Top 20 Analytics Case Studies in 2024

case study business analytics

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

  • Creation of a data-driven culture within the organization,
  • Data integration and data skills training across the organization,
  • And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

  • AI in analytics: How AI is shaping analytics
  • Edge Analytics in 2022: What it is, Why it matters & Use Cases
  • Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

case study business analytics

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Next to Read

14 case studies of manufacturing analytics in 2024, what is data virtualization benefits, case studies & top tools [2024], iot analytics: benefits, challenges, use cases & vendors [2024].

Your email address will not be published. All fields are required.

Related research

Top 10 Healthcare Analytics Use Cases & Challenges in 2024

Top 10 Healthcare Analytics Use Cases & Challenges in 2024

Exploring Analytics & AI in 2024: A Detailed Primer

Exploring Analytics & AI in 2024: A Detailed Primer

Online Manipal

Find courses from the top Manipal universities

university

Career Dreams, now within reach!

Manipal universities are helping you learn without limits. Start by clicking here.

Years of educational excellence 

Learner footprint across towns & cities of India

Student nationalities

Expert faculty

Recruiters from Fortune 500 companies

Explore our online degree courses & certifications

course

No-cost EMIs & more

With our no-cost EMIs, we let your learning take the spotlight without the stress of financing.

Scholarships up to 30%

Exclusive scholarships designed for meritorious students, defense personnel, government employees, differently abled people, Manipal alumni & learners from Sikkim and other Northeast regions of India.

Online Manipal advantages

UGC-entitled degrees Access UGC-entitled degrees from world-class universities that are NAAC accredited.

Prestigious Manipal alumni status Benefit from 70+ years of Manipal legacy and become a member of a reputed 150,000+ member alumni network.

Free Coursera access Get exclusive access to 10,000+ courses on Coursera.

Industry webinars & simulations Attend webinars by industry experts to gain industry-specific knowledge.

Om Advantage

Global classroom Join learners from 1500+ cities & towns and 50+ countries to connect & network.

Exhaustive e-content & virtual lab Gain access to vast e-libraries with 2,00,000+ e-books. Gain programming skills

In-person campus immersion Attend our exclusive in-person event- Ekam, to connect with batchmates

Convenient class schedule Attend live classes & access recorded lectures on-the-go.

Om Advantage

Choose our online programs to avail all these advantages & more

UGC-entitled degrees

Access UGC-entitled degrees from world-class universities that are NAAC accredited. Pursue online degrees that are at par with conventional on-campus degrees and accepted by governments, corporate organizations, and higher education institutions.

Choose our online programs to avail all these advantages & more

Attractive scholarships

Attractive scholarships for defense personnel, government employees, differently-abled people & meritorious students, and special scholarships for select female applicants through Online Manipal’s #EmpowerHer initiative.

Placement assistance

Increase your chances of getting a job with dedicated career and placement assistance services. Attend career-readiness sessions, resume building workshops & webinars by experts, and participate in virtual placement drives.

Prestigious Manipal alumni status

Benefit from 70+ years of Manipal legacy and become a member of a reputed 150,000+ member alumni network with top professionals & business leaders like Mr Satya Nadella, Chef Vikas Khanna, Dr Devi Prasad Shetty, and more.

Industry webinars & simulations

Attend webinars by industry experts to gain industry-specific knowledge. Participate in hands-on workshops and get certified in emerging technologies like Metaverse, AI Modelling, Blockchain, and more.

Global classroom

Join learners from 1500+ cities & towns and 50+ countries to connect & network. Exchange ideas with a diverse peer group from various industries, domains, geographies, and experience levels.

In-person campus immersion

Attend our exclusive in-person event- Ekam, to connect with batchmates & faculty members of your online program. Participate in day-long fun activities & interactive sessions and create lasting memories.

Exhaustive e-content & virtual lab

Gain access to vast e-libraries with 2,00,000+ e-books. Gain programming skills and implement coding-related projects in an exclusive state-of-the-art programming environment.

Convenient class schedule

Attend live classes & access recorded lectures on-the-go. Engage in live interactions with faculty members to get your doubts clarified and write online-proctored exams from the comfort of your homes by booking slots as per your convenience.

To avail all these advantages & more

Career support services

Our experienced team helps you choose the right career path that aligns with your goals, interests, and skills by providing you valuable guidance and support.

Resume and Linkedln profile building workshops

Resume & LinkedIn profile building workshops

Create impactful profiles with the help of our resume and linkedin profile building workshops and increase your chances of securing interviews for relevant job roles..

Access to Alumni during and after the program

Alumni interactions during & after program

Interact and receive first-hand information & guidance from alumni during and after the program..

Career advisory and counselling by industry experts

Career advisory & counselling by industry experts

Make informed decisions while choosing your career path by gaining valuable insights on various career opportunities from our expert career counsellors..

Industry-readiness sessions to make you job ready

Industry-readiness sessions

Familiarize yourself with industry trends, organizational expectations, and recruiter behavior to develop relevant skills and become job ready..

Employability skill assessment and enhancement

Employability skill assessment & enhancement

Identify your strengths & weaknesses through skill assessments and build competencies to improve your employability quotient., learner experience.

testimonial

I always wanted to pursue my higher education dream without quitting my job, and MUJ has made it possible for me through their online degrees. My online MCA degree has given me wings to fly and chase my career aspirations.

testimonial

With one year of work experience in a hospital, I wanted to hone my managerial skills. So, I decided to pursue an online MBA in Healthcare Management. Since I’m also preparing for UPSC, pursuing an online MBA is the perfect choice and Online Manipal is playing a key role in enhancing my knowledge.  

testimonial

I wanted to specialize in marketing, which is why I decided to start by pursuing an online BBA. As a working professional, an online degree was the best choice for me. The faculty at MUJ are experienced & guide us well and the student portal is user-friendly.

testimonial

I have 2 years of work experience in IT as an Application Engineer. Through this program, I hope to expand my knowledge in business analytics and apply it to my current job role. Online Manipal has enabled me to learn at my convenience and the free access to Coursera content has helped me gain industry-relevant skills.

testimonial

Having completed my master’s in business, I wanted to switch to the in-demand domain of business analytics, and I found MAHE’s certification program to be one of the best picks for me. The best part about this online certification program is that I can study at my own pace.    

testimonial

With 12 years of work experience in procurement and supply chain, I wanted to upskill in this domain. The curriculum of the online PGCP program by MAHE is industry-relevant and is helping me in applying my skills on the job. The e-tutorials are very helpful and cover in-depth topics.  

testimonial

I have been working as a lab technician in Manipal University Jaipur for 8 years, I have good technical skills like video recording and editing. However, I wanted to improve my knowledge, so I decided to pursue an online MA JMC. I want to pursue my PhD after this online program, and I also hope to become a news anchor one day. 

Speak with our counselor to get started on your learning journey

Video vault

showreel

Hamari University; Apke Aur Apke Sapnon Ke Liye | #DrivingTheChange | Online Manipal

showreel

Brand Film | Online Manipal | #AzadiWaliDegree​​

showreel

Learner Testimonial: Devyani's Journey With Online BBA (MUJ)​​

showreel

Learner Testimonial: Akhil's Journey With Online MBA (MUJ)​

showreel

Learner Testimonial: Romila's Journey With Online MA JMC (MUJ)

Read our blogs

AI - boon or bane: The revolution in our midst

AI - boon or bane: The revolution in our midst

Bhavana Gowda D.M

National Productivity Day: Why AI is every online learner’s buddy 

Priyankaa Srinivasan

Career advancement strategies for mid-career professionals

Online Manipal Editorial Team

Difference between MSc Computer Science & MCA

case study business analytics

Interested in our courses? Share your details and we'll get back to you.

Course Master of Business Administration Bachelor of Business Administration Bachelor of Computer Applications Bachelor of Commerce Master of Computer Applications MBA- Banking and Financial Services Master of Commerce Master of Arts in Journalism & Mass Communication MSc Data Science PGCP in Data Science & Machine Learning MSc Business Analytics PGCP Business Analytics PGCP Logistics and Supply Chain Bachelor of Arts MA in English MA in Sociology MA in Political Science

Institution Manipal University Jaipur Manipal Academy of Higher Education Manipal Institute of Technology

I authorize Online Manipal and its associates to contact me with updates & notifications via email, SMS, WhatsApp, and voice call. This consent will override any registration for DNC / NDNC.

Enter the code sent to your phone number to proceed with the application form

+91-9876543210 Edit

COURSE SELECTED Edit

Bachelor of Business Administration (BBA) Manipal University Jaipur

Please leave this field empty. Submit

  • Explore AI by Industry PLUS
  • Consumer goods
  • Heavy industry
  • Natural resources
  • Professional services
  • Transportation
  • AI Best Practice Guides PLUS
  • AI White Paper Library PLUS
  • AI Business Process Explorer PLUS
  • Enterprise AI Newsletter
  • Emerj Plus Research
  • AI in Business Podcast
  • The AI Consulting Podcast
  • AI in Financial Services Podcast
  • Precisely – Building Trust in Data
  • Shift Technology – How Insurers are Using AI
  • Uniphore – The Future of Banking CX in APAC
  • Uniphore – The Economic Impact of Conversational AI and Automation
  • Uniphore – The Future of Complaints Management
  • Uniphore – Conversational AI in Banking

5 Business Intelligence & Analytics Case Studies Across Industry

avatar

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

business intelligence case studies

When businesses make investments in new technologies, they usually do so with the intention of  creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first  (a question at the core of one of Emerj’s most recent expert consensuses.)

Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward “cognitive cloud” analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.

In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.

1 – Global Tech LED :Google Analytics Instant Activation of Remarketing

5 Case Studies of AI in Business Intelligence and Analytics 2

Company description:  Headquartered in Bonita Springs, Florida, Global Tech LED is a LED lighting design and supplier to U.S. and international markets, specializing in LED retrofit kits and fixtures for commercial spaces.

How Google Analytics is being used: 

  • Google Analytics’ Smart Lists were used to automatically identify Global Tech LED prospects who were “most likely to engage”, and to then remarket to those users with more targeted product pages.
  • Google’s Conversion Optimizer was used to automatically adjust potential customer bids for increased conversions.

Value proposition:

  • Remarketing campaigns triggered by Smart Lists drove 5 times more clicks than all other display campaigns.
  • The click-through rate of Global Tech LED’s remarketing campaigns was more than two times the remarketing average of other campaigns.
  • Traffic to the company’s website grew by more than 100%, and was able to re-engage users in markets in which it was trying to make a dent, including South Asia, Latin America, and Western Europe.
  • Use of the Conversion Optimizer allowed Global Tech LED to better allocate marketing costs based on bid potential.

2 – Under Armour : IBM Watson Cognitive Computing

5 Case Studies of AI in Business Intelligence and Analytics 3

Company description:  Under Armour, Inc. is an American manufacturer of sports footwear and apparel, with global headquarters in Baltimore, Maryland.

How IBM Watson is being used:

  • Under Armour’s UA Record™ app was built using the IBM Watson Cognitive Computing platform. The “Cognitive Coaching System” was designed to serve as a personal health assistant by providing users with real-time, data-based coaching based on sensor and manually input data for sleep, fitness, activity and nutrition.   The app also draws on other data sources, such as geospatial data, to determine how weather and environment may affect training.   Users are also able to view shared health insights based on other registered people in the UA Record database who share similar age, fitness, health, and other attributes.
  • The UA Record app has a rating of 4.5 stars by users; based on sensor functionality, users are encouraged (via the company’s website and the mobile app) to purchase UA HealthBox devices (like the UA Band and Headphones) that synchronize with the app.
  • According to Under Armour’s 2016 year-end results , revenue for Connected Fitness accessories grew 51 percent to $80 million.

3 – Plexure (VMob) : IoT and Azure Stream Analytics

Company description:  Formerly known as VMob, Plexure is a New Zealand-based media company that uses real-time data analytics to help companies tailor marketing messages to individual customers and optimize the transaction process.

How Azure Stream Analytics is being used:

  • Plexure used Azure Stream to help McDonald’s increase customer engagement in the Netherlands, Sweden and Japan, regions that make up 60 percent of the food service retailer’s locations worldwide.
  • Azure Stream Analytics was used to analyze the company’s stored big data (40 million+ endpoints) in the cloud, honing in on customer behavior patterns and responses to offers to ensure that targeted ads were reaching the right groups and individuals.
  • Plexure combined Azure Analytics technology with McDonald’s mobile app, analyzing with contextual information and social engagement further customize the user experience. App users receive individualized content based on weather, location, time of day, as well as purchasing a and ad response habits. For example, a customer located near a McDonald’s location on a hot afternoon might receive a pushed ad for a free ice cream sundae.
  • McDonald’s in the Netherlands yielded a 700% increase in customer redemptions of targeted offers.
  • Customers using the app returned to stores twice as often and on average spent 47% more than non-app users.

4 – Coca-Cola Amatil : Trax Retail Execution

5 Case Studies of AI in Business Intelligence and Analytics 4

Company description:  Coca-Cola Amatil is the largest bottler and distributor of non-alcoholic, bottled beverages in the Asia Pacific, and one of the largest bottlers of Coca-Cola products in the region.

How Trax Image Recognition for Retail is being used:

  • Prior to using Trax’s imaging technology, Coca-Cola Amatil was relying on limited and manual measurements of products in store, as well as delayed data sourced from phone conversations.
  • Coca-Cola Amatil sales reps used Trax Retail Execution image-based technology to take pictures of stores shelves with their mobile devices; these images were sent to the Trax Cloud and analyzed, returning actionable reports within minutes to sales reps and providing more detailed online assessments to management.
  • Real-time images of stock allowed sales reps to quickly identify performance gaps and apply corrective actions in store. Reports on shelf share and competitive insights also allowed reps to strategize on opportunities in store and over the phone with store managers.
  • Coca-Cola Amatil gained 1.3% market share in the Asia Pacific region within five months.

5 – Peter Glenn : AgilOne Advanced Analytics

5 Case Studies of AI in Business Intelligence and Analytics 5

Company description:  Peter Glenn has provided outdoor apparel and gear to individual and wholesale customers for over 50 years, with brick-and-mortar locations along the east coast, Alaska, and South Beach.

How AgilOne Analytics is being used:

  • AgilOne Analytics’ Dashboard provides a consolidated view across online and offline channels, which allowed Peter Glenn to view trends between buyer groups and make better segmentation decisions.
  • Advanced segmentation abilities included data on customer household, their value segment, and proximity to any brick-and-mortar locations.
  • Peter Glenn used this information to launch integrated promotional, triggered, and lifecycle campaigns across channels, with the goal of increasing sales  during non-peak months and increasing in-store traffic.
  • Once AgilOne’s data quality engine had combed through Peter Glenn’s customer database, the company learned that more than 80% of its customer base had lapsed; they were able to use that information to re-target and re-engage stagnant customers.
  • Peter Glenn saw a 30% increase in Average Order Value (AOV) as a result of its automated marketing campaigns.
  • Access to data points, such as customer proximity to a store, allowed Peter Glenn to target customers for store events using advanced segmentation and more aligned channel marketing strategies.

Image credit: DSCallards

Related Posts

Digitally-native eCommerce businesses are used to working with their customer data in order to write…

Reuters referenced a Stratistics MRC figure estimating the size of the business intelligence industry around $15.64…

In the past few decades, insurance companies have collected vast amounts of data relevant to…

In 2017, Emerj conducted research into the applications of machine learning in marketing with 51…

Decision-makers in the banking sector have a unique set of business intelligence needs, and artificial…

Related posts (5)

Business Intelligence in Insurance

Business Intelligence in Insurance – Current Applications

In the past few decades, insurance companies have collected vast amounts of data relevant to their business processes, customers, claims, and so on. This data can be unstructured in the form of PDFs, text documents, images, and videos, or structured, organized and curated for big data analytics.

Business Intelligence in Retail - Current Applications

Business Intelligence in Retail – Current Applications

In 2017, Emerj conducted research into the applications of machine learning in marketing with 51 different AI-focused marketing executives. The AI marketing vendors we spoke to named retail and eCommerce as the top sectors ripe for applying marketing AI software. Below is a graphic from our research showing the sectors that AI marketing vendors sell into most:

Business Intelligence in Finance - Current Applications

Business Intelligence in Finance – Current Applications

Reuters referenced a Stratistics MRC figure estimating the size of the business intelligence industry around $15.64 billion in 2016. It follows that AI would find its way into the business intelligence world. In our previous report, we covered 6 use-cases for AI in business intelligence. As of now, numerous companies claim to assist business leaders in the finance domain, specifically, in aspects of their roles using AI.

Artificial Intelligence in Business Intelligence 950×540

6 Examples of AI in Business Intelligence Applications

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time.

Business Intelligence in Healthcare - Current Applications

Business Intelligence in Healthcare – Current Applications

According to Deloitte, global healthcare spending is expected to grow annually by 4.1% from 2017-2021, up from just 1.3% in 2012-2016. The report suggests this growth will be fuelled by aging, rising populations, the growth of developing markets, advances in medical treatments, and rising labor costs.

  • Market Reasearch and Advisory
  • AI Presentations and Keynotes
  • Emerj Plus Membership
  • AI In Business Podcast
  • AI In Finance Services Podcast
  • Subscribe to our AI Newsletter
  • Advertise with us
  • Terms and Conditions
  • Refund and Cancellation Policy
  • Privacy Policy

case study business analytics

Cart

  • SUGGESTED TOPICS
  • The Magazine
  • Newsletters
  • Managing Yourself
  • Managing Teams
  • Work-life Balance
  • The Big Idea
  • Data & Visuals
  • Reading Lists
  • Case Selections
  • HBR Learning
  • Topic Feeds
  • Account Settings
  • Email Preferences

Analytics and data science

  • Technology and analytics
  • AI and machine learning

case study business analytics

Why Your Organization Needs a Bill of Rights for Employee Data

  • Kaelyn Lowmaster
  • Jonah Shepp
  • April 20, 2023

case study business analytics

Whether You’re Qualified Depends on How You’re Quantified

  • Michael Schrage
  • October 12, 2015

case study business analytics

Don’t Let Metrics Undermine Your Business

  • Michael Harris
  • Bill Tayler
  • From the September–October 2019 Issue

case study business analytics

How P&G and American Express Are Approaching AI

  • Thomas H. Davenport
  • March 31, 2017

case study business analytics

How to Gain a Competitive Advantage on Customer Insights

  • Paul Leinwand
  • Mahadeva Matt Mani
  • October 19, 2022

Give Me That Real-Time Information

  • April 01, 2004

case study business analytics

The Best Data Scientists Know How to Tell Stories

  • October 13, 2015

case study business analytics

How Companies Are Benefiting from "Lite" Artificial Intelligence

  • Seth Earley
  • July 19, 2016

case study business analytics

AI Won't Replace Humans - But Humans With AI Will Replace Humans Without AI

  • August 04, 2023

case study business analytics

The Dangers of Categorical Thinking

  • Bart de Langhe
  • Philip Fernbach

The Trillion-Dollar R&D Fix

  • Anne Marie Knott
  • From the May 2012 Issue

case study business analytics

A Simple Tactic That Could Help Reduce Bias in AI

  • November 04, 2020

case study business analytics

No-Nonsense Guide to Measuring Productivity (HBR OnPoint Enhanced Edition)

  • W. Bruce Chew
  • April 15, 2000

case study business analytics

One Obstacle to Curing Cancer: Patient Data Isn’t Shared

  • Richard G. Hamermesh
  • Kathy Giusti
  • November 28, 2016

case study business analytics

Infusing Digital Responsibility into Your Organization

  • Tomoko Yokoi
  • Lazaros Goutas
  • Michael Wade
  • Nicolas Zahn
  • Niniane Paeffgen
  • April 28, 2023

case study business analytics

Data Can Do for Change Management What It Did for Marketing

  • Michael L. Tushman
  • Mary Elizabeth Porray
  • July 31, 2017

How Velcro Got Hooked on Quality

  • K. Theodor Krantz
  • From the September–October 1989 Issue

case study business analytics

Making Sustainability Count

  • George Serafeim
  • Robert G. Eccles
  • Mary Johnstone-Louis
  • Colin Mayer
  • Judith C. Stroehle
  • Simon MacMahon
  • From the September–October 2020 Issue

case study business analytics

Use Data to Revolutionize Project Planning

  • Yael Grushka-Cockayne
  • February 26, 2020

case study business analytics

Getting Machine Learning Projects from Idea to Execution

  • Eric Siegel
  • January 23, 2024

case study business analytics

Cross Country Group: A Piece of the Rock (A)

  • Robert Simons
  • Indra A. Reinbergs
  • March 15, 1999

SK Group: Social Progress Credits

  • Ethan Rouen
  • David Freiberg
  • January 15, 2020

TradeIX: Blockchain-Enabled Trade Finance in Global Supply Chains

  • Erik Hofmann
  • Maximilian Enthoven
  • Sara Fallegger
  • August 11, 2020

Grupo Familia: Monetizing a Digital Marketing Campaign in Colombia

  • John C. Parker
  • Theodore Anderson
  • October 11, 2017

Zalora: Data-Driven Pricing

  • Sunil Gupta
  • Pavel Kireyev
  • Srinivas K. Reddy
  • October 25, 2018

Measure of Delight: The Pursuit of Quality at AT&T Universal Card Services (A)

  • Roy D. Shapiro
  • Michael D. Watkins
  • Susan Rosegrant
  • October 25, 1993

Cross Country Group: A Piece of the Rock (B)

  • January 24, 2000

Quality Wireless (A): Call Center Performance

  • Sunil Chopra
  • March 31, 2006

Algorithmic Bias in Marketing

  • Ayelet Israeli
  • Eva Ascarza
  • September 25, 2020

case study business analytics

Performance Management Collection: Effective Techniques For Managers Looking To Get The Best From Their People

  • Harvard Business Review
  • April 25, 2016

Facebook Confronts a Crisis of Trust

  • William W. George
  • Amram Migdal
  • June 11, 2018

Business Implications from Regulating Carbon Emissions in the EU

  • Benjamin Maletta
  • June 01, 2022
  • V.G. Narayanan
  • Dennis Campbell
  • February 07, 2003

Machine Learning Concepts: An Educational Game Simulation

  • Seema Chokshi
  • Lipika Bhattacharya
  • May 22, 2022

Adobe Systems: Working Towards a "Suite" Release (A)

  • David A. Thomas
  • Lauren Barley
  • September 24, 2008

Hurricane Island Outward Bound School

  • Thomas V. Bonoma
  • Bruce H. Clark
  • October 08, 1987

Ubiquitous Surveillance (B)

  • Mary Gentile
  • David Danks
  • Maralee Harrell
  • July 07, 2022

Worldwide Equipment (China) Ltd.: A Sales Performance Dilemma

  • Alan Wenchu Yang
  • February 25, 2003

Operation Overlord

  • Boris Groysberg
  • Greg Goullet
  • Katherine Connolly Baden
  • Sarah L. Abbott
  • July 18, 2022

Netflix: Valuing a New Business Model

  • Francois Brochet
  • Suraj Srinivasan
  • Michael Norris
  • August 29, 2012

case study business analytics

THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI), Teaching Note

  • May 06, 2021

case study business analytics

1. "Here's How You Should Think About Ethics" from Ethical Machines: Your Concise Guide to Totally Unbiased, Transparent, and Respectful AI

  • Reid Blackman
  • July 12, 2022

The Next One Hundred Days: Realizing the Potential of Managing Data and Information as Business Assets

  • Thomas C. Redman
  • September 22, 2008

Why Smart Companies Are Giving Customers More Data

  • Barbara H. Wixom
  • Ronny M. Schuritz
  • Killian Farrell
  • May 19, 2020

Popular Topics

Partner center.

Using people analytics to drive business performance: A case study

People analytics— the application of advanced analytics and large data sets to talent management—is going mainstream. Five years ago, it was the provenance of a few leading companies, such as Google (whose former senior vice president of people operations wrote a book about it ). Now a growing number of businesses are applying analytics to processes such as recruiting and retention, uncovering surprising sources of talent and counterintuitive insights about what drives employee performance.

Much of the work to date has focused on specialized talent (a natural by-product of the types of companies that pioneered people analytics) and on individual HR processes . That makes the recent experience of a global quick-service restaurant chain instructive. The company focused the power of people analytics on its frontline staff—with an eye toward improving overall business performance—and achieved dramatic improvements in customer satisfaction, service performance, and overall business results, including a 5 percent increase in group sales in its pilot market. Here is its story.

The challenge: Collecting data to map the talent value chain

The company had already exhausted most traditional strategic options and was looking for new opportunities to improve the customer experience. Operating a mix of franchised outlets, as well as corporate-owned restaurants, the company was suffering from annual employee turnover significantly above that of its peers. Business leaders believed closing this turnover gap could be a key to improving the customer experience and increasing revenues, and that their best chance at boosting retention lay in understanding their people better. The starting point was to define the goals for the effort and then translate the full range of frontline employee behavior and experience into data that the company could model against actual outcomes.

Would you like to learn more about our People Analytics ?

Define what matters. Agreeing in advance on the outcomes that matter is a critical step in any people-analytics project—one that’s often overlooked and can involve a significant investment of time. In this case, it required rigorous data exploration and discussion among senior leaders to align on three target metrics: revenue growth per store, average customer satisfaction, and average speed of service (the last two measured by shift to ensure that the people driving those results were tracked). This exercise highlighted a few performance metrics that worked together and others that “pulled” in opposite directions in certain contexts.

Fill data gaps. Internal sources provided some relevant data, and it was possible to derive other variables, such as commute distance. The company needed to supplement its existing data, however, notably in three areas (Exhibit 1):

  • First was selection and onboarding (“ who gets hired and what their traits are”). There was little data on personality traits, which some leaders thought might be a significant factor in explaining differences in the performance of the various outlets and shifts. In association with a specialist in psychometric assessments, the company ran a series of online games allowing data scientists to build a picture of individual employees’ personalities and cognitive skills.
  • Second was day-to-day management (“ how we manage our people and their environment”). Measuring management quality is never easy, and the company did not have a culture or engagement survey. To provide insight into management practices, the company deployed McKinsey’s Organizational Health Index (OHI), an instrument through which we’ve pinpointed 37 management practices that contribute most to organizational health and long-term performance. With the OHI, the company sought improved understanding of such practices and the impact that leadership actions were having on the front line.
  • Third was behavior and interactions (“ what employees do in the restaurants”). Employee behavior and collaboration was monitored over time by sensors that tracked the intensity of physical interactions among colleagues. The sensors captured the extent to which employees physically moved around the restaurant, the tone of their conversations, and the amount of time spent talking versus listening to colleagues and customers.

The insights: Challenging conventional wisdom

Armed with these new and existing data sources—six in all, beyond the traditional HR profile, and comprising more than 10,000 data points spanning individuals, shifts, and restaurants across four US markets, and including the financial and operational performance of each outlet—the company set out to find which variables corresponded most closely to store success. It used the data to build a series of logistic-regression and unsupervised-learning models that could help determine the relationship between drivers and desired outcomes (customer satisfaction and speed of service by shift, and revenue growth by store).

Then it began testing more than 100 hypotheses, many of which had been strongly championed by senior managers based on their observations and instincts from years of experience. This part of the exercise proved to be especially powerful, confronting senior individuals with evidence that in some cases contradicted deeply held and often conflicting instincts about what drives success. Four insights emerged from the analysis that have begun informing how the company manages its people day to day.

Personality counts. In the retail business at least, certain personality traits have higher impact on desired outcomes. Through the analysis, the company identified four clusters or archetypes of frontline employees who were working each day: one group, “potential leaders,” exhibited many characteristics similar to store managers; another group, “socializers,” were friendly and had high emotional intelligence; and there were two different groups of “taskmasters,” who focused on job execution (Exhibit 2). Counterintuitively, though, the hypothesis that socializers—and hiring for friendliness—would maximize performance was not supported by the data. There was a closer correlation between performance and the ability of employees to focus on their work and minimize distractions, in essence getting things done.

Careers are key. The company found that variable compensation, a lever the organization used frequently to motivate store managers and employees, had been largely ineffective: the data suggested that higher and more frequent variable financial incentives (awards that were material to the company but not significant at the individual level) were not strongly correlated with stronger store or individual performance. Conversely, career development and cultural norms had a stronger impact on outcomes.

Management is a contact sport. One group of executives had been convinced that managerial tenure was a key variable, yet the data did not show that. There was no correlation to length of service or personality type. This insight encouraged the company to identify more precisely what its “good” store managers were doing, after which it was able to train their assistants and other local leaders to act and behave in the same way (through, for example, empowering and inspiring staff, recognizing achievement, and creating a stronger team environment).

Shifts differ. Performance was markedly weaker during shifts of eight to ten hours. Such shifts were inconsistent both with demand patterns and with the stamina of employees, whose energy fell significantly after six hours at work. Longer shifts, it seems, had become the norm in many restaurants to ease commutes and simplify scheduling (fewer days of work in the week, with more hours of work each day). Analysis of the data demonstrated to managers that while this policy simplified managerial responsibilities, it was actually hurting productivity.

The results (so far)

Four months into a pilot in the first market in which the findings are being implemented, the results are encouraging. Customer satisfaction scores have increased by more than 100 percent, speed of service (as measured by the time between order and transaction completion) has improved by 30 seconds, attrition of new joiners has decreased substantially, and sales are up by 5 percent.

The CEO's guide to competing through HR

The CEO’s guide to competing through HR

We’d caution, of course, against concluding that instinct has no role to play in the recruiting, development, management, and retention of employees—or in identifying the combination of people skills that drives great performance. Still, results like these, in an industry like retail—which in the United States alone employs more than 16 million people and, depending on the year and season, may hire three-quarters of a million seasonal employees—point to much broader potential for people analytics. It appears that executives who can complement experience-based wisdom with analytically driven insight stand a much better chance of linking their talent efforts to business value.

Carla Arellano  is a vice president of, and Alexander DiLeonardo is a senior expert at, People Analytics, a McKinsey Solution—both are based in McKinsey’s New York office;  Ignacio Felix is a partner in the Miami office.

The authors wish to thank Val Rastorguev, Dan Martin, and Ryan Smith for their contributions to this article.

Explore a career with us

Related articles.

HR-competing_thumb_1536x1536_200_Standard

The CEO’s guide to competing through HR

Power to the new people analytics

Power to the new people analytics

People analytics reveals three things HR may be getting wrong

People analytics reveals three things HR may be getting wrong

  • Business Essentials
  • Leadership & Management
  • Credential of Leadership, Impact, and Management in Business (CLIMB)
  • Entrepreneurship & Innovation
  • *New* Digital Transformation
  • Finance & Accounting
  • Business in Society
  • For Organizations
  • Support Portal
  • Media Coverage
  • Founding Donors
  • Leadership Team

case study business analytics

  • Harvard Business School →
  • HBS Online →
  • Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

  • Career Development
  • Communication
  • Decision-Making
  • Earning Your MBA
  • Negotiation
  • News & Events
  • Productivity
  • Staff Spotlight
  • Student Profiles
  • Work-Life Balance
  • Alternative Investments
  • Business Analytics
  • Business Strategy
  • Business and Climate Change
  • Design Thinking and Innovation
  • Digital Marketing Strategy
  • Disruptive Strategy
  • Economics for Managers
  • Entrepreneurship Essentials
  • Financial Accounting
  • Global Business
  • Launching Tech Ventures
  • Leadership Principles
  • Leadership, Ethics, and Corporate Accountability
  • Leading with Finance
  • Management Essentials
  • Negotiation Mastery
  • Organizational Leadership
  • Power and Influence for Positive Impact
  • Strategy Execution
  • Sustainable Business Strategy
  • Sustainable Investing
  • Winning with Digital Platforms

Business Analytics: What It Is & Why It's Important

Data Analytics Charts on Desk

  • 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

  • Improve efficiency and productivity (64 percent)
  • Achieve more effective decision-making (56 percent)
  • Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

  • Descriptive : The interpretation of historical data to identify trends and patterns
  • Diagnostic : The interpretation of historical data to determine why something has happened
  • Predictive : The use of statistics to forecast future outcomes
  • Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

A Beginner's Guide to Data and Analytics | Access Your Free E-Book | Download Now

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses, and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

case study business analytics

About the Author

case study business analytics

  • Onsite training

3,000,000+ delegates

15,000+ clients

1,000+ locations

  • KnowledgePass
  • Log a ticket

01344203999 Available 24/7

Business Analysis Case Study: Unlocking Growth Potential for a Company 

Have you ever wondered what are the necessary steps for conducting a Business Analyst Case Study? This blog will take you through the steps for conducting it.

stars

Exclusive 40% OFF

Training Outcomes Within Your Budget!

We ensure quality, budget-alignment, and timely delivery by our expert instructors.

Share this Resource

  • Business Process Mapping Training
  • BCS Practitioner Certificate in Requirements Engineering
  • BCS Foundation Certificate in Organisational Behaviour
  • BCS Certificate in Business Analysis Practice
  • Creating Effective Stakeholder Engagement Training

course

Table of Contents  

1) An overview of the Business Analysis Case Study 

2) Step 1: Understanding the company and its objectives 

3) Step 2: Gathering relevant data 

4) Step 3: Conducting SWOT analysis 

5) Step 4: Identifying key issues and prioritising 

6) Step 5: Analysing the root causes 

7) Step 6: Proposing solutions and developing an action plan 

8) Step 7: Monitoring and evaluation 

9) Conclusion 

An overview of the Business Analysis Case Study  

To kickstart our analysis, we will gain a deep understanding of the company's background, industry, and specific objectives. By examining the hypothetical company's objectives and aligning our analysis with its goals, we can lay the groundwork for a focused and targeted approach. This Business Analysis Case Study will demonstrate how the analysis process is pivotal in driving growth and overcoming obstacles that hinder success. 

Moving forward, we will navigate through various steps involved in the case study, including gathering relevant data, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and developing an action plan. By following this step-by-step approach, we can address the core challenges and devise actionable strategies that align with the company's objectives. 

The primary focus of this Business Analysis Case Study is to highlight the significance of Business Analysis in identifying key issues, evaluating potential growth opportunities, and developing effective solutions. Through a comprehensive examination of the hypothetical company's strengths, weaknesses, opportunities, and threats, we will gain valuable insights that drive informed decision-making. 

By the end of this Business Analysis Case Study, we aim to provide a holistic view of the analysis process, its benefits, and the transformative impact it can have on unlocking growth potential. Through real-world examples and practical solutions, we will showcase the power of Business Analysis in driving success and propelling companies towards achieving their goals. So, let's dive into the fascinating journey of this Business Analysis Case Study and explore the path to unlocking growth potential for our hypothetical company. 

Unlock your potential as a Certified Business Analyst Professional and transform the world of business with our expert CBA-PRO training .  

Step 1: Understanding the company and its objectives  

In this initial step, we need to gain a thorough understanding of the hypothetical company's background, industry, and specific objectives. Our hypothetical company, TechSolutions Ltd., is a software development firm aiming to expand its customer base and increase revenue by 20% within the next year. 

TechSolutions Ltd. operates in the dynamic software solutions market, catering to various industries such as finance, healthcare, and manufacturing. The company's primary objective is to leverage its technical expertise and establish itself as a leading provider of innovative software solutions. This objective sets the foundation for our analysis, enabling us to align our efforts with the company's goals. 

Accelerate your career as a Business Analyst with our comprehensive Business Analyst training courses !  

Step 2: Gathering relevant data  

To conduct a comprehensive analysis, we need to gather relevant data pertaining to the company's operations, market trends, competitors, customer preferences, and financial performance. This data serves as a valuable resource to gain insights into the company's current position and identify growth opportunities. 

For our case study, TechSolutions Ltd. collects data on various aspects, including customer satisfaction levels, market penetration rates, and financial metrics such as revenue, costs, and profitability. Additionally, industry reports, market research, and competitor analysis provide insights into market trends, emerging technologies, and the competitive landscape. This data-driven approach ensures that our analysis is well-informed and grounded in reality. 

DATA Analysis Insights

Master the fundamentals of Business Analysis and pave your path to success with our immersive Business Analyst Fundamentals training .  

Step 3: Conducting SWOT analysis  

A SWOT analysis is a powerful tool to assess the company's internal strengths and weaknesses, as well as external opportunities and threats. By conducting a thorough SWOT analysis, we can gain valuable insights into the company's strategic position and identify factors that impact its growth potential. 

Conducting SWOT analysis

Step 4: Identifying key issues and prioritising  

Outdated Technology Infrastructure

In the case of TechSolutions Ltd., the analysis reveals two primary issues: an outdated technology infrastructure and limited marketing efforts. These issues are prioritised as they directly impact the company's ability to meet its growth objectives. By addressing these key issues, TechSolutions Ltd. can position itself for sustainable growth. 

Embark on your journey to becoming a skilled Business Analyst with our Business Analyst Green Belt training , unlocking endless opportunities!  

Step 5: Analysing the root causes  

To develop effective solutions, we must analyse the root causes behind the identified issues. This involves a detailed examination of internal processes, conducting interviews with key stakeholders, and exploring market dynamics. By identifying the underlying factors contributing to the issues, we can tailor our solutions to address them at their core. 

In the case of TechSolutions Ltd., the analysis reveals that the outdated technology infrastructure is primarily due to budget constraints and a lack of awareness about the latest software solutions. Limited marketing efforts arise from a shortage of skilled personnel and inadequate allocation of resources. 

Understanding these root causes provides valuable insights for developing targeted and impactful solutions. 

Master the art of Business Process Mapping and streamline your organisation's efficiency with our expert Business Process Mapping training !  

Step 6: Proposing solutions and developing an action plan  

Action Plan

For TechSolutions Ltd., the following solutions are proposed: 

a) Allocate a portion of the budget for technology upgrades and training: TechSolutions Ltd. should allocate a dedicated portion of its budget to upgrade its technology infrastructure and invest in training its employees on the latest software tools and technologies. This will ensure that the company remains competitive and can deliver cutting-edge solutions to its customers. 

b) Hire a dedicated marketing team and allocate resources for targeted campaigns: To overcome the limited marketing efforts, TechSolutions Ltd. should invest in building a skilled and dedicated marketing team. This team will focus on developing comprehensive marketing strategies, leveraging digital platforms, and conducting targeted campaigns to reach potential customers effectively. 

c) Strengthen partnerships with industry influencers: Collaborating with industry influencers can significantly enhance TechSolutions Ltd.'s brand visibility and credibility. By identifying key industry influencers and forming strategic partnerships, the company can tap into their existing networks and gain access to a wider customer base. 

d) Implement a customer feedback system: To enhance product quality and meet customer expectations, TechSolutions Ltd. should establish a robust customer feedback system. This system will enable the company to gather valuable insights, identify areas for improvement, and promptly address any customer concerns or suggestions. Regular feedback loops will foster customer loyalty and drive business growth. 

The proposed solutions are outlined in a detailed action plan, specifying the timeline, responsible individuals, and measurable milestones for each solution. Regular progress updates and performance evaluations ensure that the solutions are effectively implemented and deliver the desired outcomes. 

Unleash the potential of Mathematical Optimisation for solving complex business problems with our specialised Mathematical Optimisation for Business Problems training !  

Step 7: Monitoring and evaluation  

Monitoring and evaluation

Conclusion  

In this detailed Business Analysis Case Study, we explored the challenges faced by a hypothetical company, TechSolutions Ltd., and proposed comprehensive solutions to unlock its growth potential. By following a systematic analysis process, which includes understanding the company's objectives, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and monitoring progress, businesses can effectively address their challenges and drive success. 

Business Analysis plays a vital role in identifying areas for improvement and implementing strategic initiatives. By leveraging data-driven insights and taking proactive measures, companies can navigate competitive landscapes, overcome obstacles, and achieve their growth objectives. With careful analysis and targeted solutions, TechSolutions Ltd. is poised to unlock its growth potential and establish itself as a leading software development firm in the industry. By implementing the proposed solutions and continuously monitoring their progress, the company will be well-positioned for long-term success and sustainable growth. 

Discover the power of Business Analytics with our comprehensive Introduction to Business Analytics training , gaining valuable insights for success!  

Frequently Asked Questions

Upcoming business analysis resources batches & dates.

Thu 6th Jun 2024

Thu 10th Oct 2024

Thu 19th Dec 2024

Get A Quote

WHO WILL BE FUNDING THE COURSE?

My employer

By submitting your details you agree to be contacted in order to respond to your enquiry

  • Business Analysis
  • Lean Six Sigma Certification

Share this course

New year big sale, biggest christmas sale .

red-star

We cannot process your enquiry without contacting you, please tick to confirm your consent to us for contacting you about your enquiry.

By submitting your details you agree to be contacted in order to respond to your enquiry.

We may not have the course you’re looking for. If you enquire or give us a call on 01344203999 and speak to our training experts, we may still be able to help with your training requirements.

Or select from our popular topics

  • ITIL® Certification
  • Scrum Certification
  • Change Management Certification
  • Business Analysis Certification
  • Microsoft Azure
  • Microsoft Excel & Certification Course
  • Microsoft Project
  • Explore more courses

Press esc to close

Fill out your  contact details  below and our training experts will be in touch.

Fill out your   contact details   below

Thank you for your enquiry!

One of our training experts will be in touch shortly to go over your training requirements.

Back to Course Information

Fill out your contact details below so we can get in touch with you regarding your training requirements.

* WHO WILL BE FUNDING THE COURSE?

Preferred Contact Method

No preference

Back to course information

Fill out your  training details  below

Fill out your training details below so we have a better idea of what your training requirements are.

HOW MANY DELEGATES NEED TRAINING?

HOW DO YOU WANT THE COURSE DELIVERED?

Online Instructor-led

Online Self-paced

WHEN WOULD YOU LIKE TO TAKE THIS COURSE?

Next 2 - 4 months

WHAT IS YOUR REASON FOR ENQUIRING?

Looking for some information

Looking for a discount

I want to book but have questions

One of our training experts will be in touch shortly to go overy your training requirements.

Your privacy & cookies!

Like many websites we use cookies. We care about your data and experience, so to give you the best possible experience using our site, we store a very limited amount of your data. Continuing to use this site or clicking “Accept & close” means that you agree to our use of cookies. Learn more about our privacy policy and cookie policy cookie policy .

We use cookies that are essential for our site to work. Please visit our cookie policy for more information. To accept all cookies click 'Accept & close'.

100% Success Icon

  • BA Bootcamp
  • Skill Training
  • BA Mentoring Support
  • Data Analysis Bootcamp
  • BA as a Service
  • BA Process Definition
  • BA Center of Excellence
  • Agile Business Analysis Toolkit
  • Data Analytics Toolkit
  • Enterprise Architecture Toolkit
  • Business Resilience Toolkit
  • Information Security Management Toolkit
  • BA Mentoring
  • BA Tools Training
  • Requirements Warehouse
  • Free Quizzing Software - SimpleSim
  • Free Skill Assessment Software - CompetencyPro
  • Upcoming Webinars
  • Past Webinars
  • Free Previews
  • Business Analyst Videos
  • Business Analyst Publications
  • Success Stories
  • Current Offers
  • Certification Process
  • Rewards for Certification
  • Impact of Failure
  • Impact of Wrong Training Partner

Business Analyst Case Study | Free Case Study Template

LN Mishra, CBAP, CBDA, AAC & CCA

Business analyst case studies blog describes an actual business analyst case study. This provides real-world exposure to new business analysts.

In this blog, we will be discussing what is business analysis case study, why develop them, when to develop them and how to develop them. We will provide a real business case analysis case study for better understanding.

Let’s start with understanding what is business analysis before we go to analyst case studies.

Topics Below

What is a business analysis case study 

Why prepare business analysis case study 

When to prepare business analysis case study

How to prepare business analysis case study

Example Business Analysis Case Studies

What is Business Analysis Case Study?

Before we try to understand, Business Analysis Case Study, let's understand the term case study and business analysis.

As per Wikipedia, a case study is:

"A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context."

For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like, world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.

So, we can define Business Analysis Case Study as

"A Business Analysis case study is an in-depth, detailed examination of a particular business analysis initiative."

What is Business Analysis?

The BABOK guide defines Business Analysis as the “Practice of enabling change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders”. Business Analysis helps in finding and implementing changes needed to address key business needs, which are essentially problems and opportunities in front of the organization.

Business analysis can be performed at multiple levels, such as at:

  • The enterprise level, analyzing the complete business, and understanding which aspects of the business require changes.
  • The organization level, analyzing a part of the business, and understanding which aspects of the organization require changes.
  • The process level, analyzing a specific process, understanding which aspects of the process require changes.
  • The product level, analyzing a specific product, and understanding which aspects of the product require changes.  

Why Develop Business Analyst Case Study

Business analysis case studies can be useful for multiple purposes. One of the purpose can be to document business analysis project experiences which can be used in future by other business analysts.

This also can be used to showcase an organizations capabilities in the area of business analysis. For example, as Adaptive is a business analysis consulting organization, it develops multiple business analysis case studies which show cases the work done by Adaptive business analysts for the client. You can read one such case study for a manufacturing client .

When To Develop Business Analyst Case Study

Business analysis case studies are typically prepared after a project or initiative is completed. It is good to give a little time gap before we develop the case study because the impact of a change may take a little while after the change is implemented.

Most professionals prepare business analysis case studies for projects which are successful. But it is also important to remember that not all changes are going to be successful. There are definitely failures in an organizations project history.

It is also important to document the failure case studies because the failures can teach us about what not to do in future so that risks of failures are minimized.

How To Develop A Business Analyst Case Study

Document business problem / opportunity.

In this section of the business analyst case studies, we discuss the actual problem of the business case analysis example.

ABC Technologies has grown rapidly from being a tiny organization with less than 5 projects to one running 200 projects at the same time. The number of customer escalations has gone up significantly. Profitability is getting eroded over a period of time. Significant management time is spent in fire-fighting than improving the business.

Top management estimated a loss of 10% profitability due to poor management of projects which is estimated at about 10 Million USD per annum.

Document Problem / Opportunity Analysis

For our above business problem, we captured the following analysis details.

Discussions with key stakeholders revealed the following challenges in front of ABCT management:

  • There is very little visibility of project performances to top management
  • Non-standard project reporting by various projects makes it harder for top management to assess the correct health of the project
  • Practically there is no practice of identifying risks and mitigating them
  • Project practices are largely non-standardized. Few project managers do run their projects quite well because of their personal abilities, but most struggle to do so.
  • Due to rapid growth, management has no option but to assign project management responsibilities to staff with little or no project management experience.

Document Identified Solutions 

Based on root cause analysis, management decided to initiate a project to standardize management reporting. This required the organization to implement a project management system. The organization initially short-listed 10 project management tools. After comparing the business needs, tools, their costs, management decided to go with a specific tool.

Document Implementation Plan

The purchased tool lacked integration into the organizations existing systems. The vendor and organization’s IT team developed a project plan to integrate the new system with the existing systems.

Document Performance Improvements 

After a year, the effectiveness of the project was assessed. Projects showed remarkable improvement wrt reduced customer escalations, better on-time billing, and better risk management. The system also allowed the organization to bid for larger contracts as the prospective customers demanded such a system from their suppliers. The application was further enhanced to cater to the needs of other businesses in the enterprise as they were different legal entities, and their policies were different.

Document lessons learnt

Some of the key lessons learnt during this business analysis initiative were:

1. Stakeholder buy-in in extremely important to the success of the project

2. It is always better to go with iterative approach achieve smaller milestones and then go for larger milestones

BA Case Study template

Other articles of interest

No more cold feet, be best prepared to ace the Business analyst job Interview with Business Analyst Interview Questions . Join Adaptive Inner Circle and get '1000 BA Interview Questions' book for free.  Checkout the more information about CBAP Training from Adaptive US   

100 IIBA Exam Tips

You May Also Like

These Related Stories

case study business analytics

How to conquer your CBAP exam anxiety

 alt=

CBAP Certification Exam Preparation in 10 Simple steps

case study business analytics

User Story vs. Use Case - How They Stack Up - Adaptive US

Get email notifications, comments (25).

  • CUSTOMERLINK

Mainline

Home   »   Case Studies   »   Business Analytics for Financial Services

Business Analytics for Financial Services

In financial services, business requirements are complex and accuracy of information is paramount..

Deploying and optimizing a business analytics solution often involves significant systems integration challenges-so it’s important to engage a services provider with deep expertise. Here’s how IBM Premier Business Partner Mainline helped three very different financial services organizations turn their information into intelligence.

COMPANY:   Global insurance and financial services company HEADQUARTERS:   Northeast U.S. EMPLOYEES: 50,000

The Benefits:

  • 3-fold more frequent reporting (monthly vs. quarterly)
  • 75% reduction in time required to produce reports
  • 90% fewer people involved in report production, enhancing productivity
  • Improved accuracy of reports by reducing potential for human error
  • Able to understand data better and faster, enhancing decision-making

The Business Challenge:

For years, this financial service company’s global compliance group struggled to manually collect data from multiple sources such as Excel spreadsheets, Word documents, and other report summaries. The lengthy compliance reports they needed to generate took weeks to compile, and with so many manual steps involved, accuracy was less than optimal; often, reports had to be re-run due to errors. The customer needed an end-to-end business analytics solution that would automatically collect and analyze a wide range of compliance metrics.

The Solution:

The company engaged Mainline to implement IBM Business Analytics and IBM DB2 for AIX data server to automatically collect data from source systems into a data mart and analyze compliance metrics. Mainline leveraged its expertise with the IBM Business Analytics Software Development Kit and provided an annotations manager tool to add more context into reports and tie comments back to other data elements-for example, adding dynamic notes to explain the reasons behind skewed or outlier data during a certain quarter. The solution eliminated the need to copy charts into Word and add associated verbiage.

The Result:

Mainline created a central data warehouse for all global compliance metrics with 14 sub-reports acting as one, reducing the time needed to collect data and product reports from weeks to days. Notes and charts can be produced at the same time as the report is executed. Users can choose which reports execute and change the chart structure on the fly to subjectively focus on relevant data points. They can understand data better and with much less effort. Role-based security in IBM Business Analytics allows business leaders to view only the data they are allowed to see. Mainline is now a trusted partner, and has been tasked with establishing an Analytics Center of Excellence.

case study business analytics

Download the PDF

For more information, call your Mainline account representative or call Mainline directly at 866.490.MAIN(6246) or complete our contact us form.

COMPANY:   Midsize investment management firm HEADQUARTERS:   Northeast U.S. EMPLOYEES: 17,000

  • 50% improvement in operational efficiency for generating client statements
  • Created a highly customizable, user-friendly reporting environment
  • Reduced demands on IT, enabling developers to focus on other projects
  • Improved data accuracy
  • Richer data helps customers understand how investments are performing

The reporting tool that an investment firm used for generating client statements was not meeting business requirements. The process of generating statements was complex, since data was based not only on the asset types that people owned, but also on variables that account executives had set up governing what they wanted their customers to see. In order to make the reports “pixel perfect” in terms of layout and positioning, developers from the IT staff had to be involved, taking time away from other internal projects.

Mainline provided a configuration utility to create a bridge between the legacy interface and IBM Business Analytics, solving the systems integration challenge. Separate reports have to come together and look like a unified document that has its own table of contents, and this required customization of IBM Business Analytics. The integration was an iterative process, developing and defining in tandem as the customer’s requirements changed. Mainline’s agility allowed IT to deliver exactly what marketing, client services, and other stakeholders wanted.

The customer now has enhanced functionality and flexibility in producing client statements. Investment portfolio statements can be generated much faster and contain more graphical depictions and footnoting than before, making them easier for customers to interpret. Account executives can add their own personalized annotations for customers, strengthening relationships. And because client statement generation is now entirely user driven, IT no longer needs to be involved. With richer data and reporting comes the potential for increased sales. The customer has increased its usage of IBM Business Analytics and is now developing a self-service reporting portal that will allow clients to generate reports online at any time.

COMPANY:   Diversified financial services company HEADQUARTERS:   Southeastern U.S. EMPLOYEES: 6,200+

  • Seamlessly integrated multiple systems into a single user interface
  • Provided the groundwork for better customer service
  • Enhanced security
  • Saved users valuable time with single sign-on
  • Improved ability to recruit top-notch financial advisors

Having made a strong investment in Microsoft technologies, including SharePoint and SQL Server, a financial services company wanted to continue to use these tools for document management and workflow while implementing a powerful business analytics platform. The legacy portal that the customer was using was old and had no integration with SharePoint. Financial advisors had to locate reports in this separate system, which was often slow, and frequently they had to contact IT to resolve issues and get necessary reports. The customer needed more efficiency and interactivity in the reporting process.

Mainline conducted a highly customized implementation that integrated IBM Business Analytics with SharePoint and Microsoft SQL Server Analysis Services. The customer’s user interface requirements were to retain the Microsoft look and feel while creating an enterprise portal powered by IBM Business Analytics “behind the scenes.” Mainline’s expertise with the IBM Business Analytics Software Development Kit allowed it to achieve this level of integration between the IBM and Microsoft technology stacks, as well as a SiteMinder security appliance. Mainline drove the architecture and solutions while remaining agile, as business requirements were continually being revised.

Financial advisors now have direct access to all of their reports in a unified environment. Because it’s no longer necessary to go to different systems to pull reports, the advisors can present accurate reports to their clients instantly and in-person, improving client satisfaction. The reporting environment is more stable as well, and financial advisors now have a high level of trust in the data. Due to integration with the SiteMinder security appliance, user credentials are passed seamlessly down to the data source, eliminating the need for users to log in multiple times. The new portal is being used as a recruiting tool for financial advisors, and Mainline continues to provide support for change management requests as the customer’s business changes and grows.

  • Cybersecurity & Networking
  • Cyber Storage
  • Data & Analytics
  • DevSecOps & Automation
  • Enterprise Systems
  • Hybrid Cloud
  • CIO Advisory Services
  • Data Center Optimization Services
  • Managed IT Services
  • Managed Maintenance & Support
  • Software Asset Management
  • Staffing Services
  • Dell Technologies
  • Pure Storage
  • Eco System Partners
  • Case Studies
  • Events / Webinars
  • News / Press Releases
  • OnDemand Videos / Podcasts
  • Tech Training
  • Why Mainline?
  • At a Glance
  • Executive Team
  • Community Involvement
  • Mainline Innovation Center

case study business analytics

  • Digital Marketing
  • Facebook Marketing
  • Instagram Marketing
  • Ecommerce Marketing
  • Content Marketing
  • Data Science Certification
  • Machine Learning
  • Artificial Intelligence
  • Data Analytics
  • Graphic Design
  • Adobe Illustrator
  • Web Designing
  • UX UI Design
  • Interior Design
  • Front End Development
  • Back End Development Courses
  • Business Analytics
  • Entrepreneurship
  • Supply Chain
  • Financial Modeling
  • Corporate Finance
  • Project Finance
  • Harvard University
  • Stanford University
  • Yale University
  • Princeton University
  • Duke University
  • UC Berkeley
  • Harvard University Executive Programs
  • MIT Executive Programs
  • Stanford University Executive Programs
  • Oxford University Executive Programs
  • Cambridge University Executive Programs
  • Yale University Executive Programs
  • Kellog Executive Programs
  • CMU Executive Programs
  • 45000+ Free Courses
  • Free Certification Courses
  • Free DigitalDefynd Certificate
  • Free Harvard University Courses
  • Free MIT Courses
  • Free Excel Courses
  • Free Google Courses
  • Free Finance Courses
  • Free Coding Courses
  • Free Digital Marketing Courses

Top 10 Marketing Analytics Case Studies [2024]

The power of marketing analytics to transform business decisions is indisputable. Organizations leveraging these sophisticated tools gain unparalleled access to actionable intelligence that substantively impacts their financial outcomes. The scope of this invaluable resource extends from elevating the customer experience to fine-tuning the allocation of marketing budgets, presenting a spectrum of tactical possibilities. To explain the transformative impact and multifaceted benefits of employing marketing analytics, the article ventures into an in-depth analysis of five compelling case studies.

Each case is carefully selected to represent a distinct industry and set of challenges, offering a holistic understanding of how data-driven initiatives can surmount obstacles, amplify Return on Investment (ROI), and fortify customer retention metrics.

Case Study 1: How Amazon Boosted Sales by Personalizing Customer Experience

The situation: a tricky problem in early 2019.

Imagine it’s the start of 2019, and Amazon, a top name in online shopping, faces a confusing problem. Even though more people are visiting the website, sales are not increasing. It is a big deal, and everyone at Amazon wonders what’s happening.

The Problem: Complex Challenges

Figuring out the root problem was not easy. Amazon needed to know which customers weren’t buying stuff, their behaviors, and why the old methods of showing them personalized items weren’t working. It was a complicated issue that needed a smart and modern solution.

Related: Role of Data Analytics in B2B Marketing

The Solution: Using Advanced Tools

That’s when Amazon decided to use more advanced marketing tools. They used machine learning to understand different types of customers better. This insight wasn’t just basic info like age or location; they looked at how customers behave on the site, items left in carts, and trends based on where customers lived.

The Key Numbers: What They Tracked

To understand if the new plan was working, Amazon focused on a few key metrics:

1. Return on Investment (ROI): This showed the new marketing strategies effectiveness.

2. Customer Lifetime Value (CLV): This KPI helped Amazon understand how valuable customers were over the long term.

3. Customer Acquisition Cost (CAC): This measured how costly it was to get new customers.

4. Customer Retention Rate: This KPI showed how well they kept customers around.

5. Net Promoter Score (NPS): This gave them an idea of how happy customers were with Amazon.

The Results: Big Improvements

The new plan worked well, thanks to advanced marketing analytics tools. In just three months, Amazon increased its sales by 25%. Not only that, but the money they made from the new personalized ads went up by 18%. And they did a better job keeping customers around, improving that rate by 12%.

Lessons Learned: What We Can Take Away

So, what did we learn from Amazon’s success?

1. Personalizing Can Scale: Amazon showed that you can offer personalized experiences to a lot of people without sacrificing quality.

2. Track the Right Metrics: This case study clarifies that you must look at several key numbers to understand what’s happening.

3. Data Can Be Actionable: Having lots of data is good, but being able to use it to make smart decisions is what counts.

Related: Tips to Succeed with Marketing Analytics

Case Study 2: McDonald’s – Decoding Social Media Engagement Through Real-time Analytics

Setting the stage: a tantalizing opportunity beckons.

Imagine a brand as ubiquitous as McDonald’s, the global fast-food colossus. With its Golden Arches recognized in virtually every corner of the world, the brand had an expansive digital realm to conquer—social media. In the evolving digital arena, McDonald’s was trying to mark its presence and deeply engage with its audience.

The Maze of Complexity: A Web of Challenges

Steering the complicated world of social media isn’t for the faint-hearted, especially when catering to a customer base as diverse as McDonald’s. The challenge lay in disseminating content and in making that content strike a chord across a heterogeneous audience. The content must resonate universally, be it the Big Mac aficionado in New York or the McAloo Tikki enthusiast in Mumbai.

The Game Plan: A Data-driven Strategy

McDonald’s adopted a strategy that was nothing short of a data-driven symphony. Utilizing real-time analytics, the brand monitored a series of Key Performance Indicators (KPIs) to track the impact of its social media content:

1. Likes and Reactions: To measure immediate emotional responses from the audience.

2. Shares and Retweets: To gauge the virality potential of their content.

3. Impressions and Reach: To assess the scope and scale of engagement.

4. Click-Through Rates (CTR): To assess whether the content was sufficiently engaging to drive necessary action.

Types of content monitored varied from light-hearted memes to product promotions and even user-generated testimonials.

Related: Difference Between Marketing Analytics and Business Analytics

The Finale: Exceptional Outcomes and a Standing Ovation

The result? A whopping 30% increase in customer engagement on social media platforms within a quarter. But that’s not the end of the story. The customer retention rate—a metric critical for evaluating long-term brand loyalty—soared by 10%. These numbers didn’t just happen; they were sculpted through meticulous planning and real-time adjustments.

The Wisdom Gleaned: Eye-opening Insights and Key Takeaways

Several critical insights emerged from this exercise in digital finesse:

1. Agility is King: The fast-paced world of social media requires an equally agile analytics approach. Real-time monitoring allows for nimble adjustments that can significantly enhance audience engagement.

2. Diverse Audiences Require Tailored Approaches: The ‘one-size-fits-all’ approach is a fallacy in today’s digital age. Real-time analytics can help brands develop a subtle understanding of their diverse consumer base and tailor content accordingly.

3. Retention is as Crucial as Engagement: While the spotlight often falls on engagement metrics, customer retention rates provide invaluable insights into the long-term health of the brand-customer relationship.

4. Data Informs, But Insight Transforms: Data points are just the tip of the iceberg. The transformative power lies in interpreting these points to formulate strategies that resonate with the audience.

Related: VP of Marketing Interview Questions

Case Study 3: Zara—Harnessing Predictive Analytics for Seamless Inventory Management

The prelude: zara’s global dominance meets inventory complexities.

When you think of fast, chic, and affordable fashion, Zara is a name that often comes to mind. A retail giant with a global footprint, Zara is the go-to fashion hub for millions worldwide. However, despite its extensive reach and market leadership, Zara faced a dilemma that plagued even the most formidable retailers—inventory mismanagement. Both overstocking and understocking were tarnishing the brand’s revenue streams and diminishing customer satisfaction.

The Conundrum: A Dynamic Industry with Static Models

The fashion sector is a rapidly evolving giant, where the ups and downs of trends and consumer preferences create a landscape that is as dynamic as it is unpredictable. Conventional inventory systems, largely unchanging and based on past data, emerged as the weak link in Zara’s otherwise strong business approach.

The Tactical Shift: Machine Learning to the Rescue

Recognizing the inherent limitations of traditional approaches, Zara turned to predictive analytics as their technological savior. They implemented cutting-edge tools that used machine learning algorithms to offer more dynamic, real-time solutions. The tools were programmed to consider a multitude of variables:

1. Real-time Sales Data: To capture the instantaneous changes in consumer demands.

2. Seasonal Trends: To account for cyclical variations in sales.

3. Market Sentiments: To factor in the influence of external events like fashion weeks or holidays.

Related: MBA in Marketing Pros and Cons

The Metrics Under the Microscope

Zara’s analytics model put a spotlight on the following KPIs:

1. Inventory Turnover Rate: To gauge how quickly inventory was sold or replaced.

2. Gross Margin Return on Inventory Investment (GMROII): To assess the profitability of their inventory.

3. Stock-to-Sales Ratio: To balance the inventory levels with sales data.

4. Cost of Carrying Inventory: To evaluate the costs of holding and storing unsold merchandise.

The Aftermath: A Success Story Written in Numbers

The results were startlingly positive. Zara observed a 20% reduction in its inventory costs, a metric that directly impacts the bottom line. Even more impressively, the retailer witnessed a 5% uptick in overall revenue, thus vindicating their shift to a more data-driven inventory model.

The Gold Nuggets: Key Takeaways and Strategic Insights

1. Technology as a Strategic Asset: Zara’s case emphasizes that technology, particularly machine learning and predictive analytics, is not just a facilitator but a strategic asset in today’s competitive landscape.

2. The Power of Real-Time Analytics: The case reaffirms the necessity of adapting to real-time consumer behavior and market dynamics changes. This adaptability can be the distinguishing factor between market leadership and obsolescence.

3. Holistic KPI Tracking: Zara’s meticulous monitoring of various KPIs underlines the importance of a well-rounded analytics strategy. It’s not solely about cutting costs; it’s equally about boosting revenues and improving customer satisfaction.

4. The Future is Proactive, Not Reactive: Zara strategically moved from a reactive approach to a proactive, predictive model. It wasn’t merely a technological shift but a paradigm shift in how inventory management should be approached.

Related: Hobby Ideas for Marketing Leaders

Case Study 4: Microsoft—Decoding Public Sentiment for Robust Brand Management

Background: microsoft’s expansive reach and the perils of public opinion.

Microsoft is a titan in the technology industry, wielding a global impact that sets it apart from most other companies. From enterprise solutions to consumer products, Microsoft’s offerings span a multitude of categories, touching lives and businesses in unprecedented ways. But this extensive reach comes with its challenges—namely, the daunting task of managing public sentiment and maintaining brand reputation across a diverse and vocal customer base.

The Intricacies: Coping with a Data Deluge

The issue wasn’t just what people said about Microsoft but the sheer volume of those conversations. Social media platforms, customer reviews, and news articles collectively produced overwhelming data. Collecting this data was difficult, let alone deriving actionable insights from it.

The Playbook: Employing Sentiment Analysis for Real-time Insights

Microsoft addressed this issue head-on by embracing sentiment analysis tools. These tools, often leveraging Natural Language Processing (NLP) and machine learning, parsed through the voluminous data to categorize public sentiments into three buckets:

1. Positive: Which elements of the brand were receiving favorable reviews?

2. Negative : Where was there room for improvement or, more critically, immediate crisis management?

3. Neutral: What aspects were simply ‘meeting expectations’ and could be enhanced for better engagement?

Related: How to Become a Marketing Thought Leader?

Metrics that Mattered

Among the KPIs that Microsoft tracked were:

1. Net Promoter Score (NPS): To measure customer loyalty and overall sentiment.

2. Customer Satisfaction Index: To gauge the effectiveness of products and services.

3. Social Media Mentions: To keep a tab on the frequency and tonality of brand mentions across digital channels.

4. Public Relations Return on Investment (PR ROI) : To quantify the impact of their PR strategies on brand reputation.

Outcomes: A Leap in Brand Reputation and Diminished Negativity

The result was a 15% improvement in Microsoft’s Brand Reputation Score. Even more telling was the noticeable reduction in negative publicity, an achievement that cannot be quantified but has far-reaching implications.

Epilogue: Lessons Learned and Future Directions

Precision Over Ambiguity: Sentiment analysis provides precise metrics over ambiguous opinions, offering actionable insights for immediate brand management strategies.

1. Proactive Vs. Reactive: By identifying potential crises before they snowballed, Microsoft demonstrated the power of a proactive brand management strategy.

2. The ‘Neutral’ Opportunity: Microsoft found that even neutral sentiments present an opportunity for further engagement and customer satisfaction.

3. Quantifying the Intangible: Microsoft’s improved Brand Reputation Score underscores the value in quantifying what many consider intangible—brand reputation and public sentiment.

Related: Reasons Why Marketing Managers Get Fired

Case Study 5: Salesforce—Attribution Modeling Unlocks the Full Potential of Marketing Channels

Background: salesforce’s prowess meets marketing complexity.

Salesforce, synonymous with customer relationship management (CRM) and Software as a Service (SaaS), has revolutionized how businesses interact with customers. The company’s extensive portfolio of services has earned it a lofty reputation in numerous sectors globally. Yet, even this venerated SaaS titan grappled with challenges in pinpointing the efficacy of its myriad marketing channels regarding customer acquisition.

The Challenge: Decoding the Marketing Mix

Salesforce diversified its marketing investments across multiple channels—from search engine optimization (SEO) to pay-per-click (PPC) campaigns and email marketing. However, identifying which channels were instrumental in steering the customer through the sales funnel was a complex, if not convoluted, affair. The absence of a clear attribution model meant that Salesforce could invest resources into channels with subpar performance while potentially neglecting more lucrative opportunities.

The Solution: Attribution Modeling as the Rosetta Stone

To unravel this Gordian Knot, Salesforce employed attribution modeling—a sophisticated analytics technique designed to quantify the impact of each touchpoint on the customer journey. This model shed light on crucial metrics such as:

1. Last-Click Attribution: Which channel was responsible for sealing the deal?

2. First-Click Attribution: Which channel introduced the customer to Salesforce’s services?

3. Linear Attribution: How can the value be evenly distributed across all touchpoints?

4. Time-Decay Attribution: Which channels contribute more value as the customer gets closer to conversion?

The Dashboard of Key Performance Indicators (KPIs)

Among the KPIs that Salesforce monitored were:

1. Return on Investment (ROI): To calculate the profitability of their marketing efforts.

2. Customer Lifetime Value (CLV): To gauge the long-term value brought in by each acquired customer.

3. Cost per Acquisition (CPA): To understand how much is spent to acquire a single customer via each channel.

4. Channel Efficiency Ratio (CER): To evaluate the cost-effectiveness of each marketing channel.

Related: How to Become a Chief Marketing Officer?

Results: A Refined Marketing Strategy Paying Dividends

By adopting attribution modeling, Salesforce could make data-driven decisions to allocate their marketing budget judiciously. The outcome? A notable 10% surge in overall revenue and a 5% increase in ROI. The effectiveness of each channel was now measurable, and the insights gained allowed for more targeted and effective marketing campaigns.

Postscript: Reflective Takeaways and Industry Wisdom

1. Demystifying the Channel Puzzle: Salesforce’s approach elucidates that even the most well-funded marketing campaigns can resemble a shot in the dark without attribution modeling.

2. Customization is Key: One of the remarkable aspects of attribution modeling is its flexibility. Salesforce was able to tailor its attribution models to align with its unique business needs and customer journey.

3. Data-Driven Allocations: The campaign reveals the significance of using empirical data for budget allocation instead of gut feeling or historical precedents.

4. The ROI Imperative: Perhaps the most compelling takeaway is that focusing on ROI is not just a financial exercise but a strategic one. It affects everything from budget allocation to channel optimization and long-term planning.

Related: How Can CMO Use Marketing Analytics?

Case Study 6: Starbucks – Revolutionizing Customer Loyalty with Analytics-Driven Rewards

The backdrop: starbucks’ quest for enhanced customer loyalty.

Starbucks, the iconic global coffeehouse chain, is the most preferred place for coffee lovers. Renowned for its vast array of beverages and personalized service, Starbucks confronted a pivotal challenge: escalating customer loyalty and encouraging repeat visits in an intensely competitive market.

The Dilemma: Deciphering Consumer Desires in a Competitive Arena

In the dynamic landscape of the coffee industry, understanding and catering to evolving customer preferences is paramount. Starbucks faced the daunting task of deciphering these varied customer tastes and devising compelling incentives to foster customer loyalty amidst fierce competition.

The Strategic Overhaul: Leveraging Analytics in the Loyalty Program

Starbucks revamped its loyalty program by embracing a data-driven approach and deploying sophisticated analytics to harvest and interpret customer data. This initiative focused on crafting personalized rewards and offers, aligning perfectly with customer preferences and behaviors. The analytics framework delved into:

1. Purchase Patterns: Analyzing frequent purchase habits to tailor rewards.

2. Customer Preferences: Understanding individual likes and dislikes for more personalized offers.

3. Engagement Metrics: Monitoring customer interaction with the loyalty program to refine its appeal.

The Analytical Lens: Focused KPIs

Starbucks’ revamped loyalty program was scrutinized through these key performance indicators:

1. Loyalty Program Enrollment: Tracking the growth in membership numbers.

2. Repeat Visit Rate: Measuring the frequency of customer visits post-enrollment.

3. Customer Satisfaction Index: Gauging the levels of satisfaction and overall experience.

4. Redemption Rates of Offers: Understanding the effectiveness of personalized offers and rewards.

The Triumph: A Narrative of Success through Numbers

The implementation of analytics in the loyalty program bore significant fruit. Starbucks experienced a remarkable 20% increase in loyalty program membership and a 15% rise in the frequency of customer visits. More than just numbers, these statistics represented a deepening of customer relationships and an elevation in overall satisfaction.

The Crux of Wisdom: Essential Insights and Strategic Perspectives

1. Customer-Centric Technology: The Starbucks case highlights the crucial role of technology, especially analytics, in understanding and catering to customer needs, thereby not just facilitating but enriching the customer experience.

2. Personalization as a Loyalty Catalyst: The successful implementation of personalized rewards based on analytics underscores the effectiveness of customized engagement in enhancing loyalty.

3. Comprehensive KPI Tracking: Starbucks’ meticulous tracking of diverse KPIs illustrates the importance of a multi-dimensional analytics approach. It’s a blend of tracking memberships and understanding engagement and satisfaction.

4. Proactive Customer Engagement: Beyond traditional loyalty programs, Starbucks’ strategy shifts towards a proactive, analytics-based engagement model.

Related: Marketing Executive Interview Questions

Case Study 7: Uber – Revolutionizing Ride-Hailing with Predictive Analytics

Setting the scene: uber’s mission to refine ride-hailing.

Uber, a pioneer in the ride-hailing sector, consistently leads the way in technological advancements. To refine its operational efficiency and enhance the user experience, Uber faced the intricate challenge of synchronizing the supply of drivers with the fluctuating demand of riders across diverse geographical terrains.

The Challenge: Harmonizing Supply and Demand

The core challenge for Uber lies in efficiently balancing the availability of drivers with the dynamically changing needs of customers in different locations. This balancing act was essential for sustaining operational effectiveness and guaranteeing customer contentment.

The Strategic Move: Embracing Real-Time Data Analytics

In response, Uber turned to the power of real-time analytics. This strategic shift involved:

1. Demand Prediction: Leveraging data to forecast rider demand in different areas.

2. Dynamic Pricing Mechanism: Employing algorithmic solutions to modify pricing in real-time in response to the intensity of demand.

3. Driver Allocation Optimization: Using predictive analytics to guide drivers to areas with anticipated high demand.

Results: Measurable Gains in Efficiency and Satisfaction

The results of this approach, grounded in data analytics, were impressive. Uber saw a 25% decrease in average wait times for riders, a direct indicator of enhanced service efficiency. Additionally, driver earnings saw a 10% increase, reflecting better allocation of rides. Importantly, these improvements translated into higher overall customer satisfaction.

Related: Is Becoming a CMO Worth It?

Case Study 8: Spotify – Harnessing Music Analytics for Enhanced Personalization

Backstory: spotify’s pursuit of personalized music experience.

Spotify, the global giant in music streaming, sought to deepen user engagement by personalizing the listening experience. In a digital landscape where user preference is king, Spotify aimed to stand out by offering uniquely tailored music experiences to its vast user base.

The Challenge: Navigating a Sea of Diverse Musical Tastes

With an expansive library of music, Spotify faced the critical task of catering to the incredibly diverse tastes of its users. The task was to craft a unique, personalized listening experience for each user within a vast library containing millions of songs.

The Strategy: Leveraging Machine Learning for Custom Playlists

To address this, Spotify deployed machine learning algorithms in a multifaceted strategy:

1. Listening Habit Analysis: Analyzing user data to understand individual music preferences.

2. Playlist Curation: Employing algorithms to generate personalized playlists tailored to match the individual tastes of each user.

3. Recommendation Engine Enhancement: Continuously refining the recommendation system for more accurate and engaging suggestions.

Results: A Symphony of User Engagement and Loyalty

Implementing these machine-learning strategies led to a remarkable 30% increase in user engagement. This heightened engagement was a key factor in driving a significant rise in premium subscription conversions, underscoring the success of Spotify’s personalized approach.

Related: How Can Creating a Course Lead to Marketing Your Business?

Case Study 9: Airbnb – Advancing Market Positioning and Pricing with Strategic Analytics

Overview: airbnb’s quest for pricing and positioning excellence.

Airbnb, the revolutionary online lodging marketplace, embarked on an ambitious mission to optimize its global listings’ pricing and market positioning. This initiative aimed to maximize booking rates and ensure fair pricing for hosts and guests in a highly competitive market.

The Challenge: Mastering Competitive Pricing in a Diverse Market

Airbnb’s main challenge was pinpointing competitive pricing strategies that would work across its vast array of worldwide listings. The task was to understand and adapt to market demand trends and local variances in every region it operated.

The Strategic Approach: Dynamic Pricing Through Data Analytics

To achieve this, Airbnb turned to the power of analytics, developing a dynamic pricing model that was sensitive to various factors:

1. Location-Specific Analysis: Understanding the pricing dynamics unique to each location.

2. Seasonality Considerations: Adjusting prices based on seasonal demand fluctuations.

3. Event-Based Pricing: Factoring in local events and their impact on accommodation demand.

Results: A Story of Enhanced Performance and Satisfaction

This analytical approach reaped significant rewards. Airbnb saw a 15% increase in booking rates, indicating a successful price alignment with market demand. Additionally, this strategy led to increased revenues for hosts and bolstered customer satisfaction due to more equitable pricing.

Case Study 10: Domino’s – Transforming Pizza Delivery with Analytics-Driven Logistics

Background: domino’s drive for enhanced delivery and service.

Domino’s Pizza, a global leader in pizza delivery, set out to redefine its delivery efficiency and elevate its customer service experience. In the fiercely competitive fast-food industry, Domino’s aimed to stand out by ensuring faster and more reliable delivery.

The Challenge: Streamlining Deliveries in a Fast-Paced Environment

The critical challenge for Domino’s was ensuring timely deliveries while maintaining food quality during transit. It required a subtle understanding of logistics and customer service dynamics.

The Strategy: Optimizing Delivery with Data and Technology

Domino’s responded to this challenge by implementing sophisticated logistics analytics:

1. Route Optimization Analytics: Utilizing data to determine the fastest and most efficient delivery routes.

2. Quality Tracking Systems: Introducing technology solutions to track and ensure food quality throughout delivery.

Results: Measurable Gains in Efficiency and Customer Satisfaction

Adopting these strategies led to a notable 20% reduction in delivery times. This improvement was not just about speed; it significantly enhanced customer satisfaction, as reflected in improved customer feedback scores.

Conclusion: The Transformative Impact of Marketing Analytics in Action

Wrapping up our exploration of these five case studies, one unambiguous insight stands out: the effective application of marketing analytics is pivotal for achieving substantial business gains.

1. Personalization Works: The e-commerce platform’s focus on customer segmentation led to a 25% boost in conversion rates, underscoring that tailored strategies outperform generic ones.

2. Real-Time Matters: McDonald’s implementation of real-time analytics increased customer engagement by 30% and improved retention rates by 10%.

3. Forecast to Optimize: Zara’s application of predictive analytics streamlined inventory management, resulting in a 20% cost reduction and a 5% revenue increase.

4. Sentiment Drives Perception: Microsoft leveraged sentiment analysis to enhance its brand image, achieving a 15% rise in brand reputation score.

5. Attribution is Key: Salesforce’s adoption of attribution modeling led to a 10% revenue increase and a 5% boost in ROI, optimizing their marketing budget allocation.

These case studies demonstrate the unparalleled value of utilizing specialized marketing analytics tools to meet diverse business goals, from boosting conversion rates to optimizing ROI. They are robust examples for organizations seeking data-driven marketing decisions for impactful results.

  • Top 30 Finance Leadership Interview Questions and Answers [2024]
  • Top 40 COO Interview Questions and Answers [2024]

Team DigitalDefynd

We help you find the best courses, certifications, and tutorials online. Hundreds of experts come together to handpick these recommendations based on decades of collective experience. So far we have served 4 Million+ satisfied learners and counting.

case study business analytics

How Can Data Engineering Be Used in Marketing? [2024]

case study business analytics

Pros and Cons of Marketing Specialization in MBA [2024]

case study business analytics

12 Pros and Cons of Influencer Marketing [2024]

case study business analytics

Use of Predictive Analytics For Risk Management and Fraud Detection[2024]

case study business analytics

How Can CTOs Excel at Marketing? [2024]

case study business analytics

Why Marketing Leaders need to be Lifelong Learners [2024]

AUXIS

  • Shared Services
  • Nearshore Outsourcing
  • Digital Transformation
  • Finance Operations
  • Business Operations
  • IT Operations
  • M&A and Private Equity
  • GBS & Shared Services
  • Implementation
  • Optimization
  • Business Process Outsourcing
  • IT Outsourcing
  • Intelligent Automation
  • Digital Strategy Consulting
  • RPA (Robotic Process Automation)
  • Intelligent Document Processing
  • Test Automation
  • BI & Analytics
  • Key Partnerships
  • Cloud Transformation
  • Cloud Strategy
  • Cloud Implementation
  • Cloud Managed Services
  • Modern Finance
  • Finance Transformation Services
  • Business Intelligence
  • Finance & Accounting Outsourcing
  • Accounts Payable (P2P)
  • Accounts Receivable (O2C)
  • General Accounting (R2R)
  • Customer Service
  • Customer Service Support
  • Human Resources
  • HR Outsourcing
  • Revenue Cycle Management
  • Banking & Financial Services
  • Loan Processing
  • Restaurants
  • Restaurant Audit & Brand Protection Services
  • Infrastructure Management
  • Hybrid Infrastructure Management
  • End-User Support
  • Service Desk
  • IT Staffing
  • Nearshore Software Development
  • Cloud Partners
  • Azure Services
  • AWS Services
  • Integrations and Carve Outs
  • Private Equity
  • Private Equity Services
  • Delivery Centers
  • Support Hubs

Industry expertise matters. We provide specialized IT and business services across all the major industries.

  • Financial Services
  • Other Industries
  • Webinars on Demand
  • Upcoming Events
  • Success Stories

Learn how Auxis’ nearshore solutions strengthen processes and increase efficiency.

  • Case Studies
  • Our Clients & Testimonials
  • About Auxis

Experience matters. We help clients overcome challenges, embrace change, and adapt their businesses for future success.

  • Leadership Team

></center></p><h2>Business Analytics Case Study for Global Hospitality & Restaurant Company</h2><p>Client profile.</p><p>Our hospitality client is a leading developer of global, multi-channel food service brands, delivering 100+ products and $1B+ in annual retail sales. Founded in 2004, the private equity-backed corporation franchises and operates 6,400+ restaurants, cafes, ice cream shops, and bakeries in the U.S., Puerto Rico, and 55+ foreign countries.</p><h2>Business Challenge</h2><p>Foodservice corporations like our client maintain thousands of stores across a wealth of global markets. With so many franchised locations, ensuring customers receive consistent, positive experiences and product quality across stores wherever they go is a major priority.  </p><p>The ability to make informed, agile decisions about product mix, sales, and business development opportunities like rebranding or remodeling are also essential ingredients for growing revenue and measuring performance in the hyper-competitive foodservice industry.</p><p>However, this client lacked   consolidated, real-time visibility into sales, foot traffic, and brand quality across its 1,650+ international locations .</p><p>While some information existed piecemeal across different reports, the inability to combine sources made it difficult to accurately measure sales and quality in terms of single stores, franchisees, and regions. For instance, comparing data on Thanksgiving sales in a region to the previous year or actual vs. planned revenue for a franchisee.  </p><p>As a result, leadership often spent many cycles identifying locations with areas of opportunity .  </p><p>The client had also recently partnered with Auxis to build a Customer Experience Center of Excellence (CoE) at the  Auxis Global Outsourcing Center in Costa Rica . Rapid-fire growth and pandemic restrictions have made it difficult for our client field operators or brand coaches to visit every international store to ensure they meet quality standards. </p><p>Instead, brand coaches at the Auxis CoE leverage top-notch virtual tools to help franchisees operate at the excellence the client expects for its locations without physically being in the stores, gaining the ability to visit more often and more cost-effectively.  </p><p>A real-time, consolidated data view would also maximize the benefits of CoE quality audits ; for instance, helping leadership gauge correlations between improved audit scores and sales at a single store. </p><h2>Solution & Approach</h2><p>In this Business Analytics Case Study, we show how this client partnered with Auxis once again to build an advanced analytics team led by an in-house subject matter expert with a Ph.D. in data science.</p><p>Key steps included:</p><h2>1. Determining key business questions.</h2><p>Auxis came to the table with 25+ years of delivering advisory services that help businesses achieve peak performance and deep restaurant industry experience. It began by helping our client leadership identify key business questions for driving business strategy and growth. For instance, is foot traffic up or down? Does the cleanliness of a store impact long-term performance? Is this region performing better than that region?</p><p>Our client leadership provided an initial checklist of data it wanted to track. But unlike technical analytics providers who don’t also provide business expertise, the Auxis team worked as a strategic advisor to the client , helping design KPIs and metrics that effectively monitor and manage its international business.</p><p>Auxis experts led daily brainstorming sessions with leadership to build analytics that made the most sense for their business goals, ensuring they understand decisions that different data points could enable and business benefits.</p><h2>2. Identifying 4 key data dimensions.</h2><p>As teams continued to identify strategic questions, Auxis provided the flexibility to tweak dashboards and add new data points throughout the project . Ultimately, the Auxis team helped the client zero in on 4 impactful data dimensions:</p><ul><li>Sales.  Leadership has visibility into key data points such as year-over-year growth percentages, foot traffic, budget vs. actual sales, sales trends at different locations like airports and hospitals, regional comparisons, and more.</li><li>Brand quality.  Leadership can measure quality performance as well as the success of the CoE coaching program within various regions. For instance, they can easily view the CoE’s market penetration and determine key areas of improvement by market or individual stores based on audit scores.</li><li>Product mix.  Data points help identify the biggest drivers from a product perspective, drilling down into upsale drivers for other items like beverages, as well as time of day and channels like Uber Eats or to-go orders that deliver the best sales.</li><li>Business development.  Data helps leadership determine the best ways to invest marketing and business development dollars, tracking the impact of store openings, rebrandings, remodelings, product/category launches, and more.</li></ul><h2>3. Data gap analysis.</h2><p>Data quality stands as a common stumbling block to a successful analytics journey. Many businesses know their data isn’t good enough to enable informed decisions but are unsure where to start fixing problems.</p><p>For the client, the Auxis team determined which business questions could be answered immediately with available data. Then Auxis identified necessary changes to provide answers to other important questions in the long-term , such as improving data accuracy and timeliness. </p><h2>4. Microsoft Power BI analytics.</h2><p>After building a roadmap for answering key business questions in the short- and long-term, Auxis delivered a single Power BI app that offers the client leadership visualizations that provide detailed and customizable visibility into their business. Not only do dashboards offer a 10,000-foot view, but analysis can also be drilled down by market, country, region, or single stores .</p><p>To seamlessly support the Power BI dashboards, Auxis consolidated the client data from different sources into a centralized data warehouse – ensuring data flows from a single location and is summarized properly . </p><h2>Download the Case Study to see the Results</h2><p>Complete the form to receive your PDF of this case study.</p><p>" * " indicates required fields</p><h2>Submit the form to get your copy</h2><p>Related content, brand audit: how often should you visit your stores.</p><ul><li>February 16, 2024</li></ul><h2>2024 Guide: Best RPA Tools and Why UiPath is #1</h2><ul><li>February 1, 2024</li></ul><h2>A New Brand Protection Strategy for Restaurants</h2><ul><li>January 30, 2024</li></ul><h2>Private Equity Carve-Out Best Practices in Finance & Accounting</h2><ul><li>November 28, 2023</li></ul><h2>5 Steps for Building a Successful Cloud Migration Strategy</h2><ul><li>October 30, 2023</li></ul><h2>AI-Powered Automation at UiPath’s Forward VI</h2><ul><li>October 25, 2023</li></ul><h2>Get the latest from Auxis in your Inbox</h2><p>Email subscription footer.</p><ul><li>M&A Private Equity</li><li>Social Responsibility</li><li>Whitepapers & Guides</li><li>Career Opportunities</li></ul><h2>Supporting Hubs</h2><ul><li>Barranquilla, Colombia Medellin, Colombia Mexico City, Mexico</li></ul><p>© 2024 Auxis. All Rights Reserved</p><p><center><img style=

Join us at the #1 GBS Conference

Shared Services & Outsourcing Week

March 25-28, 2024

Rosen Shingle Creek, Orlando, FL

Get an exclusive 20% Discount Code

As a proud SSOW sponsor, we are pleased to offer you an exclusive 20% registration discount.

case study business analytics

New Client Special Offer

JavaScript seems to be disabled in your browser. You must have JavaScript enabled in your browser to utilize the functionality of this website.

  • My Wishlist
  • Customer Login / Registration

FB Twitter linked in Youtube G+

Buy Case Studies Online

  • ORGANIZATIONAL BEHAVIOR
  • MARKETING MANAGEMENT
  • STATISTICS FOR MANAGEMENT
  • HUMAN RESOURCE MANAGEMENT
  • STRATEGIC MANAGEMENT
  • OPERATIONS MANAGEMENT
  • MANAGERIAL ECONOMICS
  • FINANCIAL MANAGEMENT
  • CONSUMER BEHAVIOR
  • BRAND MANAGEMENT
  • MARKETING RESEARCH
  • SUPPLY CHAIN MANAGEMENT
  • ENTREPRENEURSHIP & STARTUPS
  • CORPORATE SOCIAL RESPONSIBILITY
  • INFORMATION TECHNOLOGY
  • BANKING & FINANCIAL SERVICES
  • CUSTOMER RELATIONSHIP MANAGEMENT
  • ADVERTISING

BUSINESS ANALYTICS

  • BUSINESS ETHICS
  • DIGITAL MARKETING
  • HEALTHCARE MANAGEMENT
  • SALES AND DISTRIBUTION MANAGEMENT
  • FAMILY BUSINESS
  • MEDIA AND ENTERTAINMENT
  • CORPORATE CASES
  • Case Debate
  • Course Case Maps
  • Sample Case Studies
  • IIM KOZHIKODE
  • VINOD GUPTA SCHOOL OF MANAGEMENT, IIT KHARAGPUR
  • GSMC - IIM RAIPUR
  • IMT GHAZIABAD
  • INSTITUTE OF PUBLIC ENTERPRISE
  • IBM Corp. & SAP SE
  • Classroom Classics
  • Free Products
  • Case Workshops
  • Home       
  • Case Categories       

case study business analytics

A Bookseller’s Dilemma on the Subscription Decision

Phillips carbon black limited: drives lean, cost-efficient growth with integrated workflows on sap s/4hana in the aws cloud, parking space dilemma at express mall, mcpl: brand switching analysis and forecasting using markov chain model, analyzing consumer behavior of detergent purchases: local vs national brands, dilemma of selecting the right students for the business analytics program, arogya juice products’ expansion: examining consumers’ preferences of products, demand estimation of online budget hotel service, differential choice behavior among mba aspirants, analytics for all - how analytics can solve organizational and individual problems simultaneously: a case from the global learning industry*, competitive intelligence for a medical device company in india*.

  • last 6 months (0)
  • last 12 months (0)
  • last 24 months (0)
  • older than 24 months (11)
  • BUSINESS ANALYTICS (11)
  • Electronics Manufacturing (1)
  • CASE STUDY (4)
  • CASELET (7)

Information

  • Collaborations
  • Privacy Policy
  • Terms & Conditions
  • Case Format
  • Pricing and Discount
  • Subscription Model
  • Case Writing Workshop
  • Case Submission
  • Reprint Permissions

CUSTOMER SERVICE

Phone: +91 9626264881

             

Email:  [email protected]

ET CASES develops customized case studies for corporate organizations / government and non-government institutions. Once the query  is generated, one of ET CASES’ Case Research Managers will undertake primary/secondary research and develop the case study. Please send an e-mail to [email protected] to place a query or get in touch with us.

Don’t miss out!

Be the first to hear about new cases, special promotions and more – just pop your email in the box below.

Data Analytics Case Study Guide (Updated for 2023)

Data Analytics Case Study Guide (Updated for 2023)

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

Data Analytics Case Study Vide Guide

With data analyst case questions, you will need to answer two key questions:

  • What metrics should I propose?
  • How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

Data Analytics Case Study Question: Sample Solution

Data Analytics Case Study Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

  • If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
  • If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

product analytics on screen

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

  • Who uses this social network? How do they interact with each other?
  • Has there been any performance issues that might be causing the problem?
  • What are the goals of this particular launch?
  • Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

  • The decreasing comments issue isn’t a result of a declining user base.
  • The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

  • January: 10000 users, 30000 comments, 3 comments/user
  • February: 20000 users, 50000 comments, 2.5 comments/user
  • March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

7 Favorite Business Case Studies to Teach—and Why

Explore more.

  • Case Teaching
  • Course Materials

FEATURED CASE STUDIES

The Army Crew Team . Emily Michelle David of CEIBS

ATH Technologies . Devin Shanthikumar of Paul Merage School of Business

Fabritek 1992 . Rob Austin of Ivey Business School

Lincoln Electric Co . Karin Schnarr of Wilfrid Laurier University

Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth . Gary Pisano of Harvard Business School

The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron . Francesca Gino of Harvard Business School

Warren E. Buffett, 2015 . Robert F. Bruner of Darden School of Business

To dig into what makes a compelling case study, we asked seven experienced educators who teach with—and many who write—business case studies: “What is your favorite case to teach and why?”

The resulting list of case study favorites ranges in topics from operations management and organizational structure to rebel leaders and whodunnit dramas.

1. The Army Crew Team

Emily Michelle David, Assistant Professor of Management, China Europe International Business School (CEIBS)

case study business analytics

“I love teaching  The Army Crew Team  case because it beautifully demonstrates how a team can be so much less than the sum of its parts.

I deliver the case to executives in a nearby state-of-the-art rowing facility that features rowing machines, professional coaches, and shiny red eight-person shells.

After going through the case, they hear testimonies from former members of Chinese national crew teams before carrying their own boat to the river for a test race.

The rich learning environment helps to vividly underscore one of the case’s core messages: competition can be a double-edged sword if not properly managed.

case study business analytics

Executives in Emily Michelle David’s organizational behavior class participate in rowing activities at a nearby facility as part of her case delivery.

Despite working for an elite headhunting firm, the executives in my most recent class were surprised to realize how much they’ve allowed their own team-building responsibilities to lapse. In the MBA pre-course, this case often leads to a rich discussion about common traps that newcomers fall into (for example, trying to do too much, too soon), which helps to poise them to both stand out in the MBA as well as prepare them for the lateral team building they will soon engage in.

Finally, I love that the post-script always gets a good laugh and serves as an early lesson that organizational behavior courses will seldom give you foolproof solutions for specific problems but will, instead, arm you with the ability to think through issues more critically.”

2. ATH Technologies

Devin Shanthikumar, Associate Professor of Accounting, Paul Merage School of Business

case study business analytics

“As a professor at UC Irvine’s Paul Merage School of Business, and before that at Harvard Business School, I have probably taught over 100 cases. I would like to say that my favorite case is my own,   Compass Box Whisky Company . But as fun as that case is, one case beats it:  ATH Technologies  by Robert Simons and Jennifer Packard.

ATH presents a young entrepreneurial company that is bought by a much larger company. As part of the merger, ATH gets an ‘earn-out’ deal—common among high-tech industries. The company, and the class, must decide what to do to achieve the stretch earn-out goals.

ATH captures a scenario we all want to be in at some point in our careers—being part of a young, exciting, growing organization. And a scenario we all will likely face—having stretch goals that seem almost unreachable.

It forces us, as a class, to really struggle with what to do at each stage.

After we read and discuss the A case, we find out what happens next, and discuss the B case, then the C, then D, and even E. At every stage, we can:

see how our decisions play out,

figure out how to build on our successes, and

address our failures.

The case is exciting, the class discussion is dynamic and energetic, and in the end, we all go home with a memorable ‘ah-ha!’ moment.

I have taught many great cases over my career, but none are quite as fun, memorable, and effective as ATH .”

3. Fabritek 1992

Rob Austin, Professor of Information Systems, Ivey Business School

case study business analytics

“This might seem like an odd choice, but my favorite case to teach is an old operations case called  Fabritek 1992 .

The latest version of Fabritek 1992 is dated 2009, but it is my understanding that this is a rewrite of a case that is older (probably much older). There is a Fabritek 1969 in the HBP catalog—same basic case, older dates, and numbers. That 1969 version lists no authors, so I suspect the case goes even further back; the 1969 version is, I’m guessing, a rewrite of an even older version.

There are many things I appreciate about the case. Here are a few:

It operates as a learning opportunity at many levels. At first it looks like a not-very-glamorous production job scheduling case. By the end of the case discussion, though, we’re into (operations) strategy and more. It starts out technical, then explodes into much broader relevance. As I tell participants when I’m teaching HBP's Teaching with Cases seminars —where I often use Fabritek as an example—when people first encounter this case, they almost always underestimate it.

It has great characters—especially Arthur Moreno, who looks like a troublemaker, but who, discussion reveals, might just be the smartest guy in the factory. Alums of the Harvard MBA program have told me that they remember Arthur Moreno many years later.

Almost every word in the case is important. It’s only four and a half pages of text and three pages of exhibits. This economy of words and sparsity of style have always seemed like poetry to me. I should note that this super concise, every-word-matters approach is not the ideal we usually aspire to when we write cases. Often, we include extra or superfluous information because part of our teaching objective is to provide practice in separating what matters from what doesn’t in a case. Fabritek takes a different approach, though, which fits it well.

It has a dramatic structure. It unfolds like a detective story, a sort of whodunnit. Something is wrong. There is a quality problem, and we’re not sure who or what is responsible. One person, Arthur Moreno, looks very guilty (probably too obviously guilty), but as we dig into the situation, there are many more possibilities. We spend in-class time analyzing the data (there’s a bit of math, so it covers that base, too) to determine which hypotheses are best supported by the data. And, realistically, the data doesn’t support any of the hypotheses perfectly, just some of them more than others. Also, there’s a plot twist at the end (I won’t reveal it, but here’s a hint: Arthur Moreno isn’t nearly the biggest problem in the final analysis). I have had students tell me the surprising realization at the end of the discussion gives them ‘goosebumps.’

Finally, through the unexpected plot twist, it imparts what I call a ‘wisdom lesson’ to young managers: not to be too sure of themselves and to regard the experiences of others, especially experts out on the factory floor, with great seriousness.”

4. Lincoln Electric Co.

Karin Schnarr, Assistant Professor of Policy, Wilfrid Laurier University

case study business analytics

“As a strategy professor, my favorite case to teach is the classic 1975 Harvard case  Lincoln Electric Co.  by Norman Berg.

I use it to demonstrate to students the theory linkage between strategy and organizational structure, management processes, and leadership behavior.

This case may be an odd choice for a favorite. It occurs decades before my students were born. It is pages longer than we are told students are now willing to read. It is about manufacturing arc welding equipment in Cleveland, Ohio—a hard sell for a Canadian business classroom.

Yet, I have never come across a case that so perfectly illustrates what I want students to learn about how a company can be designed from an organizational perspective to successfully implement its strategy.

And in a time where so much focus continues to be on how to maximize shareholder value, it is refreshing to be able to discuss a publicly-traded company that is successfully pursuing a strategy that provides a fair value to shareholders while distributing value to employees through a large bonus pool, as well as value to customers by continually lowering prices.

However, to make the case resonate with today’s students, I work to make it relevant to the contemporary business environment. I link the case to multimedia clips about Lincoln Electric’s current manufacturing practices, processes, and leadership practices. My students can then see that a model that has been in place for generations is still viable and highly successful, even in our very different competitive situation.”

5. Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth

Gary Pisano, Professor of Business Administration, Harvard Business School

case study business analytics

“My favorite case to teach these days is  Pal’s Sudden Service—Scaling an Organizational Model to Drive Growth .

I love teaching this case for three reasons:

1. It demonstrates how a company in a super-tough, highly competitive business can do very well by focusing on creating unique operating capabilities. In theory, Pal’s should have no chance against behemoths like McDonalds or Wendy’s—but it thrives because it has built a unique operating system. It’s a great example of a strategic approach to operations in action.

2. The case shows how a strategic approach to human resource and talent development at all levels really matters. This company competes in an industry not known for engaging its front-line workers. The case shows how engaging these workers can really pay off.

3. Finally, Pal’s is really unusual in its approach to growth. Most companies set growth goals (usually arbitrary ones) and then try to figure out how to ‘backfill’ the human resource and talent management gaps. They trust you can always find someone to do the job. Pal’s tackles the growth problem completely the other way around. They rigorously select and train their future managers. Only when they have a manager ready to take on their own store do they open a new one. They pace their growth off their capacity to develop talent. I find this really fascinating and so do the students I teach this case to.”

6. The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron

Francesca Gino, Professor of Business Administration, Harvard Business School

case study business analytics

“My favorite case to teach is  The United States Air Force: ‘Chaos’ in the 99th Reconnaissance Squadron .

The case surprises students because it is about a leader, known in the unit by the nickname Chaos , who inspired his squadron to be innovative and to change in a culture that is all about not rocking the boat, and where there is a deep sense that rules should simply be followed.

For years, I studied ‘rebels,’ people who do not accept the status quo; rather, they approach work with curiosity and produce positive change in their organizations. Chaos is a rebel leader who got the level of cultural change right. Many of the leaders I’ve met over the years complain about the ‘corporate culture,’ or at least point to clear weaknesses of it; but then they throw their hands up in the air and forget about changing what they can.

Chaos is different—he didn’t go after the ‘Air Force’ culture. That would be like boiling the ocean.

Instead, he focused on his unit of control and command: The 99th squadron. He focused on enabling that group to do what it needed to do within the confines of the bigger Air Force culture. In the process, he inspired everyone on his team to be the best they can be at work.

The case leaves the classroom buzzing and inspired to take action.”

7. Warren E. Buffett, 2015

Robert F. Bruner, Professor of Business Administration, Darden School of Business

case study business analytics

“I love teaching   Warren E. Buffett, 2015  because it energizes, exercises, and surprises students.

Buffett looms large in the business firmament and therefore attracts anyone who is eager to learn his secrets for successful investing. This generates the kind of energy that helps to break the ice among students and instructors early in a course and to lay the groundwork for good case discussion practices.

Studying Buffett’s approach to investing helps to introduce and exercise important themes that will resonate throughout a course. The case challenges students to define for themselves what it means to create value. The case discussion can easily be tailored for novices or for more advanced students.

Either way, this is not hero worship: The case affords a critical examination of the financial performance of Buffett’s firm, Berkshire Hathaway, and reveals both triumphs and stumbles. Most importantly, students can critique the purported benefits of Buffett’s conglomeration strategy and the sustainability of his investment record as the size of the firm grows very large.

By the end of the class session, students seem surprised with what they have discovered. They buzz over the paradoxes in Buffett’s philosophy and performance record. And they come away with sober respect for Buffett’s acumen and for the challenges of creating value for investors.

Surely, such sobriety is a meta-message for any mastery of finance.”

More Educator Favorites

CASE TEACHING

Emily Michelle David is an assistant professor of management at China Europe International Business School (CEIBS). Her current research focuses on discovering how to make workplaces more welcoming for people of all backgrounds and personality profiles to maximize performance and avoid employee burnout. David’s work has been published in a number of scholarly journals, and she has worked as an in-house researcher at both NASA and the M.D. Anderson Cancer Center.

case study business analytics

Devin Shanthikumar  is an associate professor and the accounting area coordinator at UCI Paul Merage School of Business. She teaches undergraduate, MBA, and executive-level courses in managerial accounting. Shanthikumar previously served on the faculty at Harvard Business School, where she taught both financial accounting and managerial accounting for MBAs, and wrote cases that are used in accounting courses across the country.

case study business analytics

Robert D. Austin is a professor of information systems at Ivey Business School and an affiliated faculty member at Harvard Medical School. He has published widely, authoring nine books, more than 50 cases and notes, three Harvard online products, and two popular massive open online courses (MOOCs) running on the Coursera platform.

case study business analytics

Karin Schnarr is an assistant professor of policy and the director of the Bachelor of Business Administration (BBA) program at the Lazaridis School of Business & Economics at Wilfrid Laurier University in Waterloo, Ontario, Canada where she teaches strategic management at the undergraduate, graduate, and executive levels. Schnarr has published several award-winning and best-selling cases and regularly presents at international conferences on case writing and scholarship.

case study business analytics

Gary P. Pisano is the Harry E. Figgie, Jr. Professor of Business Administration and senior associate dean of faculty development at Harvard Business School, where he has been on the faculty since 1988. Pisano is an expert in the fields of technology and operations strategy, the management of innovation, and competitive strategy. His research and consulting experience span a range of industries including aerospace, biotechnology, pharmaceuticals, specialty chemicals, health care, nutrition, computers, software, telecommunications, and semiconductors.

case study business analytics

Francesca Gino studies how people can have more productive, creative, and fulfilling lives. She is a professor at Harvard Business School and the author, most recently, of  Rebel Talent: Why It Pays to Break the Rules at Work and in Life . Gino regularly gives keynote speeches, delivers corporate training programs, and serves in advisory roles for firms and not-for-profit organizations across the globe.

case study business analytics

Robert F. Bruner is a university professor at the University of Virginia, distinguished professor of business administration, and dean emeritus of the Darden School of Business. He has also held visiting appointments at Harvard and Columbia universities in the United States, at INSEAD in France, and at IESE in Spain. He is the author, co-author, or editor of more than 20 books on finance, management, and teaching. Currently, he teaches and writes in finance and management.

Related Articles

INTERACTIVE LEARNING

We use cookies to understand how you use our site and to improve your experience, including personalizing content. Learn More . By continuing to use our site, you accept our use of cookies and revised Privacy Policy .

case study business analytics

Table of Contents

Business analytics defined, applications of business analytics in various industries, business analytics applications, usage of business analytics, become a business analyst, business analytics applications and notable use cases.

Business Analytics Applications and Notable Use Cases

Businesses today are faced with two very stark realities—the world is hyper-competitive, and data drive it. Companies that have the best information make the fewest mistakes, which in turn helps them to stay ahead of the pack.

Today’s digital society, through the explosion of Big Data and the Internet of Things (IoT) , has produced a ton of information. The challenge is to make any sense of all this data. With all of that information, who can sort out what’s useful and what’s not? That’s why business analytics is essential for today’s industries, and business analysts are in high demand. Today, we’re taking a look at popular business analytics applications, and some of the often-used cases.

Before launching into the meat of the matter, let’s take a moment to review. What’s the definition of business analytics? Business analytics involves the collating, sorting, processing, and studying of business-related data using statistical models and iterative methodologies. The ultimate goal is to glean practical and actionable business insights to solve an organization’s problems—boosting efficiency, productivity, and revenue.

Note that there’s a difference between business analytics and business intelligence (BI), though they are related. Business intelligence falls within the discipline of business analytics, the process of gathering the needed data from all sources, and preparing it for use by business analysts. In short, BI tells you what’s going on, and business analytics tells you why it’s happening and when it will occur again. So, a business analyst identifies a company’s weak areas, collects and sifts through data, creates a plan based on those findings, and helps to implement it.

Become a Business and Leadership Professional

  • Top 10 skills in demand Business Analysis As A Skill In 2020
  • 14% Growth in Jobs Of Business Analysis Profile By 2028

Business Analyst

  • In collaboration with IBM
  • IIBA Endorsed Education Provider

Post Graduate Program in Business Analysis

  • Certificate from Simplilearn in collaboration with Purdue University
  • Become eligible to be part of the Purdue University Alumni Association

Here's what learners are saying regarding our programs:

Sauvik Pal

Assistant Consultant at Tata Consultancy Services , Tata Consultancy Services

My experience with Simplilearn has been great till now. They have good materials to start with, and a wide range of courses. I have signed up for two courses with Simplilearn over the past 6 months, Data Scientist and Agile and Scrum. My experience with both is good. One unique feature I liked about Simplilearn is that they give pre-requisites that you should complete, before a live class, so that you go there fully prepared. Secondly, there support staff is superb. I believe there are two teams, to cater to the Indian and US time zones. Simplilearn gives you the most methodical and easy way to up-skill yourself. Also, when you compare the data analytics courses across the market that offer web-based tutorials, Simplilearn, scores over the rest in my opinion. Great job, Simplilearn!

Vy Tran

I was keenly looking for a change in my domain from business consultancy to IT(Business Analytics). This Post Graduate Program in Business Analysis course helped me achieve the same. I am proficient in business analysis now and am looking for job profiles that suit my skill set.

Although business analytics is being leveraged in most commercial sectors and industries, the following applications are the most common.

Credit and debit cards are an everyday part of consumer spending, and they are an ideal way of gathering information about a purchaser’s spending habits, financial situation, behavior trends, demographics, and lifestyle preferences.

2. Customer Relationship Management (CRM)

Excellent customer relations is critical for any company that wants to retain customer loyalty to stay in business for the long haul. CRM systems analyze important performance indicators such as demographics, buying patterns, socio-economic information, and lifestyle.

The financial world is a volatile place, and business analytics helps to extract insights that help organizations maneuver their way through tricky terrain. Corporations turn to business analysts to optimize budgeting, banking, financial planning, forecasting, and portfolio management.

4. Human Resources

Although HR is often the punchline of many office jokes, its value in keeping a company successful is not to be underestimated. Great businesses are composed of a great staff, and it’s HR’s job to not only find the ideal candidates but keep them on board. Business analysts help the process by pouring through data that characterizes high performing candidates, such as educational background, attrition rate, the average length of employment, etc. By working with this information, business analysts help HR by forecasting the best fits between the company and candidates.

5. Manufacturing

Business analysts work with data to help stakeholders understand the things that affect operations and the bottom line. Identifying things like equipment downtime, inventory levels, and maintenance costs help companies streamline inventory management, risks, and supply-chain management to create maximum efficiency.

6. Marketing

Which advertising campaigns are the most effective? How much social media penetration should a business attempt? What sort of things do viewers like/dislike in commercials? Business analysts help answer these questions and so many more, by measuring marketing and advertising metrics, identifying consumer behavior and the target audience, and analyzing market trends.

As you can see, business analytics plays a valuable role in many different industries. You may also notice that some of the applications merge into each other, but that’s hardly surprising. By leveraging business analytics, multiple departments and teams can coordinate their efforts based on the information gathered and processed. It’s up to the business analyst to identify roadblocks and areas that need improvement, helping different departments to work together to achieve a common goal.

1. Customer Segmentation

Customer segmentation is a vital business analytics application that helps companies group their customers based on shared characteristics such as demographics, buying behavior, and preferences. By analyzing customer data, businesses can tailor their marketing strategies, product offerings, and customer service to target specific segments effectively, increasing customer satisfaction and overall profitability.

2. Predictive Analytics

Predictive analytics leverages historical and real-time data to forecast future trends and events. This application is used extensively in industries like finance, healthcare, and e-commerce for tasks such as predicting stock prices, patient outcomes, and product demand. It enables proactive decision-making, risk mitigation, and optimization of business operations.

3. Supply Chain Optimization

Businesses utilize analytics to optimize their supply chains by analyzing data related to inventory levels, supplier performance, transportation logistics, and demand forecasting. By identifying inefficiencies and bottlenecks in the supply chain, companies can reduce costs, improve product availability, and enhance overall operational efficiency.

4. Fraud Detection

Fraud detection analytics employs advanced algorithms and machine learning models to identify and prevent fraudulent activities, such as credit card fraud, insurance fraud, and cyberattacks. By analyzing transactional data patterns and anomalies, organizations can minimize financial losses and maintain the trust of their customers.

5. Market Basket Analysis

Market basket analysis involves examining customer purchase history to discover patterns in product co-purchases. Retailers use this application to optimize product placement, cross-selling, and promotional strategies. By understanding which products are frequently bought together, businesses can increase sales and enhance the customer shopping experience.

6. Churn Analysis

Churn analysis focuses on identifying and reducing customer churn, which is the rate at which customers stop using a company's products or services. By analyzing customer behavior and feedback, businesses can implement retention strategies to retain valuable customers and reduce revenue loss.

7. A/B Testing

A/B testing is a fundamental analytics application for optimizing digital marketing campaigns and website performance. It involves conducting controlled experiments by randomly assigning users to different versions of a webpage or marketing content. By comparing the performance of these versions, companies can make data-driven decisions to improve conversion rates and user engagement.

8. Employee Performance Analytics

Employee performance analytics helps organizations evaluate the productivity and engagement of their workforce. By analyzing data on key performance indicators (KPIs), attendance, and employee feedback, companies can make informed decisions about talent management, training, and workforce optimization.

9. Quality Control and Process Improvement

In manufacturing and production industries, analytics is employed to monitor product quality, detect defects, and optimize production processes. By analyzing data from sensors and production lines, businesses can reduce defects, improve efficiency, and minimize waste.

10. Sentiment Analysis

Sentiment analysis, also known as opinion mining, uses natural language processing and machine learning techniques to assess public sentiment and opinions from sources like social media, customer reviews, and surveys. Companies can gain insights into how their brand is perceived and use this information to shape marketing strategies and product development.

Become a business analysis expert with our Business Analyst Master's Program . Explore and enroll today!

These business analytics applications collectively empower organizations to make data-driven decisions , improve operations, enhance customer experiences, and stay competitive in today's data-centric business landscape.

Business analytics helps organizations run more efficiently and profitably. Here are six cases where business analytics proves its worth in the commercial sector.

1. Churn Prevention

Churn is the customer attrition rate, a percentage of subscribers, or customers who stop doing business with a company. Successful companies must keep the churn rate low and replace any customer losses that inevitably occur. Furthermore, it’s more expensive to acquire new customers than it is to retain existing ones. By using predictive analysis, a business analyst helps identify customer dissatisfaction and the most likely risks or departure.

2. E-Commerce Personalization

Online businesses, like Amazon, collect, process, and analyze customer data to personalize their customers’ shopping experiences. By customizing the experience, vendors can make recommendations and increase the likelihood of further sales.

3. Predictive Maintenance

Companies must face the inevitability of equipment maintenance, both scheduled and unplanned. Business analysts work with data to create metrics about maintenance lifecycles to predict future maintenance needs and avoid costly unplanned downtime.

4. Insurance Fraud Detection

Insurance fraud is costly to companies and their customers alike. This is especially true in the medical insurance industry, where fraud costs organizations in the US approximately $68 billion a year . Business analysts use big data to process billions of claims and billing records, enabling investigators to identify and mitigate any fraudulent activity.

5. Automated Candidate Placement

As mentioned earlier, hiring new staff comes with its share of risks and uncertainty. Business analysts leverage data-driven recruitment platforms to get a better picture of any given candidate—improving the likelihood of a successful job match much faster. In some cases, the information can even help anticipate job needs before a position is posted.

Simplilearn’s  Post Graduate Program in Business Analysis , in partnership with Purdue University, provides a unique Blended Learning method, which will equip you with all the latest skills and methodologies you will need to create a fantastic career in business analysis. You will also get to work on real-world projects with guidance from top experts from Purdue University and in the industry. Enroll today to become an AI-powered business analytics professional!

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Get Free Certifications with free video courses

Business Analysis Basics

Business and Leadership

Business Analysis Basics

Business Intelligence Fundamentals

Data Science & Business Analytics

Business Intelligence Fundamentals

Learn from Industry Experts with free Masterclasses

Unleash Your 2024 Data Analytics Career with IIT Kanpur

Career Information Session: Find Out How to Become a Business Analyst with IIT Roorkee

Why Data Science Should Be Your Top Career Choice for 2024 with Caltech University

Recommended Reads

Business Analytics Basics: A Beginner’s Guide

What is Business Analytics and Why it is Important

Data Analytics with Python: Use Case Demo

Business Analytics in 2021: A Comprehensive Trends Report

What’s the Difference Between Data Analytics and Business Analytics

What is Data Analytics and its Future Scope in 2024

Get Affiliated Certifications with Live Class programs

  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

What is business analytics?

Man displays data on a screen to a conference room of individuals.

It should come as no surprise that big business decisions are made every single day at companies small and large. 

It is also well assumed that the best big decisions are ones with evidence and back them up—in the form of data. But how does data go from being raw information like surveys and click-through rates to being part of sometimes world-altering decision-making? Business analytics is how.

Pepperdine Graziadio Business School logo

The Online MS in Business Analytics from Pepperdine

The emphasis on data-powered decision-making is nothing new; in fact, businesses have known about its significance for years. A decade ago, Deloitte noted in a 2013 study that focus on big data and analytics were to be the “new normal” for maintaining growth. “Companies must focus on evolving their analytical maturity in addition to developing capabilities around rapid experimentation and trial and error. Remaining agile will be essential for handling this “new normal,” it stated.

So, with today there being hundreds of thousands of workers who describe themselves as business analysts (not to mention there now being an entire international organization dedicated to the field, the IIBA ), an important question lingers: what exactly even is business analytics? Fortune has you covered.

In the simplest terms, business analytics is the process or the ability to drive decisions using data and analytics, according to Anindya Ghose, the director of the master’s of science in business analytics program at New York University’s Stern School of Business. The school Stern is home to the no. 9 best MBA program , based on Fortune ’s ranking.

Business analytics is a field that is constantly evolving in accordance with technological developments. A few decades ago, business analytics was a much simpler domain in the typical business-tech space: spreadsheets could house information, trends could be identified using basic formulas, and data could be visualized to the team of decision-makers.

But today, business analytics is everywhere—in tech, healthcare, education, retail, media, and beyond. 

“The way we think about business analytics now—it’s a little bit of everything for everybody,” says Devanshu Mehrotra, curriculum developer and lead instructor at General Assembly, with a background in the world of analytics.

Business analytics is more so the art of data translating, says Mehrotra.

“And the idea is, since data is being democratized, and the idea is that specific organizations should own their data, they should be responsible for their data, then it’s important for there to be data translators,” he adds.

What skills do you need for business analytics?

While the exact skills needed to excel in business analytics may differ depending on industry, company, and level of experience, there are several foundations that are important to have, including:

  • Domain expertise: business fundamentals and relevant industry knowledge
  • Technical know-how: programming, data analysis, data visualization
  • Storytelling: translating data trends to business needs

The last point in particular was something Mehrotra and Ghose both emphasized as an area that really sets excellent business analysts apart from other fields. 

Additionally, knowledge of both high and low code tools are important technical aspects of the job, including for, as Mehrotra notes:

Because there are many data-related tools available—and every company may use something different—Mehrotra says it is important to be tool agnostic. 

“Multiple tools should be in your repertoire, (so) that you pick the tool based on the problem, not try and shove every problem into the two tools that you know,” he says. “And that’s why I’m always like—it’s do you understand the why before you understand the how.”

Ghose adds that in order to succeed in business analytics, having training in these two areas are of great importance:

  • Econometrics (advanced statistics and modeling)
  • Experimental design (creation and understanding of tests and behaviors)

It would also be remiss to not mention the criticality of AI in space. Like other fields, the tech is streamlining some of the day-to-day activities of business analytics. 

How can you learn business analytics?

Those wanting to get involved in business analytics are in luck because there are numerous ways to learn the in-demand skills.

When looking at traditional degree pathways, many universities have undergraduate and graduate degrees focused specifically on business analytics. ( Fortune ranks the best online master’s in business analytics ). And even if there is no program labeled business analytics directly, you can also gain through a combination of business and data science endeavors.

If a longer degree program is not for you, checking out a bootcamp or course in business analytics may provide a quicker, cheaper, and/or more flexible opportunity.

A few years ago, Mehrotra explains he may have recommended going down a traditional degree route, but because the world of analytics is always changing, a shorter program may be a better way to get the most up-to-date skills from instructors with recent industry experience.

“To me, I think long form education, specifically around these areas are not very impactful and not a good return on investment,” Mehrotra says. “I think short form and creating your own journey, so as to speak, is important and I do think that some kind of short form educational programs are a very important part of that.”

Regardless, what’s key to sticking out in a competitive job ecosystem is gaining hands-on projects, creating a portfolio, and learning from instructors with real-world experience, Mehrotra notes.

Studying business analytics also does not necessarily mean you are boxed in to becoming a business analyst. Other job titles may include data scientist , data analyst , market researcher, chief digital officer, chief data officer, head of product, and intelligence analyst.

“It’s now increasingly difficult, if not impossible to imagine—taking decisions without the help of computers, algorithms and data,” Ghose says. “So, you will almost certainly see lots of benefits from that. I think that is just the way of the world today will just continue to be even more ubiquitous as we proceed. So, jump in and join the party.”

Harvard Business Analytics Program logo

Harvard Business Analytics Program

Fortune mba rankings.

  • Best Online MBA Programs in 2023
  • Best Online Master’s in Accounting Programs in 2023
  • Best MBA Programs in 2023
  • Best Executive MBA Programs in 2022-23
  • Best Part-Time MBA Programs in 2022-23
  • 25 Most Affordable Online MBAs in 2022-23
  • Best Online Master’s in Business Analytics Programs in 2023

Fortune Information Technology & Data Rankings

  • Best Online Master’s in Data Science Programs in 2023
  • Most Affordable Master’s in Data Science in 2023
  • Best Master’s in Cybersecurity Degrees in 2023
  • Best Online Master’s in Cybersecurity Degrees in 2023
  • Best Online Master’s in Computer Science Degrees in 2023
  • Best Master’s in Data Science Programs in 2023
  • Most Affordable Online Master’s in Data Science Programs in 2022-23
  • Most Affordable Online Master’s in Cybersecurity Degrees in 2022-23

Fortune Health Rankings

  • Best Online MSN Nurse Practitioner Programs in 2022-23
  • Accredited Online Master’s of Social Work (MSW) Programs
  • Best Online Master’s in Nursing (MSN) Programs in 2022-23
  • Best Online Master’s in Public Health (MPH) Programs in 2023
  • Most Affordable Online MSN Nurse Practitioner Programs in 2022-23
  • Best Online Master’s in Psychology Programs in 2022-23

Fortune Leadership Rankings

  • Best Online Doctorate in Education (EdD) Programs in 2022-23
  • Most Affordable Online Doctorate in Education (EdD) Programs in 2022-23
  • Coding Bootcamps in New York

Fortune Bootcamp Overviews

  • Best Data Science and Analytics Bootcamps in 2023
  • Best Cybersecurity Bootcamps in 2023

Fortune Boarding School Guide

  • World’s Leading Boarding Schools in 2023
  • Top Boarding School Advisors in 2023

UC Berkeley School of Information logo

Berkeley's Data Science Master's

Book cover

The International Conference on Cybersecurity, Situational Awareness and Social Media

CYBER SCIENCE 2023: Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media pp 35–46 Cite as

Using Data Analytics to Derive Business Intelligence: A Case Study

  • Ugochukwu Orji 8 ,
  • Ezugwu Obianuju 8 ,
  • Modesta Ezema 8 ,
  • Chikodili Ugwuishiwu 8 ,
  • Elochukwu Ukwandu   ORCID: orcid.org/0000-0003-1350-4438 9 &
  • Uchechukwu Agomuo 8  
  • Conference paper
  • First Online: 18 February 2024

Part of the Springer Proceedings in Complexity book series (SPCOM)

The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics (BIA) has become a huge trend in today’s IT world as companies of all sizes are looking to improve their business processes and scale up using data-driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike-share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as “Chicago Divvy Bicycle Sharing Data” on Kaggle. The authors used the R-Tidyverse library in RStudio to analyze the data and followed the six data analysis steps of; ask, prepare, process, analyze, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real-world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users.

  • Data analytics
  • Data analysis cycle
  • Business intelligence
  • Big data analytics

This is a preview of subscription content, log in via an institution .

Huang, S.C., McIntosh, S., Sobolevsky, S., Hung, P.C.: Big data analytics and business intelligence in industry. Inf. Syst. Front. 19 (6), 1229–1232 (2017). https://doi.org/10.1007/s10796-017-9804-9

Article   Google Scholar  

Akter, S., Michael, K., Uddin, M.R., McCarthy, G., Rahman, M.: Transforming business using digital innovations: the application of AI, blockchain, cloud, and data analytics. Ann. Oper. Res. 1–33 (2022). https://doi.org/10.1007/s10479-020-03620-w

Mikalef, P., Boura, M., Lekakos, G., Krogstie, J.: Big data analytics and firm performance: findings from a mixed-method approach. J. Bus. Res. 98 , 261–276 (2019). https://doi.org/10.1016/j.jbusres.2019.01.044

Orji, U.E., Ugwuishiwu, C.H., Nguemaleu, J.C., Ugwuanyi, P.N.: Machine learning models for predicting bank loan eligibility. In: 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), pp. 1–5. IEEE (2022). https://doi.org/10.1109/NIGERCON54645.2022.9803172

Hočevar, B., Jaklič, J.: Assessing benefits of business intelligence systems—a case study. Manag.: J. Contemp. Manag. Iss. 15 (1), 87–119 (2010)

Google Scholar  

George, B., Walker, R.M., Monster, J.: Does strategic planning improve organizational performance? A meta-analysis. Public Adm. Rev. 79 (6), 810–819 (2019). https://doi.org/10.1111/puar.13104

Sun, Z., Zou, H., Strang, K.: Big data analytics as a service for business intelligence. In: Conference on e-Business, e-Services and e-Society, pp. 200–211. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25013-7_16

Ram, J., Zhang, C., Koronios, A.: The implications of big data analytics on business intelligence: a qualitative study in China. Procedia Comput. Sci. 87 , 221–226 (2016). https://doi.org/10.1016/j.procs.2016.05.152

Statista: Global BI & Analytics Software Market Size 2019–2024 (2020). https://www.statista.com/statistics/590054/worldwide-business-analytics-software-vendor-market/ . Accessed 03 March 2023

NewVantage Partners: Big Data and AI Executive Survey 2019 (2019). http://newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings-122718.pdf . Accessed 03 March 2023

Malhotra, D., Rishi, O.: An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics. J. King Saud Univ. Comput. Inf. Sci. 33 (2), 183–194 (2021). https://doi.org/10.1016/j.jksuci.2018.02.015

Orji, U.E., Ezema, M.E., Ujah, J., Bande, P.S., Agbo, J.C.: Using Twitter sentiment analysis for sustainable improvement of business intelligence in Nigerian small and medium-scale enterprises. In: 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), pp. 1–5. IEEE (2022). https://doi.org/10.1109/NIGERCON54645.2022.9803087

Caseiro, N., Coelho, A.: The influence of business intelligence capacity, network learning, and innovativeness on startups performance. J. Innov. Knowl. 4 (3), 139–145 (2019). https://doi.org/10.1016/j.jik.2018.03.009

Hopkins, J., Hawking, P.: Big data analytics and IoT in logistics: a case study. Int. J. Logist. Manag. (2018). https://doi.org/10.1108/IJLM-05-2017-0109

Dixon, M.: How Netflix Used Big Data and Analytics to Generate Billions (2019). https://seleritysas.com/blog/2019/04/05/how-netflix-used-big-data-and-analytics-to-generate-billions/ . Accessed 11 Feb 2023

Beall, A.: Big Data in Health Care: How Three Organizations Are Using Big Data to Improve Patient Care and More? (2020). https://www.sas.com/en_gb/insights/articles/big-data/bigdata-in-healthcare.html . Accessed 11 Feb 2023

Elmes, S.: Delicious Data: How Big Data Is Disrupting the Business of Food (2019). https://adimo.co/news/delicious-data-how-big-data-is-disrupting-the-business-of-food . Accessed 03 March 2023

Aleksandrova, M.: Big Data in the Banking Industry: The Main Challenges and Use Cases (2019). https://easternpeak.com/blog/big-data-in-the-banking-industry-the-main-challengesand-use-cases/ . Accessed 02 March 2023

Sigler, R., Morrison, J., Moriarity, A.K.: The importance of data analytics and business intelligence for radiologists. J. Am. Coll. Radiol. 17 (4), 511–514 (2020). https://doi.org/10.1016/j.jacr.2019.12.022

Data Analytics and the Auditor | ACCA Global. https://www.accaglobal.com/gb/en/student/exam-support-resources/professional-exams-study-resources/p7/technical-articles/data-analytics.html . Accessed 3 March 2023

Chicago Divvy Bicycle Sharing Dataset. https://www.kaggle.com/datasets/orjiugochukwu/cyclistic-dataset

Download references

Author information

Authors and affiliations.

Department of Computer Science, Faculty of Physical Science, University of Nigeria, Nsukka, Enugu State, Nigeria

Ugochukwu Orji, Ezugwu Obianuju, Modesta Ezema, Chikodili Ugwuishiwu & Uchechukwu Agomuo

Department of Applied Computing, Cardiff School of Technologies, Cardiff Metropolitan University, Wales, UK

Elochukwu Ukwandu

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Elochukwu Ukwandu .

Editor information

Editors and affiliations.

Research Series Limited, London, UK

Cyril Onwubiko

University of Galway, Galway, Ireland

Pierangelo Rosati

Temple University, Philadelphia, PA, USA

Aunshul Rege

University of Oxford, Oxford, UK

Arnau Erola

Lupovis, Glasgow, UK

Xavier Bellekens

Ain Shams University, Cairo, Egypt

Hanan Hindy

University of Stavanger, Stavanger, Norway

Martin Gilje Jaatun

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Orji, U., Obianuju, E., Ezema, M., Ugwuishiwu, C., Ukwandu, E., Agomuo, U. (2024). Using Data Analytics to Derive Business Intelligence: A Case Study. In: Onwubiko, C., et al. Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media. CYBER SCIENCE 2023. Springer Proceedings in Complexity. Springer, Singapore. https://doi.org/10.1007/978-981-99-6974-6_3

Download citation

DOI : https://doi.org/10.1007/978-981-99-6974-6_3

Published : 18 February 2024

Publisher Name : Springer, Singapore

Print ISBN : 978-981-99-6973-9

Online ISBN : 978-981-99-6974-6

eBook Packages : Physics and Astronomy Physics and Astronomy (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

IMAGES

  1. Business Analytics Case Study Template

    case study business analytics

  2. What is a Business Case Study and How to Write with Examples

    case study business analytics

  3. 😱 Business case analysis report. How to Write a Business Case: Template

    case study business analytics

  4. Business Case Analysis: Definition, Format & Examples of a Case Study

    case study business analytics

  5. Case Study

    case study business analytics

  6. FREE 6+ Sample Business Case Analysis Templates in PDF

    case study business analytics

VIDEO

  1. Case Study Review and Q&A

  2. Introducing The Business Strategy Analyst

  3. 1.5 Case study on Analytics Value Escalator

  4. Gwennia Kartikasuri Wibisono

  5. What does Business Analyst Do ?

  6. Adelaide Business School

COMMENTS

  1. Business Analytics and AI Case Studies

    Trustworthy AI™ Bridging the ethics gap surrounding AI A video and podcast series intended to ignite applied AI conversations in the Age of With™ See how Deloitte professionals helped companies with their big data needs in the gaming, food and beverage, consumer packaged goods, and telecom industries.

  2. Top 20 Analytics Case Studies in 2024

    What are some analytics case studies? How to measure analytics success? According to Gartner CDO Survey, the top 3 critical success factors of analytics projects are: Creation of a data-driven culture within the organization, Data integration and data skills training across the organization,

  3. Case studies in business analytics with ACCENTURE

    Case studies in business analytics with ACCENTURE This course is part of Strategic Business Analytics Specialization Taught in English 20 languages available Some content may not be translated Instructor: Nicolas Glady Enroll for Free Starts Feb 12 Financial aid available 26,435 already enrolled About Outcomes Modules Recommendations Testimonials

  4. Examples of Business Analytics in Action

    Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions. There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive.

  5. Business Analysis Case Study Examples and Solutions

    Glenn has been a business analyst for over 15 years, working primarily in the highly regulated pharmaceutical industry. He enjoys the variability of his role, building relationships, teamwork, creating useful requirements artifacts and especially the celebration of a valued delivery.

  6. Interesting case studies in business analytics

    Below we have featured case studies for business analytics from various sectors. Case studies for business analytics Here, we've discussed business analytics examples that demonstrate how artificial intelligence (AI) and machine learning (ML) technologies are being employed in various fields to aid in the making of more wiser business decisions.

  7. 5 Business Intelligence & Analytics Case Studies Across Industry

    Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first (a question at the core of one of Emerj's most recent expert consensuses.)

  8. Analytics and data science

    How Velcro Got Hooked on Quality. Analytics and data science Magazine Article. K. Theodor Krantz. The phone call came out of the blue one morning in August 1985. It was from our Detroit sales ...

  9. Using people analytics to drive business performance: A case study

    Using people analytics to drive business performance: A case study | McKinsey. People analytics— the application of advanced analytics and large data sets to talent management—is going mainstream. Five years ago, it was the provenance of a few leading companies, such as Google (whose former senior vice president of people operations wrote a ...

  10. How to Make the Business Case for Analytics

    In making the business case for analytics, business intelligence and analytics leaders must ensure that they establish clear linkages between analytics solutions and business benefits. ... Gartner research, which includes in-depth proprietary studies, peer and industry best practices, trend analysis and quantitative modeling, enables us to ...

  11. Business Analytics: What It Is & Why It's Important

    Business analytics is a powerful tool in today's marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.. According to a study by MicroStrategy, companies worldwide are ...

  12. Business Analyst Case Study: A Complete Overview

    An overview of the Business Analysis Case Study To kickstart our analysis, we will gain a deep understanding of the company's background, industry, and specific objectives. By examining the hypothetical company's objectives and aligning our analysis with its goals, we can lay the groundwork for a focused and targeted approach.

  13. Learn Analytics Using A Business Case Study

    Learn Analytics using a business case study : Part I. Tavish Srivastava 17 Apr, 2015 • 6 min read. Best way to learn analytics is through experience and solving case studies. Here, I will present you a complete business model and take you through a step by step process of how analytics is set up in a new business, how is it used in daily ...

  14. Business Analyst Case Study

    4 min read May 3, 2022 12:00:00 AM Business analyst case studies blog describes an actual business analyst case study. This provides real-world exposure to new business analysts. In this blog, we will be discussing what is business analysis case study, why develop them, when to develop them and how to develop them.

  15. Case Study: Business Analytics financial services from Mainline

    The customer has increased its usage of IBM Business Analytics and is now developing a self-service reporting portal that will allow clients to generate reports online at any time. Download the PDF. For more information, call your Mainline account representative or call Mainline directly at 866.490.MAIN (6246) or complete our contact us form ...

  16. Top 10 Marketing Analytics Case Studies [2024]

    2. Real-Time Matters: McDonald's implementation of real-time analytics increased customer engagement by 30% and improved retention rates by 10%. 3. Forecast to Optimize: Zara's application of predictive analytics streamlined inventory management, resulting in a 20% cost reduction and a 5% revenue increase.

  17. Business Analytics Case Study for Global Hospitality ...

    Solution & Approach. In this Business Analytics Case Study, we show how this client partnered with Auxis once again to build an advanced analytics team led by an in-house subject matter expert with a Ph.D. in data science. Key steps included: 1. Determining key business questions.

  18. Business Analytics Case Studies

    Monday - Friday. : 09:00 AM - 05:30 PM (IST) Business analytics case studies deals with logical exploration of organization's data by using statistical data analysis and different business intelligence tools to improve the business. Business analytics can be used to solve organizational and individual problems simultaneously.

  19. Lesson 1: Introduction

    Trying to break into analytics? You may be a great fit for our Analytics Apprenticeship Program. Learn more here: https://learn.silvertoneanalytics.com/appre...

  20. Data Analytics Case Study Guide (Updated for 2023)

    Step 1: Ask Clarifying Questions Specific to the Case. Hint: This question is very vague. It's all hypothetical, so we don't know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

  21. 7 Favorite Business Case Studies to Teach—and Why

    Francesca Gino, Professor of Business Administration, Harvard Business School. FRANCESCA GINO Professor, Harvard Business School. "My favorite case to teach is The United States Air Force: 'Chaos' in the 99th Reconnaissance Squadron. The case surprises students because it is about a leader, known in the unit by the nickname Chaos, who ...

  22. Business Analytics Applications and Notable Use Cases

    Usage of Business Analytics. Business analytics helps organizations run more efficiently and profitably. Here are six cases where business analytics proves its worth in the commercial sector. 1. Churn Prevention. Churn is the customer attrition rate, a percentage of subscribers, or customers who stop doing business with a company.

  23. What is business analytics?

    Business analytics is at the intersection of data and decision-making. Fortune explains what exactly the field entails. ... Deloitte noted in a 2013 study that focus on big data and analytics were ...

  24. Business Analytics

    Using business analytics, we will be solving a case study. Below is the last of the 20+ services it offers: https://www.gojek.io/blog/food-debarkation-tensoba Transport and Logistics Go-ride - Your two-wheeler taxi, the indigenous Ojek Go-car - Comfort on wheels. Sit back. Sleep. Snore. Go-send - Send or get packages delivered within hours.

  25. Using Data Analytics to Derive Business Intelligence: A Case Study

    This paper also demonstrates why data analytics and business intelligence have emerged as the new frontier of innovation for businesses and startups looking to be competitive in today's markets. ... Using Data Analytics to Derive Business Intelligence: A Case Study. In: Onwubiko, C., et al. Proceedings of the International Conference on ...

  26. What Can You Do With A Business Analytics Degree?

    Businesses rely on technology to create products and services that meet consumer needs. A business analytics career path allows you to use mathematics and statistics to help clients understand how ...