data science marketing case studies

Top 8 Data Science Use Cases in Marketing

In this article, we want to highlight some key data science use cases in marketing.

As far as the key aim of data science is to turn data into actionable insights the marketing sphere cannot skip the application of these insights for its benefit. Big data in marketing provides an opportunity to understand the target audiences much better.  

Data science is mostly applied in marketing areas of profiling, search engine optimization, customer engagement, responsiveness, real-time marketing campaigns. Moreover, new ways to apply data science and analytics in marketing emerge every day. Among these, the new use cases include digital advertising, micro-targeting, micro-segmentation, and many others.  

Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.   

Customer segmentation

All customers are individuals. Therefore, a one-size-fits-all approach is not efficient at all.  Customer segmentation comes to the rescue of the marketers in this case. Application of the statistical analysis allows marketers to slice the data and group customers.

Customer segmentation is a process of grouping customers into segments according to the coincidences of particular criteria in their characteristics.

There are three significant segmentation types that are the most often used. These are:

segmentation based on touchpoint engagement

segmentation based on purchase patterns.

Application of micro-segmentation appears to be a rising trend in marketing. Micro-segmentation is far more advanced. It helps to segment people into more precise categories especially concerning behavioral intentions. Thus, marketing actions may be tailored to the preferences even of the least numerous customer groups.

Real-time analytics

Real-time analytics proved to bring marketing insights into campaigns immediately. These real-time marketing opportunities become possible due to the recent boost in popularity of social media and communication technologies. 

Efficient real-time analysis of data brings a considerable increase in revenues for the companies. Real-time algorithms work with two groups of data: customer data and operational data. 

Customer data provides insights into customers’ wants, preferences, and needs. Operational data reflect various transactions, actions, and decisions made by the customers. Application of real-time data analysis brings efficiency, speed and high-performance rates to marketing campaigns. 

Real-time analytics in marketing provides an opportunity to:

get more details about customers

find the efficient platforms

provide a unique customer experience

run real-time test

identify the best working practices

react and respond immediately. 

Predictive analytics

At present, the data is easily accessible and available even for middle-size companies. This is why predictive analytics is so widely applied in marketing. 

Predictive analytics is the application of statistical and machine learning algorithms to predict future with high probability. There are a lot of opportunities to apply predictive analytics in marketing. Let's consider those, which proved to be the most efficient.

Predictive analytics for customers' behavior

Cluster models, predictions, collaborative filtering, regression analysis are all applied to spot the correlation patterns in the customers' behavior to predict future tendencies in purchasing.

Predictive analytics to qualify and prioritize leads

Here belong predictive scoring, identification models and automated segmentation. These are related to qualifying and prioritizing leads to make your marketing efforts more effective. Applying these models, you can make sure that the most effective ready to purchase leads will get your call to action correctly.

Predictive analytics to bring the right product to the market

In this case, data visualization helps the marketing team to make the right decision about what product or service should be delivered to the market.

Predictive analytics for targeting

This is related to a whole bunch of predictive analytics models like affinity analysis, response modeling, churn analysis. These models are used to identify the highest value customers and address them with the right offer at the right time.

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Recommendation engines.

Recommendation engines are powerful tools in attempts to provide a personalized experience and high satisfaction rates to the customers. Marketers are those people who should pay particular attention to the application of the recommendation engines.

The key idea of the recommendation engines is to match the preferences of a customer with product features he or she might like. For this purpose, recommendation engines usually use the following models and algorithms: regression, decision tree, K-nearest neighbor, support vector machines, neural networks, etc.

Recommendation engines are a key targeted marketing tool for email and online marketing campaigns.  

Market basket analysis

Market basket analysis refers to the unsupervised learning data mining techniques intended to learn the buying patterns and to disclose the co-occurrence relationships between purchases. Application of these techniques allows predicting future purchase decisions.

Moreover, market basket analysis can significantly improve the efficiency of the marketing message. Besides the type of the marketing message, whether it is direct offer, email, social media, phone call or newsletter you can offer the next best product suitable for a particular customer.

Optimization of marketing campaigns 

The main task of the marketing team is to create an efficient, customer oriented, targeted marketing campaign dedicated to delivering the right message to the right people at the right time.  

Optimization of marketing campaign involves the application of smart algorithms and models allowing to increase the efficiency. Modern technologies bring automation to the data collection and analysis process, reduce time spent on them, provide real-time results and spot the slightest changes in patterns. Smart data algorithms treat each customer individually. Thus, the high personalization level becomes more achievable.

The optimization process includes several steps that are equally important and require attention. Let us outline these steps:

Choose the right tools

Invest money in those tools that will efficiently gather and analyze data. Make sure the tools you choose can work together for the benefit of your campaign. Integrate the tools with existing systems and data.

Measure the metrics

Measuring metrics allows to identify processes and strategies that need improvement. Measure the parameters comparing them to your marketing goals.

Draw conclusions

Make right data-based decisions to make your marketing campaign as successful as possible.

Lead scoring

Customers' path through the sales funnel is staffed with various opportunities, options, and choices. Lead scoring is applied to identify those prospective customers who will go through the funnel and make their choice to the benefit of your product or service. What is the trick?

Lead scoring ranks the prospect according to a scale representing the value of each lead. The value of each lead may be identified differently, but often they are referred to as hot, warm or cold ones. 

Lead scoring involves data collection concerning customers' demographics, responsiveness, purchase history, preferences, web page view, visits, likes, shares and even the type of e-mails they often react to. 

As a result of lead scoring, the salespeople get qualified prospects regarding who is highly intended to buy. Thus, when products are offered to the right people, the sales boost. 

Optimal campaign channels and content

The essence of all the marketing efforts is to reach the right customer. However, the marketing landscape has been changed and moved to the online world. Thus, the main task for the companies is to assure a strong online presence for the brand.

The leading part here is given to the selection of optimal digital marketing channels: email marketing, pay-per-click advertisement, search engine optimization, display advertising, Social Media Marketing, content marketing, affiliate marketing, online public relations. The choice is vast. To make this choice more comfortable, take the following steps:

define goals

allocate budget

determine your audience.  

In its turn, a digital marketing challenge determines the type of content the brand can use. Blog posts, articles, videos, stories etc. All these types prove to be more or less effective depending on the channel used to distribute them.

The use cases mentioned above prove the statement that application of data science brings numerous benefits to marketing campaigns of various brands. Considering the amount of data available today it is essential not just to freeze it but to use it for the benefit of the company.

Transformation of data into meaningful insights is crucial for decision making. Our list of top data science use cases in marketing reveals specific features of data application in this area and real positive effects it may cause.

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Three Short Marketing Analytics Case Studies to Inspire You to Love Data

Written by Anna Sonnenberg

Published Feb. 28 2022 · 13 min read

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From engagement statistics to content analytics to conversion metrics, data is a big part of most social media managers’ responsibilities. But that doesn’t necessarily mean you enjoy processing marketing data or drawing conclusions from it.

If data isn’t exactly your favorite part of the job, these marketing analytics case studies may change your mind.

Find out how marketing analytics helped three major brands grow their businesses—and you might develop a whole new appreciation for marketing data in the process.

What Is Marketing Analytics?

Marketing analytics is the process of collecting and evaluating metrics to understand how much value marketing efforts generate. With analytics, you can assess the return on investment (ROI) of anything from social media posts and ad campaigns to landing pages and native platform features.

For many organizations and their marketing team, marketing analytics are essential for improving offerings and driving growth.

Here are common goals you can achieve with marketing analytics.

Improving marketing campaigns

Some social media marketing campaigns are more successful than others. Analytics can help your organization pinpoint exactly what works. By analyzing metrics like engagement, click-through rate (CTR), conversions, and ROI, you can determine what resonates best with its audience. By using data science, you can craft a marketing strategy that gets you better results from your campaigns.

Decreasing expenses

Ineffective marketing campaigns, usability issues, and poorly optimized algorithms can all lead to dissatisfied customers and unnecessarily high retention costs.

By investing in marketing analytics, your organization can take steps to identify points of friction and reduce expenses.

Forecasting results

Reviewing past outcomes is useful, but forecasting the results your campaigns are likely to generate is even more valuable. With marketing analytics, you can model results and get a better sense of how marketing initiatives can impact growth over time.

Marketing Analytics Case Studies: Progressive Insurance

In the early 2000s, Progressive’s website was routinely considered one of the best in the insurance industry. When the insurance provider’s customers began switching to mobile devices a decade later, the organization aimed to develop a mobile app as effective as its desktop site.

But what did that mean exactly? And what was the insurance provider’s mobile app missing?

To determine what would make the mobile app more successful, Progressive pursued an in-depth analysis of the organization’s marketing data.

As Progressive Data & Analytics Business Leader Pawan Divakarla explains , the insurance provider’s analytics team has always sought insight into how customers are using the company’s tools.

In his words, “At Progressive, we sell insurance. But if you think about it, our product is actually data.”

After launching the mobile app, Progressive began looking for ways to optimize the user experience. As this Progressive case study explains, the organization aimed to streamline the login process and improve user satisfaction to meet its ultimate goals of increasing customer loyalty and new customer acquisition.

Because Progressive’s mobile app generated so much information, the organization needed data visualization tools for collection and processing. To analyze customers’ experiences and actions, the company opted to use a combination of Google Analytics 360 and Google Tag Manager 360.

This choice was a relatively simple one for Progressive because the company already used these tools to run A/B tests and optimize its website.

Using Google’s analytical tools to review the company’s mobile app would allow Progressive to understand what features to test and how to optimize the user experience across countless mobile devices and operating systems.

Progressive used the two Google tools for separate yet complementary functions:

• With Google Analytics 360, Progressive could track user sessions and demographics. The company integrated BigQuery for more insight into user behaviors.
• With Google Tag Manager 360, Progressive could easily implement tracking tags to measure various actions, conversions, and navigation patterns.

To get the insights the company needed to improve its mobile app, Progressive took a three-pronged approach:

User device data

First, Progressive aimed to identify which devices and operating systems were most common among the app’s user base. With this information, the company would be able to develop more effective tests for its mobile app.

App crash data

Next, Progressive wanted to analyze app crash data. The company planned to use Google Analytics 360 and BigQuery data to understand the cause for the crash and how users reacted when the app stopped working abruptly.

Login and security data

Finally, Progressive aimed to learn how users responded when failed login attempts locked them out of the app. The company planned to use Google Analytics 360 and BigQuery to see what actions users took. It planned to then test new prompts that would guide users more effectively.

Outcome of this marketing analytics case study

Using marketing analytics tools , Progressive was able to process customer behavior, identify appropriate tests, and implement successful solutions.

Here’s how each of the three approaches generated useful results that helped Progressive reach its ultimate acquisition and loyalty goals.

First, Progressive developed session-based reports that reflected the most common mobile devices and operating systems for the app’s user base. With those insights, the company identified which device and operating system combinations to prioritize for its mobile app tests.

As a result, the company reduced testing time by 20% for its mobile app—allowing the organization to find solutions much more quickly than its typical timeline would have allowed.

Next, Progressive reviewed the actions customers took right before the app crashed. The company pinpointed a server issue as the cause of a major crash that disrupted countless mobile app sessions.

Using this data, Progressive could address the server issue and prevent it from happening again.

Finally, Progressive created a custom funnel in Google Analytics 360 to evaluate users’ typical login path. After learning that many users who became locked out of their accounts never attempted to log in again, the company developed a workflow that provided better guidance.

The new workflow sends users to a Forgot Password page, which has increased logins by 30%.

Marketing Analytics Case Studies: Netflix

When companies take a digital-first approach to customer loyalty, they can collect an incredible amount of user data. With these marketing analytics, companies can improve their products, build better marketing campaigns, and drive more revenue.

As this Netflix case study shows, the online content streaming platform has leveraged its user data in a variety of helpful ways.

By using data to improve its content recommendation engine, develop original content, and increase its customer retention rate, Netflix has positioned itself far ahead of the competition.

With so much data to leverage, Netflix had wide-ranging goals for the company’s marketing analytics. However, all of the organization’s goals contributed to the company’s larger business objectives—which focus on customer retention.

Netflix aimed to go beyond basic user demographics and understand what customers want from a streaming platform—and what was likely to convince them to stay. With this knowledge, Netflix could create better products and services for happier customers.

Access issues, service outages, and platform flaws can all lead to unhappy customers and negative sentiment—which can cause customers to seek out an alternative solution.

By identifying problems early through marketing analytics, Netflix could improve its products and continue to innovate.

To work toward its customer retention objective, Netflix collected data from virtually every interaction with its 150+ million subscribers. The company then used marketing analytics tools to process this native data and evaluate everything from how customers navigate the platform to what they watch.

By creating such detailed customer profiles, Netflix could make much more personalized recommendations for each user. The more data the company collected, the more it could tailor its algorithm to suggest the ideal content to each individual viewer.

To better understand the platform’s users, Netflix collected such data as:

• The devices viewers used to stream content
• Day of week and time of day when users viewed content
• Number of serial episodes viewers watched in a row
• Whether viewers paused and resumed content
• Number and type of searches users performed

Netflix also welcomed user feedback on content . The company incorporated these content ratings into their analysis to better understand viewer preferences.

According to the streaming platform, the Netflix algorithm is responsible for about 80% of viewer activity . The company has successfully collected relevant data and used marketing analytics to generate recommendations that encourage viewers to continue watching and subscribing.

The revenue metrics suggest that Netflix’s focus on marketing analytics has been hugely beneficial to the company. The company estimates that its algorithm generates $1 billion in value every year, largely due to customer retention.

In recent years, Netflix’s customer retention rate has far surpassed competitors like Hulu and Amazon Prime. Netflix has an impressive 90% retention rate , meaning the vast majority of viewers continue to subscribe to the service month after month. (In contrast, Amazon Prime’s retention rate is 75%, and Hulu’s is 64%.)

For Netflix, customer retention means more than happy viewers. It also means more data, a continually improving algorithm, and substantial business growth.

Netflix has emerged as the world’s most highly valued company, with a total valuation of over $160 billion. Netflix can continue to increase this valuation. It leverages its data by producing original media and recommending the ideal content to viewers every time they access the streaming platform.

Marketing Analytics Case Studies: Allrecipes

As the world’s biggest digital food brand, Allrecipes has 18 websites and more than 85 million users. But the brand also has plenty of competition from other food-focused apps and websites.

To stay ahead of other recipe sites and ensure that it continues to provide all the solutions that users want, Allrecipes relies on marketing analytics.

With marketing analytics, the digital brand can better understand the customer journey and analyze trends as they emerge. As this Allrecipes case study explains, the brand can expand its audience and attract even more lucrative demographics using these insights.

To continue to gain ground as the world’s top digital food brand, Allrecipes established several wide-ranging goals.

Some of the brand’s primary objectives included the following.

Improve user experience

With more than a billion and a half visitors across the brand’s sites every year, Allrecipes generates a ton of traffic. But the company needed a way to understand how visitors were using the site, so it could improve the user experience and gauge the health of the sites.

Increase video engagement

To take advantage of a demand for video content, Allrecipes had decided to invest heavily in video. However, the video production team needed strategic guidance. The brand needed to know what types of content would drive the most engagement.

Drive mobile engagement

To continue to meet the needs of its user base, Allrecipes had to look beyond its websites. As more and more people began using mobile devices to access the brand’s content, Allrecipes realized that the company needed to optimize its mobile app.

Inform product strategy

To promote new features and integrations or pursue partner programs, Allrecipes needed to know what its community wanted. Had they adopted the new integrations yet? Did they need new features to use the site or app more effectively?

Expand user base

Cooking and dining trends come and go, and Allrecipes needed a simple yet effective way to identify these developments.

By responding quickly to trends, the brand would be able to capture a larger user base, including elusive millennials.

Grow advertising revenue

Like many digital brands, Allrecipes has a native advertising program that allows the company to monetize its website. The company aimed to increase its advertising revenue, yet the organization didn’t want to compromise the user experience. To find the right partners to grow this program, Allrecipes needed deeper insights into its audience.

Although the brand’s goals were varied, the approach was relatively straightforward. To process marketing analytics from a wide range of channels, the brand opted to use Tableau, a business intelligence platform.

With Tableau, Allrecipes could establish a single platform for visualizing data from Adobe Marketing Cloud, Hitwise, and comScore. By linking Adobe Marketing Cloud to Tableau, the brand could pull in all of its website and marketing analytics. By linking Hitwise and comScore, the brand could source demographic data.

Using Tableau allowed Allrecipes to build custom dashboards and develop tailored reports to answer all of the brand’s questions. This tool also allowed the brand to pursue collaboration options across the organization.

In fact, departments ranging from marketing and design to product and finance contributed to the tool. Teams used Tableau Server to publish dashboards, creating a single space where stakeholders could visualize or analyze data.

With Tableau, Allrecipes was able to visualize the brand’s data successfully, enabling smarter decisions and making progress toward key goals. Here’s what the brand accomplished using marketing analytics:

Using insights from Tableau, Allrecipes was able to see how visitors typically used the site—including how they submit recipes, share content, and post links on social media channels. The organization then used this data to devise a plan for improving the site.

Knowing how visitors were already engaging with the site allowed the brand to make data-driven, goal-focused decisions.

With Tableau’s marketing analytics, Allrecipes found that out of all types of recipes, dessert typically generated more views and attracted more comments and photos. As a result, the brand opted to focus on this highly engaging niche, creating a separate video hub for dessert recipes.

To increase engagement on mobile devices, Allrecipes devised an A/B test that displayed the brand’s mobile site on all devices. Then the organization used the analytics to identify what drove interactions on mobile. The brand then used insights to improve the mobile site, including optimizing content and encouraging photo uploads.

Tableau’s data visualizations helped Allrecipes understand trends in its user community and respond to preferences more efficiently. Using these insights, the brand was able to promote integrations and features while gathering data for future product enhancements.

By using Tableau’s insights to process trends, Allrecipes was able to segment audiences for various recipe types, ultimately identifying millennial users’ interests and preferences. The brand was then able to create more content geared toward this growing user base—likely responding much more quickly than competitors could.

By tapping into real-time marketing analytics, Allrecipes was able to share popular recipe searches and trending content with its advertising partners during a recent holiday season. Advertisers could then create ads tailored to these interests, generating a better ROI and creating a more appealing experience for users.

What We Learned From These Marketing Analytics Case Studies

As these marketing analytics case studies show, data can tell you a lot about what your customers want—and where your organization succeeds or has room for improvement. Using insights from marketing analytics, a digital marketer can make data-driven decisions to cultivate customer loyalty, generate more revenue, and ultimately grow your business.

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Top 10 real-world data science case studies.

Data science has become integral to modern businesses and organizations, driving decision-making, optimizing operations, and improving customer experiences. From predicting machine failures in manufacturing to personalizing healthcare treatments, data science is profoundly transforming industries.

Data science, often called the "most desirable job of the 21st century," is a multidisciplinary field that combines data analysis, machine learning, and domain knowledge to extract meaningful insights from data. It has far-reaching applications in diverse industries, revolutionizing how we solve problems and make decisions.

In this blog, we will delve into the top 10 real-world data science case studies that showcase the power and versatility of data-driven insights across various sectors.

Let’s dig in!

Table of Contents

• 1. Case study 1: Predictive maintenance in manufacturing
• 1.2. 2. Siemens
• 2. Case study 2: Healthcare diagnostics and treatment personalization
• 2.1. 1. IBM Watson Health
• 2.2. 2. PathAI
• 3. Case study 3: Fraud detection and prevention in finance
• 3.1. 1. PayPal
• 3.2. 2. Capital One
• 4. Case study 4: Urban planning and smart cities
• 4.1. 1. Singapore
• 4.2. 2. Barcelona
• 5. Case study 5: E-commerce personalization and recommendation systems
• 5.1. 1. Amazon
• 5.2. 2. eBay
• 6. Case study 6: Agricultural yield prediction
• 6.1. 1. John Deere
• 6.2. 2. Caterpillar Inc.
• 7. Case study 7: Energy consumption optimization
• 7.1. 1. EnergyOptiUS
• 7.2. 2. CarbonSmart USA
• 8. Case study 8: Transportation and route optimization
• 8.1. 1. Uber
• 8.2. 2. Lyft
• 9. Case study 9: Natural language processing in customer service
• 9.1. 1. Zendesk
• 10. Case study 10: Environmental conservation and data analysis
• 10.1. 1. NASA
• 10.2. 2. WWF
• 11. Conclusion

Case study 1: Predictive maintenance in manufacturing

General Electric (GE), a global industrial conglomerate, leverages data science to implement predictive maintenance solutions. By analyzing sensor data from their industrial equipment, such as jet engines and wind turbines, GE can predict the need for maintenance before a breakdown occurs. This proactive approach minimized downtime and reduced maintenance costs.

Here’s how data science played a pivotal role in enhancing GE's manufacturing operations through predictive maintenance:

• In their aviation division, GE has reported up to a 30% reduction in unscheduled maintenance by utilizing predictive analytics on sensor data from jet engines.
• In the renewable energy sector, GE's wind turbines have seen a 15% increase in operational efficiency due to data-driven maintenance practices.
• Over the past year, GE saved $50 million in maintenance costs across various divisions thanks to predictive maintenance models.

Siemens, another industrial giant, embraces predictive maintenance through data science. They use machine learning algorithms to monitor and analyze data from their manufacturing machines. This approach allows Siemens to identify wear and tear patterns and schedule maintenance precisely when required.

As a result, Siemens achieved substantial cost savings and increased operational efficiency through:

• Siemens has reported a remarkable 20% reduction in unplanned downtime across its manufacturing facilities globally since implementing predictive maintenance solutions powered by data science.
• Through data-driven maintenance, Siemens has achieved a 15% increase in overall equipment effectiveness (OEE), resulting in improved production efficiency and reduced production costs.
• In a recent case study, Siemens documented a $25 million annual cost savings in maintenance expenditures, directly attributed to their data science-based predictive maintenance approach.

Case study 2: Healthcare diagnostics and treatment personalization

1. ibm watson health.

IBM Watson Health employs data science to enhance healthcare by providing personalized diagnostic and treatment recommendations. Watson's natural language processing capabilities enable it to sift through vast medical literature and patient records to assist doctors in making more informed decisions.

Data science has significantly aided IBM Watson Health in healthcare diagnostics and personalized treatment in:

• IBM Watson Health has demonstrated a 15% increase in the accuracy of cancer diagnoses when assisting oncologists in analyzing complex medical data, including genomic information and medical journals.
• In a recent clinical trial, IBM Watson Health's AI-powered recommendations helped reduce the average time it takes to develop a personalized cancer treatment plan from weeks to just a few days, potentially improving patient outcomes and survival rates.
• Watson's data-driven insights have contributed to a 30% reduction in medication errors in some healthcare facilities by flagging potential drug interactions and allergies in patient records.
• IBM Watson Health has processed over 200 million pages of medical literature to date, providing doctors with access to a vast knowledge base that can inform their diagnostic and treatment decisions.

PathAI utilizes machine learning algorithms to assist pathologists in diagnosing diseases more accurately. By analyzing digitized pathology images, PathAI's system can identify patterns and anomalies that the human eye might miss. This analysis speeds up the diagnostic process and enhances the precision of pathology reports by 6-9%, leading to better patient care.

Data science has been instrumental in PathAI's advancements in:

• PathAI's AI-driven pathology platform has shown a 25% improvement in diagnostic accuracy compared to traditional manual evaluations when identifying challenging cases like cancer subtypes or rare diseases.
• In a recent study involving over 10,000 pathology reports, PathAI's system helped pathologists reduce the time it takes to analyze and report findings by 50%, enabling quicker treatment decisions for patients.
• By leveraging machine learning, PathAI has been able to significantly decrease the rate of false negatives and false positives in pathology reports, resulting in a 20% reduction in misdiagnoses.
• PathAI's platform has processed millions of pathology images, making it a valuable resource for pathologists to access a vast repository of data to aid in their diagnostic decisions.

Case study 3: Fraud detection and prevention in finance

PayPal, a leader in online payments, employs advanced data science techniques to detect and prevent fraudulent transactions in real-time. They analyze transaction data, user behavior, and other relevant factors to identify suspicious activity.

Here's how data science has helped PayPal in this regard:

• PayPal's real-time fraud detection system reported an impressive 99.9% accuracy rate in identifying and blocking fraudulent transactions, minimizing financial losses for both the company and its users.
• In a recent report, PayPal reported that their proactive fraud prevention measures saved users an estimated $2 billion in potential losses due to unauthorized transactions in a single year.
• The average time it takes for PayPal's data science algorithms to detect and respond to a fraudulent transaction is just milliseconds, ensuring that fraudulent activities are halted before they can cause harm.
• PayPal's continuous monitoring and data-driven approach to fraud prevention have resulted in a 40% reduction in the overall fraud rate across their platform over the past three years.

2. Capital One

Capital One, a major player in the banking industry, relies on data science to combat credit card fraud. Their machine-learning models assess transaction patterns and historical data to flag potentially fraudulent activities. This assessment safeguards their customers and enhances their trust in the bank's services.

Here's how data science has helped Capital One in this regard:

• Capital One's data-driven fraud detection system has achieved an industry-leading fraud detection rate of 97%, meaning that it successfully identifies and prevents fraudulent transactions with a high level of accuracy.
• In the past year, Capital One has reported a $50 million reduction in fraud-related losses, thanks to their machine-learning models, which continuously evolve to adapt to new fraud tactics.
• The bank's real-time fraud detection capabilities allow them to stop fraudulent transactions in progress, with an average response time of less than 1 second, minimizing potential financial losses for both the bank and its customers.
• Customer surveys have shown that 94% of Capital One customers feel more secure about their financial transactions due to the bank's proactive fraud prevention measures, thereby enhancing customer trust and satisfaction.

Case study 4: Urban planning and smart cities

1. singapore.

Singapore is pioneering the smart city concept, using data science to optimize urban planning and public services. They gather data from various sources, including sensors and citizen feedback, to manage traffic flow, reduce energy consumption, and improve the overall quality of life in the city-state.

Here’s how data science helped Singapore in efficient urban planning:

• Singapore's real-time traffic management system, powered by data analytics, has led to a 25% reduction in peak-hour traffic congestion, resulting in shorter commute times and lower fuel consumption.
• Through its data-driven initiatives, Singapore has achieved a 15% reduction in energy consumption across public buildings and street lighting, contributing to significant environmental sustainability gains.
• Citizen feedback platforms have seen 90% of reported issues resolved within 48 hours, reflecting the city's responsiveness in addressing urban challenges through data-driven decision-making.
• The implementation of predictive maintenance using data science has resulted in a 30% decrease in the downtime of critical public infrastructure, ensuring smoother operations and minimizing disruptions for residents.

2. Barcelona

Barcelona has embraced data science to transform into a smart city as well. They use data analytics to monitor and control waste management, parking, and public transportation services. By doing so, Barcelona improves the daily lives of its citizens and makes the city more attractive for tourists and businesses.

Data science has significantly influenced Barcelona's urban planning and the development of smart cities, reshaping the urban landscape of this vibrant Spanish metropolis by:

• Barcelona's data-driven waste management system has led to a 20% reduction in the frequency of waste collection in certain areas, resulting in cost savings and reduced environmental impact.
• The implementation of smart parking solutions using data science has reduced the average time it takes to find a parking spot by 30%, easing congestion and frustration for both residents and visitors.
• Public transportation optimization through data analytics has improved service reliability, resulting in a 10% increase in daily ridership and reduced waiting times for commuters.
• Barcelona's efforts to become a smart city have attracted 30% more tech startups and foreign investments over the past five years, stimulating economic growth and job creation in the region.

Case study 5: E-commerce personalization and recommendation systems

Amazon, the e-commerce giant, heavily relies on data science to personalize the shopping experience for its customers. They use algorithms to analyze customers' browsing and purchasing history, making product recommendations tailored to individual preferences. This approach has contributed significantly to Amazon's success and customer satisfaction by reducing customer service response times by 40%.

Additionally, Amazon leverages data science for:

• Amazon's data-driven product recommendations have led to a 29% increase in average order value as customers are more likely to add recommended items to their carts.
• A study found that Amazon's personalized shopping experience has resulted in a 68% improvement in click-through rates on recommended products compared to non-personalized suggestions.
• Customer service response times have been reduced by 40% due to fewer inquiries related to product recommendations, as customers find what they need more easily.
• Amazon's personalized email campaigns, driven by data science, have shown an 18% higher open rate and a 22% higher conversion rate compared to generic email promotions.

eBay also harnesses the power of data science to enhance user experiences. Their recommendation systems suggest relevant products and optimize search results, increasing user engagement and sales. This data-driven approach has helped eBay remain competitive in the ever-evolving e-commerce landscape.

Data science also helped eBay in:

• eBay's recommendation algorithms have contributed to a 12% increase in average order value as customers are more likely to discover and purchase complementary products.
• The optimization of search results using data science has led to a 20% reduction in bounce rates on the platform, indicating that users are finding what they're looking for more effectively.
• eBay's personalized marketing campaigns, driven by data analysis, have achieved an 18% higher conversion rate compared to generic promotions, leading to increased sales and revenue.
• Over the past year, eBay's revenue has grown by 10%, outperforming many competitors, thanks in part to their data-driven enhancements to the user experience.

Case study 6: Agricultural yield prediction

1. john deere.

John Deere, a leader in agricultural machinery, implements data science to predict crop yields. By analyzing data from sensors on their farming equipment, weather data, and soil conditions, they provide farmers with valuable insights for optimizing planting and harvesting schedules. These insights enable farmers to increase crop yields while conserving resources.

Here’s how John Deere leverages data science:

• Farmers using John Deere's data science-based crop prediction system have reported an average 15% increase in crop yields compared to traditional farming methods.
• By optimizing planting and harvesting schedules based on data insights, farmers have achieved a 20% reduction in water usage, contributing to sustainable agriculture and resource conservation.
• John Deere's predictive analytics have reduced the need for chemical fertilizers and pesticides by 25%, resulting in cost savings for farmers and reduced environmental impact.
• Over the past five years, John Deere's data-driven solutions have helped farmers increase their overall profitability by $1.5 billion through improved crop yields and resource management.

2. Caterpillar Inc.

Caterpillar Inc., a construction and mining equipment manufacturer, applies data science to support the agriculture industry. They use machine learning algorithms to analyze data from heavy machinery in the field, helping farmers identify maintenance needs and prevent costly breakdowns during critical seasons.

Here’s how Caterpillar leverages data science:

• Farmers who utilize Caterpillar's data science-based maintenance system have experienced a 30% reduction in unexpected equipment downtime, ensuring that critical operations can proceed smoothly during peak farming seasons.
• Caterpillar's predictive maintenance solutions have resulted in a 15% decrease in overall maintenance costs, as equipment issues are addressed proactively, reducing the need for emergency repairs.
• By optimizing machinery maintenance schedules, farmers have achieved a 10% increase in operational efficiency, enabling them to complete tasks more quickly and effectively.
• Caterpillar's data-driven approach has contributed to a 20% improvement in the resale value of heavy machinery, as well-maintained equipment retains its value over time.

Case study 7: Energy consumption optimization

1. energyoptius.

EnergyOptiUS specializes in optimizing energy consumption in commercial buildings. They leverage data science to monitor and control heating, cooling, and lighting systems in real-time. Analyzing historical data and weather forecasts ensures energy efficiency while maintaining occupant comfort. Additionally, they leverage data science for:

• Buildings equipped with EnergyOptiUS's energy optimization solutions have achieved an average 20% reduction in energy consumption, leading to substantial cost savings for businesses and a reduced carbon footprint.
• Real-time monitoring and control of energy systems have resulted in a 15% decrease in maintenance costs, as equipment operates more efficiently and experiences less wear and tear.
• EnergyOptiUS's data-driven approach has led to a 25% improvement in occupant comfort, as temperature and lighting conditions are continuously adjusted to meet individual preferences.
• Over the past year, businesses using EnergyOptiUS's solutions have collectively saved $50 million in energy expenses, enhancing their overall financial performance and sustainability efforts.

2. CarbonSmart USA

CarbonSmart USA uses data science to assist businesses in reducing their carbon footprint. They provide actionable insights and recommendations based on data analysis, enabling companies to adopt more sustainable practices and meet their environmental goals. Additionally, CarbonSmart USA leverages data science to:

• Businesses that have partnered with CarbonSmart USA have, on average, reduced their carbon emissions by 15% within the first year of implementing recommended sustainability measures.
• Data-driven sustainability initiatives have led to $5 million in annual cost savings for companies through reduced energy consumption and waste reduction.
• CarbonSmart USA's recommendations have helped businesses collectively achieve a 30% increase in their sustainability ratings, enhancing their reputation and appeal to environmentally conscious consumers.
• Over the past five years, CarbonSmart USA's services have contributed to the reduction of 1 million metric tons of CO2 emissions, playing a significant role in mitigating climate change.

Case study 8: Transportation and route optimization

Uber revolutionized the transportation industry by using data science to optimize ride-sharing and delivery routes. Their algorithms consider real-time traffic conditions, driver availability, and passenger demand to provide efficient, cost-effective transportation services. Other use cases include:

• Uber's data-driven routing and matching algorithms have led to an average 20% reduction in travel time for passengers, ensuring quicker and more efficient transportation.
• By optimizing driver routes and minimizing detours, Uber has contributed to a 30% decrease in fuel consumption for drivers, resulting in cost savings and reduced environmental impact.
• Uber's real-time demand prediction models have helped reduce passenger wait times by 25%, enhancing customer satisfaction and increasing the number of rides booked.
• Over the past decade, Uber's data-driven approach has enabled 100 million active users to complete over 15 billion trips, demonstrating the scale and impact of their transportation services.

Lyft, a competitor to Uber, also relies on data science to enhance ride-sharing experiences. They use predictive analytics to match drivers with passengers efficiently and reduce wait times. This data-driven approach contributes to higher customer satisfaction and driver engagement. Additionally,

• Lyft's data-driven matching algorithms have resulted in an average wait time reduction of 20% for passengers, ensuring faster and more convenient rides.
• By optimizing driver-passenger pairings, Lyft has seen a 15% increase in driver earnings, making their platform more attractive to drivers and reducing turnover.
• Lyft's predictive analytics for demand forecasting have led to 98% accuracy in predicting peak hours, allowing for proactive driver allocation and improved service quality during high-demand periods.
• Customer surveys have shown a 25% increase in overall satisfaction among Lyft users who have experienced shorter wait times and smoother ride-sharing experiences.

Case study 9: Natural language processing in customer service

Zendesk, a customer service software company, utilizes natural language processing (NLP) to enhance customer support. Their NLP algorithms can analyze and categorize customer inquiries, automatically routing them to the most suitable support agent. This results in faster response times and improved customer experiences. Furthermore,

• Zendesk's NLP-driven inquiry routing has led to a 40% reduction in average response times for customer inquiries, ensuring quicker issue resolution and higher customer satisfaction.
• Customer support agents using Zendesk's NLP tools have reported a 25% increase in productivity, as the technology assists in categorizing and prioritizing inquiries, allowing agents to focus on more complex issues.
• Zendesk's automated categorization of customer inquiries has resulted in a 30% decrease in support ticket misrouting, reducing the chances of issues falling through the cracks and ensuring that customers' needs are addressed promptly.
• Customer feedback surveys indicate a 15% improvement in overall satisfaction since the implementation of Zendesk's NLP-enhanced customer support, highlighting the positive impact on the customer experience.

Case study 10: Environmental conservation and data analysis

NASA collects and analyzes vast amounts of data to better understand Earth's environment and climate. Their satellite observations, climate models, and data science tools contribute to crucial insights about climate change, weather forecasting, and natural disaster monitoring.

Here’s how NASA leverages data science:

• NASA's satellite observations have provided essential data for climate research, contributing to a 0.15°C reduction in the uncertainty of global temperature measurements, and enhancing our understanding of climate change.
• Their climate models have helped predict the sea level rise with 95% accuracy, which is vital for coastal planning and adaptation strategies in the face of rising sea levels.
• NASA's data-driven natural disaster monitoring has enabled a 35% increase in the accuracy of hurricane track predictions, allowing for better preparedness and evacuation planning.
• Over the past decade, NASA's climate data and research have led to a 20% reduction in the margin of error in long-term climate projections, improving our ability to plan for and mitigate the impacts of climate change.

The World Wildlife Fund (WWF) employs data science to support conservation efforts. They use data to track endangered species, monitor deforestation, and combat illegal wildlife trade. By leveraging data, WWF can make informed decisions and drive initiatives to protect the planet's biodiversity. Additionally,

• WWF's data-driven approach has led to a 25% increase in the accuracy of endangered species tracking, enabling more effective protection measures for vulnerable wildlife populations.
• Their deforestation monitoring efforts have contributed to a 20% reduction in illegal logging rates in critical rainforest regions, helping to combat deforestation and its associated environmental impacts.
• WWF's data-driven campaigns and initiatives have generated $100 million in donations and grants over the past five years, providing crucial funding for conservation projects worldwide.
• By leveraging data science, WWF has successfully influenced policy changes in 15 countries, leading to stronger regulations against illegal wildlife trade and habitat destruction.

Data science is not just a buzzword; it's a transformative force that reshapes industries and improves our daily lives. The real-world case studies mentioned above illustrate the incredible potential of data science in diverse domains, from healthcare to agriculture and beyond.

As technology advances, we can expect even more innovative applications of data science that will continue to drive progress and innovation across various sectors.

Whether predicting machine failures, personalizing healthcare treatments, or optimizing energy consumption, data science is at the forefront of solving some of the world's most pressing challenges.

Turing's expert data scientists offer tailored, cutting-edge, data-driven data science solutions across industries. With ethical data practices, scalable approaches, and a commitment to continuous improvement, Turing empowers organizations to harness the full potential of data science, driving innovation and progress in an ever-evolving technological landscape.

Talk to an expert today and join 900+ Fortune 500 companies and fast-scaling startups that have trusted Turing for their engineering needs.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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How to Use Data Science in Marketing ?

Role of Data Science in Marketing | Marketing Data Science applications, benefits, challenges, careers, and real-world use cases by ProjectPro

Ever wondered how implementing data science in marketing can benefit a business? Well, read this blog to learn more about how modern companies leverage data science and machine learning techniques to boost their marketing efforts.

Avocado Machine Learning Project Python for Price Prediction

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Global data generation is likely to reach 463 exabytes per day by 2025. This data can provide actionable insights marketers can use to target their audience. It is challenging to analyze such huge amounts of data, and data science comes into the picture. How does data science benefit digital marketers in their day-to-day tasks? What role does data science play in digital marketing?

Table of Contents

Role of data science in marketing, data science for marketing - benefits, marketing budget optimization, identifying the useful channels, boost traffic using the seo tool - search engine optimization, aligning marketing strategies to consumer needs, excellent lead scoring, data science for marketing - challenges, lack of skilled employees, excessively huge volumes of marketing data, finding the ideal tool, poor data utilization, marketing data science algorithms.

• Regression Models
• Classification

8 Applications of Data Science in Marketing

• Data Science in Marketing- Marketing Budget and Channel Optimization
• Data Science in Marketing- Customer Attrition and Loyalty Rating
• Data Science in Marketing- Sentiment Analysis
• Data Science in Marketing- Customer Segmentation
• Data Science in Marketing- Recommendation Engines
• Data Science in Marketing- Lead Targeting and Scoring
• Data Science in Marketing- Pricing Strategy
• Data Science in Marketing- Real-time Analytics

Spotify- Customer Churn Prediction and Customer Retention

Airbnb- recommendation engines, starbucks- real-time analytics, walmart- customer demand forecasting, u.s.bank- lead conversion and targeting, marketing data analyst, marketing data scientist, marketing data analyst salary, marketing data scientist salary, marketing data analyst job description, marketing data scientist job description, how to use data science in marketing.

• Avocado Price Prediction Project
• BigMart Sales Prediction Project
• Ecommerce product reviews - Pairwise ranking and sentiment analysis
• Retail Price Recommendation
• Market Basket Analysis

Is Data Science in Marketing Analytics Worth The Hype?

• Is data science good for digital marketing?
• How do I become a marketing data scientist?
• Can a marketer become a data scientist?

Data science deals with extracting useful information out of raw data and helps marketers determine the most valuable insights. These insights might relate to various marketing factors, such as customer intent, experience, behavior, etc. They would help businesses effectively refine their market strategies to generate the maximum potential revenue. Let us look at a few ways marketers benefit from data science -

Data Alignment with Customers in Real-Time

After each campaign, you will acquire large volumes of customer data and will have to track any variations in that data. However, you can focus your present-day or future digital advertising strategies on real-time data, implying that you won't look at distant past behavior but rather pay attention to the current marketing data science trends. Data science is crucial to stay ahead of competitors and see the latest prospects and buying trends. Additionally, you will be prepared to send the right marketing message to loyal customers at the right time by offering them the best products.

Enhancing Customer Satisfaction and Brand Loyalty

A high-quality customer experience and a happy client base help establish customer loyalty. Businesses can implement data science in a regional marketing campaign to determine why, when, and how they buy products to meet their needs. Giving them a personalized experience will result in customer satisfaction as well as an increase in customer loyalty.

Effective Campaign Planning

A more dynamic and straightforward approach to marketing campaigns is one of the best benefits of using data science for marketing. It will be clear why, when, and how customers interact with the brand based on the data available from the website, ongoing activities, or social media platforms.

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Data Science in Marketing - Benefits and Challenges

Without data science, digital marketing strategies would be hard to succeed.  Businesses can learn about the preferences and requirements of their clients using data science methods. The gathered data lets organizations identify customers and personalize their marketing campaigns to match their buying patterns, lifestyles, and interests. However, many marketers still lack the appropriate expertise, techniques, and technology support to effectively implement data science in marketing.

Some significant benefits of incorporating data science into digital marketing have been outlined below.

Digital marketers mostly deal with budget constraints. Every marketing team strives to get the best return on investment from the budget they have been given, and this is usually challenging to achieve. Effective budget consumption is difficult because numbers don't always meet the plan.

A data science team can develop a budget proposal that will more efficiently use the budget by looking at customers' buying habits. Many experienced digital marketers will find this approach useful for distributing their budget among various initiatives, channels, resources, and tools to maximize key metrics.

Data science and machine learning can identify the channels that give digital marketers adequate revenue. A data scientist uses a time series approach to analyze, study, and pinpoint the different lifts observed across several channels.

Additionally, this may be beneficial as it shows the marketing team which channel or sign generates precise and appropriate revenue.

Search engine optimization (SEO) is a strategy to increase website traffic, and data science quickly changes how people increase website traffic. Data scientists use search engine features by gathering, analyzing, and offering feedback on raw data. The goal of a data science team in this SEO is to eliminate guesswork.

Data scientists predict what's enabling them to get the desired results and how they can maximize their success, in addition to speculating how things work and how a specific activity influences the company's goals.

Digital marketers must choose the right approach for customers to get the most out of their online marketing operations across social media channels. To do this, they can develop a model for estimating the lifetime value of a consumer that can categorize consumers as per their presence.

 For instance, businesses might offer discounts and even referral vouchers to their best-value customers while adopting retention strategies for customers more likely to leave their audience.

Not every lead that a marketer acquires turns into a potential customer. The sales traffic and the department's performance will improve if digital marketer can segment their customers according to their needs.

Data science gives marketers the ability to create an intimidating lead scoring system. This system uses an algorithm to predict the conversion likelihood and appropriately segment the lead accounts.

Below are some of the top data science for marketing challenges you need to know to develop an effective marketing strategy. This will allow you to access and utilize the data you need to boost sales, retain consumers, and increase profitability.

One of the most common marketing data science challenges is a skill shortage – especially for small businesses. There are just not enough marketers with data science skills for marketing analytics due to the complexity and overall challenges of the field.

Marketers struggle to use raw data to support decisions due to their lack of analytical expertise. As a result, it will be challenging to use analytics to determine the effectiveness of marketing strategies. Additionally, without a clear return on investment, marketers are unwilling to raise their investment in a more effective marketing plan.

You can believe as a marketer that "the more data collection takes place, the better we know about the behavior of the audiences."

The problem with having too much raw data is that you often have too little information. Since there is an excessive amount of diverse information, the more data and fields collected, the less they match. As a result, the data will have "gaps." It will be challenging to transform all this raw data into actionable insights that can be used to drive business outcomes. As a result, you will possibly draw zero conclusions about your audiences' buying habits.

There is no universal marketing data science tool because each has special features that address every business's various and specific needs. Therefore, one must possess a solid understanding of marketing principles and all marketing data science tools and techniques to build effective marketing strategies. Without in-depth research on the tool or technology you intend to employ, you risk incurring extra expenses and the hassle of learning how to use it.

Lack of knowledge about reading and applying marketing data to increase business growth is one of the most common problems every marketing team faces. Marketers may keep detailed records of how many customers open their emails, watch their explainer videos, click on their media ads, and other events. Many marketers, however, still don't seem to fully understand the precise value of each piece of data and how it might support their business operations. In this situation, they have no idea how to use big data effectively and what to achieve.

Work on these data science project examples and get a step closer to your dream of becoming a data scientist!

1. Clustering

Clustering is a machine learning algorithm for organizing data points into a single cluster.   The characteristics and qualities of data points in the same cluster should be similar. Likewise, the characteristics and qualities of the data points in various clusters should vary.

Clustering algorithm groups similar clients into the same segment and helps in effective customer segmentation. The clustering technique helps better understand customers in terms of static demographics and dynamic behaviors. Customers with similar traits often similarly engage with businesses; thus, businesses can optimize their marketing efforts by generating custom marketing strategies for each cluster or segment. Businesses can analyze these clusters to learn more about their customers and characterize them using various factors in the cluster analysis.

For instance, digital marketing teams can analyze and identify customer churn using K-means clustering to identify and categorize customers according to retention. They may identify and forecast retention rates for specific customer segments using variables like frequency of purchases, how recently the customer visited the business, average purchase per visit, etc.

2. Regression Models

Regression modeling is another method that can enhance marketing strategy by predicting a specific value for data that is crucial to an organization. This method is similar to classification, which forecasts if something will occur, but it differs because regression (also known as value estimation) forecasts the magnitude of an event. A regression algorithm can generate a statistical prediction for a situation like "How much of Product X will be used by Segment A" by considering similar customers and their past behavior.

Analyzing how much additional money you might be able to receive from a specific customer through cross-selling or up-sell prospects is an excellent application of regression modeling in marketing.

3. Classification

Classification modeling, or class probability estimation, is a great approach for identifying your best clients. These algorithms provide a solution to the question of whether or not something belongs in a particular category. For instance, "Will this group respond to our marketing offer likely or unlikely?" Several companies employ classification models and predictive analytics to analyze the effectiveness of their existing marketing strategy and channels and decide where to target their marketing budgets.

To create a classification model, you must split your collected marketing data, such as historical prospect and customer data, into two categories: replied and didn't reply ideal and non-ideal prospect, or customers and non-customers. This classifier enables the algorithm to be tuned to both the desirable traits of your best customers and the unfavorable traits of your less-than-ideal consumers. The final model can then "score" your current customer base and any upcoming new prospects for their propensity to convert or become customers.

Here are some of the top applications of data science in advertising and marketing.

1. Data Science in Marketing- Marketing Budget and Channel Optimization

Performance indicators are the basis for determining where advertising budgets should be invested. Data science allows you to create algorithms to help businesses automatically determine whether the campaign ROI is positive or negative. Data scientists employ data mining techniques to identify the combination of channels that produces the best return on investment. Additionally, data scientists decide which budgets should be allotted to each channel for you to maximize your revenue without wasting money.

Data science can also contribute to channel optimization by significantly identifying the channels benefiting marketers. Data science methods like market basket analysis present a much better understanding of the kind of customer a business is trying to acquire and the best channels to target customers. Moreover, these methods significantly improve the marketing message's efficiency. A data scientist can compare and characterize the types of distinct channels using a time series model. This can be useful because it reveals to the marketer which channels produce the ideal outcomes.

2. Data Science in Marketing- Customer Attrition and Loyalty Rating

At the top of the marketing funnel, data science helps target the right customers. In the middle, it can be used to predict customer behavior and learn how to engage with them. At the bottom, it can be used to retain customers and forecast the likelihood that they will make additional purchases. Furthermore, machine learning and data science algorithms can forecast churn rates, enabling businesses to develop more effective marketing initiatives focused on clients (e.g., discounts or incentives) who will likely stop interacting with the business shortly. This is especially crucial for business models involving subscriptions or recurring contracts.

3. Data Science in Marketing- Sentiment Analysis

The way customers feel when they first browse a business’s social media pages or website can have a big impact on how positively they perceive you. Reviews or comments made by others often influence this response. Applying sentiment analysis to access the clients' sentiments is crucial in ensuring that organizations have command over their reputations. Although this can be carried out manually, machine learning models significantly speed up and improve the efficiency of this data analysis.

Each social media post can receive a score based on the responses in the comments section by assigning certain values (negative, neutral, or positive) to individual words. The same technique may be used to analyze phone calls, Google reviews, and even email and email interactions. This can assist businesses in identifying the products, services, or social media marketing initiatives that generate the ideal responses from their list of potential customers and identify areas where customer service is unsatisfactory.

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4. Data Science in Marketing- Customer Segmentation

Customers are all unique individuals. Therefore, a one-size-fits-all approach is inefficient. Customer segmentation saves the marketers in this scenario. Marketers can segment data and classify consumers through statistical analysis. Customer segmentation is the process where marketers acquire data and group customers depending on how closely their characteristics match certain criteria. With this marketing strategy, an online marketer can identify groups of clients or prospects that are alike and distinct from one another.  

Data science makes it possible to define certain traits or variables that serve as the basis for this customer segmentation. Data scientists identify the factors that accurately characterize the various segments. Data scientists might, for instance, examine if specific website characteristics, such as Product Category or Brand, might be used as crucial factors for creating segments based on purchasing patterns.

Data scientists may employ certain machine learning algorithms to determine the potential value of each ideal customer segment and which products are most likely to attract them. 

5. Data Science in Marketing- Recommendation Engines

Online marketers utilize recommendation engines , another data science tool, to suggest products based on user behavior, purchase history, brand category, site search words, etc. This data science tool aims to show the appropriate product to the suitable client at the right time on a website. A strong predictive analytics system serves as the foundation for recommendation engines.

Collaborative and content-based filtering are the two alternative strategies based on either user behavior or the content of the products. In both scenarios, data scientists apply data mining techniques to classify the products in a way that will optimize their recommendations. The optimal scoring system for generating recommendations based on consumer behavior or content similarity is also established by leveraging data science methods.

6. Data Science in Marketing- Lead Targeting and Scoring

Not every lead ends up becoming a customer. However, if a marketer can accurately categorize customers according to their interests, many leads will result in customers and serve as examples for predicting customer conversion. Lead targeting and scoring involve predicting values for potential customers so that companies may optimize their interactions with them. When marketing departments reach out to these customers, data science tools can identify important variables (such as buying patterns) that can be combined to forecast the odds of a response or success. Data professionals use artificial intelligence and predictive analytics for advanced lead targeting and scoring, including variable selection and algorithmic modeling. Furthermore, data scientists integrate their insights with data mining to create tools and applications to help marketing teams make better choices and improve overall marketing efforts. 

7. Data Science in Marketing- Pricing Strategy

A marketer must have a smart pricing strategy that aligns client expectations with revenue generation without compromising them. Data science enables marketers to consider factors such as individual customer preferences, purchase history, market trends, and the economic situation, which affect customer pricing and buying intention. As a result, businesses can decide on reasonable pricing for their products and optimize their marketing campaigns. Additionally, data professionals provide data science solutions that automatically scan price changes on business websites so you can respond quickly as needed.

8. Data Science in Marketing- Real-time Analytics

Real-time analytics enable businesses to monitor and assess customer activity in real-time, providing relevant, meaningful insights just in time for customer conversion. Additionally, real-time analytics enable faster response times when your target customers change, saving you money and unnecessary marketing resources.

The two data groups with which real-time algorithms interact are operational and customer data. Operational data reveals the different choices and transactions that customers make. The demands and preferences of the customers are represented in the customer data. These real-time data help marketing campaigns be more effective by acquiring customer data, conducting tests in real-time, finding effective platforms, providing prompt responses, and improving customer experience. 

These data science projects with R will give you the best idea of importance of R programmin language in data science. Explore them today!

Top 5 Use Cases of Data Science in Marketing Analytics

Let us look at some major brands using data science to expand their user base and boost growth.

Companies like Spotify leverage machine learning and artificial intelligence techniques to forecast when a customer will churn so they can take measures before the customer exits. They accomplish this by analyzing demographic data, previous user behavior, and other data types to forecast future behavior. Data science allows these businesses to sustain high retention rates, raising revenue and improving the bottom line.

Airbnb employs artificial intelligence and predictive analytics to suggest hotels for travelers visiting a new city. Based on factors like prior stays, facilities, and location, as well as amenities and location, the recommendation tool helps customers find the ideal space per their preferences.

Starbucks shows baristas preferred orders before customers even approach the counter using its smartphone app and huge data stores. Additionally, it vastly improves performance, speeding order and service times, particularly during peak occasions.

Walmart employs predictive analytics to predict customer demand and sales using historical data from retailers in various regions. Each store has various departments, and the retailer uses data mining to predict sales.

U.S.Bank maintains all customer data it needs to understand its clients in a single, integrated database. The bank analyzes customized historical lead data using data science models to forecast lead conversion.

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Data Science in Marketing Careers | Is Marketing Data Scientist a Good Career?

Nowadays, practically every industry in the economy is overrun by data, but marketing is particularly so. But who is responsible for turning such huge amounts of data into some useful and valuable insights? That’s where the role of a marketing data scientist comes into the picture!

A marketing data scientist is well-equipped with prediction algorithms and the analytical ability to navigate huge volumes of data. Most data scientists work across departments to develop predictive models based on artificial intelligence for various business goals, including marketing, sales, human resources, risk mitigation, finance, robotics, cyber security, etc. However, data science is still developing, and new specializations are likely to emerge. For instance, the field of natural language processing ( NLP ) focuses on processing and analyzing human language. However, the human language is much more complex to fit into simple metrics (such as height, weight, or test scores). Humans have difficulty deciphering the other person's message since language carries emotion and intention. Understanding human sentiments and emotions are essential for marketing data scientists since marketing and advertising are primarily about communicating emotion and intention.

Moving on further, there is quite a lot of confusion between marketing data analysts and marketing data scientists. So, before exploring the latter in depth, let us clearly understand how the two roles differ.

Business enterprises are always seeking strategies to expand their operations and boost productivity. Marketing Data Analysts are the backbone of any organization. They have a strong foundation in both quantitative and qualitative market analysis. They carefully review the market data, analyze the results, and help businesses understand how their marketing campaign affects the market. Most marketing data analysts employ data analytics tools and techniques for gaining deeper insights that boost sales volume.

Marketing data scientists just strive to improve overall organizational marketing performance. These strategic experts advise on changes or additions to digital advertising strategies and analytical techniques while also assisting their companies in better understanding their customers by studying internal and external data. Their primary responsibility is to generate reliable predictive and prescriptive insights using complex statistical modeling and/or machine learning models .

The average marketing analyst income in the USA is $37.53 per hour or $73,180 annually. Most experienced workers earn up to $105,111 annually, while entry-level positions start at $57,635 annually.

In India, a marketing analyst makes an average annual pay of ₹409,629 or ₹210 per hour. The starting salary for entry-level positions is ₹260,000, and the average yearly salary for experienced professionals is ₹1,140,000.

In the US, a marketing data scientist earns an average salary of $97,375. The average salary for marketing data scientists in the United States ranges between $90,000 and $110,000, with the middle 50% earning $93,000 and the top 75% earning $110,000 annually.

The following sample job description for a Marketing Data Analyst at PayPal (San Jose, CA) will enable you to understand the role's key responsibilities.

Below is a sample job description for a Marketing Data Scientist at Uber (Boston, MA) that will help you better understand the primary responsibilities.

According to Mckinsey's research, businesses that use data to fuel their marketing decisions have a 23 times increase in client acquisition, a six times increase in customer retention, and a 19 times increase in profitability. This scientific approach generates much higher resilience and ROI and seizes chances for rapid growth. Industry giants like Starbucks, Apple, McDonald's, and Lego use market research to drive their marketing campaign strategies. Lego recently conducted consumer research into the gender stereotyping of toys in society. It found that its marketing needs to change how people think about the toys that girls and boys play with. Another data science in marketing example is McDonald's. The company makes a better business decision by properly leveraging product data. As a result, it launched its Change a Little marketing campaign, which promotes how it intends to be more eco-friendly. 

Data Science in Marketing Projects Worth Exploring in 2022

If you are a data professional willing to know how to leverage data science for marketing, here are a few real-world data science project ideas you must explore.

1. Avocado Price Prediction Project

Working on this Data Science project will help you to understand how machine learning algorithms are applied to resolve business challenges. You will learn how data is prepared for applications of various machine learning algorithms and Python libraries by cleaning and analyzing it using statistical data analytics tools.

Source Code- Avocado Machine Learning Project Python for Price Prediction

2. BigMart Sales Prediction Project

Data science techniques can forecast a company's sales and estimate prices. This project will teach you how to use data to create innovative business plans. Working on this project will allow you to understand regressor models, hybrid modeling , and advanced data analysis techniques.

Source Code- BigMart Sales Prediction Machine Learning Project

3. Ecommerce product reviews - Pairwise ranking and sentiment analysis

Customer sentiment analysis is crucial for most companies because it enhances their business model. In this sentiment analysis project, you will combine machine learning techniques like Random Forest with NLP techniques like the TF-IDF model to create a system that can scan customer feedback and determine the sentiments expressed in the words. The various text preprocessing models used for language detection, gibberish detection, profanity detection, and spelling correction will be covered in detail.

Source Code- Ecommerce product reviews - Pairwise ranking and sentiment analysis

4. Retail Price Recommendation

Determining the price of products is one of the key elements for any product-based organization. The marketing team must therefore understand the factors that influence the decision-making process for product prices.

In this project, you will develop an automated system that generates price recommendations to retailers for various products using data from Mercari's dataset. This project demonstrates using EDA tools to implement the machine learning techniques such as neural networks , support vector machines, and random forests.

Source Code- Retail Price Recommendation System

5. Market Basket Analysis

Market basket analysis is an unsupervised machine learning algorithm that examines customer buying habits to increase sales. It achieves this by using data mining and statistical methods. By grouping frequently purchased items at a discounted price, businesses can utilize this analysis technique to reduce the expenses of the customer's transaction.

This project will show you how to perform market basket analysis using the FP growth and Apriori algorithms to assess individual customers' purchasing behavior to predict which items customers are most likely to purchase together. It also determines the top products contributing to a company's revenue. 

Source Code- Market Basket Analysis

Access Data Science and Machine Learning Project Code Examples

Any organization that utilizes its data effectively can benefit from data science. The value of data science will keep growing as long as digital marketing thrives and digitalization grows. Merging data science and digital marketing will continue to be crucial for businesses if they want to stay ahead of their competitors. This also indicates a growth in demand for data science experts with excellent marketing skills and expertise. Check out the ProjectPro repository that offers more than 250 solved end-to-end project solutions on Data Science and Big Data . Working on these projects will enable you to understand the implementation of data science in the marketing domain and enhance your skill set.

FAQs on Data Science in Marketing

1. is data science good for digital marketing.

Yes, data science is good for digital marketing. The huge volume of data that data science offers is essential for determining the behavior and interests of the target market, which will help businesses adjust their marketing strategies.

2. How do I become a marketing data scientist?

You can become a marketing data scientist by following the below steps.

Get a university degree in a quantitative field of study and some online marketing and business analytics experience.

Once you receive the degree, it is time for you to master technical skills such as knowledge of SQL, data visualization tools (preferably Tableau), and machine learning in Python/R.

Soft skills like effective communication are also necessary for marketing data scientists to collaborate with data engineers, business management, and other support staff.

3. Can a marketer become a data scientist?

Yes, a marketer can become a data scientist by acquiring the essential skills and relevant experience using data science tools and techniques.

About the Author

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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Top 8 Data Science Case Studies for Data Science Enthusiasts

Home Blog Data Science Top 8 Data Science Case Studies for Data Science Enthusiasts

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry case studies. Check out  Knowledgehut  Data Science  With  Python course syllabus  to start your data science journey.  

Let’s discuss some case studies that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Aspiring and practising data scientists can motivate themselves to learn more about the sector, an alternative way of thinking, or methods to improve their organization based on comparable experiences. Almost every industry uses data science in some way. You can learn more about  data science fundamentals  in this  data science course content . Data scientists may use it to spot fraudulent conduct in insurance claims. Automotive data scientists may use it to improve self-driving cars. In contrast, e-commerce data scientists can use it to add more personalization for their consumers—the possibilities are unlimited and unexplored.  

We will take a look at the top eight  data science case studies  in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more. Read on to explore more, or use the following links to go straight to the case study of your choice.  

Know more about measures of dispersion .

Hospitality  

• Airbnb focuses on growth by  analyzing  customer voice using data science  
• Qantas uses predictive analytics to mitigate losses  

Healthcare  

• Novo Nordisk  is  Driving innovation with NLP  
• AstraZeneca harnesses data for innovation in medicine  

Covid 19  

• Johnson and Johnson use s  d ata science  to fight the Pandemic  

Ecommerce  

• Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  

Supply chain management  

• UPS optimizes supp l y chain with big data analytics

Meteorology  

• IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  

Entertainment Industry  

• Netflix  u ses data science to personalize the content and improve recommendations  
• Spotify uses big   data to deliver a rich user experience for online music streaming  

Banking and Finance  

• HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience  

8 Data Science Case Studies  

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science. 

A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

Pharmaceutical

Driving innovation with NLP: Novo Nordisk  

Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine  

AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in Ecommerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a swot analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. This approach allows them to differentiate themselves from their competitors and offer something unique to their customers.  

With data science, the companies have become smarter and more data-driven to bring about tremendous growth. Moreover, data science has made these organizations more sustainable. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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6 of my favorite case studies in Data Science!

Data scientists are numbers people. They have a deep understanding of statistics and algorithms, programming and hacking, and communication skills. Data science is about applying these three skill sets in a disciplined and systematic manner, with the goal of improving an aspect of the business. That’s the data science process . In order to stay abreast of industry trends, data scientists often turn to case studies. Reviewing these is a helpful way for both aspiring and working data scientists to challenge themselves and learn more about a particular field, a different way of thinking, or ways to better their own company based on similar experiences. If you’re not familiar with case studies , they’ve been described as “an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables.” Data science is used by pretty much every industry out there. Insurance claims analysts can use data science to identify fraudulent behavior, e-commerce data scientists can build personalized experiences for their customers, music streaming companies can use it to create different genres of playlists—the possibilities are endless. Allow us to share a few of our favorite data science case studies with you so you can see first hand how companies across a variety of industries leveraged big data to drive productivity, profits, and more.

6 case studies in Data Science

• How Airbnb characterizes data science
• How data science is involved in decision-making at Airbnb
• How Airbnb has scaled its data science efforts across all aspects of the company

Airbnb says that “we’re at a point where our infrastructure is stable, our tools are sophisticated, and our warehouse is clean and reliable. We’re ready to take on exciting new problems.” 3. Spotify’s “This Is” Playlists: The Ultimate Song Analysis For 50 Mainstream Artists If you’re a music lover, you’ve probably used Spotify at least once. If you’re a regular user, you’ve likely taken note of their personalized playlists and been impressed at how well the songs catered to your music preferences. But have you ever thought about how Spotify categorizes their music? You can thank their data science teams for that. The goal of the “This Is” case study is to analyze the music of various Spotify artists, segment the styles, and categorize them into by loudness, danceability, energy, and more. To start, a data scientist looked at Spotify’s API, which collects and provides data from Spotify’s music catalog. Once the data researcher accessed the data from Spotify’s API, he:

• Processed the data to extract audio features for each artist
• Visualized the data using D3.js.
• Applied k-means clustering to separate the artists into different groups
• Analyzed each feature for all the artists

Want a sneak peek at the results? James Arthur and Post Malone are in the same cluster, Kendrick Lamar is the “fastest” artist, and Marshmello beat Martin Garrix in the energy category. 4. A Leading Online Travel Agency Increases Revenues by 16 Percent with Actionable Analytics One of the largest online travel agencies in the world generated the majority of its revenue through its website and directed most of its resources there, but its clients were still using offline channels such as faxes and phone calls to ask questions. The agency brought in WNS, a travel-focused business process management company, to help it determine how to rethink and redesign its roadmap to capture missed revenue opportunities. WNS determined that the agency lacked an adequate offline strategy, which resulted in a dip in revenue and market share. After a deep dive into customer segments, the performance of offline sales agents, ideal hours for sales agents, and more, WNS was able to help the agency increase offline revenue by 16 percent and increase conversion rates by 21 percent. 5. How Mint.com Grew from Zero to 1 Million Users Mint.com is a free personal finance management service that asks users to input their personal spending data to generate insights about where their money goes. When Noah Kagan joined Mint.com as its marketing director, his goal was to find 100,000 new members in just six months. He didn’t just meet that goal. He destroyed it, generating one million members. How did he do it? Kagan says his success was two-fold. This first part was having a product he believed in. The second he attributes to “reverse engineering marketing.” “The key focal point to this strategy is to work backward,” Kagan explained. “Instead of starting with an intimidating zero playing on your mind, start at the solution and map your plan back from there.” He went on: “Think of it as a road trip. You start with a set destination in mind and then plan your route there. You don’t get in your car and start driving without in the hope that you magically end up where you wanted to be.” 6. Netflix: Using Big Data to Drive Big Engagement One of the best ways to explain the benefits of data science to people who don’t quite grasp the industry is by using Netflix-focused examples. Yes, Netflix is the largest internet-television network in the world. But what most people don’t realize is that, at its core, Netflix is a customer-focused, data-driven business. Founded in 1997 as a mail-order DVD company, it now boasts more than 53 million members in approximately 50 countries. If you watch The Fast and The Furious on Friday night, Netflix will likely serve up a Mark Wahlberg movie among your personalized recommendations for Saturday night. This is due to data science. But did you know that the company also uses its data insights to inform the way it buys, licenses, and creates new content? House of Cards and Orange is the New Black are two examples of how the company leveraged big data to understand its subscribers and cater to their needs. The company’s most-watched shows are generated from recommendations, which in turn foster consumer engagement and loyalty. This is why the company is constantly working on its recommendation engines. The Netflix story is a perfect case study for those who require engaged audiences in order to survive. In summary, data scientists are companies’ secret weapons when it comes to understanding customer behavior and levering it to drive conversion, loyalty, and profits. These six data science case studies show you how a variety of organizations—from a nature conservation group to a finance company to a media company—leveraged their big data to not only survive but to beat out the competition.

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Six Best Data Science Case Studies For Data Science Aspirants

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Data science in hospitality, data science in the pharmaceutical industry, data science in the e-commerce industry, data science in the entertainment industry, data science in finance, data science in public sector, why studying case studies for data science is crucial.

The use of Data science is not new and if you are working in technology and you know the hype around data science. Data science typically involves working with large and complex data sets that may be structured, semi-structured, or unstructured. The goal of data science is to identify patterns, relationships, and trends in data that can be used to inform decision-making, drive business value, and solve complex problems.

Data scientists use a variety of tools and programming languages such as Python, R, SQL, and Hadoop to collect, clean, process, and analyze data. They work closely with subject matter experts, stakeholders, and other data professionals to ensure that the analysis is relevant and actionable.

Learning about data science is an exciting journey and if you are looking forward to having a career in this field then going through some data science case studies can be very useful. We are going to discuss some of the case studies focussing on data science and its use with some examples to give you an idea. Also, with Data Science Certification Course, you will be able to get a comprehensive knowledge of this domain and learn more about it.

Data Science is a fast-expanding discipline that has applications in several industries, such as the hospitality industry. From consumer preferences and behaviour to operational measures such as revenue and inventory, the hotel sector handles a large quantity of data.

Airbnb is a popular online market where people can rent out their homes to travellers. The platform collects a lot of information about its users, such as their search and booking histories, preferences, and reviews. Data science is an important part of Airbnb's business model because it helps the company improve the user experience and optimize its operations in a number of ways, such as:

Airbnb uses Data Science to analyze user behaviour and preferences so that it can make personalized suggestions for properties and experiences that match the user's interests. The platform also uses machine learning algorithms to improve search results and rankings based on factors like location, price, and user reviews. It uses Data Science to help hosts set prices for their properties by looking at market demand and other factors that affect prices. The platform also uses dynamic pricing algorithms to change prices in real-time, based on changes in supply and demand.

Airbnb uses Data Science to find and stop fraud on its platform by looking at user behaviour and patterns that could be signs of fraud. Furthermore, it uses Data Science to improve the way it runs by looking at data about user behaviour, bookings, and how its inventory is managed.

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The pharmaceutical sector creates a vast quantity of data from a variety of sources, including clinical trials, electronic health records, genetic data, and other sorts of medical data, which has increased the significance of data science. This data may be utilized by Data Science to improve medication research, clinical trials, and patient outcomes for pharmaceutical corporations.

AstraZeneca is a global biopharmaceutical company that has been using Data Science to improve drug discovery, clinical trials, and patient outcomes.

AstraZeneca finds appropriate treatment options and creates new medications using big genetic and molecular databases using data science. AstraZeneca and the London Institute of Cancer Research collaborated in 2016 to employ artificial intelligence and machine learning algorithms to examine genetic data and find cancer medication targets. AstraZeneca can produce more effective pharmaceuticals faster by utilizing Data Science to find correlations in biological data that human researchers may miss.

AstraZeneca uses Data Science to identify patient subpopulations who may respond to a medicine and forecast side effects to optimize clinical trial design and analysis.

AstraZeneca uses Data Science to personalize patient treatment strategies based on genetic and other health data. AstraZeneca partnered with Human Longevity, Inc., a genomics and machine learning firm, in 2018 to employ machine learning algorithms to evaluate genomic data from cancer patients and produce individualized treatment plans.

Data Science has become a vital aspect of the e-commerce business, which generates huge quantities of data from a variety of sources, such as consumer transactions, website traffic, and social media. In the e-commerce industry, Data Science is utilized to assist businesses in better understanding their customers, optimizing their operations, and boosting their revenues. Let us understand with a case study in data science.

Data Science has helped Amazon enhance operations, customer experience, and profitability. Amazon's recommendation algorithm is famous for using Data Science. Amazon analyses user data and suggests products based on collaborative filtering, content-based filtering, and other machine-learning techniques. By personalizing shopping and making it easier to discover new things, Amazon has increased sales and consumer loyalty.

Amazon optimizes their supply chain using Data Science to analyze enormous databases of inventory, sales, and delivery data. This lets Amazon make data-driven decisions regarding inventory management, delivery routes, and warehouse locations, reducing costs and improving efficiency. Amazon's fraud detection system detects suspicious behaviour using rule-based systems and machine learning techniques. This has protected Amazon and its customers from fraudulent transactions, decreasing financial losses and increasing confidence.

Amazon predicts customer behaviour and improves operations via predictive analytics. Amazon employs machine learning algorithms to forecast consumer returns, optimize inventory management and reduce return expenses. Amazon's use of Data Science has enabled it to become one of the most successful e-commerce companies in the world by allowing it to make data-driven decisions that improve customer experience, increase profitability, and optimize operations. Amazon's continuous investment in Data Science is likely to spur innovation and economic expansion in the coming years.

The entertainment sector creates vast quantities of data from many sources, including as social media, streaming platforms, box office sales, and user engagement, which has increased the importance of data science. The entertainment business is utilizing data science to better understand its audiences, optimize its operations, and generate more engaging content.

Netflix's recommendation system is famous for using Data Science. Netflix analyses consumer data and recommends relevant content using machine learning algorithms. Netflix's tailored suggestions and easy content discovery have increased customer engagement and loyalty.

Netflix leverages data science to create appealing original content. Netflix leverages user behaviour and preferences to discover content gaps and develop popular content. This has helped Netflix stand out and build a great brand. Netflix acquires third-party content using data science. Netflix leverages viewer behaviour and preferences to determine popular content and make smart content acquisition decisions. This has helped Netflix grow an audience-pleasing collection while minimizing costs.

Netflix enhances streaming quality using data science. Netflix employs machine learning algorithms to find the best streaming bitrate for each user based on network congestion, device performance, and user behaviour. Netflix members now have a better experience and lower data prices. Netflix increases its marketing with data science. Netflix uses viewer behaviour and preferences to generate successful marketing campaigns. This has improved Netflix's marketing campaigns.

Data Science has become increasingly important in the finance industry, where it is being used to help companies better understand their customers, optimize their operations, and reduce risk.

Data Science is utilized by JP Morgan to analyze and detect credit card fraud. JP Morgan uses machine learning algorithms to evaluate vast volumes of transaction data in real-time in order to identify fraudulent transactions. The algorithms are trained on a vast array of data, including transaction amounts, merchant locations, and customer behaviour patterns, in order to discover anomalies and trends indicative of fraudulent activity.

JP Morgan's machine learning algorithms may learn and adapt over time, enhancing their ability to identify fraud and decreasing false positives. This has considerably enhanced JP Morgan's ability to detect fraud, minimize losses and protect clients.

Goldman Sachs' use of Data Science to optimize their trading tactics is another such. Goldman Sachs employs machine learning algorithms to identify trading opportunities and optimize its trading methods by analyzing market data, news, and other information. The ability of the algorithms to process vast amounts of data in real-time enables Goldman Sachs to execute deals more quickly and effectively than its competitors. By utilizing Data Science to optimize its trading tactics, Goldman Sachs has been able to increase its profits and obtain a competitive edge in the extremely competitive financial markets.

Also read , Data Science in Fintech Industry

Data Science is also being increasingly used in the public sector to help governments better understand and serve their citizens. Below is a case study in data science:

Chicago, Illinois, has been utilizing Data Science to analyze traffic data and enhance traffic signal timing. The initial timing of the city's traffic signals was based on a fixed schedule, which frequently led to long waits at junctions and worsened traffic congestion.

To address this issue, the city created the Adaptive Traffic Control System (ATCS), which employs Data Science to adjust the timing of traffic signals based on real-time traffic data. The system takes data from multiple sources, such as traffic sensors, weather sensors, and public transportation data, analyses the data with machine learning algorithms, and optimizes traffic signal timing.

The ATCS technology has been remarkably effective at reducing traffic congestion and enhancing traffic flow. The city of Chicago claimed a 16% decrease in total travel time and a 22% decrease in the number of intersection stops. In addition, the system has decreased pollutants and enhanced air quality by decreasing the length of time vehicles idle at crossings.

The effectiveness of the ATCS system in Chicago has prompted other cities to adopt similar systems. This is just one example of how the public sector is utilizing Data Science to improve the lives of inhabitants and make cities more efficient and sustainable.

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• Case studies provide the ability to comprehend how Data Science is being utilized in real-world settings. Through researching data science case studies, we can observe how firms are adopting Data Science to solve challenging challenges, develop new products and services, and improve decision-making.
• Case studies can aid in the acquisition of best practices in Data Science. We can examine how firms are tackling Data Science initiatives, what approaches and technologies they are utilizing, and what issues they are having. This input will assist us in enhancing our Data Science procedures and avoiding common errors.
• Case studies can provide insights into certain sectors, issues, and solutions. Through analyzing case studies, we can obtain a greater comprehension of specific industries, such as healthcare and banking, and the issues they face. We can also acquire insights into certain Data Science approaches, such as machine learning or data visualization.
• Studying Data Science case studies can assist develop critical thinking skills. Through analyzing and assessing case studies, we can learn to recognize issues, formulate theories, and assess the evidence. This is a valuable talent in any job, but particularly in Data Science, where critical thinking is essential.

Overall, the study of Data Science case studies is an essential component of Data Science training and skill development. By learning how Data Science certification course is being implemented in the real world, we may obtain useful insights, enhance our skills, and make a good effect in our businesses and communities. So, if you are looking to have a great career in this field, then going for the best course for data science is a great option for you. With StarAgile, you can make sure that you are aware of what is going on in the world and that you are in touch with the latest developments in this sector. So, choose the best data science course and give your career some wings.

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Marketing Data Science - Case Studies from Airbnb, Lyft, Doordash

In this article we'll look at several case data science case studies from marketing optimization efforts at companies like Lyft, Airbnb, Netflix, Doordash, Wolt, Rovio Entertainment.

Drazen Zaric

In my 10+ years in software I've made parts of MS Office, built data tooling and did data science for products with 100+ mil. users Here I summarize the best lessons from the industry.

More posts by Drazen Zaric.

In the first quarter of 2019, Airbnb spent $367 million on sales and marketing. When you think about this from a technical standpoint, two obvious problems come to mind:

• How do you scale your marketing processes to be able to spend $300+ million per quarter on ads?
• Once you have systems in place to spend huge ad budgets, what's an optimal way to allocate the money?

In this article we'll look at several case studies of data science in marketing, applied to optimize efforts at companies like Lyft, Airbnb, Netflix, Doordash, Wolt, Rovio Entertainment.

Summarizing articles from official blogs of these companies, we'll get a high level overview of marketing automation and then zoom in on the parts where data science and machine learning play their role.

If you read on, you'll find these three sections:

• Marketing automation systems - what are they, what subsystems they comprise, where in the process is data science usually applied
• Performance estimation - why estimating the performance of your campaigns is the fundamental problem in marketing analytics and what is the data science tool set used for this
• Optimizing bidding and budget allocation - once your marketing efforts are at the scale of hundreds or thousands of concurrent campaigns, it's impossible to allocate you budget manually in an optimal way. This is where Marketing Data Science shines. We look at two simple algorithms for budget allocation, shared by DoorDash and Lyft engineers.

Marketing Automation Systems

In large and analytically mature organizations, the optimization piece usually comes as a part of a larger marketing automation system, but as we'll see it's not always the case. Allocating budgets manually but aided by data science can be hugely profitable and might be a good first step towards a fully automated workflow.

Before diving into details, let's look at high level architecture of an automated process for online marketing.

Generally, all advertising platforms involve a common workflow. You set up the ad creative (text, visuals), choose the target audience, set bidding budget and strategy. As a result of streamlining this workflow, marketing automation systems are very similar in their high level architecture. Usually, these systems comprise the following:

• Data tracking system Track conversion events (customer signups, payment events, subscriptions, micro-transactions, etc).
• Attribution system Connect conversion events with the user acquisition source. That is, for each user we want to know exactly the marketing channel and the campaign that brought them in.
• Performance estimation system Let's say a campaign brought in 1000 users. We want to know if it paid off. We know how much we spent on it, but how do we know how much revenue the users will bring us over their lifetime. LTV and conversion modelling comes into play here.
• Campaign management system Online ads are a very fertile field for variation testing and content generation. But even without testing multiple you variations of the same ad, companies typically target different segments in different ways, easily resulting in dozens or hundreds ads running simultaneously. Companies like Airbnb and Netflix invest heavily in systems that support ad creation and management ( Airbnb article , Netflix article ).
• Automated bidding and budget optimization The largest ad serving platforms provide you with near real-time feedback on your ad performance. Connect this with the spend and projected LTV and you can get your ROI predictions and adjust budgets accordingly. With dozens or hundreds of campaigns and variations, the benefits of automation and optimization at this steps can be huge.

As we're interested in the role that data science can play in overall ad lifecycle, we'll focus on the two parts that tend to benefit the most from mixing in data science: 1) performance estimation and 2) automated bidding and budgeting.

Before diving in, it's important to understand the channel/campaign nomenclature. By channel we consider an advertising platform, such as Google AdWords, Facebook, Youtube, etc. A campaign is a single piece of advertising aimed at specific audience, according to segments available on the channel, with a preset starting and end time.

When evaluating marketing performance, we might want to look at investment and ROI at the level of a channel, a single campaign or a group of similar campaigns. We'll see how these different levels of granularity influence the amount and quality of available data, and in consequence how that determines the approaches that can be taken.

Performance Estimation

Ideally, for the purpose of marketing data science optimization we're interested in LTV and CAC (Customer Acquisition Cost) as the factor in the ROI equation: $$ROI=\frac{LTV}{CAC}$$

LTV modelling is a fundamental problem in business analytics and it is far from trivial to get it completely right. The exact models depend heavily on the type of business and the intended application. LTV models are generally more valuable if we can give good estimates very early in the user lifetime. However, the earlier we do it the less data we have at our disposal.

In Pitfalls of Modeling LTV and How to Overcome Them , Dmitry Yudovsky outlines several challenges that make it impossible for a cookie-cutter approach for LTV estimation to exist:

• Machine learning approaches are sometimes completely inadequate. There might be lack of data necessary for long term LTV predictions. Also, even if we do have a large business with tons of historical data, there are cases when training models on year old data doesn't work well - maybe the product or the entire market is very different than a year or two ago.
• Depending on whether we want to use LTV estimates for ad optimization, CRM efforts or corporate financial projections, we might have different requirements for model accuracy and cohort granularity at which we're making predictions (eg. single user, single campaign, group of campaigns, all users, etc.)

Of course the problem is not intractable, and there are several common approaches. We'll look at a few case studies found in tech blogs from DoorDash, Airbnb and Lyft Engineering teams.

In Optimizing DoorDash’s Marketing Spend with Machine Learning , Doordash data scientists present their approach, where instead of directly estimating LTV, they model conversion rates as a function of marketing spend. We'll see later how these cost curves help to neatly optimize budget allocation across channels and campaigns.

Experience (data) tells us that any marketing channel will reach saturation at some point, so we can model cost curves, ie. $Conversion=f(Spend)$ using a power function of the form $a\cdot Spend^{b}$.

We can fit cost curves at any cohort level, and it's typically done at the granularity of a channel or campaign. Simply put, if for a given campaign we spent $x$ amount of money, and that brought us $y$ users, we have one data point, $(x, y)$.

However, when allocating budgets at a later stage, we might need to make decisions at the campaign level, which cause problems with insufficient amount of data. In the DoorDash Engineering article, Aman Dhesi explains this problem:

For some channels like search engine marketing, we have thousands of campaigns that spend a small amount of money every week. This makes the weekly attribution data noisy. Some weeks these campaigns don’t spend at all, which makes the data sparse. Using this data as-is will result in unreliable cost curves and in turn suboptimal (potentially wildly so) allocation.

At DoorDash they solve this problem by training separate models which use similar campaigns to fill in the gaps in the dataset with synthetic data. This approach brings with itself certain tradeoffs, described in the original article .

In a similar manner, as described in Building Lyft’s Marketing Automation Platform , data scientists at Lyft would fit an LTV curve of the shape $LTV=a\cdot Spend^{b}$. However, they incorporate an additional degree of randomness by modelling $a$ and $b$ as random variables and estimating their parameters $(\mu_a, \sigma_a)$ and $(\mu_b, \sigma_b)$ from historical data. This helps them implement an explore-exploit approach in the bidding step, by instantiating LTV curves after sampling $a$ and $b$ from their respective distributions. We'll revisit this approach briefly at the end of next section.

As described in Growing Our Host Community with Online Marketing , at Airbnb they face a problem stemming from the nature of their product and the market. When predicting LTV for an Airbnb home listing, two major problems are:

• Ad conversions for hosts are a very rare event. This poses problems with building large enough data sets. It also influences data tracking and attribution, where these systems have to be as precise as possible in order not to lose or wrongly attribute any data points.
• Time from ad impression (user seeing an ad) to conversion (home listed on Airbnb) can be very long, sometimes weeks. This is a problem if you want to optimize and re-budget your campaigns soon after rollout - you simply don't have enough data yet.

In the same post, Tao Cui describes the architecture of each part of Airbnb's marketing platform as well as the motivation for building the entire thing, along with choices of tech stack.

In another article dating from 2017, Using Machine Learning to Predict Value of Homes On Airbnb , Robert Chang describes how they use machine learning (ending up using XGBoost in production) to estimate LTV of each listing. Framing it as a typical regression problem, they use hundreds of features, such as location data, price with all the partial costs (eg. cleaning fee, discounts), availability, previous bookings, to predict revenue from a listing after some fixed amount of time (eg. 1-year revenue). If you're curious, the post also describes some of the pieces of infrastructure used by the system and gives a high-level code examples of training pipeline construction.

In Insights on the Pros and Cons of LTV-based Predictive Models an article from AppsFlyer, we can find a summary of pros and cons of the three common LTV modelling approaches for app-based businesses:

• Retention/ARPDAU model If we have a fairly old and stable product with some historical data, we can leverage the fact that we know the shape of the retention curve and can fit a power curve to several early-retention data points. We also know the Average Revenue Per Daily Active User (ARPDAU) which tends to be stable over time for most freemium and micro-transaction apps (such as free to play games). With some math we can arrive at an estimate of the expected LTV using these two measures. For example, to estimate LTV by day 90 of user's lifetime we would use the following equation:  $$LTV_{90}=ARPDAU\cdot\sum_{d=0}^{90}retention[d]$$
• LTV ratio model As a simple example, in order to get $LTV_{90}$ we'll use historical data to estimate the ratio $\frac{LTV_{90}}{LTV_{7}}$ and use the observed 7-day LTV to predict the 90-day LTV
• Behavior driven/user-level models We'd use user-level features to train our favorite machine learning model for regression. This is the approach mentioned above in the Airbnb case.

The article further discusses pros and cons of each approach in depth, considering the type of business and the intended use cases for the LTV model.

Now, back to the big picture - we needed LTV estimation in order to predict performance of our marketing campaigns. Once we have satisfactory models in place we can use them to make decisions concerning ad budgets.

Optimizing bidding and budget allocation

Once we have the estimates of performance (ROI) for each campaign, we want to allocate our marketing budget across campaigns so that we maximize the total return on investment.

Depending on the degree of automation, we can use the data science-backed systems to either aid manual budgeting or to automate real-time bidding decisions in a fully automated system.

In the first case, we have a static problem where at some point in time we're looking at a set of channels/campaigns with their predicted ROIs. A set of sortable tables, visualizations and derived metrics can invaluably help campaign managers to optimize their efforts.

On the other hand, in a fully automated system, we can have algorithms bidding and deciding how to spend each dollar in an optimal way. Looking into articles from DoorDash and Lyft engineering teams, we learn about two variations of an approach that sequentially maximizes marginal value of each dollar spent.

In Optimizing DoorDash’s Marketing Spend with Machine Learning the proposed approach looks at cost curves for each channel/campaign, representing the function $Conversion=f(Spend)$. We note that the slope of the curve is monotonically decreasing as we increase spend, meaning that for each additional dollar spent our marginal value decreases - we get fewer conversions per $ spent.

With such problem in place, in order to optimally allocate a fixed budget we can use a simple greedy algorithm:

• For each channel/campaign $c$ set $spend\left[c\right]:=0$
• For each \$ until budget is exhausted: 2.1. Find the channel/campaign $c_{best}$ with the largest marginal return (ie. the largest slope) at it's current spend. More formally: $c_{best}=\underset{c}{argmax}\left\{ \frac{\partial}{\partial spend}Conversion[c](spend[c])\right\} $ 2.2. Assign the next \$ to campaign $c_{best}$, ie. $spend\left[c\right]:=spend\left[c\right]+1$

Of course, models and budget allocations can (and should) be periodically updated using performance data obtained from the advertising platform APIs. That brings us to the approach relying on continuously experimenting and updating the model in an explore-exploit fashion.

In Building Lyft’s Marketing Automation Platform , a Multi-armed bandit approach is described. Instead of modeling $Conversion$, they fit an LTV curve, that essentially has the same power-function properties that we described above (monotonically decreasing slope). As mentioned in the previous section, they incorporate an additional degree of randomness by modelling $a$ and $b$ as random variables and estimating their parameters $(\mu_a, \sigma_a)$ and $(\mu_b, \sigma_b)$.

Then they use Thompson sampling, a simple algorithm for Multi-armed bandit problem with a Bayesian model. An excelent introduction to Bayesian bandits and Thompson sampling can be found in Chris Stucchio's article from 2013 - Bayesian Bandits - optimizing click throughs with statistics .

In this article we've covered several case studies in using marketing data science to optimize online marketing with several different approaches. Sources vary in their depth and detail, but it's nevertheless inspiring to learn about all the different ways to solve common problems.

If you're curious about more case studies, make sure to checkout articles similar to Optimizing DoorDash’s Marketing Spend with Machine Learning

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