Top 5 Uses of Data Science in Marketing 2024

The Marketing Value of Data Science

Have you ever listened to a song on Spotify, only to find similar recommendations appear on your playlist shortly after? Or maybe you browsed the Internet for a pair of shoes to then receive targeted ads from Nike on your Instagram feed for an entire week?

These are both examples of the marketing capabilities of data science in action.

Nowadays, organizations have the technical capacity and processing power to collect and store large amounts of user data. As this data builds up over time, companies use it to identify trends in customer behaviour. They utilize these insights to upsell, send consumers targeted advertisements, and come up with personalized product recommendations.

In this article, we will go over the top 5 data science use cases in marketing in order to provide you with some practical insights on how data changes the user experience. Then we will explore some ways you can land a job in marketing as a data scientist.

Top 5 Applications of Data Science in Marketing:

Recommendation Systems
Churn Prediction
Customer Segmentation
Market Basket Analysis
Sentiment Analysis

Why You Should Pursue a Career in Marketing as a Data Scientist?

Top 5 Applications of Data Science in Marketing

1. Recommendation Systems

E-commerce websites and streaming platforms such as Amazon, Netflix, and YouTube use recommendation systems to come up with customized suggestions based on your browsing behaviour.

For example, notice how your Netflix movie recommendations start off generic, then get more precise the more time you spend on the platform. This is because the site’s built-in system picks up on your streaming patterns over time. The algorithm captures the amount of time you spend watching a movie or show and adjusts your genre preference based on the ratings you give them. Such data science techniques also take into consideration the actions of members who have similar streaming preferences as you and use this to come up with more refined suggestions.

Recommender systems are a popular application of data science for marketing among subscription service providers. Netflix can only provide you with decent movie recommendations if their algorithm has monitored your behavior over time. As you get suggestions ever more tailored to your tastes, you become more likely to renew your subscription come next month.

2. Churn Prediction

Organizations who leverage marketing with data science are able to determine how likely you are to stop frequenting their sites and using their products or services even before you are aware of it yourself. This is called customer churn prediction and it is an extremely powerful user-retention strategy.

Here is an example of why customer churn prediction works so well:

Think of a time you signed up for a service with a monthly subscription, only to terminate it soon after. You must have received multiple follow-up emails, promotions, and customized discounts from the service provider. That is because the company didn’t want to lose out on a client and attempted to entice you back with special offers. However, by this time, it’s often too late for them to re-gain your interest since you’ve already made the decision to leave.

Now, imagine if the business was able to predict a month ago that you were planning to unsubscribe. At this time, you might have still been on the fence about quitting. Customized promotions could have made it far more likely for you to continue your subscription.

And what if the company’s data scientists went a step further and found a way to identify the pain-point driving you to leave? Then they could have implemented measures to fix it and ensure that you’d continue doing business with them.

The scenario above is very straightforward but it encapsulates just how useful a predictive model can be for consumer retention. Customer churn prediction is often a supervised machine learning problem. Data scientists will collect the attributes of customers who have left the company in the past and use this information to build a classification model that can distinguish between churners and non-churners.

3. Customer Segmentation

Sometimes, you get an ad for a product that you’ve never used before and didn’t even know you wanted. After seeing the ad, however, the product offering resonates with you, and you decide to make a purchase. Someone was able to predict your affinity towards an item even before you were aware of it.

How does this happen?

Predictions like this are generally made with the help of customer segmentation models.

Machine learning algorithms are built to segment individuals with similar traits and group them together using cluster analysis methods. For example, I often do yoga and Pilates during my free time. I’ve browsed yoga mats, searched for virtual classes, and purchased sportswear online. Not long ago, I received an ad from a plant-based restaurant despite never having searched for any food online.

This is most likely because an algorithm realized that I displayed similar traits to individuals who enjoyed practicing yoga, and a majority of them were on the lookout for healthy vegan food.

Customer segmentation models can be incredibly useful for identifying similarities between groups of people that often go unnoticed by the human eye.

4. Market Basket Analysis

Basket analysis is the most popular marketing application of data science among retail stores and restaurants. These companies use algorithms to identify items frequently purchased together. The products are then placed next to each other on shelves or menus so that customers have easy access to them.

Placing highly correlated items in the same line of sight is great for upselling. For example, a person who buys flour is also likely to purchase baking powder, so these products are displayed on the same shelf.

Market basket analysis allows companies to uncover more complex correlations that aren’t necessarily visible to us. A good, albeit fictional, example of this is the famous “beer and nappies” case study. According to a statistical analysis conducted by Walmart, beer and diapers are items frequently purchased together on Friday nights.

While you might think the correlation is purely coincidental, it has a logical explanation. In fact, the phenomenon was caused by working men buying diapers for their kids on the way back from work. As they didn’t have much time left to go to the bar after that, these men picked up a bottle of beer to take home with them. Based on these findings, Walmart started to place beer and diapers on the same shelf, which led to a massive increase in sales on Fridays.

While this particular use case is fake, it has been rehearsed time and again to demonstrate just how powerful statistics is in uncovering hidden correlations in consumer behavior.

5. Sentiment Analysis

When a company makes the decision to launch a new addition to their assortment, they need to ensure that it will resonate with customers. Products need to have a Unique Value Proposition and should be able to address an existing pain point in the market. The combination of methods used by marketers to maximize the appeal of products and make them stand out from the crowd is known as marketing mix.

Sentiment analysis is a great way to identify gaps in existing product line-ups and help companies decide what to launch next.

To build a product that addresses market demand, it is important to answer the following questions:

What are the existing solutions on the market, and how are customers responding to them?
If a company launches a new product and posts about it on social media, is public sentiment mostly positive or negative?
If negative, what are people saying? What are their pain points, and how can they be addressed?
Has customer sentiment towards a specific product changed over time? Can we predict how people will respond to similar offerings in the future?

Sentiment analysis is powered by cutting-edge Natural Language Processing technology that is creating new opportunities for bridging the gap between marketing and data science. It allows marketers to monitor customer behavior in real-time and make better decisions faster. Learn more in the dedicated Sentiment Analysis in Python tutorial where I show you how to build your own model, step by step.

Is data science useful for digital marketing?

Data science can be used to create a highly detailed picture of consumer behavior and thus maximize the value of marketing campaigns. Targeted advertising, customer retention strategies, effective product placement – these are just some of the ways data science can drive marketing ROI. In a line of work where the chief prerequisite for success is understanding the target persona in minute detail, data science gives savvy marketers the upper hand. Making decisions based on intuition, years of experience, or detailed research has been the norm in the field for many years. But with the advent of data, this approach is becoming outdated. While nothing will ever replace the ‘human touch’ that creative marketers bring to the table – data is helping such professionals act on insights faster and with better results.

Do marketers need to know data science?

While it is not imperative for marketers to be data-science-savvy, the ability to build highly detailed models with data is becoming more and more valuable in the field. For years, marketing teams have relied on a combination of knowing what has worked in the past, intuition about what might work in the future, and varying degrees of target audience research. The advent of data is quickly changing these deep-rooted standards and with them the face of marketing as a whole. Even though marketers have always been data-driven, the level of complexity that data science adds to these efforts is simply unattainable otherwise. Tools such as predictive analytics, cluster analysis, NLP, sentiment analysis, and others, are able to uncover connections in the data that won’t be visible to the naked eye. Based on these connections, marketers can make better decisions on the fly and thus achieve optimal returns.

Is marketing analytics a data science?

As marketing analytics becomes an ever more forward-looking field, it is getting closer to data science. Traditionally, market analytics looks at past and present data to determine what works and what doesn’t based on historic patterns and current market conditions. But the trend is shifting and many marketing experts are now employing predictive modelling to maximize the ROI of their campaigns. Moreover, marketing analytics and data science are using very similar tools and systems. A clear example of this is A/B testing. The staple in product optimization, it is where marketing analytics and data science converge.

Why You Should Pursue a Career in Marketing as a Data Scientist?

Data practitioners are experts at building highly accurate models, cleaning data, and performing statistical analysis with technical precision. On the other hand, marketing professionals are able to come up with strategies to attract customers to a business.

If you acquire the combined skillset of both these roles, you will be invaluable to companies looking to extract value from customer data. Not only will you possess the ability to build predictive models and munge large amounts of data, but you will also be in a position to come up with actionable insights based on your findings.

If you’d like to start learning data science approaches to marketing, 365 Data Science offers 2 courses that can equip you with the skills necessary to launch a career in the field — Introduction to Business Analytics and Customer Analytics in Python. Packed with real-world use cases, top-shelf industry know-how, and hard technical expertise, these courses are the perfect introduction to data-driven sales strategies. If you already have a basic understanding of Python and other analytics tools, they will help you take the next step toward applying your skills to generating product market value.