Data Science in Marketing: A Comprehensive Guide (With Examples)

Data Science in Marketing: A Comprehensive Guide (With Examples)

Learn how data science methods like machine learning, clustering, and regression have moved marketing from a creative domain to a scientific one.

Most marketing teams are leaving a lot of money on the table. According to Sitecore, the average US brand collects eight pieces of data per user, ranging from address to behavioral insights. Brands are collecting an extensive amount of data at various stages of the customer journey. Data science helps us leverage this data into actionable insight that results in a greater return on investment. 

Data science methods like machine learning, clustering, and regression have moved marketing from a creative domain to a scientific one. By leveraging data science, marketing teams can extend their top-funnel approach to incorporate the full-funnel and uncover product and customer insights at scale in an unprecedented way. To do this, growth marketers should understand what data scientists can and cannot do as well as some of the methods and how marketing teams use data scientists.

What Data Science Is and Is Not?

There is a lot of confusion about what a data scientist does and does not do. Specifically, people often interchange the terms of data science and data analytics. The easiest way to differentiate between the two is that a data scientist looks to predict the future, while a data analyst looks to summarize the past. Data scientists make predictive models using regression, machine learning, and other advanced statistical methods, while a data analyst uses descriptive statistics to analyze past patterns. 

A data scientist is not a software engineer. Their programming ability is enough to run machine learning and statistical analyses they need using platforms like R, Python, and SAS, but not to develop software or manage infrastructure like an engineer would. Data science is the intersection between business expertise, programming, and statistics, where programming is simply a medium to derive insights using statistics and business or domain expertise. 

The data scientist toolbox uses artificial intelligence and mathematical modeling to unlock a new set of insights. A marketing data scientist can answer questions such as: Who are your most promising customers? What choice alternatives do consumers of your product have? How do people feel about your brand? What other products do your customers want to buy? By leveraging the data scientist, a marketing team can eliminate waste and target customers in ways that are cost-effective and personalized. 

Understanding Data Science Workflow

Understanding the data science workflow will allow your marketing team to communicate with the data scientist effectively. After you have defined your task and gotten access to your data, the data scientist will perform some exploratory data analysis to get an idea of the right model to find the insight we are looking for. This could mean testing models on historical data sets and measuring its accuracy or a variety of other methods to create a benchmark against which to measure the success of whatever model we pick. After the model is chosen, the data is formatted in a workable way. This could involve figuring out how to deal with missing values, duplicates, or other variables that make the model harder to apply. The model is then run on a partition of the data in order to train it. The method chosen will mold itself to the data and then will allow you to apply the model to any dataset with the same parameters. Finally comes fine-tuning the model. This means the model isn’t overfitted to the data and that it runs as it is supposed to. 

Examples and Use Cases of Data Science Applications in Marketing

Let us take a look at a scenario that most marketing professionals are deeply familiar with. A company is spending a small fortune on marketing, and the ads are getting a lot of visibility, but the return on investment is nowhere near expectations. Enter the data scientist. Through data collected on the website and social media pages, the data scientist can understand the customer base’s demographics. This understanding goes beyond age, geographic location, and gender of yesteryear. A simple affinity analysis (also known as a market basket analysis), wherein we analyze certain consumer behaviors’ co-occurrence, will give you details about what else this customer is likely to shop for. 

Below is a visual depiction of an affinity analysis for grocery items. Using an affinity analysis, a marketer can see patterns like people who buy male cosmetics are also likely to buy bottled water. 

While the market basket analysis has been employed for years by retailers, in the new age, it gives you insights beyond people who buy almond butter are also likely to buy bread. It might give you a less intuitive, but equally actionable insight such as foodies are also likely to be home décor enthusiasts and are likely to watch, “Yoga with Adriene,” on YouTube (affinities produced with the Think with Google toolbox). This allows you to market in new places where your client base is present, while still exposing you to a new audience, increasing your visibility without breaking the bank on marketing material. 

Data Scientists Seek to Optimize Every Chance They Get

In data science and growth marketing, the name of the game is optimization. A growth marketer is aware that business success is, in large part, driven by profitable revenue. Tying a business’s marketing strategy to key performance indicators like customer lifetime value, incrementality, and cost per customer acquisition is absolutely necessary for a competitive business landscape. Businesses simply cannot afford to spend on marketing that does not contribute to their bottom line. A favorite tool of every data scientist and one that is absolutely necessary to the modern-day marketer to help with this is segmentation. 

Some Key Benefits of Segmentation

  • Helps determine market opportunities
  • Tailor-made marketing initiatives
  • Product development and design insights
  • Pricing model insights

We can define segmentation as grouping or clustering of customers into groups based on different characteristics. Every marketer knows that different audiences respond to different narratives. This is where our data science toolkit comes in. Clustering customer segments together when you only have a few input variables is easy. However, the task becomes much more challenging as the number of variables grows. Data scientists use a machine learning method called clustering to figure out where the segments really are. 

How Does Clustering Work?

Clustering algorithms are unsupervised, meaning the algorithm figures out what variables are similar to each other without input from the user. These clustering algorithms strive for the most mutually exclusive, collectively exhaustive segments. When employed correctly, this approach optimizes clusters in the most efficient way possible. It does not segment where segments are not needed and do not miss segments necessary for a targeted campaign. Not only are points within clusters similar to each other, the clusters themselves are dissimilar, meaning marketers can then tailor what each segment will respond to, rather than a generic campaign with low ROI. 

If we look at the graph below, we see that the clustering algorithm groups the raw data into three separate clusters optimized for minimum distance between points and maximum distance between clusters. This particular algorithm works by cycling through a series of cluster centers and finding which one is most mutually exclusive, and collectively exhaustive. 

Full Funnel Optimization Leveraging AI

Machine learning and artificial intelligence are how marketing reaches the full funnel. Marketing campaigns have traditionally focused on awareness, acquisition, and activation. Through the use of data science, the growth marketer gets all the way down to retention, revenue, and referral. A business can forecast the customer lifetime value of new customers through the use of several machine learning and artificial intelligence methodologies. Rather than just focusing on a first sale, growth marketers employ data science insights to help companies to target longer customer relationships.

Data science allows you to understand the customer journey in a much deeper way than previously possible. Where do your best customers come from? Data scientists give you the insights to curate your marketing strategy to the customer that makes the most sense for you. Suddenly, your marketing team is directly impacting your revenue growth. Machine learning can predict churn rates, helping you develop a strategy to target customers who are not as engaged with the brand as you would like them to be. Your marketing team is now working on retention. This works all the way down to referrals. Artificial intelligence can help you determine which customers are influencers for your brand through qualitative analyses on the quality of content, brand affinity, and brand engagement. You can then target them to make their referral process simpler and more effective. 

The insights artificial intelligence give you are seemingly endless. Marketers and data scientists alike know that at the end of the day, data is really about understanding people. In the age of social media, businesses have access to unfiltered, unbiased, real-time commentary from their customer base and people exposed to their brand. In the past, companies would spend a small fortune to hire a research firm to survey how people feel about a product or campaign. In the 21st century, the data scientist is your research firm. 

Simple sentiment analysis will give you an idea of how the public feels about your product. This information is invaluable. Sentiment analysis works by associating words with sentiments and then mining sources for text to measure the overall themes in the text. Most commonly for marketing, this is done through polarity, meaning words are assigned a value of positive, negative, or neutral. Finally, the outcome of the analysis is measured, giving the marketer feedback on how people responded to an ad. Here is a graphic by Irena Spasic, a data mining professor at Cardiff University, outlining the components and expertise required for sentiment analysis. 

This process is completely automated through the power of machine learning. Growth marketing bridges the gap between data and society. By understanding the power of data and its insights, growth marketers can make sure their campaigns speak to the society they live in. 

Insights and Experimentation

Equally important as predicting these segments is understanding “why.” Data scientists look for causal relationships that growth marketers can then reverse engineer into an effective campaign. For example, let us hypothesize that clicks and conversion rate are positively correlated. The data scientist can test if that is true with regression analysis, then the growth marketer, in partnership with the data scientist, can come up with experiments to test which campaigns produce more clicks, therefore a higher conversion rate. 

The growth marketer is not just a creative, but a scientist, whose process involves constant experimentation. 

An effective marketing strategy must always attempt to be ahead of the curve. This requires some level of risk, but with our data science toolkit, that risk is minimized. In the business world, we often get caught up in the “best practice,” rhetoric, when in reality, every business we have seen scale has done so by taking risks. The best practices of sixty years ago were risks at the time. Progression happens through experimentation, and data science equips you to perform a large volume of micro-experiments that together give you immense insight without making drastic or sudden changes, thereby mitigating the risk inherently present when trying something new. 

The growth marketer can communicate the pain points of a business, while simultaneously understanding the public sentiment and developing a marketing strategy around that. 

An oft-told adage in the data science world is that data scientists are storytellers. The insights derived from the data science toolkit are not just numbers. They help tell your business a narrative. What are our customers feeling? Where are we doing well? Where are we struggling? Data science in marketing is about answering these questions in a more efficient and cost-effective way. Similarly, marketers understand the importance of the narrative. Studies show that a consumer is much more likely to remember an ad when there is a narrative attached to it. The most effective advertisements are told as a story. The growth marketer is the 21st-century hybrid—a master of data and storytelling both to the business and the public. The growth marketer can communicate the pain points of a company, while simultaneously understanding the public sentiment and developing a marketing strategy around that. 

Should You Hire a Data Scientist?

So, as a marketing executive, where should you start? It is just as important to recognize where your company is not ready to employ artificial intelligence as it is to recognize where it is necessary. Someone considering hiring a data scientist should be asking themselves questions like do I have enough data? Is that data sourced in a way a data scientist can access? An early-stage startup may not have the infrastructure or volume of data to necessitate hiring a data scientist. A large enterprise may need to consider its data pipelines before it can consider hiring one. Understanding what a data scientist can and cannot do is essential in deciding whether one is right for your team. 

Maybe your company has some need for data science tools but has not employed them before. As is in marketing, when thinking about where to integrate data science and growth marketing tools into your marketing strategy, it is often best to capitalize on the low hanging fruit first. Certain tools and methods are easy to implement using data your company is already likely to have. For example, your company has likely already done demographic research and has the raw data somewhere. Clustering is a natural next step from there. Similarly, most businesses that are past the startup stage have the data to do at least some churn rate prediction, and from there, they can take steps to minimize it. Once your business has a big enough pool of data, you can venture into natural language processing and sentiment analysis to understand how consumers feel towards your product. 

If you think you’re team could benefit from some advanced data analysis but not enough to hire a full-time employee we would love to talk about your needs to see if a NoGood squad acting as an extension of your team could solve your problems.

Examples of How Brands Incorporate Data Science Into Their Marketing Mix 

Today’s growth teams don’t stand without a growth data science function. Companies like Google, Facebook, and Netflix have created a new position in their marketing departments called the growth data scientist to find the insights while the growth marketer applies them. As the business landscape becomes more and more competitive, the growth marketer’s skillset, combining data literacy and marketing know-how, is essential to finding that path of least resistance to acquiring new customers and growing your business sustainably.

Here are examples of some leading brands and how they apply data science in marketing:


At Netflix, they have a team of data scientists devoted to driving the messaging of their marketing campaigns. The growth data science team at Netflix measures the effectiveness of marketing and messaging through a causal approach, specifically incrementality. The Netflix model employs experimentation to understand how its audience is responding to notifications sent out for new content. At Netflix, the data scientist becomes both an engineer and a strategist. The data science team is responsible for driving the innovation roadmaps to improve experimentation and modeling techniques. This means that the data scientist decides what questions to ask to get to know the audience and work with business stakeholders to shape the strategy. A similar model is being employed by most companies running large marketing campaigns, including Facebook and Google. 


Facebook has a marketing science team that consults across a variety of insights to give insights on understanding the impact and effectiveness of their client’s marketing campaigns. The goal of the team is to quantify, measure, and create strategies that effectively target consumers. The Facebook model is built on research and empirical methods and provides services such as Ads Research, Client Measurement, Consumer and Advertising Insights, and Auction and Delivery, everything from marketing strategy to programmatic buying. 


The marketing data scientist at Google develops, optimizes, and implements actionable quantitative models for advertiser and publisher customers to drive marketing effectiveness and return on investment for Google’s clients. Google’s marketing data science team aims to improve data savvy to its clients by helping them interpret insights. Additionally, Google helps clients develop and implement new processes to optimize marketing efficiency and return on investment. 

Today’s Most Common Applications of Data Science in Marketing:

Today’s application of data science in marketing is more essential than ever. The marketing data assets have exceeded our ability to analyze them. In addition, there is a tremendous amount of noise and incomplete data that makes it hard for marketers to understand what drives their growth engine. 

Real-time growth experimentation:

Using data to analyze customer sentiment about product or brand attributes is becoming a core competency for today’s marketing team. The key differentiator here is that scenarios and experiments can now be tested in real-time rather than in retrospect or on an intermittent basis which means that today’s brands have an opportunity to immediately engage with and connect with their customers in ways that they were never able to achieve before.

Channel Optimization

So what do we do with all of this insight? The answer is to optimize. Here are a few practical applications that data science can help marketers trim the fat on their campaigns and better understand and target their customers. By taking a look at where your greatest conversions are, you can choose what channels to use to bring your product to market. Data science can help you automate this process and make sure you are always getting the greatest possible ROI. 

Targeting and micro-segmentation: 

Conducting statistical analysis of structured and unstructured data sets allows data scientists to organize and re-arrange data in ways that reflect creative or content performance and inform creative executions against micro-targeting campaigns. This helps marketers deliver hyper targeted messaging and personalized offerings to smaller, highly focused consumer groups.

Customer Persona Development

Marketing and data science both take common approaches in their strategies, making assumptions, then validating or invalidating them. Data science can help you test the research and assumptions you make to develop who your customers are and then pivot if need be. Once you have a solid understanding of who your true customer persona is, the data will show you deeper insights about what channels they prefer and what content they are likely to respond to, hence further increasing marketing efficiency. 

Lead Targeting and Lead Scoring

Seasoned marketers know that a business or product does not have just one customer persona, but several. The problem often arises that businesses are not sure which one will provide the greatest ROI. Data science allows you to track which customers have the greatest lifetime value (LTV) and then create a model to rank and target leads by LTV or any other KPI that makes sense for your business. 

Sentiment Analysis

Sentiment analysis is a marketer’s natural best friend. Any marketer knows that the most important trait a marketer should possess is empathy. Sentiment analysis allows you to collect data at scale to help you empathize with the customer. It allows you to monitor their reactions and beliefs towards the information they receive and gives you feedback on content and how people are engaging with your campaign. Your customer’s initial reaction when they find your social media accounts or website can go a long way to shaping how they feel about your brand view, even before they have experienced your service. This reaction is often shaped by multiple factors including reviews or social posts and comments. 

Sentiment analyses are typically set up by data engineers who assign specific values (negative, neutral, or positive) to individual words, to give each piece of content a score based on the user’s reactions in the comments. This same method can be applied to emails, reviews, and even phone conversations using speech-to-text analytics. There are also plenty of social media listening and monitoring tools that offer this type of analysis as an out of box service.

Product Development and Pricing Strategy

Data science will help you match the right product with your customer. By looking at insights given to you by customer persona data you can perform various clustering analyses to see what else they are likely to buy and what price they are likely to buy it at. These insights let you know exactly what your customer is looking for both from your current collection and give you data to develop new products they might be interested in. 

Real-Time Data Insights

Data science also gives you the power to communicate with your customers quickly based on real-time data. For example, a marketer may want to target customers who have delayed flights. Data science allows you to find customers who fit the mold and market to them immediately. This helps marketers improve their customers’ experience by further personalizing content.

Mostafa ElBermawy
As NoGood CEO and Founder, Mostafa is a seasoned growth engineer and venture builder with experience accelerating revenue/user growth, as well as leading and advising growth and product teams for VC-backed startups, venture funds, and Fortune 500 brands.

Ahmed Ouda
Ahmed is a seasoned startup strategist having worn different hats across a range of different industries as a data scientist, product manager, founder, and operator, though he is particularly passionate about fintech. He is a regular contributor to Bloomberg Asharq where he comments on the intersection of tech and policy. Currently, he is interested in crypto, DeFi and web3 (who's not).


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