data science

Companies use data science to understand large data sets surrounding their customer base. With this information, a business can discover what is and isn’t working in their marketing tactics and take action to improve their overall campaigns. Using data science in business helps advertisers decide where to spend marketing dollars, what messaging is working, and which channels to focus their efforts on.

Read on to learn more about data science and how it can be used in your company’s marketing campaigns to improve your human connections.

What is data science in marketing?

Companies use marketing data science to analyze large batches of information regarding customer demographics, purchasing history, behavior, and more. Data scientists can use this information to identify unique insights and patterns that traditional analysis options may have missed. Marketers can use these insights to create data-driven marketing strategies better suited to their consumer base.

Data science vs. data analysis

Data science is the process of analyzing large batches of information to gain valuable customer insights and identify problems that companies didn’t even know were there yet.

Data analytics takes smaller data collections and finds common patterns and insights regarding problems that companies know exist but don’t know how to fix.

How to use data science in marketing

Here are several ways to use data science in business to enhance your company’s marketing campaigns.

Customer segmentation

Customer segmentation groups customers by certain factors, including location, lifestyle, buying patterns, demographics, and more. Then, companies use these segments to create targeted data-driven marketing toward different groups.

Customer loyalty and churn score

If a customer is considering leaving your brand, data science can help you identify their intent and what may be contributing to their risk of leaving. With this information, you can send targeted offers to re-engage the customer and bring them back to your brand.

Attribution models

Data science in business can be used to create attribution models, which accurately measure which marketing channels are most successful in converting customers. To determine successful campaign performance, data scientists track various touchpoints in the customer’s journey, including product research, the time before buying, and where the customer is purchasing.

Social media marketing

Social media significantly influences most age groups, so any data-driven marketing strategy should include it. With data science, you can see how different ads and campaigns perform on various social media channels and design future campaigns after what is working.

Sentiment analysis of product rating

Customers rate their online purchases. Data scientists use that information to understand the ratings and determine what a business can do to improve the rating

Lead scoring

Data science can be used to score potential customer leads on the likelihood that they will convert into actual customers. When you know which leads are more likely to convert, you can focus your marketing efforts on them, leading to a more efficient and effective sales process.

Channel optimization

With marketing data science, you can analyze various channels, like content marketing, online advertising, and social media marketing, to determine which is most effective. From there, you can put more money toward the channels that bring you the most business.

Matching customers with the right strategies

Not every campaign will work for all your customers, so using data science can help you determine which data-driven marketing strategy will likely work best for your different customer groups.

Email campaigns

Emails can be an effective marketing strategy, though it may be challenging to determine campaign performance without additional information. With marketing data science, you can gain insight into email open and click-through rates as well as the effectiveness of personalized content.

Product pricing

Customers often choose not to purchase a product, even if they want it, simply because of the price. Data scientists can see how a product sells at one price point amongst a particular customer group and then make adjustment recommendations based on that data.

Recommendation engines

Marketing data science uses recommendation engines to make product recommendations to a customer based on the purchase habits of other customers with similar shopping and purchase histories. This predictive data approach can help promote specific products at the right time in the purchasing process to increase the chances of a sale.

Marketing budget optimization

You want to get the most from your marketing dollars. Marketing data science helps you maximize your budget by monitoring campaign performance and identifying which approaches drive the highest return on investment.

Communication with customers

Do you know how your customers prefer to be contacted? Some prefer email campaigns, and others only want SMS messages. Data science can help you identify how customers want to communicate with you, increasing the likelihood of a sale.

Predictive analytics

Predictive analytics uses a combination of machine learning and artificial intelligence to predict what might happen in certain situations for businesses or customers. This predictive data can help companies target valuable consumers, distribute content effectively, evaluate digital advertising campaigns, and increase sales.

Tools for marketing data science

Here are a few tools you can use to make it easier to understand data science information.

Data visualization

To help companies see their information more easily, data scientists use charts, infographics, and more to show detailed marketing data science information. This is known as data visualization, a technique that can provide further insights into a company’s customer base and allow them to create more personalized marketing campaigns designed to attract existing or potential customers.

Automation of customer support

When customers visit a company’s website, they can be connected with an automated bot or virtual assistant that answers common questions. These virtual assistants are driven by artificial intelligence. To make these bots sound more natural, data scientists can add known data into the bot’s algorithm, ensuring they provide accurate information to customers. Automation also helps companies save money since they don’t need manual labor to cover the chat feature.

Regression analysis

In marketing data science, regression analysis predicts what customers will buy next based on past purchasing and shopping behaviors. From there, a company can advertise specific products to recommend similar ones to increase the chance of a purchase. For example, if someone buys detergent online, they might get ads for more detergent when their current supply is running low. The same goes for phone accessories when someone buys a new phone. This is how regression analysis is used for data-driven advertising.

Successful marketing data science examples

Marketing data science is used throughout every industry. Many large companies harness it to influence their campaign performance and improve user experiences. Here are a few examples of data-driven marketing in various industries.


Facebook was initially created to connect people with their family and friends online. Since then, it has evolved into one of the most popular advertising social media platforms in the world. This is due, in part, to Facebook using data science to analyze user behaviors and interests with machine learning. Then, they turn that information into data-driven advertising personalized for each user.


Spotify uses data science to create personalized music recommendations based on several user factors, including what songs they listen to, preferred genres, and when they are listening during the day. Users can get curated playlists based on one song or artist, and the system learns preferences by tracking thumbs up, thumbs down, and skipped songs.


Netflix is known for having an extensive — and pretty accurate — “Recommended for You” feature on their platform. To create these recommendations, Netflix uses data science to analyze what users watch, how long they watch for, and how they rate what they’ve watched. Netflix took this a step further by including a percent rating on a movie or TV show, letting users know how close that option is to their learned preferences.


Google is the most popular search engine worldwide, partially due to marketing data science. Google uses various data science techniques, including predictive data, analyzing and improving search results, and showing user-targeted ads. This means the more a person uses Google, the better the system gets at predicting what they’re looking for and showing content they are likely to click on.


Airbnb uses data science to personalize its search results and recommendations to users. It analyzes user behavior, such as what properties they prefer and what neighborhoods they like to stay in, to serve personalized recommendations.

Do you need to hire a data scientist?

Data scientists aren’t necessary for every business. However, if you want to improve marketing efforts, gain more customer insight, and find new ways to meet your audience, consider implementing marketing data science and hiring a data scientist.
A data scientist needs various skills and qualifications to be effective for your business, including:

  • Structured Query Language (SQL)
  • Data visualization
  • Machine learning in Python and R
  • Great collaborative skills
  • Related university degree
  • Internet marketing experience
  • Business analytics skills

How soon can data science deliver results?

Several factors impact the timeline of data science results, including the quality and availability of the data, how complex the problem is, and the resources used for the analysis. Some results may be delivered quickly if the data pool is small, while others could take longer if there are large, complex data sets.

Experian can help you improve your marketing with data science

Marketing data science provides in-depth details into the success of your marketing strategies. However, it’s important to remember that data science is not a short-term solution. Experian can help you invest in long-term solutions to maximize your business efforts. We can help improve your marketing efforts by continually collecting and analyzing data surrounding your marketing performance metrics and customer details.

Improve your data science strategy with Experian’s data hygiene, enrichment, and identity resolution services.

How Experian can help

We can help you create marketing strategies catered to the specific preferences and behaviors of your current and prospective customers. We specialize in helping brands discover data-driven insights to make an everlasting impact on consumers.

Our data and identity products and services can help you learn more about customers and target audiences, leverage data resources, improve targeted marketing, create personalized campaigns, and optimize marketing strategies.

With us, you’ll understand your consumers better, make more effective data-informed decisions, and increase your customer base for bigger revenue.

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