Advanced Customer Segmentation Techniques for Data Scientists

December 29, 2025 By

Data scientists are already adept at delving deeper into the numbers to find insights businesses need. However, sometimes the numbers are just that—numbers. Without a customer segmentation approach, they’ll only see the top-level trends, such as lifetime value or churn rates. Advanced customer segmentation can help them divide the data into meaningful segments that outline hidden insights for revenue growth and happier customers.

What is advanced customer segmentation, and why does it matter today?

Segmentation is the “process of grouping current and potential customers into groups (or segments) based on shared traits and metrics”. Advanced segmentation is the approach and process of going beyond traditional methods and data for segmentation by incorporating complex data sources and analytical techniques to identify subtle patterns. In plain language, that means looking beyond LTV and churn rates by combining data sources (traditional and non-traditional, such as social media data or EASI’s Life Stages) to discover insights that other companies miss or don’t have.

Companies use customer segmentation to develop more personalized communication and solutions or products, thereby increasing conversion rates and improving customer satisfaction. This type of data and the apps that do it used to be the domain of enterprise companies. Today, however, everyone has access to it through their email newsletter platform, free website, and digital tracking software like Google Analytics and Fathom, as well as CRM software like Salesforce and e-commerce apps like Shopify. 

That means basic segmentation is becoming table stakes for companies. And it’s why many companies are having to go deeper in their data to derive actionable insights that’ll move the needle for their business. It’s why they’re pairing their data scientists with other data professionals in their organizations to create the required data sets and align them with business goals and objectives. 

Why advanced segmentation matters to business

Data scientists who collaborate with sales and marketing data pros will help their companies develop the advanced segmentation insights they need, such as:

  • Identifying the marketing, messaging, and sales tactics that resonate more deeply with customers
  • Identifying at-risk customers so businesses can develop strategies earlier to keep them (most companies wait till they’re already on their way out the door)
  • Developing product features tailored to specific customer needs that go beyond their typical personas and previous customer profiles
  • Focusing on high-value segments and freeing up internal resources for their high-value work on those segments, as they offer a higher ROI

Plus, this type of work challenges data scientists to move beyond basic data analysis and use their skills and expertise to the fullest. This reduces employee churn, keeps them engaged with their work, and increases company profits as internal budgets decrease and revenues go up.

Techniques data scientists can use for advanced customer segmentation

Here are a few ways data scientists can use advanced customer segmentation techniques:

  • Combine basic demographic data with behavioral segmentation, such as age groups, online shoppers who abandon carts, and frequent buyers versus one-time buyers of multiple products who reside in specific geographic areas, along with product searches and income levels.
  • Identify unusual psychographic segments, such as values, interests, and lifestyles. Then, create a segment for them specifically and send them useful information. For example, a food brand created a segment for women with digestive problems and sent them useful and helpful tips, as well as special offers.
  • Align value-based segmentation with product releases and sales to better match customer behavior and improve satisfaction, retention, and revenues. For example, sending high-spending customers exclusive offers during low-revenue cycles to entice large sales and boost revenue, or sending non-spending ones highly discounted offers that are shareable across their community or network to increase engagement, retention, and potential sales. 

Beyond that, data scientists can create custom advanced segmentation by combining data in ways they or their companies might not have thought of before. Approaching the data differently can spark new business opportunities and insights that no one in the organization might’ve considered.

Future trends in advanced segmentation for data scientists

Looking ahead at advanced customer segmentation ideas, it’ll change dramatically as apps evolve and automation is incorporated into more workflows. Data gathering, collation, and analysis may become easier, but it’s the data scientists who’ll find the value in the output.

Other trends that are popping up include: incorporating real-time data streams into data sources for dynamic segmentation; combining traditional segmentation with sentiment analysis; focusing on understudied groups for quick wins and improved engagement; and tapping into non-traditional data sources, such as online searches, social media use, and other online databases that companies aren’t used to mining. Any data, anywhere, is available for advanced customer segmentation use and analysis if scientists know where to look.

Get more advanced with customer segmentation for deeper insights

The bottom line is that if you want to stay ahead of the competition and become the go-to brand for your customers, advanced customer segmentation is the way to go. It’s becoming essential for uncovering the insights other companies are missing.

By combining traditional data with newer sources, such as social media and behavioral patterns, data scientists can help companies create more personalized experiences that boost revenue and keep customers satisfied. Enjoy better customer experiences and relationships without increasing your employees’ workloads.

Ready to take your customer segmentation to the next level? Reach out to EASI to discover the hidden insights your data may reveal.