How to Use Machine Learning for Customer Segmentation

Hrvoje Smolic

Machine Learning for Customer Segmentation

Machine learning is the branch of Artificial Intelligence (AI) that provides systems with the ability to learn without being explicitly programmed. The applications of machine learning span many industries, including marketing, sales, finance, healthcare, and manufacturing.

This blog post will explore how to use machine learning for customer segmentation as a part of predictive analytics for marketing initiative. We will look at customer segmentation, why it's essential for an organization and artificial intelligence's role in this process. We'll wrap up by looking at specific cases where companies use machine learning for segmentation purposes.

Let's get started.

What Is Machine Learning?

At the most fundamental level, machine learning is a computer system's ability to learn without having to follow explicit instructions to do so. With this, computers can learn from past experiences or observations and make smarter decisions moving forward without any human intervention. 

Machine learning can also be defined as a type of computer programming that enables a computer to learn for itself with exposure to data and lots of processing time. 

As the amount of data available for analysis grows, machine learning is becoming an invaluable tool for analysts across various industries.

Why Use Machine Learning to Segment Customers?

The idea behind customer segmentation is to identify distinct groups, segments, or clusters of customers who are similar in some way and act differently from each other. Then, decision-makers can take appropriate steps to meet their needs.

A one-size-fits-all approach to business will result in lower profits because of less engagement and lower click-through rates. It is intelligent to approach all your customers individually with a different campaign, product, email, ad, or product. After all, customers do have different needs. Customer segmentation is the solution to this problem.

Machine learning will find patterns and similarities in data via unsupervised learning methods. Whey visualized correctly, analysts can easily spot logical segments, like

  • Customers that buy often, and spend much, that are <50 years old, ...
  • Customers that buy often, but don't spend much, that are >50 years old, ...
How to Use Machine Learning for Customer Segmentation
Image by the Author: Why Use Machine Learning to Segment Customer

"Nearly 63% of digital marketing leaders continue to struggle with delivering personalized experiences to their customers." - Gartner, Inc

Although companies could certainly create customer segmentation strategies manually, using machine learning algorithms to automate the process has many advantages.

One of the biggest is that machine learning can sort through large volumes of data to look for patterns and find hidden insights. It can examine every aspect of a customer's experience with a company or brand. These might be purchasing histories, demographic details, or any other data points that can be collected on an individual or group level.

Through this process, data scientists can help companies answer questions like

  • Who are the most valuable customers? 
  • What are customers' pain points with a particular product or service?
  • Who are the customers that stop purchasing from us?

Before companies can determine precisely how to segment their customers using machine learning, they must first select the right questions to ask.

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Questions to Consider Before Implementing Machine Learning for Customer Segmentation

Before using machine learning for customer segmentation, companies need to ask themselves several questions. These will help them determine how to approach the problem of customer segmentation and how best to design their algorithms.

For example, marketing leaders should consider which aspects of customers' experiences they want to measure, then figure out how they can collect data on those factors. The "right" data will depend on the objectives and goals a company wants to achieve by creating its customer segments.

Capital One is an excellent example. The company's goal with customer segmentation was to improve customer experiences and build trust. 

To achieve these goals, the company needed to figure out how to build an algorithm to identify its best customers' characteristics. 

It did this by identifying the measurable behaviors of these individuals, then curating a set of questions that would hone in on these behaviors.

The result was a survey that would automatically be sent to new credit card customers after they enrolled in the service. The survey asked questions like how long they had been Capital One customers, how many cards they had, and how they had heard about Capital One.

After analyzing the customers' answers, Capital One was able to identify several distinct groups of individuals based on spending behavior and acquisition channels. These segments remained relatively stable over time and had different needs, driving Capital One to develop unique marketing campaigns to target each one specifically.

Real-world Use Cases of Machine Learning for Customer Segmentation

Deciding how to approach the problem of customer segmentation is only part of the challenge. Companies must also implement their machine learning algorithms and find ways to generate actionable insights that marketers can use to improve the customer experience.

There are usually four parameters used to determine the methodology used in machine learning customer segmentation. 

Machine Learning for Customer Segmentation dataset
Image by the Author: Machine Learning for Customer Segmentation dataset example

Let's take a closer look:


This parameter defines the geographic location of a group of customers. It can be used to segment customers based on where they live and shop. 

For example, you could use a geographic parameter to segment customers according to the state or country they reside in. This can help build loyalty among your best customers and create specific marketing materials to target their localities.


Age, gender, and education level are examples of demographics. These personal characteristics are the most basic data points that can be collected on an individual level. Using these data points to segment customers can help marketers understand why they behave the way they do and where to focus their efforts. 

For example, a company might not offer baby product loyalty programs to non-parents.


Behavioral data points describe customer habits before and after a purchase. This includes details like how much money they spend or how often they shop on a specific website. 

By segmenting your customers based on their spending patterns and behaviors, you can identify market segments suited to receive certain types of marketing messages.


This parameter is used to describe specific customer behaviors and interests. This information can be captured using surveys and questionnaires. These data points can help marketers understand which customers have similar needs, with the ultimate goal of creating a set of customer segments that are more likely to use the product or service.

The key to successful customer segmentation is in the design of your algorithms and survey questions. You should aim to determine what characteristics make up each segment, then work on collecting data on those factors.

Machine Learning for Customer Segmentation in the no-code platform Graphite Note
Image by the Author: Machine Learning for Customer Segmentation in the no-code platform Graphite Note

Conclusion on Machine Learning for Customer Segmentation

The bottom line is that you can use machine learning to automate most elements of the customer segmentation process. You can analyze large volumes of data, collect demographic and psychographic data, and create survey questions to help you determine what makes a good customer segment.

Above everything else, you should strive to focus your efforts on personalization. Successful customer segmentation doesn't just mean dividing your customers into groups; it means developing marketing clusters that offer individualized customer experiences based on their unique interests, needs, and preferences.

You want to give your customers exactly what they want. You need to find an optimal number of unique customer groups to do that. That will help you understand how your customers differ. As a result, customer segmentation improves customer experience and boosts company revenue. And doing it with machine learning is fast, unbiased, and efficient.

Customer Segmentation requires extensive machine learning and statistics knowledge. Luckily, if you don't have the in-house talent to do the job, there are no-code machine learning solutions like Graphite with ready-to-go prebuilt models. You can run your customer segmentation without writing a single line of code. Reach out if you want to hear more, or start free today!

Further Reading

Machine Learning Unsupervised : 3 Main Tasks

Segment Customers or Products with Graphite Note

Essential Guide to RFM Analysis and Its Role in Customer Segmentation

What are Machine Learning Methods: a Gentle Introduction to 3 Types of ML


This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies.  Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.


At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.

Author Bio

Hrvoje Smolic, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.

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