What are customer segmentation models, and why every business needs them
Many companies are confident that sales are growing thanks to advertising, new channels, or the launch of another product. Formally, these steps yield a significant increase. However, in practice, growth begins earlier, with an understanding of who exactly buys, why they come, and what influences their decision.
In today’s article, I will reveal exactly how your business will change the moment you implement machine learning development models. I won’t hide it from you for a long time, because you don’t have so much time, and that’s what I appreciate about you.
How is customer segmentation different from market segmentation?
These two terms are often confused, although their difference is fundamental.
Marketing customer segmentation offers a comprehensive view of the population residing in each region, including the groups of consumers that exist and their demographics, such as age and profession. This is useful for a go-to-market strategy, but it is often too general for internal company decisions.
Here, in the consumer segmentation model, we focus solely on your audience – those who have already come, bought, doubted, or left. Approximately 76% of buyers are more willing to consider a company’s offer if it contacts them personally.
Client segmentation reveals where your real resources are, where the leaks are, and what can be addressed immediately.
3 reasons to choose segmentation
Option #1: You don’t know why people leave without buying
A person goes to the site. Put the goods in the basket, and… disappear.
You see a 2% conversion. But you don’t see that there are completely different people behind this 98%: some compare prices and go to a competitor if you don’t give a discount right now. Others don’t understand how your product differs from its counterparts; they don’t need a discount banner, but a clear explanation of its value.
Still others are ready to buy, but doubt delivery, return, or reliability; they need guarantees, not a reminder of an abandoned basket.
Option #2: You spend money on customer acquisition that will never pay off
A classic situation: you run ads, leads go, sales grow. Everything is fine. But profits aren’t increasing proportionally.
Why? Because some customers buy once for the minimum amount and are no longer returned. The cost of attracting them is the same as for everyone else.
You pay for both equally. Invest in holding both equally, and you lose money without even understanding what went wrong.
Option #3: You retain the wrong customers and lose those who could be interested
Imagine: you have two clients. Both bought in two months.
The first was bought by accident once. He came for a promotion, and he didn’t really need the goods. The probability that he will return is 5%.
The second was bought regularly for the last six months, but suddenly fell silent. His situation has changed. Maybe he went to a competitor. Possibly, he just forgot about you. The probability of returning it is 60%.
Without a consumer segmentation model, you will send the same letter to both and not get the desired result.
What does business gain from segmentation?
Let’s analyze the changes in work that follow the introduction of segmentation, and why these changes directly affect ROI.
Marketing stops spending too much
Without segmentation, the business pays for advertising that goes “nowhere”.
Segmentation helps understand which groups make a profit and which groups make only expenses, disable campaigns that don’t work, and redistribute the budget to those who actually buy.
Put simply, you only start paying for things that generate revenue.
Increased retention – the most expensive pain of any CEO
Attraction rises in price every year. To lose a customer means to lose the amount of money you have already invested.
Segmentation creates a system where you know whom you lose, at what stage, why, and what can be done to hold?
Retention is a direct contribution to earnings. The short path to growth often lies not in attracting, but in working with those who are already nearby.
Segmentation makes personalization the norm, not a rarity
People quickly recognize mass communication. Segmentation allows you to speak with the client in detail: about the product that suits him, at the moment when it is more convenient for him, in the channel he prefers.
Personalized recommendations boost repeat sales and reduce friction in communication without sophisticated technology.
The manager gets clarity on where exactly the business is growing and where it’s not:
When data is collected by segment, a lively picture of the business is visible. You can see which segment gives 80% of the profit, what pulls marginality down; where investments are needed, and where, on the contrary, a pause is required.
What is a customer segmentation model?
The customer segmentation model is a system that automatically divides your audience into groups based on data: purchases, site behavior, interaction with content, and support history.
If manual segmentation involves a marketer sitting in Excel and trying to figure out who is who, then the customer segmentation models do it in seconds based on hundreds of parameters at a time.
The primary difference between the audience segmentation model and manual segmentation is that the former identifies patterns that a person cannot physically notice. For example: customers who shop on Tuesdays after reading three blog articles have an LTV that is 40% higher than the average. Users who add goods to their favorites but not to the basket go to competitors in 70% of cases. Customers who open emails but don’t click are highly likely to return within a month, if not sooner.
The model doesn’t just group people. She predicts behavior and suggests the next steps.
If you need a customer segmentation types analysis with the introduction of a model tailored to your specifics, this is a solvable task. The only question is what data you already have and what business task you need to close first.
How do companies achieve the data-driven decision-making process?
At Data Science UA we assist companies in extracting real value from unstructured data sources
4 main types of market segmentation
There are 4 basic types of segmentation that almost all companies use. However, it’s essential to understand that these are not mutually exclusive approaches. Most often, they are combined.
Demographic segmentation
This division is based on basic characteristics: age, gender, income, education, marital status, and profession.
The simplest and most common type of segmentation. But it has a trap: demographics reveal who your customer is, but not why they’re making a purchase.
Parents of children aged 8-12 tend to make fewer purchases, but for large sums: bicycles, gadgets, sports equipment.
Each group requires different goods, different communication, and different contact frequency.
However, it’s essential to understand that demographics don’t explain why one parent only buys at sales and another is willing to pay full price for premium brands. It requires other types of segmentation.
Geographic segmentation
In practice, geography affects not only logistics. It affects needs, seasonality, cultural characteristics, and solvency.
Let’s say you have a clothing store. You can segment your audience as follows:
-Customers from large cities buy more frequently, adopt trends, and are willing to pay for expedited delivery.
-Customers from small towns tend to buy less frequently, but in larger quantities, because they have fewer offline alternatives. Free shipping is more important to them than speed.
-Clients from the southern regions do not buy winter jackets, but they take summer collections 2-3 months earlier than those in the northern areas.
If geography is ignored, you’ll be advertising down jackets in warm regions of your country in October, and wondering why the conversion is zero.
Psychographic segmentation
Psychography answers the question of why a person makes a purchase. It often turns out to be more important than demographics.
Two people of the same age, gender, and income can buy in completely different ways – because they have other priorities.
The same fitness club can segment the audience not by age, but by motivation:
“Health and longevity” – people who train to prevent diseases, improve well-being. They are important medical indicators that the coach and safety team closely monitor.
“Aesthetics and appearance” – those who want to lose weight, gain muscle mass, and improve their figure. They care about fast visual results, motivational content, and before/after photos.
One needs care, the other needs the result. If you talk to everyone the same way, you will not get into the pain of your CA.
Behavioral segmentation
Behavioral segmentation is the strongest for decision-making, because it isn’t based on what the client says about themselves, but on what they really do.
Behavioral segmentation enables the implementation of ML models that predict outflow, recommend the next purchase, or determine the optimal moment for contact.
Without understanding behavior, you work blindly. With him, you see who is doing what, and you can take proactive steps.
Additional customer segmentation models
Yes, as you understand, the 4 basic types of customer segmentation cover most tasks. However, there are situations where more specific optics are needed, especially in B2B, SaaS, or products with a long decision cycle.
Here are the customer segmentation models that work where classic segmentation can’t handle it.
Technographic segmentation
Division by technological stack: what tools, platforms, devices the client uses.
It is very effective for B2B and tech products. Because if your solution doesn’t integrate with what the client is already using, it won’t even be considered.
Firmographic segmentation
This is a B2B version of demographics, including company size, industry, number of employees, annual turnover, and decision-making structure.
A startup for 5 people and a corporation for 5000 aren’t only different scales, but also different pains, different procurement processes, and different decision-making periods.
If you are trying to sell to everyone equally, you waste money. A startup doesn’t require enterprise-level functionality. A startup solution isn’t suitable for a corporation.
Needs-based segmentation
Finally, we have reached a crucial point, namely, why your client buys. The same product can cover completely different needs. If you understand the client’s task, you can offer him not just a product, but a solution to his problem. It often requires the assistance of artificial intelligence consulting to identify patterns of need that are not immediately visible.
Value-based segmentation
Division by the economic value of the client for the business: how much he brings profit now and will bring in the future.
This is the toughest yet most honest segmentation. Because it forces us to admit an inconvenient truth: not all clients are equally important. Value-based segmentation avoids wasting resources on those who do not pay for them and concentrates efforts on those who truly drive the business growth.
Lifecycle stage segmentation
Division by stage of interaction with the product: beginners, active users, loyal customers, and those who are on the verge of leaving.
The person on the first day after registration and the person who has been with you for three years are two different clients. They need other things.
Lifecycle segmentation reveals who needs help, who is untouchable, and where you’re currently losing people.
Social media segmentation
Division by activity and behavior on social networks: what they post, what they react to, with whom they interact, and what content they consume.
It works for brands in which social media serves as both a communication channel and a platform where purchases take place or loyalty is formed.
If you ignore social media segmentation, you can invest in people who will never buy and forgive those who are already ready to be your lawyers.
How to choose the right segmentation model(s)
The problem isn’t that there are a few customer segmentation models. The problem is that there are too many. Businesses often fall into a state of choice paralysis: which one to use? All at once? One, but which one?
The correct answer: depends on what you’re trying to decide.
Here is a comparison table that will help you understand which model for which task:
| Segmentation model | When to use it | Which data is required | Main benefits |
| Demographic | Mass market, B2C, physical goods | Age, gender, income, and education | Rapid primary segmentation |
| Geographic | Local business, delivery, climate specifics | Address, region, time zone | Optimization of logistics and seasonality |
| Psychographic | Premium products, lifestyle brands | Interests, values, motivation | Emotional “hit” in the audience |
| Behavioral | E-commerce, SaaS, subscriptions | Purchase history, site activity | Predicting the next action |
| Technographic | B2B SaaS, tech products | Tools used, stack | Personalize sales and integrations |
| Firmographic | B2B sales | Company size, industry, turnover | Prioritizing leads |
| Needs-Based | Complex products with different use cases | Clients’ tasks, job-to-be-done | Precise positioning |
| Value-Based | Subscriptions, high-margin products | LTV, profit per customer, frequency of purchases | Resource optimization |
| Lifecycle Stage | SaaS | Date of registration, activity, engagement | Reduced outflow |
| Social Media | Brands with a strong community | Social media activity, UGC, impact | Working with brand advocates |
Define your marketing goals and objectives
Before choosing a model, answer a simple question:
What are you trying to fix?
- High cost of attraction, low ad conversion.
- Customers leave after the first purchase.
- We sell to everyone the same way, but some of the customers are unprofitable.
- B2B sales are slow; we don't get into customer pain.
Identify the types of customer data that matter
Not all data is equally useful. Moreover, an excess of data often hurts more than a shortage of data.
What exactly is needed:
Transactional data: what they bought, when, for what amount, how often.
Behavioral data: what they did on the site, what pages they watched, where they got stuck, what they added to the basket, but did not buy.
Interaction data: Did they open letters, click on links, contact support, or leave reviews?
What might be useful (but not always):
Demographics – if they affect purchases (for example, age is critical for children’s products, but not for B2B SaaS).
Psychographic data – if your product is tied to values or lifestyle (for example, eco-products, premium segment).
Choose tools to collect and analyze data
Segmentation only works when you have the infrastructure to collect and analyze data.
Minimum stack:
– CRM (HubSpot, Salesforce, Pipedrive) for storing customer data and interaction history.
– Analytics (Google Analytics, Mixpanel, Amplitude) to track behavior on the site.
– Email platform (Mailchimp, SendGrid, Klaviyo) – to track the reaction to communications.
Advanced stack:
– CDP (Customer Data Platform) to combine all data in one place.
– BI tools (Tableau, Looker, Power BI) for visualization and analysis of types of customer segments.
– ML models for automatic segmentation and prediction of behavior (this is already a level where the help of specialists is needed).
Steps to implement customer segmentation
Segmentation isn’t a one-off project. This is a process that is built into the company’s work and operates independently.
Here’s a step-by-step guide on how to implement different types of customer segmentation so that it works rather than collecting dust in a presentation.
Define the target market and audience
It sounds trite, but most companies cannot clearly answer the question: “Who is your client?” It usually sounds like: “Everyone who needs our product.”
For example, the company sells online programming courses. It would seem that the target audience is “everyone who wants to learn how to program.” But in practice it can be: students who want their first job; specialists from other fields who wish to transition into a new profession; current developers who want to master a new language or framework, managers who need to understand the technical context for work.
Each group has different motives, different pains, different levels of training, and different expectations from the course.
If you try to sell to everyone the same way, you don’t sell to anyone. The first step is to recognize that you have not one audience, but several. Each requires its own approach.
Understand your market and customer journey
Segmentation only works when you understand a customer’s journey from first contact to purchase, and beyond, to re-purchase or exit.
At each stage, different questions, different doubts, different triggers. If you know at what stage a customer is stuck, you can help them move on.
Without understanding the customer journey, segmentation becomes a meaningless categorization of the audience based on signs that don’t affect the purchase decision.
Create customer personas
Persona is the story of a particular person: who he is, what worries him, what problems he solves, what he pays attention to, what annoys him.
Consider a company that sells email marketing tools. Imagine that they create a user persona:
Name: Anna, marketer at e-commerce company
Age: 28
Pain: She needs to launch campaigns quickly, but the current tool is too complex.
Management requires ROI reports, but collecting data is time-consuming.
The budget is limited, and we cannot afford expensive enterprise solutions.
What motivates her:
She wants to show results and advance in her career.
Appreciates simplicity and speed – there is no time to understand complex interfaces.
Cases and reviews are important, afraid of making a mistake with the choice of tool.
How she makes the decision:
Read reviews and comparisons on blogs.
Watching video tutorials on YouTube.
Requests trial and tests itself.
If everything is ok, she shows the numbers to the management and asks for a budget.
Now the company knows how to talk to Anna: not through technical specifications, but through ease of use and quick results. Not through cold calls, but through educational content and demos.
Creating persons helps the team stop speaking abstractly (“our customers”) and start speaking specifically (“Anna from e-commerce”).
Segment the market and validate correlations
A common mistake: to create segments based on hypotheses, but not to check if there is a real difference in behavior.
One company suggested that young customers (18-25 years old) buy more often than older customers (40+). We divided the base by age and began sending different letters. After a month, they look at the statistics, and the conversion rate remains unchanged.
Why? Because age did not affect the purchase. The income and stage of life significantly influence someone at 22, who is working and buying, vs. someone at 45, who is retired and also buys, and someone at 30, who is a student and has no money.
How to check correlation:
-Divide the data into different customer segments.
-Examine the key metrics: conversion, average check, frequency of purchases, and LTV.
-If the metrics are significantly different, segmentation works.
-If the metrics are the same, this sign is not important; look for another.
Segmentation for the sake of segmentation is a waste of time. Only the one that helps make different decisions for different groups is important.
Monitor changes and evolving customer needs
The market is changing, customers are changing, and their needs are evolving.
Let’s remember the 2020 pandemic. People go online en masse. Companies that sold offline services suddenly found their segments completely changed:
Customers who previously preferred live meetings now want to do everything remotely.
A new segment has emerged: individuals who have never bought online before but are forced to do so. Priorities have shifted: security, home delivery, and contactless payment have become more important than speed.
How can it be monitored?
Once a quarter, at least look at the key metrics by segment: have the patterns of purchases, activity, and outflows changed?
Follow external factors, including the economy, competition, technological changes, and social trends.
Interview customers: what has changed in their lives, what has become more important, what is no longer relevant.
Learn how to use customer segmentation models.
Challenges and tips for effective market segmentation
Issue 1: Too many segments
It must be acknowledged that a passion for segmentation leads to an additional problem. The marketing team doesn’t align with the tasks, the budget is spread thinly, and there is no result.
In this case, I recommend starting with 3-5 key segments. Those that give maximum profit or maximum headache. The rest can be postponed for now and added as tasks arrive.
Issue 2: Segmentation is divorced from action
The company effectively segmented the audience, created a presentation, and presented it to management. Everyone is happy. But nothing has changed. Marketing continues to send everyone the same letters. Sales representatives continue to call everyone from the same script.
Each segment should receive something of its own: its own letter, its own proposal, its own call script, its own landing page. If you are not ready to do this, do not waste time on segmentation.
Issue 3: Insufficient data
The company wants to segment by behavior, but there is no data. There is no analytics on the site. CRM is not maintained. No purchase history is collected. As a result, segmentation is based on guesses – and does not work.
First, set up data collection. Connect the dimension. Set up CRM. Start collecting at least basic information: who buys what, when, and how often. In 2-3 months, there will be enough data for the first segmentation.
Issue 4: Segments not updated
The company segmented its audience a year ago. Since then, the market has evolved, competitors have appeared, and customers have begun to behave differently. But the segmentation remains the same. As a result, the company works with an outdated reality map.
It’s essential to review segments at least once a quarter. Even better, set up dynamic segmentation, where customers automatically transition from one segment to another based on their behavior.
Issue 5: Segmentation not related to business objectives
Each segment must answer a specific business question:
How to reduce outflows? → Customers at risk segment.
How to increase the average check? → Segment “buyers with upside potential.”
How to optimize marketing? → Segment “high-margin vs low-margin customers.”
As you understand, segmentation is just one of the tools to achieve results. But like any tool, it is useless if not used.
Therefore, I want to summarize today’s article with the following advice: start small. Select one segment that gives maximum profit or creates maximum problems. Start working with him differently. Measure the result. If it worked, feel free to scale. If not, adjust.
After all, our task is to get closer to understanding our clients every day and act based on this understanding.
FAQ
How can small businesses segment customers without large datasets?
Start with qualitative methods: conduct 10-15 in-depth interviews with customers, analyze reviews and feedback, and study purchasing patterns in your CRM. Even 100-200 transactions yield useful insights. Use simple RFM (Recency, Frequency, Monetary) segmentation – it works with minimal data. Add basic post-purchase surveys with 3-5 questions. Monitor website behavior in Google Analytics, which pages customers visit, what they add to their cart. This will provide initial segmentation, which can be refined as the data grows.
What common mistakes should be avoided when segmenting customers?
Too many segments. If you have 15 segments, you won’t be able to create a personalized approach for each one. Optimum: 3-5 key segments.
Segments without action. You’ve identified a segment, but don’t understand how to approach it differently. Each segment should require a separate strategy.
Static segmentation. Customers change, but segments remain the same for years. This quickly becomes outdated.
Ignoring small but profitable segments. Focusing only on mass groups can miss VIP clients, who account for 30-40% of revenue.
Segmentation for the sake of segmentation. Doing something because “that’s what everyone else is doing”, without understanding why it benefits your business.
How often should customer segments be reviewed and updated?
Depends on the dynamics of your business:
Fast-growing startups – every 3 months. The customer base changes rapidly.
E-commerce and B2C – every 6 months. Consumer behavior changes with the seasons and trends.
B2B and established companies – once a year or during significant changes (new product, new market entry, crisis).
Triggers for an unscheduled audit: a sharp change in conversion, the emergence of a new type of customer, a change in the product line, the entry of a major competitor.
At a minimum, automatically monitor key metrics monthly. If you see anomalies, dig deeper.
Can multiple segmentation models be combined for better results?
Yes, and this often yields better results than a single approach.
Demographics + behavior: age and income (who they are) + purchase frequency and average order value (what they do). You get “young active shoppers with low order value” vs. “mature infrequent shoppers with high order value”.
RFM + psychographics: when they last purchased + what values are important. “Haven’t purchased in a while, but value eco-friendliness” – target them with a new eco-friendly line.
Geography + product preferences: region + what they buy.
It’s important not to overcomplicate things. Combine 2-3 dimensions maximum, otherwise you’ll end up with segments that are too small to work with.
What metrics indicate that segmentation strategies are successful?
Conversion by segment. If personalized campaigns for a segment convert better than mass campaigns, they’re working.
Customer Lifetime Value (CLV) increase. Proper segmentation increases customer lifetime value because you provide them with something relevant.
Retention rate improvement. Customers stay longer when they receive a personalized experience.
CAC (Customer Acquisition Cost) decrease. Target the right people, spend less on acquisition.
Engagement metrics: email open rate, CTR, time on site, and return rates increase for segmented campaigns versus mass campaigns.
ROI of personalization. Calculate the additional revenue from segmented campaigns minus the costs of creating them. If ROI > 1, segmentation is profitable.
Churn decreases in the segments you focus on.
Key principle: compare the results of a segmented approach with a mass approach on control groups through A/B testing.
