Traditional segmentation is no longer sufficient. Dividing by age, gender, or location gives you a superficial view, but it doesn’t explain how people buy, why they buy, or when they will buy again.
By 2025, leading companies are using machine learning (ML) to segment with unprecedented accuracy. ML doesn’t just group customers; it predicts their behavior, detects hidden patterns, and enables personalized campaigns that significantly increase repeat purchases.
In LMS, this type of algorithm is key to building more profitable, efficient, and user-centric loyalty programs.
Why did machine learning change the way customers are segmented?
Because machine learning doesn’t segment by intuition, it segments by real behavior. It identifies patterns that humans can’t see at first glance.
Among its main advantages:
- Find high-value micro-segments: Identify small (and extremely valuable) groups that respond best to rewards, promotions, or gamified dynamics.
- Predicts the “next best action”: Suggests which promotion, reward, or message is most likely to convert.
- Detect churn risk: Find early signs of churn before the customer disappears.
- Create rich and dynamic profiles: Every interaction—web, WhatsApp, call center, physical store—feeds back into the model.
What data do you need to apply machine learning?
Machine learning does not require thousands of data points, but consistent data.
The most important elements for a loyalty program are:
- Transactional data:
- Purchase frequency
- Average ticket
- Product type
- Margin
- Purchase location
- Program usage data:
- Exchanges
- Accumulations
- Participation in challenges or gamification
- Reaction to previous promotions
- Digital behavior data:
- Opening emails
- Navigation on the platform
- Inquiries via the call center
- WhatsApp conversations
Types of segmentation with machine learning
These are the most commonly used models in the industry and in LMS.
- Unsupervised clustering (K-means, DBSCAN): Groups customers according to patterns without telling the algorithm what to look for. Ideal for discovering new or unexpected groups.
- Predictive repurchase models: They predict when each customer will buy again in order to activate offers at just the right time.
- Propensity models: Indicate the probability that a customer:
- Buy a certain product
- Participate in a dynamic
- Cancel your registration
- Use a reward
- Customer Life Value (CLV) models: Calculate the expected future value of each user. Perfect for allocating smart reward budgets.
Practical example: real segmentation with ML
(This section would show a detailed example of the application of a Machine Learning model, such as the RFM matrix or a churn propensity graph, in a real-world loyalty use case.)
How to activate these segmentations in a loyalty program
- Integrate a robust CRM: Unify data and enable personalized campaigns. Internal example: CRM + AI in an LMS.
- Connect the program to communication channels: WhatsApp, email, push notifications or call center.
- Apply dynamic rules: The system changes rewards or promotions based on customer behavior… automatically.
- Measure and optimize: Each campaign feeds into the model to improve the next one.
To delve deeper:
https://towardsdatascience.com/machine-learning-customer-segmentation
Frequently Asked Questions (FAQ)
Do I need a lot of data to use machine learning?
No. You need clean, well-organized data.
How long does it take to see results?
Between 4 and 8 weeks depending on the size of the program.
Can I implement ML if my company doesn’t have an advanced ERP or CRM?
Yes. LMS can be integrated with simple databases and build models from scratch.
Call to Action
Want to implement smart segmentation in your loyalty program? Schedule a meeting with our team here.
Signed:
Daniel Velasco Rallo – Strategic Planner.
