Customer Segmentation and Churn Prediction using Machine Learning in Loyalty & Marketing (CRM)

We helped one of the leading Loyalty companies to enrich the platform with self-learning Machine Learning components for automatically drawing analytics conclusions from CRM data and then taking the best business actions with help of Machine Learning.


Identify the best customers

An investment in building loyalty makes sense when you can identify the most loyal customers, monitor them and create a personalized offer for each segment.

Increase retention

The purpose of loyalty programs is to keep customers. Retention provides a steady stream of revenues and reduces the cost of acquiring customers. Therefore, it is necessary to predict whether a particular client will leave to try to stop him.

Increase marketing offers personalization

Personalization drives loyalty in today’s consumers. Today, the company must determine which awards reward specific customers.


Churn prediction model

We have created a model that estimates the probability of customers who will stop using your product or service. Results support the strategy of maintaining clients, providing valuable information to managers and automatically taking action to maintain clients. Churn prediction software was integrated with companies CRM system and a personalized message was sent to risky customers.

Customer lifetime value prediction model

We developed Machine Learning algorithms, which help to recognize how much money a particular client will spend.
The most profitable clients were identified and rewarded. Solution was integrated with advertising software which automatically with the use of predictions targeted right customers.

Download our White Paper on Get Benefits from LTV Predictions and Use it For Your Marketing Campaigns

Recommendation engine

 Created a data-driven product recommendation engine that analyzes a huge amount of transactions and customer behavioral data to increase sales, overall business performance, client engagement, and satisfaction. The recommendation engine also supported cross-sell and up-sell activities.

Custom segmentation algorithm

We developed an algorithm for automatic customer segmentation, which adapts to each retailer and to the data structure changes. Segmentation enables offering appropriate discounts and promotions to various consumer profiles.

Analytical dashboard

We created an analytics dashboard which gave the possibility for multidimensional analysis of all available data (machine learning results, customer activity data, etc.). Deployed dashboards gave an opportunity for business departments to better understand customer 360 views.


We have enriched the platform with self-learning Machine Learning components for automatically drawing analytics conclusions from data for and then taking the best business actions.


Interactive analytics dashboard


Increase in sales


Increase in customer retention


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