Machine Learning

This module helps to create a long-term relationship with you clients and reduce a human factor risks of making a wrong decision.

Personal promos for your customers based on machine learning

Personalization of offers for each client

Transformation of the loyalty program and making it truly personal.

Combined strategies

Impact on all strategies at the same time, work with the return, retention and churn of customers. Stand out from the competition by generating millions of personalized promos for millions of customers per day.

Working with churn

Reduction in churn return costs (communications and rewards)

Average receipt

The module analyzes the behavior of each customer and suggests products that fit into their shopping cart. This helps to increase the average receipt.

Purchase frequency

The client is interested in personal promotions and is ready to make purchases more often.

Advantages of a recommender system based on machine learning

Only an automated system will be able to run a promo for each client

Marketing Team Capabilities

  • Run up to 500 promotions per month.
  • Analyze up to 100 target segments.
  • Generation of up to 200 different product offerings (product or product group).

Capabilities of the Machine Learning module

  • Run up to 10,000,000 promotions per day.
  • Analyze up to 100,000,000 clients.
  • Generation of up to 1,000,000 different product offerings.

Machine Learning Module

Use of the module

  • Fits businesses with a customer base of 300,000 or more (loyalty cards).
  • Fully autonomous and can be integrated into any IT infrastructure.

Business Value

  • Save on discounts by diversifying offers.
  • Improving the efficiency of loyalty programs by increasing the average check, the frequency of purchases and the share of high-margin goods in the receipt.
  • Reducing the load on the marketing team, which can focus on massive promotions and attracting new customers.

Customer Journey
Informing the customer

How It Works

Historical Data Analysis

EVERY client for more than 1000 parameters

Product offer selection

10 relevant products from the entire range

Selecting the size of the discount

Enough to make a purchase

Efficiency evaluation (system learning)

For selecting a new offer



The result is a relevant personal promo for each client

Client Offer Goal
Product Discount Promotion of high-margin products, reducing the churn in the category
Discount on limited selection items Growth of the average receipt
Increased product discount Growth of the average receipt
Discount on a product or category (each on its own day) Increase the frequency of purchases/td>
Increased discount on the entire purchase from a certain amount Reducing the churn

IT Infrastructure Integration

Recommender system

Implementation scheme

Core + Recommender system