Churn Prediction

We detect customers at risk of leaving.

We detect customers at risk of leaving before they go - and trigger retention actions exactly when they have the best chance of succeeding.

Book a consultation → Case studies

Retaining a customer costs 5-7× less than acquiring a new onePersooa retention deployments: +27% conversion, +14% ARPUAutomated retention scenarios triggered at the optimal moment

Starting point

Why do you lose customers before you can react?

Most companies learn about churn too late - when the customer has already stopped buying, cancelled their subscription or moved to a competitor. Reactivation costs many times more than retention, and mass campaigns reach everyone instead of those who truly need an incentive. Churn prediction models reverse this logic: AI analyzes behavioral and transactional signals, assigns each customer a churn score and triggers automated actions before the customer leaves for good.

Retaining a customer costs 5-7 times less than acquiring a new one.

After deploying triggered retention campaigns, Homla achieved a 14% increase in revenue per user and an 8% increase in the number of transactions.

Retention deployments at Persooa clients translate on average into a 27% increase in conversion and a 14% increase in ARPU.

A churn score for every customer

The AI model analyzes historical, transactional and behavioral data - purchase frequency, intervals between transactions, basket value, product category history, responses to communication and channel activity - and assigns each customer an individual level of churn risk.

You no longer act on hunches or demographic segments - you have a ranked list of customers worth addressing first.

Business value: data-driven retention priorities, a more efficient campaign budget, earlier detection of declining engagement

Data integration from multiple sources

A good churn prediction model learns from the full customer profile - not just e-commerce purchase data, but also mobile app behavior, customer service contact history, loyalty program activity and brick-and-mortar store data.

We consolidate these sources into a single 360 view of each customer, because the better the data integration, the more accurate the predictions and the more real the impact on business results.

Business value: more accurate predictions than on data from a single system, full customer context for personalizing retention actions, elimination of data silos between departments

Automated retention campaigns

Knowing the risk of leaving only has value when you can act on it.

When AI detects a customer with a high churn score, it automatically triggers a retention campaign - a personalized offer based on purchase history, products from the most frequently bought category and an incentive matched to the customer's value to the brand. The campaign starts at the optimal time and through the best contact channel for that specific person - email, SMS, RCS, push, WhatsApp or iMessage.

Business value: churn reduction without manual team work, a higher rate of returning customers, lower retention cost per customer

Complementary predictions: likelihood to buy and best time to contact

Churn score is one of four predictors that enrich the customer profile.

Alongside it we activate likelihood to buy, best time to contact (best time to buy) and best contact channel. Together they create a complete picture of each customer - you know not only who might leave, but also when and how to talk to them to retain them most effectively.

Business value: more accurate timing of retention campaigns, higher open rate and response rate, consistency between churn prediction and other marketing activities

Complementary predictions: likelihood to buy and best time to contact

Segmentation by risk level

Not every customer at risk of churn requires the same action.

A customer with moderate risk receives a personalized product recommendation and a reminder about loyalty points. A high-risk customer - a win-back offer with a strong incentive. A premium VIP customer - a dedicated invitation or direct contact. We segment risk levels and match the tactic to the customer's value to the brand, rather than treating everyone the same.

Business value: a more efficient retention budget, higher CLV of premium segments, a more accurate message matched to the customer's situation

Monitoring model and campaign effectiveness

A churn prediction model requires continuous calibration - customer behavior changes seasonally, and the effectiveness of individual retention tactics differs between segments.

We monitor prediction accuracy, the retention rate after campaigns and the impact of actions on CLV. Reports show how much revenue was saved through retention and where the model needs tuning - without having to build a dedicated data science team inside the company.

Business value: transparent ROI from retention actions, continuous model optimization without in-house resources, faster detection of segments with rising churn risk

Monitoring model and campaign effectiveness

The loyalty program as a retention mechanism

The most effective protection against churn is a relationship built before the customer starts to leave.

The myRewards loyalty program integrates directly with the churn prediction model - customers with rising risk of leaving are automatically activated with reminders about expiring points, dedicated status rewards and offers available exclusively to program members. Loyalty built through a program is more durable than a one-off reactivation discount.

Business value: higher CLV of loyalty program members, lower churn in the segment of active members, reduced win-back campaign costs

Blog

Latest articles

Knowledge and practical tips on Synerise, AI and personalization.

View all

Tools June 21, 2026 Interactive calculators

E-commerce growth calculators: CLV, churn cost, loyalty program

Three free, interactive Persooa calculators: compute Customer Lifetime Value (CLV), the annual cost of churn and the ROI of a loyalty program for your e-commerce.

Knowledge June 20, 2026 11 min read

Synerise vs MarTech - how the platform differs from others on the market

A comparison of Synerise with the largest MarTech platforms. A proprietary foundation model trained on behavioral data, the TerrariumDB database, 31 billion events per month and 50 features you won't find at the competition.

E-COMMERCE

7 reasons for cart abandonment

An analysis of the 7 main causes of cart abandonment in e-commerce and concrete ways to eliminate them.

Get started

Let us talk about your growth.

Book a free call - we will analyze your situation and show where we can deliver measurable results fastest.

Book a consultation