Predicting Churn and Favorite Brands to Retain Lapsing Customers

Create personalized email campaigns that target high-risk customers with recommended products from their favorite brands

Difficulty: Medium · Modules: AI Hub, Experience Hub, Automation Hub

Channels

Industries

Cosmetics
Electronics
Fashion & Apparel
FMCG
Home & Furniture
Jewelry
Multi
Retail

Challenges addressed

Increase loyalty
Increase the revenue
Personalize communication

Summary

This use case focuses on leveraging Synerise's AI recommendation engine to analyze behavioral data, purchase history, and contextual signals to deliver highly personalized product or content suggestions in real time. Specifically, "Predicting Churn and Favorite Brands to Retain Lapsing Customers" enables your team to create personalized email campaigns that target high-risk customers with recommended products from their favorite brands. This implementation balances ease of setup with meaningful business impact.

Example Scenario

A customer browses your website or app and the system instantly analyzes their browsing history, past purchases, and similar customer patterns to surface the most relevant products. The recommendation engine uses Cleora graph embeddings and collaborative filtering to identify items with the highest conversion probability, updating suggestions in real time as the customer interacts.

Business Challenge

Increase loyalty
Increase the revenue
Personalize communication

Solution

Synerise's AI Recommendation Engine uses proprietary Cleora embeddings to build vector representations of products and customers in a shared latent space. The system calculates similarity scores using approximate nearest-neighbor search, combines them with business rules (margin, stock levels, category affinity), and delivers ranked recommendations via API or Dynamic Content widgets. The engine supports multiple recommendation strategies including similar items, frequently bought together, trending products, and personalized picks.

This implementation leverages Synerise's AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations and Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints. The solution delivers results through email campaigns with dynamic templates, send-time optimization, and behavioral triggers.

Expected Impact

Implementing this use case delivers measurable improvements across multiple business dimensions. Organizations typically see results within the first 2-4 weeks of deployment, with compound gains as the system learns from additional data. The medium-level implementation complexity means a balanced investment of time and effort yields strong returns.

Key KPIs

Click-through rate on recommendations
Recommendation-driven revenue share
Average order value uplift
Items per transaction increase
Conversion rate from recommendation clicks

Data Inputs

email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers
AI Hub — AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations
Experience Hub — Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints
Automation Hub — Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention

Implementation Steps

1. Configure product catalog feed and ensure item attributes (category, price, brand) are synchronized
2. Set up behavioral event tracking (page views, add-to-cart, purchases) via SDK or API
3. Select and configure recommendation model type (similar, cross-sell, personalized) in AI Hub
4. Define business rules such as category filters, stock thresholds, and margin preferences
5. Deploy recommendation widgets using Dynamic Content or Brickworks schemas
6. Monitor model performance and retrain on the latest behavioral data

Technology Explanation

The recommendation engine is powered by Cleora — Synerise's proprietary graph embedding algorithm that produces high-quality vector representations from sparse interaction data. Unlike traditional collaborative filtering, Cleora handles cold-start scenarios effectively by propagating information through the product-customer interaction graph. Recommendations are served with sub-100ms latency through a dedicated serving infrastructure.

Platform Components

AI Hub — AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations
Experience Hub — Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints
Automation Hub — Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention

Channels

email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers

Target Industries

Cosmetics
Electronics
Fashion & Apparel
FMCG
Home & Furniture
Jewelry
Multi
Retail

Functionality Used

AI recommendations — leveraging Synerise's AI recommendation engine to analyze behavioral data, purchase history, and contextual signals to deliver highly personalized product or content suggestions in real time
Predictions — using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action
Segmentation — creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing
Email communication — creating and sending targeted, personalized email campaigns using Synerise's built-in email capabilities, including dynamic templates, behavioral triggers, and deliverability optimization

Implementation Complexity

Medium — This use case requires moderate technical knowledge and some configuration effort.

Time to Value

2-4 weeks

Documentation

https://help.synerise.com/docs/use-cases/lapsing-customer/

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