Propensity-based customer segmentation

Create a segmentation based on the propensity prediction to find out who among your customers are most likely to purchase specific items

Difficulty: Medium · Modules: Decision Hub, AI Hub

Channels

website

Industries

Automotive
Books
Cosmetics
Electronics
Fashion & Apparel
Finance
FMCG
Home & Furniture
Jewelry
Multi
Retail
Telco
Travel

Challenges addressed

Increase loyalty

Summary

This use case focuses on creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing. Specifically, "Propensity-based customer segmentation" enables your team to create a segmentation based on the propensity prediction to find out who among your customers are most likely to purchase specific items. This implementation balances ease of setup with meaningful business impact.

Example Scenario

Your marketing team needs to identify high-value customers who have purchased in the last 30 days, have a preference for premium brands, and have engaged with email campaigns in the past week. Using Synerise's segmentation tools, this complex query runs in real time across millions of profiles, producing a dynamic segment that updates automatically as customers meet or exit the criteria.

Business Challenge

Increase loyalty

Solution

Synerise Segmentation Engine supports both rule-based and AI-assisted segment creation. Marketers define segments using a visual builder with conditions on events, attributes, aggregates, expressions, and prediction scores. Segments evaluate in real time — adding or removing customers as their data changes. Segments integrate with all activation channels including email, push, SMS, Dynamic Content, and automation flows.

This implementation leverages Synerise's Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring and AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations. The solution delivers results through website interactions including browsing behavior, on-site engagement, and web-based content delivery.

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

Segment precision and recall rates
Campaign performance by segment
Segment-level conversion rate
Revenue per segment
Segment growth or shrinkage trends

Data Inputs

website — website interactions including browsing behavior, on-site engagement, and web-based content delivery
Decision Hub — Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring
AI Hub — AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations

Implementation Steps

1. Define segment criteria using the visual segment builder with behavioral and attribute conditions
2. Test segment size and composition before activation
3. Connect segments to marketing channels and automation flows
4. Set up segment-level reporting and KPI dashboards
5. Monitor segment dynamics and refine criteria based on performance
6. Create lookalike segments for audience expansion

Technology Explanation

Synerise's segmentation engine operates on a streaming architecture that evaluates segment membership in real time as events arrive. Complex conditions including event sequences, time windows, and aggregate functions are compiled into an optimized query plan. The engine handles segments with millions of members with sub-second evaluation latency.

Platform Components

Decision Hub — Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring
AI Hub — AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations

Channels

website — website interactions including browsing behavior, on-site engagement, and web-based content delivery

Target Industries

Automotive
Books
Cosmetics
Electronics
Fashion & Apparel
Finance
FMCG
Home & Furniture
Jewelry
Multi
Retail
Telco
Travel

Functionality Used

Aggregates
Segmentation — creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing
Predictions — using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action
Propensity

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/segmentation-propensity-based/

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