Reach customers with high propensity to buy

Create a workflow to reach customers with high propensity to buy through all channels

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

Channels

email
SMS communication
Web push
website

Industries

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

Challenges addressed

Engage new customers
Increase the revenue
Personalize communication
Prepare analysis

Summary

This use case focuses on harnessing Synerise's full AI capabilities including machine learning models, natural language processing, and computer vision to automate decisions and discover insights from customer data. Specifically, "Reach customers with high propensity to buy" enables your team to create a workflow to reach customers with high propensity to buy through all channels. This implementation balances ease of setup with meaningful business impact.

Example Scenario

Your data team leverages Synerise AI Hub to build and deploy custom machine learning models for specific business problems — from predicting which customers will respond to a specific campaign, to automatically categorizing support tickets, to detecting fraudulent transactions. Models are trained on your proprietary data and deployed directly into production workflows without manual data pipeline management.

Business Challenge

Engage new customers
Increase the revenue
Personalize communication
Prepare analysis

Solution

Synerise AI Hub provides an end-to-end machine learning platform with automated feature engineering, model training, hyperparameter tuning, and production deployment. Pre-built model types cover common use cases (churn prediction, product recommendations, customer scoring), while the custom model framework supports any supervised or unsupervised learning task. All models integrate directly with segmentation, automation, and reporting.

This implementation leverages Synerise's Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring and Automation Hub for building event-driven workflows, multi-step customer journeys, and triggered actions that run 24/7 without manual intervention. The solution delivers results through email campaigns with dynamic templates, send-time optimization, and behavioral triggers and SMS messages for time-sensitive notifications, transactional alerts, and promotional campaigns.

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

Model accuracy and performance metrics
AI-driven revenue attribution
Automation rate through AI decisions
Model retraining frequency and drift monitoring
ROI of AI-powered campaigns vs. rule-based

Data Inputs

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

Implementation Steps

1. Define the business problem and identify available training data
2. Configure feature engineering using customer attributes and behavioral events
3. Train and evaluate models using the AI Hub interface
4. Deploy the best-performing model as a real-time scoring endpoint
5. Integrate model scores into segmentation and automation workflows
6. Monitor model performance and set up automated retraining schedules

Technology Explanation

Synerise AI Hub runs on a scalable ML infrastructure that supports distributed training across GPU clusters. The feature store pre-computes and caches commonly used features for sub-millisecond model inference. Model serving uses containerized microservices with automatic scaling and A/B testing support for model comparison.

Platform Components

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

Channels

email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers
SMS communication — SMS messages for time-sensitive notifications, transactional alerts, and promotional campaigns
Web push
website — website interactions including browsing behavior, on-site engagement, and web-based content delivery

Target Industries

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

Functionality Used

AI — harnessing Synerise's full AI capabilities including machine learning models, natural language processing, and computer vision to automate decisions and discover insights from customer data
Automation — orchestrating complex, multi-step customer journeys using Synerise Automation Flows that trigger actions based on events, conditions, and time-based rules across all channels
Email communication — creating and sending targeted, personalized email campaigns using Synerise's built-in email capabilities, including dynamic templates, behavioral triggers, and deliverability optimization
Mobile
Personalization — delivering individualized experiences across channels by adapting content, offers, and messaging to each customer's unique profile, preferences, and real-time behavior
Predictions — using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action
Propensity
Segmentation — creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing

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/communication-high-propensity-to-buy/

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