Predicting the likelihood of offline visit

Use custom predictions to find customers most likely to visit the store and send them mobile push at the best time

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

Channels

mobile application
mobile push
offline

Industries

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

Challenges addressed

Increase loyalty
Increase the revenue
Personalize communication

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, "Predicting the likelihood of offline visit" enables your team to use custom predictions to find customers most likely to visit the store and send them mobile push at the best time. This expert-level implementation delivers maximum impact for mature, data-driven organizations.

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

Increase loyalty
Increase the revenue
Personalize communication

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 mobile app experiences including in-app messaging, push notifications, and native UI personalization and mobile push notifications with personalized content, deep links, and delivery optimization.

Expected Impact

Implementing this use case delivers measurable improvements across multiple business dimensions. Organizations typically see results within the first 8-12 weeks of deployment, with compound gains as the system learns from additional data. The expert-level implementation complexity means dedicated platform expertise is recommended for optimal results, but the payoff is substantial.

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

mobile application — mobile app experiences including in-app messaging, push notifications, and native UI personalization
mobile push — mobile push notifications with personalized content, deep links, and delivery optimization
offline — offline touchpoints including in-store interactions, call center, and physical mail
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

mobile application — mobile app experiences including in-app messaging, push notifications, and native UI personalization
mobile push — mobile push notifications with personalized content, deep links, and delivery optimization
offline — offline touchpoints including in-store interactions, call center, and physical mail

Target Industries

Books
Cosmetics
Electronics
Fashion & Apparel
FMCG
Home & Furniture
Jewelry
Multi
Retail
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
Mobile push
Mobile
Time optimizer
Coupons — managing and distributing personalized coupon campaigns through Synerise's promotion engine, with unique codes, redemption rules, and real-time tracking
Custom predictions
Expressions
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
Segmentation — creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing

Implementation Complexity

Expert — This is an expert-level use case requiring deep platform knowledge and complex integration.

Time to Value

8-12 weeks

Documentation

https://help.synerise.com/docs/use-cases/predicting-likelihood-of-offline-visit/

Related use cases

Cross-sell recommendations with category tabs based on last purchased product — Display cross-sell recommendations grouped into category tabs based on the customer's most recently purchased product
Saving and recovering product configuration — Help customers return to unfinished product configurations and complete their purchase
Dynamic Content Scratch Card — Engage visitors with an interactive dynamic content scratch card offering a discount on selected product categories
Brickworks product template with AI-based similar recommendations — Create a Brickworks schema for a product template enriched with AI-driven similar item recommendations
Brickworks loyalty template with tiered offer — Create a Brickworks schema for an email template with tiered offer based on an expression
Displaying a set of promotion banners powered by a Brickworks schema — A Brickworks-powered schema that returns multiple promotion banners in JSON, enabling client applications to freely design carousels or lists