Recommendations for new and returning app users

Prepare mobile app recommendations for new and returning users by means of screen views

Difficulty: Advanced · Modules: Experience Hub, Data Modeling Hub, Decision Hub

Channels

mobile application

Industries

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

Challenges addressed

Engage new customers
Personalize communication
Increase the revenue

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, "Recommendations for new and returning app users" enables your team to prepare mobile app recommendations for new and returning users by means of screen views. This advanced implementation unlocks significant value for organizations with established data pipelines.

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

Engage new customers
Personalize communication
Increase the revenue

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 Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints and Data Modeling Hub for defining custom data structures, managing catalog data, and building complex data relationships. The solution delivers results through mobile app experiences including in-app messaging, push notifications, and native UI personalization.

Expected Impact

Implementing this use case delivers measurable improvements across multiple business dimensions. Organizations typically see results within the first 4-8 weeks of deployment, with compound gains as the system learns from additional data. The advanced-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

mobile application — mobile app experiences including in-app messaging, push notifications, and native UI personalization
Experience Hub — Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints
Data Modeling Hub — Data Modeling Hub for defining custom data structures, managing catalog data, and building complex data relationships
Decision Hub — Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring

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

Experience Hub — Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints
Data Modeling Hub — Data Modeling Hub for defining custom data structures, managing catalog data, and building complex data relationships
Decision Hub — Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring

Channels

mobile application — mobile app experiences including in-app messaging, push notifications, and native UI personalization

Target Industries

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

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
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
Personalization — delivering individualized experiences across channels by adapting content, offers, and messaging to each customer's unique profile, preferences, and real-time behavior
New customers — acquiring and onboarding new customers through targeted campaigns, welcome journeys, and first-purchase incentives powered by Synerise's platform capabilities
Personalized recommendations
Segmentation — creating precise customer segments using Synerise's real-time segmentation engine that combines behavioral, transactional, and demographic data for targeted marketing

Implementation Complexity

Advanced — This is an advanced use case requiring significant technical expertise and detailed configuration.

Time to Value

4-8 weeks

Documentation

https://help.synerise.com/docs/use-cases/recommendations-for-new-and-returning-users/

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