Carousel with recently viewed products

Display a moving banner on the website with the recently viewed products

Difficulty: Medium · Modules: Experience Hub, Decision Hub

Channels

website

Industries

Home & Furniture
Retail

Challenges addressed

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, "Carousel with recently viewed products" enables your team to display a moving banner on the website with the recently viewed products. 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 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 Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints and Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring. 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

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

Data Inputs

website — website interactions including browsing behavior, on-site engagement, and web-based content delivery
Experience Hub — Experience Hub for crafting personalized customer journeys, dynamic content, and interactive engagement experiences across all digital touchpoints
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
Decision Hub — Decision Hub for data-driven business decisions, analytics dashboards, and real-time performance monitoring

Channels

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

Target Industries

Home & Furniture
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
Similar recommendations
Item catalog
Dynamic content — using Synerise Dynamic Content to create and deliver interactive, context-aware content experiences that adapt in real time based on customer data and behavior

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/product-recommended-for-recently-viewed/

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