Landing page with personalized listing and brand-focused product recommendations

Send an email to customers with high propensity to buy from the specific brand. Direct them to a personalized Landing Page with product listing filtered to this brand

Difficulty: Medium · Modules: Experience Hub, AI Hub

Channels

email
website

Industries

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

Challenges addressed

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, "Landing page with personalized listing and brand-focused product recommendations" enables your team to send an email to customers with high propensity to buy from the specific brand. direct them to a personalized landing page with product listing filtered to this brand. 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

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 AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations. The solution delivers results through email campaigns with dynamic templates, send-time optimization, and behavioral triggers and 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

email — email campaigns with dynamic templates, send-time optimization, and behavioral triggers
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
AI Hub — AI Hub for training, deploying, and managing machine learning models, predictions, and AI-powered recommendations

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

Target Industries

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

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
Landing page — creating targeted landing pages with personalized content, product listings, and conversion-optimized layouts using Synerise's Dynamic Content and recommendation capabilities
Email communication — creating and sending targeted, personalized email campaigns using Synerise's built-in email capabilities, including dynamic templates, behavioral triggers, and deliverability optimization
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
Predictions — using Synerise's AI prediction models to forecast customer behavior such as churn risk, purchase propensity, lifetime value, and next best action
Propensity
Personalization — delivering individualized experiences across channels by adapting content, offers, and messaging to each customer's unique profile, preferences, and real-time 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/landing-page-personalized-listing/

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