Personalized recommendations compliant with the Omnibus Directive
Create personalized recommendations that show the lowest price over the last 30 days for discounted products
Difficulty: Medium · Modules: Experience Hub
Channels
Industries
- Books
- Cosmetics
- Electronics
- Fashion & Apparel
- Home & Furniture
- Jewelry
- Retail
- Media
- Multi
Challenges addressed
- Increase loyalty
- Optimize your website and campaigns
Tags
- AI recommendations
- Dynamic content
- Item catalog
- Personalization
- Personalized recommendations
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, "Personalized recommendations compliant with the Omnibus Directive" enables your team to create personalized recommendations that show the lowest price over the last 30 days for discounted 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 loyalty
- Optimize your website and campaigns
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. 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
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
Channels
- website — website interactions including browsing behavior, on-site engagement, and web-based content delivery
Target Industries
- Books
- Cosmetics
- Electronics
- Fashion & Apparel
- Home & Furniture
- Jewelry
- Retail
- Media
- Multi
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
- 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
- Item catalog
- Personalization — delivering individualized experiences across channels by adapting content, offers, and messaging to each customer's unique profile, preferences, and real-time behavior
- Personalized recommendations
Implementation Complexity
Medium — This use case requires moderate technical knowledge and some configuration effort.
Time to Value
2-4 weeks
Documentation
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