We built a real-time AI Personalization Engine that dynamically customized every touchpoint of the shopping journey—from the homepage to post-purchase emails.
1. Behavioral Intelligence Layer
We deployed a lightweight JavaScript tracker (GDPR-compliant, cookie-consent-gated) that captured rich behavioral signals in real-time:
* Products viewed, time spent on each, scroll depth
* Search queries and filter selections
* Add-to-cart and wishlist actions
* Purchase history and return patterns
This data fed into a real-time customer profile stored in a Redis-powered feature store, updated with every click.
2. Dynamic Homepage & Product Recommendations
Using collaborative filtering ("customers who bought X also bought Y") combined with content-based filtering (style, color, price range preferences), the AI generated personalized product grids for every visitor.
* First-time visitor: Sees trending products in their inferred category (based on referral source and initial clicks).
* Returning visitor: Sees "Continue where you left off" + new arrivals matching their taste profile.
* High-intent visitor (viewed product 3+ times): Sees targeted discount nudge.
3. Intelligent Cart Recovery
When a cart was abandoned, the AI triggered a multi-step recovery sequence:
* +1 hour: Personalized email with abandoned items + "You might also like" suggestions.
* +24 hours: SMS/WhatsApp with a time-limited 10% discount on their specific cart.
* +72 hours: Final email repositioning the products with social proof ("87 people bought this today").
Each message was dynamically generated—no two customers received the same email.
4. AI-Powered Search & Merchandising
We replaced their basic keyword search with semantic search powered by vector embeddings. Customers could search "cozy reading nook" and get curated results combining throw blankets, reading lamps, and small bookshelves—even though no product was tagged with "reading nook."
5. Predictive Inventory Optimization
The personalization data also fed into a demand forecasting model. If the AI detected a trending product category (e.g., "outdoor furniture" spiking in March), it alerted the merchandising team to increase stock before the rush.
