Fordel StudiosE-Commerce Recommendation Engine
Real-time product recommendations that improve with every click.
The problem
being solved
An e-commerce store with 5,000+ SKUs shows the same "bestsellers" to every visitor. Static merchandising ignores browsing signals: categories viewed, time on product pages, cart adds/removes, price sensitivity, brand preferences. Conversion sits at 2-3%.
Algolia Recommendations operates with sub-10ms response times. Nosto delivers individualized experiences. Dynamic Yield offers ML-powered strategies with A/B testing. These prove personalization lifts conversion and AOV.
Mid-market merchants face meaningful monthly costs and integration complexity. Many default to Shopify's built-in "you may also like" using simple co-purchase data.
How this
agent works
The agent builds real-time behavioral profiles from clickstream data: pages viewed, products examined, time spent, cart activity, price sensitivity, and category affinity. For returning customers, purchase history and reviews layer on top.
Hybrid recommendations: collaborative filtering (similar customers bought these), content-based (similar along style, price, brand dimensions), and contextual signals (time, device, referral). Different weights for different placements: discovery on homepage, complementary on PDP, completion at checkout.
Recommendations update within the session. A visitor shifting from running shoes to hiking boots sees recommendations adapt immediately. Email uses the most recent profile, not stale purchase data.
A senior engineer integrates directly with your storefront (Shopify, WooCommerce, or custom) via a lightweight JS snippet that streams clickstream events into a FastAPI service backed by Redis for session-level profile state and PostgreSQL for long-term interaction history. Recommendations run through a hybrid model combining collaborative filtering and content-based similarity, with placement-specific feature weights trained in TensorFlow on your historical order and browse data. Initial model training and storefront integration takes 2–3 weeks, with A/B test infrastructure included from day one.
- 01
Session-Level Behavioral Profiles
The JS snippet streams click, add-to-cart, and dwell events in real time. Redis holds a live per-visitor profile that shifts recommendation weights as behavior evolves within the same session — no login required.
- 02
Hybrid Ranking Models
Collaborative filtering identifies what similar buyers purchased next. Content-based similarity fills cold-start gaps for new SKUs using catalog attributes. Each placement — homepage, PDP, checkout — uses separately tuned feature weights rather than one global model.
- 03
Context-Aware Placement Strategy
Homepage widgets optimize for discovery (broad affinity signals). PDP widgets optimize for complementary and substitute items. Checkout widgets target bundle completion. Email triggers use purchase-gap and win-back signals from the PostgreSQL interaction log.
- 04
Built-In A/B Testing
Experiment infrastructure is part of the base build, not a bolt-on. Compare recommendation strategies head-to-head with statistical significance tracking across conversion rate, AOV, and revenue per visitor — no third-party tool required.
Build this agent
for your workflow.
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call