The right product, to the right person, at the right time
Amazon generates 35% of its revenue from recommendations. Netflix retains subscribers with personalized suggestions. This capability is no longer reserved for tech giants. Wikolabs' personalized recommendation system adapts to your catalogue and audience, regardless of your size, to increase average basket, retention and engagement.
"Popular products" or "You might like" carousels based on manual rules are generic and ineffective. They show the same products to everyone, ignore context and miss moments when a user is ready to buy. Result: low click rates, stagnant average basket, missed cross-sell opportunities.
The system combines multiple approaches: collaborative filtering (what similar users purchased), content-based filtering (products similar to the user's profile) and contextual matching (time, device, location, recent history). Recommendations are calculated in real time and personalized for each session.
Tracking integration: product views, add-to-cart, purchases, time spent, searches. Interaction history.
Collaborative filtering (matrix factorization), content-based and hybrid models trained on your data.
High-availability REST API (<50ms) for frontend integration: product pages, cart, emails, push notifications.
Continuous testing of recommendation strategies. CTR, conversion rate and average basket optimization.
Contextual, personalized recommendations increase the value of each order by surfacing relevant add-ons.
Users who find relevant content or products come back. Personalization creates a memorable experience.
The model improves automatically with interactions. The more data you have, the more accurate the recommendations.
Free 30-minute audit. We analyze your context and deliver a concrete roadmap.