Personalized Recommendation

The right product, to the right person, at the right time

WhatsApp
Agent sheet
Category
Search and structured data
Model
open, installed on your premises
Data
never leaves
Subscription
€0 per month
01Overview

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.

The problem

"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.

Our answer

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.

02How it works

Live in four steps.

  1. 01
    Behavioral data collection

    Tracking integration: product views, add-to-cart, purchases, time spent, searches. Interaction history.

  2. 02
    Model training

    Collaborative filtering (matrix factorization), content-based and hybrid models trained on your data.

  3. 03
    Real-time recommendation API

    High-availability REST API (<50ms) for frontend integration: product pages, cart, emails, push notifications.

  4. 04
    A/B testing & optimization

    Continuous testing of recommendation strategies. CTR, conversion rate and average basket optimization.

03What you gain

Results you can measure.

Average basket +30%

Contextual, personalized recommendations increase the value of each order by surfacing relevant add-ons.

Retention +20%

Users who find relevant content or products come back. Personalization creates a memorable experience.

Zero manual maintenance

The model improves automatically with interactions. The more data you have, the more accurate the recommendations.

04Frequently asked

What we get asked before signing.

How much data is needed to start?
A minimum of 10,000 interactions (views, purchases, clicks) produces useful recommendations. Models improve with more data.
Does the system handle cold start (new users)?
Yes. For new users, popularity-based and session-context recommendations are used until data accumulates.
Can business rules be integrated (exclude certain products)?
Yes. Business filtering rules can be applied: exclude out-of-stock products, promote certain categories, etc.
Is the system GDPR compliant?
Yes. No identifiable personal data is required. The system works on anonymous session or cookie identifiers.

Let's talk about your case for thirty minutes.

A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.

WhatsApp
+261 38 66 261 00team@wikolabs.com