Personalized recommendations
The "You might like" widget takes into account the customer's behavior in the online store
Every customer sees something different
Key tags: AI personalization, Individual, Behavioral, Conversion growth
Personalization means that every customer sees products tailored exactly to their preferences. The system learns from their behavior – what they've browsed, searched for, bought – and creates unique recommendations.
What we track
Browsed products
The system remembers which products the customer visited and looks for similar or complementary ones.
Search history
Based on queries, we understand the customer's needs and recommend relevant products.
Purchase history
If the customer has bought from you before, we recommend products in line with their earlier preferences.
Interest categories
We identify which categories the customer is interested in and prioritize them in recommendations.
Brands and style
If the customer prefers specific brands or a specific style, recommendations take that into account.
Collaborative filtering
We compare the customer with similar users and recommend what they liked.
A unique experience for every customer increases the relevance and conversion of recommendations.
How does personalization work?
The personalization system works in three layers:
1. Short-term personalization (session)
During the current visit we track what the customer is doing right now. If they're browsing the women's clothing category, recommendations adapt immediately.
2. Medium-term personalization (days/weeks)
Based on repeated visits we build a preference profile. If the customer regularly searches for organic products, we prioritize them.
3. Long-term personalization (months)
Based on purchase history and long-term behavior we build a comprehensive profile that allows us to predict future purchases.
All three layers combine and produce the final recommendation, optimized for the specific customer.
Personalization examples
Scenario 1: First visit The customer searches for "running shoes" → We recommend running apparel, sports accessories → Priority: Similar products + bestsellers
Scenario 2: Returning visitor The customer regularly browses the "Eco cosmetics" category → We recommend new arrivals in eco cosmetics → Priority: Organic brands + certified products
Scenario 3: Loyal customer The customer has bought Apple electronics 5× → We recommend new Apple products and accessories → Priority: Apple brand + premium segment
Benefits for your online store
- Up to 35% higher click-through rate (CTR) on personalized recommendations
- A better customer experience – everyone sees relevant products
- Higher Customer Lifetime Value thanks to more relevant offers
- Automatic learning with no manual configuration required
- Encouraging customer return visits by remembering preferences
- A competitive advantage over online stores with generic recommendations
