// COMMERCE INTELLIGENCE
Recommender Systems: Show Customers Exactly What They Need Next.
Static "similar products" lists ignore the individual context of your buyers. With Xanevo's AI recommender systems you use deep learning to find, from millions of combinations, the one with the highest purchase probability, seamlessly integrated into your existing tech stack and based on the clean data structures of your golden records.
More than "others also bought".
01
Algorithms
Collaborative vs. content-based filtering
02
Business impact
Basket value and customer loyalty
03
Integration
PIM and tracking connection
How do recommender systems increase e-commerce revenue?
Recommender systems lift revenue via two levers: higher average order value (AOV) through context-relevant cross-sells and higher repeat-purchase rate through personalized recommendations along the customer journey. Unlike static "others bought" lists, modern recommenders combine behavioral data (behavioral) with product attributes (content-based) into hybrid recommendations. The semantic distribution of products in the PIM defines how precise the matches are. The cleaner the attribute matching, the higher the conversion on the recommendation.
// COMPARISON
Static lists vs. AI recommender
Criterion
Static "similar products"
Xanevo recommender
Data base
Manually maintained cross-sell list
Behavioral + content-based hybrid
Personalization
Same for all users
Per session and device individual
Cold start
Only for maintained products
Attribute-based even for new items
B2B capability
Generic
Spare-part logic, quantity tiers, compatible accessories
Performance
Static, simple DB query
Milliseconds via multi-step pipelines
// PRODUCT MODULES
The functional modules for personalized recommendations
Module 1: Hybrid algorithms, Behavioral plus content-based
Collaborative filtering uses behavior patterns ("customers who bought X also bought Y"), content-based filtering uses product attributes ("products with similar properties to X"). The Xanevo recommender combines both and solves the cold-start problem: for new products without behavior data content-based kicks in, for products with rich history behavioral takes over. Hybrid instead of either-or.
Module 2: Business impact, More than just "customers also bought"
Basket optimization on the product detail page (PDP) through fitting accessories, last-minute offers in checkout, personalized newsletter content based on purchase history. For B2B assortments special cases come in: spare-part logic (which part fits the purchased device?), quantity tiers (is there a cheaper pack size?), compatible accessories (what would the customer additionally need?).
Module 3: PIM and tracking connection
The recommender reads product data from the PIM (through golden records or direct API access) and behavioral data from the shop tracking. Both feed the model. The recommendation gets served through a frontend API called directly from the shop, newsletter, or service tool. No frontend rebuilds, performant through vector caching.
// FAQ
Three questions e-commerce managers and heads of sales ask
No. Through cold-start strategies we first use your PIM data for attribute-based recommendations until enough behavior data is available. The system improves gradually instead of being useless for months. First productive recommendations from day one.
Yes, our systems allow business rules (boosting) to push e.g. high-margin products or inventory overhangs. The AI logic stays untouched, business rules act as a multiplier on the recommendation score. Fully transparent, adjustable anytime.
Through highly efficient API connections and multi-step pipelines, delivery happens in milliseconds without slowing the frontend. Recommendations are precomputed in the backend and only the final hits delivered via lightweight API. No noticeable performance impact.
// RECOMMENDER SYSTEMS
We calculate your conversion potential
Give us an excerpt of your product data and typical customer behavior from your shop. We concretely simulate how much higher your average basket would be with personalized recommendations and deliver an implementation concept including cold-start strategy. In two weeks.