Maximize AOV With Gen AI Product Recommendations

Experro analyzes live customer behavior—like what they click, add to cart, or ignore—and matches it with real-time catalog data to suggest the right products.

For example, Experro can display reminder tiles on the homepage for products users viewed but didn’t buy, show similar items on PDPs to reduce bounce, or recommend accessories at checkout to lift AOV—all using real-time behavior and context.

An agentic AI recommendation engine is designed to act autonomously, learning from every interaction and adjusting recommendations accordingly. This means AI agents continuously refine what shoppers see without needing daily input from your team.


These agents detect patterns, test variations, and update recommendations automatically, freeing up merchandisers while ensuring the system evolves with customer behavior.

Context-aware recommendations adapt based on the shopper’s environment - like location, time, and device type. Experro uses this context to personalize suggestions and boost relevance in real time.


Examples include:

• Showing winter jackets to users in colder regions
• Recommending gift bundles near holiday events
• Promoting wishlist items when stock is low or limited
  This kind of targeting builds trust and improves the overall shopping experience.

Yes, Experro functions as a self-learning recommendation system. It observes every customer action—searches, clicks, bounces, and purchases—and automatically updates future recommendations based on what’s performing best.

The more your customers engage, the smarter it gets—without needing manual tagging, rules, or campaign setups. That means better results, with less effort.

Absolutely. Experro allows you to A/B test product recommendations to compare which strategies perform better across your audience.


You can test:

• Affinity-based recommendations vs. behavior-based models
• Placement on homepage vs. PDP vs. cart
• "Trending" vs. "New for You" logic
• Different layouts or recommendation widgets


Performance insights help you optimize your strategy and scale what works best.

Affinity-based recommendations are suggestions based on a shopper’s interests, such as their favorite brands, color preferences, or categories they often visit. Experro tracks these signals to create a curated experience that evolves with every interaction.

These models can even be tailored by industry or merchant type—whether you’re showing similar fashion pieces or bundling accessories in tech.

Intent-aware recommendations anticipate why the customer is browsing—whether they’re here to explore, buy a gift, or pick up where they left off. Experro uses signals like dwell time, search phrasing, and cart behavior to shape recommendations around shopper goals.


This helps:

• Gift buyers find curated options quickly
• Returning users get reminded of unfinished journeys
• High-intent shoppers discover personalized add-ons faster


The result? Less scrolling, more conversions.

Hyper-personalized product recommendations in Experro combine live behavioral data with historical preferences to suggest exactly what each user is most likely to buy. It goes beyond segments—every customer gets a unique experience, shaped by their actions and interests.

These personalized journeys guided by AI agents help reduce decision fatigue, increase product discovery, and keep shoppers engaged longer—without requiring complex manual targeting. These recommendations can also be extended to emails or in-store displays, maintaining a seamless journey across every channel.