Today’s shoppers don’t follow clean paths. They browse during lunch, pick up where they left off on their phone, then buy something completely different a week later. That makes predicting intent tricky. Most recommendation systems can’t keep up.

Wunderkind’s AI Catalog changes that. It uses affinity modeling to understand user behavior and surface products that actually match what a user wants. The result is a smarter, more personal shopping experience that works across devices and adapts with every session.

Here’s how it works, and why it matters.

What Is Affinity Modeling?

Affinity modeling is the process of figuring out what someone is likely to care about, based on how they behave. Every click, view, or return visit tells a story. Some actions carry more weight than others. Affinity modeling learns from all of it.

It’s different from broad segmentation. Instead of grouping people by rough traits, like age or location, it builds a dynamic profile for each user. That profile changes over time. As new patterns emerge, so do new predictions.

This allows brands to go beyond assumptions. You’re not targeting a “shopper.” You’re targeting a person with a history, preferences, and intent.

How Wunderkind Uses Affinity Modeling

Wunderkind connects signals from different visits and devices to build a clearer picture of shopper preferences. When someone visits a site, their actions get translated into a set of weighted signals:

• Did they scroll through a category?
• Have they come back to it more than once?
• What types of items did they purchase or add to cart?

Each signal shapes an evolving profile. These profiles aren’t etched in stone; they shift with each and every interaction. If someone moves from browsing sandals to exploring backpacks, the catalog will reflect that. Quickly.

Because the system recognizes users across devices, it keeps context. You don’t start from zero when switching from phone to laptop. That continuity is key to relevance.

The Advantage for Retailers

Most recommendation engines treat all clicks the same. Wunderkind’s doesn’t. It looks at how users engage. It considers not just what they click, but how often, how recently, and in what context.

This lets retailers separate high-intent behavior from casual browsing. If a user circles back to the same product multiple times, explores related items, or dives deep into a category, the system recognizes that pattern. It adjusts accordingly.

AI Catalog uses this behavioral intelligence to surface the right products for each individual. That means fewer generic recommendations and more relevance. Brands can speak to the specific interests of each shopper, not just the average trend.

Built With Modern AI

Behind the scenes, Wunderkind’s AI Catalog uses modern AI models to improve how it understands product relationships. This allows the system to make smarter associations.

For example, someone who looks at neutral-toned linen clothing might also be shown wide-leg trousers or natural-fiber bags, even if they didn’t search for them directly. The model infers aesthetic preferences, not just keywords.

This combination of behavioral data and deep product intelligence makes recommendations feel less like guesses, and more like help.

7 Reasons Affinity Modeling Drives Better Results

1. Cross-Device Tracking: Keeps context when users switch devices.
2. Fast Adaptation: Updates recommendations as soon as user behavior changes.
3. Deeper Personalization: Goes beyond “people also bought” logic.
4. Intent-Based Ranking: Surfaces products based on actual interest, not just availability.
5. Subtle Pattern Detection: Picks up on repeated interest or slow-burn curiosity.
6. Noise Reduction: Filters out products that are irrelevant to a user’s history.
7. Behavior-Driven Creative: Supports personalization at the asset and messaging level.

These benefits compound. Better targeting means better engagement. That means stronger long-term customer value.

From Data to Real Impact

Affinity modeling isn’t only about technology. It’s about measurable outcomes for retailers. Brands using AI Catalog have seen three consistent shifts:

• Higher Conversion Rates: When recommendations match actual interest, the path to purchase gets shorter.
• Better Product Discovery: Shoppers don’t just find what they came for; they uncover new products they didn’t know they wanted.
• Stronger Retention: Personalized experiences help make customers feel understood. This keeps them coming back.

This shift changes how brands think about their catalogs. Instead of hoping shoppers stumble on the right product, retailers can guide them naturally toward what fits best. Over time, this builds trust and makes every interaction more valuable.

Designed to Scale with You

Whether you’re managing a catalog of a few hundred items or tens of thousands, AI Catalog adjusts to suit your needs. The system factors in stock, seasonality, and brand-specific priorities.

That means your recommendations aren’t just personalized; they’re aligned with your broader goals, too.

Final Thoughts

Affinity modeling isn’t just another feature. It’s the foundation of a smarter, more responsive marketing strategy. Wunderkind’s AI Catalog brings this approach to life with real-time behavior tracking, cross-device continuity, and the latest advances in machine learning.

To see how it works or how it might work for you, learn more about Wunderkind’s AI Catalog here.