Summary
Personalization is no longer a competitive edge, it’s a customer expectation, and Wunderkind’s AI Experiences offer a glimpse into how deeply behavioral data can be operationalized to meet it. Far beyond rule-based automation, each AI module—Abandonment, Catalog Price Drop, Post-Purchase Recommendations, and Behavioral Out-of-Stock—is designed to respond to distinct behavioral signals with precision in timing, content, and channel. These systems use models like purchase propensity, product affinity, and send-time optimization to determine what to say, when to say it, and how. The strategic insight lies in their modularity: each experience addresses a different inflection point in the customer journey, yet they interlock to form a dynamic, AI-powered marketing continuum. For leaders, the question isn’t whether AI can enhance engagement, but how to orchestrate these specialized tools into a coherent, value-generating customer lifecycle.
In today’s fast-paced digital world, brands must connect with customers on a personal level. AI is transforming this landscape, moving beyond simple automation to create smarter, more personalized customer experiences. Wunderkind’s suite of AI Experiences—AI Abandonment, AI Catalog (Price Drop), AI Post-Purchase Recommendations, and AI Behavioral Out-of-Stock—is designed for this very purpose.
These strategies are designed to work independently or as part of a larger, unified marketing journey. This blog will explore how each AI Experience is optimized for a specific behavioral opportunity, all powered by AI to enhance timing, channel selection, and recommendation logic.
The Under-the-Hood Look at AI Experiences
Wunderkind’s AI Experiences use different proprietary models to deliver personalized messages designed to improve key metrics like clicks and conversions.
AI Abandonment
AI Abandonment is a powerful tool that personalizes the timing and channel for each shopper after they leave a product, category, or cart. It replaces older, rules-based systems with dynamic, adaptive logic. This AI advantage helps drive higher performance with more clicks and conversions than classic, fixed-time journeys. The system uses a shopper’s behavior and engagement patterns to predict the best channel (email or text) and the optimal time to send a message. After a user is identified, the system tracks their session activity and uses click propensity, purchase likelihood, and texting thresholds to make its decisions.
AI Catalog – Price Drop
AI Catalog uses affinity modeling to expand the audience for triggered messages. It targets shoppers who may have an interest in a product, even if they didn’t browse that exact item. The Price Drop feature works by using AI-driven product affinity modeling to predict which shoppers will likely engage and with which products. This model analyzes past behavior, including product interactions and conversion history, to score a shopper’s likelihood of buying certain products. When a product’s price drops, the system matches it with high-intent shoppers based on their scores. This creates a broader reach and higher relevance, ultimately leading to more revenue.
AI Post-Purchase Recommendations
AI Post-Purchase Recommendations is all about extending customer engagement after a purchase. It recommends products that are frequently bought together, helping to increase revenue from existing customers. When a user’s session ends after a conversion, the system looks at the items they bought. It then uses Wunderkind’s behavioral and affinity models to suggest the next best offer. The message is then sent through the shopper’s preferred channel, which is also determined by AI.
AI Behavioral Out-of-Stock
Out-of-stock items can be a lost opportunity, but AI Behavioral Out-of-Stock turns them into a chance for engagement. It targets users who abandon their session after viewing an out-of-stock product and recommends similar items that are in stock. The system uses affinity modeling and behavioral scoring to choose the best recommendations. Like the other AI Experiences, it also uses send-time and channel optimization to ensure the message is delivered effectively.
The Data and Logic Behind the AI Experiences
These AI Experiences rely on a blend of data signals and sophisticated decision-making logic. The systems track all site sessions via a PrivacyID trigger. They then analyze a variety of data, including click and purchase propensities, and even texting thresholds, to make decisions. The models used for these experiences include:
- Purchase Propensity: This model predicts the likelihood that a person will make a purchase. It scores individuals based on behavioral and purchase data. This score is used in campaigns like AI Abandonment to determine the best time to send a message.
- Product Affinity: This model measures a person’s engagement with a specific product and finds other related products. This is crucial for AI Catalog and AI Behavioral Out-of-Stock. For instance, it can find lookalike products that a user might be interested in, even if they didn’t browse them.
- Send-time Optimization: This model predicts the optimal hour of the day and day of the week to send a message to a specific user. It uses past open, click, and conversion data (in addition to many other inputs) to recommend the best time for each send. This is a core part of the AI Abandonment and AI Post-Purchase strategies.
- Channel Optimization: This model predicts whether email or text is the best channel to engage a user. It uses historical open and click data to determine the channel with the highest likelihood of engagement. This model is used to dynamically choose the best channel for AI Abandonment and AI Post-Purchase messages.
Where They Overlap and Where They Specialize
The four AI Experiences share a common foundation. All of them are built on Wunderkind’s AI infrastructure and use models like Purchase Propensity and Product Affinity to some degree. They also leverage send-time and channel optimization to ensure messages are delivered at the right moment and through the right medium. However, each strategy specializes in a different behavioral opportunity.
- AI Abandonment is a reactive strategy, designed to re-engage shoppers who have already left the site. It specializes in optimizing the timing and channel of these follow-up messages.
- AI Catalog is a proactive strategy, expanding the audience for triggered messages to reach people who haven’t yet shown direct interest in a specific product. Its unique strength is in its predictive targeting.
- AI Post-Purchase Recommendations focuses on a different part of the customer journey, aiming to drive repeat business from converted customers. Its logic is specifically designed to recommend complementary products.
- Finally, AI Behavioral Out-of-Stock is designed to recover revenue from a specific type of abandonment, turning a dead-end experience into a new sales opportunity by recommending alternative, in-stock products.
Each AI Experience is optimized for a specific behavioral opportunity. This means they are purpose-built for smarter engagement. These tools are designed to work either on their own or together as part of a unified customer journey. By using AI to power the timing, channel, and recommendation logic, brands can create personalized connections with every user.
Ready to see how AI Experiences can work for you? Contact us today.
