Jetlore’s AI-powered Prediction Platform develops a deep understanding of customer preference by analyzing each customer-centric impression compared directly to millions of semantic product attributes. 

 
 

Increased sales and conversions in email, web, and homepage channels

Jetlore’s patent pending “learning-to-rank” engine moves beyond solutions that rely on business rules. By ranking your entire inventory for each customer based on their semantic attribute preferences, you can now predict and serve the exact content that will inspire each individual's purchase intent.

 
 

Higher conversions in Product Listing Pages

Modern PLP experiences force the customer to sort, filter, and search for the products they're interested in, needlessly creating purchase obstacles.  Jetlore creates a PLP experience that automatically adapts to retail customers’ needs in real time, raising conversions more than 20% for e-commerce retailers.

 
 

AI-Powered, Long term Adaptation

Jetlore’s Artificial Intelligence monitors and understands each user impression and each user's reaction to displayed content.  This allows retailers to automatically adapt to changing preferences and new information for each user as time goes on, which greatly increases sales and customer lifetime value.

 
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Deep Customer Insights

Jetlore tracks each piece of content displayed and provides detailed reports and insights.  Learn real time content metrics for all your assets (products, sub-catalogs, promotions) like what received the most views, clicks, purchases, etc.

 

 

Take a closer look at how The Jetlore Platform works
within the most important retail verticals.

There are several unique challenges to fashion and apparel that business-rule-heavy systems can't accomodate. Because early recommendation systems rely on collaborative filtering (similar users also bought), it's not possible to surface accurate content without recent customer data. Fashion retailers are forced to display broad, discount focused content.

The Jetlore Platform breaks down each product into semantic attributes, and compares that information directly to behavior from each tracked customer impression. The Jetlore Platform then re-ranks entire fashion inventories for each customer in real time, allowing marketers to recommend the most accurate and specific content assets possible.

Traditional recommendation systems that focus on product-to-product relationships are unable to recommend items across categories, and are ineffective in online marketplaces.

The Jetlore Platform Jetlore has consistently driven exceptional results with large online marketplaces like Linio and eBay. Jetlore's unique data model focuses on the relationship between the product and the customer, by analyzing billions of customer impressions compared to millions of semantic product attributes.

With that information, Jetlore can dynamically re-rank every content asset for each customer in real time, and predict exactly what each customer will want to see across a wide variety of categories.

Inventory in Flash Sale models moves quickly; new items are constantly introduced, and deals expire fast. This creates an additional hurdle for marketers: on top of the challenge of creating relevant content in general, displaying merchandised recommendations can often result in sending customers out of stock inventory or expired deals.

The Jetlore Platform accounts for this by automatically surfacing content at the time of interaction, instead of pre-programmed messages dictated by business rules. If an item or deal is expired, Jetlore immediately removes that from display and populates the next highest ranking content.

The ability to learn, adapt, and refresh content is imperative for home goods retailers like Living Spaces or Home24. Customers who purchase a big ticket item exhibit infrequent purchase cycles. Jetlore's natural ability to understand preference allows marketers to predict and surface cross category items and content, and frequently send relevant content until the time customers are ready to purchase again. This leads to additional sales, and higher customer LTV.