Predictive Product Recommendations

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Create and manage contextually relevant content with ease to personalize every message, every page, every ad and every customer interaction with Listrak’s Predictive Product Recommendations.

Listrak’s Predictive Product Recommendations ingest and respond to millions of individual consumer signals. By using advanced machine learning algorithms and providing the ability to layer deeply customizable merchandising decisioning, Listrak is able to understand and predict the most relevant, data-driven and personalized products to influence the unique customer journey toward the path to purchase.

Feel confident that you are showcasing the right products to deliver the best user experience with the highest propensity to convert at that specific point in time.

  • Intelligent recommendations: show products that haven’t been purchased or previously suggested
  • Open-time optimized: never recommend an out-of-stock item
  • Full creative control: use one of our templates or custom build from our easy-to-use interface
  • Transparency: always understand why an item is being recommended
  • Ease of integration: use across multiple campaigns and channels

Use machine learning and predictive analytics to automatically put the most relevant products in front of each customer across channels and devices.

Predictive Product Recommendations

Apply custom algorithms to meet your business needs

Create specific parameters to only recommend merchandise within the same price point, category or sub-category, brand, gender, color or size variance simply by sliding a button to indicate the level of importance.

Predict likely purchase behavior and merchandise products accordingly without ever having to manually update existing email creative or website pages. Introduce new product offerings that the shopper is most likely to buy.

Listrak’s Recommender has transformed the way we communicate with shoppers. We are able to create messages that are much more personal and relevant and customers are responding positively.

Chaim Posen, Marketing Director, JomaShop

Easily create new algorithms based on predictive modeling, including
  • Purchased this / purchased that
  • Viewed this / purchased that
  • Frequently bought together
  • Best-sellers
  • Currently trending
  • New arrivals / markdowns

Base personal recommendations on each customer’s browse and purchase behavior.Preview results by product or customer. Define global products to recommend as a backup if needed. Customize design of content blocks.