Introduction
Aampe’s recommender system combines user behavior data with catalog information to deliver personalized product recommendations in push notifications and surfaces. The system also allows a cool-down period to be set, so if a recommendation is made today, N days can elapse before the recommendation can be used again in a communication. The customer can control how long or short this cool-down period is. The process starts with catalog data ingestion through CSV files, data aggregators, or cloud buckets, capturing essential product details like IDs, names, prices, and deep-links. This data can then be leveraged using CMS filters to write messages specifically for sub-categories in your catalog. Aampe offers different recommenders for all your use-case ideas, e.g., abandoned cart, browse abandonment, new products, and more. The recommendation engine tracks user consumption patterns and creates an item-to-item transition matrix based on factors like sequence frequency and timing. It generates personalized recommendations by matching user history against this matrix, selecting high-scoring items while avoiding previous recommendations. Implementation is straightforward - recommendations are inserted into messages through a simple interface during content creation. The system continuously learns from user interactions, improving recommendation accuracy over time while effectively handling both popular and niche products across large catalogs.Integration Steps
Our dedicated developers will be with you every step of the way to ensure that the integration is smooth and easy. Here’s a step-by-step overview of the process:Prepare your CMS data: Ensure your product catalog includes essential fields such as:
- Item ID/SKU
- Item name
- Item price (Optional for in-depth analytics)
- Other relevant catalog properties you wish to include in your messaging (e.g., brand, category, description)
Choose your integration method:
- Cloud bucket integration