Improve Product Recommendations for Your Repeat Buyers with AI

Most ecommerce stores start with their platform's built-in tools. Shopify's default "You may also like" widget is based on product-to-product similarity. It shows that customers who bought X also bought Y. For a repeat buyer who just purchased their third bag of the same coffee beans, the widget might suggest the exact same coffee beans, showing it has no memory of that customer's history.

Plugins like Rebuy or Wiser try to fix this with manual rules: "if a customer buys product X, show product Y." This handles simple cross-sells, like suggesting a camera lens with a camera body. These rules fail with nuance. A customer who buys a specific brand of running shoes every 6 months does not need to see ads for other shoes. They need a reminder in 5 months to re-order their preferred model. The rules engine cannot handle time-based cadence or learned personal preference.

Consider a 15-person company selling high-end skincare. A loyal customer buys the same Vitamin C serum every 90 days. Their Shopify store, using a standard recommendation app, shows them ads for the same serum they just bought. Or it shows a generic bestseller from a brand they've never purchased. The system ignores the customer's established buying pattern and brand loyalty, missing a clear opportunity to surface a complementary night cream from the same brand they trust.

The structural problem is that off-the-shelf tools operate on a session-level or product-level data model. They are stateless regarding the individual customer's journey over time. They cannot build a persistent, evolving profile for each buyer that incorporates their full history, returns, and buying frequency. These tools are designed for one-time conversions, not for maximizing lifetime value from your most loyal customers.

The first step is a data audit. Syntora would connect to your store's database or API to analyze the last 24 months of order data. This process maps purchase frequency, category preferences, returns data, and other customer behaviors to identify the most predictive signals for a custom model. You receive a report on data quality and the specific features that will drive recommendations.

A custom model would be built using a combination of collaborative filtering and sequence modeling to understand both product affinity and purchase timing. The model would be developed in Python, using libraries like scikit-learn, and wrapped in a FastAPI service. This service is deployed on AWS Lambda for low-cost, on-demand processing, designed to return recommendations in under 200ms.

The delivered system is a simple API endpoint. Your ecommerce frontend calls this endpoint with a customer ID and receives a list of 5 recommended product SKUs to display. You get the full Python source code in your GitHub repository, a runbook for maintenance, and a Supabase dashboard to monitor model performance and click-through rates. The model retrains automatically on a nightly schedule to incorporate the latest customer activity.