Boost AOV with AI-Powered Product Recommendations

Many small ecommerce teams start with their platform's built-in recommendation tools, like Shopify's 'Product recommendations'. These tools typically only offer a basic 'customers who bought this also bought' logic. They cannot use individual browsing history, like a customer viewing a product three times in a day, to inform suggestions. The result is generic recommendations that ignore powerful user intent signals.

Next, teams often install a third-party app like Rebuy or LimeSpot. While more powerful, these apps run as black boxes. You cannot inspect the model's logic or add your own domain knowledge. For example, a 5-person team selling high-end cycling gear knows a specific carbon wheelset is incompatible with a certain frame. The app, unaware of this rule, recommends the incompatible part, leading to a customer purchase, a frustrating return, and erosion of brand trust. The app's data sync might also be delayed by 15 minutes, causing it to recommend recently out-of-stock items.

The structural problem is that off-the-shelf apps are built for the median store, not your specific catalog and customer behavior. They are architected for easy installation, which prevents deep integration with your business rules and real-time data. To truly personalize, you need a model that is trained on your data, reflects your unique product relationships, and is controlled by you.

The first step would be a data audit. Syntora would connect to your store's database, whether it's on Supabase, PlanetScale, or directly through a platform API, and pull 12-24 months of order and browsing history. We would join this with data from other tools like Klaviyo or Google Analytics to create a unified customer profile. You would receive a data readiness report and a list of over 50 potential features to power the model.

Based on the audit, the technical approach would involve building a collaborative filtering model using a Python library like LightFM, which excels with sparse data typical in ecommerce. This model would be wrapped in a FastAPI service and deployed on AWS Lambda. This serverless architecture ensures responses are fast, typically under 200ms, and keeps hosting costs low, often under $50 per month, as you only pay for compute when a recommendation is requested.

The delivered system is a private API endpoint that your developers can easily integrate into your custom storefront. When a customer browses, your front end sends their user ID to the API and receives a list of personalized product SKUs to display. You get the full source code in your GitHub repository, a runbook for retraining the model, and a simple monitoring dashboard to track performance.