Increase Ecommerce Sales with a Custom Product Recommendation Algorithm

Most small online shops install a Shopify app like 'Also Bought' or 'Frequently Bought Together'. These tools work by analyzing store-wide purchase history to find common pairings. This approach fails for stores with diverse catalogs or niche products because it treats all customers the same. The recommendations are generic and often surface the same handful of bestsellers to every visitor, missing valuable cross-sell opportunities.

Consider a shop selling specialized camera gear. A customer buys a specific Sony camera body. A generic app recommends the most popular tripod, which might not be compatible. The customer actually needs a lens with a specific E-mount, a battery compatible with the NP-FZ100 model, and a memory card fast enough for 4K video. The generic app cannot grasp these nuanced relationships because it only sees co-purchase frequency, not product attributes.

Furthermore, these apps often slow down your website. They inject heavy JavaScript files that can add 500ms or more to your page load time, which directly harms conversion rates. You also have no control over the logic. If you want to push high-margin accessories or clear out overstocked inventory, you cannot configure the app's algorithm to prioritize those items. The app's logic is a black box designed to work for ten thousand stores, not optimized for your specific business goals.

The structural issue is that these plugins are one-size-fits-all products, not tailored solutions. They are built on the assumption that aggregate data is good enough. For a small shop whose competitive advantage is deep product knowledge and curation, a generic model that ignores that expertise undermines the brand and leaves money on the table.

The project would begin with a data audit of your ecommerce platform, whether it's Shopify, WooCommerce, or another system. Syntora connects to your sales history (typically 12-24 months of order data) and product catalog. This initial analysis identifies the strength of the purchasing signals and determines the best modeling approach, such as collaborative filtering for stores with sufficient user data or a content-based model for stores with rich product attributes. You receive a brief report outlining the data quality and proposed model strategy.

The technical system would be a Python service using the FastAPI framework, deployed on AWS Lambda. This serverless architecture is ideal for ecommerce; it costs pennies to run and automatically handles traffic spikes during sales events. The model would process your product catalog to create embeddings (numerical representations) stored in a Supabase Postgres database. When a user views a product, your website makes an API call to the FastAPI endpoint, which returns a list of recommended product IDs in under 100ms.

The final deliverable is more than just an API. You get a lightweight system that plugs directly into your site's theme, with a simple runbook explaining how to trigger a model retrain as new sales data accumulates. The system runs in your own AWS account, and you receive the full Python source code. It's a business asset you own completely, not another monthly SaaS subscription.