Increase Order Value with a Custom Recommendation Engine
A custom recommendation engine increases average order value by showing customers hyper-relevant products. It learns from your unique sales history, not just generic "people also bought" rules.
Syntora designs custom e-commerce recommendation engines to increase average order value by analyzing unique sales history and product metadata. We propose architectures that integrate with existing e-commerce platforms, using machine learning to surface hyper-relevant product suggestions.
Building such a system depends on your product catalog size and the quality of your historical order data. A site with a consistent SKU structure and robust historical purchase data (e.g., 24 months of Shopify order history) allows for a more direct implementation. Conversely, sites with inconsistent product tags, fragmented user data across multiple systems, or limited transaction history would require an initial data audit and unification phase. Syntora has extensive experience in preparing and structuring complex datasets for machine learning applications across various domains, which directly informs our approach to e-commerce data.
What Problem Does This Solve?
Most e-commerce sites start with their platform's built-in "Related Products" feature. On Shopify, this feature relies solely on product collections or manually assigned tags. It cannot learn from customer behavior, so it will never suggest a complementary product from a different collection, missing major cross-sell opportunities.
Next, store owners install a third-party app like Wiser or Also Bought. These apps use basic collaborative filtering, showing what other customers purchased. This fails for new products that have no purchase history, known as the cold start problem. It also leads to popularity bias, where the same best-sellers are recommended over and over again, preventing discovery of your long-tail catalog.
A platform like Amazon Personalize seems like the next step, but it is a black box. You cannot inject your own business logic, such as boosting high-margin items or filtering out low-stock products. The per-request pricing model also becomes expensive for sites with over 100,000 monthly visitors, creating a penalty for growth.
How Would Syntora Approach This?
Syntora's approach to building a custom recommendation engine begins with a discovery and data audit phase. We would collaborate with your team to understand specific business goals and access your historical order data, typically spanning 12-24 months, via your e-commerce platform's API. This data would then be enriched with available product metadata. Our engineering team would employ libraries like Pillow for extracting dominant colors from product images and spaCy for converting text descriptions into vectors, creating a rich feature set suitable for content-based analysis. We have applied similar data processing and feature engineering techniques in projects involving complex financial documents using Claude API, demonstrating our capability with diverse data types.
The core of the system would involve designing a hybrid recommendation model. This model would combine a collaborative filtering component, using libraries such as Surprise in Python to generate user-item interaction matrices from purchase history, with a content-based component. The content-based element would calculate cosine similarity on product feature vectors to identify similar items. By applying a weighted average of these two models, the system would be able to recommend both popular and newer products effectively. The model training pipeline would be designed for efficiency, allowing for regular retraining on updated data.
The final recommendation model would be packaged as a FastAPI service for efficient, asynchronous request handling. This service would be deployed on a scalable serverless architecture, such as AWS Lambda, to manage varying request loads. When a user visits a product page, a light JavaScript snippet embedded in your site's theme would send the product ID to our API, which would then return a list of recommended product IDs.
For ongoing performance monitoring, every recommendation served and subsequent user interaction would be logged to a managed database like Supabase. This allows for tracking metrics such as click-through rate (CTR) and potential conversion uplift. We would configure monitoring and alerting systems, such as AWS CloudWatch, to notify relevant stakeholders if API latency or error rates exceed defined thresholds, helping maintain system stability. The model's training pipeline would be automated to retrain at a scheduled cadence, incorporating the latest user interaction and product data.
What Are the Key Benefits?
Go Live in 4 Weeks, Not 6 Months
Our focused build process delivers a production-ready engine in 20 business days, integrated directly into your e-commerce platform's theme.
Pay for the Build, Not Per Request
A one-time engagement for development and a flat, predictable monthly hosting fee. No variable SaaS costs that penalize you for high traffic.
You Get the GitHub Repo and Runbook
We deliver the complete Python source code and technical documentation. Your system is an asset you own, not a service you rent.
Automated Weekly Model Retraining
The system pulls the latest order data and retrains every Sunday at 2 AM. Your recommendations stay fresh without any manual intervention.
Works with Shopify, Magento, or BigCommerce
The API-first design integrates with any e-commerce platform via a simple webhook and a front-end JavaScript snippet.
What Does the Process Look Like?
Data and Access (Week 1)
You provide read-only API access to your e-commerce platform. We perform a data audit and present our findings on data quality and volume.
Model Prototyping (Week 2)
We build and test the core recommendation logic in a Jupyter Notebook. You receive a report showing sample recommendations for key products.
API Deployment (Week 3)
We deploy the FastAPI service and provide the API endpoint. You receive the integration snippet and documentation for your developers to review.
Integration and Handoff (Week 4+)
We assist your team with front-end integration and monitor performance for 30 days post-launch. You receive the final runbook and GitHub repository.
Frequently Asked Questions
- How much does a custom recommendation engine cost?
- Scope depends on data sources and catalog complexity. A standard Shopify integration with clean data is typically a 4-week project. Pricing is a fixed fee for the build, not a recurring subscription. Book a discovery call at cal.com/syntora/discover to discuss your specific requirements and receive a detailed proposal.
- What happens if the recommendation API goes down?
- The JavaScript snippet on your site has a 200ms timeout. If our API does not respond in time, the recommendation section simply remains hidden. Your site functionality is never affected. We use PagerDuty for alerts and have a 2-hour service restoration SLA for any production outages covered by our support plan.
- How is this different from a Shopify App Store app?
- App Store solutions are one-size-fits-all, using basic logic that creates popularity bias. They cannot incorporate your specific business rules, like boosting high-margin items. We build a hybrid model tailored to your catalog and customer behavior, which you own completely and can customize as your business evolves.
- How does the engine handle new products with no sales history?
- This is the 'cold start' problem, which our hybrid model solves. The content-based component recommends new items by matching them to popular existing products based on attributes like tags, descriptions, and colors. Once a new product gets a few sales, the collaborative filtering component's behavioral data takes over.
- Can we influence the recommendations with our own business logic?
- Yes. The API can accept parameters to boost certain categories, exclude out-of-stock items, or prioritize high-margin products in the results. This logic is defined during the discovery phase and built directly into the FastAPI service, giving you granular control over the output that a generic app cannot provide.
- What is the minimum amount of data we need to get started?
- We need at least 12 months of order history, containing a minimum of 1,000 total orders across a catalog of at least 50 distinct products. This provides enough data for the collaborative filtering model to find meaningful patterns. We verify your data volume and quality during the initial audit before any build work begins.
Ready to Automate Your Technology Operations?
Book a call to discuss how we can implement ai automation for your technology business.
Book a Call