Build a Recommendation Engine That Uses Your Actual Sales Data

Most small businesses first try the built-in 'related products' features in their CRM. In HubSpot, this is often a static list. A sales rep selling 'Product A' is always told to pitch 'Service B', even if customer data shows a different pattern. The rules are manual and cannot learn from your actual sales history.

This leads to a specific failure. An industrial supply company used Salesforce to suggest safety gloves with every power tool sale. Their own data, however, showed that customers buying a specific German-made saw almost always bought a high-end blade set within 30 days, a far more profitable upsell. The static rule-based system could never discover this non-obvious relationship, leaving money on the table with every sale.

External recommendation platforms built for e-commerce are not a good fit. They require massive data volumes (millions of interactions) to be effective and charge monthly fees based on API calls. An SMB with 5,000 customers and 10,000 historical deals does not have enough data to train these large models, resulting in poor recommendations for a high price.

Syntora would approach this problem by first conducting a data audit and discovery phase. This involves understanding your CRM setup and available historical data. We would then work with your team to extract 18-24 months of closed-won deal data, typically via your CRM's API. Using Python with the pandas library, this history would be transformed into a user-item interaction matrix, mapping customers to their past purchases to establish the ground truth for model training.

For the recommendation model, a collaborative filtering approach, specifically matrix factorization with the lightfm library, is suitable. This method is effective for the sparse datasets often found in SMBs, where individual customers have purchased only a subset of available products. The model would learn underlying patterns to predict likely future purchases. The trained model would then be wrapped in a lightweight FastAPI service. Syntora has experience deploying similar data-driven services for financial document processing, where fast and accurate predictions are critical.

This FastAPI application would be containerized using Docker and deployed as a serverless function on AWS Lambda, triggered by an API Gateway endpoint. This architecture offers cost-effectiveness, with typical operational costs under $50 per month for up to 20,000 monthly requests, and scales automatically without requiring server management. Structured logs would be sent to AWS CloudWatch using structlog for effective monitoring.

The integration with your CRM would involve configuring a webhook to call our API endpoint. When a sales representative views a customer account, the webhook would send the customer ID to the API. The API would process this request and return the top 3 recommended product SKUs. This allows suggestions to appear directly within your CRM's customer page, minimizing changes to existing sales workflows. The delivered system would be a deployed, functional API, along with documentation and knowledge transfer for your team.