Build a Recommendation Engine That Uses Your Actual Sales Data
The best way is to build a custom API that uses your deal history to find similar customers. This API connects to your CRM to suggest relevant products to your sales team.
Syntora designs custom recommendation engines for SMBs using existing CRM data. These systems suggest relevant products to sales teams by identifying patterns in deal history. This approach focuses on technical architecture and engineering engagement rather than selling a pre-built product.
Building a recommendation engine like this typically requires 3 to 4 weeks for initial deployment. The project scope is influenced by the number of products offered and the quality of your historical sales data. A business with two years of clean HubSpot deal data would allow for a faster build. In contrast, a company needing to unify sales records from Salesforce and an external billing system would require additional data preparation steps.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Get a Live API in Four Weeks
From CRM data export to a production-ready API integrated into your workflow in 20 business days. Your team gets useful recommendations immediately.
Pay Once, Own the System Forever
A single fixed-price build with no recurring license fees. You are not penalized for growing your sales team or increasing API call volume.
You Get the Full Source Code
We deliver the complete Python codebase to your company's GitHub repository, along with documentation. You are never locked into a proprietary platform.
Alerts When Performance Changes
We configure AWS CloudWatch alerts that trigger if API latency exceeds 500ms or error rates pass 1%. We know about problems before your sales team does.
Works Inside HubSpot or Salesforce
The system writes recommendations to a native panel in your existing CRM. No new software for your team to learn, no extra tabs to manage.
What Does the Process Look Like?
CRM Data Connection (Week 1)
You provide read-only API access to your CRM. We perform a data audit and deliver a report on data quality and the feasibility of the model.
Model Training (Week 2)
We build and train the recommendation model on your historical data. You receive a validation report showing model accuracy with real examples.
API Deployment and Integration (Week 3)
We deploy the API to AWS and connect it to a sandbox version of your CRM. You receive access to test the live recommendations yourself.
Production Launch and Handoff (Week 4)
We connect the API to your live CRM. After a 90-day monitoring period, we deliver the final code, documentation, and a runbook for maintenance.
Frequently Asked Questions
- What is the typical timeline and cost for a recommendation engine?
- A standard build takes 3-4 weeks. The cost is a fixed price determined by scope. Key factors include the number of data sources (e.g., just a CRM vs. CRM plus an ERP) and the cleanliness of your historical product data. We provide a firm quote after a 30-minute discovery call where we assess these factors.
- What happens if the recommendation API goes down?
- The CRM integration is built to fail gracefully. If the API is unreachable, the 'Recommended Products' panel on the customer page will simply appear empty or show a 'temporarily unavailable' message. It will not cause an error or disrupt the user's workflow. We receive a CloudWatch alert and typically restore service in under an hour.
- How is this different from Salesforce Einstein Product Recommendations?
- Einstein requires a large volume of data to work effectively and is only available on expensive license tiers. Our approach is designed for the sparser data typical of SMBs (200-500 customers). You also own the model and the code, which means you have full transparency into why a recommendation is made, unlike Einstein's 'black box' approach.
- How does the system handle new products with no sales history?
- This is the 'cold start' problem. For new products, we use a content-based approach. The model analyzes the new product's category and description to find similar existing products. It is then recommended to customers who previously bought those similar items. Once a new product has about 15-20 sales, the primary collaborative filtering model takes over.
- Where is our customer data stored and processed?
- The entire system is deployed in your own AWS account. Your customer data never leaves your cloud infrastructure. Syntora only requires temporary, read-only access to your CRM during the build phase. Unlike SaaS vendors, we do not process or store your data on our servers, which gives you full control and privacy.
- How do we measure the ROI of this system?
- The API logs every recommendation shown and we can track whether a suggested product is added to an open deal within a set timeframe, like 30 days. We provide a simple dashboard built with Supabase that shows the conversion rate of recommendations and the total pipeline value generated from them, giving you a clear view of its financial impact.
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