Improve Lead Scoring Accuracy with a Custom AI Model

Many small marketing teams start with their CRM's built-in lead scoring, like the tool inside HubSpot. It allows you to assign points for actions like opening an email or filling out a form. The problem is that all actions are not equal. A lead from a referral might close at a 40% rate, while a lead from an ebook download closes at 2%, but a simple rules engine can't distinguish that nuance and may score them the same.

Consider a 10-person marketing team trying to improve lead quality for their 3 sales reps. They use HubSpot's point system but the reps still complain MQLs are poor. The marketing manager wants to create a rule that scores leads higher if they are from a company with over 20 employees and visited the pricing page twice in a week. HubSpot's tool cannot handle this multi-conditional logic, so the score remains a blunt instrument based on simple email clicks.

More advanced platforms like Salesforce Einstein require thousands of lead outcomes and an expensive Enterprise-tier plan, pricing out most small teams. Even if you qualify, the model is a black box. A sales rep sees a score of 82 but has no idea why, so they can't tailor their opening call. The structural failure is that these tools are features inside a platform, designed for mass-market simplicity, not for your specific sales motion. They cannot learn what actually predicts a closed-won deal from your unique data.

The engagement would begin with a data audit. Syntora would connect to your CRM and marketing platforms to pull the last 12-24 months of lead and deal history. This audit identifies which data fields are clean enough to be used as features and which signals are most predictive of conversion. You receive a report on your data readiness and a list of the top 5 most promising features before any model building begins.

The core system would be a gradient boosting model built with Python, wrapped in a FastAPI service, and deployed on AWS Lambda for efficient, serverless execution. When a new lead is created in your CRM, a webhook triggers the function. The API ingests the lead data, generates a 0-100 score, and writes it back to a custom field in your CRM. The entire process takes under 500 milliseconds.

The delivered system integrates directly into your team's existing workflow. Your sales reps see a score and a plain-English explanation (e.g., 'High score from referral source + pricing page visits') inside their CRM. You receive the full source code in your GitHub repository and a Supabase dashboard to monitor model accuracy. The system can be configured to retrain automatically every 90 days on new sales data, ensuring the scores stay accurate.