Focus Your Sales Team on Leads That Actually Convert

Small sales teams often start with their CRM's built-in scoring, like in HubSpot. This assigns static points for actions like opening an email (1 point) or filling a demo form (10 points). The system cannot learn from outcomes. It gives the same score to a high-fit lead from a target account and a student who downloaded a whitepaper, forcing reps to manually re-qualify every single lead.

This leads to workflows built with external tools to add more logic. A common setup involves a multi-step process that triggers on a new lead, uses a filter to check for a work email, enriches the contact, and then posts to a Slack channel. This can burn 4-5 tasks per lead. At 500 leads/month, that is 2,500 tasks and a bill that exceeds $120/month just to triage leads before a human even sees them.

The fundamental issue is that these tools are not built for probabilistic scoring. They operate on fixed rules. They cannot answer the most important question: based on the patterns of all our past closed-won deals, how likely is this new lead to convert? This requires a model that learns from data, not a branching path in a visual editor.

Syntora's engagement would begin with a discovery phase to understand your data environment. This typically involves exporting 12-24 months of your CRM deal history using platforms like HubSpot or Salesforce API. We would then identify opportunities to enrich this with web analytics data, pulling page view and session data from tools such as Plausible or Google Analytics. This raw data would be loaded into a Python environment using pandas for cleaning and feature engineering, where the team would identify and develop predictive features relevant to your sales cycle, such as 'days since last visit' or 'viewed pricing page more than twice'.

Syntora would then train several machine learning models using scikit-learn. A gradient boosting model often provides strong performance for this type of prediction, as it can capture non-linear relationships and interactions that simpler point systems might miss. The model's objective would be to predict the probability of a lead converting to a 'closed-won' deal. The final model would be selected based on its predictive performance for identifying high-value leads.

The selected model would be packaged into a lightweight REST API using FastAPI. This API would expose an endpoint designed to accept lead data and return a calculated score. For deployment, Syntora typically uses serverless functions on AWS Lambda, which allows for cost-effective and scalable operations without constant server management. The infrastructure supporting this API can be defined as code, allowing for clear version control and straightforward updates.

Finally, Syntora would integrate the API with your existing CRM. This often involves configuring a webhook in systems like HubSpot or Salesforce that triggers when a new lead is created or updated. This webhook would send the lead's details to the FastAPI endpoint. The API would process the data, return the score, and Syntora would write this score back to a custom field within your CRM. This integration ensures your sales team receives lead scores automatically, fitting into their current workflow without disruption.