Build a B2B Lead Scoring Model for Your Agency

Most marketing agencies start with HubSpot's built-in scoring. You can assign points for form fills or page views, but the logic is static. A form submission for a 'brand awareness' webinar gets the same score as one for a 'PPC management pricing' guide. Your sales reps waste time chasing leads who are researching, not buying.

Consider an agency generating 250 leads weekly. The sales team of two spends every Monday morning manually sifting through a HubSpot list, guessing intent based on job titles. A lead who visited the high-value services page three times is buried under fifty webinar attendees. That high-intent lead gets a generic email 24 hours late because the system could not see the behavioral signal.

Upgrading to a tool like Marketo or Salesforce Pardot introduces machine learning, but it's a black box. You get a score, but you cannot see why a lead scored 85. More importantly, these platforms cannot incorporate agency-specific signals. Your best leads might come from companies using a specific marketing tech stack or having a high ad spend, data which is not native to Salesforce.

The structural problem is that these are mass-market platforms. They are not built to learn from an agency’s unique sales cycle or integrate external data sources that signal true buying intent. A system built for ten thousand companies cannot optimize for the specific patterns that drive revenue for your two-person sales team.

The engagement would begin with a data audit. Syntora connects to your CRM, Google Analytics, and ad platforms to extract 12-24 months of historical lead data. This process identifies the most predictive signals in your data, from specific page visits to email engagement patterns, and surfaces any data quality issues. You receive a report detailing the potential feature set before any model building begins.

Syntora's technical approach uses a gradient boosted tree model, written in Python with scikit-learn, because it excels at finding complex patterns in sales data. The model is wrapped in a FastAPI service and deployed on AWS Lambda. This serverless architecture means you only pay for compute time when a lead is scored, typically costing under $50 per month to run, and responds in under 200ms. Pydantic schemas enforce data quality for every API call.

The delivered system integrates with your existing CRM via a webhook. When a new lead arrives, the system sends the data to the API, receives a 0-100 score, and writes it to a custom field. Your sales team works from a single, prioritized list inside the tool they already use. You receive the full source code in your GitHub repository and a runbook for maintenance.