AI-Powered Customer Segmentation and Lead Targeting

Marketing teams often start with the built-in lead scoring in their CRM, like HubSpot or Marketo. These systems use static rules: 'opened an email' is +5 points, 'visited pricing page' is +10. This logic cannot distinguish between a CEO who visits the pricing page once and an intern who visits ten times. The score is identical, but the lead quality is vastly different.

Consider a 20-person B2B software company whose ideal customer is a VP at a firm with 100-500 employees. Their sales reps spend 5-10 minutes per lead checking LinkedIn Sales Navigator for job titles and company sizes. For a rep handling 50 new leads a week, that is over 4 hours of manual, non-selling work. The CRM's native tools cannot automate this specific, multi-step qualification process.

The structural problem is that off-the-shelf tools are not built for your specific business logic. They provide generic fields and rule-based triggers, but they cannot connect to your production database to see which features a trial user engaged with. They cannot weigh a combination of signals—like 'job title is VP' AND 'company size > 100' AND 'used feature X'—because your most predictive signals live outside the CRM's data model.

The first step would be a data audit. Syntora would connect to your CRM, analytics platform, and any other relevant data sources using their APIs. We would analyze the last 12-24 months of data, requiring at least 300-500 closed-won and closed-lost deals to identify statistically significant patterns. You receive a data readiness report that outlines usable features and any data quality gaps before the build begins.

The technical approach would use a gradient boosting model, like LightGBM, built in Python. This model is wrapped in a FastAPI service and deployed on AWS Lambda for an efficient, serverless architecture that typically costs under $30/month to operate. When a new lead is created or updated in your CRM, a webhook sends the data to the FastAPI endpoint. The service returns a 0-100 score and a plain-English explanation in under 250 milliseconds.

The delivered system integrates directly into your team's existing workflow. Two new custom fields appear in your CRM: 'AI Lead Score' and 'Scoring Rationale'. Your sales reps see 'Score: 95, Rationale: VP title, visited pricing 3x, active trial user' without leaving their CRM. You receive the complete Python source code, a technical runbook, and a dashboard for monitoring model accuracy over time.