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AI / ML·5 weeks

Custom ML Model for Lead Scoring

Sales efficiency up 40% with intelligent lead prioritization

Pythonscikit-learnFastAPIDocker
91% accuracy
Key Result
B2B SaaS · Sales team of 25 · HubSpot CRM
Client

The Problem

A 25-person sales team wasted 60% of their time on low-quality leads. CRM had basic manual scoring that missed behavioral signals. Conversion rate at 3.2%.

The Approach

End-to-end ML pipeline: HubSpot data extraction (3 years), feature engineering with behavioral signals, XGBoost training, and a real-time scoring API with HubSpot webhook integration.

Technical Decisions

XGBoost over neural networks (12k samples — simpler models generalized better). Top predictive features: pricing page time, case study views, email response latency. FastAPI microservice with model drift monitoring.

The Result

91% accuracy on test data. Sales efficiency up 40%. Conversion rate from 3.2% to 5.8%. Weekly retraining keeps accuracy at 89-92% after 6 months.

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Custom ML Model for Lead Scoring | Case Study