PREDICTIVE SALES ANALYTICS ENGINE

Project Overview

A B2B SaaS company was losing deals because their sales team couldn't prioritize effectively across 3,000+ monthly leads. We built a predictive analytics engine that scores every lead based on behavioral signals, firmographic data, and engagement patterns — telling reps exactly who to call and when. Built with Python, scikit-learn, PostgreSQL, and a Next.js dashboard.

Scope of Work

Temnix built the complete predictive pipeline — from the data warehouse to the ML models to the sales team's daily-driver dashboard.

Key deliverables included:

• Lead Scoring ML Model: A gradient-boosted model trained on 2 years of CRM data that scores incoming leads from 0-100, predicting conversion probability with 82% accuracy. The model factors in email engagement, website behavior, company size, and 40+ other signals.

• Revenue Forecasting Engine: Time-series forecasting models that predict monthly and quarterly revenue with 90% accuracy, giving leadership confidence in pipeline projections and resource allocation.

• Sales Intelligence Dashboard: A real-time dashboard showing prioritized lead lists, deal health scores, rep performance analytics, and AI-generated next-best-action recommendations for each opportunity.