🎯 Project Overview

Developed a complete ML-based lead scoring workflow that identifies high-intent customers using behavioral and transactional data. The system predicts which leads are most likely to convert, helping sales teams focus on the top 20% of leads that generate 80% of revenue.

💼 Business Impact

3× increase in conversion rates
Focus on top 20% leads generating 80% revenue
Reduced sales cycle by prioritizing high-value prospects
Data-driven decisions replace gut-feel prioritization
Automated lead routing to appropriate sales reps

🛠️ Technical Architecture

Workflow Components

1. Data Collection

Aggregates data from multiple sources: CRM logs (Salesforce, HubSpot), website behavior (Google Analytics, custom tracking), purchase history, email engagement, and social media interactions.

2. Data Cleaning & Feature Engineering

Cleans raw data, handles missing values, normalizes formats, and creates predictive features like engagement score, time-to-response, page views, download frequency, and interaction patterns.

3. Model Training

Trains multiple models (Random Forest, XGBoost, Gradient Boosting) and selects best performer. Also uses LLM-based embeddings for semantic understanding of lead behavior patterns.

4. Scoring Engine

Real-time lead score API that evaluates new leads instantly. Scores range from 0-100, with higher scores indicating higher conversion probability.

5. Dashboard

Streamlit-based dashboard where sales teams view top potential customers, score distributions, conversion predictions, and performance metrics.

6. Automation

Sends real-time alerts when high-score leads appear, automatically assigns leads to sales reps, and triggers follow-up workflows based on score thresholds.

Core Technologies

Python - Data processing and ML pipeline
Scikit-Learn - Machine learning algorithms and preprocessing
XGBoost - Gradient boosting for high-accuracy predictions
Airflow - Workflow orchestration and scheduled retraining
PostgreSQL - Data storage and feature store
Streamlit - Interactive dashboard for sales teams

🔧 Technical Challenges Solved

Challenge 1: Feature Engineering at Scale

Problem: Creating meaningful features from diverse data sources (CRM, web, email, social).

Solution: Built automated feature engineering pipeline that extracts temporal patterns, engagement metrics, and behavioral sequences. Used feature importance analysis to focus on high-impact features.

Challenge 2: Model Drift

Problem: Lead behavior patterns change over time, causing model accuracy to degrade.

Solution: Implemented automated retraining pipeline with Airflow that runs weekly, monitors model performance metrics, and alerts when accuracy drops below threshold.

Challenge 3: Real-Time Scoring

Problem: Need to score leads instantly as they come in, not in batch.

Solution: Built lightweight scoring API that pre-computes features and uses optimized model inference. Response time < 100ms for real-time lead evaluation.

📊 Performance Metrics

3×

Conversion Increase

85%

Model Accuracy

<100ms

Scoring Latency

20%

Top Leads (80% Revenue)

💡 Key Features

Multi-source data integration: CRM, web analytics, email, social media
Ensemble models: Combines multiple algorithms for better accuracy
Explainable AI: Shows why a lead received a specific score
Automated retraining: Models stay current with changing patterns
CRM integration: Seamless sync with Salesforce, HubSpot

🚀 Results

✅ 3× increase in conversion rates for scored leads
✅ Sales team efficiency improved by 60%
✅ Revenue per lead increased by 2.5×
✅ Reduced sales cycle by 30% through better prioritization
✅ ROI of 400%+ within first 6 months

AI-Powered Sales Lead Scoring Pipeline