Predictive Lead Scoring

Predictive Lead Scoring (Python + Airflow)

We designed and deployed a predictive lead scoring engine that ranks every lead by its probability of converting, not just by form fields or gut feel. Sales and marketing teams get a single, consistent score they can use to prioritize outreach, SLAs, and sequences.

Using behavioral events (emails, page views, product usage) and firmographic data (industry, size, region), the pipeline trains and refreshes XGBoost models on historical opportunities. Scores are written back into the CRM so that routing, sequences, and dashboards all operate on the same signal.

XGBoost models Behavioral & firmographic features Airflow orchestration CRM sync & routing

Impact at a glance

↑ Win rate

Higher conversion on top leads

↓ Response time

Better adherence to SLAs

100%

Lead coverage with scores

Recurring

Model retraining via Airflow

Reps see exactly which leads to work first today, RevOps gets measurable uplift in pipeline efficiency, and leadership can track score performance over time.

Problem

Before predictive scoring, lead management looked like:

Reps cherry-picking leads based on feel, not data.
Static MQL/SQL rules failing to adapt to new segments.
Marketing generating volume, but sales lacking clarity on where to focus.

Solution

We introduced a data-driven scoring pipeline:

Feature engineering on historical closed-won/closed-lost opportunities.
XGBoost models trained to predict conversion probability.
Lead, account, and opportunity-level scores computed on a schedule.
Scores synced back to CRM fields to drive routing and sequences.

Outcome

The predictive lead scoring system delivered:

Clear prioritization so reps focus on the top slice of leads each day.
Higher efficiency in campaigns and outbound sequences.
Better alignment between marketing, sales, and RevOps around one scoring model.

Architecture overview

The lead scoring engine runs as a Python-based modeling stack orchestrated by Airflow. It connects to the data warehouse and CRM, computes scores on schedule, and exposes them everywhere GTM teams need them.

Data extraction – Lead, account, and opportunity data is pulled from CRM and product analytics into a modeling dataset.
Feature engineering – Python jobs build behavioral and firmographic features such as web activity, email engagement, usage events, and ICP fit.
Model training – XGBoost models are trained on historical outcomes with proper splits, cross-validation, and class balance handling.
Scoring – Newly created and existing leads are scored regularly, producing probabilities and score buckets (e.g., A/B/C or 0–100).
Sync & monitoring – Airflow tasks write scores back to CRM fields and log model metrics (AUC, lift, calibration) to dashboards.

Key features in production

Unified scoring model

One canonical score shared across SDR, AE, and marketing flows, instead of multiple competing rules.

Actionable segments

Leads are grouped into tiers (e.g., A/B/C or hot/warm/cold) that plug directly into views, workflows, and sequences.

Model governance

Performance metrics are tracked over time, with alerts when drift appears or scores stop correlating with conversion.

Auditability

Feature importance and example explanations help RevOps understand why certain leads are scored higher, improving trust and adoption.

The architecture is designed to evolve: new features, new segments, and new objective functions can be added without disrupting how scores flow into day-to-day sales tools.

ML capabilities

Supervised learning for conversion probability prediction.
Feature engineering on behavioral and firmographic data.
Model evaluation using lift curves, AUC, and calibration.
Champion/challenger setup for iterative model improvement.

Engineering & data platform

Python-based modeling and scoring services.
Airflow DAGs for daily/weekly retraining and scoring.
Connectors to CRM and data warehouse for bidirectional sync.
Logging, monitoring, and alerting for pipeline reliability.

Typical use cases

Inbound lead triage for SDR teams.
Scoring accounts and contacts for ABM programs.
Prioritizing free-to-paid trials based on product usage.
Any GTM motion where capacity is limited and focus matters.