Marketing ScienceADA Asia2022 – 2024Production

Campaign Performance Prediction Engine

Two-stage system predicting campaign pipeline potential before launch and monitoring active campaigns daily.

Daily

Campaign Scoring

2-Stage

Architecture

The Problem

Marketing teams were reactive; they only knew a campaign underperformed after it ended. They needed forward-looking predictions to decide which campaigns to scale, which to cut, and how to reallocate budget.

What Was Built

Two-stage architecture: (1) XGBoost Classifier predicting high vs. low potential campaigns (pipeline threshold classification). (2) XGBoost Regressor estimating expected pipeline value/volume for predicted high-potential campaigns. Features include campaign attributes, budget, type, date-derived features, pipeline context at campaign start, active campaign counts, and historical averages for similar campaigns.

Business Impact

Enabled proactive campaign management by predicting success before launch and flagging underperformers for budget reallocation, shifting marketing teams from post-campaign hindsight to forward-looking intelligence.

Tech Stack

PythonXGBoostscikit-learnBigQuery

Domain Tags

Campaign PredictionMarketing IntelligenceBudget OptimizationMarketing Science

Details

Role
Primary Owner
Status
Production
Tier
Tier 1
Period
2022 – 2024
Employment
ADA Asia