Think of your marketing as a sports team where every player (Google Ads, Facebook, email, TV, etc.) helps score sales. These methods figure out which players actually contributed to each goal so you know who deserves more time and money.
Marketers struggle to understand which channels, campaigns, and touchpoints truly drive conversions and how to optimally distribute limited budget across them. Traditional last-click or rule-based attribution misallocates spend and hides underperforming and overperforming channels.
Proprietary historical marketing and conversion data combined with domain-specific attribution models and continuous feedback loops for budget optimization form the main defensible asset; integration into existing marketing workflows and spending decisions increases stickiness.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data integration and feature engineering across many ad platforms and tracking systems, plus model retraining latency as campaigns and user behavior change.
Early Majority
Focus on algorithmic, data-driven attribution and budget optimization instead of static rules or last-click models; uses multi-touch behavioral data and ML to estimate incremental impact per channel and scenario, enabling scenario testing and more granular budget shifting.