Imagine every customer sale is a relay race where many marketing touches (ads, emails, social posts, referrals) pass the baton before someone finally buys. Classic “last-click” gives the medal only to the last runner. An AI attribution model watches the whole race and fairly credits each runner so you know which parts of your marketing truly drive revenue.
Traditional last-click attribution over-credits the final touchpoint and under-values upper- and mid-funnel channels, making marketers misallocate budget, under-invest in impactful campaigns, and struggle to prove ROI across complex, multi-touch customer journeys.
Proprietary historical marketing performance data and customer journey logs, combined with bespoke model tuning to a specific brand’s mix of channels and audiences, can create a defensible advantage that is hard for competitors to copy quickly.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Data volume and quality across channels (joining ad platforms, web analytics, CRM, and offline conversions) plus model retraining cost as campaigns and user behavior shift.
Early Majority
The focus is on custom, brand-specific multi-touch attribution tailored to a company’s real customer journeys rather than relying solely on black-box platform attribution (e.g., Google or Meta) or generic rule-based models such as first/last click or simple linear attribution.