Multi-Touch Attribution System
Probabilistic multi-touch attribution using Markov Chain removal-effect modeling — crediting each channel based on its actual influence on conversion probability across the full customer journey.
Daily
Attribution Scoring
2-Tier
Model Architecture
6
Funnel Stages Covered
The Problem
Marketing teams were using first-touch and last-touch attribution — both systematically over-crediting one touchpoint and ignoring the influence of others. They needed statistically-grounded attribution to justify channel investment and understand which audience-campaign combinations drove conversion at each funnel stage.
What Was Built
Built a Markov Chain MTA system — customer journeys modeled as Markov states, transition matrix built from historical paths, attribution computed via removal effect (drop in conversion probability when a channel is removed). Enriched with firmographic attributes (region, industry, company size, persona) and campaign-level metadata (channel type, content format, offer type, message theme), enabling attribution of not just which channels work but why specific audience × campaign combinations are effective at each funnel stage.
Business Impact
Replaced rule-based first/last-touch attribution with daily probabilistic multi-touch attribution. Enabled stage-by-stage conversion credit by channel, audience segment, and campaign type — giving marketing teams evidence-backed answers to budget allocation questions.
Tech Stack
Domain Tags
Details
- Role
- Primary Owner
- Status
- Production
- Tier
- Tier 1
- Period
- 2022 – 2024
- Employment
- ADA Asia