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.
Akash Sharma
Primary Owner
Daily
Attribution Scoring
2-Tier
Model Architecture
6
Funnel Stages Covered
Overview
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.
The problem
Marketing teams were using first-touch and last-touch attribution, which systematically over-credited one touchpoint and ignored 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.
How the system works
- 01
Customer journeys
ordered touchpoints
- 02
Markov states
channels and funnel stages
- 03
Transition matrix
built from historical paths
- 04
Removal effect
drop in conversion probability per channel
- 05
Channel credit
actual influence on conversion
Engineering decisions
Why removal effect?
It credits each channel by how much conversion probability falls when that channel is removed, not by its position in the path.
Why enrich with firmographics?
Firmographic and campaign metadata show why specific audience and campaign combinations work at each funnel stage, not just which channels do.
Results
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.
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