Attribution Shares: Normalising Removal Effects into Credited Conversions and Revenue
This lesson is part of the course
In this lesson
You see why raw removal effects double-count, normalise them into shares that add up to 100%, and credit each channel with conversions and revenue.
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Lessons in this course
- From Customer Journeys to Transition Counts: Start, Channel Steps, Conversion and Null Locked
- The Transition Probability Matrix: Rows that Sum to 1, Absorbing States and Two-Step Paths Locked
- Conversion Probability from Start: Step-by-Step Convergence and the Exact (I − Q)⁻¹ Solution Locked
- Removal Effects: What Happens to Conversions when a Channel Disappears Locked
- Attribution Shares: Normalising Removal Effects into Credited Conversions and Revenue Locked
- Markov vs Last Click vs Linear: Who Gains, Who Loses (with a Chart) Locked
- Budget Implications: Cost per Attributed Conversion, ROAS and a Naive Reallocation Locked
- Sensitivity & Limitations: How Stable Is the Credit, and What Markov Attribution Cannot Tell You Locked