Tabellab
Marketing Analytics Advanced

Markov Chain Attribution: Channel Paths, Removal Effects & Fair Credit

Turn customer journeys into transition counts and a probability matrix, compute the conversion probability step by step and exactly with (I − Q)⁻¹, remove channels to get removal effects, credit conversions and revenue, compare with last click and linear, price CPA and ROAS and test stability, in 8 graded labs with MMULT and MINVERSE.

8 lessons 8 labs 1.9 h Certificate

The first lesson is free — no account needed.

About this course

A Markov chain attribution course that turns the customer journeys of one fictional online home-goods store, Fernhill Home, into fair channel credit in a spreadsheet, for marketing analysts, performance marketers and data analysts. You count transitions from Start through the channels to Conversion or Null, divide them into a transition probability matrix with absorbing states and multiply it with MMULT, walk a visitor through the chain until the conversion probability converges and get the exact value from the fundamental matrix (I − Q)⁻¹ with MINVERSE, remove each channel to measure its removal effect, normalise the effects into credited conversions and revenue, compare Markov credit with last click and linear in a chart, price CPA and ROAS under each model, and test how stable the credit is across three months. The first lab is free.

Lessons

  1. From Customer Journeys to Transition Counts: Start, Channel Steps, Conversion and NullLab15 minFree
  2. The Transition Probability Matrix: Rows that Sum to 1, Absorbing States and Two-Step PathsLab14 min Pro
  3. Conversion Probability from Start: Step-by-Step Convergence and the Exact (I − Q)⁻¹ SolutionLab15 min Pro
  4. Removal Effects: What Happens to Conversions when a Channel DisappearsLab16 min Pro
  5. Attribution Shares: Normalising Removal Effects into Credited Conversions and RevenueLab12 min Pro
  6. Markov vs Last Click vs Linear: Who Gains, Who Loses (with a Chart)Lab15 min Pro
  7. Budget Implications: Cost per Attributed Conversion, ROAS and a Naive ReallocationLab14 min Pro
  8. Sensitivity & Limitations: How Stable Is the Credit, and What Markov Attribution Cannot Tell YouLab13 min Pro