/attribution-model

Marketing channel attribution model with Markov Chains in Python

Primary LanguageJupyter Notebook

Advanced attribution

Attribution is a key part of the Growth strategy, as it helps us to measure the impact of each channel on our strategy. There are many ways to measure attribution. Traditionally, channel attribution has been tackled by a handful of simple but powerful approaches such as First Touch, Last Touch, and Linear.

Customer journey

Standard attribution models:

  • First Touch: The revenue generated by the purchase is attributed to the first marketing channel the user engaged with, on the journey towards the purchase.

  • Last Touch: As the name suggests, Last Touch is the attribution approach where any revenue generated is attributed to the marketing channel that a user last engaged with.

  • Linear: In this approach, the attribution is divided evenly among all the marketing channels touched by the user on the journey leading to a purchase.

Markov Chains:

Markov chains are named after the Russian mathematician Andrey Markov, and describe a sequence of possible events in which the probability of each event depends only on the state attained in the previous event

Markov chain

In the case of channel attribution, it helps us to figure out how user journeys work, and how they are influenced by each channel in order to travel from one state to another, or not.