Implementing a Markov Chains-based Multi-Touch attribution model at BlaBlaCar
Marketing attribution models are used to attribute conversions to marketing channels and hence monitor the performance of online marketing investments. Rules-based attribution models are frequently used in the industry with models such as ‘last interaction’, ‘first interaction’ or ‘position based’. They all follow a simple set of rules to perform the attribution, however the rules are set arbitrarily and may therefore be biased. At BlaBlaCar, we developed a data-driven Multi-Touch attribution model based on Markov chains. We will present the challenges that we have overcome from the idea to the roll-out and the impact on performance marketing of this project.
Ken is a Data Analyst at BlaBlaCar with a master in Business Management and a Msc in Data Science from Edhec Business School. His business and data science background helps the understanding of business application of data science projects and smoothen the communication between technical data teams and business teams. He is working on various data-related marketing subjects such as the development of a Multi-Touch attribution model based on Markov Chains and the implementation of a Marketing Mix Modeling.