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.
Nicolas is a Data Scientist at BlaBlaCar specialized in marketing topics: he is currently developing a Multi-Touch attribution model based on Markov Chains and participates in carrying out an incrementality framework to evaluate the performance of marketing channels. He previously worked for an insurance company developing end-to-end NLP models to automate and ease customer services tasks. He holds a MSc from CentraleSupélec.