Abstract
This paper presents an exploration of market attribution methods and the integration of user behaviour. Attribution is the measurement of interaction between marketing touchpoints and channels along the customer journey, improving customer insights and driving smarter business decisions. Improving the accuracy of attribution requires a deeper understanding of user behaviour, not just marketing channel credit assignment. Evidence has been provided regarding the problems in the standardized approach to behavioural modelling and alternatives have been presented. The study explores data provided by a British based jewellery company with an investigation into pre-existing data features that can aid with the analysis of user behaviour. The study contains over 10 million rows collected over 2 years and presents the initial findings made in the first 15 months of a PhD study.
Recommended Citation
Thornton, Tanisha Naomi; Thorne, Simon; and Calderon, Ana, "Modelling User Behaviour in Market Attribution: finding novel data features using machine learning" (2021). UK Academy for Information Systems Conference Proceedings 2021. 18.
https://aisel.aisnet.org/ukais2021/18