Causal Inference Methods for Understanding Attribution in Marketing Analytics Pipelines

Abstract

In strategic decision-making, the limits of conventional predictive analytics have become more apparent as businesses negotiate more complex, data-rich settings. Because predictive models often just reveal correlations rather than the fundamental causes of change, they expose organisations to misunderstandings and inefficient responses. A revolutionary development is provided by causal machine learning models, which separate cause-and-effect correlations from big, multidimensional datasets. By simulating the possible effects of business decisions prior to execution, these models help decision-makers close the gap between insight and consequence. In these kinds of campaigns, many channels often provide ads to specific individuals. The industry is very interested in "attribution," which is the process of allocating conversion credit to the different channels. Marketing researchers have a plethora of options to better forecast and maybe explain customer behaviour because to the massive amount of data. In this work, a causally justified approach to conversion attribution in online advertising campaigns is presented. However, as this article will argue, academics studying marketing should not hastily forsake methodological and cognitive processes that have been honed over centuries of scientific and philosophical contemplation. By combining the literature from many hard sciences, we talk about the importance of machine learning in causal inference as well as current issues with data management and measurement in the age of digital data.