Marketing is principally a communication challenge. Companies use it to convey a value proposition and consumers either accept it or reject it. Historically, marketers received very poor feedback on their decisions because customer conversion data was highly aggregated, fragmented or disconnected. However, digital advertising and the availability of user-level data have dramatically changed things.
Marketers are now consolidating datasets and methods to more accurately measure the performance of their media investments. Multi-touch attribution models are designed to assign credit to the touch points that affect user journeys on the path to conversion.
Conventionally, multi-touch attribution models have been built with logistic regression, but advances in machine learning have produced incredibly powerful classification algorithms that are more accurate in many cases. Some attribution vendors have been slow to adopt such algorithms because they are difficult to explain. This is a mistake. Imagine what air travel would be like if pilots had to explain to every passenger how jet engines work.
This document details how multi-touch attribution models based on a machine learning ensemble approach provides more accuracy, better measurement of marketing contribution and opportunity for innovation.
Download this white paper to learn more.