Advertisers need to estimate the contribution from touch-points (view, clicks, conversions) on different channels. This process of contribution measurement is often referred to as multi-touchribution (MTA) We propose CAMTA, a novel deep recurrent neural network architecture which is a casual attribution
Advertising channels have evolved from conventional print media, billboards
and radio advertising to online digital advertising (ad), where the users are
exposed to a sequence of ad campaigns via social networks, display ads, search
etc. While advertisers revisit the design of ad campaigns to concurrently serve
the requirements emerging out of new ad channels, it is also critical for
advertisers to estimate the contribution from touch-points (view, clicks,
converts) on different channels, based on the sequence of customer actions.
This process of contribution measurement is often referred to as multi-touch
attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent
neural network architecture which is a casual attribution mechanism for
user-personalised MTA in the context of observational data. CAMTA minimizes the
selection bias in channel assignment across time-steps and touchpoints.
Furthermore, it utilizes the users' pre-conversion actions in a principled way
in order to predict pre-channel attribution. To quantitatively benchmark the
proposed MTA model, we employ the real world Criteo dataset and demonstrate the
superior performance of CAMTA with respect to prediction accuracy as compared
to several baselines. In addition, we provide results for budget allocation and
user-behaviour modelling on the predicted channel attribution.