Visible to the public CAMTA: Causal Attention Model for Multi-touch Attribution

TitleCAMTA: Causal Attention Model for Multi-touch Attribution
Publication TypeConference Paper
Year of Publication2020
AuthorsKumar, Sachin, Gupta, Garima, Prasad, Ranjitha, Chatterjee, Arnab, Vig, Lovekesh, Shroff, Gautam
Conference Name2020 International Conference on Data Mining Workshops (ICDMW)
Keywordsadvertising, Attention, attribution, causality, Channel estimation, composability, Conversions, Data models, Human Behavior, Metrics, Multi-touch attribution, Predictive models, pubcrawl, Recurrent neural networks, Resource management, social networking (online), temporal modeling, Touchpoints
AbstractAdvertising 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 causal 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 per-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 modeling on the predicted channel attribution.
Citation Keykumar_camta_2020