Paper Info
Title
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution.
Abstract
Customers are usually exposed to online digital advertisement channels, such as email marketing, display advertising, paid search engine marketing, along their way to purchase or subscribe products( aka. conversion). The marketers track all the customer journey data and try to measure the effectiveness of each advertising channel. The inference about the influence of each channel plays an important role in budget allocation and inventory pricing decisions. Several simplistic rule-based strategies and data-driven algorithmic strategies have been widely used in marketing field, but they do not address the issues, such as channel interaction, time dependency, user characteristics. In this paper, we propose a novel attribution algorithm based on deep learning to assess the impact of each advertising channel. We present Deep Neural Net With Attention multi-touch attribution model (DNAMTA) model in a supervised learning fashion of predicting if a series of events leads to conversion, and it leads us to have a deep understanding of the dynamic interaction effects between media channels. DNAMTA also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects. We used computational experiment of large real world marketing dataset to demonstrate that our proposed model is superior to existing methods in both conversion prediction and media channel influence evaluation.
Year
Venue
Field
2018
arXiv: Learning
Display advertising,Inference,Search engine optimization,Communication channel,Supervised learning,Control variable,Artificial intelligence,Deep learning,Artificial neural network,Mathematics,Machine learning
DocType
Volume
Citations 
Journal
abs/1809.02230
1
PageRank 
References 
Authors
0.35
3
5
Name
Order
Citations
PageRank
Ning li110.35
Sai Kumar Arava210.35
Chen Dong393.51
Zhenyu Yan417614.57
Abhishek Pani510.68