  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.
