  In online advertising, users may be exposed to a range of different
advertising campaigns, such as natural search or referral or organic search,
before leading to a final transaction. Estimating the contribution of
advertising campaigns on the user's journey is very meaningful and crucial. A
marketer could observe each customer's interaction with different marketing
channels and modify their investment strategies accordingly. Existing methods
including both traditional last-clicking methods and recent data-driven
approaches for the multi-touch attribution (MTA) problem lack enough
interpretation on why the methods work. In this paper, we propose a novel model
called DeepMTA, which combines deep learning model and additive feature
explanation model for interpretable online multi-touch attribution. DeepMTA
mainly contains two parts, the phased-LSTMs based conversion prediction model
to catch different time intervals, and the additive feature attribution model
combined with shaley values. Additive feature attribution is explanatory that
contains a linear function of binary variables. As the first interpretable deep
learning model for MTA, DeepMTA considers three important features in the
customer journey: event sequence order, event frequency and time-decay effect
of the event. Evaluation on a real dataset shows the proposed conversion
prediction model achieves 91\% accuracy.
