  We propose a novel statistical model to answer three challenges in direct
marketing: which channel to use, which offer to make, and when to offer. There
are several potential applications for the proposed model, for example,
developing personalized marketing strategies and monitoring members' needs.
Furthermore, the results from the model can complement and can be integrated
with other existing models.
  The proposed model, named Dynamic Propensity Model, is a latent variable time
series model that utilizes both marketing and purchase histories of a customer.
The latent variable in the model represents the customer's propensity to buy a
product. The propensity derives from purchases and other observable responses.
Marketing touches increase a member's propensity, and propensity score
attenuates and propagates over time as governed by data-driven parameters. To
estimate the parameters of the model, a new statistical methodology has been
developed. This methodology makes use of particle methods with a stochastic
gradient descent approach, resulting in fast estimation of the model
coefficients even from big datasets. The model is validated using six months'
marketing records from one of the largest insurance companies in the U.S.
Experimental results indicate that the effects of marketing touches vary
depending on both channels and products. We compare the predictive performance
of the proposed model with lagged variable logistic regression. Limitations and
extensions of the proposed algorithm are also discussed.
