  This study utilizes an ensemble of feedforward neural network models to
analyze large-volume and high-dimensional consumer touchpoints and their impact
on purchase decisions. When applied to a proprietary dataset of consumer
touchpoints and purchases from a global software service provider, the proposed
approach demonstrates better predictive accuracy than both traditional models,
such as logistic regression, naive Bayes, and k-nearest neighbors, as well as
ensemble tree-based classifiers, such as bagging, random forest, AdaBoost, and
gradient boosting. By calculating the Shapley values within this network, we
provide nuanced insights into touchpoint effectiveness, as we not only assess
the marginal impact of diverse touchpoint types but also offer a granular view
of the impact distribution within a touchpoint type. Additionally, our model
shows excellent adaptability and resilience with limited data resources. When
the historical data is reduced from 40 to 1 month, our model shows only a
modest 19% decrease in accuracy. This modeling framework can enable managers to
more accurately and comprehensively evaluate consumer touchpoints, thereby
enhancing the effectiveness and efficiency of their marketing campaigns.
