CustomerBase Sequential Data Analysis: An Application of Attentive Neural Networks to Sales Forecasting This study applies a machinelearning model to analyze marketing sequential data to improve the performance of sales forecasting. Marketing data such as pointofsale data reflects the heterogeneity of customers, and it is difficult to understand the feature quantity, unlike image data. To properly understand the characteristics of customers and implement high accurate marketing measures, a model analysis that considers customer heterogeneity is required. This study focuses on artificial intelligence, which has been rapidly developing in recent years, and the study incorporates customer heterogeneity into machinelearning model parameters. To extract a certain trend from a customeru0027s purchase history and utilize this information for sales prediction, we focus on convolutional neural networks and attention mechanisms. We conducted experiments using real data to verify that the proposed model demonstrates good analytical performance.

Convolutional Neural Networks on Time Series for Smartphone Application Activations Using Wavelet Transform This research focuses on time series of smartphone application activation (SAA) analyses. The inputs are big data of three categories of time series automatically collected from a single source panel of individuals from smartphones. The types of consumer activation outputs are precategorized. This study extracts effective features to identify categories of individual website access activities. This is an important but challenging task in the age of digital marketing. Most existing studies rely on conventional simple statistical analyses, timeseries analyses, or shallow neural networks that cannot identify features to accurately classify plural categories of website access activities. This study proposes an automatic feature learning model from the raw inputs for the SAA problem by using a deep convolutional neural network (CNN) and Bayes optimization. Data are nonconverted or converted with two levels by wavelet transformation for possible application in digital marketing.