Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Consumer behavior patterns related to home digital media use are changing due to technological innovations. We examine them in the presence of two-way cable television (CATV) set-top boxes. They permit viewers to change channels, switch to the Internet, and order paid programming, among other functions. We focus on micro-level data that are generated from consumer CATV viewing behavior. We capture clickstreams of channel-changing behavior when consumer use a remote control handset to interact with the set-top box in their home. We explore a variety of data analytics results that characterize patterns of consumer channel-switching behavior, as a basis for suggesting different clusters of observed behavior. We also probe the explanatory elements that give rise to what we see.