LSTM-based network churn classification from EDA phasic data
2023 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI(2023)
Abstract
Understanding television watching behavior of consumers can be useful in many contexts, such as evaluating the influence of a TV network, building recommendation systems, or providing insights regarding commercials for advertisers. Electrodermal activity (EDA) is a psychophysiological indicator of emotional arousal and attention that reflects the variation of the electrical properties of the skin. Given that it is a measure that reflects the emotional status of consumers and has advantages over self-report of emotions, it has been widely used in consumer research studies. In this study, we built a classification model using long-short term memory networks and EDA phasic signals to classify network switch/churn occurrence. The developed model had an accuracy of 71%, which demonstrates that EDA phasic activity is a good candidate to predict channel churn occurrence.
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Key words
electrodermal activity, emotion, consumer behavior, TV watching
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