Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models

• Published in: Information processing & management Vol. 56; no. 4; pp. 1339 - 1355
• Main Authors: Alemán Carreón, Elisa Claire, Nonaka, Hirofumi, Hentona, Asahi, Yamashiro, Hirochika
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-07-2019; Elsevier Science Ltd
• Subjects: Advertisements; Algorithms; Behavior; Consumer behavior; Customers; Demographics; Digital media; Effects; Exposure; Investment; Machine learning; Purchase behavior; SVM; Television; Television advertising; Television adverts; XGBoost; SVM; Television adverts; XGBoost; Purchase behavior; Machine learning
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2019.03.007

•Models based on exposure time to television adverts have significantly lower predictability of actual purchase in comparison with models using demographic data.•Actual Purchase behavior predictability was not significantly different in models including both demographic data and exposure time data as opposed to those with only demographic data.•Results suggest that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.
Since its introduction, television has been the main channel of investment for advertisements in order to influence customers purchase behavior. Many have attributed the mere exposure effect as the source of influence in purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is questionable and their environments do not reflect reality. With the advent of the internet, social media and new information technologies, many recent studies focus on the effects of online advertisement, meanwhile the investment in television advertisement still has not declined. In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement exposure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months. If we were able to predict purchase behaviors with models based on exposure time more reliably than with models based on demographic data, the obvious strategy for businesses would be to increase the number of adverts. On the other hand, if models based on exposure time had unreliable predictability in contrast to models based on demographic data, doubts would surface about the effectiveness of the hard investment in television advertising. Based on our results, we found that models based on advert exposure time were consistently low in their predictability in comparison with models based on demographic data only, and with models based on both demographic data and exposure time data. We also found that there was not a statistically significant difference between these last two kinds of models. This suggests that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.