Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models
Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models
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DOI：
10.1016/j.ipm.2019.03.007
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发表时间：
2019-07-01
影响因子：
8.6
通讯作者：
Yamashiro, Hirochika
中科院分区：
文献类型：
--
作者：
Carreon, Elisa Claire Aleman;Nonaka, Hirofumi;Yamashiro, Hirochika
电视自问世以来，一直是广告投入的主要渠道，以影响消费者的购买行为。许多人认为，单纯的曝光效应是影响购买意愿和购买决策的来源;然而，大多数关于电视广告效应的研究不仅过时，而且样本量值得怀疑，而且它们的环境不能反映现实。随着互联网、社交媒体和新信息技术的出现，近年来许多研究都集中在网络广告的效果上，而电视广告的投入也没有减少。针对这一点，我们应用机器学习算法SVM和XGBoost以及Logistic回归，根据家庭广告曝光时间和人口统计数据构建了一些预测模型，研究了36种不同产品的3000名客户在3个月内的实际购买和购买意向行为的可预测性。如果我们能够用基于曝光时间的模型比基于人口统计数据的模型更可靠地预测购买行为，那么企业的明显策略将是增加广告数量。另一方面，如果与基于人口统计数据的模型相比，基于曝光时间的模型具有不可靠的可预测性，那么对电视广告硬投资的有效性就会产生怀疑。根据我们的研究结果，我们发现，与仅基于人口统计数据的模型以及基于人口统计数据和曝光时间数据的模型相比，基于广告曝光时间的模型的可预测性始终较低。我们还发现，这后两种模型之间没有统计学上的显着差异。这表明，广告曝光时间在短期内对增加积极的实际购买行为几乎没有影响。
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purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is questionable and their environments do not reflect reality.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.