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Since its introduction, television has been the main channel of investmentfor advertisements in order to influence customers purchase behavior. Many haveattributed the mere exposure effect as the source of influence in purchaseintention and purchase decision; however, most of the studies of televisionadvertisement effects are not only outdated, but their sample size isquestionable and their environments do not reflect reality. With the advent ofthe internet, social media and new information technologies, many recentstudies focus on the effects of online advertisement, meanwhile, the investmentin television advertisement still has not declined. In response to this, weapplied machine learning algorithms SVM and XGBoost, as well as LogisticRegression, to construct a number of prediction models based on at-homeadvertisement exposure time and demographic data, examining the predictabilityof Actual Purchase and Purchase Intention behaviors of 3000 customers across 36different products during the span of 3 months. If models based on exposuretime had unreliable predictability in contrast to models based on demographicdata, doubts would surface about the effectiveness of the hard investment intelevision advertising. Based on our results, we found that models based onadvert exposure time were consistently low in their predictability incomparison with models based on demographic data only, and with models based onboth demographic data and exposure time data. We also found that there was nota statistically significant difference between these last two kinds of models.This suggests that advert exposure time has little to no effect in theshort-term in increasing positive actual purchase behavior.

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