Title: Targeting of online advertising using logistic regression
Authors: Šoltés, Erik
Táborecká-Petrovičová, Janka
Šipoldová, Romana
Citation: E+M. Ekonomie a Management = Economics and Management. 2020, roč. 23, č. 4, s. 197-214.
Issue Date: 2020
Publisher: Technická univerzita v Liberci
Document type: article
URI: http://hdl.handle.net/11025/42225
ISSN: 2336-5604 (Online)
1212-3609 (Print)
Keywords: online marketing;cílení;logistická regrese;klasifikační metriky
Keywords in different language: online marketing;targeting;logistic regression;classification metrics
Abstract in different language: Recently, the internet became the dominant medium in marketing and comparing the development of expenditures into advertising indicates the dominance of online advertising will be inevitably stronger. Internet advertising compared to traditional media advertising has plenty of advantages hence online marketing exhibits a huge expansion in recent era. To fully utilize the potential of online marketing, it is necessary to effectively target activities of relevant internet users with the real presumption they will purchase promoted products or services. The paper is focused on demographic targeting by the mean of logistic regression models. Explanatory variables in presented application are arising from affinities of internet webpages visited by particular users and areas of their interests that are identified from their online behaviour. Our paper provides binomial logistic mode whose role is to predict the gender of internet user and multinomial logistic model constructed for the estimation of age category the user may be assigned to. The only variables exploited in the model by the mean of stepwise regression are variables with significant influence. The impact of particular factors is quantified via odds ratios that are used for the identification of areas of interests typical for women, men and for considered age categories. The paper demonstrates how it is possible to utilise estimated logistic models for the estimation of probabilities that the internet user is from a target group – in our case, women aged 25–44 years old. Prediction quality of models is assessed by the set of classification measures arising from confusion matrix that is generally acceptable in machine learning. Presented analyses are conducted in statistical software SAS Enterprise Guide on data provided from the real advertising campaign. More than 160,000 statistical units enabled the confirm results gained on training dataset of a relatively huge validation dataset.
Rights: CC BY-NC 4.0
Appears in Collections:Číslo 4(2020)
Číslo 4(2020)

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