Marketing a obchod
TARGETING OF ONLINE ADVERTISING USING LOGISTIC REGRESSION
In the era of big data and digital technologies, marketing and especially digital marketing are rapidly developing. This development is significantly supported by utilisation of various quantitative methods allowing for a lot of useful information from different marketing fields. Digital marketing brings access to mass market for a reasonable price and unlike advertisement in traditional media (TV commercials, print), it enables personalized marketing. Digital marketing applies digital channels, devices and platforms to develop or implement a marketing strategy. One subset of digital marketing is online marketing usually defined as internet marketing.
Expansion of internet marketing is considerable and evident by the increase of expenditures into internet advertisement. In the year 2018, 68% of Slovaks and 75% of Czechs had daily access to internet, whereas in 2012 it was 60% and 44%, respectively.
Jméno a příjmení autora:
Erik Šoltés, Janka Táborecká-Petrovičová, Romana Šipoldová
Online marketing, targeting, logistic regression, classification metrics
DOI (& full text):
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…více
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.
Marketing a obchod