اهمیت متغیرها در داده های پرسشنامه بر روی تبلیغات

In this article, we deal with the problem of measuring the importance of features, that determine the purchase of the product after being exposed to an advertisement. We use an algorithm called Monte Carlo feature selection, which is based on multiple usage of decision trees, to achieve a ranking of variables from the questionnaire data. Our data generation process relies on low-involvement during the advertisement watching phase and the comparison of advertised products is based on purchase in a virtual shop.

In marketing, the effective advertisement is one of the keys to success. Marketers responsible for the decision, which advertisement should be applied, are interested, prior to the emission, what is the expected result of the emission on customers. For example, whether customers will like it, find it interesting and convenient. Marketers are interested in what assotiations are created with their brand and product as an effect of watching an advertisement by their customers. Sometimes, a few versions of the same advertisement are produced and the question is, which version should be chosen and shown in mass media. Usually, before the advertisement is broadcasted on TV or printed in newspaper, it is tested whether it will perform its functions well. Answering to these questions may be done using various types of market research techniques. A focus study may be the illustrative example. A small group of potential clients is shown a commercial and then they discuss, among each other, their opinion about it. This discussion is recorded by researchers and then the conclusions are drawn. Sometimes, a wider group of potential customers is shown a commercial and then they fill a questionnaire about the just watched commercial. However, results obtained from the above mentioned procedures are severly biased, due to the fact, that respondents concentrate during watching advertisements, which is usually not true in real life (people usually switch channels during commercial breaks). One of the key concerns in market research of this type is therefore providing an experimental design, as similar to the real life situation as possible. Also, the effect of advertisement on willingness to purchase in real life may differ from the one declared in questionnaire. In order to diminish this difference, often the research based on low-involvement of respondents is conducted.

In this article, we presented a methodology to achieve a hierarchy of attributes, that determine the purchase, when the purchase is influenced by the advertisement. This ranking has a clear interpretation and can be easily understood by marketers without the machine learning background. We used questionnaire data, that were gathered using the low-involvement principle. We also analyzed the problem, how to choose best advertisement of the same product, having a few competing versions. We employed, in this marketing study, the Monte Carlo feature selection algorithm, that was earlier used in genetics. We analyzed the FMCG products, but our approach can be applied to any product, that is advertised, so there is a wide range of potential applications of this idea. The algorithm presented in this paper can be easily applied to other types of marketing research. Due to the fact that it uses decision trees as the inbuilt classifiers it can also cover various types of data, which are typical in marketing research. The application of the method presented here can also contribute to cutting costs of research, due to the fact that it is designed to find relationships in small samples.