Prediktivni model segmentacije tržišta: Primena modela logističke regresije i CHAID procedure
Predictive market segmentation model An Application of Logistic Regression Model and CHAID Procedure
Apstrakt
Segmentacija tržišta predstavlja jedan od najvažnijih koncepata savremenog marketinga. Cilj segmentacije jeste podela tržišta na grupe - segmente kupaca koji imaju slične karakteristike, potrebe, želje i/ili slično tržišno ponašanje. Na osnovu dobijenih segmenata kompanije mogu razvijati specifične marketing planove za svaki segment posebno, i na taj načini steći konkurentsku prednost, kako na kratak, tako i na dugi rok. U zavisnosti od cilja segmentacije mogu se primeniti različite procedure i tehnike. U ovom radu predstavljen je prediktivni model segmentacije tržišta koji se bazira na primeni modela logističke regresije i CHAID analize. Model logističke regresije je primenjen samo za potrebe izdvajanja iz datog skupa promenljivih onih promenljivih koje su statistički značajne u objašnjavanju zavisne promenljive. Zatim su tako izdvojene promenljive uključene u CHAID analizu kao prediktor promenljive. Na konkretnom empirijskom primeru prikazane su mogućnosti CHAID analize u generisanju prediktivnog modela segmentacije tržišta. Rezultati modeliranja predstavljeni su u formi: tabele sumarnih rezultata modela, CHAID drveta, dijagrama dobiti (gain chart) i indeks dijagrama, kao i tabele rizika i klasifikacione tabele, koje otkrivaju prediktivnu moć modela.
Abstract
Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments) of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables) which are statisticaly significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.
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