Analysing preferences to iPad attributes using two-stage segmentation and latent class procedures

Abstract

In these recent years, market research has turned out to be an essential tool for a company in order to take the right decisions and as well guarantee a future progress. The most important objective for any company is that of identifying and targeting distinct consumers' choices and preferences on a particular product or service. Subsequently, cluster analysis has been widely used in market research so as to tackle this objective. Through cluster analysis groups of customers with similar views can be accessed more easily. This research study analyses the consumer preferences for the different iPads having distinct attributes. The three selected attributes include the capacity, connectivity and price of the iPad. Using a full profile and full factorial design, twenty-four profiles were generated and the respondents were asked to rate these profiles on a 7-point scale. Additionally, two statistical procedures were employed to relate the respondents' rating scores to the attributes. The former procedure is the Two-Stage Segmentation and the latter procedure is the Latent Class Analysis. The Two-Stage Segmentation procedure involves the estimation of the individual level parameter by the usage of a least squares regression of the respondents' preference ratings, in the first stage. Then in the second stage, these individual-level parameter estimates are used to cluster the respondents into segments based on the similarity of their estimated parameters. This can be carried out using both hierarchical and non-hierarchical non-overlapping clustering algorithms. For this research study it is going to be carried out by the two-step cluster analysis. In Latent Class Analysis, the prediction models are derived by the maximization of the expected complete log-likelihood function, given that the number of segments is known. Furthermore, the EM algorithm also yields the posterior probabilities which are in turn used to assign the respondents in their respective segments. The main merit of the Latent Class Analysis over the Two-Stage Segmentation procedure is that the classification of the respondents into segments and the estimation of a regression model within each segment are conducted simultaneously.