Examining the segment retention problem for the “Group Satellite” case

The purpose of this work is to determine how well, criteria designed to help the selection of the adequate number of market segments, perform in recovering small niche segments, in mixture regressions of normal data, with experimental data. The simulation experiment compares several segment retention criteria, including information criteria and classification-based criteria. We also address the impact of distributional misspecification on segment retention criteria success rates. This study shows that Akaike’s Information criterion with penalty factors of 3 and 4, rather than the traditional value of 2, are the best segment retention criteria to use in recovering small niche segments. Although these criteria were designed for the specific context of mixture models, they are rarely applied in the marketing literature.Information criteria; Latent Class Segmentation.

Econometric modeling of business Telecommunications demand using Retina and Finite Mixtues

In this paper we estimate the business telecommunications demands for local, intra-LATA and inter-LATA services, using US data from a Bill Harvesting R survey carried out during 1997. We model heterogeneity, which is present among firms due to a variety of different business telecommunication needs, by estimating normal heteroskedastic mixture regressions. The results show that a three-component mixture model fits the demand for local services well, while a two-component structure is used to model intra-LATA and inter-LATA demand. We characterize the groups in terms of their differences among the coefficients, and then use Retina to perform automatic model selection over an expanded candidate regressor set which includes heterogeneity parameters as well as transformations of the original variables.Telecommunication Demand Models, Local calls, Inter-LATA calls, intra-LATA calls, Retina, Flexible Functional Forms, Heterogeneity, Finite Mixtures.

Econometric Modeling of Business Telecommunications Demand using RETINA and Finite Mixtures.

In this paper we estimate the business telecommunications demands for local,intra-LATA and inter-LATA services, using US data from a Bill Harvesting (R) survey carried out during 1997. We model heterogeneity, which is present among firms due to a variety of different business telecommunication needs, by estimating normal heteroskedastic mixture regressions. The results show that a three-component mixture model fits the demand for local services well, while a two-component structure is used to model intra-LATA and inter-LATA demand. We characterize the groups in terms of their differences among the coefficients, and then use RETINA to perform automatic model selection over an expanded candidate regressor set which includes heterogeneity parameters as well as transformations of the original variables. Our models improve substantially the in-sample fit as well the out-of-sample predictive ability over alternative candidate models. RETINA suggests that the final demand specification should include telephone equipment variables as relevant regressors. On the other hand, the output of the firm, as well as its physical extension, have second order, yet significant effects on the demand for telecommunication services. Estimated elasticities are different for the three demands but always positive for access form (single-line or private network).Telecommunication Demand Models, Local calls, inter-LATA calls, intra-LATA calls, RETINA, Flexible Functional Forms, Heterogeneity, Finite Mixtures.

Latent class mixture models for analyzing rating responses

Latent class methodology has been used extensively in market research. In this approach, segment membership and parameter estimates for each derived segment are estimated simultaneously. A popular approach for fitting latent class models to rating responses is to assume mixtures of multivariate conditional normal distributions. An alternative approach is to assume a Proportional Odds model. These two approaches are compared empirically in a Monte Carlo study, assessing segment membership and parameter recovery, goodness of fit and predictive accuracy.peer-reviewe

Assessing the Number of Components in Mixture Models: a Review.

Despite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available approaches designed to help the selection of the adequate number of mixture components (including Monte Carlo test procedures, information criteria and classification-based criteria); we also provide some published simulation results about their relative performance, with the purpose of identifying the scenarios where each criterion is more effective (adequate).Finite mixture; number of mixture components; information criteria; simulation studies.

Identification of Segments of European Banks with a Latent Class Frontier Model

This paper analyses technical efficiency of European banks over the period 1996-2003 with unbalanced panel data techniques. A latent class frontier model is used which allows the identification of different segments in the production frontier. We find that there are three statistically significant segments in the sample. Therefore, we conclude that no common banking policy can be effective for all the banks included in the sample, and that banking policies by segments are required instead.European banking, latent class frontier model, technical efficiency

Organizational Design and Control across Multiple Markets: The Case of Franchising in the Convenience Store Industry

Many companies operate units which are dispersed across different types of markets, and thus serve significantly diverging customer bases. Such market-type dispersion is likely to compromise the headquarters' ability to control its local managers' behavior and satisfy the divergent needs of different types of customers. In this paper we find evidence that market-type dispersion is an important determinant of delegation and the provision of incentives. Using a sample of convenience store chains, we show that market-type dispersion is related to the degree of franchising at the chain level as well as the probability of franchising a given store within a chain. Our results are robust to alternative definitions of market-type dispersion and to other determinants of franchising such as the stores' geographic distance from headquarters and geographic dispersion. Additional analyses also suggest that chains that do not franchise at all, may cope with market-type dispersion by decentralizing operations from headquarters to their stores, and, to a weaker extent, by providing higher variable pay to their store managers.Control, Market Dispersion, Decentralization, Incentives, Franchising, Retailing