Mónica Gómez Suárez

Shelf space assigned to store and national brands

Purpose – To empirically analyse the relationship between the shelf space assigned to brands and several factors related to store management.Design/methodology/approach – The data come from a study of 40 product categories in a sample of superstores in Spain. The variables are: shelf space occupied by private labels, private labels market share, number of promotions, assortment (number of brands and number of varieties) and prices gap between private labels and national brands. A neural network analysis is then applied to the data. Methodologically, this method is shown to have better predictive power than a multiple regression.Findings – There is a direct relationship between the space occupied by store brands and the market share, and an indirect relationship between the space and the price differential gap, the number of national brands and the range of choice.Research limitations/implications – The main limitation of this research is the time spent on collecting the data. Another limitation is that so...

Focusing resources for customer loyalty: An application to the Chilean banking industry

A model is proposed that should enable banking industry executives to better focus resources on loyalty programs by determining which factors are most valued by customers in their decisions to stay with their present bank. The model factors include intangibles drawn from concepts of intellectual capital that influence customers’ perceptions of image, service quality, satisfaction and loyalty. A structural equation model developed for the Chilean banking industry and incorporating eight factors that impact the loyalty of current account customers found that those with the greatest direct effects are satisfaction, image and quality while those with the strongest indirect effects are attention to customers and personalization.

Espacio ocupado en el lineal por las marcas de distribuidor: estimación mediante redes neuronales vs regresión multiple

This paper analyses the influence of some variables in the shelf space occupied by store brands. We propose and test a theoretical model of store brand shelf space. Data were collected for 29 product categories in 55 retail stores. A two-phase procedure was adopted: (1) multiple regression analyses; (2) neural network simulation (ANN). The application of this last method improves the goodness of fit obtained through the regression method. Furthermore, it presents additional advantages since ANN does not need to fulfil the main assumptions needed in regression analyses. The findings corroborate our proposed model, in that all hypothesized relationships and directions are supported. On this basis, we draw theoretical as well as useful managerial implications for both retailers and manufacturers.