Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction

An important aspect of the new orientation on customer relationship marketing is the use of customer transaction databases for the cross-selling of new services and products. In this study, we propose a mixed data factor analyzer that combines information from a survey with data from the customer database on service usage and transaction volume, to make probabilistic predictions of ownership of services with the service provider and with competitors. This data-augmentation tool is more flexible in dealing with the type of data that are usually present in transaction databases. We test the proposed model using survey and transaction data from a large commercial bank. We assume four different types of distributions for the data: Bernoulli for binary service usage items, rank-order binomial for satisfaction rankings, Poisson for service usage frequency, and normal for transaction volumes. We estimate the model using simulated likelihood (SML). The graphical representation of the weights produced by the model provides managers with the opportunity to quickly identify cross-selling opportunities. We exemplify this and show the predictive validity of the model on a hold-out sample of customers, where survey data on service usage with competitors is lacking. We use Gini concentration coefficients to summarize power curves of prediction, which reveals that our model outperforms a competing latent trait model on the majority of service predictions. (C) 2003 Elsevier Science B.V. All rights reserved

Efecto de la atribución causal en un modelo de falla de servicio

Service recovery has been extensively studied and is a relevant issue for markets in which consumers repurchase products or services. There are two normative aspects of service recovery: how a company should act after a service failure and the consequences of the service failure regarding the company-customer relationship. This study presents a service failure model that combines these two aspects, investigating how causal attribution affects the customer’s perception regarding repurchase when a solution is provided after a service failure. A survey was conducted with users of a telecommunications service provider in Brazil, exploring two situations: a) the customer accidentally caused a service failure, and b) the company caused a service failure. The item response theory (IRT) was used, adopting PLS-SEM. Trust level and switching barriers were highlighted as important factors to keep repurchasing intentions positive. Customers trust more in the company when the failure is attributed to the organization, and it solves the problem, which induces a higher repurchase intention than when the failure is attributed to the customer. © 2022,RAE Revista de Administracao de Empresas.All Rights Reserve

Técnicas de preferência declarada na análise do nível de serviço hoteleiro Techniques in stated preference for the analysis of hotel service

O caminho usual para que os consumidores dêem suas respostas aos atributos de qualidade de serviço passa pelo uso efetivo do serviço. No entanto, estas ocasiões únicas não cobrem todos os casos de interesse. Uma alternativa para reduzir parcialmente essa limitação consiste em coletar e analisar as preferências dos consumidores com o auxílio de um experimento estatístico chamado pesquisa de preferência declarada ou análise conjunta. Para isso, os atributos são combinados, variando-se sistematicamente seus valores de forma a cobrir uma grande área de interesse, e representando assim cenários realistas. Estes cenários são usualmente apresentados na forma de cartões (textos, esquemas). Pede-se, às pessoas entrevistadas, após uma explicação introdutória, que classifiquem os cenários em ordem decrescente de preferência. Num segundo estágio, uma técnica de calibração é utilizada para ajustar os coeficientes de uma função utilidade. A classificação hoteleira no Brasil considera somente atributos físicos, tais como, quartos, garagem, recepção, etc. Os elementos relacionados ao serviço, como atendimento, acessibilidade e conforto não são explicitamente incluídos. Este trabalho descreve uma pesquisa de preferência declarada e um modelo de preferência do consumidor, com aplicação específica ao serviço hoteleiro de Balneário Camboriú.<br>The usual way that customers show their response to service quality attributes is when they use the service effectively. But those occasions alone do not cover all the cases of interest. One way to partially reduce this limitation is to collect and analyze the customers' preferences with the aid of a statistical experiment called stated preference surveying, or conjoint analysis. In order to do this, the attributes are combined such that their values cover a range of interest, representing realistic scenarios. These scenarios are usually presented in the form of cards (text, drawings), and the interviewed person is asked, after the introductory explanations, to rank them in a decreasing order of preference. A two-stage calibration technique is used to adjust a logit model, yielding the coefficients of a utility function. In Santa Catarina, a southern Brazilian state, resort beaches receive lots of Argentinean tourists in the summer. The official hotel classificatory system in Brazil considers only physical attributes of the facilities (bedroom and reception characteristics, garage, etc). Service factors such as comfort, attendance, accessibility, etc are not included. The paper describes a stated preference survey and customers' preference modeling with regard to hotel services in Balneário Camboriú

Dengue

Lecture 49 ISBN e-book : 9781615045754International audienc