Annibal Parracho Sant'Anna and Rodrigo Otavio de Araujo Ribeiro
Data mining registers of transactions allows for benchmarking customer's evaluation strategies. The purpose of this paper is to provide information on the application of different…
Abstract
Purpose
Data mining registers of transactions allows for benchmarking customer's evaluation strategies. The purpose of this paper is to provide information on the application of different approaches to explore this kind of data.
Design/methodology/approach
Traditionally, heuristics based on variables such as recency, frequency, and monetary (RFM) value of transactions are used to determine the best customers. In this paper, a new form of directly combining the values of these variables is compared to an approach based on fitting a stochastic model. This last model is a mixture of a model for the number of transactions and another for the value spent. The new direct form of evaluation is based on computing the joint probability of maximizing quality indicators.
Findings
Good fit of the different models tested to the series of individual data as well as coherent predictions are registered. Patterns found provide empirical confirmation of results that theoretically should be expected.
Research limitations/implications
These results are valid for a particular supermarkets network in a Brazilian city. The inner consistency of the results, nevertheless, and the coherence of the results obtained with what was expected, encourage application to other places and sectors of activity.
Practical implications
The results obtained show clearly the effectiveness of the approach based on RFM value measurement.
Originality/value
The models studied are applied for the first time for the kind of data treated, where determination of which customers remain active is a problem of special interest.
Details
Keywords
Annibal Parracho Sant’Anna, Lidia Angulo Meza and Rodrigo Otavio Araujo Ribeiro
The purpose of this paper is to discuss the application of a method for combining multiple criteria based on the transformation of numerical evaluations into probabilities of…
Abstract
Purpose
The purpose of this paper is to discuss the application of a method for combining multiple criteria based on the transformation of numerical evaluations into probabilities of preference. It is applied to compare failure risks and to measure efficiency in the retail trade sector.
Design/methodology/approach
The main conceptual aspect of the method employed is taking into account uncertainty. Its other important feature is allowing for the combination of evaluations in terms of joint probabilities. This avoids the need of assigning weights to the criteria. In the context of failure modes and effects analysis (FMEA) it provides a probabilistic derivation for priority scores. An application of FMEA to the sector of services is discussed. Another area of application investigated is the assessment of efficiency.
Findings
Details of the application of the probabilistic composition in the evaluation of modes of failure and in the comparison of operational efficiencies of retail stores are evidenced.
Research limitations/implications
The study is limited to the retail market. Other factors might be considered in the reliability analysis and other inputs and outputs might be added to the productivity evaluation. The extension of the study to other cases and sectors is straightforward.
Practical implications
Features of the evaluation of modes of failure and of productivity in the retail sector are revealed.
Originality/value
The main contribution of this paper is showing how to use a probabilistic framework to measure efficiency in services management.