Fuzzy Logic–Based Comparison of Predicted Sales Records of Competing Products

Abstract

The issue of comparing sales records of competitors is gaining increased importance to both marketing academicians and practitioners to get an idea about approximate trend of customer inclination to their products. Actual sales records of competing products for past few years can be compared in two ways. If sales records exhibit normal distribution, then they can be tested for dominance over the other using t test (paired or unpaired). On the other hand, if normality is violated, then non-parametric tests like Kruskal–Wallis test by ranks or one-way ANOVA (analysis of variance) can be applied to test whether samples originate from the same distribution. One-way ANOVA is very flexible in the sense that it can work with two or more independent samples, and sample sizes need not be equal. This article emphasizes the fact that marketing strategies of today must take care of predicted consumer inclination, at least in the near future. Prediction of future sales records of competing products can be obtained using many techniques available in the literature, like linear regression, auto-regressive moving average (ARMA) model etc. All these predictions come up with a certain percentage of error. Therefore, it is wise to fuzzify them by dividing into ranges, before comparison. Here, a novel fuzzy logic–based technique is proposed that compares predicted sales records of competing products and accordingly finds out which one is the best.