Research on Industry Data Analytics on Processing Procedure of Named 3-4-8-2 Components Combination for the Application Identification in New Chain Convenience Store

Abstract

With the rapid economic boom of Asian countries, the president of Country-A has made great efforts to reform in recent years. The prospect of economic development is promising, and business opportunities are emerging gradually, depicting a prosperous scene; accordingly, people’s livelihood consumption also has changed significantly. The original main point of consumption for urban and rural people was the old and traditional grocery store with poor sanitation, but due to the economic improvement, the quality of consumption has also improved, and convenience stores are gradually replacing grocery store. However, convenience store management involves performance, logistic, competition, and personnel costs. Both whether the store can create a net profit and evaluate and select a new store will be important keys that significantly influence business performance. Therefore, this study attempts to use the industry data analysis method for highlighting a concept of processing an experience procedure of named 3-4-8-2 components combination in two stages. First, in the data preprocessing stage, this research considers 22 condition attributes and two types of decision factors, that include net profit and new store selection, and use both techniques of attribute selection and data discretization through the analysis and prediction of data mining tools. Next, in the experiment execution stage, three well-known classifiers (Bayes net, logistic regression, and J48 decision tree) with past good performance and four models (without preprocessing, with attribute selection, with data discretization, and with attribute selection and data discretization) are used for eight different experiments through two data verification methods (percentage split and cross-validation). Conclusively, three key results are identified from empirical analysis: (1) It is found that the prediction accuracy of the J48 decision tree classifier is relatively high and stable among the three classifiers in this study; at the same time, the J48 decision tree can yield comprehensible knowledge-based rules to instruct interested parties. (2) The results of this study show that the important attributes for the net profit decision attribute include the store type, POS number, and cashier number, while the important attributes for the new store selection include the store type and cashier number. (3) There is a difference in the selection of important attributes. Furthermore, four key valuable contributions are addressed from the empirical results, including academic contributions, enterprise contributions, application contributions, and management contributions. It is expected that the direction of store layout expansion can be found and identified through this study, but there are still many risks hidden behind the considerable business opportunities that need to be carefully managed.