Exploring Consumer Behavior: Use of Association Rules

Abstract

This paper focuses on problematic of use of association rules in exploring consumer behavior and presents selected results of applied data analyses on data collected via questionnaire survey on a sample of 1127 Czech respondents with structure close to representative sample of population the Czech Republic. The questionnaire survey deals with problematic of shopping for meat products. The objective was to explore possibilities of less frequently used data-mining techniques in processing of customer preference. For the data analyses, two methods for generating association rules are used: Apriori algorithm and FP-grow algorithm. Both of them were executed in Weka software. The Apriori algorithm seemed to be a better tool, because it has provided finer data, due to the fact that FP-growth algorithm needed reduction of preference scale to only two extreme values, because the input data must be binary. For consumer preferences we also calculated their means. This paper explores the different preferences and expectations of what customers’ favorite outlet should provide, and offer. Customers based on the type of their outlet loyalty were divided into five segments and further explored in more detail. Some of the found best association rules suggest similar patterns across the whole sample, e.g. the results suggest that the respondents for whom a quality of merchandise is a very important factor typically also base their outlet selection on freshness of products. This finding applies to all types of retail loyalty categores. Other rules seem to indicate a behavior more specific for a particular segment of customers. The results suggest that application of association rules in customer research can provide more insight and can be a good supplementary analysis for consumer data exploration when Likert scales were used.

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

“…This research uses several aspects or attributes such as teaching learning, tutoring, and consulting, research and community service, and other tasks beyond the main task as a consideration in judgment Subsequent research that utilizes the Rough Set method, which uses a variable of education, academic position and Department of office is then used to determine the desired rector candidate in accordance with the existing provisions. The results of the study show that education is a major factor in the election of foreign rector which is then supported by the academic position and structural department owned by the lecturer (Chen et al, 2020;Turčínek and Turčínkova, 2015;Dhandayudam and Krishnamurthi, 2013;Shyng et al, 2007;Suchacka and Chodak, 2017;Sembiring and Azhar, 2017;Hadiansah, 2017;Wang and Chou, 2013;Sanny, Susastra, Roberts, and Yusramdaleni, 2020).…”

A novel consumer preference mining method based on improved weclat algorithm

“…To the best of the authors' knowledge, although association rule mining has not been applied in the fruit market, it has been widely used to analyze consumer preferences in other fields (Guan et al, 2018;Park and Kim, 2018;Wang, 2019). Further, Tur cínek and Tur cínkov a have also confirmed that the application of association rule mining in customer research can provide more insights and good supplementary analysis related to consumer data exploration, which makes it possible to compensate for the defects of BWS, descriptive statistics and hypothesis testing (Tur cínek and Tur cínkov a, 2015). Based on this, in the present study, association rule mining is applied to analyze the preferences of fruit consumers.…”

Data Mining Framework for Treating both Numerical and Text Data

“…Because TETDM can not treat such input data currently, only text part of the data is given to TETDM as an ordinary input. Then, numerical part of the data is prepared as csv format and given to the data mining tool directly 3 .…”

“…Electronic data is collected in everywhere and many products and services are widely used in our daily lives [1]. Data mining techniques such as association analysis and cluster analysis are used for marketing analysis [2,3], because those can discover relationships and rules hiding in enormous numerical data. On the other hand, text mining [4] techniques such as keywords extraction or opinion extraction are used for questionnaire and review text analysis, because those can support us to comprehend consumers' opinion in text data.…”