Consumer Customs Analysis Using the Association Rule and Apriori Algorithm for Determining Sales Strategies in Retail Central

Abstract

The sustainability of a company will not be separated from the role of consumers in conducting transactions. In fact, a consumer has different behaviour and character, therefore as a company owner must be able to analyze the patterns or habits of consumers in making transactions. This also happens in the retail center X, which has problems in the sales process, such as products running out of stock and unsold products and the most popular products and products that are not in demand by consumers. Therefore we need an analysis of consumer habits in conducting transactions. The method of association rule with Apriori algorithm is able to be applied well in the analysis of the habits of consumer transactions in the central retail X. The results of the calculation obtained an average percentage of the value of support 33%-40% and the value of confidence 43%-80%. The results of applying the association rule method with Apriori algorithm can help recommend central retail X owners in structuring product and determine strategic steps in increasing sales, such as providing discounts or promos for certain products.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

“…• SAHM Led Smart Tv → HOMMER Microwave Oven • ARROW Led Smart TV → Impex Led Smart TV • Impex Led Tv → Moulinex Food Preparation It has been proven that removing or replacing a product in the market can impact the sales of associated products [3,19]. The sales volume of the associated products: HOMMER microwave, Impex led smart tv, Molineux food preparation, al-Saif flask deva, etc., in this example is over 400,000 SR which is not a small amount and may affect the overall financial balance.…”

“…Last but not least, there is one important point before start applying our post-processing approach is to decide and specify the support threshold value to work with. Generally, there is no single "best" support value for association rule mining, as the optimal value can vary depending on the specific dataset and analysis goals, as suggested by researchers [18][19][20]. On one hand, it can be argued that execution times generally increase inversely with the support values, as shown in [18], where most algorithms have higher execution times for the lowest support value of 0.1 compared to 0.5.…”

The Sales Combination Strategy of Fresh Supermarket Vegetables Based on Apriori

“…In the process of association rule mining, this study not only pays attention to the discovery of frequent item sets but also pays attention to the quality of the mined association rules and the feasibility of practical application. By setting parameters such as lift degree, confidence degree, and support degree, this paper carefully adjusts the threshold of the algorithm to ensure that the mining results are more accurate and practical [12] .…”

A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study

“…Based on the discussions on results obtained by [12], using association rule learning with the Apriori algorithm, it is possible to decode consumer habits when making purchases with confidence levels between 43% and 80% and a 33-40% value of support. As such, these two are recommended for retailers to arrange products and remain aggressive in marketing.…”

“…Some researchers directly observe and monitor customers' physical behavior [3][4][5], while others utilize specific data features to make predictions and analyze large-scale datasets to forecast consumer behavior [6][7][8][9]. Then, after approaching the consumer, a technical approach is implemented to predict consumer behavior, such as classification [3,5,8,10,11], association rule learning [9,[12][13][14][15][16][17], clustering [15,18], and other techniques like neural networks [4,6,7].…”