Abstract

Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

Introduction

Analyzing consumers’ online purchasing habits can provide many advantages for commercial entities, marketing professionals, and consumers. Classifiers based on decision trees can be applied to predict market trends, specifically for determining when to buy or sell. Decision trees are the transparent and efficient option for machine learning because they sort data attributes at each node to arrive at a decision (Samarth 2023; Vaca et al. 2020). Several researchers proposed a system to enhance the shopping experience at fashion retail outlets. This system smartly groups together various items and customer profiles, which it encounters online and in physical stores. It leverages the power of mining association rules to foresee the shopping patterns of new customers (Bellini et al. 2023; Fan et al. 2023).

Formal concept analysis (FCA) (Carpineto and Romano 2004; Ganter and Wille 1999; Poelmans et al. 2013) is a method of data analysis based on a lattice theory that has great potential to study how people behave online. This method offers visualization capabilities to explore the relationships in the object-attribute tables that are present, for example, by customer transactions and their characteristics. Combining well-known methods like decision trees and pattern-finding has made these studies more attractive to a broader audience.

Conclusion

In this paper, we presented the analysis of real e-shop consumer behavior data in eastern Europe, which contains data from 9123 orders of sports nutrition, healthy foods, fitness clothing, and accessories from 2014 to 2021. For analysis, we applied the methods of decision trees, GUHA association rules, and Formal concept analysis. We found that each of these methods provides various possibilities to have a greater look at consumer behavior of online e-shops.