The objective of this paper is to determine factors that affect the consumers’ willingness to purchase product from the online store. We evaluated the criteria based on which users make decisions when purchasing online. We conducted principal components analysis to reduce the number of these criteria and created seven factors. To confirm factors are accurate, we executed confirmatory factor analysis that proved that model consisting of the newly created factors fits the data well.

The knowledge of high-value customers provides the possibility to make decisions ensuring profitability of the company. By analyzing and optimizing a buyer’s journey, companies can better understand their customers and optimize marketing costs in the way that will generate a higher return on investment. The primary objective of this paper is to define the current state of multichannel attribution and, based on the literature, study and analyze the data regarding the buyer’s journey of high- and low-value customers of selected e-commerce business. To accomplish the main objective of our study, we retrieved and analyzed top conversion paths from Google Merchandise Store, the e-commerce website selling goods branded by Google, with the use of Markov chains and heuristic models. A difference between high- and low-value customers regarding the acquisition by marketing channels before the purchase was found. Moreover, it was found that high-value customers' journeys consist of more interactions compared to those of low-value customers.

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.

This study introduces two simplified composite indicators, each capturing a distinct dimension of marketing and financial performance, based on aggregated monthly data from 2019 to 2024. By analyzing ten key metrics, including costs, revenue, profit margins, average order value, and marketing returns, Principal Component Analysis was employed to reduce the complexity of the dataset into two interpretable indices. The resulting indicators together explain over 90% of the data’s variability. The first indicator, Business Scale and Profitability, reflects business scale, profitability and operational efficiency. The second indicator, Marketing Pressure, captures marketing spending efficiency, investment intensity, and return on marketing efforts. By distilling a broad range of variables into two measures, this approach could help managers and analysts to quickly assess performance, identify trends, and make data-driven decisions without being overwhelmed by technical complexity. While based on a single corporate case, the proposed methodology and practical interpretation guidelines offer a transferable framework for managers seeking to assess and optimize marketing strategies with minimal technical overhead.

Peer-reviewed journal on social and business aspects of informatics that covers the theory and application of enterprise information systems, as well as interactions between ICT and people.