Enhancing Multi-Channel Consumer Behavior Analysis: A Data-Driven Approach using the Optimized Apriori Algorithm

Abstract

Purpose –In the dynamic arena of retail and e-commerce, understanding customer behavior is crucial. This research paper investigates the strategies customers use to balance convenience and cost. It notes that consumers often divide their purchases, choosing online platforms for items they perceive as reasonably priced, while preferring physical stores for products associated with high shipping costs, lack of express shipping, or higher online prices. Traditional retail analysis methods, such as identifying frequently purchased and co-occurring items, are critical to the sector. However, these insights may be skewed if the analysis is solely confined to the online shopping cart, neglecting related purchases made at physical stores either before or after the online transaction. This paper addresses this analytical gap by looking into the complex interplay between online and offline purchases. The objective is to provide a more comprehensive, detailed understanding of multi-channel shopping behaviors, allowing retailers to better cater to their customers' needs and preferences while optimizing their strategies for a more effective market presence. This comprehensive approach attempts to uncover deeper insights into the interactions between online and offline purchases, thereby contributing to a more comprehensive understanding of consumer behavior in the retail and e-commerce industries. Design/methodology/approach- A detailed survey questionnaire was administered, obtaining 2465 responses. The questionnaire was specifically designed to capture the complexities of online and offline purchases, with an emphasis on Apple's purchase channels. The Apriori algorithm was used twice to find frequently co-occurring items that were purchased online and then online clubbed with offline. Essential metrics like support and confidence were calculated for both online and offline purchases. To determine any significant differences between the groups, an independent-samples t-test was used. This comprehensive methodology ensures a thorough examination of multi-channel shopping behaviors, providing valuable insights into consumer behavior in the retail and e-commerce sectors. Findings–We find that cross channel switching in a multi-channel distribution environment occurs due to price differences, convenience and flexibility. The paper illustrates the flaws of drawing inferences with data from a single channel emphasizing the need for a more holistic, multi-channel approach to data analysis in order to capture the full range of consumer behavior. Research limitations/implications – This study assumes that self-reported purchases or acquisitions of products accurately reflect actual consumer behavior. The insights derived from this research have significant implications for various marketing strategies, including pricing, product bundling, cross-selling, and promotional activities. However, it’s important to note that the reliance on self-reported data may introduce certain limitations, as it may not fully capture the complexity of actual consumer behavior. Despite these limitations, the insights gained from the study will add value for several marketing decisions like pricing, bundling, cross-selling and promotions. Originality value– While there is an abundance of literature on the application of Association Rule Mining for Market Basket Analysis, the existing body of work exploring its application within a multi-channel context is notably sparse. This study aims to address this gap in the literature, offering a unique perspective and making a substantial contribution to the existing body of knowledge in this area.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 20 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.