Differences in Buyer Journey between High- and Low-Value Customers of E-Commerce Business

Jung

et al. 2021

JTAER

Machine learning technology is recently being applied to various fields. However, in the field of online consumer conversion, research is limited despite the high possibility of machine learning application due to the availability of big data. In this context, we investigate the following three research questions. First, what is the suitable machine learning model for predicting online consumer behavior? Second, what is the good data sampling method for predicting online con-sumer behavior? Third, can we interpret machine learning’s online consumer behavior prediction results? We analyze 374,749 online consumer behavior data from Google Merchandise Store, an online shopping mall, and explore research questions. As a result of the empirical analysis, the performance of the ensemble model eXtreme Gradient Boosting model is most suitable for pre-dicting purchase conversion of online consumers, and oversampling is the best method to mitigate data imbalance bias. In addition, by applying explainable artificial intelligence methods to the context of retargeting advertisements, we investigate which consumers are effective in retargeting advertisements. This study theoretically contributes to the marketing and machine learning lit-erature by exploring and answering the problems that arise when applying machine learning models to predicting online consumer conversion. It also contributes to the online advertising literature by exploring consumer characteristics that are effective for retargeting advertisements.

Section: Data Collectionmentioning

confidence: 99%

Section: Measurementmentioning

confidence: 99%

A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion

Jung

et al. 2021

JTAER

“…Like customer characteristics, then, the type of product or service affects channel choice indirectly. Expensive products, for instance, motivate a higher need for information and risk reduction (Kakalejcík et al 2019 ; Xu and Jackson 2019a ). The influence of involvement (Chocarro et al 2013 ), risk level (Heitz-Spahn 2013 ), and complexity (Keyser et al 2015 ; Kim et al 2019 ), each factors closely related to price, is similar.…”

Section: Analysis and Findingsmentioning

confidence: 99%

Factors of customers’ channel choice in an omnichannel environment: a systematic literature review

Wolf

Steul-Fischer

2022

Manag Rev Q

The proliferation of mobile devices and the continuous development of online technologies has led to an increasing variety of channels, leaving customers with a choice of channel alongside the choice of product, service, or retailer. Any attempt to optimize customer experience and engage in successful omnichannel management will require a complete, multifaceted understanding of the processes around channel choice of customers. To date, the many existing studies around multi- and omnichannel research have failed to yield an integrated, comprehensive synthesis of factors involved in customers´ channel choice. Our study conducted a systematic literature review to the end of identifying the factors involved in channel choice which appear in the scientific literature on this topic over the last two decades. We retrieved 128 papers from three bibliographic databases (EBSCO Host, Scopus, and Web of Science) and carried out descriptive analysis on them. Qualitative thematic analysis inductively extracted 66 different factors of channel choice, each assignable to five broader categories, from the studies included in the review. The findings indicate that perceived channel characteristics, customer needs and situational or contextual factors influence customers´ channel choice directly, and customer characteristics and characteristics of products or services influence it indirectly. Alongside its presentation of an integrated conceptual framework comprising these relationships, our study details a comprehensive research agenda with regards to theories, contexts, and methods and, in particular, with regards to factors influencing customers´ channel choice. Our findings advance the academic understanding of channel choice behavior and provide researchers and practitioners in this area with information on important implications for omnichannel management.

Int. J. Interact. Mob. Technol.

“…Research Development [6], [10], [13], [14], [15], [16], [18], [19] Being tested with larger sample [4], [20], [21] Being tested in different countries [4], [17] The relationship between customers and sellers [22], [23] Qualitative assessment [24], [21] Being tested with different case study [25], [19] The test on customer's perspective [26] Research opportunity is organized based on numbers of supporting literatures. There are six literatures which support research development, both for research model and technology aspect.…”

Section: Opportunity Referencesmentioning

confidence: 99%

The Role of e-Commerce: A Systematic Literature Review

Qurtubi

Yudhistira

Febrianti

et al. 2022

The implementation of digitalization in industrial sector surely gives impact to several sectors, particularly in business. The development of information technology and rapid economic globalization have initiated the role of e-commerce in economic trading activities over the globe. It takes numerous of researches to initiate online new business. Therefore, it is important to reveal the existence of gap among previous researches. Systematic literature review method is applied to analyse the role of e-commerce in trading activity as well to provide the improvement for future researches. As many as 28 e-commerce related literatures are analysed comprehensively and systematically based on protocol review. The result of research confirms the opportunity for future research on e-commerce system that enables the integration on business processes. The supporting variables of e-commerce include business branding, social and economic development, efficient system e-commerce platform, framework application.