Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review

Policarpo, Lucas Micol; Silveira, Diórgenes Eugênio da; Righi, Rodrigo da Rosa; Stoffel, Rodolfo Antunes; Costa, Cristiano André da; Barbosa, Jorge Luís Victória; Scorsatto, Rodrigo; Arcot, Tanuj

doi:10.1016/j.cosrev.2021.100414

Cited by 35 publications

(17 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While clickstream data hold promise for understanding customer behavior in e-commerce, studies on this topic are relatively sparse due to data availability limitations and privacy concerns. Future research is anticipated to concentrate on extracting user behavior information, such as mouse movement and clicks, session times, searched items, and product customization [ 37 ]. Studies on real-time customer purchase prediction based on navigation data [ 38 , 39 , 40 , 41 ] have yielded promising results using various ML algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Necula

2023

Behavioral Sciences

View full text Add to dashboard Cite

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer’s purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Necula

2023

Behavioral Sciences

View full text Add to dashboard Cite

show abstract

“…The application of machine learning, as in other sales business models, extends to almost every area of e-commerce (e.g., security [22], fraud detection [23,24], profit maximisation [25], sales prediction [26,27], inventory management [28,29], product categorisation [30], and portfolio management [31]). Literature reviews exploring machine learning applications in different e-commerce scenarios can mainly be found in [32][33][34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

2022

View full text Add to dashboard Cite

Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

show abstract

“…As for ML, researchers and digital designers recognize that the ML trend has become especially interesting since it opens many new design opportunities for UX designers [4], [5]. The authors of [6], viewed ML as the new UX. Even though ML The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .…”

Section: Introductionmentioning

confidence: 99%

“…L. M. Policarpo et al [6] gave an overview of the obstacles UX designers face when using ML as a design element. One example of the obstacles is the struggle UX designers go through when collaborating with data scientists in a proactive manner.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most sought-after and valuable applications of ML in UX design is its capacity to provide users with new levels of personalization. L. M. Policarpo et al [6] described ML as an untapped possibility for UX designers and that it has yet to be fully realized as a design material. Algorithms that learn from underlying data sources are frequently used to improve UX [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User Experience Design Using Machine Learning: A Systematic Review

2022

View full text Add to dashboard Cite

User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML's existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

show abstract

Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review

Cited by 35 publications

References 45 publications

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

User Experience Design Using Machine Learning: A Systematic Review

Contact Info

Product

Resources

About