Data mining and machine learning in retail business: developing efficiencies for better customer retention

M, Rajesh Kumar; Venkatesh, J.; Rahman, Abdur

doi:10.1007/s12652-020-02711-7

Cited by 19 publications

(11 citation statements)

References 13 publications

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“…Customers first engage with the webstore through multiple interactions (Shamim et al , 2021). These interactions will develop trust with the service provider which will in the next stage enhance the chances of customer retention (Kumar et al , 2021). The findings of the study reveal significant insights where website design positively develops e-trust among the customers and enhances the chances of customer e-retention in the online shopping context.…”

Section: Discussion Implications and Agenda For Future Researchmentioning

confidence: 99%

Building e-trust and e-retention in online shopping: the role of website design, reliability and perceived ease of use

Din

Shamim

Suki

et al. 2023

SJME

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Purpose This study aims to examine the impact of website design, reliability and perceived ease of use as an engagement motivational factors on customer e-trust and e-retention in online shopping. Design/methodology/approach By using deductive approach, quantitative methods and purposive sampling technique, this study has collected the data from 295 young online customers to enhance an understanding of website design, reliability and perceived ease of use in an online shopping context. Findings The findings revealed interesting insights where reliability is the most significant predictor of customer e-trust in online shopping, followed by perceived ease of use and website design. In addition, a significant mediating effect of e-trust is found between customer e-retention, website design, reliability and perceived ease of use. Research limitations/implications Future research is recommended to predict the antecedents of online engagement motivational factors with value co-creation and co-creation experience in online shopping context. Originality/value This study offers fresh insights about driving elements and impediments of online customer retention. Customer engagement comprising of website design, reliability and perceived ease of use appear to influence the online customer retention through direct and indirect effect.

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Section: Discussion Implications and Agenda For Future Researchmentioning

confidence: 99%

Building e-trust and e-retention in online shopping: the role of website design, reliability and perceived ease of use

Din

Shamim

Suki

et al. 2023

SJME

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“…This section presents an extensive review of the literature on all studies related, directly or indirectly, to exploring users' interests mining methods [17,18]. Another name for mining users' interests is personalized recommendation systems.…”

Section: Related Studiesmentioning

confidence: 99%

“…After that, we adjusted the weight factor to control the percentage of similarity between users. Both demographic and personality data take an equal weight in importance, while the interested topics take a higher weight for their matter in evaluating the similarity results (line [17][18]. Finally, similarities between users were identified based on these three factors, which makes a significant contribution to effectively solving the cold start problem (line 19-20).…”

Section: User Modelingmentioning

confidence: 99%

“…For the topics graph, the same procedure as the user graph was followed; the difference will be in assigned G z as the new empty graph where the node represents topics Z in the cluster and the edge E ZZ represents a similar topic based on threshold value. Furthermore, topic nodes were created and added to the topic graph and clustering is used to assign the topics node to a particular group (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). H ← Initialize semantic similarity matrix between topics 5:…”

Section: Graph Modelingmentioning

confidence: 99%

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The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

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“…Loan approval at the banks has seen the application of ML in detecting high-risk applicants and fraudulent paper documents [16]. In the retail industry, ML is presently employed as chatbots that perform scripted functions and leverage natural language processing for customised conversational discussions [17]. Intelligent transportation systems (ITS) have seen the integration of ML for price calculation, ridesharing, ride surge demand locations, and trafc pattern detection [18].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions

Dake

Buabeng-Andoh

2022

Mobile Information Systems

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Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An attrition model was built and validated among support vector machine, decision tree, multilayer perceptron, and random forest algorithms. The machine learning algorithms were tested for accuracy, precision, recall, F-measure, and ROC using the 10-fold and the 5-fold comparative cross-validation techniques. In addition to the cross-validation technique, the chi-square feature selection mechanism was implemented to understand the algorithms’ training time and accuracy. The random forest emerged as the best-performing algorithm, with an accuracy of 70.98% and 69.74% for the 10-fold and the 5-fold cross-validation implementations, respectively.

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Data mining and machine learning in retail business: developing efficiencies for better customer retention

Cited by 19 publications

References 13 publications

Building e-trust and e-retention in online shopping: the role of website design, reliability and perceived ease of use

Building e-trust and e-retention in online shopping: the role of website design, reliability and perceived ease of use

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions

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