<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online, where the purchase decisions are increasingly made and often based on product reviews, ratings and recommendations available in social media networks. The original data consists of 23486 customer reviews with ten variables/features of the reviewing customers, the products under review and the feedback to their reviews from online retail clothing business, and about half of the dataset is analyzed after cleaning the data. To find out, which features are the most important factors leading to a recommendation, the naïve Bayes and logistic regression methods are applied. Earlier research has shown that the sentiment of textual reviews and the given numerical ratings are key factors for the decision to recommend or not recommend products. The focus of this paper is to identify and rank-order the most relevant (numerical) factors affecting the review process leading to a recommendation. After applying the logistic regression classifier, we have found that rating, positive feedback count and age are statistically significant factors, in that order. The results support online retailers and manufacturers, as well, in adjusting their product portfolios and marketing efforts optimally to obtain recommendations for their products, reach potential customers and expose them to the given recommendations leading to positive purchase decisions. Further, the results indicate some future research opportunities.</p></abstract>

Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online, where the purchase decisions are increasingly made and often based on product reviews, ratings and recommendations available in social media networks. The original data consists of 23486 customer reviews with ten variables/features of the reviewing customers, the products under review and the feedback to their reviews from online retail clothing business, and about half of the dataset is analyzed after cleaning the data. To find out, which features are the most important factors leading to a recommendation, the naïve Bayes and logistic regression methods are applied. Earlier research has shown that the sentiment of textual reviews and the given numerical ratings are key factors for the decision to recommend or not recommend products. The focus of this paper is to identify and rank-order the most relevant (numerical) factors affecting the review process leading to a recommendation. After applying the logistic regression classifier, we have found that rating, positive feedback count and age are statistically significant factors, in that order. The results support online retailers and manufacturers, as well, in adjusting their product portfolios and marketing efforts optimally to obtain recommendations for their products, reach potential customers and expose them to the given recommendations leading to positive purchase decisions. Further, the results indicate some future research opportunities.

Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses

Proceedings of the International Conference on Applied Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online,...

Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses

Publié en ligne: 31 mai 2021

Pages: 120 - 128

DOI: https://doi.org/10.2478/icas-2021-0011

Mots clés
consumers, social media, logistic regression, naïve Bayes, ROC curve

© 2021 Irina Georgescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses

Publié en ligne: 31 mai 2021

Pages: 120 - 128

DOI: https://doi.org/10.2478/icas-2021-0011

Mots clésconsumers, social media, logistic regression, naïve Bayes, ROC curve

© 2021 Irina Georgescu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
consumers, social media, logistic regression, naïve Bayes, ROC curve