A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry

Round 1

Reviewer 1 Report

The paper is interesting with adequate methodology used. I suggest publication after making minor revision:

- There are too many subchapters in the text

- Table 1 could go in Appendix

- Many of the bullets in the text are not necessary

- Page 13. - Tables are not numerated correctly, there should be Table 10,11...

- There are too many small tables, it could be explained in the text

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Using an interesting study, this paper emphasizes the need to understand RS and the premises of hyper-personalization to develop long-lasting customer relationships. The study is presented with many valuable details and the results are compelling. In reading the manuscript, I commend the Author/s’ attention to details and providing visual aids that help summarize the research and its phases in an accurate manner.

The recommendations for improvements are presented for each section of the manuscript.

1. The Abstract should focus more on the original contributions of the study and the scope of the paper.

Introduction

2. To create the background of the paper, new and current academic references should be included in the Introduction. It is quite uncommon not to use academic references in the Introduction, so I advise adjusting this aspect.

3. In its current form, the background is briefly mentioned. Thus, the Author/s could consider extending the background of the study with regard to hyper-personalization and digital clienteling, as a result of technology development, by also providing industry statistics in this area.

4. The concept of ‘hyper-personalization’ could also be enhanced upon, considering the fact that section 2 does not address it in a comprehensive manner. 

5. The introduction of the paper should also clearly address the aims and the research gaps encountered in existing literature on this matter. 

6. To enhance the Introduction section, the Author/s might find the following studies:

a. Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics, 11(1), 141.

b. Valdez Mendia, J. M., & Flores-Cuautle, J. D. J. A. (2022). Toward customer hyper-personalization experience—A data-driven approach. Cogent Business & Management, 9(1), 2041384.

c. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. applied sciences, 10(21), 7748.

2. Related Work

7. The classification of RSs could also be presented in a summarizing figure, before subsections 2.1.1-2.1.4

8. Section 2.2. Structure of collaborative filtering algorithms could use additional explanations in terms of linking the ideas of this subsection to the scope of the paper.

9. Section 2.3. could be retitled. Overall, the whole section 2 highlights literature review. But this subsection focuses on highlighting approaches used in studies on RS.

10. Overall, I would advise more cohesiveness in the presentation of the studies, in section 2.3. 

11. For instance, the paragraph of rows 271-280 highlight the scarcity of RS studies in the fashion industry, then paragraph of rows 281-285 highlight the trend of examining customer segmentation in general, then the next paragraph of rows 286-289 reconsiders the premises of studies in fashion industry.

12. In addition to my previous point of cohesiveness, the manuscript should present flow in ideas and smooth transition between ideas. In its current form, the literature review seems a bit fragmented.

13. The references’ list could be enhanced with current studies beyond the 55 list of sources.

3. Proposed Recommendation System

14. Section 3 needs additional explanations regarding the proposed model, especially by providing academic background for the methodology.

15. The manuscript should provide accurate descriptions of each stage.  There are missing references for RFM, clustering…

4. Experimental Design

16. In the experimental design, the manuscript should emphasize the research context. Why is this study relevant for this industry and in this country?

17. Also, in presenting the ideas related to the company (rows 457-467), the manuscript should reorganize these ideas. Details regarding the company’s location (and other profiling elements) should be presented at the beginning, not at the very end. 

18. The manuscript mentions: “The company uses RSs to increase conversion rates and offer the right product to 463 customers, especially in e-commerce operations. However, since the RS used belongs to a 464 third-party company, the company needs to develop and implement a new system”. Could the Author/s expand on these ideas? What type of RS  is used presently and how is this research meant to expand or innovate customer knowledge for future implementation? 

19. In mentioning “in the last three years” with regard to the data, could the Author/s be more specific on the timeline? 

20. In 4.1., the Author/s only seem to present details on R measurement and codifications. The M and F should also be briefly addressed. 

21. In 4.2., the Author/s mention ‘customer location information’. However, is this related to shipping address and billing address? Because for e-commerce, these 2 addresses can be different.

22. Due to the wide range of abbreviations (and the need to review these abbreviations in Annex 1), it would be helpful for provide a short explanation/description of the results presented in Table 4-7. Also, to increase the readability factor of the research, the explanations should present the whole concepts, not just abbreviations.

23. Moreover, Table 5 could also highlight the full concepts of product category.

24. The transition of the analysis from 1 customer to 1,478 customers needs additional explanations. Also, which year is the research referencing?

25. Tables 10-13 are misnumbered causing confusion.

26. A % difference in Table 11 and Table 12 would be useful to enhance the results.

Conclusions and Future Studies

27. The Conclusions’ section should accurately describe the original value of the research.

28. Considering the practical aspects presented by this research, I would recommend including a separate section (in the Conclusions section) that highlights the managerial implications of this study. 

29. The limitations of the study should also be addressed for research transparency.

All the best to the Author/s with their academic efforts!

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Dear Authors,

Please find below some remarks and recommendations regarding your manuscript.  

The proposed manuscript concentrates on a recommendation system based on the RFM technique for customer segmentation, the k-means clustering algorithm to create customer segments with customer-based RFM values, an apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. The authors decided that product recommendations will be presented with a rule-based heuristic algorithm. The proposed  approach is aimed to produce product recommendations that the authors call hyper-personalized.

It is a great value of the paper that the proposed approach is verified on actual customer data obtained from an electronic commerce site of a company operating in the fashion retail industry. 

Conceptual research model should be introduced and described more clearly in Section 3. Figure 3 is currently illegible, it should be larger and the description should be more elaborate. The Figure should be introduced before the detailed description of the research approach.

Section 4 should be better organized and include a scheme (figure) of the experimental design. Subection 4.1 is too short in comparison with other subsections. Please bear in mind that the reader should be able to easily follow the paper when reading. 

It is valuable that Authors provide an Appendix to understand their experiment better. However, in Table 4 I would appreciate much better the more informative product names without the need to look into the Appendix.

Since there is no separate Discussion Section I would expect in Section 4 a deeper discussion of the achieved results and reflections on what the Authors’ ideas on them are. Were such results expected by them or not and why? Perhaps the Subsection 4.5 should be split into 2, one for results and one for an overall discussion, which would provide references to papers, to which a proper discussion should refer to.

I suggest that some findings presented in short in Conclusions should be developed more in Section 4. 

I would also like to see in Conclusions clearer directions of future study, with regard to limitations of the experiment presented in the reviewed paper.

Furthermore, I would expect a wider literature review, in particular in Section 2.1. While the general introduction to recommender systems was provided in preceding subsections, and that seems to be well covered also in the Introduction, there is no background to the evaluation aspect of systems. I would suggest an extra paragraph after the first one to briefly introduce recommender systems evaluation, referring to survey papers such as one titled Recommender systems survey published in Knowledge-based systems. 

What is more, the aspects of recommendation content evaluation and optimization should be introduced in Section 2.1 or 2.3 (incl. techniques used such as eye-tracking, mouse tracking, other implicit behavior tracking, etc.) together with references to some most up-to-date papers in the field: paper titled Evaluation of varying visual intensity and position of a recommendation in a recommending Interface towards reducing habituation and improving sales; paper titled Diversified personalized recommendation optimization based on mobile data; paper titled Horizontal vs. vertical recommendation zones evaluation using behavior tracking; paper titled Deep learning-enhanced framework for performance evaluation of a recommending interface with varied recommendation position and intensity based on eye-tracking equipment data processing; and paper titled Attempts to attract eyesight in e-commerce may have negative effects. Also, the topic of purchase intent modeling could be introduced in Section 2.3, e.g. paper titled Fuzzy approach to purchase intent modeling based on user tracking for e-commerce recommenders.

In Table 1 and the discussion which follows I suggest to include more papers where recommendations in fashion e-commerce sites where discussed, in particular from the last few years, for example paper titled Online store aesthetics impact efficacy of product recommendations and highlighting.

The paper also needs some language editing. I will not mention a few examples of mistakes with suggestions of corrections, because there is plenty of work to be done in this realm. I hope the authors will be able to improve on that. 

Please consider the recommendations as constructive remarks and suggestions to improve the already good quality of your manuscript.  

With best regards

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

I commend the Authors for all the improvements they have added for this study, including extending the reference list, providing additional explanations on the study design and analysis. 

As mentioned by the Authors in this new version of the paper: “The data of the company's e-commerce operation in Turkey between 2015-2017 are used for implementation.”

Based on the new details provided by the Authors with regard to the timeline, I would recommend presenting the context of this dataset for this timeframe and its relevance in today’s context. It is important to address the context and its relevancy for the manuscript’s topicality.

Also, perhaps Tables 8-11 could use additional details with regard to ‘Period’ (e.g., year 1 of the analysed timeframe), to increase the readability factor of the paper.

Author Response

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Author Response File: Author Response.pdf