PONE-D-24-55477Application of machine learning in predicting consumer behavior and precision marketingPLOS ONE

Dear Dr. LIN,

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Originality

While some aspects show promise, it does not sufficiently demonstrate groundbreaking insights. The claims, while relevant, need further validation to establish originality and significance.

2. Technical Soundness

The manuscript employs specific methods or techniques, which seem appropriate on a surface level. However, the link between the data and conclusions lacks robustness. Some areas need clarification, especially in how findings directly support the stated outcomes.

3. Main Claims and Significance

The primary claims potentially impactful, their significance for the discipline is moderate. The work but does not convincingly position itself as transformative within the field.

4. Relationship to Literature

The paper demonstrates familiarity with existing literature but falls short in thoroughly contextualizing its contribution. These omissions detract from the paper's credibility and integration into the wider academic discourse.

5. Placement of Claims in Literature Context

The claims are not adequately framed within the context of existing literature. For instance, specific claim could be better supported by referencing to significant work. The discussion occasionally seems disconnected from the broader field.

6. Statistical Analysis

Statistical methods, including the analysis techniques, are employed but lack detailed explanation. The justification for the statistical choice is insufficient, and key assumptions are not thoroughly addressed. A more rigorous and transparent presentation of the analysis is needed to substantiate claims.

7. Potential for Resubmission

Despite its current shortcomings, the study has potential. Encouraging the authors to resubmit with revisions could elevate the work's quality and impact.

8. Data Availability

The paper does not clearly confirm that all underlying data are accessible. Transparent data sharing, including detailed datasets, would enhance the reproducibility and credibility of the findings.

9. Repository Information

No explicit mention is made of depositing data in public repositories. Providing accession numbers or repository links for specific data elements (e.g., genes, proteins, or other significant findings) is critical.

10. Methodology

The methodology appears reasonably designed but lacks sufficient theoretical underpinning in some areas. Specific steps of methodology need clearer justification. Without this, reproducibility and confidence in the results are compromised.

11. Reproducibility

Details of the methodology are insufficient to ensure reproducibility. Key parameters are omitted or underexplained.

12. Results

The results section is descriptive but occasionally unclear. Graphs and tables, while helpful, would benefit from additional commentary to elucidate their connection to the hypotheses and conclusions.

13. Implications for Research, Practice, and Society

The paper briefly touches on its implications but does not fully explore their relevance to research, practice, or society. Expanding on these would strengthen its practical and theoretical contributions.

14. Quality of Communication

The writing is technically accurate but lacks clarity in some areas. Sentence structure and jargon usage occasionally hinder accessibility. Simplifying language without sacrificing technical precision would improve communication.

15. Accessibility to Non-Specialists

The manuscript is primarily aimed at specialists, and non-specialists may find it difficult to engage with the content due to dense technical language and insufficient background context.

16. Standard English Usage

The manuscript generally adheres to standard English conventions but contains minor grammatical inconsistencies and awkward phrasing.

17. Comments to the Author

• Ensure all data and methodologies are transparently presented to facilitate reproducibility.

• Address gaps in the literature review and align claims more closely with existing studies.

• Enhance clarity in the statistical analysis section, providing rationale and assumptions for methods used.

• Expand on the societal and practical implications of the research findings.

The paper demonstrates potential but requires substantial revision to meet publication standards. Key areas needing attention include data transparency, methodological clarity, and integration with existing literature. Encouraging a resubmission after thorough revision is recommended.

Reviewer #2: ABSTRACT

1. Ensure all abbreviations in the abstract, such as SVM, XGBoost, CatBoost, and BPANN, are clearly defined upon first use for better readability and comprehension.

2. Include performance metrics like F1-score etc. in the abstract for better clarity and impact.

3. "Future research can further enhance the predictive power..." - This statement is overly generic and uninspiring. Specify actionable directions for future research, such as incorporating unstructured data (e.g., text, images) or experimenting with deep learning models like Transformers for consumer behavior analysis.

INTRODUCTION:

1. Please format citations in square brackets (e.g., “[19]”) as per the submission guidelines.

2. “Consumer behavior has become increasingly complex and unpredictable..." - This sentence reiterates what is already mentioned in the abstract without adding new information. Use this space to provide detailed background or evidence, such as specific challenges faced by industries.

3. "Traditional marketing models are unable to effectively cope with this change..." - This statement lacks support. Briefly explain why traditional models fail. For example, are they too rigid, or do they lack adaptability to real-time data?

4. Does not provide sufficient evidence or citations to justify the selection of the machine learning models (SVM, XGBoost, CatBoost, BPANN) over other alternatives. Including references to studies or benchmarks demonstrating the superior performance, efficiency, or suitability of these models for consumer behavior prediction tasks would strengthen the argument.

5. The introduction fail to address potential limitations, such as computational complexity, data quality issues, or overfitting risks. Discussing these would strengthen the credibility of your research.

6. Replace vague terms like "significant impact" and "great potential" with specific examples, data points, or case studies to ground your claims in reality.

7. The stated research objective, "to explore how to optimize consumer behavior prediction and precision marketing through machine learning," lacks originality and does not sufficiently highlight the study's unique contribution. To strengthen this section, clearly articulate the research gap.

8. Please provide a suitable comparison with other state-of-the-art models to highlight the strengths and limitations of your approach in relation to existing methodologies.

Modeling Algorithms:

1. The section titled "Modeling Algorithms" should be renamed to "Methods and Materials" to align with standard academic conventions.

2. Additionally, within this section, include a detailed subsection that thoroughly explains the methodology of the study. This should cover how each machine learning model (SVM, XGBoost, CatBoost, BPANN) is implemented, the data collection, data preprocessing steps, feature selection, model architecture, model training and testing . performance metrics and any other relevant aspects of the experimental setup.

3. Within the "Methods and Materials" section, for each machine learning algorithm used, provide a detailed explanation of why the selected model is the best choice for this study. Include supporting evidence from relevant literature or prior research that demonstrates the model's effectiveness in similar contexts.

4. For each machine learning algorithm used in the study, please include a visual representation (e.g., flowchart or diagram) of the algorithm’s implementation.

RESULTS AND DISCUSSION

1. The Results and Discussion section should focus on how the results relate to the hypothesis presented at the start of the study. It should provide a succinct explanation of the implications of the findings, particularly in relation to previous related studies. The detailed methodology, including Data Collection, Data Preprocessing, Feature Engineering, model training and OPtimization should be moved to the Methods and Materials section, as these describe the process rather than the outcomes. Please revise the manuscript to reflect this distinction.

2. Clarify the source of the dataset more explicitly. The mention of "UCI machine learning library" is sufficient, but you should also include the specific version or year to ensure reproducibility.

3. Please add a statistical metrics table (such as mean, standard deviation, etc.) to support the data analysis and provide a clearer summary of the key data characteristics.

4. Mention why SMOTE was chosen for addressing class imbalance and justify this choice with a brief explanation of its advantages.

5. It would be useful to explain why the threshold for variance filtering and mutual information was set to 0.05 and 0.05 respectively.

6. Clarify the performance metrics used (e.g., ROC AUC, accuracy, recall) and why they are important in assessing model performance.

7. Consider discussing the trade-offs between model accuracy and model interpretability.

8. Include more details on the comparison metrics for the models.

9. Provide clearer interpretations of results in Table 2 and Figure 3.

10. Add more context to the practical implications of feature importance analysis. How should companies act on these insights beyond the optimization suggestions?

11. The section on marketing strategy optimization seems theoretical. Provide examples or case studies where these strategies have been successfully implemented, or reference studies to support these suggestions.

12. Clarify the methodology used to optimize personalized recommendations, dynamic pricing, etc.

Conclusion and Prospect

1. The section can be renamed as "Conclusion and Future Scope".

2. The conclusion appropriately summarizes the key findings but could benefit from clearer connections to the research questions and hypotheses stated earlier in the paper. Emphasize how the findings align with the initial objectives of the study.

3. The suggestion to introduce additional data types such as social media behavior and geolocation is insightful but could be expanded. Explain how incorporating these data types could enhance model performance, and provide examples of their potential impact on prediction accuracy.

4. The mention of combining deep learning or reinforcement learning is promising. However, it would be beneficial to elaborate on how these advanced techniques could be integrated into the current model framework, and what challenges might be encountered when doing so.

5. The term "real-time decision optimization" could be clarified. It would be useful to specify which specific real-time applications, such as personalized recommendations, dynamic pricing, or user loyalty management, could benefit from the improved model.

6. The future scope mentions incorporating emerging technologies but should also emphasize how future work could address the limitations or gaps identified in the current study. This could include areas for refinement in model performance or alternative strategies to overcome challenges observed.

Reviewer #3: 1- In marketing we need to study about 4Ps at least ....what about product and placing data ? have you consider such a parameters in your modeling ?

2-Authers must put data collection as section two and make the results and discussion section four and methodology as a section three.

3-The literature review is very limited and need to be extended to include several other researches published in 2023-2024.

4-The methodology modeling flowchart is not presented and need to be added for the readers benefits.

5-The discussion is very shallow and need to be extended with other feature dimensions.

Reviewer #4: The manuscript titled “Application of Machine Learning in Predicting Consumer Behavior and Precision Marketing” addresses a highly relevant topic for today’s digital economy. The comparative evaluation of machine learning models—SVM, XGBoost, CatBoost, and BPANN—adds value by providing insights into consumer behavior prediction. The study’s feature importance analysis and its practical suggestions, such as personalized recommendations and dynamic pricing, are particularly noteworthy. That said, there are areas where the paper could be improved to enhance clarity, depth, and overall impact.

Clarify the research gap and intent in the introduction. While the introduction highlights the broader issues of consumer behavior and precision marketing, it does not clearly identify the specific research gap this study addresses. What problem in consumer behavior prediction does this work solve that others have not? Additionally, the paper needs a sharper focus on its novelty. For instance, what makes this comparison of models unique in the current landscape of machine learning applications? Explicitly stating these elements will position the study more clearly and strengthen its contribution.

Add a dedicated literature review section. Currently, there is no structured discussion of prior research, which makes it difficult to understand the groundwork that led to this study. A literature review should summarize recent advancements in machine learning for marketing, highlight key works, and point out gaps that justify the need for this research. Make sure to include references to recent studies from 2023 and 2024, as the field of machine learning and marketing is evolving rapidly. This will not only make the paper more current but also situate it within the broader academic conversation.

Include a clear methodology section before modeling. The paper jumps into describing the models without providing a proper methods section. This section should outline the research design, the choice of models, and why these specific techniques (SVM, XGBoost, CatBoost, BPANN) were chosen over alternatives like LightGBM or Random Forest. Additionally, elaborate on preprocessing decisions, such as feature selection, handling imbalanced data with SMOTE, and outlier removal. Providing detailed justifications here will improve the paper’s reproducibility and transparency.

Develop a discussion section to interpret the results. The results section does a good job of presenting the model performance and feature importance analysis, but it lacks a deeper discussion. Explain why CatBoost and XGBoost outperformed other models—what aspects of these techniques make them more suitable for handling complex e-commerce data? Also, compare your findings with similar works done in this area to highlight alignment or differences. To make this section more engaging for practitioners, consider incorporating feedback from real-life e-commerce marketers to frame the results in terms of their business impact, such as ROI improvement or customer segmentation strategies.

Add a limitations and implications section. It’s important to acknowledge the limitations of the study, such as the use of a static dataset that may not capture the dynamic nature of consumer behavior. Discuss how this could affect the results and what steps can be taken to address it in future research. On the implications side, connect the findings to practical applications—how can businesses use these models to improve marketing outcomes? This section would be particularly valuable for bridging the gap between research and industry practice.

Conclude with a strong conclusion section. The paper ends abruptly without summarizing the key findings or outlining future directions. A clear conclusion should highlight the main contributions of the study, such as the performance of CatBoost and XGBoost, and their potential for e-commerce applications. It should also suggest future work, like exploring deep learning approaches, real-time data analysis, or expanding the dataset to include other industries and consumer segments.

Consider integrating a theoretical framework. The study would benefit from being grounded in a marketing or behavioral theory to connect the findings to a broader context. For example, frameworks like Behavioral Economics (decision-making) or Customer Relationship Management (CRM) could add depth to the study. This would elevate the paper’s academic value and provide readers with a stronger conceptual understanding of the work.

Minor refinements for clarity and engagement. While the paper is generally clear, there are areas where the writing could be polished for better flow and readability. For instance, some figures and tables could benefit from more descriptive captions to help readers quickly understand the takeaways. Additionally, improving the grammar and refining terminology (e.g., "administrative pages") would make the paper more professional and accessible.

In summary, this paper tackles a critical and timely issue in precision marketing and demonstrates the value of machine learning models in predicting consumer behavior. The comparative analysis and practical insights are strengths of the study. To further improve its quality, I recommend clarifying the research gap, adding a literature review and methodology section, developing a robust discussion, and including practical business perspectives. With these refinements, the paper will be far more impactful for both academic researchers and industry professionals

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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Application of machine learning in predicting consumer behavior and precision marketing

PONE-D-24-55477R1

Dear Dr. Lin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Evans Otieno Omondi, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Abstract:

Clearly highlight the novel insights compared to past studies.

Introduction:

Clearly define your primary research questions or hypotheses upfront to guide the readers through the manuscript clearly and effectively.

Modeling Algorithms:

Explicitly justify why deep learning methods (such as CNN or RNN) were not included in your comparative analysis, given their prominence in recent literature on consumer behavior prediction.

Clearly articulate the rationale behind selecting the RBF kernel for SVM, and briefly discuss why alternative kernels (linear, polynomial) were not suitable or selected.

Dataset Presentation:

Provide a detailed table clearly describing all dataset features, including feature type, detailed descriptions, and value ranges. This will significantly enhance the manuscript's clarity and reproducibility.

Efficiency and Scalability:

Explicitly present details regarding computational resources (runtime, CPU/GPU requirements, memory usage) for each machine learning model.

Clearly suggest practical scenarios and explicit limitations when deploying these machine learning models at large-scale or real-time contexts.

Minor Recommendations:

Correct typographical errors, such as labeling inaccuracies (e.g., correcting Fig. 1 from "SWM" to "SVM").

Enhance readability by reducing repetitive content and maintaining consistent terminology throughout the manuscript.

Reviewer #3: no further comments; the manuscript was revised accordingly. Now the manuscript can be considered for publication

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #2: No

Reviewer #3: No

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