Predictive Customer Engagement Using Machine Learning Models for Retail Business
DOI:
https://doi.org/10.47672/ejt.2804Keywords:
Customer Engagement, Retail Business, Machine Learning, Online Retail Dataset, Retail AnalyticsAbstract
Purpose: Customer interaction in retail business is one of the key sources of loyalty, profitability, and long-term development since it shows the ability of a business to establish relations with its customers. Using the Online Retail data set on Kaggle which contains over half of the transactions, totaling 540,000 this article investigates how machine learning approaches might be utilized to predict consumer participation.
Materials and Methods: The research methodology includes data preprocessing in the form of data cleaning, data normalization, and outlier identification. K-Nearest Neighbors (KNN) and Random Forest (RF) are two algorithms that were tested and evaluated using key performance indicators such as recall, accuracy, precision, F1-score, and ROC-AUC.
Findings: Based on the results of the trial, KNN was the most accurate with a 97.90% score, while RF was the most efficient with a 96.46% score, thanks to its higher recall and F1-score. Comparative analysis with other models, including CNN and XGBoost, confirmed the robustness of the proposed models in outperforming traditional approaches. Retail analytics that incorporate machine learning have the ability to boost decision-making, inventory management, and tailored marketing, according to the results. This study demonstrates that predictive modeling can provide powerful tools for fostering customer engagement and achieving competitive advantage in the retail sector.
Unique Contribution to Theory, Practice and Policy: Future improvements, incorporating advanced models such as Gradient Boosting or deep learning, as well as integrating real-time data streams and customer sentiment from social media or reviews, could provide deeper insights into engagement patterns.
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References
[1] A. Kumar, “Retail Sector : Growth and challenges perspective in India,” Int. J. Emerg. Technol., vol. 5, no. 1, pp. 69–73, 2014.
[2] V. Shankar, “Big Data and Analytics in Retailing,” NIM Mark. Intell. Rev., vol. 11, no. 1, pp. 36–40, May 2019, doi: 10.2478/nimmir-2019-0006.
[3] H. Mahobia and R. K. D. Dubey, “A Study on Factors Influencing Consumer Engagement in Retail,” Int. J. Res. IT Manag., vol. 6, no. 2, 2016.
[4] T. K. Rao, “Reimagining Retail : AI-Driven Personalization and the Future of Customer Experience,” Int. J. Innov. Res. Sci. Eng. Technol., vol. 8, no. 2, 2019, doi: 10.15680/IJIRSET.2019.0802106.
[5] A. M. Choudhury and K. Nur, “A Machine Learning Approach to Identify Potential Customer Based on Purchase Behavior,” in 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), IEEE, Jan. 2019, pp. 242–247. doi: 10.1109/ICREST.2019.8644458.
[6] Z. H. Kilimci et al., “An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain,” Complexity, vol. 2019, no. 1, Jan. 2019, doi: 10.1155/2019/9067367.
[7] S. S. S. Neeli, “The Significance of NoSQL Databases : Strategic Business Approaches and Management Techniques,” J. Adv. Dev. Res., vol. 10, no. 1, p. 11, 2019.
[8] F. Naz and F. Popowich, “Mining Retail Telecommunication Data to Predict Profitability,” in 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), IEEE, Aug. 2019, pp. 1–5. doi: 10.1109/PACRIM47961.2019.8985083.
[9] M. A. I. Arif, S. I. Sany, F. I. Nahin, and A. S. A. Rabby, “Comparison Study: Product Demand Forecasting with Machine Learning for Shop,” in 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), IEEE, Nov. 2019, pp. 171–176. doi: 10.1109/SMART46866.2019.9117395.
[10] A. Bhatnagar and S. Srivastava, “A Robust Model for Churn Prediction using Supervised Machine Learning,” in Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing, IACC 2019, 2019. doi: 10.1109/IACC48062.2019.8971494.
[11] L. Liu, B. Zhou, Z. Zou, S. C. Yeh, and L. Zheng, “A Smart Unstaffed Retail Shop Based on Artificial Intelligence and IoT,” in IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD, 2018. doi: 10.1109/CAMAD.2018.8514988.
[12] K. Kim and J.-H. Lee, “Bayesian Optimization of Customer Churn Predictive Model,” in 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), IEEE, Dec. 2018, pp. 85–88. doi: 10.1109/SCIS-ISIS.2018.00024.
[13] H. Amnur, “Customer Relationship Management and Machine Learning Technology for Identifying the Customer,” JOIV Int. J. Informatics Vis., vol. 1, no. 1, pp. 12–15, Mar. 2017, doi: 10.30630/joiv.1.1.10.
[14] Y. Kaneko and K. Yada, “A Deep Learning Approach for the Prediction of Retail Store Sales,” IEEE Int. Conf. Data Min. Work. ICDMW, vol. 0, pp. 531–537, 2016, doi: 10.1109/ICDMW.2016.0082.
[15] A. J. Gallego, J. Calvo-Zaragoza, J. J. Valero-Mas, and J. R. Rico-Juan, “Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation,” Pattern Recognit., vol. 74, pp. 531–543, 2018, doi: 10.1016/j.patcog.2017.09.038.
[16] A. Generosi, S. Ceccacci, and M. Mengoni, “A deep learning-based system to track and analyze customer behavior in retail stores,” in 2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), IEEE, Sep. 2018, pp. 1–6. doi: 10.1109/ICCE-Berlin.2018.8576169.
[17] A. P. Patil, M. P. Deepshika, S. Mittal, S. Shetty, S. S. Hiremath, and Y. E. Patil, “Customer churn prediction for retail business,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput. ICECDS 2017, pp. 845–851, 2018, doi: 10.1109/ICECDS.2017.8389557.
1. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Big Text Data Analysis for Sentiment Classification in Product Reviews Using Advanced Large Language Models. International Journal of AI, BigData, Computational and Management Studies, 2(2), 55-65.
2. Gangineni, V. N., Tyagadurgam, M. S. V., Chalasani, R., Bhumireddy, J. R., & Penmetsa, M. (2021). Strengthening Cybersecurity Governance: The Impact of Firewalls on Risk Management. International Journal of AI, BigData, Computational and Management Studies, 2, 10-63282.
3. Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., & Gangineni, V. N. (2021). An Advanced Machine Learning Models Design for Fraud Identification in Healthcare Insurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 26-34.
4. Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., & Polam, R. M. (2021). Advanced Machine Learning Models for Detecting and Classifying Financial Fraud in Big Data-Driven. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(3), 39-46.
5. Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2021). Enhancing IoT (Internet of Things) Security Through Intelligent Intrusion Detection Using ML Models. International Journal of Emerging Research in Engineering and Technology, 2(1), 27-36.
6. Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2021). Smart Healthcare: Machine Learning-Based Classification of Epileptic Seizure Disease Using EEG Signal Analysis. International Journal of Emerging Research in Engineering and Technology, 2(3), 61-70.
7. Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., & Kamarthapu, B. (2021). Big Data and Predictive Analytics for Customer Retention: Exploring the Role of Machine Learning in E-Commerce. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 26-34.
8. Penmetsa, M., Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., & Pabbineedi, S. (2021). Next-Generation Cybersecurity: The Role of AI and Quantum Computing in Threat Detection. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 54-61.
9. Polu, A. R., Vattikonda, N., Gupta, A., Patchipulusu, H., Buddula, D. V. K. R., & Narra, B. (2021). Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques. Available at SSRN 5297803.
10. Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
11. Polu, A. R., Vattikonda, N., Buddula, D. V. K. R., Narra, B., Patchipulusu, H., & Gupta, A. (2021). Integrating AI-Based Sentiment Analysis With Social Media Data For Enhanced Marketing Insights. Available at SSRN 5266555.
12. Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Vattikonda, N., & Gupta, A. K. (2021). INTEGRATING AI-BASED SENTIMENT ANALYSIS WITH SOCIAL MEDIA DATA FOR ENHANCED MARKETING INSIGHTS. Journal Homepage: http://www. ijesm. co. in, 10(2).
13. Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Narra, B., & Vattikonda, N. (2021). An Analysis of Crime Prediction and Classification Using Data Mining Techniques.
14. Rajiv, C., Mukund Sai, V. T., Venkataswamy Naidu, G., Sriram, P., & Mitra, P. (2022). Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic. J Contemp Edu Theo Artific Intel: JCETAI/102.
15. Sandeep Kumar, C., Srikanth Reddy, V., Ram Mohan, P., Bhavana, K., & Ajay Babu, K. (2022). Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks. J Contemp Edu Theo Artific Intel: JCETAI/101.
16. Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2020). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341
17. Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in Healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340
18. Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., & Chalasani, R. (2022). Designing an Intelligent Cybersecurity Intrusion Identify Framework Using Advanced Machine Learning Models in Cloud Computing. Universal Library of Engineering Technology, (Issue).
19. Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2022). Leveraging Artificial Intelligence Algorithms for Risk Prediction in Life Insurance Service Industry. Available at SSRN 5459694.
20. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.
21. Gangineni, V. N., Pabbineedi, S., Penmetsa, M., Bhumireddy, J. R., Chalasani, R., & Tyagadurgam, M. S. V. Efficient Framework for Forecasting Auto Insurance Claims Utilizing Machine Learning Based Data-Driven Methodologies. International Research Journal of Economics and Management Studies IRJEMS, 1(2).
22. Vattikonda, N., Gupta, A. K., Polu, A. R., Narra, B., Buddula, D. V. K. R., & Patchipulusu, H. H. S. (2022). Blockchain Technology in Supply Chain and Logistics: A Comprehensive Review of Applications, Challenges, and Innovations. International Journal of Emerging Research in Engineering and Technology, 3(3), 99-107.
23. Narra, B., Vattikonda, N., Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., & Polu, A. R. (2022). Revolutionizing Marketing Analytics: A Data-Driven Machine Learning Framework for Churn Prediction. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 112-121.
24. Polu, A. R., Narra, B., Buddula, D. V. K. R., Patchipulusu, H. H. S., Vattikonda, N., & Gupta, A. K. BLOCKCHAIN TECHNOLOGY AS A TOOL FOR CYBERSECURITY: STRENGTHS, WEAKNESSES, AND POTENTIAL APPLICATIONS.
25. Bhumireddy, J. R., Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., & Penmetsa, M. (2022). Big Data-Driven Time Series Forecasting for Financial Market Prediction: Deep Learning Models. Journal of Artificial Intelligence and Big Data, 2(1), 153–164.DOI: 10.31586/jaibd.2022.1341
26. Nandiraju, S. K. K., Chundru, S. K., Vangala, S. R., Polam, R. M., Kamarthapu, B., & Kakani, A. B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer’s Disease (AD) in Healthcare. Journal of Artificial Intelligence and Big Data, 2(1), 141–152.DOI: 10.31586/jaibd.2022.1340
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Copyright (c) 2023 Rami Reddy Kothamaram, Dinesh Rajendran, Venkata Deepak Namburi, Vetrivelan Tamilmani, Aniruddha Arjun Singh Singh, Vaibhav Maniar
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