AI Enhanced User Feedback Systems for Product Managers: Leveraging Data to Drive Insights |
||
 |
 |
|
| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-1 |
||
| Year of Publication : 2025 | ||
| Authors : Dhruv Sawhney, Abhai Pratap Singh | ||
| DOI : 10.14445/22312803/IJCTT-V73I1P114 | ||
How to Cite?
Dhruv Sawhney, Abhai Pratap Singh, "AI Enhanced User Feedback Systems for Product Managers: Leveraging Data to Drive Insights," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 119-127, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P114
Abstract
This paper examines whether AI-enhanced user feedback systems can transform product managers when consumer expectations change dynamically in the digital era. It looks at how two of the fastest-evolving technologies, Artificial intelligence and machine learning, make a big difference in collecting, processing, and interpreting user feedback information. This research points out a number of the positive attributes of AI-driven systems: real-time sentiment analysis, predictive trend forecasting, and automated categorization of user comments. This paper discusses challenges and other ethical issues in implementing such advanced systems. Our results show that AI-enhanced feedback mechanisms have significantly enhanced product development cycles, customer satisfaction, and overall business performance. This research will provide valuable insights for product managers leveraging bleeding-edge technology to drive data-informed decisions and stay ahead of the competition in today's dynamic market landscape.
Keywords
Artificial Intelligence, Customer Success Management, Data engineering, Feedback analysis, Natural Language Processing, Product management.
Reference
[1] Evelyn Tio et al., “Measuring Holistic User Experience: Keeping an Eye on What Matters Most to Users,” Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Yuanzhu Zhan et al., “Unlocking the Power of Big Data in New Product Development,” Annals of Operations Research, vol. 270, pp. 577-595, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Q. Vera Liao et al., “Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience,” Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Aleksander Fabijan, Helena Holmström Olsson, and Jan Bosch, “Customer Feedback and Data Collection Techniques in Software R&D: A Literature Review,” Lecture Notes in Business Information Processing, vol. 210, pp. 139-153, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Emmanuel Kumah, Felix Osei-Kesse, and Cynthia Anaba, “Understanding and using Patient Experience Feedback to Improve Health Care Quality: Systematic Review and Framework Development,” Journal of Patient-Centered Research and Reviews, vol. 4, no. 1, pp. 24-31, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Dookun Park et al., “Large-Scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents,” arXiv, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Tongshuang Wu, Michael A Terry, and Carrie Jun Cai, “AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts,” arXiv, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sai Vishwanath Venkatesh, Atra Akandeh, and Madhu Lokanath, “MetaPix: A Data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured Computer Vision Data,” arXiv, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sara Abdali et al., “Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-Context Learning,” arXiv, pp. 1 7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Haiju Hu, Yonghui Wei, and Yu Zhou, “Product-Harm Crisis Intelligent Warning System Design based on Fine-Grained Sentiment Analysis of Automobile Complaints,” Complex & Intelligent Systems, vol. 9, pp. 2313-2320, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yue Wang et al., “Configuring Products with Natural Language: A Simple Yet Effective Approach based on Text Embeddings and Multilayer Perceptron,” International Journal of Production Research, vol. 60, no. 17, pp. 5394-5406, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Chris Parnin et al., “Building Your Product Copilot: Challenges, Opportunities, and Needs,” arXiv, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Xikui Wang, and Michael J. Carey, “An IDEA: An Ingestion Framework for Data Enrichment in AsterixDB,” Proceedings of the VLDB Endowment, vol. 12, no. 11, pp. 1485-1498, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Youngja Park, and Stephen C. Gates, “Towards Real-Time Measurement of Customer Satisfaction using Automatically Generated Call Transcripts,” Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1387-1396, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Janet R. McColl-Kennedy et al., “Gaining Customer Experience Insights That Matter,” Journal of Service Research, vol. 22, no. 1, pp. 8-26, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Shi Feng et al., “InterSentiment: Combining Deep Neural Models on Interaction and Sentiment for Review Rating Prediction,” International Journal of Machine Learning and Cybernetics, vol. 12, pp. 477-488, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Paolo Zicari et al., “Discovering Accurate Deep Learning-Based Predictive Models for Automatic Customer Support Ticket Classification,” Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1098-1101, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Huu-Thanh Duong, and Tram-Anh Nguyen-Thi, “A Review: Preprocessing Techniques and Data Augmentation for Sentiment Analysis,” Computational Social Networks, vol. 8, pp. 1-16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Nina Yan, Xun Xu, and Tingting Tong, “Should Businesses Keep Consistent Performance between Channels? Empirical Evidence from the Impact of Rating Discrepancy on Demand,” Decision Sciences, vol. 53, no. 2, pp. 260-276, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jiahao Wanga, and Amer Shalabya, “Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model,” arXiv, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mengxin Wang, Dennis J. Zhang, and Heng Zhang, “Large Language Models for Market Research: A Data-augmentation Approach,” arXiv, pp. 1-47, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Nikhil Bangad et al., “A Theoretical Framework for AI-driven Data Quality Monitoring in High-Volume Data Environments,” arXiv, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mark C. Ballandies et al., “Constructing Effective Customer Feedback Systems -- A Design Science Study Leveraging Blockchain Technology,” arXiv, pp. 1-36, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shu-Ting Pi, Michael Yang, and Qun Liu, “Contact Complexity in Customer Service,” arXiv, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Liangyu Teng et al., “End-Cloud Collaboration Framework for Advanced AI Customer Service in E-Commerce.” 2024 IEEE 10th World Forum on Internet of Things (WF-IoT), Ottawa, ON, Canada, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Aisha Zahid Huriye, “The Ethics of Artificial Intelligence: Examining the Ethical Considerations Surrounding the Development and use of AI,” American Journal of Technology, vol. 2, no. 1, pp. 37-45, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Blake Murdoch, “Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era,” BMC Medical Ethics, vol. 22, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Brij Mohan Dutta, “The Ethics of Artificial Intelligence in Legal Decision Making: An Empirical Study,” Psychology and Education Journal, vol. 55, no. 1, pp. 1-11, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Stefania Montani, and Manuel Striani, “Artificial Intelligence in Clinical Decision Support: A Focused Literature Survey,” Yearbook of Medical Informatics, vol. 28, no. 1, pp. 120-127, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Renwen Zhang et al., “Editorial: Responsible AI in Healthcare: Opportunities, Challenges, and Best Practices,” Frontiers in Computer Science, vol. 5, pp. 1-3, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Sofia Morandini et al., “Assessing the Transparency and Explainability of AI Algorithms in Planning and Scheduling Tools: A Review of the Literature,” Human Interaction & Emerging Technologies, vol. 111, pp. 609-619, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Sandeep Reddy Gudimetla, and Niranjan Reddy Kotha, “Ethical Consideration In AI-Driven Security Solutions Development,” International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 5, pp. 1-3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Anna Jobin, Marcello Ienca, and Effy Vayena, “The Global Landscape of AI Ethics Guidelines,” Nature Machine Intelligence, vol. 1, pp. 389-399, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Frank Phillipson, Tariq Bontekoe, and Irina Chiscop, “Energy Storage Scheduling: A QUBO Formulation for Quantum Computing,” Innovations for Community Services, pp. 251-261, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Fristi Riandari, Aisyah Alesha, and Hengki Tamando Sihotang, “Quantum Computing for Production Planning,” International Journal of Enterprise Modelling, vol. 15, no. 3, pp. 163-175, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Beatriz Rey et al., “Development and Testing of a Portable Virtual Reality-Based Mirror Visual Feedback System with Behavioral Measures Monitoring,” International Journal of Environmental Research and Public Health, vol. 19, no. 4, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Musarath Jahan Karamthulla et al., “Navigating the Future: AI-Driven Project Management in the Digital Era,” International Journal for Multidisciplinary Research, vol. 6, no. 2, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Ramesh Nyathani, “AI-Driven HR Analytics: Unleashing the Power of HR Data Management,” Journal of Technology and Systems, vol. 5, no. 2, pp. 15-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Sichun Luo et al., “Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation,” arXiv, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]