Salesforce CRM Framework for Real Time DeFi Portfolio Intelligence and Customer Engagement Forecasting in Web3 Based Decentralized Finance Ecosystems Using ML Techniques
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P111Keywords:
Salesforce CRM, DeFi Portfolio Intelligence, Web3 Ecosystems, Machine Learning Forecasting, Customer Engagement PredictionAbstract
With the evolution of Web 3.0 and the rise of the DeFi ecosystems it is only natural that the paradigms of customer relationship management have changed, and are in urgent need of novel and real-time ways of gaining insights into portfolios and predicting engagement. In this work, we explore the fusion of Salesforce CRM frameworks with machine learning methodologies such as DeFi portfolio management and customer engagement prediction in the context of Web3. The study uses a quantitative research method that targets a sample of 250 users of DeFi platforms among Indian cryptocurrency exchanges. The questionnaires were structured and dealt with portfolio performance metrics, customer engagement scores, and ML model accuracy indicators for the primary data collection. It includes some predictive analytics methodology such as decision tree algorithms, K-Nearest Neighbors (KNN), and hybrids between deep learning models. Their results show that Salesforce CRM systems integrated with ML achieve 87.3 percent accuracy in forecasting the portfolio and achieve 82.6 percent precision in predicting their customer engagement factors. Results show interesting, statistically relevant correlations between the ability to perform real-time data processing technologies and user satisfaction. The results suggest that blockchain-based CRM models improve transparency, security, and personalization within DeFi ecosystems. We add to the ongoing Web3 customer relationship management dialogue and offer guidance in bringing intelligent CRM solutions to existence within decentralized financial platforms
References
1. Cheng, S. (2024). Future of Web 3.0. In Web 3.0: Concept, Content and Context (pp. 195–217). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-6319-5_9
2. S. K. Gunda, "Fault Prediction Unveiled: Analyzing the Effectiveness of Random Forest, Logistic Regression, and KNeighbors," 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), Erode, India, 2024, pp. 107-113, https://doi.org/10.1109/ICSSAS64001.2024.10760620.
3. Dube, M., & Tshuma, S. (2024). Redesigning consumer engagement through metaverse strategies: Blockchain and Web 3.0 technologies. In AI-Driven Marketing Research and Data Analytics (pp. 309–324). IGI Global.
4. I. Manga, "Scalable Graph Neural Networks for Global Knowledge Representation and Reasoning," 2025 9th International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2025, pp. 1399-1404, doi: 10.1109/ICISC65841.2025.11188341.
5. Durovic, M. (2024). Adaptability of the Unfair Commercial Practices Directive to the metaverse. In Research Handbook on the Metaverse and Law (pp. 311–334). Edward Elgar Publishing. https://doi.org/10.4337/9781035324866.00029
6. S. K. Gunda, "A Deep Dive into Software Fault Prediction: Evaluating CNN and RNN Models," 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India, 2024, pp. 224-228, https://doi.org/10.1109/ICESIC61777.2024.10846549.
7. S. K. Gunda, "Machine Learning Approaches for Software Fault Diagnosis: Evaluating Decision Tree and KNN Models," 2024 Global Conference on Communications and Information Technologies (GCCIT), BANGALORE, India, 2024, pp. 1-5, https://doi.org/10.1109/GCCIT63234.2024.10861953.
8. I. Manga, "Towards Explainable AI: A Framework for Interpretable Deep Learning in High-Stakes Domains," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1354-1360, doi: 10.1109/ICSCSA66339.2025.11170778.
9. Gebre, D., Hadish, S., Sbhatu, A., Aloqaily, M., & Guizani, M. (2024). Establishing trust and security in decentralized metaverse: A Web 3.0 approach. ACM Transactions on Multimedia Computing, Communications and Applications, 20(12), 1–17. https://doi.org/10.1145/3696454
10. I. Manga, "Federated Learning at Scale: A Privacy-Preserving Framework for Decentralized AI Training," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 110-115, doi: 10.1109/ICSCSA66339.2025.11170780.
11. Gunda, S. K. (2024). Smart device for object-oriented software prototype (UK Registered Design No. 6400739). UK Intellectual Property Office, Class 14-02.
12. Murtas, G., Pedeliento, G., & Mangiò, F. (2024). Luxury fashion brands at the gates of the Web 3.0: An analysis of early experimentations with NFTs and the metaverse. Journal of Global Fashion Marketing, 15(1), 90–114. https://doi.org/10.1080/20932685.2023.2249476
13. Gunda, S.K. (2026). A Hybrid Deep Learning Model for Software Fault Prediction Using CNN, LSTM, and Dense Layers. In: Bakaev, M., et al. Internet and Modern Society. IMS 2025. Communications in Computer and Information Science, vol 2672. Springer, Cham. https://doi.org/10.1007/978-3-032-05144-8_21.
14. SanMiguel, P., Sádaba, T., & Sayeed, N. (2024). Fashion brands in the metaverse: Achievements from a marketing perspective. Journal of Global Fashion Marketing, 15(3), 320–340. https://doi.org/10.1080/20932685.2024.2331518
15. Shaikh, A. A., Mutanov, G., & Karjaluoto, H. (Eds.). (2024). Blockchain, metaverse, and digital payments: A global digital consumer perspective. Taylor & Francis. https://doi.org/10.4324/9781003401452
16. Shen, M., Tan, Z., Niyato, D., Liu, Y., Kang, J., Xiong, Z., Zhu, L., Wang, W., & Shen, X. (2024). Artificial intelligence for Web 3.0: A comprehensive survey. ACM Computing Surveys, 56(10), 1–39. https://doi.org/10.1145/3657284
17. I. Manga, "Unified Data Engineering for Smart Mobility: Real-Time Integration of Traffic, Public Transport, and Environmental Data," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1348-1353, doi: 10.1109/ICSCSA66339.2025.11170800.
18. Gunda, S. K., Yalamati, S., Gudi, S. R., Manga, I., & Aleti, A. K. (2025). Scalable and adaptive machine learning models for early software fault prediction in agile development: Enhancing software reliability and sprint planning efficiency. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.74
19. I. Manga, "AutoML for All: Democratizing Machine Learning Model Building with Minimal Code Interfaces," 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2025, pp. 347-352, doi: 10.1109/ICSCDS65426.2025.11167529.
20. Vertakova, Y., & Shkarupeta, E. (2024). Strategic management of the metaverse ecosystem in the context of Web 3.0: Theory, framework and tools. Review of Business and Economics Studies, 12(4), 29–41. https://doi.org/10.26794/2308-944X-2024-12-4-29-41
21. Gunda, S. K. (2025). Accelerating Scientific Discovery With Machine Learning and HPC-Based Simulations. In B. Ben Youssef & M. Ben Ismail (Eds.), Integrating Machine Learning Into HPC-Based Simulations and Analytics (pp. 229-252). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3795-7.ch009.
22. Wan, S., Lin, H., Gan, W., Chen, J., & Yu, P. S. (2024). Web3: The next internet revolution. IEEE Internet of Things Journal, 11(21), 34811–34825. https://doi.org/10.1109/JIOT.2024.3432116.
