Hyperfocused Customer Insights Based On Graph Analytics and Knowledge Graphs

Authors

  • Sarbaree Mishra Program Manager at Molina Healthcare Inc., USA. Author
  • Vineela Komandla Vice President - Product Manager, JP Morgan, USA. Author
  • Srikanth Bandi Software Engineer, JP Morgan Chase, USA. Author

DOI:

https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P110

Keywords:

Graph Analytics, Knowledge Graphs, Customer Insights, Personalization, Data Analytics, Predictive Analytics, Data-Driven Decisions, Customer Behavior, Hyperfocus, Behavioral Analytics, Customer Segmentation, AI-Powered Insights, Data Visualization, Real-Time Analytics, Customer Journey Mapping, Machine Learning, Contextual Data, Customer Retention, Targeted Marketing, Data Integration, Decision-Making Models, Consumer Preferences, Dynamic Profiling, Data-Driven Personalization, User Behavior Analysis, Deep Learning, Data Mining

Abstract

Businesses are opting for graph analytics and knowledge graphs more than ever to extract high-quality customer insights. These tools allow companies to establish relationships with data points, thus revealing hidden patterns and connections that traditional methods are unlikely to detect. By graph analytics, businesses are aware of the customer's behavior more clearly, which in turn enables them to create more personalized experiences and targeted strategies. Knowledge graphs amplify this notion by restructuring complex data into simple and accessible formats and then providing a bird's-eye view of the interactions between different elements. Such a picture of customer interactions enables companies to go beyond isolated data points and identify the relationships that result in customers's actions. Thus, with such insights, businesses become capable of forecasting future behaviors, reading customer minds, and making decisions more efficiently. The potential of graph analytics and knowledge graphs runs through all sectors, from customer service and marketing campaigns all the way to product development and sales forecasting. For instance, graph analytics might help companies to identify the hot topics and the products that fit customers' tastes, hence, engagement and sales going up. By drawing out and connecting the data from different sources, knowledge graphs give businesses the power to see the grand view and make strategic decisions that enhance the overall customer experience. This transition from fundamental data analysis to a deeper, more connected understanding of customer behavior is a significant milestone in how businesses engage with their audience and make data-backed decisions

References

1. Loshin, D. (2013). Big data analytics: from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. Elsevier.

2. Arthur, L. (2013). Big data marketing: engage your customers more effectively and drive value. John Wiley & Sons.

3. Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).

4. Abdul Jabbar Mohammad. “Timekeeping Accuracy in Remote and Hybrid Work Environments”. American Journal of Cognitive Computing and AI Systems, vol. 6, July 2022, pp. 1-25

5. Graham, H. (2018). Marketing to life scientists: Fact and fiction from the frontlines.

6. Nookala, G. (2023). Secure multiparty computation (SMC) for privacy-preserving data analysis. Journal of Big Data and Smart Systems, 4(1).

7. Manda, Jeevan Kumar. "Privacy-Preserving Technologies in Telecom Data Analytics: Implementing Privacy-Preserving Techniques Like Differential Privacy to Protect Sensitive Customer Data During Telecom Data Analytics." Available at SSRN 5136773 (2023).

8. Olson, C., & Levy, J. (2018). Transforming marketing with artificial intelligence. Applied Marketing Analytics, 3(4), 291-297.

9. Talakola, Swetha. “Automating Data Validation in Microsoft Power BI Reports”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Jan. 2023, pp. 321-4

10. Shaik, Babulal. "Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns." Journal of Bioinformatics and Artificial Intelligence 1.2 (2021): 71-90.

11. Allam, Hitesh. "Declarative Operations: GitOps in Large-Scale Production Systems." International Journal of Emerging Trends in Computer Science and Information Technology 4.2 (2023): 68-77.

12. Fader, P., & Toms, S. E. (2018). The customer centricity playbook: Implement a winning strategy driven by customer lifetime value. University of Pennsylvania Press.

13. Balkishan Arugula. “AI-Driven Fraud Detection in Digital Banking: Architecture, Implementation, and Results”. European Journal of Quantum Computing and Intelligent Agents, vol. 7, Jan. 2023, pp. 13-41

14. Jani, Parth. “Azure Synapse + Databricks for Unified Healthcare Data Engineering in Government Contracts”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 2, Jan. 2022, pp. 273-92

15. Gemignani, Z., Gemignani, C., Galentino, R., & Schuermann, P. (2014). Data fluency: Empowering your organization with effective data communication. John Wiley & Sons.

16. Patel, Piyushkumar. "The Implementation of Pillar Two: Global Minimum Tax and Its Impact on Multinational Financial Reporting." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 227-46.

17. Veluru, Sai Prasad. "Streaming Data Pipelines for AI at the Edge: Architecting for Real-Time Intelligence." International Journal of Artificial Intelligence, Data Science, and Machine Learning 3.2 (2022): 60-68.

18. Manda, J. K. "DevSecOps Implementation in Telecom: Integrating Security into DevOps Practices to Streamline Software Development and Ensure Secure Telecom Service Delivery." Journal of Innovative Technologies 6.1 (2023): 5.

19. Upadhyay, S., & McCormick, K. (2018). The Revenue Acceleration Rules: Supercharge Sales and Marketing Through Artificial Intelligence, Predictive Technologies and Account-Based Strategies. John Wiley & Sons.

20. Datla, Lalith Sriram. “Postmortem Culture in Practice: What Production Incidents Taught Us about Reliability in Insurance Tech”. International Journal of Emerging Research in Engineering and Technology, vol. 3, no. 3, Oct. 2022, pp. 40-49

21. Balkishan Arugula. “From Monolith to Microservices: A Technical Roadmap for Enterprise Architects”. Journal of Artificial Intelligence & Machine Learning Studies, vol. 7, June 2023, pp. 13-41

22. Abdul Jabbar Mohammad. “Cross-Platform Timekeeping Systems for a Multi-Generational Workforce”. American Journal of Cognitive Computing and AI Systems, vol. 5, Dec. 2021, pp. 1-22

23. David, L. (2013). Big Data Analytics From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph.

24. Allam, Hitesh. “Unifying Operations: SRE and DevOps Collaboration for Global Cloud Deployments”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 89-98

25. Suwelack, T., Stegemann, M., & Ang, F. X. (2022). Creating a Customer Experience-Centric Startup. Springer International Publishing.

26. Chaganti, Krishna Chaitanya. "AI-Powered Threat Detection: Enhancing Cybersecurity with Machine Learning." International Journal of Science And Engineering 9.4 (2023): 10-18.

27. Immaneni, J. (2023). Detecting Complex Fraud with Swarm Intelligence and Graph Database Patterns. Journal of Computing and Information Technology, 3.

28. West, M. (2019). People analytics for dummies. John Wiley & Sons.

29. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).

30. Patel, Piyushkumar. "Transfer Pricing in a Post-COVID World: Balancing Compliance With New Global Tax Regimes." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 208-26

31. Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Voice AI in Salesforce CRM: The Impact of Speech Recognition and NLP in Customer Interaction Within Salesforce’s Voice Cloud”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 3, Aug. 2023, pp. 264-82

32. Kaufman‐Scarborough, C., & Cohen, J. (2004). Unfolding consumer impulsivity: An existential–phenomenological study of consumers with attention deficit disorder. Psychology & Marketing, 21(8), 637-669.

33. Mohammad, Abdul Jabbar. “Predictive Compliance Radar Using Temporal-AI Fusion”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 1, Mar. 2023, pp. 76-87

34. Shaik, Babulal. "Automating Compliance in Amazon EKS Clusters With Custom Policies." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 587-10.

35. Olson, A. B. (2022). What to Ask: How to Learn what Customers Need But Don't Tell You. BenBella Books.

36. Jani, Parth. "Predicting Eligibility Gaps in CHIP Using BigQuery ML and Snowflake External Functions." International Journal of Emerging Trends in Computer Science and Information Technology 3.2 (2022): 42-52.

37. Datla, Lalith Sriram. “Infrastructure That Scales Itself: How We Used DevOps to Support Rapid Growth in Insurance Products for Schools and Hospitals”. International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 1, Mar. 2022, pp. 56-65

38. Misirlis, N. (2019). Social media behavior analysis: exploring the paradigm in eHealth.

39. Chaganti, Krishna C. "Leveraging Generative AI for Proactive Threat Intelligence: Opportunities and Risks." Authorea Preprints.

40. Manda, Jeevan Kumar. "Augmented Reality (AR) Applications in Telecom Maintenance: Utilizing AR Technologies for Remote Maintenance and Troubleshooting in Telecom Infrastructure." Available at SSRN 5136767 (2023).

41. Marincolo, S. (2010). High: Insights on marijuana. Dog Ear Publishing.

42. Burgess, C. (2020). The new marketing: how to win in the digital age.

43. Govindarajan Lakshmikanthan, Sreejith Sreekandan Nair (2022). Securing the Distributed Workforce: A Framework for Enterprise Cybersecurity in the Post-COVID Era. International Journal of Advanced Research in Education and Technology 9 (2):594-602.

Downloads

Published

2023-12-30

Issue

Section

Articles

How to Cite

1.
Mishra S, Komandla V, Bandi S. Hyperfocused Customer Insights Based On Graph Analytics and Knowledge Graphs. IJAIBDCMS [Internet]. 2023 Dec. 30 [cited 2025 Oct. 30];4(4):88-99. Available from: https://ijaibdcms.org/index.php/ijaibdcms/article/view/209