VIJ Digital library
Articles

AI-Powered Personalization in Salesforce: Enhancing Customer Engagement through Machine Learning Models

Ravi Teja Potla
Department of Information Technology, Slalom Consulting,
Vamsi Krishna Pottla
Department of Information Technology, United Health Group,

Submission to VIJ 2024-08-25

Keywords

  • Salesforce, AI, Machine Learning, Personalization, Customer Engagement, CRM, Predictive Analytics, Customer Relationship Management

Abstract

In a fast-moving, digitized, customer-centric world, the convergence of Artificial Intelligence and Machine Learning within CRM platforms serves as a potent agent of change. This paper presents the possibility of integrating Artificial Intelligence and machine learning models into Salesforce CRM for delivery of highly personalized customer experiences to enhance customer engagement. As businesses are fast embracing AIdriven solutions, it has become very critical to understand how these technologies shape customer relationships and, in turn, impact overall business success.

The various machine learning techniques used within Salesforce, in particular, are investigated with a view to delivering personalized interactions that best resonate with customers: recommendation systems, clustering algorithms, and predictive analytics. Such AI-driven models are supposed to analyze large amounts of customer data to spot patterns and preferences that allow for tailoring communications, offers, and services to the particular needs of every customer.

This in-depth analysis of case studies and real-world empirical data in the research reflects major improvements in the key customer engagement metrics, such as click-through rates, conversion rates, and customer satisfaction scores, right after the execution of AI-driven personalization strategies in Salesforce. It also covers some challenges and discussions for the deployment of AI in a CRM environment regarding keeping customers' data private, ethical concerns, and the need for transparency of AI decision-making processes.

These results give very useful insight into an organization's future in effectively using AI within CRM strategy. With AI-driven personalization inside Salesforce, businesses will achieve a lot more in customer engagement and in building a strong relationship with customers for their businesses to grow and be competitive in the market.

 

References

  1. Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).
  2. Kaliuta, K. (2023). Personalizing the user experience in Salesforce using AI technologies. Computer-Integrated Technologies: Education, Science, Production, (52), 48-53.
  3. Tadimarri, A., Jangoan, S., Sharma, K. K., & Gurusamy, A. AI-Powered Marketing: Transforming Consumer Engagement and Brand Growth.
  4. VORSOBINA, M. The impact of AI-powered digital marketing operations: empirical evidence from case studies.
  5. Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how?. International Journal of Information Management, 77, 102783.
  6. Ajiga, D. I., Ndubuisi, N. L., Asuzu, O. F., Owolabi, O. R., Tubokirifuruar, T. S., & Adeleye, R. A. (2024). AI-driven predictive analytics in retail: a review of emerging trends and customer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(2), 307-321.
  7. Reddy, S. R. B. (2022). Enhancing Customer Experience through AI-Powered Marketing Automation: Strategies and Best Practices for Industry 4.0. Journal of Artificial Intelligence Research, 2(1), 36-46.
  8. Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmantation in Augmented reality. Innovative Computer Sciences Journal, 9(1).
  9. Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).
  10. Wang, Z., Zhu, Y., Li, Z., Wang, Z., Qin, H., & Liu, X. (2024). Graph neural network recommendation system for football formation. Applied Science and Biotechnology Journal for Advanced Research, 3(3), 33-39.
  11. Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680.
  12. Bhuiyan, M. S. (2024). The role of AI-Enhanced personalization in customer experiences. Journal of Computer Science and Technology Studies, 6(1), 162169.
  13. Parmar, D. (2023). Enhancing Customer Relationship Management with Salesforce Einstein GPT.
  14. Aluri, A., Price, B. S., & McIntyre, N. H. (2019). Using machine learning to cocreate value through dynamic customer engagement in a brand loyalty program. Journal of Hospitality & Tourism Research, 43(1), 78-100.
  15. Kliestik, T., Zvarikova, K., & Lăzăroiu, G. (2022). Data-driven machine learning and neural network algorithms in the retailing environment: Consumer engagement, experience, and purchase behaviors. Economics, Management and Financial Markets, 17(1), 57-69.
  16. Mosa, M., Agami, N., Elkhayat, G., & Kholief, M. (2020). A literature review of data mining techniques for enhancing digital customer engagement. International Journal of Enterprise Information Systems (IJEIS), 16(4), 80-100.
  17. Wang, Z., Zhu, Y., He, S., Yan, H., & Zhu, Z. (2024). LLM for Sentiment Analysis in E-Commerce: A Deep Dive into Customer Feedback. Applied Science and Engineering Journal for Advanced Research, 3(4), 8-13.
  18. Lin, Z., Wang, Z., Zhu, Y., Li, Z., & Qin, H. (2024). Text Sentiment Detection and Classification Based on Integrated Learning Algorithm. Applied Science and Engineering Journal for Advanced Research, 3(3), 27-33.
  19. Rane, N. (2023). Enhancing customer loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data technologies: improving customer satisfaction, engagement, relationship, and experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023).
  20. Hollebeek, L. D., Sprott, D. E., & Brady, M. K. (2021). Rise of the machines? Customer engagement in automated service interactions. Journal of Service Research, 24(1), 3-8.
  21. Perez-Vega, R., Kaartemo, V., Lages, C. R., Razavi, N. B., & Männistö, J. (2021). Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. Journal of Business Research, 129, 902910.
  22. Wu, K., & Chi, K. (2023). Enhanced e-commerce customer engagement: A comprehensive three-tiered recommendation system. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 348-359.
  23. Vuong, N. A., & Mai, T. T. (2023). Unveiling the synergy: exploring the intersection of AI and NLP in redefining modern marketing for enhanced consumer engagement and strategy optimization. Quarterly Journal of Emerging Technologies and Innovations, 8(3), 103-118.
  24. Lyu, H., Wang, Z., & Babakhani, A. (2020). A UHF/UWB hybrid RFID tag with a 51-m energy-harvesting sensitivity for remote vital-sign monitoring. IEEE transactions on microwave theory and techniques, 68(11), 4886-4895.
  25. Zhu, Z., Wang, Z., Wu, Z., Zhang, Y., & Bo, S. (2024). Adversarial for Sequential Recommendation Walking in the Multi-Latent Space. Applied Science and Biotechnology Journal for Advanced Research, 3(4), 1-9.
  26. Force, S., & Mantrala, M. K. (2021). Sales Force Productivity Models Chapter Draft Appearing in History of Marketing Science (Eds: Russell Winer and Scott Neslin) World Scientific-Now Publishers.
  27. Dutta, S. K. (2024). Implementing the Salesforce Enablement Playbook: A Guide to Best Practices and Organizational Success. The American Journal of Engineering and Technology, 6(07), 13-23.
  28. Christ, P., & Anderson, R. (2011). The impact of technology on evolving roles of salespeople. Journal of Historical Research in Marketing, 3(2), 173-193.
  29. Akimova, O. (2019). Tracking user behavior on the web for digital marketing personalization with Salesforce.
  30. Kim, W. (2002). Personalization: Definition, status, and challenges ahead. Journal of object technology, 1(1), 29-40.
  31. Kumar, A. (2007). From mass customization to mass personalization: a strategic transformation. International Journal of Flexible Manufacturing Systems, 19, 533-547.
  32. Qihong, Z., Guangzong, W., Zeyu, W., & Huihui, L. (2018, July). Development of Horizontal Stair-Climbing Platform for Smart Wheelchairs. In Proceedings of the 12th International Convention on Rehabilitation Engineering and Assistive Technology (pp. 57-60).
  33. Khambati, A. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4726-4734.
  34. JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.
  35. Germanakos, P., & Mourlas, C. (2009). Adaptation and personalization of web-based multimedia content. In Multimedia transcoding in mobile and wireless networks (pp. 160-177). IGI Global.
  36. R. Novais, L., Maqueira, J. M., & Bruque, S. (2019). Supply chain flexibility and mass personalization: a systematic literature review. Journal of Business & Industrial Marketing, 34(8), 1791-1812.
  37. Zimmermann, A., Specht, M., & Lorenz, A. (2005). Personalization and context management. User modeling and user-adapted interaction, 15, 275-302. 38) Polatidis, Nikolaos, and Christos K. Georgiadis. "Recommender systems: The Importance of personalization in E-business environments." International Journal of E-Entrepreneurship and Innovation (IJEEI) 4, no. 4 (2013): 32-46.
  38. Dixit, S. (2022). Artifical intelligence and crm: A case of telecom industry. In Adoption and Implementation of AI in Customer Relationship Management (pp. 92-114). IGI Global.
  39. Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 32(2), 144-157.
  40. Amershi, S., Chickering, M., Drucker, S. M., Lee, B., Simard, P., & Suh, J. (2015, April). Modeltracker: Redesigning performance analysis tools for machine learning. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 337-346).
  41. Esfahani, M. N. (2024). Content Analysis of Textbooks via Natural Language Processing. American Journal of Education and Practice, 8(4), 36-54.
  42. Khambaty, A., Joshi, D., Sayed, F., Pinto, K., & Karamchandani, S. (2022, January). Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World. In Proceedings of International Conference on Wireless Communication: ICWiCom 2021 (pp. 335-343). Singapore: Springer Nature Singapore.
  43. Narayanan, B. N., Djaneye-Boundjou, O., & Kebede, T. M. (2016, July). Performance analysis of machine learning and pattern recognition algorithms for malware classification. In 2016 IEEE national aerospace and electronics conference (NAECON) and ohio innovation summit (OIS) (pp. 338-342). IEEE.
  44. Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., & Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171, 1251-1260.
  45. Bağcan, S., & Duygun, A. (2022). A multidimensional marketing communication model on social media for global brands: The case of coca-cola Turkey. Turkish Online Journal of Design Art and Communication, 12(2), 469-482.
  46. Deighton, J., & Kornfeld, L. (2011). Coca-Cola on Facebook. Harvard Business School Marketing Unit Case, (511-110).
  47. Rainsberger, L. (2022). Practice: AI Tools and Their Application Possibilities. In AI-The new intelligence in sales: Tools, applications and potentials of Artificial Intelligence (pp. 41-102). Wiesbaden: Springer Fachmedien Wiesbaden.
  48. Wachtler, V. M. (2020). From information transaction towards interaction: social media for efficient services in CRM. Data-Centric Business and Applications: Evolvements in Business Information Processing and Management (Volume 2), 371-407.
  49. Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2022). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 1-33.
  50. Heinrich, C., & Betts, B. (2003). Adapt or die: transforming your supply chain into an adaptive business network. John Wiley & Sons.
  51. Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean.
  52. McAndrew, S. T. (2009). Collaborative technologies for mobile workers and virtual project teams (Doctoral dissertation, Loughborough University).
  53. Grize, Y. L., Fischer, W., & Lützelschwab, C. (2020). Machine learning applications in nonlife insurance. Applied Stochastic Models in Business and Industry, 36(4), 523-537.
  54. Leiria, M. A. P. M. (2022). Customer loyalty in non-life insurance: antecedents, determinants and future directions.
  55. Rane, N., Choudhary, S., & Rane, J. (2023). Hyper-personalization for enhancing customer loyalty and satisfaction in Customer Relationship Management (CRM) systems. Available at SSRN 4641044.
  56. Reddy, S. R. B. (2021). Predictive Analytics in Customer Relationship Management: Utilizing Big Data and AI to Drive Personalized Marketing Strategies. Australian Journal of Machine Learning Research & Applications, 1(1), 1-12.
  57. Riyaz, M., Sawant, P. D., Raju, S., Nijhawan, G., Deepika, N. M., & Muralidhar, L. B. (2023, December). Artificial Intelligence for Customer Relationship Management: Personalization and Automation. In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (Vol. 10, pp. 547-551). IEEE.
  58. Ángeles Oviedo-García, M., Muñoz-Expósito, M., Castellanos-Verdugo, M., & Sancho-Mejías, M. (2014). Metric proposal for customer engagement in Facebook. Journal of research in interactive marketing, 8(4), 327-344.
  59. Bielski, L. (2008). Guided by feedback: measuring customer engagement. American Bankers Association. ABA Banking Journal, 100(8), 44.
  60. Kumar, V. (2024). Customer Engagement Value. In Valuing Customer Engagement: Strategies to Measure and Maximize Profitability (pp. 37-59). Cham: Springer Nature Switzerland.
  61. Kaliuta, K. (2023). Integration of AI for routine tasks using salesforce. Asian Journal of Research in Computer Science, 16(3), 119-127.
  62. Batt, S., Grealis, T., Harmon, O., & Tomolonis, P. (2020). Learning Tableau: A data visualization tool. The Journal of Economic Education, 51(3-4), 317-328.
  63. Murray, D. G. (2013). Tableau your data!: fast and easy visual analysis with tableau software. John Wiley & Sons.
  64. Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.
  65. Milligan, J. N. (2019). Learning Tableau 2019: Tools for Business Intelligence, data prep, and visual analytics. Packt Publishing Ltd.
  66. Patel, A. (2021). Data Visualization Using Tableau.
  67. Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS quarterly, 865-890.
  68. Tintarev, N., & Masthoff, J. (2012). Evaluating the effectiveness of explanations for recommender systems: Methodological issues and empirical studies on the impact of personalization. User Modeling and User-Adapted Interaction, 22, 399-439.
  69. Li, C. (2016). When does web-based personalization really work? The distinction between actual personalization and perceived personalization. Computers in human behavior, 54, 25-33.
  70. Sheng, H., Nah, F. F. H., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the Association for Information Systems, 9(6), 1.
  71. Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).
  72. JALA, S., ADHIA, N., KOTHARI, M., JOSHI, D., & PAL, R. SUPPLY CHAIN DEMAND FORECASTING USING APPLIED MACHINE LEARNING AND FEATURE ENGINEERING.
  73. Wu, H. (2022). Probabilistic Design and Reliability Analysis with Kriging and Envelope Methods (Doctoral dissertation, Purdue University).
  74. Wu, H., Khan, M., Du, X., Sawchuk, A. P., & Yu, H. W. (2019). Reliability analysis for image-based non-invasive pressure quantification in Aortorenal artery systems. Circulation Research, 125(Suppl_1), A122-A122.
  75. Chengying, L., Hao, W., Liping, W., & Zhi, Z. H. A. N. G. (2017). Tool wear state recognition based on LS-SVM with the PSO algorithm. Journal of Tsinghua University (Science and Technology), 57(9), 975-979.
  76. Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).
  77. Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmantation in Augmented reality. Innovative Computer Sciences Journal, 9(1).
  78. Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).
  79. Zhu, Z., Wang, Z., Wu, Z., Zhang, Y., & Bo, S. (2024). Adversarial for Sequential Recommendation Walking in the Multi-Latent Space. Applied Science and Biotechnology Journal for Advanced Research, 3(4), 1-9.
  80. 刘华山, 金元林, 程新, 王泽宇, 齐洁, & 刘洋. (2019). 力矩输入有界的柔性关节机器人轨迹跟踪控制. Control Theory & Applications/Kongzhi Lilun Yu Yinyong, 35(6).
  81. Wang, Z. (2024, August). CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10) (pp. 143-151).
  82. Wu, H., & Du, X. (2022, August). Envelope Method for Time-and Space-Dependent Reliability-Based Design. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 86236, p. V03BT03A002). American Society of Mechanical Engineers.
  83. Wu, H., Bansal, P., Liu, Z., Li, Y., & Wang, P. (2023, August). Uncertainty Quantification on Mechanical Behavior of Corroded Plate With Statistical Shape Modeling. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 87318, p. V03BT03A051). American Society of Mechanical Engineers.
  84. Chengying, L., Hao, W., Liping, W., & Zhi, Z. H. A. N. G. (2017). Tool wear state recognition based on LS-SVM with the PSO algorithm. Journal of Tsinghua University (Science and Technology), 57(9), 975-979.