AI-Enabled Customer Relationship Management: Personalization, Segmentation, and Customer Retention Strategies
Keywords:
Artificial Intelligence, Customer Relationship Management, Personalization, Segmentation, Predictive Analytics, Churn Prediction, Virtual Reality, Data Privacy, Algorithmic Bias, Customer Experience, Consumer Behavior, LoyaltyAbstract
Artificial intelligence (AI) and machine learning are transforming customer relationship management (CRM) strategies. This paper provides an extensive review of how AI-enabled capabilities like predictive analytics, personalization engines, and customer segmentation are enabling more tailored, relevant experiences that strengthen customer relationships and loyalty over time. Current CRM systems generate massive datasets on customer interactions and behaviors, which feed AI algorithms to uncover hidden insights around individual preferences, likely future behaviors, and optimal cross-sell recommendations unique to each customer. We analyze key AI methodologies powering next-generation CRM including reinforcement learning, neural networks, natural language processing, and computer vision. The paper discusses sample use cases and real-world examples of AI-driven CRM initiatives from leading companies that focus on personalization, predictive churn models, next-best action recommendations, and automated customer service agents. We also examine emerging technologies on the horizon such as affective computing, virtual reality, and the metaverse that present new opportunities to understand customers and meet their needs in highly tailored, emotionally intelligent ways. The paper concludes with an analysis of critical considerations as firms implement AI-enabled CRM including data privacy, transparent AI, and avoiding algorithmic bias. With responsible implementation, AI stands poised to revolutionize CRM with previously impossible levels of personal relevance at scale, ultimately growing customer lifetime value.
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Ngai, E.W.T.; Xiu, L.; Chau, D.C.K. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using big data analytics capability and business strategy alignment?. Int. J. Prod. Econ. 2016, 182, 113–131.
Hiziroglu, A.; Sengul, M. Investigating two customer lifetime value models from segmentation perspective. Procedia Soc. Behav. Sci. 2012, 62, 766–774.
Huang, M.H.; Rust, R.T. Artificial intelligence in service. J. Serv. Res. 2018, 21, 155–172.
Brian R. Gaines, Mildred L. G. Shaw, Concepts, technologies and applications of the evolving discipline of knowledge science, Knowledge-Based Systems, 2021, 227
Keramati, A.; Jafari-Marandi, R.; Aliannejadi, M.; Ahmadian, I.; Mozaffari, M.; Abbasi, U. Improved churn prediction in telecommunication industry using data mining techniques and multiple classifiers. Appl. Soft Comput. J. 2014, 24, 994–1002.
Hbr.org. 2022. To Make Better Decisions, Combine Human and Machine Intelligence. [online]
Huang, M.H.; Rust, R.T. Engaged to a robot? The role of AI in service. J. Serv. Res. 2021, 24, 30–41.
Bhat, S.; Darzi, M.A. Customer relationship management: An approach to competitive advantage in the banking sector by exploring the mediational role of loyalty. Int. J. Bank Mark. 2016, 34, 388–410.
Ryals, L.; Knox, S. Cross-functional issues in the implementation of relationship marketing through customer relationship management. Eur. Manag. J. 2001, 19, 534–542.
Payne, A.; Frow, P. A strategic framework for customer relationship management. J. Mark. 2005, 69, 167–176.
Trainor, K.J.; Andzulis, J.; Rapp, A.; Agnihotri, R. Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. J. Bus. Res. 2014, 67, 1201–1208.
Chen, I.J.; Popovich, K. Understanding customer relationship management (CRM): People, process and technology. Bus. Process. Manag. J. 2003, 9, 672–688.
Krasnikov, A.; Jayachandran, S.; Kumar, V. The impact of customer relationship management implementation on cost and profit efficiencies: Evidence from the US commercial banking industry. J. Mark. 2009, 73, 61–76.
Mortenson, M.J.; Doherty, N.F.; Robinson, S. Operational research from Taylorism to Terabytes: A research agenda for the analytics age. Eur. J. Oper. Res. 2015, 241, 583–595.
Ngai, E.W.; Xiu, L.; Chau, D.C. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Keramati, A.; Jafari-Marandi, R.; Aliannejadi, M.; Ahmadian, I.; Mozaffari, M.; Abbasi, U. Improved churn prediction in telecommunication industry using data mining techniques and multiple classifiers. Appl. Soft Comput. 2014, 24, 994–1002.
Nilsson, N.J. Principles of Artificial Intelligence; Morgan Kaufmann: Burlington, MA, USA, 2014..
Cui, D.; Wong, M.L.; Wan, X. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Manag. Sci. 2006, 52, 597–612.
Ngai, E.W.; Xiu, L.; Chau, D.C. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Ballings, M.; Van den Poel, D. CRM in social media: Predicting increases in Facebook usage frequency. Eur. J. Oper. Res. 2015, 244, 248–260.
Khajvand, M.; Zolfaghar, K.; Ashoori, S.; Alizadeh, S. Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Comput. Sci. 2011, 3, 57–63.
Larivière, B.; Van den Poel, D. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 2005, 29, 472–484.
Gao, F.; Cai, Y.; Zhou, K.; Sun, Y.; Xu, H.; Su, J.; ... & Fu, D. Probabilistic multi-view matrix completion for item recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 393-402.
Gupta, S. Customer-based valuation. J. Interact. Mark. 2009, 23, 169–178.
Reinartz, W.J.; Krafft, M.; Hoyer, W.D. The customer relationship management process: Its measurement and impact on performance. J. Mark. Res. 2004, 41, 293–305.
Ngai, E.; Xiu, L.; Chau, D.C.K. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Prinzie, A.; Van den Poel, D. Predicting home loans default for a credit union with cost sensitive decision trees and ensembles. In Proceedings of the European Marketing Academy Conference, Brussels, Belgium, 23–26 May 2006.
Aggarwal, C.C.; Zhai, C. Mining text data. Springer Science & Business Media: New York, NY, USA, 2012.
Goodfellow, I.J.; Bengio, Y.; Courville, A.C. Deep learning. MIT Press: Cambridge, MA, USA, 2016.
Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
Liébana-Cabanillas, F.J.; Marinković, V.; Ramos-de-Luna, I.; Helguera-García, M. Predicting customer churn in mobile telecommunications industry using Probabilistic Neural Networks. Neural Comput. Appl. 2016, 27, 2669–2679.
Ngai, E.W.; Xiu, L.; Chau, D.C. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Cambria, E.; White, B. Jumping NLP curves: A review of natural language processing research. IEEE Comput. Intell. Mag. 2014, 9, 48–57.
Mostafa, M.M. More than words: Social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 2013, 40, 4241–4251.
Mechael, P.N.; Massana, X.T.; Gagneja, H.K. Chatbot use in healthcare: Scoping review. J. Med. Internet Res. 2021, 23, e22860.
Larivière, B.; Bowen, D.; Andreassen, T.W.; Kunz, W.; Sirianni, N.J.; Voss, C.; ... & De Keyser, A. “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers. J. Bus. Res. 2017, 79, 238–246.
Aggarwal, C.C. Neural networks and deep learning. Springer Nature: 2018.
Mcfarland, M.; Nielson, J. Emotional intelligence and selling-a myth or reality in the business-to-business (B2B) context? Industrial Marketing Management 2017, 64, 57-70.
Craciun, G.; Manolea, B.; Hiticas, C. Trends Engaging Conversational Agents with Customers during and after the COVID 19 Pandemic. Informatica Economică, 25, 1, 18-30. 10.24818/issn14531305/25.1.2021.02.
Zhong, S.; Liu, J.; Chen, J.; Huang, D.; Zhou, M. Multi-view custumer profiling for personalized recommendation. In Pacific Rim Conference on Multimedia, Xi'an, China, 28–29 September 2018; Springer: Berlin, Germany, 2018; pp. 450–459.
Qiu, J.; Wu, Q.; Wang, H.; Tan, L.; Huang, K. Online product review helpfulness: Impact of reviewer profile image. Decision Support Systems, 2019, 126, 113159.
Brusilovsky, P.; Kobsa, A.; Nejdl, W. Adaptive Web Technologies and Personalization. In The Adaptive Web; Brusilovsky, P., Kobsa, A., Nejdl, W., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; Volume 4321, pp. 869–890. ISBN 978-3-540-72078-2.
Ricci, F.; Rokach, L.; Shapira, B. Recommender Systems Handbook; Springer US: Boston, MA, USA, 2015; ISBN 978-1-4899-7637-6.
Ho, S.Y.; Bodoff, D. The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer search theory. MIS Q. 2014, 38, 497–A10.
Kobielus, J. How A.I. is transforming marketing campaigns through personalization and optimization. Available online: https://bit.ly/3MA1SKN (accessed on 7 February 2024).
Aguirre, E.; Mahr, D.; Grewal, D.; de Ruyter, K.; Wetzels, M. Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. J. Retail. 2015, 91, 34–49.
Ascarza, E. Retention futility: Targeting high-risk customers might be ineffective. J. Mark. Res. 2018, 55, 80–98.
Larivière, B.; Van den Poel, D. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 2005, 29, 472–484.
Vafeiadis, T.; Diamantaras, K.I.; Sarigiannidis, G.; Chatzisavvas, K.C. A comparison of machine learning techniques for customer churn prediction. Simul. Model. Pract. Theory 2015, 55, 1–9.
Amin, A.; Khan, A.R.; Tat, H.H.; Chin, T.A. A hybrid intelligent system framework for the prediction of customer churn in telecommunication companies. Expert Syst. Appl. 2016, 36, 3514–3522.
Larivière, B.; Van den Poel, D. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Syst. Appl. 2005, 29, 472–484.
Verbeke, W.; Dejaeger, K.; Martens, D.; Hur, J.; Baesens, B. New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. Eur. J. Oper. Res. 2012, 218, 211–229.
Spotify AB, Stockholm Sweden. Personalization at Spotify. Spotify Labs Machine Learning & Data Science. 2016 Nov 1.
Bonnin, G.; Jannach, D. Automated generation of music playlists: Survey and experiments. ACM Computing Surveys (CSUR), 47(2), 1-35.
Kamehkhosh, I.; Jannach, D. User perception of next-track music recommendations. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017 Jul, pp. 113-121. ACM.
Vagliano, I.; Monti, D.; Morisio, M. ... & Maurino, A. Measuring ambiguity and surprise in music recommendation. In Proceedings of the Symposium on Applied Computing, 2017 Mar, pp. 1816-1823.
Madrigal A. How Netflix reverse engineered Hollywood. The Atlantic [Internet]. 2014 Jan [cited 2020]. Available from: https://www.theatlantic.com/technology/archive/2014/01/ how-netflix-reverse-engineered-hollywood/282679/
Bell J. What Makes Netflix So Addictive?. The Huffington Post [Internet]. 2016 Dec [cited 2020]. Available from: http://www.huffingtonpost.in/ jenifer-sparkles/what-makes- netflix-so-addictive_b_8846526.html
Park Y, Choi S, Lee HJ, Kim HK ... An intelligent fashion recommendation system based on style attribute identification. Icts. 2022 Jun 1;536-9.
Yadav SK, Bharadhwaj SK. Personalized fashion recommender system. Procedia computer science. 2018 Jan 1;132:1256-65.
Sudhakar RR, Vires T, Priya N. Personalised clothing recommendation system using hybrid filtering technique. Materials Today: Proceedings. 2022 Jan 1;46:3140-5.
HungryRoot » Our AI Powered Grocery Service. Hungryroot.com. https://www.hungryroot.com/how-it-works
Hungryroot personalizes your grocery shopping with AI – TechCrunch. (2018). TechCrunch. https://techcrunch.com/2018/01/11/hungryroot-personalizes-your-grocery-shopping-with-ai/
Oracle. AI for B2B Sales. https://www.oracle.com/cx/sales/b2b-sales-ai/
LinkedIn Sales Navigator Team. AI Series: Lead Scoring. LinkedIn. https://business.linkedin.com/sales-solutions/blog/sales-ai/2018/ai-series--lead-scoring
Oracle. Marketing. https://www.oracle.com/marketingcloud/products/marketing-automation/lead-management.html
Patil, S., Joshi, M., Toshniwal, D. et al. Predicting potential customers using classification models in marketing automation systems. J Big Data 8, 54 (2021). https://doi.org/10.1186/s40537-021-00449-8
Gerbert P, Lorenz M, Ransbotham S, Schmitz C, Zerwas D. Masters of machines. The Boston Consulting Group. 10 January 2018.
Conversational AI Solutions | Kasisto. Kasisto. https://kasisto.com/kai-platform/
Cosmopolitan. I Curated My Perfect Skincare Routine Using Sephora’s App. https://www.cosmopolitan.com/style-beauty/beauty/a34774384/sephora-skincare-bot/
Sephora virtual artist. Sephora. https://www.sephora.com/virtual-artist
McStay, A. Emotional AI and EdTech: Serving the public good?. Learning, Media and Technology, 45(3), 2020, pp.312-326.
Yao, Q.; Chen, Y.; Xiong, J.; Li, C.; Huang, B. BLSE: Sentiment analysis for business with bert-based domain adaptation and lexicon enhancement. Decision Support Systems, 2020, 137, 113363.
Hussain, S.S. Service chatbots–the next frontier in CRM and customer experience. Available at SSRN 3723251, 2021.
Mcfarland, M.; Nielson, J. Emotional intelligence and selling-a myth or reality in the business-to-business (B2B) context? Industrial Marketing Management 2017, 64, 57-70.
Huang, M.H.; Rust, R.T. Artificial intelligence in service. J. Serv. Res. 2018, 21, 155–172.
Koutsogiannis, A.; Berre, A.J. The metaverse–part 1: A next generation matrix where physical and digital realities are merged? Applied Sciences, 12(1), 2022, p.293.
Jung, T.; tom Dieck, M.C. Augmented reality, virtual reality and 3D printing for the co-creation of value for the visitor experience at cultural heritage places. J. Place Manag. Dev. 2017, 10, 140–151.
Flavián, C.; Ibáñez-Sánchez, S.; Orús, C. The impact of virtual, augmented and mixed reality technologies on the customer experience. J. Bus. Res. 2019, 100, 547–560.
Spreer, P.; Kallweit, K. Augmented reality in retail: Assessing the acceptance and potential for multimedia product presentation at the PoS. Trans. Emerg. Telecommun. Technol. 2014, 25, 20–35.
Hilken, T.; de Ruyter, K.; Chylinski, M.; Mahr, D.; Keeling, D.I. Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. J. Acad. Mark. Sci. 2017, 45, 884–905.
Volvo Cars R&D SITE Volvo Cars Technology Award FINALIST: Virtual Car Configuration using VR - Jonatan Ström & Dennis Gustafsson
Chen, H.; Chiang, R.H.; Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS Q. 2012, 36, 1165–1188.
Learned, A. Viewing customer data usage through the lens of trust: Comparing attitudes of marketers and consumers. J. Direct Data Digit. Mark. Pract. 2019, 20, 230–243.
Eckerson, W.W. Predictive Analytics: Extending the Value of Your Data Warehousing Investment. TDWI Best Practices Report. 1st Quarter 2009. Available online: https://bit.ly/3Due3pt (accessed on 11 February 2024).
Ngai, E.W.; Xiu, L.; Chau, D.C. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 2009, 36, 2592–2602.
Zhang, J.; Chen, Q.; Hayat, T.; Alsaedi, A.; Ahmed, B. Feature selection for customer churn prediction based on random forests model. Pak. J. Stat. Oper. Res. 2018, 14, 513–526.
Solove, D.J. The myth of the privacy paradox. Geo. Wash. L. Rev. 2021, 89, 1.
Martin, K. Ethical implications and accountability of algorithms. J. Bus. Ethics 2021, 160, 835–859.
Tankard, C. What the GDPR means for businesses. Netw. Secur. 2016, 2016, 5–8.
Edelman. 2022 Edelman Trust Barometer. [online] Available at: https://www.edelman.com/trust/2022-trust-barometer [Accessed February 2024].
Zhong, S.; Liu, J.; Chen, J.; Huang, D.; Zhou, M. Multi-view custumer profiling for personalized recommendation. In Pacific Rim Conference on Multimedia, Xi'an, China, 28–29 September 2018.
Wetsch, L.R. Artificial intelligence, privacy and data protection. Inf. Commun. Technol. Law 2021, 30, 266–283.
Bietz, M.J.; Baumer, E.P.; Lee, C.P. Synergizing in cyberinfrastructure development. Comput. Support. Coop. Work 2019, 28, 115–127.
Raisch, S.; Krakowski, S. Artificial Intelligence and Management: The Automation-Augmentation Paradox. Acad. Manag. Rev. 2021, 46, 192–210.
Martin, K. Designing ethical algorithms. MIS Q. Exec. 2019, 18, 129–142.
Suresh, H.; Guttag, J.V. A framework for understanding unintended consequences of machine learning. arXiv 2019, arXiv:1901.10002.
Aguirre, E.; Mahr, D.; Grewal, D.; de Ruyter, K.; Wetzels, M. Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. J. Retail. 2015, 91, 34–49.
Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115.
Ribeiro, M.T.; Singh, S.; Guestrin, C. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. arXiv 2016, arXiv:1602.04938.
Ntoutsi, E.; Fafalios, P.; Gadiraju, U.; Iosifidis, V.; Nejdl, W.; Vidal, M.E.; Ruggieri, S.; Turini, F.; Papadopoulos, S.; Krasanakis, E.; et al. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, e1356.
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; Galstyan, A. A survey on bias and fairness in machine learning. arXiv 2019, arXiv:1908.09635.
Verma, S.; Rubin, J. Fairness definitions explained. 2018. In Proceedings of the International Workshop on Software Fairness (FairWare ’18), Gothenburg, Sweden, 29 May 2018; ACM: New York, NY, USA, 2018.
Zliobaite, I.; Custers, B. Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. Artif. Intell. Law 2020, 24, 1–35.
Martin, K. Addressing the data crisis in AI ethics and algorithmic auditing. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, 19–21 May 2021.
Hagendorff, T. The Ethics of AI Ethics: An Evaluation of Guidelines. Minds Mach. 2020, 30, 99–120.
Crawford, K. The Atlas of AI. Yale University Press: New Haven, CT, USA, 2022; ISBN 9780300209570.
Madasu, R. "Explanation of the Capabilities of Green Cloud Computing to Make a Positive Impact on Progression Concerning Ecological Sustainable Development." Research Journal of Multidisciplinary Bulletin 2, no. 2 (2023): 5-11.
Kamuni, Navin, Sathishkumar Chintala, Naveen Kunchakuri, Jyothi Swaroop Arlagadda Narasimharaju, and Venkat Kumar. "Advancing Audio Fingerprinting Accuracy with AI and ML: Addressing Background Noise and Distortion Challenges." In Proceedings of the 2024 IEEE 18th International Conference on Semantic Computing (ICSC), 341-345. 2024.
A. Srivastav and S. Mandal, "Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges," in IEEE Access, vol. 11, pp. 97147-97168, 2023, doi: 10.1109/ACCESS.2023.3312382.
Madasu, Ram. "A Research to Study Concerns Regarding the Security of Cloud Computing." International Journal of Research 10, no. 08 (August 2023): 270-274. DOI: https://doi.org/10.5281/zenodo.8225399.
Satish, Karuturi S R V, and M Swamy Das. "Review of Cloud Computing and Data Security." IJAEMA (The International Journal of Analytical and Experimental Modal Analysis) 10, no. 3 (2018):1- 8.
A. Srivastav, P. Nguyen, M. McConnell, K. A. Loparo and S. Mandal, "A Highly Digital Multiantenna Ground-Penetrating Radar (GPR) System," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 7422-7436, Oct. 2020, doi: 10.1109/TIM.2020.2984415.
Satish, Karuturi S R V, and M Swamy Das. "Quantum Leap in Cluster Efficiency by Analyzing Cost-Benefits in Cloud Computing." In Computer Science and Engineering by Auroras Scientific Technological & Research Academy Hyderabad, vol. 17, no. 2, pp. 58-71. Accessed 2018. https://www.ijsr.in/article-description.php?id=ZU9rWnA5d3R1Q1dzK2tLSTNTbDRZZz09.
Satish, Karuturi S R V, and Naseemuddin Mohammad. "Big Data Security and Data Encryption in Cloud Computing." International Journal of Engineering Trends and Applications (IJETA) 7, no. 4 (2020): 35-40. Eighth Sense Research Group™. All Rights Reserved.
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