Citation:

Chetankumar Patel, 2023. "Customer Experience Optimization Using Machine Learning: A Systematic Review", ESP Journal of Engineering & Technology Advancements  3(4): 176-187.

Abstract:

Customer experience (CX) is now a pivotal factor in the success of organizations in the digital and customer-focused markets and has necessitated the data-driven approaches to comprehend, forecast, and optimize customer engagement. The continuous growing volume of customer data that is generated by various digital touchpoints has provided possibilities to use machine learning (ML) and deep learning (DL) methods to maximize CX in different fields, such as e-commerce, banking, retail, and customer service. The systematic review is based on the research published in the period between 2013 and 2022 and it focuses on the general overview of ML and DL applications in CX optimization. On a predetermined PRISMA-based approach, 27 articles were picked by undertaking a thorough screening and quality checks of leading academic databases. The review investigates the tendencies of methodology strategies, application fields, data sources, and customer experience dimensions presenting the transformation of the traditional ML models of structured data to advanced DL models and mixed frameworks that are able to handle unstructured and multimodal data. The main methodological issues that were observed are the low use of real-time and streaming data, lack of explainability, no longitudinal or causal modelling, uneven evaluation measures, and little cross-domain validation. In summing up the available evidence, outlining key gaps, and emphasizing emerging trends, this review would serve as a fundamental source of information to scholars and practitioners who need to create strong, interpretable, and effective ML/DL-based methods to improve customer experience in various industry environments.

References:

[1] A. Chinnalagu and A. K. Durairaj, “Context-based sentiment analysis on customer reviews using machine learning linear models,” PeerJ Comput. Sci., vol. 7, no. 04, p. e813, Dec. 2021, doi: 10.7717/peerj-cs.813.

[2] M. A. Daqar and A. Smoudy, “The Role of Artificial Intelligence on Enhancing Customer Experience,” Int. Rev. Manag. Mark., vol. 9, no. 4, pp. 22–31, Jul. 2019, doi: 10.32479/irmm.8166.

[3] R. Carvalho, S. A. Pushkala, and R. Saxena, “Systems and methods for rapid processing of file data,” US9594817B2, 2017

[4] H. Malik, A. Afthanorhan, N. A. Amirah, and N. Fatema, “Machine Learning Approach for Targeting and Recommending a Product for Project Management,” Mathematics, vol. 9, no. 16, p. 1958, Aug. 2021, doi: 10.3390/math9161958.

[5] G. Sarraf, “Resilient Communication Protocols for Industrial IoT: Securing CyberPhysical-Systems at Scale,” Int. J. Curr. Eng. Technol. E-ISSN 2277 – 4106, P-ISSN 2347 – 5161, vol. 11, no. 6, pp. 1–9, 2021.

[6] C. G. Chatzopoulos and M. Weber, “Digitization and Lean Customer Experience Management: success factors and conditions, pitfalls and failures,” Int. J. Ind. Eng. Manag., vol. 12, no. 2, pp. 73–85, Jun. 2021, doi: 10.24867/IJIEM-2021-2-278.

[7] S. Garg, “Predictive Analytics and Auto Remediation using Artificial Inteligence and Machine learning in Cloud Computing Operations,” Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 7, no. 2, 2019, doi: 10.5281/zenodo.15362327.

[8] S. Achouche, U. B. Yalamanchi, and N. Raveendran, “Method, apparatus, and computer-readable medium for performing a data exchange on a data exchange framework,” 2019

[9] S. B. Aher and L. M. R. J. Lobo, “Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowledge-Based Syst., vol. 51, pp. 1–14, Oct. 2013, doi: 10.1016/j.knosys.2013.04.015.

[10] S. Lee and J. Y. Choeh, “Predicting the helpfulness of online reviews using multilayer perceptron neural networks,” Expert Syst. Appl., vol. 41, no. 6, pp. 3041–3046, May 2014, doi: 10.1016/j.eswa.2013.10.034.

[11] J. Kirchner, A. Heberle, and W. Lowe, “Classification vs. Regression - Machine Learning Approaches for Service Recommendation Based on Measured Consumer Experiences,” in 2015 IEEE World Congress on Services, IEEE, Jun. 2015, pp. 278–285. doi: 10.1109/SERVICES.2015.49.

[12] S. Kumar and C. D., “A Survey on Customer Churn Prediction using Machine Learning Techniques,” Int. J. Comput. Appl., vol. 154, no. 10, pp. 13–16, Nov. 2016, doi: 10.5120/ijca2016912237.

[13] Z. Singla, S. Randhawa, and S. Jain, “Sentiment analysis of customer product reviews using machine learning,” in 2017 International Conference on Intelligent Computing and Control (I2C2), IEEE, Jun. 2017, pp. 1–5. doi: 10.1109/I2C2.2017.8321910.

[14] A. Singh and C. S. Tucker, “A machine learning approach to product review disambiguation based on function, form and behavior classification,” Decis. Support Syst., vol. 97, pp. 81–91, May 2017, doi: 10.1016/j.dss.2017.03.007.

[15] S. Khodabandehlou and M. Zivari Rahman, “Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior,” J. Syst. Inf. Technol., 2017, doi: 10.1108/JSIT-10-2016-0061.

[16] R. N. Bolton et al., “Customer experience challenges: bringing together digital, physical and social realms,” J. Serv. Manag., vol. 29, no. 5, pp. 776–808, Nov. 2018, doi: 10.1108/JOSM-04-2018-0113.

[17] G. Spedicato, C. Dutang, and L. Petrini, “Machine Learning Methods to Perform Pricing Optimization: A Comparison with Standard Generalized Linear Models,” Ratemaking Prod. Inf., vol. 12, no. 1, pp. 69–89, 2018.

[18] K. Ebrah and S. Elnasir, “Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms,” J. Comput. Commun., vol. 07, no. 11, pp. 33–53, 2019, doi: 10.4236/jcc.2019.711003.

[19] A. De Keyser, K. Verleye, K. N. Lemon, T. L. Keiningham, and P. Klaus, “Moving the Customer Experience Field Forward: Introducing the Touchpoints, Context, Qualities (TCQ) Nomenclature,” J. Serv. Res., vol. 23, no. 4, pp. 433–455, Nov. 2020, doi: 10.1177/1094670520928390.

[20] W. D. Hoyer, M. Kroschke, B. Schmitt, K. Kraume, and V. Shankar, “Transforming the Customer Experience through New Technologies,” J. Interact. Mark., vol. 51, no. 1, pp. 57–71, Aug. 2020, doi: 10.1016/j.intmar.2020.04.001.

[21] S. Nosratabadi et al., “Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods,” Mathematics, vol. 8, no. 10, p. 1799, Oct. 2020, doi: 10.3390/math8101799.

[22] C.-J. Liu, T.-S. Huang, P.-T. Ho, J.-C. Huang, and C.-T. Hsieh, “Machine learning-based e-commerce platform repurchase customer prediction model,” PLoS One, vol. 15, no. 12, p. e0243105, Dec. 2020, doi: 10.1371/journal.pone.0243105.

[23] N. Patankar, S. Dixit, A. Bhamare, A. Darpel, and R. Raina, “Customer Segmentation Using Machine Learning,” in Recent Trends in Intensive Computing, 2021. doi: 10.3233/APC210200.

[24] H. Riaz, U. Baig, I. Meidute-Kavaliauskiene, and H. Ahmed, “Factors Effecting Omnichannel Customer Experience: Evidence from Fashion Retail,” Information, vol. 13, no. 1, p. 12, Dec. 2021, doi: 10.3390/info13010012.

[25] C. C. Imediegwu and O. Elebe, “Customer Experience Modeling in Financial Product Adoption Using Salesforce and Power BI,” Int. Sci. Ref. Res. J., vol. 4, no. 4, pp. 83–105, 2021.

[26] N. S. Egbuhuzor, A. J. Ajayi, E. E. Akhigbe, O. Oluwadamilola, Agbede, and C. P.-M. Ewim, “Cloud-based CRM systems: Revolutionizing customer engagement in the financial sector with artificial intelligence,” Int. J. Sci. Res. Arch., vol. 3, no. 1, pp. 215-234, Oct. 2021, doi: 10.30574/ijsra.2021.3.1.0111.

[27] T. Nguyen, S. Quach, and P. Thaichon, “The effect of AI quality on customer experience and brand relationship,” J. Consum. Behav., vol. 21, no. 3, pp. 481–493, May 2021, doi: 10.1002/cb.1974.

[28] C. V. Misischia, F. Poecze, and C. Strauss, “Chatbots in customer service: Their relevance and impact on service quality,” Procedia Comput. Sci., vol. 201, pp. 421–428, 2022, doi: 10.1016/j.procs.2022.03.055.

[29] A. De Mauro, A. Sestino, and A. Bacconi, “Machine learning and artificial intelligence use in marketing: a general taxonomy,” Ital. J. Mark., vol. 2022, no. 4, pp. 439–457, Dec. 2022, doi: 10.1007/s43039-022-00057-w.

[30] J. Rana, L. Gaur, G. Singh, U. Awan, and M. I. Rasheed, “Reinforcing customer journey through artificial intelligence: a review and research agenda,” Int. J. Emerg. Mark., vol. 17, no. 7, pp. 1738–1758, Jul. 2022, doi: 10.1108/IJOEM-08-2021-1214.

[31] V. Sharma, “AI-Driven Automation in Retail: Delivering Personalized Customer Experiences at Scale,” IJIRMPS, vol. 10, no. 1, pp. 1-7, 2022.

[32] A. M. H. Abbas, K. I. Ghauth, and C.-Y. Ting, “User Experience Design Using Machine Learning: A Systematic Review,” IEEE Access, vol. 10, pp. 51501–51514, 2022, doi: 10.1109/ACCESS.2022.3173289.

[33] L. Nicolescu and M. T. Tudorache, “Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review,” Electronics, vol. 11, no. 10, p. 1579, May 2022, doi: 10.3390/electronics11101579.

[34] M. Ragab, S. Alshehri, N. A. Alhakamy, W. Alsaggaf, H. A. Alhadrami, and J. Alyami, “Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification,” J. Healthc. Eng., vol. 2022, pp. 1–13, Mar. 2022, doi: 10.1155/2022/6074538.

[35] H. Shafa, “Integration Of Machine Learning And Advanced Computing For Optimizing Retail Customer Analytics,” Int. J. Bus. Econ. Insights, vol. 02, no. 03, pp. 01–46, Sep. 2022, doi: 10.63125/p87sv224.

[36] A. Alsayat, “Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca,” Neural Comput. Appl., vol. 35, no. 6, pp. 4701–4722, Feb. 2022, doi: 10.1007/s00521-022-07992-x.

[37] C. Ledro, A. Nosella, and A. Vinelli, “Artificial intelligence in customer relationship management: literature review and future research directions,” J. Bus. Ind. Mark., vol. 37, no. 13, pp. 48–63, Dec. 2022, doi: 10.1108/JBIM-07-2021-0332.

[38] B. Noori, “Classification of Customer Reviews Using Machine Learning Algorithms,” Appl. Artif. Intell., vol. 35, no. 8, pp. 567–588, Jul. 2021, doi: 10.1080/08839514.2021.1922843.

[39] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “A Machine Learning Based Classification Method for Customer Experience Survey Analysis,” Technologies, vol. 8, no. 4, p. 76, Dec. 2020, doi: 10.3390/technologies8040076.

Keywords:

Customer Experience, Machine Learning, Deep Learning, Customer Engagement, Artificial Intelligence, Customer Satisfaction, Hybrid Models.