Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty

Nikhil  Patel; Sandeep  Trivedi

Authors

Nikhil Patel Bachelor of Engineering (B.E) Computer Engineering, Mumbai University (India), Principal contact for editorial correspondence, [email protected]
Sandeep Trivedi Graduated from Technocrats Institute of Technology India [email protected]

Keywords:

AI, Business, Customer loyalty, Machine learning, NLP, Predictive modeling

Abstract

AI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today's organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world. The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively. The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what's useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them.

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