ENHANCING CUSTOMER RELATIONSHIP MANAGEMENT WITH ARTIFICIAL INTELLIGENCE AND DEEP LEARNING: A CASE STUDY ANALYSIS

Abstract

Effective customer relationship management (CRM) techniques are essential in today's
business environments for companies looking to maximize client interactions and increase
revenue. This paper addresses customer churn, a significant issue that firms in various
industries confront, by providing a thorough examination of how to improve CRM through the
integration of AI and ML technologies. The importance of CRM and how AI and ML are
transforming customer interactions, customization, and operational efficiency are covered in
detail in the first section of the study. An investigation of a case study is carried out to see how
well different machine learning models predict customer attrition in CRM systems. The
following models are evaluated: GaussianNB, Artificial Neural Networks (ANN), KNeighbors
Classifier, Support Vector Classifier (SVC), Decision Tree, Random Forest, and
Logistic Regression. With an accuracy of 92.5%, Random Forest Classifier is found to be the
most successful model in the study; Decision Tree Classifier is next closest at 89.8%. It also
looks at how important features engineering, data preparation, model selection, training,
validation, deployment, and performance tracking are for AI-driven CRM systems. Data
features, outlier identification, linear correlations, and model accuracy evaluation are all made
possible by visualizations such as histograms, box plots, scatterplots, and performance metrics
for classification models. The results highlight how crucial data quality, algorithm selection,
and continuous model monitoring are to the success of CRM projects. By utilizing AI and ML
technology, this research propels CRM approaches forward, enabling firms to anticipate
consumer behaviors, tailor interactions, and cultivate enduring customer connections in highly
competitive market conditions.