CUSTOMER SEGMENTATION AND VALUE CLASSIFICATION USING DATA ANALYTICS: A PREDICTIVE APPROACH FOR STRATEGIC BUSINESS DECISION-MAKING

Tathagat Rishabh Sen; Varnika Devyani Rathore

Authors

Tathagat Rishabh Sen
Varnika Devyani Rathore

Keywords:

Customer Segmentation, Data Analytics, Predictive Modeling, Customer Value, E-commerce Analytics

Abstract

The digital transformation era is turning to be more data-driven and more relianted on data as the business to analyze the customers and make strategic decisions. The given paper develop a predictive customer segmentation and value classification model with the help of data analytics. The paper leverages machine learning to group customers into a given value-based categories with a holistic e-commerce dataset that captures all three variables of transactional, behavioral, and engagement. The analysis reveals that the key variables, such as monetary value, frequency of purchase, and customer engagement are significant contributors in the outcomes of segmentation. Predictive model is very precise, and this indicates that data analytics possesses an excellent possibility of identifying customer value patterns. The results point to the significance of combining behavioral and financial indicators to realize more specific and practical segmentation. The implications of the research in managerial terms are that the research can offer insight into how to maximize marketing activities, improved customer targeting and better business performance. The paper contributes to the literature by providing a data-driven framework to aid in closing the gap between customer analytics and strategic decision-making. Moreover, the paper highlights how machine learning and predictive analytics could revolutionize the customer relationship management in the digital business context.

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experiment with new methods of analysis.

Conclusion

The usefulness of data analytics and predictive modeling in segmenting customers and classifying their values in an e-commerce environment. Using the variables of transactional, behavioral, and engagement, the study has managed to find specific types of customer segments as well as to point out the most important factors affecting customer value. These findings confirm the importance of the factors such as monetary contribution, frequency of purchase and customer interaction to classify the segment, and therefore give valid and reliable predictive outcomes. The paper add to the existing evidence of data-driven marketing by offering a comprehensive framework, which incorporates both customer analytics and machine learning methods. The results underscore the importance of adopting advanced analytical software to facilitate decision-making, marketing and customer relationship management. Practically, the findings of the study can help companies to find high-value customers, create specific marketing campaigns, and enhance the overall profitability. The research also has limitations as it is limited to a single set of data in an e-commerce scenario. The framework can be extended in the future with real-time data, cross-industry data, and more sophisticated artificial intelligence methods to further increase segmentation accuracy and strategic relevance.