Customer Lifetime Value Modelling

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Sharma, Shreya
Major Professor
Dr. Anthony Townsend
Committee Member
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Information Systems and Business Analytics
In today’s business landscape, information systems and business analytics are pivotal elements that drive success. Information systems form the digital foundation of modern enterprises, while business analytics involves the strategic analysis of data to extract meaningful insights. Information systems have the power to create and restructure industries, empower individuals and firms, and dramatically reduce costs. Business analytics empowers organizations to make precise, data-driven decisions that optimize operations, enhance strategies, and fuel overall growth. Explore these essential fields to understand how data and technology come together, providing the knowledge needed to make informed decisions and achieve remarkable outcomes.
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In this era, when every organization competes to stay on the top in the market, organizations need to ensure that they should consider all the factors that will result in their long-term success. One of the most crucial factors among all is to provide the best customer experience. Customer Lifetime Value is an important factor that helps in understanding customers. It allows organizations to understand the importance level of each customer. By segmenting customers into different groups, analysts can build tailored strategies for customers. With data mining approaches, critical customer knowledge can be extracted, which could further help in critical decision-making. This paper aims to segment customers into groups, calculate customer lifetime value, and determine the best prediction model with maximum accuracy. The evaluation was carried out within customer segmentation, using a database of a company operating in the retail sector. The results indicated that developing prediction models by dividing CLTV into clusters is a better approach with a good accuracy rate and provided many beneficial insights.

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Fri Jan 01 00:00:00 UTC 2021