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A Study on CLTV Model in E-Commerce Domains using Python

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A Study on CLTV Model in E-Commerce Domains using Python


Rasamallu Sai Bharath Reddy | Dr. T. Narayana Reddy



Rasamallu Sai Bharath Reddy | Dr. T. Narayana Reddy "A Study on CLTV Model in E-Commerce Domains using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6, October 2022, pp.741-759, URL: https://www.ijtsrd.com/papers/ijtsrd51952.pdf

Customer Relationship Management (CRM) system is an information management and analysis tool that can help businesses and other organizations manage their interactions with customers. CRMs were originally designed to target large corporations, but the internet has allowed small business owners to take advantage of these tools as well. Customer data is collected in a CRM database, which allows for advanced analysis such as customer segmentation and contact history. Customer relationship management system (CRMs) is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information. In this article, we will be explaining how you can a E-commerce company can apply their customer relationship management system to analyze their customer base by CLTV, a key marketing metric that allows you to evaluate the impact and outcomes of the firm’s customer relationship management strategies and tactics. In order to increase revenue through better marketing campaigns. E-commerce companies consider that customers are their most important asset and that it is essential to estimate the potential value of this asset. Hence, a model for calculating customer's value is essential in these domains. We describe a general modeling approach, based on BG-NBD and Gamma-Gamma models, for calculating customer value in the e-commerce domain. This model extends existing models from the field of direct marketing, by taking into account a sample set of variables required for evaluating customers value in an e-commerce environment. In addition, we present an algorithm for generating this model from historical data, as well as an application of this modeling approach for the creation of a model for e-commerce. This model provides more accurate predictions than existing models regarding the future income generated by customers using Python.

E-commerce, Customer Life Time Value, Customer Segmentation, Recency, Frequency, Monetary, CLTV model, BG-NBD Model, Data Analytics, Python


IJTSRD51952
Volume-6 | Issue-6, October 2022
741-759
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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