Open AccessCustomer Behavior Analysis and Revenue Prediction System
Author(s) -
Rahul Gupta,
Pranil Kamble,
Vanshi Negandhi,
Ankush Hutke
Publication year - 2020
Publication title -
international journal for research in engineering application and management
Language(s) - English
Resource type - Journals
ISSN - 2454-9150
DOI - 10.35291/2454-9150.2020.0245
Subject(s) - revenue , business , revenue management , analytics , marketing , e commerce , boosting (machine learning) , yield management , customer satisfaction , computer science , artificial intelligence , data science , finance , world wide web
In the era of e-commerce there are many organizations that have implemented customer behaviour analytics for their growth in business. It is a crucial challenge for the organizations in the e-commerce world to study and analyse the behaviour of the online buyers. The success of every organization is within the satisfaction of the customers they have and to gain new customers as well, and this is done by targeting the potential customers that can generate revenue to the organizations. RFM analysis is used to indicate recently buying customers, frequently buying customers, and huge spending customers. It is one of the best methods to segment organization’s revenue generating customers around other customers. Also 80/20 rule is implemented which focuses on the 20 percent of the customers that generate 80 percent of the revenue for the organization. The model is developed using Light GBM (Gradient Boosting Method) which is a machine learning algorithm.