Improving Product Marketing by Predicting Early Reviewers on E-Commerce Websites

https://doi.org/10.55529/ijrise.43.17.25

Authors

  • Dr. Sarangam Kodati Department of Information Technology, CVR College of Engineering, Hyderabad, India.
  • Dr. M. Dhasaratham Department of Information Technology, TKR College of Engineering and Technology, Hyderabad, India.
  • Veldandi Srikanth Assistant Professor, SVS Engineering College, Hyderabad, India.
  • K. Meenendranath Reddy Assistant Professor, SVR Engineering College, Nandyal, India.

Keywords:

E-commerce, Machine Learning, KNN, SVM.

Abstract

Customers now often consult online surveys before making a smart purchase decision. Much of the time, the early surveys of an item fundamentally affect the deals of that item later on.In this study, we move up and focus on the conduct characteristics of early analysts via their posted surveys on two actual enormous web based company stages, i.e., Amazon and Cry. To be clear, we divide an item's lifecycle into three discrete phases: the beginning, the middle, and the end. Early commentators are clients who have posted surveys during the pilot phase. We provide a quantitative portrait of the first reviewers based on their rating habits, the popularity of the items they reviewed, and the ratings of support they received from other users. We have viewed that as (1) Early commenter will often downgrade a higher average rating, and (2) An early analyst tends to publish more positive polls.

Downloads

Download data is not yet available.

Published

2024-04-01

How to Cite

Dr. Sarangam Kodati, Dr. M. Dhasaratham, Veldandi Srikanth, & K. Meenendranath Reddy. (2024). Improving Product Marketing by Predicting Early Reviewers on E-Commerce Websites. International Journal of Research in Science & Engineering , 4(03), 17–25. https://doi.org/10.55529/ijrise.43.17.25

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.