Cross-Platform Analysis of Seller Performance and Churn for Ecommerce Using Artificial Intelligence

Abstract

Suppliers and sellers play a crucial role in the ecommerce ecosystem. Sellers and ecommerce firms use social media to increase user engagement, visibility, and sales. Seller ratings are as important as the product ratings on ecommerce platforms to drive buying decisions. Based on sellers' actions on social media, this study examines seller turnover and disengagement on e-commerce platforms. The study has been supported by the justice theory. Seller reviews and ratings from e-commerce platforms and conversations from social media platforms have been gathered. Using natural language processing, machine learning, partial least squares (PLS) path analysis, and statistical inferences, objectives of the study are met. The study offers recommendations for both practitioners and researchers. The sellers must focus more on interaction and communication than marketing. Through a longitudinal analysis, the study also establishes that ecommerce organizations can use seller social media performance as a predictor of future seller churn and disengagement so they can take the necessary remedial action.