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

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

Anuj Batta, Arpan Kumar Kar, Shyamali Satpathy
Copyright: © 2023 |Pages: 21
DOI: 10.4018/JGIM.322439
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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.
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Introduction

Almost half of the online purchase decisions are based on online reviews and ratings given by customers in the ecommerce context (Mosteller & Mathwick, 2016). Customer reviews and ratings are more trusted and considered highly credible in comparison to the advertisements and promotions by the brands or sellers (Thakur, 2018). It is not only the product and brand rating or reviews which are important, however, the seller reviews and ratings also have an impact on the purchase decision by customers (Chen et al., 2008). Thus, retailers, sellers, and suppliers continue to invest heavily in various techniques and methods, apart from providing the best possible customer experience, to get better online ratings and reviews. The available literature in this domain indicates that customer engagement is a primary driver which leads to several effective outcomes, including positive ratings and reviews leading to a positive buying decision by customers (Brodie et al., 2013; Kaur et al., 2020; Xiao & Li, 2019).

Sellers and suppliers use social media platforms as one of the primary channels for customer engagement and interaction. Millions of customer interactions occur every day on social media platforms such as Facebook, Twitter, YouTube, etc. (Appel et al., 2019; Gupta and Ramachandran, 2021). There is a robust interrelated phenomenon between customer relationship management, social media technologies, customer engagement, positive word of mouth and brand loyalty (Dewnarain et al., 2019). With the exponential growth in social media users and the trend reflecting a fundamental shift from primarily company-customer interactions to customer-customer interactions that impact company-customer relationships, it is imperative for sellers to have an effective social media customer engagement plan (Agnihotri, 2020). Social media appeared as, and continues to be a business phenomenon. Increasingly, current and prospective customers use social media to communicate about the products and services they plan to buy. At the same time, sellers or suppliers are also using the social media platform to express their products and services and resolve customer issues (Kaplan & Haenlein, 2010; Laroche et al., 2013).

Social media customer engagement for most companies began on external social media sites such as Facebook, Twitter, LinkedIn etc. Today, 90% of mid-size and large enterprises spent at least 11% of their total marketing expenses on these platforms (de Oliveira Santini et al., 2020). As this is a huge cost for companies, there has been an immense interest in evaluating the impact and return on investment of this spend, however, it is not an easy and a straightforward mechanism. There are several issues including limitation of the social media analysis tools to only include keywords, topics or sentiment analysis, difficulty in identifying the right questions, suggestions, desires etc., and most importantly the failure in considering the communication in the conversation context (Abbasi et al., 2018). Due to the high cost and ineffective ways to measure the impact, companies quickly realized multifold benefits of supporting customer communities on their own website. Customers can raise questions or complaints about the product either on a regular website from where they buy the product or on the social media platform. Also, companies are utilizing these platforms to communicate their brands and product to customers. Most companies that engage with customers online, do so on both of these avenues, i.e. social media platforms and their own websites (Connell et al., 2019; Kumar et al., 2022).

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