A machine learning-based assessments of customer satisfaction levels in Indian Postal Services (ML-ACSLIPS) for selected Tamilnadu districts from social media content

Abstract

The Indian Postal Services (IPSs) have above 155,333 post offices distributed globally. They are one of the most known and established government-owned institutions with sizable customer bases. They provide accessible and affordable services in India thanks to the unrivalled postal systems with various new services added in the last decades which include money transfers, distribution of mutual funds, and mail/parcel deliveries. IPSs have positioned themselves as dependable agencies for the Government of India. It has a competitive advantage of geographical accessibility. The system has also established centralized core banking solutions with alternative delivery channels enabling everywhere, anytime banking settings in India. Since the current marketplaces focus on clientele; businesses need to regard them as profit-making entities creating needs for high-quality services and management essential for customer satisfaction and retention. Globalization has added to acute competition to IPSs making it imperative to improve the overall quality of their services. This study’s goal is to identify the primary drivers of consumer happiness with IPSs. Since social media is the most widely used for views, references, and product information, this study effort suggests a system based on social media data called Machine learning-based Assessments of Customer Satisfaction Levels in Indian Postal Services (ML-ACSLIPS). Regression analysis and machine learning techniques (MLTs) were used in this study to examine Indian customer satisfaction levels in IPSs.