Innovative service recovery of customers by food aggregators using sentiment analysis. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Chauhan, Sanjeev; Upadhyay, Yogesh; Singh, S K
• Is Part Of: IOP conference series. Volume 1116:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Aggregator business model -- Online food ordering -- Sentimental analysis -- Tone analyser -- Social mention tool -- Sustainable business practices.; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1116/1/012199
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: In the global era, technology plays a vital role in determining the strong market position for the modern-day organizations. Food aggregators make efforts to identify the reasons for service failure in case of customers not in direct communication with them. The ascending online food ordering market has augmented the involvement of peers' day-to-day interaction on social media resources. Food ordering brands like Swiggy, Food panda, Zomato who have garnered quite a reputation by tapping their audience online via enticing them with the idea of getting quality restaurant meals right at their doorstep with just a click are slowly being victimised by their web of problems. Online marketing had made daily food ordering easy for the users but, the excessive demand of orders at a time can lead to delay in deliveries, loss in quality etc. henceforth, targeting brand's esteem and loss of valuable consumers. These food ordering websites collaborate with a wide network of restaurants/hotels in a city and provide quick delivery of food products to their customers using aggregator business model. They act as middlemen who deliver the food from restaurants to user's home maintaining a brands name. The current study focuses on tracking the sentiments of the customers through which these brands can identify the issues which leads to unsatisfactory responses or reviews on various online and social sites. Further study pave ways for innovative service recovery mechanism to frame a sustainable business practice for gaining competitive advantage. In this study, sentiment analysis tools like IBM Watson, social mention and tone analysers were used to observe the reactions of customers and used disruptive technology to analyse how brands could improve their service recovery behaviour to secure and reach more customers by figuring out the reasons of service failure.