Creation of unstructured big data from customer service: the case of parcel shipping companies on Twitter

Customer service provision is a growing phenomenon on social media and parcel shipping companies have been among the most prominent adopters. This has coincided with greater interest in the development of analysis techniques for unstructured big data from social media platforms, such as the micro-blogging platform, Twitter. Given the growing use of dedicated customer service accounts on Twitter, this paper investigates the effectiveness with which parcel shipping companies use the platform. This paper demonstrates the use of a combination of tools for retrieving, processing and analysing large volumes of customer service related conversations generated between parcel shipping companies and their customers in Australia, United Kingdom and the United States. Extant studies using data from Twitter tend to focus on the contributions of individual entities and are unable to capture the insights provided by a holistic examination of the interactions. This study identifies the key issues that trigger customer contact with parcel shipping companies on Twitter. It identifies similarities and differences in the approaches that these companies bring to customer engagement and identifies opportunities for using the medium more effectively. The development of consumer-centric supply chains and relevant theories require researchers and practitioners to have the ability to include insights from growing quantities of unstructured data gathered from consumer engagement. This study makes a methodological contribution by demonstrating the use of a set of tools to gather insight from a large volume of conversations on a social media platform.

  • Record URL:
  • Corporate Authors:

    University of Sydney. Institute of Transport and Logistics Studies

    University of Sydney, 144 Burren Street, Newtown, New South Wales, 2042, Australia
    Sydney, New South Wales   
  • Authors:
    • Bhattacharjva, J
    • Ellison, A B
    • Pang, V
    • Gezdur, A
  • Publication Date: 2018-10

Media Info

  • Pagination: 29p
  • Serial:
    • Issue Number: ITLS-WP-18-19

Subject/Index Terms

Filing Info

  • Accession Number: 01684193
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ITRD, ATRI
  • Created Date: Oct 25 2018 12:04PM