Long tail vs. blockbusters - a data-driven approach

Anita Kumar, C Ranjani

Research output: Contribution to journalArticle

Abstract

Business revenue generation can be typically represented as a power law distribution curve where the head of the distribution represents the 'blockbusters' (maximum revenue generation) and a 'tail' that represents products that account for a small percentage of total revenue. Traditionally, brick-and-mortar businesses have allocated their sizeable resources and attention to the blockbusters and have largely ignored the tail. In the present internet-driven era and markets, the concept of 'long tail' has emerged that, effectively demonstrates that companies can make the products, in the niche segment, profitable as well however, in practice, this concept is still limited to the digital products marketed online, and in the academic literature, this concept has received little attention. The aim of this paper is to highlight the relevance and importance of a data-driven strategy to manage both the 'blockbusters' and 'long tail', through illustrative cases drawn from the Indian SME food sector. It, then, proposes a conceptual framework that the practitioners can utilise effectively to make informed decisions, driven by data, to maintain a wide product portfolio.
Original languageEnglish
Pages (from-to)118-138
Number of pages21
JournalInternational Journal of Business and Systems Research
Volume11
Issue number1/2
DOIs
Publication statusPublished - 2017

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Long tail
Revenue
Conceptual framework
Food sector
Product portfolio
Digital products
Resources
Power-law distribution
Niche
Small and medium-sized enterprises
World Wide Web

Keywords

  • Blockbusters
  • Longtail
  • Business analytics
  • Decision making
  • India
  • Food SMEs
  • Revenue generation
  • Power law distribution
  • Product portfolio
  • Small and medium sized enterprises
  • Food industry

Cite this

Long tail vs. blockbusters - a data-driven approach. / Kumar, Anita; Ranjani, C.

In: International Journal of Business and Systems Research, Vol. 11, No. 1/2, 2017, p. 118-138.

Research output: Contribution to journalArticle

Kumar, Anita ; Ranjani, C. / Long tail vs. blockbusters - a data-driven approach. In: International Journal of Business and Systems Research. 2017 ; Vol. 11, No. 1/2. pp. 118-138.
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