Title: A uniqueness-driven similarity measure for automated competitor identification

Authors: Xin Ji; Yi-Lin Tsai; Adam Fleischhacker

Addresses: Alfred Lerner College of Business and Economics, University of Delaware, 46 Amstel Ave, Newark, DE, 19711, USA ' Alfred Lerner College of Business and Economics, University of Delaware, 46 Amstel Ave, Newark, DE, 19711, USA ' Alfred Lerner College of Business and Economics, University of Delaware, 46 Amstel Ave, Newark, DE, 19711, USA

Abstract: Uniqueness is an important source of competitive advantage and a salient aspect for firms identifying competitors and market structure. While marketing research often includes uniqueness as an important aspect of product positioning and product strategy, the existing literature has offered little guidance on operationalising this notion for use in the competitor identification process. This paper proposes a probabilistic similarity measure to quantify a competitive landscape where uniqueness is a key driver of competition. The proposed measure, when used with readily available data and combined with existing clustering algorithms, enables automation of the competitor identification process. Empirical experiments are used to validate the proposed measure. These experiments show that marketers can use readily available data, including social media tags and geographical proximity data, to reveal the same insight as is gathered when using the more laborious and time-consuming approach of traditional consumer surveys.

Keywords: uniqueness; similarity; competitor identification; related social tags.

DOI: 10.1504/IJADS.2019.098664

International Journal of Applied Decision Sciences, 2019 Vol.12 No.2, pp.179 - 204

Received: 27 Mar 2018
Accepted: 24 May 2018
Published online: 29 Mar 2019 *

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