Using Lexical Semantic Analysis to Derive Online Brand Positions: An Application to Retail Marketing Research

doi:10.1016/j.jretai.2009.03.001

Journal of Retailing

Volume 85, Issue 2, June 2009, Pages 145-158

https://doi.org/10.1016/j.jretai.2009.03.001 Get rights and content

Abstract

This paper provides an innovative approach to brand tracking in the context of online retail shopping by deriving meaning from the vast amount of information stored in online search engine databases. The method draws upon research in lexical text analysis and computational linguistics to gain insights into the structural schema of online brand positions. The paper proposes a simple-to-use method that managers can utilize to assess their brand's positioning relative to that of their competitors’ in the online environment.

Section snippets

Background and problem statement

If managers do a simple keyword search to assess what the online world says about their brands, they will usually get back thousands of hits from a search engine. They will then face the onerous task of sifting through the voluminous data generated by those hits. Currently, the usefulness of the hit information is limited by the fact that the listings contain no summary information about the content. Nor do the listings contain information on the “semantic” properties of hits containing textual

Lexical semantics—some initial ideas

Typing the name of a brand or a travel destination into a search engine will often provide thousands of hits, but the searcher does not gain information on whether these sites have positive, negative, or neutral things to say about the brand or destination (Turney 2002). To get a better insight into the meaning in the text, researchers in the fields of artificial intelligence and natural language processing are beginning to focus on evaluating the content of large text-based corpora.

One of the

Lexical semantics—the method

For the purpose of our analysis, we utilize two key properties of textual data: (1) consumers attach meanings to words and (2) meanings are inherent in the text, or more specifically in the adjectival expressions used by the author of the text. Understanding the valence or semantic orientation of a word (Hatzivassiloglou and McKeown 1997) can help describe its associated noun. That is, we can infer the evaluative nature of a sentence describing a noun by examining the association between the

Co-occurrence of brands and descriptors

In our applications, word probability p(x) will be calculated as the number of documents in an online database that have the word x mentioned in them, divided by the total number of documents relevant to that problem. Let's say we are interested in examining the associations between automobile brands (Toyota, Honda, BMW, etc.) and certain descriptor variables (reliable, efficient, expensive, etc.). In this scenario, one would calculate the PMI of “Toyota” and “reliable” as follows: $PMI = lo g_{2} (p ()$

Study 1: Brand positioning analysis

Despite the central role of positioning in marketing, there is surprisingly limited empirical work on consumer-derived typologies that reflect brand positioning strategies (Blankson and Kalafatis 2004). Branding is particularly important in the retail industry given the industry's highly competitive nature (Ailawadi and Keller 2004) and the fact that many retail chains are introducing private brands to compete with national brands (Bellizzi et al. 1981). Brand positioning relates to how brands

Study 2: Online brand personality assessment

It is now common to refer to brands as having their own personalities (Aaker, 1997, Fournier, 1998). Aaker (1997) defined brand personality as “the set of human characteristics associated with a brand” (p. 347). Through her empirical work, she identified five distinct dimensions of brand personality: sincerity, excitement, competence, sophistication, and ruggedness. She further broke down these five dimensions into 15 “facets” that were themselves composed of 42 “traits.” In Study 2, we attempt

Study 3: Attribute-level brand comparisons

In study 3, we compare two brands on a given attribute. Specifically, we compare brand pairs on “reliability” for several home appliances. Brand pairs were chosen based on the results reported in Consumer Reports’ 2004 Buying Guide. Consumer Reports’ results were based on a national survey of tens of thousands of responses it received for its annual questionnaire about products bought in the previous five years or so (Consumer Reports 2004). Thus, the Consumer Reports listing of brand

Discussion

This paper demonstrates that massive search engine databases like Google can be successfully mined to provide information of interest to online retailers. The three studies reported here are based on lexical semantic analysis which yields valuable insights into online brand representations that are of considerable value for making strategic marketing decisions.

Our approach offers a promising potential for analytical examination of a freely available database. Because the web provides an

Conclusions and limitations

The untamed jungle of the textual content on the web, coupled with the ease and frequency with which people can add content and the automated web crawlers that are commonplace today, provides a rich and detailed source of content for consumer researchers looking to better understand consumers’ relationships with products and brands. We present a significant first step at making sense of this content for marketing analysis, while encouraging and inviting other scholars to generate a stream of

Acknowledgements

The authors would like to thank Aleksey Cherfas for his significant assistance with the programming for this project. We also gratefully acknowledge the financial support of the UMD Chancellor's Grant program and additional support from CRITO/Project POINT.

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Using Lexical Semantic Analysis to Derive Online Brand Positions: An Application to Retail Marketing Research

Abstract

Section snippets

Background and problem statement

Lexical semantics—some initial ideas

Lexical semantics—the method

Co-occurrence of brands and descriptors

Study 1: Brand positioning analysis

Study 2: Online brand personality assessment

Study 3: Attribute-level brand comparisons

Discussion

Conclusions and limitations

Acknowledgements

Journal of Retailing

Journal of Retailing

Journal of Business Research

Journal of Retailing

Journal of Retailing

Journal of Retailing

Journal of Retailing

Journal of Retailing

Journal of Retailing

Journal of Retailing

Dimensions of Brand Personality

Journal of Marketing Research

Unspeakable Sentences

The Semantic Web

Scientific American

Consumer Perceptions of National, Private, and Generic Brands

Journal of Retailing

The Development and Validation of a Scale Measuring Consumer/Customer-Derived Generic Typology of Positioning Strategies

Journal of Marketing Management

Recognizing Subjectivity: A Case Study of Manual Tagging

Natural Language Engineering

Online Reviews: So Consumers Use Them?

Advances in Consumer Research

Word Association Norms, Mutual Information and Lexicography

Buying Guide 2004

Service Quality and E-Commerce: An Exploratory Analysis

Managing Service Quality

Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews

WordNet: An Electronic Lexical Database