Understanding changes in a brand’s core positioning and customer engagement: a sentiment analysis of a brand-owned Facebook site

Zhenning Xu (), Colin Vail (), Amarpreet S. Kohli () and Saeed Tajdini ()
Additional contact information
Zhenning Xu: California State University
Colin Vail: TD Bank, America’s Most Convenient Bank
Amarpreet S. Kohli: University of Southern Maine
Saeed Tajdini: Indiana University Southeast

Journal of Marketing Analytics, 2021, vol. 9, issue 1, No 2, 3-16

Abstract: Abstract The increasing power of social media has created unprecedented opportunities for marketers. In particular, brand-owned social media seems to be an increasingly popular way of enhancing a brand’s position, connecting with customers, and improving customer engagement with the brand. To guide strategic marketing communication decision-making on social media, the current study extends the relationship communication model and offers an analytical workflow to gain new insights from unstructured textual data available on brand-owned social media. The workflow utilizes an eclectic mix of analytical tools such as word clouds, and cluster and word association analyses, which collectively allow for identification of main topics and their temporal evolution in unstructured textual data from a brand’s social media. In doing so, the proposed workflow offers researchers and practitioners a step by step procedure to make sense of such textual data, which may prove unwieldy and overwhelming otherwise. Furthermore, to manifest the utility of the proposed workflow, it is applied to illustrative data collected from a brand’s Facebook page. Results from this example analysis point to a slight fading of the brand’s perceived core position as an event avenue, as well as an evolution of customer sentiments that may reflect different levels and types of customer engagement with the page. Finally, we discuss the implications of our findings for research and brand management practice, as well as the study’s limitations and future research opportunities.

Keywords: Relationship communication; NLP; Social media; Sentiment analysis; Word association analysis; Cluster analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41270-020-00099-z

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