Using big data file fusion to determine the effects of social media on retail brand preference

Don Edward Schultz, Martin Paul Block

Research output: Contribution to journalArticle


One of the primary challenges in the emerging field of data analytics is the combining of multiple sets of data through what is often called data fusion. This approach was used to analyse consumer responses to a biannual online study of reported social media usage by US consumers over a ten year period. The goal was to determine if the rapidly growing use of social media had any impact on the reported changes in retail brand preferences reported by those consumers in that decade-long time frame. Negative correlations were found for manufacturer brands and limited impact was found for retail brands. The primary correlation was with the growth of consumer-stated no brand preference. This preliminary analysis further confirms the present growth of no brand preference in multiple retail brand categories. Of equal value is the evidence that online consumer data can be aggregated and analysed over time, thus providing marketers with another needed tool to use in understanding and managing the flood of consumer-generated data now emerging.
Original languageEnglish (US)
Pages (from-to)81-102
Number of pages22
JournalApplied Marketing Analytics
StatePublished - 2014


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