Using big data file fusion to determine the effects of social media on retail brand preference
Don E. Schultz, Martin Paul Block · Applied marketing analytics · 2014
One of the primary challenges in the emerging fi eld 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 confi rms 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 fl ood of consumer-generated data now emerging.