Is the Mini Cooper seen as an environmentally friendly car? What about Tesla as a luxury car? The traditional approach to understanding these questions is to conduct a survey among Mini and Tesla buyers (and perhaps non-buyers too, if budget allows). Such studies have been conducted for decades and often involve ratings of multiple attributes and brands. While certainly feasible, they can be expensive, time consuming and can get outdated over time. Is there a better way to get at attribute perceptions of brands that can be fast, economical and automated?
Aron Culotta and Jennifer Cutler describe such an approach in a recent issue of the INFORMS journal Marketing Science, and it involves the use of social media data – Twitter, in this case. Their method is novel because it does not use conventional (if one can use that term here) approaches to mining textual data, such as sentiment analysis or associative analysis. Sentiment analysis (social media monitoring) provides reports on positive and negative sentiments expressed online about a brand. In associative analysis, clustering and semantic networks are used to discover how product features or brands are perceptually clustered by consumers, often using data from online forums.
Breaking away from these approaches the authors use an innovative method to understand brand perceptions from online data. The key insight (drawn from well-established social science findings) is that proximity in a social network can be indicative of similarity. That is, understanding how closely brands are connected to exemplar organizations of certain attributes, it is possible to devise an affinity score that shows how highly a brand scores on a specific attribute. For example, when a Twitter user follows both Smart Car and Greenpeace, it likely indicates that Smart Car is seen as eco-friendly by that person. This does not have to be true for every such user, but at “big data” levels there is likely to be a strong enough association to extract signal from the noise.
What is unique about this approach to using social media data, is that it does not really depend on what people say online (as other approaches do). It only relies on who is following a brand while also following another (exemplar) organization. The strength of the social connection becomes a signal of the brand’s strength on a specific attribute. “Using social connections rather than text allows marketers to capture information from the silent majority of brand fans, who consume rather than create content,” says Jennifer Cutler, who teaches marketing at the Kellogg School of Management in Northwestern University.
Sounds great in theory, right? But how can we be sure that it produces meaningful results? By validating it with the trusted survey data that has been used for decades. When tested across 200+ brands in four sectors (Apparel, Cars, Food & Beverage, Personal Care) and three perceptual attributes (Eco-friendliness, Luxury, Nutrition), an average correlation of 0.72 shows that social connections can provide very good information on how brands are perceived. Unlike with survey data, this approach can be run continuously, at low cost with results being spit out in real time. And there is another advantage. “The use of social networks rather than text opens the door to measuring dimensions of brand image that are rarely discussed by consumers in online spaces,” says Professor Cutler....