SoCal BMA: Using Discrete Data to Develop a Focused Brand Strategy

Posted on November 15, 2013

Earlier this week I was able to participate in the Southern California Business Marketing Association’s conference entitled Harnessing the Big Data Revolution.  My co-presenters were Ryan Rieches and Alan Brew of RiechesBaird, a B2B brand strategy agency and Dr. Bill MacElroy, Chairman of Socratic Technologies, a brand research firm.

In attendance were marketers from a variety of industries including technology, energy and industrial products.  The goal was to discuss a common for big data and themes for how it can be used to improve business performance.

Our presentation focused on the dual data methodology of using (i) primary research to discover insights about purchase intent and brand perceptions to form options for brand strategy, while (ii) using big data social monitoring to assess brand health over time.  I offered the metaphor of the former being a “garden hose” and the latter being a “fire hose” when thinking about data variety, volume and velocity.  Using discrete data (specific data sets) to form a brand strategy is pertinent to the research methodology.  That is, obtaining brand awareness, perception and preference data from customers and non-customers that represent your optimal target market is required to ensure the insights generated are relevant.  For instance, if you are a brand selling to enterprise IT decision makers then you would want to ensure your sample population of research respondents represent that target market.

However, big data is not about carefully selecting discrete data sources from which to extract insights.  To the contrary, big data is about sifting through a large amount of the “3 V’s” (variety, volume, velocity) of data to extract relevant trends and insights.  So the application of big data analysis in the case of branding would not be in formulating your strategy, but in monitoring the results of your strategy.  Specifically, monitoring large amounts of unstructured data to identify patterns in how your brand is being talked about and positioned in the mind of the buyer as evidenced by their online conversations and comments.

Dr. MacElroy raised important distinctions between what big data can reveal (typically, ways to do the same thing — only better) versus the insight discovery available from discrete data sets.  Discrete data sets permit techniques such as perceptual mapping and brand power (preference) mapping to understand new opportunities for brands.  Both research approaches are valuable but they must be applied to the correct problem.

So the conclusion of our presentation was two-fold: (i) discrete data allows brands to isolate the appropriate target markets from which to extract insights for developing brand strategy, while (ii) big data allows brands to monitor trends and brand associations to determine the alignment between a brand’s positioning and delivery of the actual brand experience.


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