Following the release of the new How Disruption Can Fuel Brand Growth report Bill Pink, Head of Brand Guidance Analytics in North America, and I have been mulling over whether big data is likely to inspire disruption. Our answer is that to do so you need more than just data, big or small.
With that in mind, we wrote up our seven principles for working with big data.
- Good strategy is informed by good analysis. Know what question you are trying to answer before you start, don’t just go on a fishing trip and expect the data to tell you anything useful.
- Recognize that existing data will likely identify many paths to make incremental gains but it is less likely to find something truly disruptive. Lots of small wins may lead to incremental growth but are easily countered by competition and may disappear once the low hanging fruit has been picked.
- Inform your analyses of big data with what is known already about how to grow successful brands If you have existing research on the brand associations that influence equity and sales for a given brand or category, then use those insights to bring structure to the unstructured big data assets to identify growth opportunities.
- Big changes in market share come from doing something disruptive and different from the norm, but historical and aggregated data sets usually reflect the status quo. This is true for both big and small data. If you want to identify how best to grow your brand, start with a set of opportunities and hypotheses and find or create the data necessary to quantify the size of the prize and whether it is achievable.
- Any form of data and analysis should ultimately lead to better business decisions. Don’t just blindly trust data just because there is a lot of it. Use the data that is shown to be effective at predicting sales performance or anticipating new trends emerging. It does not matter whether that data is small or big, just that it is available and reliable to inform decisions. And in most case, you will need a mix of small and big data to achieve reliability and predictive accuracy.
- Focus on what the customer wants, not what you want. Use the data to understand what will add value for your customer that they are willing to pay for, rather than simply trying to drive volume sales.
- Passively observed data will always give you a better measurement of purchases and choice but will tell you little about motivations and how brand memories influence consumer decisions. This needs to be included in the analysis, either from the survey or social data, to see how to better influence future consumer decisions. Sometimes attitudes lead behavior, sometimes they follow. Figure out how to create a mutually reinforcing set of attitudes and behaviors.
So do you agree with our basic proposition or do you believe that big data can create disruption? Or maybe you have something to add to our list of principles?
Please share your thoughts.
This article appeared first on Kantar Millward Brown’s Blog…