Effective brand marketing increases the probability of consumers either picking a brand or paying more for a brand when it is time to buy. But what data and analytic approaches help us to understand the influence of brand marketing on sales?
We know that brand and sales are not the same thing, and we know there are many influences on consumer choice beyond branded memories and associations such as availability, discounts, new product entrants, and changes in consumer needs. But even after controlling for these influences we still find it difficult to isolate the contribution of brand marketing. How can we do better?
The answer lies in going back to some first principles of analytics and putting them in a brand context. If we could build the ideal data set to understand brand impact, that data set would need to observe consumer choices and feelings at a highly granular level and cover a variety of users. Here is why:
First, granularity is key because we know different consumers are influenced differently by messages, product benefits, pricing, etc. When our data exists at a granular level, we can easily see these differences in action. Contrast this with analytics run solely at a macro or trended level. We may see flat trends in aggregate but we would miss movements within particular consumers, or groups of consumers, that average out to no change. Granular data uncovers these patterns, which is why so much of our work in this space focuses on bridging sales and brand data at a segment, region, or household level.
Second, we know that history matters. Ask yourself how often you are looking at two to three years of trended data for a mature brand? Or do a bit of homework and see how long that brand has been in existence in that category and market? I recently tried to understand the impact of brand equity on sales for a brand that was over 70 years old, but we had data going back two years and only at the monthly level. No surprise the data was rather flat and provided minimal variation to leverage. It’s extremely rare to have data on consumers going back so far in time to capture the initial launch, but if we were working with brand and sales data at a granular level then we would have a mix of recurring, new, and lapsed buyers in our data to work with.
Said bluntly, variation is the mother’s milk of analytics. And when trying to use analytics to understand the impact of brand marketing, we need to leverage data assets that allow for granular observations as much as possible to have variation in brand feelings, consumer outcomes, and different brand histories.