When David Ogilvy says, "Any damn fool can put on a deal, but it takes genius, faith and perseverance to create a brand," many of us nod knowingly. You spend an overwhelming amount of time (and in some cases money) defining, building, and executing on a brand that will drive your business forward. That's why you run brand workshops, that's why you create style guides, and that's why you obsess about refining a list of brand attributes until it perfectly captures who you want to be and what emotions you want to elicit from your customers. That master list of brand attributes will impact every touchpoint between your business and your customers, so it's worth getting right.
But how do you know which brand attributes matter the most? How can you be sure that you're emphasizing the brand attributes that will most effectively lead to business results? Brand is a slippery, amorphous thing, like a fish that flops around while you try to weigh it. But because brand attributes drive so many decisions, from the tone of all your messaging to the design of your website, it's critical to find ways to measure performance beyond that blunt instrument called a customer satisfaction survey.
An emerging technique for prioritizing brand attributes involves correlating each attribute to increasing share of wallet with customers. The goal is to identify which brand attributes are most closely linked to customer loyalty by looking at how much customers spend with your business out of all the money they spend in your industry. By examining which brand attributes are working most successfully with the most loyal customers, you get compelling evidence that these are the brand attributes that are most important to your business.
Here's an overview of how this process works:
What emerges from this process is a list of brand attributes that is prioritized based on measurable business results. What was formerly the touchy-feely realm of branding now has a little more science injected into it. As any statistician will tell you, correlation is not necessary causation, and this data doesn't prove with certainty that certain brand perceptions cause higher loyalty. But this data is a strong indicator that certain brand attributes matter more than others, and thus can help you prioritize and focus your brand.
All sorts of decisions become easier after prioritizing brand attributes. Rather than juggling a long list of diverse brand attributes when writing email copy or choosing a color palette for your new website, you can now focus your team on the few primary attributes that are most important. In addition, testing new concepts becomes easier, because you can have customers evaluate a concept against these prioritized brand statements, just as they did in the survey.
Brand attributes are the winds behind the sails of many decisions that you make. Isn't it about time you measure which gusts of wind are most successfully moving you forward?
Steve Mulder and Ziv Yaar are colleagues at Molecular, an internet consulting firm in Boston, where Steve is principal user experience consultant and Ziv is vice president of internet strategy. They are the authors of "The User Is Always Right: A Practical Guide to Creating and Using Personas for the Web."
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Dead on – The key is not to guess on the brand attributes but to discover them as well as the relative significance and level of positive negative sentiments. I would like to invite you to check out www.wisewindow.com – I would love to hear your thoughts on auto discovery using the social media. Sid
Is the assumption that consumers are good at providing some form of proxy for SOW or is there actual empirical evidence? Maybe higher ticket items, reported may even come close to actual SOW, but for FMCG products, I would expect consumer reported estimates on spending (SOW less likely) to rarely come close to actual. For instance, for batteries, if you ask me what percent I spend on Duracell vs. Energizer vs. Rayovac vs. Eveready, I will probably be more than a little perplexed. So why not just use brand-tracking data and correlate (or even run a multivariate model) that to actual sales? You don't have to worry about reported vs. actual consumer behavior and you will end up with a much better sample size. Agreed aggregate data may not give as much insight as individual level, but on the flip side you have more consistent estimates coming from a larger sample size vs. surveying a few thousand consumers as a proxy for millions.
brand correlation is importannt
I think this is a very interesting article, and I am looking forward to reading more of them.
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