One of the things I’ve loved most about crafting my career in online marketing is that there is lots of feedback when it comes to campaigns. You can tell almost instantly what people respond to and what falls flat.
When I started out in marketing in 1999, billboards, radio, television, print and other difficult to track media ruled, but the prevailing advertising wisdom was, “Half the money I spend on advertising is wasted; the trouble is I dont know which half.” (John Wanamaker) That seemed to satisfy most clients who were easily convinced that spending more meant more return. As the web evolved and more and more people started getting their purchasing influence and information on it, we were able to pinpoint which half was wasted and which half worked.
However, this information only tells half of the story. Yes, people clicked on this ad and not that ad and people shared this post and not that post…but why? That’s still a big gap in information.
“The trouble with data is that it asks as many questions as it answers.”
My theory is that these questions are the ones that most marketers (and their clients) dread, so the data is ignored (at best) or misinterpreted (at worst).
I was joking around with some associates of mine the other day about formulas that work. We made a game of listing off the sure-fire wins in social media marketing, “Let’s see. Cats…that guarantees a win. Puppies are good, too. Oh…and people like drunkenness! Yes! And crass humour. We just need to make a funny drunken cat video and it’ll totally go viral!” The problem with this theory,besides the obvious animal cruelty controversy it’s sure to drum up, is that, well, funny drunken cats aren’t really appropriate to every brand. Not to mention that people get tired of formulas pretty quickly. It’s not cats or drunkenness or humour. If we want to reverse engineer success, we need to go beneath the surface. Instead of just blindly accepting that people love cat videos, we need to understand why they love cat videos.
There are no formulas, but there are lots of clues for every brand and it rests in the questions behind their own data. Instead of just reading the numbers and making wild guesses on why something works and why something doesn’t, the people who work with the data have to become Data Whisperers…a skill that requires looking at the quantitative AND the qualitative and looks between the connected dots.
Data Whispering is the ability to tell a story with the numbers. Data Whispering is uncovering the human side of the numbers. Data Whispering is understanding the right question to ask of the numbers. And to do that, you need to step back from your own bias as much as humanly possible and find the story the data wants to tell you (not what you want it to tell you).
I have to admit bias here as I’m friends with the founder, but my favorite Data Whispering tool is Nexalogy. Conceived by an astrophysicist (astrophysicists are those types who look at stars and planets and seek to understand the universe and our place in it), it’s the perfect mix of BIG DATA and storytelling:
They don’t only tell you what people are talking about, but how it’s related to your product and how much weight it carries. And it’s perfect for general market research as well. If you know a few data points about your potential audience, you can find out more data points about that audience. For example, if you are running a candidate for political office in a region, you can find out what people in that region really care about – helping that candidate form their political platform to suit their constituents’ needs. If you are thinking about opening a restaurant, you can pinpoint gaps in the market or even figure out where you should be looking to locate your restaurant. For established brands, you can see how people see you versus your competitors. What terms do they use?
Unlike sentiment analysis – of which I’m not a huge fan because it, too, asks more questions than it answers – Nexalogy gives a neutral, unbiased view of why people are making the decisions they are making. Claude Theoret once demonstrated an analysis they had done for a bank, showing that people thought of them as fair, good, community-focused, providing personal service and friendly versus their bigger competitors that people thought of as secure, prestigious, cold and impersonal. According to Theoret, the bank used this information to focus on expanding their community programs and promoting their one-on-one personal touch and saw great outcomes with this campaign.
I love data, but I’m not a fan of more of it. I’m an advocate for understanding how to read it and learn to tell a story from it. It’s a skill that needs to be a regular part of a marketing curriculum. Knowing that people love that nerd kitty image is great, knowing why is even more valuable.