For the record, I’m only referring to it as ‘big data’ because it makes the title of this article more catchy. We all know that I’m of the camp that marketers need to think small about data.
Anyone who has read much of what I’ve written about the concept of big data knows that I am firmly of the belief that in order to popularize the use of data, we need to think in less grandiose terms. That said, big data can be called as much because of its big applications. Among those big data applications are the creative elements spawned from analysis. And therein lies our big gap; generally speaking (in my experience anyway) creatives tend to be creative, and statistics (and the secret powers they hold) tend to fall on the shoulders of others. Considering the malleable nature and hugely valuable potential that comes with effective data analysis, that creates a pretty serious problem.
The Problem (The Big Gap)
So what exactly is this gap? As noted, historically, the creative process has not involved data at this scale. Of course, market research and historical results have always factored heavily into the creation of new creative, but what we’re talking about in this case is something far more focused that can be measured in real time.
With the advent of new media and the facility with which marketers can measure the results of a campaign (and the responses of a targeted audience) data, even superficial data, need to be a consideration of even the most creatively-driven marketers. And that highlights the problem – or our industry’s big data gap – which marketers now face. Data proficiency and insight analysis are no longer neat skills that can be highlighted on your LinkedIn profile; a successful campaign needs creative minds that can read and adapt to real-time analytics in order to ensure every ounce of potential is extracted from an audience and the subsequent creative.
Alas, there has been a divide between numbers people and creative people since the dawn of marketing. So what is needed in order to start shrinking this gap and blend these two, crucial worlds into one?
Luckily for us, new media advertising networks (like Facebook, Twitter, and LinkedIn) have been working hard to bring these two worlds closer. Data is now a central focus in advertising dashboards, and it allows for creatives to analyze their work (from a statistical standpoint) run tests with different variables in play and come to numerically-justifiable conclusions that help improve campaigns (as well as universal branding initiatives) in the long run.
Facebook IQ, a branch of the social networking giant that conducts research, shares data and provides marketers with expert insights, recently published some findings as they related to the matter of closing this ‘creative loop’, as they called it. While the points that these marketers analyzed seem somewhat superficial, or even trivial, the process of testing the minutiae of a campaign is, for some reason, so often overlooked. There were two types of analyses reviewed in this particular publication by Facebook IQ: retrospective analysis and in-market analysis. And what exactly is the difference? Looking at subtleties in past campaigns (retrospective), and analyzing performance on a number of these elements in real time (in-market). Again, we’re talking about small adjustments (like re-wording a call-to-action, or changing the color of your creative’s background) that can lead to changes in your audience’s response in a controlled environment (keeping the targeted audience constant).
Image Credit: Facebook IQ.
The key thing to remember is that not all brands (and not all audiences) are created equal. Much like my feelings about the term ‘big data’, I have made my feelings about the field of aggregate data very clear: blindly following industry averages to craft your strategies – especially with so much of your own data available – is hugely misguided. You need to pay attention to your own creative and your own analytics in order to determine the best course of action, not simply use the call-to-action ‘Read More’ because some study found that it performed XX% better after studying 1,000 posts. Those studies were impressive at the incipience of social media marketing, now they’re dated and irrelevant.
Customizing Your Gap Closure
As noted, not all brands are created equal. There are test and research papers that you’ll read and review that might sound intriguing, but you need to remember that your test grounds might be very different. What’s more, creatives and dataheads will need to approach the bridging of this gap in a different way. First, let’s take a look at creatives.
The creative marketer will need to first become familiar with the analytics dashboards that exist. Look beyond the superficial metrics like click-through rate and start studying elements like visitor path and engagement value. Just because your content got lots of views and a high volume of ‘Likes’ it doesn’t necessarily mean that you’ve found the Holy Grail. Identify the metrics that matter to your brand and reverse engineer the successful trajectories. Then begin to identify correlations between your most successful pieces of creative in order to run the above-mentioned tests. The opposite is true for the data-inclined.
If your focus is largely on data, then the reverse engineered approach described above is something that is already far too familiar to you. Instead, get to know the creative. For numbers people, idiosyncrasies is the name of the game. Become more familiar with the inner workings of your creative in order to better understand what makes your target audience tick. Don’t simply look at the obvious, think about your creative on a more primal level; look at colors, placement and, one of my favorite emerging fields (from a tech standpoint, anyway) focus on where the audience’s attention and eyes are drawn.
By following these two approaches and learning to empathize with the other side of the equation, the gap can be shrunk significantly. With fewer misunderstandings between these two types of marketers, campaigns can be developed that are more targeted, more adaptive and, ultimately, more successful.
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