It seems a bit hard to believe we’re already in the fourth quarter of the year—but, it’s October and many PR practitioners are doing 2018 planning.
Data analysis will continue to be paramount in the PR practitioner’s toolkit in 2018, which is why it’s so important that we examine data for what it is, not what we want it to be. A recent post on Mediashift brings this point into clear focus.
The post provided an overview of the results of an Institute for Public Relations report that examined how CMOs and CCOs view management in the digital age. The responses of those interviewed were fascinating, as they point to an issue that communications will continue to struggle with—namely, the question of what exactly is digital? How do we define what is digital, when so much of communications is now either done online, or is informed by online content?
Most interesting to me, viewed from the perspective of data analysis, was this quote:
One automotive executive takes a different approach. Instead of pulling insights from the data, he commissions the research to confirm his gut instinct.
If you’re a researcher, my guess is that you just cringed a bit when reading that. It’s a dangerous path to go this route of seeking confirmation for preconceived ideas.
This is because we tend to trust our instincts more than information, even when they are wrong. A Harvard Business Review article from 2003 provides context as to why this is not a good idea in a piece titled, appropriately, “Don’t Trust Your Gut.” The crux of the article is simple: human cognition is riddled with inherent biases that can mislead us, and often do.
The piece notes that one of the most problematic aspects of our intuitive processes is the desire to identify patterns. This compulsion is so strong that we often see patterns where they don’t exist at all. It is a normal human behavior to process new information through the lens of prior experience; yet by prioritizing experience we miss important information.
And this is the problem with the executive cited above’s approach. By placing the gut instinct in the starring role, one can almost certainly find some data, somewhere, to support it—this is because when one starts with the assumption that the gut instinct is correct data that supports that conclusion will be easy to see—it will jump out at you. With that as a core assumption, research that confirms the assumption will get priority attention, perhaps receiving more weight than it should in the analysis.
This is not to argue that gut instinct should be ignored—far from it. A better approach is to view data as it comes and if there is something that seems wrong or off, to then dive further into it. For example, if a company has just had a strong quarter in sales but the quarterly media analysis shows a rise in negative coverage, it would be smart to dig into the data and see if there are factors that led to such a counterintuitive result.
For example, if the media content is being scored by humans, is there a new person on the team whose ratings are skewing results? If the content is being scored by an automated system, is there a word or phrase in the content that is throwing the automated system off—like sarcasm, or tongue-in-cheek mentions? Was the negative content carried in publications that aren’t relevant to your target audience—so it didn’t resonate and carry over to lost sales? Even more interestingly, could the negative press have caught the attention of and brought new customers in, who ignored the headlines and bought anyway?
Prioritizing gut instincts could cause a company to miss important insights. The more data we pull in, the more important it becomes to keep our biases in check.
The HBR piece makes another very interesting point. As communications are now global, there is greater potential for “gut instincts” to be more homogenized than ever before. Trusting one’s gut for business decisions could lead to a sameness in the results—rather than leading, we’re inadvertently following. The same informational inputs will lead to similar outputs.
Shedding our internal biases completely isn’t possible—as humans, we are products of our cumulative learned experiences. However, we can keep the urge to find patterns in check by making the concerted effort to view the data we collect as objectively as possible first. If something seems amiss, we can dig deeper. If there’s still no explanation, we then have a case to examine and test in an attempt to either disprove or verify.
Gut instincts have a place—it makes no sense for a person with vast experience in an industry to ignore intuition entirely. But we should resist the urge to find data that fits our instincts, which puts the intuition cart before the data horse.