As the relationship between human marketers and marketing data evolves from casual dating into something a little more serious, many are beginning to feel the growing pains. Sometimes the data tells us thing we don’t want to hear, or worse yet, can’t understand. Sometimes we ask the data to answer questions it can’t. And sometimes we have to spend more energy than we have breaking through the static and cutting to what really matters.
In short, when it comes to data, many of us feel like we’re in a dysfunctional relationship, and it’s time for a little couples counseling.
Marketers are from Mars, data platforms are from Venus
First off, as is often the case with couples, we can’t assume that we really understand each other. All too often, we come to our data full of assumptions and beliefs that have gone unchallenged, and whether we realize it or not, we’re just looking for confirmation of what we already believe.
This phenomenon is known among cognitive scientists as confirmation bias, and it’s a really nasty bug in the human operating system. In short, it’s a subroutine in the algorithm of human perception that leads us to focus on data that confirms pre-existing beliefs. It’s almost as though the data that tells us we’re right is shouting at us, while the data that suggests we’re wrong is relegated to a quiet (but potentially game-changing) whisper.
The problem is, as is also often the case with couples, that while marketers are often mercurial and full of urgency and need, our counterpart data is annoyingly level-headed and complacent. It doesn’t put up an argument. Often it will happily comply and just tell us what we want to hear.
In other words, unless you design a solution to counteract this dynamic, the data will play along with our confirmation bias disorder and lead us down the wrong path. As Ronald Coase, famed British economist and winner of the Nobel Prize in Economics in 1991, once said, “If you torture the data long enough, it will confess to anything.”
Blah, blah, blah
Confirmation bias isn’t the only thing creating relationship problems. Data has its own baggage, too, and part of it is that it just won’t shut up. Sure, once in a while it tells us something useful that might drive sales. But it also gossips, spreads rumors and occasionally starts stupid fights that could have been avoided if we’d all just counted to 10 first.
Think about it. We’re surrounded by marketing data dashboards, and our brains just can’t handle the strain. Most companies have at least a dozen or more data dashboards going at any given time, leading to massive data fatigue and information overload.
And it’s more than just a data-induced headache we have to worry about. This dynamic of too much “what” and not enough “so what” leads to some very real and problematic behaviors.
First of all, marketers are left to sort through the data in the hopes of finding a meaningful insight, even though that’s what the dashboard was supposed to do in the first place. To use the old proverb, we’re in a room filled with horse manure up the ceiling, and we’re digging through it with a shovel in the belief that with so much dung in the room, there’s got to be a horse to ride in here somewhere.
But ultimately, marketers aren’t judged by how much horse manure they shovel; they’re judged by how much hay they can make.
Fighting over nothing
What’s more, even if a marketer ultimately does dig their way through to an insight on a dashboard, oftentimes the visualizations dashboards serve up are misleading and can lead to people arguing about the wrong things. According to a recent article in the Harvard Business Review, there are three traps in particular that dashboards create for marketers (or, more likely, busy CEOs).
The first is what they call the “Importance Trap” — that is, just because something looks statistically meaningful on a chart doesn’t mean it is. Data has to be aligned with, filtered by and correlated to business KPIs before they warrant a fire drill.
The second is the “Context Trap.” One number on its own — the number of sales leads, for example — may go up and down. But without additional context, who knows whether that number is good or bad?
The final trap is the “Causality Trap,” and it’s perhaps the most dangerous and pervasive. You can overlay a social media data trend side by side with a revenue data trend, but that doesn’t mean that social media contributed to revenue. It just means they happened at the same time. The internet is full of examples of spurious correlations, and they’re worth a quick scan if you’re looking to prove this point internally.
Running the relationship intervention
The first step to improving any relationship is to own your own baggage. Focus on the assumptions and behaviors that are within your own power to change. The second step is to ask for your partner — in this case, marketing data platforms — to make some improvements on their end. Here’s a quick “relationship tune-up” guide for you and your marketing data.
1. Check your assumptions at the door.
Make a short list of all the assumptions you’re making about your business objectives, about your customers, and about what you think is really going on. Make a concerted effort to counteract your own confirmation bias and proactively look for data that proves you wrong. This is what scientists do every day. Be a scientist and explore new possibilities in the data. It might actually be fun.
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