Why So Much Marketing Data Analysis is Wrong

Why So Much Marketing Data Analysis is Wrong

Any idiot can gather data. In fact, idiots are gathering data all the time. Smart people are, too. So are ants and mosquitos.

Every time I drive a car, I’m constantly gathering data. My brain is doing what human brains do to judge distance, speed and alignment and how each might be influenced by things such as potholes, pedestrians and dump trucks.

It’s similarly easy to gather data in marketing. Smart marketers do it all the time. The subject line you might see in an email promoting your favorite store’s Flag Day sale (Target’s Annual Flag Day Mumu Sale!) might be different from the one your neighbor sees (Be a Flag Day Mumu Mama!). Somewhere behind the scenes is an evil marketer looking like Montgomery Burns dressed in equally evil ceremonial garb ready to make even more evil decisions based on which subject line inspired more people to actually open the email.

Data is how marketing people justify their existence. For so long, the connection between a marketing effort and results was tenuous at best. I might think that the pretty little ad I designed influenced more people to buy mumus, but there was no real way for me to determine causation. Sure, I could say mumu sales jumped 10% in the first two weeks after my ad hit the market. But what if some hip TikTok influencer did some stupid dance in a mumu and sparked a mumu revolution that just so happened to coincide with my mumu ad?

Because of that disconnect, marketing for decades was one of the first places management targeted when times got tough. All these folks saw was that marketing spent a ton of money developing ads and campaigns and placing them in magazines, on billboards and in stores. Only smart leaders seemed to grasp the fact that there was a return on that investment in the form of revenue for that which was promoted, and too often marketing folks did nothing to educate the more common masses.

Thanks to the rise of data companies like Google and Facebook (you do realize that’s what they are, right?) and the much-less-well-known data aggregating companies that feed them their information, marketing today can track your behavior all across the internet and draw some pretty spot-on conclusions based on how you interact with the digital world.

So now not only can I know what words trigger more people to open an email, I can know what words trigger you to open an email and tailor my marketing accordingly. Sounds creepy, right? It kinda is.

The problem, thus, is no longer marketers justifying their existence based on return on investment. I can pretty much show you how my work generated revenue, if you have the right systems.

  • Rather, the problem is that uncontrollable, unfiltered data is kind of like feeling a little thirsty while you’re outside gardening and, instead of turning on the trusty garden hose for a sip, going to the fire hydrant at the curb and opening the valve full-blast right into your maw.

Enter: The Data Analyzer!

It’s cool to call yourself a data analyst. It sounds pretty bad-ass when you start a sentence with, “I looked at the data, and it says …” The problem, however, is that data analysis isn’t just science, and not everyone who thinks they’re good at it is actually good at it. Thus, it’s extremely common for two people in the same marketing department to look at the same data from the same campaign and draw wildly different conclusions about the story that data is telling. We’re not talking “there’s room for some compromise” differences here. This isn’t tomAto/tomAHto. This is “Are you freaking kidding me? Where did you get that from?!?”

If you put a glass of clear liquid in front of me that has no discernable smell and certainly looks like water, I can do some empirical tests to determine if it is, indeed, water. But with data analysis, there’s a ton of room for subjectivity. And there’s also a ton of room for a lack of common sense.


For example!

Many people, upon seeing the data that more than 95% of shark attacks occur within 100 yards of shore, would never leave the comfort of the sandy beach for a quick dip ever again.

Far fewer would look at that data and say, “Well, duh. Of course 95% of shark attacks happen within 100 yards of shore, because that’s where most people who go in the ocean are actually in the ocean.” Were we suddenly to, as a species, start solely entering the ocean by taking huge ships 100 miles out to sea before jumping off en masse with our freshly scabbed knees, I imagine we can flip that statistic pretty fast.

Yet for so many self-professed “data analysts,” the story the data would be telling them is that being in the water close to shore is asking to become lunch.

It’s a good thing to recognize this about shark attack data, but it’s equally as important to recognize it as a marketing person about whatever campaign you’re running. What I’ve seen time and again are the evils of confirmation bias leaking into supposed “data analysis.” We’re really good at thinking the world operates like we think it operates, that other people see and experience things exactly how we see and experience them.

  • The truth is, no one thinks exactly as you do. No one. Yet I have seen countless important decisions made about the future of products and services because one manager interacts with the world a particular way and happens to think he’s the prototype for his customer base.

You as a marketing person will never experience your product or service as a customer experiences it. Ditto for the managers of the company. You’re all too intimately involved with it, too familiar with it, too knowledgeable about it. It would have been exceedingly dumb for me, when I worked in the electricity industry, to think that the average customer thinks about their electricity and electric bill as much as I did as that marketing guy. For most people, the extent of their thoughts about electricity is that, when they flip a switch, it works and that when it doesn’t, they’re pissed.

Me trying to get them excited about how electricity is generated and then takes a magnificent journey in which its voltage is increased to drastically high levels only to be reduced to safer levels closer to their homes so it doesn’t fry their electronics would be as pointless as trying to explain rational thought to a Q-Anoner. I might think the electricity generation and transmission process is cool, but the average person just doesn’t care.

And right there is the key to being someone who’s better at data analysis than the average marketing person. You have to have to have to have to be human-centered when you’re designing your campaigns and analyzing the results. It can’t be all about what you know and you think about the product or service. It can’t be focused on the features you think are most interesting or meaningful.

  • If you make a bottle opener that cures cancer and I have an unopened bottle and no cancer, I don’t really care about your bottle opener’s cancer-curing properties at that moment, and neither does the rest of the thirsty and cancer-free population.

Good data analysis is focused 100%, exclusively, all-the-time on the end user. If you’re not looking at the story the data is telling through that lens, you’re not getting an accurate picture.

What do you need to be a human-centered data analyst? Contact with actual humans! And not just in focus groups. No one is offering legit opinions in focus groups. Ever. There’s group dynamics at play, only a specific type of person is available or cares enough to participate in focus groups, and that incentive you’re paying to get people to attend ensures bias.

No, what you need is actual observational data based on people interacting with your product or service in their natural habitat.

  • Want to see what’s going wrong with the marketing of your open houses as a real estate company that isn’t turning those who attend into clients? If you’re not unobtrusively attending your own open houses and people watching, you’re throwing darts.

The consequences for bad data analysis can be severe and long-lasting. Each incorrectly made decision compounds the previous one, and pretty soon you’re so far off course that the right destination isn’t even on the map.

Which is why it is so important to hire good storytellers as data analysts. I understand the pull toward science-minded people, and they have their place. Yet in general science people aren’t as good with human subtleties as people with more experience in nuance. If you can tell a good story through marketing, you are better able to read the story the data is telling about that marketing.

All of this falls on the hiring managers for a marketing department. Even if you don’t have the luxury of hiring a data analyst, you can still strengthen your department. Look for the ones who know people. Think about those with interest in psychology and history and anything else that delves into human behavior. Then make sure they know how to develop and read a spreadsheet.

You’ll be successful when you find those who can mix those right brain and left brain traits and flow easily back and forth. They’re rare. But they’re worth it.

John Agliata is a marketing professional with more than 30 years of communications experience. Reach him at johnagliata@gmail.com or (352) 226-5852.

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