Data Does NOT Equal People

We marketers love data. We treat it like a holy grail: a thing to be worshipped. But we’re praying at the wrong altar. Or, at the very least, we’re praying at a misleading altar.

Data is the digital residue of behavior. It is the contrails of customer intent -- a thin, wispy proxy for the rich bandwidth of the real world. It does have a purpose, but it should be just one tool in a marketer’s toolbox. Unfortunately, we tend to use it as a Swiss army knife, thinking it’s the only tool we need.

The problem is that data is seductive. It’s pliable and reliable, luring us into manipulation because it’s so easy to do. It can be twisted and molded with algorithms and spreadsheets.

But it’s also sterile. There is a reason people don’t fit nicely into spreadsheets. There are simply not enough dimensions and nuances to accommodate real human behavior.

Data is great for answering the questions "what," "who," "when" and "where." But they are all glimpses of what has happened. Stopping here is like navigating through the rear-view mirror.



Data seldom yields the answer to "why." But it’s why that makes the magic happen, that gives us an empathetic understanding that helps us reliably predict future behaviors.

Uncovering the what, who, when and where makes us good marketers. But it's "why" that makes us great. It’s knowing why that allows us to connect the distal dots, hacking out the hypotheses that can take us forward in the leaps required by truly great marketing. As Tom Goodwin, the author of "Digital Darwinism," said in a recent post, “What digital has done well is have enough of a data trail to claim, not create, success.”

We as marketers have to resist stopping at the data. We have to keep pursuing why.

Here’s one example from my own experience. Some years ago, my agency did an eye-tracking study that looked at gender differences in how we navigate websites.

For me, the most interesting finding to fall out of the data was that females spent a lot more time than males looking at a website's “hero” shot, especially if it was a picture that had faces in it. Males quickly scanned the picture, but then immediately moved their eyes up to the navigation menu and started scanning the options there. Females lingered on the graphic and then moved on to scan text immediately adjacent to it.

Now, I could have stopped at “who” and “what,” which in itself would have been a pretty interesting finding. But I wanted to know “why.” And that’s where things started to get messy.

To start to understand why, you have to rely on feelings and intuition. You also have to accept that you probably won’t arrive at a definitive answer. “Why” lives in the realm of “wicked” problems, which I defined in a previous column as “questions that can’t be answered by yes or no -- the answer always seems to be maybe.  There is no linear path to solve them. You just keep going in loops, hopefully getting closer to an answer but never quite arriving at one. Usually, the optimal solution to a wicked problem is 'good enough – for now.'”

The answer to why males scan a website differently than females is buried in a maze of evolutionary biology, social norms and cognitive heuristics. It probably has something to do with wayfinding strategies and hardwired biases. It won’t just “fall out” of data because it’s not in the data to begin with.

Even half-right “why” answers often take months or even years of diligent pursuit to reveal themselves. Given that, I understand why it’s easier to just focus on the data. It will get you to “good," and maybe that’s enough.

Unless, of course, you’re aiming to “put a ding in the universe," as Steve Jobs said in an inspirational commencement speech at Stanford University. Then you have to shoot for great.

2 comments about "Data Does NOT Equal People".
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  1. Ed Papazian from Media Dynamics Inc, July 23, 2019 at 9:57 a.m.

    All very true, Gord. The key  is "why" and this requires human judgement born of experience as well as what can be gleaned from "the data".

    Sadly, many in advertising---and elsewhere---don't seem to draw the distinction between "data" and "interpretation". What they want is a simplistic formula that is widely accepted and doesn't require much thought. For example, about 25 years ago there was a major thrust in the TV commercial testing field to find a single, easy to measure metric as a way to evaluate the effectiveness of each commercial. Instead of the sometimes tedious process of establishing what was recalled, whether it was motiaving, etc. we would rely simply on "liking"---whether the viewer "liked" the commercial. Now, it must be said that "liking" did correlate well with other more specific metrics---verified recall, brand ID, sales motivation, etc. ---so there was some merit to the idea. However, many wanted to forget about the other indicators and rely only on "liking". Which would have been a huge mistake as "liking" didn't tell you why the ad was liked---hence effective---and offered little guidance regarding how to create future ads.

    We see much the same thing today in the constant chant---"data, data, data"----with no discussion of what data is being referred to or how to use it in a meaningful manner. Indeed, some are suggesting that tomorrow's CMOs must learn how to manipulate and process "data" or they will be left behind. Which is absurd. What CMO's need is people who report to them who understand "data" from a practical marketing persepctive---not a theoretical one---and can draw conclusions from the data which support the development of sensible strategies.

  2. Paula Lynn from Who Else Unlimited replied, July 23, 2019 at 1:31 p.m.

    All else would require more time and money i.e. less time on the yatch and pay interns. 

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