Resistance Is Futile

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

When you look at job descriptions of analytics leads in various organizations, you will often find the word “evangelization.” If every stakeholder is a believer of analytics, we would not need such a word to describe the position at all. We use that word because an important part of an analyst lead’s job is to convert non-believers to believers. And that is often the hardest part of our profession.

I smile when I see memes (or T-shirts) like “Science doesn’t care about your beliefs.” I’m sure some geek who got frustrated by the people who treat scientific facts as just an opinion came up with this phrase. From their point of view, it may be shocking to realize that scientifically proven facts can be disputed by people without any scientific training. But that is just human nature; most really don’t want to change either their beliefs or their behaviors.

Now, without being too political about this whole subject, I must confess that I face resistance to change all of the time in business environments, too. Why is that? How did activities of making decisions based on numbers and figures became something to resist?

My first guess is that people do not like even remotely complicated stuff. Maybe the word “analytics” or talk of “modeling” bring back all of the childhood memories of their scary math teachers. Maybe that kind of headache is so bad that some would reject things that could actually be helpful to them.

If the users of information feel that way, analysts must aspire to make analytics easier to consume and digest. Customers are not always right, but without the consumers of information, all analytical activities become meaningless — at least in non-academic places.

An 80-page report filled with numbers and figures dumped on someone’s desk should not even be called analytics. Literally, that’s still an extension of an unfiltered data dump. Analysts should never leave the most important part of the job — deriving insights out of mounds of data — to the end-users of analytics. True, the answer may lie somewhere in that pile, but that is like a weather forecaster listing all of the input variables to the general public without providing any useful information. Hey, is it going to rain this morning, or what?

I frequently talk about this issue with fellow analytics professionals. Even in more advanced organizations in terms of data and analytics infrastructure, heads of analytics often worry about low acceptance of data-based decision-making. In many instances, the size of the data and smooth flow of them, often measured in terabytes per second as a bragging point, do not really matter.

Information should be in nugget-sizes for easy consumption (refer to “Big Data Must Get Smaller”). Mining the data to come up with fewer than five bullet points is the hardest part, and should not be left to the users. That is the primary reason why less and less people are talking about “Big Data” nowadays, as even non-data professionals are waking up to realize that “big” is not the answer at all.

However, resistance to analytics doesn’t disappear, even when data are packaged in beautifully summarized reports or model scores. That is because often, the results of analytics uncover an inconvenient truth for many stakeholders — as in, “Dang, we’ve been doing it wrong all of this time?”

If a person or a department is called out as ineffective by some analytical geeks, I can see how involved parties may want to dispute the results any which way they can. Who cares about the facts when their jobs or reputations are at stake? Even if their jobs are safe, who are these analytics guys asking us to “change”? That is not any different from cases where cigarette companies disputed that smoking was actually beneficial in the past, and oil and gas companies have an allergic reaction when the words “climate” and “change” are uttered together in present days.

I’ve seen cases where analytical departments were completely decimated because their analytics revealed other divisions’ shortcomings and caused big political hoopla. Maybe the analysts should have had better bedside manners; but in some cases I’ve heard about, that didn’t even matter — as the big boss used the results of analytics to scold people who were just doing their jobs based on an old set of rules.

You can guess the outcome of that kind of political struggle. The lesson is that newly discovered “facts” should never be used to blame the followers of existing paradigms. Such reactions from the top will further alienate analytics from the rest of the company, as people get genuinely scared of it. Adoption of data-based decision-making? Not when people are afraid of the truth. Forget about the good of the company; that will never win vs. people’s desire for their job security.

Now, at the opposite end of the spectrum, too much unfiltered information forcing decisions can also hurt the organization. Some may call that “Death by KPI.” When there are too many indicators floating around, even seemingly sound decisions made based on numbers and figures may lead to unintended consequences; very often, negatively impacting the overall performance of the company. The question is always, “Which variable should get higher weight over others?” And that type of prioritization comes from clearly defined business goals. When all KPIs are treated to be equally important? Then nothing really is. Not in this complex world.

Misguided interpretation of numbers leads to distrust in analytics. Just because someone quoted an interesting figure within or without proper context, that doesn’t mean that there is just one version of an explanation behind it. Contextual understanding of data is the key to beneficial insights, and in the age of abundant information, even casual users of analytics must understand the differences. Running away from it is not the answer. Blindly driving the business just based on certain indicators should be avoided, as well. Both extremes will turn out to be harmful.

Nevertheless, the No. 1 reason why people do not adopt to analytics is many have gotten burned by “wrong” analytics in the past, often by the posers (refer to “Don’t Hire Data Posers”). In some circles, the reputation of analytics got so bad that I even met a group of executives who boldly claimed that whole practice of statistical modeling was totally bogus and it just didn’t work. Jeez. In the age of machine learning, one doesn’t believe in modeling at all? What do you think that “learning” is based on?

No matter how much data we may have in our custody, we use modeling techniques to predict the future, derive answers out of seemingly disjointed data and fill in the gaps in data — as we will never have every piece of the puzzle nicely lined up all of the time.

In a case of such deep mistrust in basic activities like modeling, I definitely blame the analysts of the past. Maybe those posers overpromised about what models could do. (No, nothing in analytics happens overnight). Maybe they aimed for a wrong target. Maybe they didn’t clean the data enough before plugging them into some off-of-the-shelf modeling engine. Maybe they didn’t properly apply the model to real-life situations, and left the building. No matter. It is their fault if the users didn’t receive a clear benefit from analytical exercises.

I often tell analysts and data scientists that analytics is not about the data journey that they embarked on or the mathematical adventure that they dove into. In the business world, it is about the bottom line. Did the report in question or model in action lead to an increase in revenue or a reduction in cost? It is really that clear-cut.

So, dear data geeks, please spare the rest of the human collectives from technical details, and get to the point fast. Talking about the sample size or arguing about the merits of neural net models – unless the users are equally geeky as you — will only further alienate decision-makers from analytics.

And the folks who think they can still rely on their gut feelings over analytics? Resistance to analytics is indeed futile. You must embrace it — not for all of the buzzwords uttered by the posers out there, but for the survival of your business. When your competitors are embracing advanced analytics, what are you going into the battle with? More unsolicited emails without targeting or personalization? Without knowing what elements of promotions are the key drivers of responses? Without even basic behavioral profiles of your own customer base? Not in this century. Not when consumers are as informed as marketers.

One may think that this whole analytics thing is overly hyped-up. Maybe. But definitely not as much as someone’s gut feelings or so-called business instincts. If analytics didn’t work for you in the past, find ways to make it work. Avoiding it certainly isn’t the answer.

Resistance is futile.

Zooming In, Zooming Out: 2 Focal Lengths for Better Audience Understanding

Instead of a scientist measuring a particle, imagine you’re a marketer and the red dot is your audience. You really want to know who they are, so you try to get close.

I truly admire artists like Keith Haring. He created an iconic style that’s known across the world — even by people who don’t know the artist’s name. And when I think about creating an iconic, signature style like that, I imagine artists have an idea that they simply can’t shake. And they keep trying, over and over, to create the perfect expression of their vision. Only with each iteration, they are creating something that may be based on the same idea … but each expression is entirely new unto itself. And is its own version of perspective.

Now … I’m not an artist. I certainly don’t have an iconic style. But I do have an idea that I can’t get out of my head. I keep returning to it over and over again. Because I know there’s a lesson for marketers in the idea. So once again, I’m diving into The Heisenberg Uncertainty Principle — a scientific principle I’ve used in many presentations before.

In the simplest terms, the principle establishes this: There is a limit to the ability that you can understand both the exact location and the exact momentum of a particle in motion.

There’s an equation that goes along with the principle, and it includes things like The Planck Constant, central to quantum mechanics. But for the sake of this marketing article, we’ll focus on this visual expression of the idea:

John Lane artIf you’re trying to measure the precise location of the red dot, you’d want to get as close to it as possible, to plot the exact X and Y axis — down to the deepest decimal. “The fifth nine,” if you will.

If you’re trying to measure the exact momentum (to understand how fast the dot is moving from one spot to another), you’d take a view from the top, open end of the cone — so you’d have context to measure the location in relation to other objects at varied times.

To get better understanding of one aspect — location or momentum — you lose the ability to focus on the other.

Now, instead of a scientist measuring a particle, imagine you’re a marketer and the red dot is your audience. You need to know both to be successful. You need to get up close to connect. You need to see the bigger picture to create a lasting relationship. To truly understand your audience, you’re going to need to zoom in and zoom out.

Zooming In: To know your audience,  you try to get close. (At least I hope so. But for most brands, it’s more like close-ish. As close as they can get without actually talking to that audience. But that’s a different post.) You should want to do this to ensure you’re going to set up camp on a channel with a storyline that will resonate greatly.

Zooming Out. To ensure the money you’re spending on that channel and that storyline is well spent — that it isn’t wasted on a trend that passes in a hot second — you need a more broad view. You need to see all the different influences on your audience that you can only get from a broad view.

Marketers’ Most Recent Answer to Solving the Riddle of Getting Both Perspectives Is Big Data

But here’s the even newer wrinkle (and why this principle is back in my head again): The growing reliance on Big Data is actually best for the broad view. And relying on big data is steadily pulling us marketers farther and farther up the cone. It’s allowing marketers to better see where people were, and where they might be headed. But it’s taking us farther and farther away from understanding the individuals within our audience. It’s creating — and even causing us to crave — an abstracted view of our audience rather than a precise view.

The Answer to This Problem Is: More Small Data!

Small Data is gathered by actually reading the comments on Instagram posts. Not just the comments they leave on your post … but the one they left on their best friend’s post yesterday. Within that comment — that comment gained through super-tight focus — are the keys to communicating in their language … to connecting with them based on a challenge, need, value or passion that that individual is expressing.

Small Data is gathered by talking to your audience. You ask for input or invite it, rather than always pushing your message. Once the input flows in — whether via suggestions or questions — you don’t stop after a two-line conversation. You cultivate the third, fourth and fifth exchanges. And then you incorporate your new discoveries based on the conversation into your storyline and lexicon. (This is qualitative input, not based solely on the Big Data algorithm breakdown of the exchange.)

Small Data Is …

Yes, Big Data is good. The bigger picture — the momentum and direction — is important. But Small Data — the highly-specific details gained by tight inspection and interaction — is just as important (if not more so) to building engagement.

So take a lesson from science! Think about the Heisenberg Uncertainty Principle. Deliberately take two views of your audience — by zooming in to Small Data, zooming out with Big Data, and continually repeating the process. Your marketing will be better for it.