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|In: Digital|Last Updated:

You heard it here first, but, he said it better, “he” being Professor Ravi Bapna of the University of Minnesota’s Carlson School of Management; “it” being a discussion of “Two Things Companies Should Do Now to Set Up for a Post-COVID-19 Future.”

Well, okay, he actually said it first too — beat me by a week.

Professor Bapna’s recommended two things are: (1) upskilling your workforce, because “as organizations shift to an AI-first world, they need a workforce which understands the world of data, analytics, and AI”; and (2) re-thinking operations and strategy toward an “AI-first strategy.”

So let me up the ante with KJR’s Thing One and Thing Two: AI-based business modeling and anticipatory customer re-identification.

AI-based business modeling

While our pre-COVID-19 fascination with Digital transformation frequently led to little more than Digital superficialities, it did lead to one salutary change in executive thinking — recognition that increasing revenue is just as legitimate a strategic outcome as cutting costs. It didn’t, sadly, overcome the metrics obsession that’s the root cause of management’s over-reliance on cost-cutting, but it was a start.

Briefly, the issue is that connecting a cost-cutting effort to an actual cost reduction is, for the most part, pretty simple, while connecting revenue-enhancement efforts to actual increased sales is frustratingly multivariate.

What’s needed to manage effectively isn’t more and better metrics. It’s the ability to model complex cause-and-effect relationships.

Start here: For many companies, strategic change isn’t really strategic in nature. Planning is based on the unstated assumption that the business details might shift from year to year, but the basic shape of the business doesn’t change. The buttons and levers management can push and pull to make profit happen are constant.

To the extent this unstated assumption is true, it should be possible to direct the attention of machine-learning technology to a business’s inputs, outputs, and operating parameters so that, after some time has passed, the AI will be able to determine the optimal mix for achieving profitable revenue growth.

And in case you’re curious … no, I’m not remotely qualified to delve very far into the specifics of how to go about this. That would call for deep expertise, and I’m a broad-and-shallow kind of guy. I do know someone who built this sort of model the hard way, and she verified that yes, it can be done and yes, machine learning would be a promising alternative to doing it the hard way.

Anyway, while I’m a broad-and-shallow kind of guy, I’m not so shallow that I can’t suggest Thing #2, which is:

Anticipatory customer re-identification

Right now, as pointed out here a couple of weeks ago, most businesses are just trying to survive until the future gets here. And please don’t misunderstand. Succeeding at this will, for most businesses, be nothing to sneeze at (insert your own COVID-19 snark here).

But smart business leaders will take their planning to another level, and it has everything to do with their expectations regarding what the economy will be like once the crisis has passed.

My own, everything-I-know-about-economics-I-learned-on-a-street-corner expectation is that as we’re reaching Great Depression levels of unemployment we shouldn’t expect the post-COVID-19 consumer population to look just like it did before we started self-isolating.

As with the Great Depression most working-age adults will be employed, so there will be consumers to sell to. If we use the Great Depression as the benchmark of our worst-case-not-including-total-societal-collapse analysis we’ll figure about 20% unemployment as the basis for customer re-identification — my just-invented term for Figuring Out Who You Want to Sell To.

The KJR point of view: There will still be consumers and they will still be spending. Fewer and less, for sure, but still well above the zero mark. The affluent and wealthy won’t go away either, and it wouldn’t surprise me if many do quite well in the aftermath and decide this is an excellent time to buy stuff.

I’m not going to try to identify specific consumer segments here. That’s for you and your fellow strategic planners in the business to do. What I’m recommending is that business leaders shouldn’t wait to find out who will be spending what, and shouldn’t undertake their survival efforts based on an expected return to status quo ante.

Make your adjustments based on positioning the business for the consumer marketplace to come, and which segments within it you want to cater to.

And yes, that includes those businesses that don’t sell to consumers, because in the end, no matter how long the business-to-business-to-business value chain, it’s always consumer spending that pays for the steps in between.

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|In: Cognitive Enterprise|Last Updated:

Think of this as KJR’s pledge week.

No, I’m not asking for donations. I’m asking for your time and attention before you let yourself read this week’s missive.

Specifically, when I decide what to write about each week I’m doing too much guessing based on too little information. I’m asking you to let me know what you’d like me to cover this year in KJR, and, almost as important, what I should be writing about so your non-KJR-subscribing colleagues would find it more compelling.

One more thing: My ManagementSpeak inventory is running low. After 23 years of one a week it’s entirely possible there just isn’t all that much more bafflegab to translate. So instead, if you don’t hear something that deserves the ManagementSpeak treatment, send me your favorite quotes instead.

I will ask you to apply one filter on these: As with all things KJR I’m looking for what’s off the beaten path — dictums that haven’t yet been widely discovered but deserve to be read by a discerning audience.

Okay, that’s enough Pledge Minute. Back to this week’s KJR.

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The private sector has discovered data. Where a decade ago, business leaders were encouraged to trust their guts, they’re now encouraged to trust their data scientists.

It’s role reversal. Back then, governmental policy-making was heavily data-driven. As Michael Lewis (no relation) explains in The Fifth Risk, an extensive, intensive, and essential responsibility of many cabinet-level agencies is the collection of highly valuable data and managing the databases that contain them.

Just in time for businesses to invest heavily in data collection, management, analytics, and interpretation, the federal government is shifting much of its policy-making to a more instinctive approach, and in doing so is shifting budget and resources elsewhere.

Here at the Keep the Joint Running Institute, the Joints in question are organizations of all sizes and shapes; our charter is how to keep them running. As a general rule we (and that’s a royal we) stay away from political matters. Politicized matters? In bounds whenever they’re relevant.

And so (you were wondering when a point might emerge, weren’t you?) as your organization, for all the right reasons, embraces data-driven decision-making, here are a few cautionary notes you and your colleagues might find helpful:

Culture before tools: If you’re a longtime subscriber you’re familiar with the idea that when trying to institutionalize data-driven decision-making, a “culture of honest inquiry” is a prerequisite for success. In case you aren’t, the principle (but not its achievement) is simple: Everyone involved wants to discover the right answer to each question, not to prove themselves right.

Solving for the number: A culture of honest inquiry is what enlightened leaders strive for. While still on the journey, though, be on the lookout for someone using these new, powerful analytical tools to manipulate filters, choices of statistical techniques, and thresholds to support their pre-determined preferred result — for ammunition, not illumination.

GIGO: “Garbage In, Garbage Out” was widely recognized back when IT was known as the Data Processing department. That Big Data lets organizations collect and manipulate bigger piles of garbage than before changes nothing: Before you release your data lakes into the watershed, make sure your data scientists assess data quality and provide appropriate cautions as to their use.

Ease vs Importance: When it comes to data, some attributes are easier to measure than others. Even professional researchers can fall prey to this fallacy — that hard to measure means it doesn’t matter — without the blind spot ever quite reaching the threshold of consciousness.

Interpolation is safer than extrapolation: Imagine a regression analysis that yields a statistically significant correlation. And imagine that, in your dataset, the lowest value of x is $20 and the highest value is $200. Predicting the outcome of spending $40 is a pretty safe bet.

Predicting the outcome of spending $10 or $300? Not safe at all. Straight lines don’t stay straight forever. They usually bend. You just know where the line doesn’t bend — you have no idea where it does, and in what direction.

Machine guts need skepticism, too: Machine learning depends on neural networks. It’s the nature of neural networks that they can’t explain their reasoning — mostly, they’re just very sophisticated correlation finders. They’re useful in that they can plow through a lot more data than their human counterparts. But they’re still correlations, which mean they don’t imply causation.

But of course, to us the unwary, they do.

Courage: Take a timid business — more accurately, a business made timid by business leaders who consider avoiding risks to be the pinnacle of business priorities. Now add data and analytics to the mix.

What human data scientists and their AI machine-learning brethren do is spot potentially useful patterns in the data. These patterns will sometimes suggest profitable actions.

When all is said and done, when a pattern like this, along with the potentially profitable actions, are put in front of a timid business leader, much more will be said than done.

It’s unfortunate but not uncommon: Taking action is inherently unsafe, an insight that’s true as far as it goes.

What it misses: Playing it safe is usually even more of a risk, as competitors constantly search for ways to take your customers away from you.

Play it too safe and not only won’t you take customers away from them. You’ll fail to give your own customers a reason to stay.

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