Catching Lightning on the Back-of-an-Envelope

“How far away is it?” Depending on the reason for the question, the precision of the answer has a different value.  “No more than a mile” may be specific enough for you to make the decision between walking and grabbing an Uber.  Every tenth of a mile may make a huge difference if you are wondering if you have enough gas to get to the next service station.  Different uses for the results of a query help to define the valuable level of precision. I love the hacks, shortcuts, and rules-of-thumb that relieve me from spending energy on precision that is not valued.

One Mississippi… Two Mississippi…

I will let the National Weather Service explain one of the most well-known guesstimates.  It is also one where the level of precision in the answer matches the question being sought to be answered.

“Since you see lightning immediately and it takes the sound of thunder about 5 seconds to travel a mile, you can calculate the distance between you and the lightning. If you count the number of seconds between the flash of lightning and the sound of thunder, and then divide by 5, you’ll get the distance in miles to the lightning: 5 seconds = 1 mile, 15 seconds = 3 miles, 0 seconds = very close.”

So it is not a terribly precise measure.  It actually only takes a little over 4.8 seconds for sound to travel a mile,  Based on that difference alone a perfect count would still be off by half a football field for every “mile” counted. And that is not even including the many variations of “Mississippi” in spoken timekeeping. But who cares? No one cares!

The questions that this guesstimate seeks to provide input to are equally imprecise.  In answering, “how far away is the storm?” fifty yards is hardly relevant.  The actions taken to prepare for a storm that is 3 miles away are identical to those for one that is only two and one-half miles away. “Is the storm moving towards us?”  the other question frequently informed by this data, is equally imprecise.  It is simply a directional measure.  Did I count fewer Mississippis this time versus the time before? Having more precision adds little value to answering the question.

The ROI Misalignment

I am thinking about this alignment as I read yet another article promising to deliver a straight forward method for capturing ROI.  I appreciate the noble quest but wonder if it is really needed.  What are the questions seeking input?  What precision is valuable to these questions?

Binary, one-time questions are what come to mind first. “Is this a worthwhile investment?” This is a simple yes or no question which does not value a highly specific percentage to be calculated. Confidence that I am going to at least get my money’s worth may be the hurdle to be cleared. The difference between 125% and 140% is negligible for confidence building.

There is a slide that used to be part of the standard startup pitch deck that made me cringe.  The slide’s objective was to reduce the perceived risk associated with competition, prove the size of the market, and get potential investors excited about the startup’s potential.  We called it the 1% slide.   Often it was little more than a large pie chart showing the billion-dollar market size with a small 1% slice.  The slide’s commentary always included some variation of, “and if we are only able to capture 1% of the market we are still a $400 million business.” Translated, “even if we suck, you win.”

So what if, rather than saying a certain learning initiative has a certain return perhaps all we need to do is show that the return clears some hurdle.  The equivalent of the 1% slide. A blog on what is called the kaizen method sums it up this way.

“It might not seem like much, but those 1% improvements start compounding on each other.”

What L&D needs is a simple back-of-the-envelope calculation that allows it to confidently say to our business sponsors, “even if the initiative only moves the [profit/revenue] needle 1% we still show a positive return on your investment.”  In my next blog I will lay out my back-of-the-envelope (BOTE) calculation. Spoiler alert: even if we suck the business wins.  I look forward to your feedback and suggestions.

Author: J.

J. Miguez has spent the last 25 years designing Learning & Development organizations and the service offerings that support them. Learning domain explorer.

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