Asking the right questions. Seeking the relevant information.

jackLet us play a card guessing game. I have a standard pack of 52 cards. I pick one at random and ask you for the chances it is a Jack. That is not that difficult. It is 1/13.

But it is not any standard pack of cards. We do not know how many cards in it. In fact we do not even know if there are any Jacks in it. It is like those card dispenser contraptions that spit out a card, except of unknown size. An acceptable answer is, “I don’t know”, because the problem is not frequentist probability question. The problem space has switched from risk to uncertainty.

But we can’t end it there. What if we need to find out to help with a business decision? After all, our output as a leader is decision making- make that informed decision making under uncertainty. So we have to push forward. What if we can reduce the uncertainty by asking questions? What if I made available volumes of varied data (BigData) about the card?

Sidebar,  if you ever followed Jonah Lehrer, renowned Bob Dylan scholar who also wrote books on creativity, or you are one of the hustling valley entrepreneur type you might answer  the question by,

“Questions? I won’t ask questions. I will force your hand and turn the card over to see”.

But let us stay realistic here and continue. What questions would you ask? What relevant data would you seek in the BigData?

You could ask: What color is the card?
But is that relevant and help to reduce uncertainty?

BigData could say: In 200,000 card pickings there were 103,568 red and rest black cards
Is that BigData relevant?

You could ask: Is that a picture card?
This  helps to reduce uncertainty. If the answer is no, you are done. If the answer is yes you still have work to do, but are at a better state than before.

BigData could say: In the past 200,000 card pickings a few picture cards were sighted.
This helps too but not as effective as you actively seeking the information.

The role of information is to reduce uncertainty. If it does not help reduce uncertainty in decision making it is useless regardless of its volume and variety (or how many ever Vs you add).  BigData is not a substitution for application of mind. You the decision maker need to ask the right questions for the decision at hand and not let it spew you with interesting findings.

Do you ask the right questions?

Not everything that counts can be counted … but did you try?

Photo Courtesy: Quote Investigator

Most people attribute this quote to Einstein,

Not everything that counts can be counted, and not everything that can be counted counts

It is repeated with deference by most. There are however alternative theories about whether or  not this quote should be attributed to Einstein or not. That is not the argument here.

The argument is how statements like

“not everything can be quantified”
“not everything can be measured”
“you have to look at this holistically”
“we have been in this business long, we know”

get  thrown around to support decisions made only with lots of hand waving and posturing. Anyone pushing back to ask for data is told about this famous quote by Einstein – “surely you are not smarter than Einstein”, is the implied message I guess.

Let us treat that famous quote as given – there are indeed factors that are relevant to a decision that cannot be quantified. But ..

  1. How many business decisions depend on such non-countable factors? Consider the many product, marketing, strategy, pricing, customer acquisition etc.  decisions you need to make in your job. Do all of them depend on factors that cannot be counted?
  2.  Considering many factors that go into making a decision what is the weight of non-countable factors in any decision? You don’t say 100% of any decision depends on non-countable factors.
  3. How many times have you tried to push through decisions based entirely on non-countable factors (stories?) with not an iota of data?
  4. Finally, have you stopped to check whether what you label as non-countable is indeed so and is not so because of your lack of trying? Are you casting those fators as non-countable either because you are not aware of the methods to count them or because you are taking shortcuts? If you made an attempt you will find most things are countable (How to Measure Anything), be it segment size, willingness to pay or brand influence.

I cannot speak for what Einstein (or someone else) meant with that quote about non-countable things that count. But I am going to interpret that for our present day decision making.

For any decision there are factors that are inputs for it. Some of those factors can be measured with certainty, some with quantifiable uncertainty* and others with unquantifiable uncertainty*. You must cover first, have a model for second and put in place methods to learn more to reduce the third.

Without this understanding if you try to invoke Einstein you are only cheating yourself (and impact your business in that process).

What is your decision making process?


Quantifiable Uncertainty means you can express the uncertainty within some known boundaries. Like saying, “I don’t know what is the click through rate is but I am 90% confident it is between  1% and 8%”

Unquantifable Uncertainty means those factors which are unknown and cannot even be expressed in terms of odds (or probabilities).

Probability of Winning MegaMillions Vs. Probability of Dating Supermodel

$640 million jackpot is hard to resist. Those who do not usually play the lottery are now playing, buying at least five tickets. The WSJ reports Megamillion sold two tickets for every adult in the country.

The probability of winning is still 1/175,711,536.

MarketPlace Radio tries to put this number in perspective by giving a list of things that are more likely to happen to us.

Chances of dating a supermodel?  1/88,0000

But there is a big difference in the definition of probability between these two scenarios that is lost.

Probability of winning MegaMillions is determined by simply counting all possible ticket numbers. This is the frequentist approach and it is correct in this case.

We cannot use the same frequentist approach for finding your chances of dating a supermodel. The 1/88,000 number is based on number of supermodels and number of men.  This is relevant only if we are estimating, “what is the probability that a randomly selected man from the population is dating a supermodel?”

When you want to measure your chances of dating a supermodel you need different definition – probability as a measure of uncertainty.  It is not 1/88,000. (For a more detailed discussion of this definition of probability see here.)

How can you measure this probability? If you imagined living your life 10,000 times, given all possible events that could happen and the many different choices you make, in how many such lives do you find yourself dating the supermodel? That is your probability and it is different for every individual.

On the other hand, if you do win tonight the probability of dating supermodel is 1. That is the conditional probability.

My Thoughts on Risk Taking

I believe one’s action deserves the label Risk Taking only if one has multiple options available, each with its own outcome, and one picks an option whose outcome is most advantageous despite the uncertainty.

If one picks the one and only option available to them, however uncertain the outcome is, it is not risk taking.

As a corollary one should take risks when one can most afford it and not as the last action.

Using NFL analogy
– Onside kick on opening kick-off is risk taking.
– Onside kick when down 3 with 30 seconds on the clock isn’t risk taking

In Business Context
– When you have a successful product line introducing a new one with potential but unproven market  and may even cannibalize your current one is risk taking
– When your current offering is commoditized, introducing the only possible product isn’t risk taking

Next time you are interviewing a candidate about a time they took risk, ask them whether it was onside kick on opening kickoff or in the last 30 seconds.

In Startup Context
– When one gives up college or leaves one’s current job with guaranteed pay to start a venture with unknown outcome, t is risk taking

– When one  fed up with growth in current job or find it difficult to land a new one decided to start a venture, it isn’t risk taking

We need regulations because our original business plan didn’t consider these disruptions …

A Cajun food cart in a food cart cluster in SE...

For anyone studying business model disruptions, a rich case study is unfolding across the nation, from coast to coast, one food cart at a time. Let us not make this a culinary argument and look at this purely from business perspective. If you have not been following the stories here is a summary,

Food carts are restaurant on wheels. They come in all flavors and with clever names (Curry up now). They are showing up in office parks and business districts, catering to the lunch crowd. And that is the problem for traditional  brick and mortar restaurant owners – the trucks are right at their doorsteps and eating their lunch (pun intended). The restaurant owners don’t like it!

Let us look at this in two parts to see the disruption. As I am wont to do, let us start with customers.

Customers/Customer Needs: Anyone with money to spend during lunch is a customer. Why are some willing to give up the sit down comfort and service of restaurants and try food carts? There is no one reason, there are always utilitarian and emotional reasons and it is the degree of intensity that varies for different customers.

Some were hiring the restaurant just to satisfy their hunger and they are happy to hire any other service that does the same job faster/better/cheaper. Some hire the restaurant for the experience but not likely all the time. In some cases they may be hiring the food cart just for the novelty and experience.

If you look at the comprehensive list of purchasing occasions you will find there were enough jobs for which the restaurants were hired only because there was no alternative. When food carts arrived the customers are happy to fire the inferior candidate and hire the new one.

The Incumbents: The restaurant owners had a nice run. Their product was hired by customers for lots of jobs even though it was not the right fit. Do not get me wrong, there are many other jobs for which restaurants are the right fit and rightly capture their share of the value add. But the rest of their customers incurred higher transaction cost for the value delivered. These segments were right for picking by anyone. Restaurants were however only too happy to serve the same product at the same price to all their customers and now see that advantage vanish.

Their reaction can be summed up nicely by the comments made by their rep in KQED’s Forum interview,

“Our original business plan did not take into account these food carts taking away our customers”.

Isn’t this a luxury every business would like to have?

That is the core problem. Disruptions don’t telegraph their arrival. Uncertainties are the unknowables. You can’t ask for protection because of your failure to model these uncertainties.

If disruptions these are the unknowables how can they model these?

There is really only one way – starting with customer segments and asking what jobs are the different segments hiring your product for.  If your product is not the best candidate for each one of those jobs it is currently hired for it will be disrupted. The flip side of my earlier statement, “anyone with money to spend is customer”, is , “anyone goes after that money is your competitor”. And they do that by doing the job faster, better, cheaper.

If the customers hired your product  only because of lack of alternatives are they really your customers to begin with?

Your failure to focus on customer needs cannot be fixed with newer regulations to stop the disruptors.

We all hear about customer loyalty and the need to focus on increasing loyalty. Loyalty cannot stop disruption. If you do not want to be disrupted it is you who should show loyalty – loyalty to the job your customer hired your product for, doing the job better than anyone else can.

Probability of Another Bear Market – Do Not Listen to Frequentists

By now you already know the market nosedived, plummeted, crashed today – losing 4.5% in just one day. We are down 10% from the peak set in May.

Analysts are quick to call this a correction – by their definition a drop of 10% from peak means we are in correction territory.

Are we going into another bear market ( 20% or more loss from the peak)?

Briniyi Associates has this to say about chances of a bear market

Since 1962, there have been 25 corrections greater than 10% during bull markets. Nine of these instances became bear markets. Historically there is a64% probability that this is only a correction and not the start of a bear market.

The math is not complex to see, in 16 of 25 past corrections we did not enter bear market so they say the chances are 64%.

This is the classic or the frequentist approach that simply counts the instances and assigns a probability to next recession.

But is that correct or relevant?

Estimating the chances of next bear market is not about counting but about estimating the (un)certainty. Will we enter a bear market? Not easy to answer – definitely not by counting the sample space or the outcomes. Probability in this cass ceases to be a ratio of two countable events and becomes a representation of hunch, degree of belief, gut feel or a hypothesis.

Again, is a bear market just around the corner? We don’t know and neither do those analysts with their incorrect use of probability.