The Most Underrated Bias: Quantification!

Mainstream management makes many mistakes - quantification bias is just one among them (well, this is chapter 10 of the series). Don’t get me wrong - metrics and quantification are needed to manage a business. Quantification by itself can’t be asserted as inherently harmful.

Having said that, our bias to focus on things that are easily quantifiable has been pointed out by many multidisciplinary thinkers over time. Here is the late Charlie Munger on this topic:

But, the mainstream management mantra in Silicon Valley has always been and still is:

I’ve always wondered about this conflict… What nuanced message are the multidisciplinary thinkers trying to convey? Today’s post will explore this topic.

Table of Contents

• The Human Element

• Slow Elevators vs Boredom

• Fast Trains vs Uncertainty

• Fast Taxis vs Uncertainty

• Inside - Focus on Your People

• Outside: Focus on your Customers

• Wrapping up

The Human Element

I’ve worked with several business, software development and cybersecurity executives - both as a consultant and as full-time employee. I have studied this phenomenon curiously as to how executives usually miss the human elements - both inside the corporation (their employees) and outside the corporation (their customers).

• Inside: They only look at a dashboard to make decisions and formulate their “strategy” - they miss reading the room, ignore the psychological disposition of their people, not factor in their aspirations and motivations (& incentives) and the conflicts that could arise from them. They don’t get that the outcomes that they seek are very much socio-technical and not just technical.

• Outside: They don’t understand what they are measuring, what it is a proxy for, and why they should optimize it and the second-order effects of such optimization. The number and the dashboard themselves become the focus of every meeting, all the teams rally behind it, but the business/customer implications are completely ignored. Here is Jeff Bezos cautioning leaders to be always on guard when it comes to ”metrics-driven” management 👇🏾

Let’s now look a couple of examples where the focus on the “not-so-easy-to-quantify” aspects led to creative solutions… As it turns out, the fundamental ideas about this stuff is well-known and used by marketing folks that understand behavioral science. I’ll set the context with a pertinent quote that is often attributed to the legendary advertising tycoon, David Ogilvy, the 'Father of Advertising':

Slow Elevators vs Boredom

Here is the story of the "slow elevator" problem, which emerged after World War II, when occupants of a New York skyscraper complained about the slow elevators. When you start with such a formulation of the problem, the obvious solutions were to objectively increase the speed of the elevators, add more elevators, or improve the algorithms that could proactively position them for efficiency. Even extensive surveys only revealed complaints about slow elevators.

Time can be measured - there is an SI unit for it to be measured objectively (seconds). Trying to improve this "objective" or "quantified" attribute is what we seem to gravitate towards. However, such solutions would require an expenditure of millions of dollars and may not have been financially or technologically feasible at that time.

An alternative approach to solving this problem is not to just focus on what your customers are telling you verbally but also think about their emotional disposition. Focus on the no-so-easy-to-quantify (i.e., no standardized SI unit) 'boredom' of the waiting occupants instead. This new approach gave rise to a creative idea that dissolved the problem and resulted in happy occupants, but cost only thousands of dollars: installing mirrors in all the elevator lobbies, so that people could stare at themselves (and each other), thereby killing the boredom of the wait.

This approach to problem-solving is the domain of behavioral economics, which takes into account the fundamental insight that our perception is leaky, our actions are not always rational, and we cannot verbalize everything we think and feel (implicit knowledge).

Fast Trains vs Uncertainty

Our perception of time is not objective. It is really dependent on the context. Our evolutionary brain also despises uncertainty.

If you can't speed up the trains objectively (i.e., as measured in seconds) or don't have the budget to increase capacity, the best thing you can do is eliminate uncertainty, which results in significantly improving the customer satisfaction. We are OK to wait happily even for nine minutes with the certainty provided by the display compared to a five-minute wait with total uncertainty.

Pound for pound, the best money spent on improving the customer satisfaction of subway riders in London was not faster or bigger trains but installing dot matrix displays that show when the next train would arrive. Here is Rory Sutherland explaining this:

Fast Taxis vs Uncertainty

As Rory explains, Uber also doesn’t have to necessary reduce the waiting time for a taxi (which depends on multiple factors and not everything is in the control of Uber - like traffic) but only have to remove uncertainty.

The Uber app does it really well - as soon as you open the app, it gives you confidence by showing a few cars nearby on the map. Once you hail a car, you get real-time updates on a map, rather than wondering if the cab driver took the wrong exit or is stuck in traffic. I’m old enough to remember the days when you had to call the taxi cab phone number again who then used wireless radio to to find out where the driver was.

Connecting this all back to the Ogilvy quote above, it is remarkable to note that people, when asked/surveyed, always wanted faster cars. You’ll notice a similar pattern in the people who complained to the property managers about the slow elevators - we are not good at articulating things like boredom and uncertainty.

Large parts of the decision-making apparatus of our brain are opaque to our introspection - we simply post-rationalize many of our decisions. Culture, emotion, context, etc., are all at play all the time and not just facts and figures alone for "rational" decision-making.

So, it is not just quantification per se, but it is clear that we have to become more self-aware about the interplay of our own biases, our leaky perception, the incentives, emotions, motivations, & other human factors, the methods of inquiry and improvement we adopt as a result, the questions we ask, etc. - all of this has to be in context. This might explain why this bias is highly invisible/underrated.

For premium-tier subscribers and the members of the Cyb3rSyn Community, I’ll now discuss two real-world examples of how leaders succeeded/failed to factor in the “not-so-easy-to-quantify” human elements - inside (their employees) and outside (customers).

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