McKinsey’s Power Curve debunked

I became quite excited about a promising new business model – McKinsey’s power curve, which I came across while researching company growth curves. It was proposed by McKinsey & Company, one of the Big Three Management Consultancies. I was on the verge of buying their book on this topic to get a better understanding of the insights that were promised. That is until I started digging deeper into the data that led to this model. I discovered that this model was a mathematical function derived almost 300 years ago by French mathematician, Abraham de Moivre, who was also a consultant on gambling and insurance (according to Encyclopedia Britannica).

Consulting is a noble profession. Consultants get marred and tarred by stories like what you are about to read. Do you remember how a consultant was defined during the days of the Enron/Arthur Andersen debacle? A consultant is someone that borrows your watch to tell you the time and then keeps it. (Kihn, 1994)

Despite this view about consultants, billions of dollars are spent on advice from big brand name consulting powerhouses. They have friends in high places in the corporate world who control large discretionary budgets. Most consulting scandals are driven by “big” consultant factories. It is smaller consultants, without a “brand”, who have to fight this negative reputational headwind to make a case that consultants can and do actually provide a great value-added service. (Full disclosure, I belong to this group of brandless consultants).

My aim is not to bury McKinsey, but to praise their profession – consulting. This is my Mark Anthony moment. McKinsey has a lot of great people who have and are still contributing to some awesome insights and advice on all facets of business management. They have volumes of good, insightful work that is freely available on the web. Besides, an article like this would hardly affect McKinsey which has a Teflon-like history. They have shrugged off of bad publicity and continued to do business as usual, according to Duff McDonald, author of the book, “The Firm: The Story of McKinsey and Its Secret Influence on American Business.” (Lewis, 2013)

I have written this article for small and medium cap companies that are mesmerized by the out-of-reach and mythical world of these “Big” consultant factories. I hope that at the end of this article, the reader will have had one of those “the-emperor-has-no-clothes” realizations.

What is McKinsey’s Power Curve Model?

I came across McKinsey’s power curve model, on the web in an article titled “Strategy to beat the odds” (Bradley, Hirt & Smit, 2018) published in the February 2018 issue of the McKinsey Quarterly. The authors introduced the Power Curve of Economic Profit that was identified during their research into the profit numbers (after taxes and interest) of 2,393 large companies. Sorting the data by profit in a descending sequence, they then plotted the data with the x-axis divided quantiles (5 parts – each with 20% of the total range of values). The resultant plot looked like an S-shaped curve (see following figure) which they called the Power Curve. To me it looked very familiar and similar to some common mathematical functions, for example the logit curve function. I digress, more on that later.

mckinsey's power curve

(source: LinkedIn)

Analyzing the data and this curve, the authors put forth several hypotheses. One such hypothesis was that most companies (in the middle three quantiles) barely made any profit because of a power (my bold and underline) of “perfect” markets to push economic surplus to zero” (Hirt, 2018, Jan 23). Wow! Really?

The authors further claimed that this middle section of the curve is a common outcome. Additional language and innuendos seemed to imply this as a preordained outcome for companies. However, they went on to say “not a necessary one. If you understand the social side of strategy, the odds of strategy revealed by our research, and the power of making big moves, you will dramatically increase your chances of success.” (Bradley, et al., 2018)

This statement is immediately followed by a question “Would you like to learn more about our Strategy & Corporate Finance Practice?” with a link to their “How we help clients”, Corporate Strategy page.

In other words, what they posit is that a company could break free of their destiny controlled by the “power of perfect markets” which they claim to have identified. To truly succeed a company would only have to make a jump into the top-most, high-profit quintile. In order to replicate that success and learn the “secret” behind that magical breakthrough, one would, of course, have engage McKinsey consultants. I wonder how many billions were paid in consulting fees and purchase of books to learn about this “secret” that drives companies to fall into their respective places on the Power Curve?

I will reveal that secret here for free. Yes free! Zip – zero – nada!

The secret is normal variation!

Yes, you did read that correctly. The secret behind that curve is good, old-fashioned, common cause variation. The bell curve. The Gaussian distribution. Whatchamacallit. The curve that has been around for 300+ years representing the predictable probability of most variables to cluster towards an average value by virtue of the “power” (ok – if you insist, we can call it that) of central tendency. Let me explain.

The so-called McKinsey’s Power Curve explained

As I attempted to learn more about the McKinsey “Power Curve”, one thing struck me as very odd. Refer to the y-axis in the following figure: Why did they use the Economic Profit average value of $180 million as the point where the x-axis intercepted the y-axis? Didn’t they just state in the article that the majority of companies make almost no economic profit – in other words close to zero. Then why was the zero point not taken as the intersection point? 180 million in profit is not zero profit! Say that to a company with $360 million in revenues and they will be offended. Why did they clump the “majority” of the companies into that squished region in the middle? Why were they trying to portray that region as a doomed zone for companies? What was happening there? This was all very puzzling, and drove additional intrigue towards understanding the mechanics behind that curve. I had to find out more about those companies in the middle three quintiles. I am a Deming disciple – “Show me the data”. I needed data to slice and dice.

After some searching, I stumbled across a dataset compiled by Gary Hoover. It was described as “2015 Profitability of Public Companies with Revenues of $30 Billion or Greater” (Hoover, 2017). This was a dataset of profitability of companies that would be similar in profile to a typical McKinsey client. While the Hoover data had only 93 data points (2,300 less than the McKinsey data set), it was close enough to the 100 deemed to be adequate for a statistically significant study, as you yourself will see in the graphs below. At least I would get an insight of what was going on in that mysterious y-axis origin of the McKinsey Power Curve.

The figure below shows the raw data in the Hoover dataset plotted as a normal distribution function. It had a long tail. The average profit of the companies in that dataset (as % of sales) was $6.3 billion.

The long tail was the result of an outlier – Apple, Inc. I deleted the Apple data point and voila – I had a decent bell curve. No surprises there. What does an iPhone sell for again? You get my point.

After viewing the data as normal distribution plots, I did what McKinsey had, for all practical purposes, done with their data. I plotted it as a Cumulative Distribution Function (CDF) and flipped the axes. Do you see any similarities in this curve with the McKinsey curve? Other than the greater slope of the curve in the mid three quintiles, and the jaggedness from the smaller number of data points, the curve was essentially similar to McKinsey’s so-called Power Curve.

The very flat shape of the graph (tight standard deviation) in the middle three quintiles of the McKinsey curve compared to greater slope in that region for the Hoover data curve also does make sense. The McKinsey data is from their clients, that most likely have a lot of similarities in their sizes, operations and profitability. This would weed out a lot of other causalities that would otherwise lead to a wider variation.

There you have it. This McKinsey “Power Curve” is simply a percentile distribution curve or a CDF; flipped on its side, probably to obfuscate the fact that it was in reality a commonly recognized S-curve function. They made it look like the lesser known Logit function.

McKinsey’s curve represents the normal variation that would be seen if anyone plotted data of any number of companies. It is NOT because a company is battling a “Darwinist force of the market that squeezes your profitability” (Hirt, 2018, Jan 23). Now we are talking about evolutionary market forces? Hmmm…

There is no magic potion or secret formula that will allow you to “jump” up the power curve. It is common knowledge that it takes hard, smart, effective work to improve your company performance and become above average and move into a higher-class interval of a histogram.

McKinsey took conventional wisdom that has been around for 300+ years and packaged it as something mystical and seemingly beyond the intellect of normal companies. I have some additional revelations from examining that same Power Curve article which I will share in a subsequent article. It is simply disingenuous for McKinsey to package this well-known mathematical function and convert that into mystical, sage consulting advise. Well, I suppose if water can be bottled and sold, so can public domain knowledge. We can’t fault them for trying as long as no damage (other than weaker bottom lines) was caused by companies trying to chase this magic power.

Based on what you now know, the statement that “global distribution of economic profit is radically uneven” where the “tails of the curve rise and fall at exponential rates, with long flatlands in the middle(Bradley, et al., 2018) is nothing but meaningless consultant mumbo-jumbo. This is why consultants get ridiculed for consultant-speak. I suppose everyone has a right to say anything. Free speech and all that.

What I see as a bigger problem is that consumers of information such as this, do not use their critical thinking skills. They simply gobble this up as Gospel. In their minds, after all, they think how can such a respected brand like McKinsey be wrong? Well, as we have seen here, even consulting Gods, with their minions, churning out expensive reports, can be and frequently are wrong.

For example, when McKinsey says, “mobility on the Power Curve is possible, but rare. Only 1 in 12 companies, or 8%, manages to move from the middle quintiles to the top over a 10-year period. Those are pretty daunting odds(Hirt, 2018, Apr 24), I say, “Show me the data”.

Thankfully, the burgeoning freelancer-powered, gig economy along with boutique, more affordable, consulting firms continue to disrupt the consultant factory dominated industry. Affordable, effective consulting is available near you. They may not be “Gods” on the pedestals of your mind, but they can certainly be the guiding angels to your ultimate success.

Effective consultants will have you taking money to your bank vaults and not the other way around!


Bradley, C., Hirt, M., & Smit, S., (2018, Feb 13). Strategy to beat the odds. McKinsey Quarterly. Retrieved June 06, 2020, from

Hirt, M., (2018, Jan 23). Is Your Strategy Good Enough to Move You Up on the Power Curve? LinkedIn. Retrieved June 6, 2020, from

Hirt, M., (2018, April 24). The 6 Numbers That Should Guide Your Strategy. LinkedIn. Retrieved June 6, 2020,

Hoover, G. (2017). Giant Corporations Profits. Retrieved June 9, 2020 from

Kihn, M., (1994). House of Lies: How Management Consultants Steal Your Watch and Then Tell You the Time. Warner, USA.

Lewis, A., (2013, Sep 25). Is global consulting giant McKinsey evil? Market Watch. Retrieved June 6, 2020, from