The Wall Streetization of Marketing

In the well intentioned pursuit of accountability and a more businesslike approach, the marketing industry has, to a large extent, lost its way.  What was once a discipline that prided itself on its ideas, has become one that reveres numbers it doesn’t truly understand.

Like Wall Street, marketers have sought to raise their art to a science through quantification and have even borrowed jargon from their financial brethren.  The result is pseudoscience in the guise of professionalism which obscures far more than it reveals.

To truly comprehend where marketing is going wrong and how to fix it, we need to go back to where finance faltered and forge a different path for marketing.

Bachelier and The Random Walk

The genesis of modern Wall Street can be traced back to Paris in 1900.  It was there and then that Louis Bachelier presented his thesis on speculation to a panel of judges including the great Henri Poincaré.  It described the fluctuation of market prices as a random walk, a revolutionary idea at the time.

The paper was an impressive piece of work anticipating not only mathematical finance, but Einstein’s famous paper of Brownian motion published four years later.  His main insight that “the mathematical expectation of the speculator is zero” allowed for the the construction of elegant mathematical models.

However, due to either the fact that Bachelier was way ahead of his time or because of his rather prickly disposition, he and his work were largely ignored and then forgotten.

The Birth of Financial Engineering

Over half a century later, Bachelier showed up in the dissertation of a student of Paul Samuelson, a man considered by many to be the father of mathematical finance.  Bachelier’s work proved to be a revelation and led to a deluge of new Brownian motion models that sought to tame financial risk.

No longer would finance be beholden to uncertainty, but would be elevated to a true science.  Gut instinct would be replaced by powerful equations.  Luck would be superseded by mathematical rigor.  Returns would not be simply hoped for, but engineered and economic life would be transformed.

As the financial revolution gathered steam, Samuelson’s colleague at MIT, Paul Cootner, collected the most promising papers in a 500 page tome, The Random Character of Stock Market Prices, which became an instant classic.  The age of financial engineering had arrived!

A Penny on the Tracks

Included in Cootner’s book was a paper by Benoit Mandelbrot, that should have been a bump in the road, but proved to be nothing more than a penny on the tracks.  In it, Mandelbrot pointed out that as impressive as the models where, they bore no resemblance to empirical data and vastly underestimated volatility (the stuff that causes market crashes).

In a response, Cootner wrote that Mandelbrot forced economists “to face up in a substantive way to those uncomfortable empirical observations that there is little doubt most of us have had to sweep under the carpet until now.” He then added, “but surely before consigning centuries of work to the ash pile, we should like to have some assurance that all of our work is truly useless.”

In other words, the fascination with certainty proved to great an enticement to bother with uncomfortable realities and good sense.

In the decades since, the failures of financial engineering have been laid bare.  Economic cycles have not been tamed.  Markets crash with increasing regularity.  Great investors such as Warren Buffet and George Soros argue vehemently against the efficient markets hypothesis spawned by Bachelier’s Brownian models

Wall Street Meets Main Street

Despite the fact that Wall Street’s equations had never passed any kind of empirical testing (and indeed failed them), the financial engineering mindset affects how companies are managed today.  Especially with respect to the Capital Assets Pricing Model (CAPM), which guides how companies evaluate business investments.

Specifically, there are three problems.  First, few corporate financial executives understand CAPM and can’t use it properly.  Second, much like their Wall Street brethren, numbers have come to be valued for numbers sake, regardless of their relationship to market realities.  Third, analysis often focuses on individual investments and ignores portfolios.

This last mistake is particularly pervasive, because Wall Street’s models penalize volatility, a measure of risk.  However, risk averse behavior is often self defeating, because at any given time “safe” investments will tend to be in the same areas and therefore, a company that seeks “sure things” might actually be taking excess risk through lack of diversification.

Financial engineering also had a more subtle, but significant effect on how managers run their businesses.  Because models imply that risk, measured as a stock’s beta value, should be compensated by greater reward, any variability in profitability would be penalized heavily by the market.  It’s no wonder that, as this McKinsey article shows, managers have come to prize predictability over profits.

Marketing Joins in

We are now told to view marketing as an investment, not an expense.  It’s a view that makes philosophical sense but results in practical chaos.  Like any investment, there is an expectation of a return.  However, marketing is nothing like a capital investment so return on investment (ROI) is a tricky business.

Nevertheless, myths about ROI pervade how marketing is done today.  Much like in the financial world, there remains a soft headed faith that certain results can be achieved if only we hit upon the right equation and quantify everything in sight.

The truth is that ROI models are descriptive, not prescriptive.  We can analyze activity only after the fact and we’ve developed some very powerful tools, such as econometric modeling, that do that extremely well.  However, outcomes will always be uncertain, no matter how many algorithms we design and how much data we process.

Nevertheless, the ROI game continues to be played and played poorly.

Science and Pseudoscience

At the crux of the problem is  the myth of scientific marketing, which inevitably leads to pseudoscience, as physicist Richard Feynman describes in this video.

As this article in the Guardian explains, science isn’t about certitude, but skepticism.  We can gather data and make some educated guesses, but we’re going to be wrong much, if not most of the time.  Failure to reconcile that simple truth is what leads to the hubris of false certainty.

Moreover, an excessively quantitative approach often blinds us to factors that we can’t measure in favor of factors that we can.  There is always danger and opportunity lurking in dark places and, by looking only at numbers that fit in Excel sheets, we often blind ourselves to what’s really important.

Operating With (and within) Competence

Just to be clear, I’m not arguing that we abandon numbers in marketing (I’m a bit of a data freak myself).  However, we do need to change how we use them.  It wasn’t the Brownian models in finance themselves that caused market crashes, but reliance on them so excessive as to preclude judgment.

Here are five things that will help:

Training: Despite the “show me the numbers” attitude that prevails, math skills in the industry remain atrocious.  I know of no marketing organization that offers training in entry level statistics.  How can we continue to rely on models that so few understand?

If people knew more about the limitations of the models that they use, they could apply them more intelligently.

Rigor: Much of the confusion in the marketplace happens because people simply don’t check their facts.  A Google search every now and then would alleviate many of the problems that we face.

Use the Simplest Model: Mathematicians have a rule that you should use the simplest model that explains the data.  All too often in marketing, we employ the most complicated techniques in a never-ending quest to confuse each other.

Speak in Plain Language: Often marketers try to mask their bewilderment behind a wall of incomprehensible jargon.  That’s a mistake.  If you truly understand something, you should be able to speak about in plain language.  As I wrote before, good communication starts and ends with the desire to be understood rather than to impress.

Believe in Magic: When we were young, we believed in magic.  It is only when we grow up that we learn to despise all that we don’t understand.  That’s a shame, because the industry was built on a healthy sense of wonder and possibility.  You can follow your dreams and still check your facts.

So we should stop mistaking quantification for rigor, complexity for intelligence and sesquipedalianism for sophistication.  Most of all we should have some fun.  Without that, we might as well be working in a bank.

– Greg