Where do you look for marketing lessons?

Today’s WSJ has an article whose theme is, “What we can learn about business from a Church?”. There are many such articles and even books that follow this theme on, “what can we learn about business, marketing, pricing, product development etc”, from completely unrelated fields (for example a street performer or a child’s Lemonade stand to which I have contributed  as well).

It is as if we think that lessons from business research, publications and successful businesses are irrelevant that we need new lessons from these unrelated wells of knowledge. May be these are indeed better sources, but I would like to caution you about these articles and studies that want to teach us:

  1. Many of these studies are cursory reviews, some just look at one individual sample. There is no rigor to the methods employed. The observer picks what is convenient and readily available to them (their neighborhood Church, lemonade stand, farmer’s market, parking lot (mea culpa)).
  2. The most common pattern is, the observer picks successful entities and look for observable positive traits. There is no attempt to study those that are not doing so well, resulting in survivorship bias.
  3. Success is defined narrowly or as an afterthought – metrics like eyeballs, sign-ups, crow etc are used. The studies do not consider alternative scenarios where success could be an order of magnitude different from the current state?
  4. There is no attempt made to look at the origins and longevity of these traits. There is one measurement made and results reported.
  5. These traits are treated as new/unique, as if these have not been reported before. The error is in not seeing the traits as examples of established marketing principles but rather as something totally new.
  6. Assuming that the traits are a result of deliberate action taken by the entity and neglecting the possibility that these could  just be random or incidental side effects.
  7. Attributing the success of the entity to the observed positive traits. That’s a causation error.
  8. Once causation is implicitly assumed, the observer makes the leap that the positive  traits are so generic (e.g., everyone should give away for free and let customers pay what they wish) that these not only apply to other entities of the same kind but also to totally unrelated entities like Tech Startups.

I believe these studies add very little value or even distract us from the main goal.  It is tempting to look for easy lessons but these so called lessons may lead us down the wrong path. Every example you see stated as a paragon of excellence should be treated as nothing more than a case study – with flaws in information reported.

Where do you look for your lessons learned?

Here are some books that will help you see the fads for they are:

  1. Hard Facts, Dangerous Half-Truths And Total Nonsense: Profiting From Evidence-Based Management by Jeffrey Pfeffer and Robert I. Sutton
  2. The Affluent Society by John Kenneth Galbraith ( chapters on Conventional Wisdom and Consumer sovereignty )
  3. Wrong: Why Experts keep Failing Us by David Freedman
  4. Fooled By Randomness By Nassim Nicholas Taleb

Metric For Predicting Profitability

Recently, I analyzed  past 10 years worth of 10-K filings of S&P 500 companies. A very interesting finding from this analysis is a high positive correlation (0.673) between number of times the word “customer” occurs in the 10-K and their profit growth. Every  20% increase in the word “customer” was associated with a 3.75% increase in profit. This finding adds to mounting evidence that companies that are customer focused stand to reap the benefits while those that are not are cast aside.

No wait. If you have not guessed already, I made all this up.  But let us pretend otherwise and dissect this for analysis errors.

Correlation does not mean causation: I imply causation with my statement about 20% increase. Yes there may be correlation but it means nothing. It could be due to any number of reasons (lurking variables and Omitted Variable Bias. You can find many other correlations if you looked for it.

Meaningless metric: I nudge you to think that being customer focused is manifested in the word count.

Cross-Sectional Analysis: This means I looked across companies and found those with low word-count are associated with low stock growth and high word-count are associated with high profit growth. This ignores all industry specific and firm specific factors.

Implying Longitudinal Analysis: Longitudinal analysis is following a firm’s performance over years and see if the correlation holds true for a single firm over the years as they increased or decreased the word-count. However by stating, “10 years” I imply as if I just did this analysis.

Surviorship Bias: The imaginary data set consists of only those companies that are publicly traded and survived for 10 years and I only looked at S&P 500 companies, which are part of S&P 500 because of specific selection criteria . Half of the companies that were in S&P 500 10 years ago are not in it any more. So the sample set is even smaller. What about all other companies that are either private, did not make it or got dropped from S&P 500?

You probably did not worry about all these errors because the initial claim is so ridiculous that no further dissection was necessary. But not all claims on customer metrics are this obviously ridiculous.

There lies the danger!

These usually come neatly packaged and branded, come from some one with “authority”, stated with backing of data and vouched for by their marquee customers. They become extremely popular – accepted as gospel by other marketing Gurus and blogs.  Authority and Acceptance become stand in for truth (Kenneth Galbraith).

But these claims are susceptible to exactly the same errors I stated above. I urge you to look beyond the surface and fanfare and look at the biases before you embrace the next big metric.

The Convenience of Conventional Wisdom

There is considerable comfort in knowing we are not alone – be it in our ideas or actions. The comfort comes from the challenges in objectively evaluating the merits of an idea to make an informed decision.When we see many others adopting an idea – especially in the social media- we treat that as mental shortcut for adopting it.  Not all the people who follow an idea or a Guru  can be wrong, can they?

On the flip side, when we see someone going against the flow – taking decisions that do not fit our accepted norm, the conventional wisdom, then we tend to see that as risky or worse, foolish.

It is crazy to raise prices in recession: Almost everyone wrote off Starbucks for increasing prices.

It is risky to cut on inventories: NYTimes described the move by luxury retailers to cut inventories as risky.

It is stupid to give up market share: Almost all stock analysts ding companies that give up market share to maintain price premium.

If it does not fit the conventional wisdom it must be wrong.

In his book The Affluent Society, Galbarith first introduced the concept of Conventional Wisdom. He wrote,

In the never ending competition between what is right vs. what is merely acceptable, even though strategic advantage lies with the former all tactical advantage is with the latter. Acceptance comes from convenience, because finding what is right is hard and not convenient. Ideas that are familiar are easy to accept because everyone does it and end up having great stability.

Decision making is not a popularity contest. If it is just about following what is accepted, familiar, (Re-Tweeted the most) and convenient where is the competitive advantage?

Are you ready to leave the convenience of conventional wisdom and do the hard work of evaluating ideas on their own merits regardless of who said it and how popular it is?