Category Archives: Mind

Beyond average thinking

Data science is changing global and local business.

One might think that immense data sets and expensive tools are required to make sense of this increasingly complex world, but that’s not true. Before we even look at data sets, we can all cultivate better data sense.

To that end, I’d encourage us all to look at one common kind of data with suspicion: the average. We all use averages on daily basis— e.g., when we split a dinner bill or keep tabs on our favorite athletes. They can be useful. But the following examples show how this easy-to-compute shorthand can also lead us astray.

Making money online

I do a lot of work in e-commerce, in part because I find it interesting. Also, all my clients, regardless of industry, care about making money. Until there’s a zombie apocalypse, knowing how to make money within and across digital ecosystems is a valuable skill.

For a pure e-commerce business, Average Order Value (AOV) is a standard measure of financial health. It’s a good metric as far as metrics go. In addition to aggregate revenue, it gives business owners a sense of how things are going compared to last week, last year, or some other benchmark.

But an averaged view of user purchase and onsite behavior isn’t all that actionable on its own. The average tells us whether “things” are up or down, but it doesn’t tell us which specific things are working or not working.

For example, if our site sells $10 socks and $200 jackets, and the average order size this month is $100 (and that’s lower than we’d like)… do we sell more socks? More jackets? Run a promotion? Raise prices? Lower them? Refresh our advertising messages? Redesign our mobile website? Streamline our checkout process? Switch performance marketing vendors? Have an existential meeting about this on a daily basis?

Thankfully, there are easy and well-known ways to quickly get beyond the simple average. Avinash Kaushik is a master at digital marketing analytics, and he is eloquent throughout his work about the necessity of looking at user behavior at a “cohort” level — paying attention not just to the average, but what individual groups do. If we review our site data at a cohort level, we might find we have many different kinds of shoppers who behave in statistically consistent ways: holiday gift-givers, power buyers, one-time visitors, socks-only-buyers, etc. Instead of optimizing for the average, we will have a higher impact if we prioritize, then optimize, for the individual groups.

Running a successful business generally

It turns out that deliberately segmenting one’s audiences at the “top of the funnel” and performing cohort analysis at the “bottom of the funnel” are essential components of business, brand, and marketing strategy generally. I’ve written about this before, most recently in the pieces “What is brand?” and “What is marketing?”.

The mechanics of doing this analysis can get quite heady. For example, see this recent strategy+business article entitled “Getting Value Propositions Right with Data and Analytics” by David Meer looking at the product development approaches of global CPG companies. The article has many interesting findings, but the main take-away is simple: companies that sub-segment their audiences optimize their footprint and their impact, while those that rely on simple averages fail by degrees.

Companies of any kind or size can commit to “top-down” and “bottom-up” audience segmentation before, and sometimes without, high volumes of data or complex analysis.

Investing for the future

Managing a personal retirement plan forces you to make iterative predictions about the future: your own and that of global capital markets. Many Americans use some variation of the “Random Walk Down Wall Street” approach, with broadly diversified portfolios, dollar cost averaging, and dynamic allocations based on age and risk tolerance. With this strategy, any big meltdowns in the market will theoretically be offset by subsequent run-ups, so it will all “average out” in the end.

There are a number of risks to this otherwise-solid approach, including an implicit assumption that future markets will behave like previous ones and that market turbulence has a cyclic cause and not a structural one. But even investors who assume that normal market conditions will persist in the coming decades — e.g., no zombie apocalypse, no crash of the US Dollar — might still be surprised to learn, per this article, that average stock market returns aren’t average. Even with a well-diversified portfolio, in mathematical simulations 69.2% of investors earn less than the average and 8.9% even lose principal over a 30-year period. This is in conflict with the conventional wisdom that investment outcomes are normally distributed and long time horizons ameliorate risk. It likely also changes how any one of us might expect our investments to perform in the future.

Making healthy decisions

One last example of how averages can lead us astray: at some point in our lives, we will all find ourselves reading new medical and scientific research, perhaps looking for promising experimental treatments that can treat or cure an illness. Scientific journals have rigorous filter criteria for publication… So, of the research reports that clear those filters, what percentage do you think are later proven to be true, in the sense that the initial results can be replicated?

You might think the number is quite high, or if you’re very conservative, you might estimate the odds as being about 50/50. But in 2005, John P. Ioannidis wrote a shocking paper asserting that the majority of published research findings are false. Bayer Labs later confirmed that only 35% of its own experiments could be replicated. That’s much worse than a coin toss.

Ioannidis’s finding is not just empirical but also mathematical. Given that a) scientists only run experiments in line with their current hypotheses, and b) only successful experiments get submitted for publication, and c) research publications have clear and rigorous, yet imperfect publication criteria, we can forecast mathematically a high rate of inaccuracy, which is indeed what we find. Nate Silver describes these two papers and their broader significance in his wonderful book The Signal and the Noise.

Ioannidis’s detailed math is interesting for those who care to read the complete article, but the takeaway can be useful to any of us regardless. Rather than assume that publishers are bad actors or bad practitioners, we can look at individual publications or subject domains and apply a percentage likelihood of accuracy to the findings. E.g., we could estimate that any new medical treatment in pre-clinical research, despite promising experimental findings, has less than a 35% probability of being replicable, with additional completely unknown risks of side effects, complications, or limitations. This likely will affect our personal risk assessments regarding which treatments we want to pursue.


Averages can of course be useful— often they’re just what we need. But whenever you hear or see an average, some questions you can ask yourself:

  • Is the data normally distributed?
  • Does a “cohort” level view provide more insight?
  • What is our % confidence in the findings?
  • What is our % confidence in our confidence assessment?
  • Are there any external variables or implicit assumptions at work?

These questions might lead to some real “aha’s” — and beyond-average thinking — even before you re-crunch the numbers.

It’s (not) complicated

We all understand simple mechanical systems like pulleys.

Complex systems, like rain forests, however, work differently.

They exhibit unique characteristics, including modularity, homeostasis, self-organization, resilience, emergence, non-linearity, inter-dependence with other complex systems, and collapse.

In work and life, we encounter complex systems every day. They include:

  • Human brains
  • Human bodies
  • Human relationships
  • Organizational cultures
  • Financial markets
  • Digital media ecosystems
  • Competitive business environments
  • Global climate

One sure-fire way to make big mistakes is to expect complex systems to behave like simple ones. You’ll notice people doing this all the time. E.g., “My investments are down right now, but you know, the pendulum always swings back.” These simple system metaphors can warp our understanding of what’s really going on.

Complex systems aren’t necessarily complicated, however.

First of all, they all obey similar principles. We may not be able to grasp the underlying algorithms perfectly, but we know what kinds of phenomena to expect.

Secondly, they can all be examined at the level of dynamic complexity or detail complexity. Dynamic complexity focuses on the key variables that matter. It merits more attention. Detail complexity either distracts us with minutiae or gives us valuable data to test whether our current algorithms are correct.

Many of the experts I admire and feature on The Next Us website understand complexity very well. Examples:

In my own strategy consulting and individual coaching work, here are some insights related to complexity that tend to recur:

  • An organization’s goals—not its starting conditions or competition—create the primary context for its choices.
  • Macroeconomic conditions determine where money gets invested, what kinds of investments succeed, and which kinds will scale.
  • Over long time horizons, indirect competition is always more concerning than direct competition.
  • An organization’s goal very quickly becomes to perpetuate itself. When this happens, it often becomes insular, loses curiosity about its customers, stops taking risks, and thereby makes its collapse more likely.
  • Inefficiency increases exponentially with organizational size.
  • The explicit and implicit contract in a relationship is more important than how any one conflict escalated.
  • A CEO’s personality is a “strange attractor” that can limit an organization’s ability to execute on an otherwise-solid strategy.
  • Co-founder relationship dynamics mirror those of marriages.
  • Changing a habit requires cultivating new behaviors, not combating the existing ones.
  • The strategies that work before a system collapses are not the ones that will work after.
  • No dogma, approach or answer is useful or right in every situation. Make informed, contextual choices.
  • It’s very helpful to be able to hold, understand, and respect opposing viewpoints. Echoing Nate Silver: be a fox not a hedgehog.


Our default way of experiencing the world is through stories.

Whether they come from the latest Good Wife episode, the companies we purchase from, or the theater of our minds, stories are safe-to-consume simulations about how things were, are, will be, or could be.

I love stories, and they can do many good things:

  • They entertain us.
  • They help us contemplate what we would do in unfamiliar situations.
  • They help us act.
  • They make abstract concepts relatable and human.
  • The create order out of apparent disorder.
  • They bind communities together.
  • They make us smarter by either challenging or reinforcing our existing ideas.
  • They sharpen our pattern recognition skills.
  • They help us restore self-control.

That said, even the best stories lie. They replace reality with an edited version. And sometimes even, they’re dead wrong. Resilient individuals and organizations therefore balance storytelling with unstorytelling.

Here’s how to do that:

  • Use stories to time travel, but always come back to the here and now. The brain has two modes: our “narrative circuitry” which essentially turns all incoming data into a story and “direct experience” which takes in sensory data without an interpretive filter. The narrative circuit is our automatic mode and takes less energy to run, which means that we have to deliberately focus if we want to savor the moment we’re having, or pick up on details that don’t fit our pre-conceived stories. The two modes engage different regions of the brain, but by creating rituals for switching between them, over time we’ll be able to pause and reflect more easily before we’re caught up in a story.
  • When push comes to shove, choose reality over a story. We all make the mistake of applying our narrative circuitry to not just our external reality, but our internal one as well. We turn ourselves into a story. From earliest childhood, we are building a narrative about how the world works and how we fit into it. Over time, these stories become self-reinforcing—we typically do not give up our essential narratives about ourselves, and directly experience who we are without overlay, except under extreme duress. In her wonderful book Wired for Story, Lisa Cron points out that novels feel unsatisfying if the protagonist has a big epiphany without going through hell to get there. Life is often like that too.

Luckily, there are shortcuts: practices like Byron Katie’s The Work can help us recognize flaws in our personal narratives before they become a crisis. Like scientists, we can lower our thresholds for noticing that an existing story isn’t working out. We can also choose to act congruently with a new story even before we’re ready to give up the old one.

Navigating through chaos

We all get overwhelmed sometimes.

Our stories, resources, and energy can be insufficient to accommodate a new reality. We become disorganized, inefficient. Like any complex system, we enter a chaos state. This is an ever-present possibility for even the most enlightened or skilled individual.

In a chaos state, we often find ourselves clinging to old ideas, to losses, to hopes for the future. We try to run the old program, even though the operating system has changed. Sometimes reality will shift back to something more pleasant and familiar. But it doesn’t always.

As St. John of the Cross said: “Swiftly, with nothing spared, I am being completely dismantled.”

It’s helpful to recognize during these times that our losses are always illusionary. We lose our ideas of what should be or should have been. But reality persists independent of our ideas.

When we stop clinging to our imaginary stories, we start the process of re-organizing in a way that’s congruent with reality. We find new resources to keep us stable. Our energy reserves start building up again. We can see alternative stories that might help us make sense of the senseless. Our old attachments die. We become something new. This way we can be okay with great loss, great hardship, and great change.


Our culture values a happy face. Positive interpretations. Continuous success. These can be virtues. But they can’t always nurture us. Life can sometimes be horrible and it changes in a blink.

The deepest wisdom comes not from avoiding or denying chaos but from learning how to move through it skillfully. If you find yourself in a dark place:

  • Remember you are not your thoughts. Do not attach to them.
  • Seek help. When we are overwhelmed, we develop tunnel vision and miss valuable resources all around us.
  • Focus on small steps. You may not be able to control your circumstances, but you can control your attention.
  • Try new things. In order to shift internally, we often must first change something externally.
  • Create a space for grief. Grief is a painful but sure-fire program for dealing with loss.
  • To the extent that you can: connect with your body, feel your emotions, challenge your thinking.

Climb the ladder

There’s a classic tool from learning psychology that I sometimes use with clients. It’s called the ladder of learning. This model says that whenever we learn a new skill, we always go through four steps:

  1. Unconscious incompetence: You are blissfully unaware that you are bad at something. You feel strongly in synch with your environment and confident in your abilities. This feels wonderful, but by the same token, you aren’t learning anything.
  2. Conscious incompetence: New information begins to intrude on your awareness, or you are beset with new challenges that demand attention. Your habitual methods for dealing with problems stop working. This is an extremely stressful situation, and it requires energy to overcome. People without the extra energy to weather these “ego crises” don’t reach this step; they will choose (wisely or foolishly) to remain ignorant of what they don’t want to know.
  3. Conscious competence: You eventually hit on methods for addressing the new challenges, but they are unfamiliar and you have to practice them. Over time, you can exhibit skillfulness but only with constant focus. Every time you practice, your neurons are firing and wiring together, making it easier to repeat the new behaviors.
  4. Unconscious competence: You have achieved mastery, and your new skills can be accessed on autopilot. After the proverbial 10,000 hours, your neural pathways for these new habits may develop myelin sheaths, making nerve conduction ultra fast. At this step, you are again in blissful harmony with your environment… until the next failure or surprise hits and then the learning process begins all over again.

My clients often like this tool because it’s a quick and easy way to frame where they are in relation to their goals. It explains the different emotions that come up at each step, and what to do to keep moving.

We all have tendencies to get stuck at the different steps above. Here are some thought patterns that might let you know you’re stuck:

    • “I think I’ve really figured it all out.” (Stuck at Step 1)
    • “The external situation or person needs to change, not me.” (Stuck at Step 2)
    • “I think I’ve really figured it all out.” (Stuck at Step 4… which is also the same as Step 1)

We sometimes wish that life would be easy and perfect, but that actually would not be satisfying. Accepting and embracing change, and iteratively climbing the steps above, makes our realities bigger, and our lives richer.

We can celebrate big achievements and rest stops along the way, but over time the journey becomes its own reward.