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.

Summary

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.


What is marketing?

Marketing is one of those words that seems to have a different definition depending on who you ask.

If you explore the huge volume of resources, history, and commentary out there regarding marketing, it quickly becomes obvious that these voices have little to do with each other. It’s hard to believe that they all describe aspects of the same general craft.

I work with companies across a wide range of industries and levels of scale, so it helps to have frameworks that are easy to understand and broadly applicable.

That in mind, I’ve found it most useful to think of all marketing as communication.

 

I’ve written previously that business strategy is essentially about opportunity — seeing clearly what you want and how you will apply physical and financial capital to make it happen.

I’ve also written that brand strategy is essentially about relationships — cultivating deliberate long-term bonds with specific groups.

Marketing strategy is the last piece and is essentially about communication — what you say and (equally important) how you listen.

Connect opportunity, relationships, and communication on an ongoing basis and you can build a powerful engine for long-term business success.

 

This definition of marketing is perhaps so simple that I can risk complicating it slightly. I’ll do so by introducing a classic marketing framework called the customer acquisition funnel, purchase funnel, or sometimes just the marketing funnel.

 

Marketing funnel

 

Everyone labels this framework differently, but the general concept is more important than the details.

If you’re unfamiliar with the tool, one important thing to note is that at each step of the funnel, you lose a very large portion of your audience. Not all people who become aware of your brand proceed to visit your website. Not all of those window shoppers pay you money. And not all of those first-time customers come back, etc.

When many people think of marketing, they picture activities at the top of the funnel that create awareness or drive new traffic: TV ads, promotions, blimps, etc. But savvy marketers know that the most value is at the bottom of the funnel, working with the existing brand evangelists who will sing your praises for free. Unincented positive word of mouth is the most cost-effective marketing technique because it works the best and costs nothing.

It’s nearly impossible to execute the funnel well without data. I’ve said that half of all communication is about listening, and to a large extent, data is how most companies listen. This is too big a topic to cover completely in this one blogpost, or even an epic series, but suffice it to say that for every type of organization, channel, activity, and level of scale, there are “gold standard” metrics for measuring brand and financial health at each step of the marketing funnel. (Note though that these two kinds of metrics — brand and short-term financials — are often in dynamic tension with each other.) Avinash Kaushik is a master of this topic when it comes to digital channel analytics, and for quant jocks, his website is well-worth reviewing in detail.

 

Top-down and bottom-up segmentation

 

A business’s efforts at the top of the funnel (TOFU) are designed to capture very specific audiences. On a regular basis, we can also perform cohort analysis to see if our converted customers, retained customers, and brand evangelists at the bottom of the funnel (BOFU) match our original audience targets. If they don’t, this will likely give us good ideas about how to revise our business, brand, or marketing strategy. (E.g., Huh, we seem to be having more traction with women than men…)

And for each of these “bottom of the funnel” cohorts, with increasing degrees of finesse, we can also track:

  • their lifetime financial value to us
  • their brand loyalty
  • the cost it took us to acquire them

With this infrastructure in place, any business can have excellent visibility into the effectiveness of all its marketing activities… everything needed to smartly evolve its marketing strategy on an ongoing basis.

 

God is in the details of course. The definitions and models above admittedly cover “what” marketing is but don’t spell out “how” to do it. Those “how” details vary significantly depending on whether you’re a local gym or General Electric.

But the beauty of these simple frameworks is that they highlight the major marketing errors that many businesses — big and small, old and new — fall into. Here are some gotchas to look out for:

  • The marketing approach doesn’t sync with the underlying business and brand strategy.
  • We don’t know who the customer is, or our unique value to that customer.
  • We’re aren’t appropriately balancing “top-of-the-funnel” traffic acquisition with “bottom-of-the-funnel” relationship-building.
  • We lack full-funnel visibility, with metrics in place to measure brand and financial health at every step.

Finally, black belt marketers always heed the advice of Meryl Streep. When asked by James Lipton, “How important is listening?” she said:

“It’s everything.”


What is brand?

Brand is a top priority for many businesses, but it often lacks a clear definition, owner, or action plan.

People frequently conflate the term “brand” with related concepts like vision, awareness, positioning, and design, and so it never really gets articulated, and therefore never really gels.

I’ve been doing brand work for a long time, across a wide range of clients—from massive global conglomerates to edgy startups to small local businesses, and everything in between.

Personally I’ve found that the most effective way to think about brand is to replace it with the word “relationships.” Not relationships in some abstract sense, but specifically: human relationships.

In my work, I often depict these relationships using simple Venn diagrams.

 

Brand diagram #1

 

Brand diagram #2

 

The health of these relationships, individually and in aggregate, is the brand.

Note that:

  • These relationships are all specific and deliberate. A strong brand doesn’t appeal to everybody: it appeals to clearly identified groups.
  • Not all of these groups are customers. They include any stakeholders who are vital to the organization’s long-term success.
  • Internal and external relationships both matter. The internal relationships (aka “culture”) are aligned and optimized to maintain, extend, and deepen the external relationships.

Brand = relationships

This simple way of looking at brand has advantages over others you might encounter. I’ll consider a few of these one at a time. Brand is not…

    • Design. Design is wonderful. It’s an integral part of sustaining relationships and facilitating clear, consistent bi-directional communication. I love high-quality design. But the truth is that some beloved businesses have mediocre design (e.g., Craigslist), and some companies and products with exquisite design fail against objectives and/or fail to connect with consumers (e.g., the original incarnation of Google+). Having the perspective that design supports brand, not equals brand, typically leads to better thinking about both.
    • Reputation. Reputation management is important, but just as managing your personal reputation doesn’t create character, managing general external perception of your business doesn’t in itself build long-term, strong relationships with specific groups. In reality, many companies survive major PR gaffes but still have strong brands. (Uber, Lululemon, Apple, and Whole Foods all leap to mind.) As with “design,” it’s helpful to look at brand and reputation as related but separate.
    • Trust. Trust is a good word, a feel-good word. But it’s also very vague. In practice, how does one become “trusted”—to whom and for what? I’ve seen many companies become smitten with the word “trust” as a key tenet of who they are and what they do and then get bogged down defining the intent and boundaries of what “trust” actually entails.
    • Magic. There are many bad actors and bad practitioners out there who insist that brand is ineffable but essential, and that only they have the mystic intuition and creativity to tell you what yours is, for a princely sum. Don’t hire these people. Good consultants can always explain the practical value of their deliverables.
    • Bullshit. Given the huge volume of muddled thinking on the topic, I’ve seen many companies reject all discussion of “brand” entirely… then be nagged by questions about how to tell their story, how much to invest in design, who their customers are or should be, or whether their culture is firing on all cylinders. “Brand” admittedly is an imperfect container for all these discussions, but right now, most companies I work with find it easier to live with it than without it.
Santa Brand Book

This Santa brand book parody from Quietroom is painfully and brilliantly representative of the nonsense you’ll see from many “brand” agencies.

Here are some alternate definitions of brand that I believe are very close to the mark, but still slightly lacking:

    • A promise. Cheryl Heller is a heavy-hitter when it comes to writing and thinking about brand, and she and others have used the phrase “brand promise” to good effect. I recommend her work highly and agree with her general line of thought, but in this instance I quibble with the word choice. To me, a promise is a very narrow agreement—e.g., I promise to take out the trash each Thursday—but a relationship is richer, with multiple dimensions, expectations, shared history, and mutual benefits. Is a relationship reducible to a promise? (E.g., is a marriage reducible to a contract?) I don’t think so.
    • A pattern. Marc Shillum of Method wrote an excellent paper a few years ago entitled “Brands as Patterns.” I agree with this paper 100%. If you’re an expert practitioner when it comes to brand, or you want to be, I recommend it highly. But for many businesses, and many CEOs especially, talking about brands as patterns is too esoteric. My plain-English and somewhat crude simplification of Shillum’s paper is that brands are relationships, and all relationships tend to be ritualized: the family dinner, the Amazon 1-click, the Apple product launch experience. With many CEOs, product teams, and marketing teams, I ask: what specific rituals bring our brand to life? This provokes good discussion without having to use the word “patterns” or assume a shared and nuanced understanding of cognitive psychology.
    • An experience. It’s vogue-ish now to talk about brand as an “experience” and there is a lot of excellent work being done under the aegis of “brand experience design.” One gotcha with this way of thinking is that it can lead to an impression that “brand” is a carefully curated, Disney-esque, self-contained reality. Experiences are designed; relationships are co-created… depending on the nature of your business, this might be an important distinction. Also, practically speaking, the organizational owner of “experience design”—whether that’s a CXO, CTO, CMO, creative director, product owner, or agency of record—often doesn’t own all aspects of external and internal relationships, and so they don’t really own the brand. Letting experience design be experience design and not “brand experience design” will often foster clearer thinking and expectations regarding accountability.

Think better

I find this overall way of looking at brand very helpful for a number of reasons:

  • It’s simple, memorable, and easy to act on.
  • It provokes the right kinds of questions.
  • It highlights the importance of human relationships to overall business success.
  • It scales across many different company sizes and types.
  • It makes useful distinctions between brand and design.
  • It also makes a useful distinction between brand and marketing, since the two are sometimes unproductively clumped together. (More about this in a future post…)

Also, it includes culture, but in a useful way:

  • It clears up a misconception that internal culture and external brand should be identical. Optimal cultures are skillful at maintaining, extending and deepening external relationships. Sometimes this means that the internal culture and external brand mirror each other, sometimes not.
  • It clears up a misconception that optimal cultures maximize “niceness” or the comfort of the internal team. Culture is focused outward, not inward. Companies run into trouble when they become too insular and caught up in their internal story. Also, many employees tend to find it vitalizing to give to the world and think beyond themselves.

In other words, brand doesn’t have to be bullshit. It can be what gives business meaning.


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.

Unstorytelling

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.