Becoming #1

Long ago a colleague recommended that I read the book Eating the Big Fish by Adam Morgan. I did. At the time, I thought it was interesting, if simplistic. And then I continued to use it as a key tool in my professional work for the next dozen years.

The core idea in Eating the Big Fish is that there are large incumbents in every market space, but “little fish” can come to dominate the ecosystem through effective brand-building and communication. The book came out at a time when “brand” was the buzziest of buzzwords, one that could smother any attempt at critical thinking. Yet the book has endured as a modern business classic.

Eating the Big Fish starts with the thesis that in every competitive environment there are two leaders: a market leader and a thought leader. The thought leader—aka the “challenger brand”—must do several things to create awareness and pull attention away from the market leader:

  • Break with the immediate past.
  • Build a “lighthouse identity.” Stand for clear and different ideas,
    communicated loudly.
  • Reframe the conversation.
  • Create symbols of re-evaluation.

There’s perhaps a mixed metaphor here, with small fish building lighthouses (underwater?), but I’ll forgive it. This is a really good list. And its logic makes sense: if you’re not the leader yet, you must create a unique, compelling, and highly visible presence. Otherwise, no one will be able to remember you or understand why your offering is better.

As I said, over the past decade or so, I have found myself often returning to this idea of the challenger brand, and using it with a wide range of clients. The wisdom of Eating the Big Fish appears to be broadly useful. But why would that be?

I think the reason the book remains relevant, and broadly so, is that its narrow focus obscures a deeper insight about our current economy and media ecology:

Every brand communicates as if they are #1 in their respective space.

To explain why this might be, I want to share an adapted version of the Eat Big Fish framework that retains its original metaphor, but extends it to make a few additional points.

In every “space,” we find:

  • A monopoly or ecosystem owner
  • Optimizers within that ecosystem
  • Niche brands who are only going after a small part of the overall opportunity

 

 

Each of these levels has an objective leader who owns the largest share of the overall market. All these leaders are #1.

Each of these levels also has a primary market challenger, a disruptor who is trying to change the terms of the overall space. Like the market leaders, these challenger brands are also #1… in leading the new categories that they themselves are defining.

 

 

This crude breakdown gives you a sense of why every organization positions itself as #1. It’s not that they’re all lying—it’s that, in a way, they are telling the truth. They are each excelling at the unique thing they are trying to do. And Jack Welsh’s famous notion, when not taken to extremes, remains correct: if you can’t be #1 (the market leader) or #2 (the thought leader) in your core category, you do not have a future.

If you’ve been in business long enough, you begin to understand that being the challenger—the category disruptor—is the only way for the mid-sized and bigger fish to survive and thrive over the long haul. Companies die quickly or slowly when they optimize within the parameters of their existing space. They run out of room to grow. Geoffrey West has written about this poignantly, and it’s one of the reasons companies like Amazon, Nike, Facebook, Google, Apple, and Tesla are always signaling that they are, in Jeff Bezos’ words, “Day 1” companies. Permanent disruptors.

The corollary is that every organization finds based on its environment that it must become either a small, niche success or a “challenger brand.”

So… the reason every brand communicates as if they’re number one is not that they’re blind, smug, stupid, dishonest, or lucky.

It’s because—they have to.


The future of consulting

I’ve been thinking a lot about the future of consulting lately. This is a bit of an occupational hazard… I’ve been a consultant for almost 20 years, based in San Francisco, and I’ve run my own practice for eight years and counting.

A long time ago I remember telling a colleague:

“The consulting space is massive: multi-billions of dollars globally. Even if it were to shrink by 50%, there’d still be money on the table for those who know how to find it.”

That remark, though not particularly insightful, is still correct and evergreen. There will always be money to be made in consulting… the question is who is going to make it and how.

Here is some of what I see looking around me and looking ahead, with some hat tips to others who are doing the same.

This has all happened before

I made the comment above back in 2001, when my consulting firm employer was between rounds of layoffs and many of our competitors were either closing their doors forever, merging with each other, or being unbundled from their previous corporate parents. Old-timers like me may still have t-shirts from that era with names like Viant, Scient, Monday, BearingPoint, General Magic, and marchFIRST.

It’s incredibly obvious to anyone in the space right now that we’re undergoing another period of rapid change in the consulting landscape. Writing in HBR in October 2013, Clayton Christensen, Dina Wang, and Derek van Bever nail the big takeaway:

“We have come to the conclusion that the same forces that disrupted so many businesses, from steel to publishing, are starting to reshape the world of consulting.” (“Consulting on the Edge of Disruption”)

The most visible forces are macroeconomic, the results of which mirror what’s going on across much of the private sector:

  • Near the top of the consulting food chain, we’re seeing a lot of value consolidation through mergers and acquisitions. For example, this year two of my past employers — Stone Yamashita Partners and Sapient — have been acquired by larger firms. PwC and Booz&Company have merged into Strategy&. In recent memory, independent, marquee firms in the Bay Area like Hot Studio, One & Company, Pivotal Labs, and Adaptive Path have all been absorbed into enterprise clients (a kind of exit that a decade ago would have been highly unusual).
  • At the tippy-top of the food chain, the “big fish” consulting firms are having a crisis of relevance, which is what you would expect from any massive organizations in times of rapid change. According to a recent New York Times piece, McKinsey is undergoing a particularly turbulent evolution as it responds to scandals and replaces long-held values with new, more stringent rules. Monitor Group has ceased to exist on its own. Some massive “full service” incumbents will weather the storm as always, but deep bench strength requires deep pockets, and accordingly the meat and potatoes clients of the big firms will always be Big Enterprise and Big Government, not the next generation of edgy startups. Success at the top of a food chain is somewhat like being the Red Queen: you have to run as fast as you can forever to stay where you already are.
  • Meanwhile, as in other industries, new innovation in the consulting space is most obvious when looking at the “small fishes.” New and smaller firms are finding they can succeed by combining differentiated and sharply positioned services with innovative organizational design. Distributed teams and new org structures supported by the latest networked tools can sometimes deliver boutique results without the overhead of a traditional agency. My own firm The Next Us falls into this category, as do other organizations like Undercurrent, Neo, and Context Partners. Here’s prominent Seattle tech investor Chris Devore forecasting a broader trend of small agency innovation back in 2011:

“[I] see an opportunity for a “virtual industry” of boutique advisory firms, led by “partner-level” refugees from the old pyramids who understand how innovation happens now. These “innovation boutiques” will call in elite teams of specialists, assembled from the emerging class of high-performance talent marketplaces (think Forrst and Dribble for visual design, GroupTalent and oDesk for software execution, Contently for content creation, etc.), to deliver short-duration, high-impact solutions to their corporate clients.”

Other small firms are thriving by becoming exquisitely specialized. Think Droga5 for big brand ad creative, Outcast for media relations, and Kepler Group for marketing strategy, analytics and optimization.

So times are good for those who are correctly positioned. That said, while some consulting work is shifting or evolving, some of it is indeed disappearing by going in-house. As part of the overall value consolidation trend, Apple has decided they can handle advertising on its own thank you very much, and Netflix and Kellogg are two of many, many companies who have decided to take their programmatic buying internally.

Agencies will still be a vital part of the ecosystem, but their assignments may be narrower and less lucrative than before. Specialization and commoditization often go hand-in-hand.

And it will all happen again

Is there a chance in these current throes of change, or near-future ones, that consulting could effectively end entirely? Many might greet such an ending happily. In the novel Life, the Universe and Everything, Douglas Adams described an advanced alien civilization that decided to maroon its telephone sanitizers, management consultants and public relations professionals on another planet. The perception that consultants are by definition useless persists in some quarters to this day.

But — and I admit I can’t be objective here — I think there will always be consultants because the roots of the consulting industry aren’t in temporary macroeconomic or talent trends but instead basic human psychology. For at least several decades more, the average human brain is still much smarter than the smartest networked machine. We need human insight to spark and execute new ideas and maintain transformative change. At the same time, the human brain gets bogged down in very predictable ways. In particular, the larger an organization, the dumber and more insular it gets. Dmitri Orlov talks about this tendency memorably and rather colorfully in his article “Understanding Organizational Stupidity.”

With more positivity, Paul Pangaro looked at organizational stagnation back in 2002 and in the wonderful Little Grey Book used the insights of cybernetics to illustrate the continual need for leaders to look outside their organizations for fresh ideas and perspectives.

Little Grey Book image

From the Little Grey Book. Designed by Dubberly Design for Sun Microsystems. Used with kind permission of Oracle. Copyright ©2002, Sun Microsystems.

Consultants fill this “outsider” role perfectly. Writing more recently, Venkatesh Rao asserts that the primary value of a consultant is indeed his or her outsider status combined with the ability to safely comment on and sometimes attack the internal organization’s sacred beliefs:

What the ideal consultant really brings to the party is a lack of a sense of the sacred. This implies a lack of a sense of the profane as well, and a lack of unexamined trust in the ideas and reasoning patterns of insiders. This state is not easy to achieve. You have to get practiced at systematically dismantling notions of sacredness, both the client’s and your own, to get to a sufficiently clean slate.

So the consultant is often just an outsider who is roughly as smart as the insiders who are hiring him or her, but processes differently by virtue of a missing or dismantled sacredness module.

This is the same reason therapy persists as a profession. Our brains are wired to seek clarity from people who are outside our group and have the requisite training not to get caught up in our internal story.

The connection between business, personal, and social transformation is in fact a strong and enduring one. In his book The Age of Heretics: A History of the Radical Thinkers Who Reinvented Corporate Management (2nd edition), Art Kleiner of strategy+business places consultants in a multi-century lineage of heretics and outcasts who have led disruptive change. And he describes in detail how many specific trends in 20th century management consulting drew inspiration from social phenomena like the human potential movement, communes, Buddhism, and LSD. (For more quality writing on this theme, I recommend Steve Silberman’s retrospective of Steve Jobs, as well as Fred Turner’s From Counterculture to Cyberculture.)

Kleiner describes an almost-linear progression of 20th century heretical ideas— from balanced scorecard and scenario planning to quality management and lean manufacturing to re-engineering and knowledge management— each of which was absorbed, metabolized, bastardized, and discarded by the general business culture. Following this line into the 21st century, we could add things like “innovation,” lean startup, mindfulness, and resilience. These ideas are all worthy, but what’s notable of course is that they all have expiration dates. Once the intended clients have grokked the essential concept, and incorporated it as Paul Pangaro describes, there’s nothing for an idea-centered consulting firm left to sell, perhaps other than simple training.

So what’s new this time?

For that reason, I’m hesitant to say that the future of consulting is all about resilience, lean startup, Big Data, crowdsourcing, or any such theme that will have its moment in the sun before we move onto the next unsolved problem and genuine new idea. I will say that some new ideas are better than others. E.g., Venkat at Ribbonfarm has skewered “design thinking” and sharply critiqued lean startup — his essay on innovation methodology generally, “Product Driven Versus Customer Driven,” is still my favorite business read this year.

But the general list of trends I expect to bear most fruit include:

  • Emphasis on “zero to one” value creation over optimization and incrementalism
  • Increasing specialization and unbundling of services
  • Deeper collaboration between and integration of skill sets: e.g., ad creative and buying, product and sales, marketing and IT, data science and everything
  • Business models that can permit slower growth
  • Increasing hybridization of services and products. (On this general theme, Aaron Dignan and Bud Caddell of Undercurrent note that for today’s organizations “Doing is cheaper than planning.”)
  • GitHub-like idea exchanges, perhaps replacing 20th century conferences. (Twitter, Reddit, and Slack to an extent are filling this role already.)
  • Overall, more activity in liminal spaces between the salaried world and the consulting world, with more porous activity at the membrane, including things like: highly involved angel investors; consultant/salaried hybrid roles; programs like Designer Fund’s Bridge that prepare freelancers for startup culture by immersing them deeply and thoughtfully in those environments; aggressive transparency à la Buffer, thereby blurring the distinction between what’s “inside” vs.“outside.”

After 17+ years in the consulting world and 41+ on Planet Earth, I’m old enough to remember where we’ve been and still be excited about the future. I’m looking forward to it.


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…


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…