Marina Gorbis's Blog, page 1362

Rotary Strengthened Their Brand by Simplifying It

It’s no surprise that simplicity sells. Too many options can overload short-term memory, inhibiting the ability to process information, creating cognitive overload. In addition, excessive options can spark feelings of remorse after transactions as customers continue to wonder if they had made the right choice.

But creating “decision simplicity” presents only part of the brand simplicity picture. Sephora, Carrefour, and Amazon are examples of successful simple brands, despite providing a vast range of options to their customers.

Simplicity should be built into the very core of the brand, beginning with the product or service itself and extending through the interactions at each touch point and in all brand communications.

Achieving simplicity at this level is not easy, but the returns can be well worth the effort. The Siegel+Gale Global Brand Simplicity Index, an annual global study of 10,000 consumers (both customers and familiar nonusers) found that three out of four people are more likely to recommend a brand that provides simpler overall experiences and communications, and that people are even willing to pay more for a simpler brand’s product or service. In addition, brands that are perceived as being simple in their “products, services, interactions, and communications” outperformed indices on the stock market by as much as 100%.

So how can a brand achieve this form of simplicity? A look at the 2013 rebranding of the nonprofit Rotary can supply some clues.

Rotary is a highly complex organization, steeped in tradition, with 1.2 million members in 34,000 autonomously run clubs in 530 districts across the globe. Navigating its extensive and varied programming was difficult for members and the public alike, making it hard for the organization to stay relevant. Rotary also discovered, through an internal survey, that members had difficulty explaining the nonprofit’s role in the world.

Working with Siegel+Gale, they conducted two additional worldwide studies. The first one assessed a donor’s motivation to give money or time by comparing the nonprofit to 12 international peers and two local charities in each of four global regions to see how people perceived Rotary, as well as the respondent’s “brand preferences” among these organizations. This survey found that while some nonprofits were positioned clearly in people’s minds, Rotary wasn’t. The second study revealed that neither their members nor their staff could consistently answer the question, “What is Rotary?”

While the results were certainly disappointing, these surveys found two recurring and motivating themes: People join and stay with Rotary because of the connections they make with others and the positive feelings they get by giving back to their communities. Seeing the potential in these themes, Rotary adopted “community and connections” as their brand essence — the core benefit, promise, or purpose of a product, service, or organization.

Rotary organized all of their activities into three core areas at aligned with this brand essence: 1) “join leaders” for their club meetings; 2) “exchange ideas” for their work finding solutions to community problems; and, 3) “take action” for their work to create positive change in their local communities and in the world. As a result, Rotary was able to imply the benefits of getting involved with the organization, as well as explain how to do it, through one simple structure.

Finally, Rotary turned their attention to their website. Prior to the rebranding, this site was focused on internal operations, making it nearly incomprehensible to the general public. But with the new brand essence and architecture in place, they were able to simplify their messaging by using the three core areas as part of their navigation. They found they needed two websites: one for the public, helping them to understand Rotary’s role, and another for their members, where they could conduct their business. In addition, they updated their logo and imagery to underscore an experience centered on community and connections.

According to Rotary’s General Secretary, John Hewko, this simplification effort is showing positive results.

Based on Rotary’s experience, here are four key action steps to keep in mind for simplifying your brand:

Find your brand essence. Understanding what your brand stands for is not only essential for helping you focus your products and services, it is the key for helping you simplify your communications. A brand essence can be used as the screen for judging the appropriateness of everything from a group’s product and service offerings to their brand experience and communications. But be careful not to go too narrow with your essence. Focusing on one idea alone will be too limiting and handcuff your brand without providing the vibrancy needed for today’s world.


Hide your complexities. Many of the brands that rank high on the Siegel+Gale Brand Simplicity Index, such as Amazon and Google, have truly complex underpinnings, while providing a simple experience. Likewise, Rotary remains highly complex with its vast number of initiatives and programs. But the brand refresh simplified the experience into their three-item menu of “join leaders,” “exchange ideas,” and “take action,” making it easier for both members and non-members to get and stay involved.


Simplify your communications. Organizations that make their communications too complicated, have inconsistencies between their message and experience, or employ the use of fine print raise transparency questions and force consumers to work to align the promise with the reality. What consumers want is a clear presentation of the value a brand provides.


Realign your metrics. Measurements you once thought were helpful might not tell the entire story after your simplification process. When Rotary launched their new websites, they found that less time was spent on the site and there were fewer page views. But upon a further look, they learned that the change was because readers were now able to find their needed information more quickly — a key benefit of simplicity.

Taking these steps just might spark added recommendations and referrals, the ability to charge a premium, and greater brand value.

The Industries Plagued by the Most Uncertainty

It’s a cliché to say that the world is more uncertain than ever before, but few realize just how much uncertainty has increased over the past 50 years. To illustrate this, consider that patent applications in the U.S. have increased by 6x (from 100k to 600k annually) and, worldwide, start-ups have increased from 10 million to almost 100 million per year.  That means new technologies and new competitors are hitting the market at an unprecedented rate.  Although uncertainty is accelerating, it isn’t affecting all industries the same way. That’s because there are two primary types of uncertainty — demand uncertainty (will customers buy your product?) and technological uncertainty (can we make a desirable solution?) — and how much uncertainty your industry faces depends on the interaction of the two.

Demand uncertainty arises from the unknowns associated with solving any problem, such as hidden customer preferences. The more unknowns there are about customer preferences, the greater the demand uncertainty. For example, when Rent the Runway founder Jenn Hyman came up with the idea to rent designer dresses over the internet, demand uncertainty was high because no one else was offering this type of service.  In contrast, when Samsung and Sony were deciding whether to launch LED TVs, which offered better picture quality than plasma TVs at a slightly higher price, there was lower uncertainty about demand because customers were already buying TVs.

Technological uncertainty results from unknowns regarding the technologies that might emerge or be combined to create a new solution. For example, a wide variety of clean technologies (including wind, solar, and hydrogen) are vying to power vehicles and cities at the same time that a wide variety of medical technologies (chemical, biotechnological, genomic, and robotic) are being developed to treat diseases. As the overall rate of invention across industries increases, so does technological uncertainty.

Consider the 2×2 matrix below. The horizontal axis plots each industry based on technological uncertainty, measured as the average R&D expenditures as a percentage of sales in the industry over the past ten years. The vertical axis plots each industry’s demand uncertainty, measured as an equal weighting of industry revenue volatility, or change, over the past 10 years and percentage of firms in the industry that entered or exited during that same time period. Although these are imperfect measures, they identify the industries facing the highest, and lowest baseline levels of uncertainty.

The table below ranks industries into the top 10 and bottom 10.

Where does your industry sit?

If your industry is in the lower left quadrant, or in the bottom 10 in the above table, you face relatively low baseline uncertainty for both demand and technology. Examples of industries here include providers of personal services, such as hair styling and dry cleaning, who have used similar technologies to provide solutions for well-known demands. By contrast, if you’re in the lower-right quadrant, you can generally predict demand but the challenge you face is technological uncertainty. For example, insurance companies face technological uncertainty that comes from how big data and analytics investments will drive revenue; whereas demand is based on highly predictable demographics.

If you’re in the upper-left quadrant, you are with industries that face high demand uncertainty but low technological uncertainty. For example, restaurants and hotels often have difficulty predicting demand for their services, because many factors influence whether, when, and where people eat out or travel. However, the technologies used to offer food or lodging have not changed dramatically over the years.  Finally, industries in the upper-right quadrant — such as software, pharmaceuticals, and medical equipment — face high uncertainty in both demand and technology. For example, who would have predicted that medical robots would perform surgeries? When Intuitive Surgical launched the Da Vinci System medical robot — which allows surgeons to operate using 3-D visualization and four robotic arms — the company faced significant technical as well as demand uncertainty.

If you’re in the upper right quadrant — or in the top 10 most uncertain industries as shown in the Table — you require greater innovation management skills than industries in the other quadrants or in the bottom 10. In fact, among the top 10 companies of the Forbes Most Innovative Companies list (since 2011 when we started the list), more than 80% of the most innovative companies compete in industries in the top right quadrant.  In other words, if you are in a high uncertainty industry, you must excel at innovation…or die.

A new set of tools and perspectives — such as, for example, design thinking, lean start-up, agile development — are emerging in many disparate fields and revolutionizing the way managers in established companies successfully create, refine, and bring new ideas to market in conditions of high uncertainty.  In our new book, The Innovator’s Method, we show how managers can adapt these tools, in an end-to-end process, for managing innovation.

For example, companies that excel at resolving demand uncertainty become experts at design thinking and validating concepts through rapid experimentation with customers.  Successful software companies like Google, Intuit, and Salesforce.com churn out their “beta” or “labs” products that effectively test demand for new products. When Google software engineer Paul Bucheit had ideas for Gmail and AdSense (the system that placed advertisements based on keywords in your Gmail messages, search, or website) he found he was often fighting against the opinions of leaders.  But fortunately, experiments with customers trump opinions at Google. Following the advice of then CEO Eric Schmidt to “get 100 happy users inside Google,” Bucheit prototyped solutions that eventually proved demand and won the day. Today, AdSense generates $10 billion in annual revenue for Google.

Companies that excel at resolving technological uncertainty often develop a broader technology palette. For example, to help start-up teams generate a broad list of solutions, Intuit identified and hired experts in technologies related to mobile devices, social media, user interaction, collaboration, data, and the like. These experts are valuable for broadening solution searches, and they help teams identify what is technologically feasible. At biotech Regeneron, the company pioneered a new experimentation platform — “humanized” mice that allowed them to test drug effects more rapidly and reliably — that dramatically increased their ability to test various technology solutions to problems.

The bottom line is that success requires an understanding of how much uncertainty you face and the ability to manage those uncertainties in new ways.

How much uncertainty does your industry face?  Ask yourself the following questions:

Have new technologies or startups started to threaten my company or my industry?
Over the past five years have new competitors entered the market and captured 10% share by targeting our customers with a different value proposition than what we offer?
Over the past five years have we begun to see customer preferences change, resulting in a different mix of products and services being demanded?
Have you recently started offering (or are planning to offer) a product or service that has never been offered before?

If you answered “yes” to the first two questions, you’re likely sitting in a business with high technological uncertainty; if “yes” to the last two questions, you’re probably dealing with high demand uncertainty.

American Wealth Gap Widens Despite Economic Recovery

The surge in the U.S. stock market over the past few years has disproportionately benefited wealthier Americans, according to the Federal Reserve and the Wall Street Journal. While nearly all families in the top 10% owned shares, the proportion of families holding stock declined from 15.1% in 2010 to 13.8% in 2013, says the Fed, with the decrease in stock ownership being most pronounced in the bottom half of the income distribution. Between 1989 and 2013, the proportion of all U.S. family wealth owned by the top 3% rose from 44.8% to 54.4%, while the proportion owned by the bottom 90% fell from 33.2% to 24.7%.

A Speech Is Not an Essay

Reading an essay to an audience can bore them to tears. I recently attended a conference where a brilliant man was speaking on a topic about which he was one of the world’s experts. Unfortunately, what he delivered was not a speech but an essay. This renowned academic had mastered the written form but mistakenly presumed that the same style could be used at a podium in the context of an hour-long public address. He treated the audience to exceptional content that was almost impossible to follow — monotone, flat, read from a script, and delivered from behind a tall podium.

He would have done well to heed the words of communication professor Bob Frank: “A speech is not an essay on its hind legs.” There is a huge difference between crafting a speech and writing an essay. And for those new to public speaking, the tendency to mimic the forms of writing we already know can be crippling.

Speeches require you to simplify. The average adult reads 300 words per minute, but people can only follow speech closely at around 150-160 words per minute. Similarly, studies have shown auditory memory is typically inferior to visual memory, and while most of us can read for hours, our ability to focus on a speech is more constrained. It’s important, then, to write brief and clear speeches. Ten minutes of speaking is only about 1,300 words (you can use this calculator), and while written texts — which can be reviewed, reread, and reexamined — can be subtle and nuanced, spoken word must be followed in the moment and must be appropriately short, sweet, and to the point.

As you focus on brevity and clarity in a speech, it’s also important to signpost and review. In a written essay, readers can revisit confusing passages or missed points. Once you lose someone in a speech, she may be lost for good. In your introduction, state your thesis and then lay out the structure of your speech ahead of time (e.g., “we’ll see this in three ways: x, y, and z”). Then, as you work through your speech, open each new point with a signpost to let your listeners know where you are with words such as, “to begin,” “secondly,” and “finally,” and close each point with a similar, review-oriented signpost (e.g., “so we see, the first element of success is x”). This lack of subtlety can be repetitive and inelegant in a written document, but it is essential to the spoken word.

Similarly, the subtleties of complex argumentation and statistical analysis can be compelling in an essay, but in a speech it’s important to drop the statistics and tell a story. Neuroscience has shown that the human brain was wired for narrative. And while I always appreciate arguments that are fact-based and grounded in sound logic, it’s easier for me to engage with a speaker when she keeps the statistics to a minimum and opts for longer and more vivid stories. Lead or end an argument with statistics. But never fall into reciting strings of numbers or citations. Your audience will better follow, remember, and internalize stories.

To bring these stories to life, remember that when delivering a speech you are your punctuation. When you’re speaking, your audience doesn’t have the benefit of visual signifiers of emphasis, change in pace, or transition — commas, semicolons, dashes, and exclamation points. They can’t see question marks or paragraph breaks. Instead, your voice, your hand gestures, your pace, and even where and how you’re standing on stage give the speech texture and range. Vary your excitement, tone, and volume for emphasis. Use hand gestures consciously and in accordance with the points you’re trying to make. Walk between main points while delivering the speech — literally transitioning your physical position in the room to signify a new part of the argument.  Standing motionless while speaking in a monotone voice doesn’t simply drain your audience’s energy, it deprives them of understanding — like writing a text in one run-on sentence with no punctuation or breaks. Resist the urge to read your speech directly from the page. Become the punctuation your audience craves.

Speeches and essays are of the same genus, but not the same species. Each necessitates its own craft and structure. If you’re a great writer, don’t assume it will translate immediately to the spoken word. A speech is not an essay on its hind legs, and great speech writers and public speakers adapt accordingly.

Every Content Marketer Needs an Editor

Content marketing is getting a lot of attention as companies strive to capture customer attention in an era in which TV ads get skipped, direct mail goes unopened, and even online ads get blocked. It seems more and more like the best—some would say, only—way to get your message out is for your customers to seek it out as content.

That’s led to an explosion in sponsored reports, videos, slideshares, and just about every other form of content that human beings might voluntarily choose to consume (as opposed to having it foisted upon them as pop-ups and banner ads). Unfortunately, as is widely acknowledged, most of the content in content marketing falls far short of the standard historically set by traditional media outlets: much of it is more marketing than it is content. I’ve lost count of the number of times I’ve made it to the end of a report or blog post, only to find myself still waiting for useful information, fresh insight, or even a coherent argument—rather than a pitch for the author’s book or a link to their consulting practice.

Disappointing readers with content that fails to rise above mediocrity is no way to build brand awareness or drive sales. Sexy headlines and quick quizzes may generate visits or even shared links, but you’re not going to win over the hearts and minds of your potential customers if they abandon your blog posts and reports after a dull or unreadable paragraph or two.  When you consider that 27 million pieces of content are created each day, but that 60 to 70% of website content goes unused, it’s clear that content marketing has emphasized producing a high volume of content at the expense of producing content that people actually want to consume.

Content marketing will only deliver on its promise if it’s good enough to deliver customers–that’s why improving the quality of content marketing is critical to business. But creating the kind of excellent original content that attracts, engages and retains an audience requires a mix of competencies that go well beyond what you find on a typical marketing team. At the top of that list of missing competencies is professional editing.

I’m painfully aware of the role that editors could and should play in the content marketing ecosystem because I’ve experienced many different sides of the content development and content marketing business. I’ve helped to develop and edit the corporate blog here at Vision Critical. I’ve worked with volunteer contributors to online community sites, balancing the need to encourage unpaid contributions with the desire to promote and showcase quality content. I’ve also written for high-profile blogs and publications and in doing so I’ve had both editors who have profoundly advanced my own writing and thinking, and ones who simply post whatever I submit. The impact of quality editing on my own work has made me appreciate the crucial role that editors play in turning information and ideas into compelling content (including right here at HBR).

I’m not talking about simple proofreading or copyediting (though gosh, that would be nice; my inner nitpicker is being driven slowly mad by all those stray apostrophes running loose across corporate blogs). Nor am I talking about overarching editorial direction and content strategy: that’s the part most marketing teams have figured out, albeit imperfectly, simply because understanding an audience (customers, potential customers and perhaps industry influencers) and defining key messages (the benefits your products and services offer, and the expertise you have in the field) are what a marketing team has traditionally needed to do best.

It’s the in-between layer that’s missing: the editor who acts as a proxy for the reader, and ensures your content offers that reader real value in return for their time.  That editor also has the ability to recognize the difference between an idea that’s worth a 140-character tweet, and one that can be developed into a blog post or report—or, for that matter, a three-minute video. They have the ability to work with an author whose ideas may be terrific but who may not be a strong communicator, and develop that author’s ideas into a compelling and engaging piece of content. And yes, the ability transform inelegant or even incoherent prose into a tight, readable argument.

Those competencies make the difference between a site full of content marketing, and a site full of content that actually acts as an effective part of your marketing strategy. Look at the stars of content marketing, and you’ll see that their content isn’t just shiny — it delivers real substance. Like Hubspot, whose widely-shared blog posts offer authoritative insight on everything from the evolution of Google+ to the the psychology of job interviews — topics that go far beyond the company’s specific business of inbound marketing software, but support its claims to expertise. Or Whole Foods, whose Dark Rye magazine holds its own against commercial lifestyle magazines. (A key test for any content marketing: does it read like content people would actually pay for?)  Or IBMblr, IBM’s much-discussed Tumblr, where the company shares a compelling mix of visual and text-based snippets that convey the company’s passion for innovation in a dynamic and charming way.

Companies that want to produce this kind of quality content don’t need more marketing genius; they need more editorial genius. Yet the scramble to build content marketing capacity has not to date translated into a scramble for talented editors. While there is no shortage of opportunities in content marketing, just a handful of these are for editors. Yes, some dispensers of content marketing wisdom include a editor-in-chief in their vision for content marketing teams, but that role is typically described more as a leadership position exerting high-level editorial control than as an in-the-trenches job ensuring high-quality execution.

To deliver the kind of content that can truly advance a brand, marketing teams need to hire editors who have the time to really dig into each piece of content they produce, and the mandate to create content that serves the reader as well as the business. That means hiring people with the experience to edit contributions from anywhere in your organization (or outside of it) — even if that means going toe-to-toe with a CEO whose sentences don’t hang together. That means your editors need the authority to make significant changes or even kill selected contributions, but shouldn’t be swamped with managerial duties that crowd out the detailed and intensive revision process — editors need the bandwidth to actually work through each and every piece of branded content you produce so that it is as good as the best unbranded content.

The companies that make this kind of investment in editorial capacity will be most successful in translating their content marketing aspirations into a daily reality of producing excellent content. Content marketing is now a central part of marketing strategy. But it won’t work for readers or for brands unless our content achieves a high and consistent level of quality. It’s time for corporate marketers to recognize what media outlets have long known: if you want quality content, you need quality editors.

Let Algorithms Decide – and Act – for Your Company

In the near future, simply having predictive models that suggest what might be done won’t be enough to stay ahead of the competition. Instead, smart organizations are driving analytics to an even deeper level within business processes—to make real-time operational decisions, on a daily basis. These operational analytics are embedded, prescriptive, automated, and run at scale to directly drive business decisions. They not only predict what the next best action is, but also cause the action to happen without human intervention. That may sound radical at first, but it really isn’t. In fact, it is simply allowing analytics to follow the same evolution that manufacturing went through during the industrial revolution.

Centuries ago everything was manufactured by hand. If you needed a hammer, for example, someone would manually produce one for you. While manually manufacturing every item on demand allows for precise customization, it doesn’t allow for scale or consistency. The industrial revolution enabled the mass production of hammers with consistent quality and lower cost. Certainly, some customization and personal touches were lost. But the advantages of mass production outweigh those losses in most cases. It remains possible to purchase custom made items when the expense is deemed appropriate, but this usually only makes sense in special situations such as when the purchaser desires a one-of-a-kind piece.

The same revolution is happening in analytics. Historically, predictive analytics have been very much an artisanal, customized endeavor. Every model was painstakingly built by an analytics professional like me who put extreme care, precision, and customization into the creation of the model. This led to very powerful, highly-optimized models that were used to predict all sorts of things. However, the cost of such efforts only makes sense for high-value business problems and decisions. What about the myriad lower value decisions that businesses face each day? Is there no way to apply predictive analytics more broadly?

There is.

Operational analytics recognize the need to deploy predictive analytics more broadly, but at a different price point. An assembly line requires giving up customization and beauty in order to achieve an inexpensive, consistent product. So, too, operational analytics require forgoing some analytical power and customization in order to create analytics processes that can increase results in situations where a fully custom predictive model just doesn’t make sense. In these cases, it is better to have a very good model that can actually be deployed to drive value than it is to have no model at all because only an optimal model will be accepted.

Let me illustrate the difference with a common example. One popular use of predictive models is to identify the likelihood that a given customer will buy a specific product or respond to a given offer. An organization might have highly robust, customized models in place for its top 10-20 products or offers. However, it isn’t cost effective to build models in the traditional way for products or offers that are far down the popularity list. By leveraging the learnings from those 10-20 custom models, it is possible to create an automated process that builds a reasonable model for hundreds or thousands of products or offers rather than just the most common ones. This enables predictive analytics to impact the business more deeply.

Operational analytics are already part of our lives today, whether we realize it or not. Banks run automated algorithms to identify potential fraud, websites customize content in real time, and airlines automatically determine how to re-route passengers when weather delays strike while taking into account myriad factors and constraints. All of these analytics happen rapidly and without human intervention. Of course, the analytics processes had to be designed, developed, tested, and deployed by people. But, once they are turned on, the algorithms take control and drive actions. In addition to simply predicting the best move to make or product to suggest, operational analytics processes take it to the next level by actually prescribing what should be done and then causing that action to occur automatically.

The power and impact of embedded, automated, operational analytics is only starting to be realized, as are the challenges that organizations will face as they evolve and implement such processes. For example, operational analytics don’t replace traditional analytics, but rather build upon them. Just as it is still necessary to design, prototype, and test a new product before an assembly line can produce the item at scale, so it is still necessary to design, prototype, and test an analytics process before it can be made operational. Organizations must be proficient with traditional analytics methods before they can evolve to operational analytics. There are no shortcuts.

There are certainly cultural issues to navigate as well. Executives may not be comfortable at first with the prospect of turning over daily decisions to a bunch of algorithms. It will also be necessary to get used to monitoring how an operational analytics process is working by looking at the history of decisions it has made as opposed to approving up front a series of decisions the process is recommending. Pushing through such issues will be a necessary step on the path to success.

The tools, technologies, and methodologies required to build an operational analytics process will also vary somewhat from those used to create traditional batch processes. One driver of these differences is the fact that instead of targeting relatively few (and often strategic) decisions, operational analytics usually target a massive scale of daily, tactical decisions. This makes it necessary to streamline a process so that it can be executed on demand and then take action in the blink of an eye.

Perhaps the hardest part of operational analytics to accept, especially for analytics professionals, is the fact that the goal isn’t to find the best or most powerful predictive model like we’re used to. When it is affordable and the decisions being made are important enough to warrant it, we’ll still put in the effort to find the best model. However, there will be many other cases where using a decent predictive model to improve decision quality is good enough. If an automated process can improve results, then it can be used with confidence. Losing sleep over what additional power could be attained in the process with a lot of customization won’t do any good in situations where it just isn’t possible due to costs and scale to actually pursue that customization.

If your organization hasn’t yet joined the analytics revolution, it is time that it did. Predictive analytics applied in batch to only high value problems will no longer suffice to stay ahead of the competition. It is necessary to evolve to operational analytics processes that are embedded, automated, and prescriptive. Making analytics operational is not optional!

To Sell is Human: The New ABCs of Moving Others

Do you sell the same way you did a decade ago?

In the classic movie Glengarry Glen Ross Alec Baldwin tells a group of salesmen that the key to selling is, “A-B-C. A – Always; B – Be; C – Closing. Always be closing.”

But this steamroller approach is now a relic.

According to bestselling author Dan Pink, sales has changed more in the last 10 years than the previous 100. Today, buyers have as much information as sellers—along with ample choices and the means to push back. Selling effectively requires a new approach.

In this interactive Harvard Business Review webinar, Dan Pink draws on cutting-edge social science and best practices from global organizations to reveal the new ABCs of selling. Pink reveals 5 ways to frame messages to increase clarity and promote action. He also discusses why problem finding is now more important than problem solving, how questioning your abilities before a sales call can actually help, and why the most effective salespeople are not extroverts.

How Microeconomists Made Amazon Possible

With the digital age has come the celebration of “platforms.” The concept is that an enterprise can add value at either of two levels. It can provide a broad foundation upon which others can profitably build, or it can be one of those latter efforts, taking advantage of an existing platform and offering solutions that more narrowly target specific opportunities.

When people talk of valuable platforms, they are typically thinking of things like the operating systems for personal computers, tablet computers, and smartphones, all of which allow and indeed encourage software developers to invent “apps”; or the Amazon selling system which provides a layer of basic logistical functions for independent retailers. But platforms have a longer lineage than these. In an earlier age, “general purpose technologies” such as electricity and communications networks were recognized by economists as their own category because they provided vital inputs for all kinds of businesses, revolutionized the way business was conducted in general, and literally redefined what it meant to live in modern society.

Platforms can also be thought of more broadly than technological systems. Government policies, whether established by legislatures, regulatory agencies, or judicial rulings, constitute legal platforms that also enable commerce. There is an extensive academic literature, for example, on how the “rule of law” — property and contract rights that can be enforced, and disputes peaceably and efficiently resolved by a trusted judicial system — is a precondition for both economic growth and advanced living standards (although some economists argue that the causation runs both ways – that economic growth also supports the rule of law by making more resources available for it, and causing more demand for it).

I’d like to argue that ideas can also qualify as platforms, to the extent that others rely or build on them to make their own marks. Further, I would claim that, in the realm of business, more of these platform ideas have been developed by economists than many entrepreneurs and business leaders imagine. I’ll offer three examples.

The Idea to Break Up AT&T

The internet would not be what it has become without huge private investments in its “backbone” – the fiber optic cables that route packets of zeroes and ones to the network nodes close to the hardware (PCs or smartphones) where they originate and end up. But it is all too easy to forget (or not to know) what encouraged those fiber investments in the first place: the federal government’s successful antitrust lawsuit against the “old AT&T” that once monopolized telecommunications in the United States.

Economists were instrumental in providing the intellectual rationale for the Justice Department’s landmark filing of that lawsuit, and just as important, of the relief the government sought: breakup of “Ma Bell’s” control over both long-distance and local telephony. Breakup was critical to the development of the Internet because as long as AT&T faced no competition in long-distance, the company had no incentive to replace its copper wires with the fiber optic technology that ironically was developed by Bell Labs. Post-breakup, long-distance competitors MCI and Sprint, among others, induced the new AT&T long-distance company (which later was acquired by Southwestern Bell but kept the AT&T name) to lay the fiber optic cables across the country that became the Internet’s backbone.

So, when browsers came along in the 1990s, the web was ready for the explosion in commercial traffic and searching that quickly followed. It is not at all clear that Sergey Brin and Larry Page, founders of Google or Jeff Bezos, the founder of Amazon, would have been interested or able to launch their now-iconic companies had not the internet been ready for them when they had hatched their ideas and were ready to implement them.

The Idea to Deregulate Transportation

While web retailing has made shopping hugely more convenient, none of it would be possible at the scale it has achieved without the retailers’ being able to tap into a highly flexible and efficient transportation system capable of delivering goods promptly to customers. We have that transportation system now, but this was not always the case.

Before 1980, all routes and fares of the airline and trucking industries were regulated by agencies of the federal government, dating from Depression-era statutes. In retrospect, it is clear that this system of “economic regulation” was designed to insulate incumbent carriers from competition; neither air nor truck traffic were natural monopolies requiring price and entry controls.

Many transportation economists had argued for decades that economic regulation of airlines and trucking was inappropriate, mainly serving to jack up prices for the consumers and businesses buying these services. Amazingly, President Carter and Senator Ted Kennedy listened to them, using their arguments to persuade Congress to dismantle price and entry controls in these industries in 1978 (airlines) and 1980 (trucking). Carter also appointed noted economists like Alfred Kahn, Elizabeth (“Betsy”) Bailey, and Darius Gaskins, to key regulatory positions, where they were able to administratively deregulate first, where possible, and also to make the case to Congress that ultimately only legislative repeal would really do the trick.

Deregulation not only lowered shipping prices; it unleashed fierce competition between Federal Express and UPS that eventually produced the efficient and flexible transportation system that turned out to be ideal for internet commerce. So when Jeff Bezos and other web retailers came along in the 1990s and later, they were able to tap into that system, without having to buy trucks and plane fleets of their own, which would have been prohibitive barriers to entry. That they didn’t have to is a tribute to the eventual power of economic ideas and research.

Ideas about Energy Price Control

A huge transformation is underway due to the recent, remarkable surge in domestic oil and natural gas production. The reason is well known: the combination of horizontal drilling technology with hydraulic fracturing (“fracking”) has enabled energy producers to locate and bring to the surface oil and gas in “tight” rock formations. The unforeseen energy turnaround has been a boon to U.S. manufacturers, inducing some to rethink their location decisions.

What many don’t fully realize, however, is the unsung role that economists have played in this story. I am old enough to remember the dark days after the Arab oil embargo in 1973-74, which triggered a quadrupling of world oil prices at a time when monetary policy makers and elsewhere were already struggling to contain inflation. Even Republicans in the White House (Nixon and then Ford) were not comfortable with the political implications of the higher gasoline prices this jump in crude prices entailed, and so they implemented a complicated system of controlling the price of “old oil” (that discovered before the price jump) and new oil, alongside a preexisting and even more complicated system of controlling different vintages of natural gas. The results were prices to consumers that were below market clearing levels, which any economist could have predicted would result in shortages: long lines at gas stations for motorists and rationing of natural gas for heating homes.

Economists in both the Ford and Carter Administration were instrumental in decisions dismantling energy price controls, which were gone by the early 1980s. This took some guts, especially by President Carter (again), who agreed to decontrol oil prices toward the end of his term by which time crude oil prices again had soared (Carter and a Democratically controlled Congress did enact a time-limited windfall profits tax on energy producers, however).

Why is this history important? Because when oil prices again rose in the 2000s, politicians had learned their lesson and did not rush to impose price controls. Higher prices did what economists predicted they would do, but admittedly even more successfully than many probably thought: they provided the economic impetus for energy companies to combine horizontal drilling and fracking, a coupling that has produced remarkable results. So score several points for the technologists and the risk-taking oil and gas production companies, but score at least one point (maybe two) for the economists in the background.

*  *  *  *  *

Is it proper to cast these ideas as “platforms”? Based on what has been built since, it seems fair to say yes. The first two examples were hugely influential in enabling the Internet economy; the third benefited all energy-using industries. The people behind them all belong in the club I call the Trillion Dollar Economists.

So perhaps the so-called dismal science of economics should be celebrated a bit more. Mention the word “economist” and most people conjure up mental images of macro forecasters, making pronouncements about, or in the case of the Fed, actually influencing measures of the overall economy: GDP growth, inflation, and unemployment. We should shine more light on the other, “micro” economists – the ones studying individual firms and markets. Among these are the thinkers who are even now hatching new platforms. Years from now, we’ll see the new companies that were enabled, and the existing businesses whose growth was accelerated, by their most powerful, policy-bending ideas.

How Apple Pushes Entire Industries Forward

Yesterday, hardware stole the show at the Apple unveiling. But Apple’s most impressive achievement on display at yesterday’s announcement was not a technological feat — although the technology on display was certainly impressive.

Apple’s great feat was the use of their scale to swiftly get the world lined up behind a new model for payments. Apple Pay will be more secure, it will be easier, and it will probably be more profitable for the payments industry as a whole by shifting people away from cash (at least for the time being). But putting it into practice required an entire ecosystem to move in unison — merchants, consumers, credit card companies, and banks. Something that only a company with the massive reach of Apple could do.

Big companies’ struggles with innovation have been well documented — including by me. But there are some things you need to be big in order to achieve.

What Apple demonstrated yesterday was its power as an “impatient convener.”

The term was coined by the first CTO of the US Government, Aneesh Chopra. Chopra, and his successor Todd Park, have thoughtfully used the unique position of the White House to bring together disparate leaders to drive innovation through mutually beneficial agreements. Their thesis, which Chopra describes elegantly in his book Innovative State, is that the White House has the pull to sit people down at the table. When the President calls, you answer. When the President says, you need to come to Washington to discuss something like rolling out a smart-grid technologies nationally, you come. And if you are there and the proposal makes sense, you may actually opt in as well – even if there are no demands or formal requests from on high.

Yesterday, Tim Cook demonstrated the benefits to driving innovation as a massive, mature company. Apple used its power as an impatient convener to move an industry. Yesterday, Tim Cook showed that, like the White House, when Apple calls, you answer.

Fixing payments isn’t a novel concept. Years ago, the team at Paypal set out to change the payments industry and free us from crushing payment fees by enabling ACH based payments. More recently, Square decided that archaic payment hardware needed to be refreshed for a world where people carried a supercomputer in their pockets. And Google perennially updates their offering with Google Wallet.

But despite all of these efforts, each of us still carries stuffed wallets and heavy purses around wherever we go. Why? Mobile payment systems need ubiquity before we can replace our old systems. And if we aren’t going to stop carrying our wallets around – why change anything at all? Sure, we’ll swipe when someone has a Square reader. We’ll send a friend cash with Paypal if we don’t have it in our pocket. And we might pay with Google Wallet if there is a big enough coupon to justify re-installing the app. But nothing foundational has really changed.

Consider: last year Square was rumored to process around ~$30B in sales. In the same year, the Federal Reserve suggested Americans swiped credit cards ~30 billion times (and debit cards about 50 billion more times). With the average credit card swipe at somewhere between 80 and 100 dollars, that gives Square a tiny fraction of the processing pie. And Square still relies on the swipe of a plastic card.

On the other hand, on launch date, the Apple Pay will be accepted by terminals across the globe who process hundreds of billions of dollars of payments. Major retailers from Walgreens to Walt Disney will let you pay with Apple’s secure payment technology. And now, according to Jack Dorsey, so will millions of Square merchants around the world.

Sure, the security features Apple is prepared to offer resonate with us in a time where our identities and financial information are constantly at risk of being stolen. Obviously, the backwards compatibility with all our existing cards is compelling. And the fact that Apple already has our card on file makes it pretty easy. But the real brilliance of Apple’s offer is much simpler: it’s scale.

Apple Pay is impressive, it’s groundbreaking, and it may just unleash a wave of innovation. To do what Apple just did requires deep industry collaboration. It must have taken months of careful negotiations, a lot of trust, and a real threat to inaction. The companies who would have been happy to sit back on their laurels probably felt that failure to engage would result in large losses. It harkens back to the negotiations that must have occurred with record labels before Apple launched the iTunes store. It foreshadows the type of collaboration that Tesla may require in order to retrofit an ecosystem of fuel stations for EV charging. It’s an expert use of product design, network effects, and game theory all wrapped up in one move.

And it also makes me think. How can each of us use the power of impatient convening to our advantage? If Apple is audacious enough to use its power to transform a 50-year old industry, how could you use your company’s reach to create meaningful change?

Power Cues: New Science on Influencing Others

How leaders communicate has a tremendous impact on their ability to lead and influence others, and on their personal success. Yet unknown by many is that most communication is unconscious.

Nick Morgan, communications expert and author of Power Cues: The Subtle Science of Leading Groups, Persuading Others, and Maximizing Your Personal Impact, has looked at recent brain and behavioral science revelations about how humans communicate, and how effective communicators use subtle gestures, visual cues, sounds, and signals that elicit emotion.

In this interactive Harvard Business Review webinar, Morgan shares insights from science on new ways to prompt unconscious responses to connect with people powerfully and persuasively. By leveraging these communications insights—these “power cues”—leaders can maximize their personal impact, enhance their leadership skills, and improve their ability to influence others.