Marina Gorbis's Blog, page 1369

The Condensed October 2014 Issue

Amy Bernstein, editor of HBR, offers executive summaries of the major features. For more, see the October 2014 issue of HBR.

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Why Your Marketing Metrics Don’t Add Up

Few things can be as jarring for marketers as losing trust in your metrics. There are many sources of marketers’ trust issues with their data, but the one I hear cited more than any other is conflicting or competing data sources. Perhaps the numbers are emerging from two different reporting systems or third-party publishers, or maybe it’s a case of misattributed engagement, but all too often marketers slap down side-by-sides that simply don’t add up. When 60% of customers are coming from one channel, and the remaining 50% from another, then what? Anxiety, distress, distrust—and the cycle continues.

This discomfort is an unwelcome side effect of some welcome advances in omnichannel marketing—you’re everywhere and so is your marketing, and that’s bound to lead to at least some static. However, what we’re seeing now takes that even further, leaving marketers with significant internal and external roadblocks in the journey to become data-driven. How can you and your organization determine a single source of truth and, ultimately, leverage this to make smart, effective, efficient decisions, when the data not only doesn’t provide a clear-cut next step, but may not even make sense?

Give more sources “credit” for the sale

Attribution is one of the most common sources of confusion and distress. Even the most seemingly airtight campaigns can wind up in a situation where, at least at the surface, the numbers don’t tell a steadfast tale. One of the most common sources of data disconnect in marketing and advertising is misattribution—very simply, which platform, offer, or campaign gets the “credit” for a conversion?

Think about the last time you logged into Facebook and saw an article or video that was making the rounds. It was probably posted on a few of your friends’ walls, and maybe you saw some other people tweet about it. By the time your friend texted it to you, you’d already seen it, because you subscribe to daily updates from the site that originally posted it. Sound familiar? To which source should your click be attributed? Didn’t they all, in some way, drive you to engage with the content? Does it matter which link you clicked through when you actually consumed the content? What’s more, it’s possible you clicked through from more than one source. So who wins?

Trust issues bubble to the surface because marketers feel misled by the seemingly conflicting data when, in fact, it’s all correct—just different interpretations of what ultimately led to the conversion. Because consumers’ journeys are becoming increasingly circular, attribution can come from a variety of sources and shouldn’t incite distress signals. A greater understanding of and appreciation for this circuitous system can help dissuade some of this distrust.

Embrace multiple data sources

The best answer to data trust issues is to understand the various sources of legitimate confusion, like in the attribution example above. To accomplish this, marketers should be prepared to determine campaign key performance indicators (KPIs) ahead of launch, and ensure proper measurement across multiple internal and third party data sources.

While relying on a single data source sounds appealing, in practice digging through multiple sources is more likely to yield an accurate picture.

Global PC manufacturer Lenovo, for example, implemented a multichannel analytics strategy to measure customer satisfaction. By analyzing data from six sources—the web, postpurchase surveys, its customer relationship management (CRM) system, call center, email, and live chat—Lenovo ascertained why customers were phoning the call center and what actions they took before calling. That insight helped them improve areas of the website to reduce calls and avoid customer dissatisfaction with long wait times.

Learn to live with inflated metrics

Another common source of confusion is artificially inflated metrics. Are consumers flying through a slideshow, paying little or no mind to the images and native or promotional content? Although those aren’t great engagement experiences from a marketing perspective, they’re certainly being counted both by in-house solutions and third-party reporting systems. This doesn’t necessarily mean these are bad platform choices; but it is something you need to take into account in order to have a concrete understanding of how your marketing budget is being spent and the effectiveness of your campaigns.

And when all else fails and definitively determining the truth seems impossible, simply admit it. Decide what your organization will use to measure success, how it will be articulated and leveraged, and what you’ll look for from campaign to campaign, test to test—and make sure everyone falls in line. By having a universal set of KPIs, metrics, and vernacular, you’ll save time and key resources from ongoing debate, chatter, distress, and distrust, freeing up time for meaningful optimization activity.

Convincing Employees to Use New Technology

All of our companies are digital now – or quickly becoming that way. Almost any enterprise you can think of, no matter the industry or sector, is trying (or being pressured by competitors) to use new technology to harness the vast new oceans of data being generated by smartphones, sensors, digital cameras, GPS devices, and myriad other sources of information originating from customers and markets.

Yet how many millions of dollars have been spent on analytics technology, but with no parallel improvements – or even any changes – to the way decisions are made within a business?  How many companies have deployed internal wikis and social networks with great fanfare only to see slow take-up or a huge slow down after a few months?  Even among digital natives, adoption of things like enterprise digital tools often doesn’t live up to lofty expectations.  “We’ve spent an awful lot of money on technology, but I still see people working in the old way,” complained the CFO of a large hospitality company.  The result is often widely deployed internal applications that no one actually uses effectively. Why does this happen?

When these platforms are introduced, organizations too often focus only on deployment, not adoption. It’s remarkable how commonplace it is for leaders to lose sight of the true ROI of their digital investments: collaboration among actively engaged users, smarter decision making, increased sharing of best practices and, over time, sustained behavior change.

There are three related problems. First, CIOs and technical leaders too often take a limited “tech-implementation” view and measure success on deployment metrics like live sites or licenses. They consider business adoption someone else’s job, but in fact no one is made accountable for it. Second, platform vendors often oversell the promise of instant change through digital technology. They make their money by selling products and software, rarely by getting them used at scale. And finally, the bottom line: user adoption programs cost money.

The real return on digital transformation comes from embedding new work practices into the processes, work flows, and ultimately the culture of organizations. But even in cases where the value of adoption is understood, cost containment often takes over. Faced with limited budgets, companies focus on the most tangible part first – deploying the technology. Adoption is left for later, and often “later” never comes.

This drives negativism that can spread through the organization. Business users don’t see the value and fail to engage in the new digital platforms. The platforms are themselves blamed for the failure. Cynicism sets in. Every additional digital investment gets negatively scrutinized and the whole digital transformation program slows down.

When the process works, the benefits become obvious. Sometimes adopting a new technology can even become an irreversible movement. “We have started a ‘digital movement’ that affects all aspects of our company and requires all of our people to be engaged in the program. We will only win together,” explained Pernod Ricard’s CEO Pierre Pringuet.

Business adoption of digital tools has to be led. So what do you need to do?

Do fewer things better. Despite the myriad opportunities that exist to invest time, effort, and dollars into making your business more digital, you can’t do them all. Focus on the initiatives that you believe, once adopted by the business, will create real value — and that you believe you can actually finish. Prioritize those initiatives by both the size of the business impact and relative ease of execution. Put a time limit on execution and allocate the resource level it will take to succeed in that timeframe. And clearly communicate the value of adoption to your employees.

Plan and budget for adoption from the start. Plan for what it will take to realize the benefits beyond the technology deployment efforts. Take into account the people, process and structural changes. Budget for the  communication, training and organizational development required to succeed. And ensure that proper governance and metrics are in place to monitor progress.

Lead by example. You can influence the transition to new digital ways of working by modeling the change you want to see happen – and by encouraging your colleagues to do so. For instance, actively participating on digital platforms and experimenting with new ways of communicating, collaborating, and connecting with employees. It is the first important step to earning the right to engage your organization. Coca-Cola faced huge challenges when it deployed its internal social collaboration platform. Only when Coca-Cola’s senior executives became engaged on the platform did the community become active. As the implementation leader put it, “With executive engagement, you don’t have to mandate activity.”

Engage true believers. Drawing on influential employees in the front line is one of the most effective vehicles for promoting change in an organization. Identify your committed digital champions early – individuals who network well and can create horizontal influence to help implement behavior change across silos. Devise a program to nurture your digital champions, as they are key to transformation success and will most likely be your organization’s future digital leaders.

Engage your HR and Organizational Development people early. Encourage them to take a leadership role in the transformation. It will be essential for them to adapt management and HR processes to ensure the new practices get institutionalized – for instance, designing a new digital competency model or formalizing a reverse mentoring program. They also need to ensure that the metrics and goals that describe adoption success are monitored, as well as to provide regular analytics on progress.

Align rewards and recognition. Transformation goals and measures are inextricably linked. It’s natural that conflicts in your reward structures will occur as you get into the depth of your digital conversion, and this can slow down execution. Many retailers, for instance, have had to align their online and in-store sales incentives to avoid channel conflicts. Use all available reward structures to foster adoption, not just financial ones. And consider new forms of employee engagement, such as games, which can also yield positive results.

Remember, creating a digital organization is not just about implementing new technology. If you want to see true and lasting value from your technology investments, people need to change their mindsets and behaviors, and you need to lead that change.

When Star Talent Grew More Powerful than Capital

Any mention of the Sixties elicits an immediate response: hippies, Vietnam protests, Woodstock, sexual freedom, and psychedelic drugs. But along with all of that something quite dramatic happened to corporate America as well, and it started showing up in the data in 1960. It was the first salvo in an economic revolution, one that was largely in keeping with the above phenomena: the rise of talent.

As I describe in my article in this month’s HBR, up to 1960, the U.S. economy had evolved at a glacial pace and had exhibited remarkably narrow and limited creative intensity.   At the turn of the 20th century, the proportion of workers who had to exercise significant independent judgment and decision-making in their jobs had been just 13%, and the remaining 87% of workers across all job classifications worked in routine-oriented jobs in which their superior determined what they were supposed to do all day and, to a great extent, how they were supposed to do it. By 1960, the percentage of creativity-oriented jobs had risen to just 16%, representing a change in job content for only 3 in every 100 workers over a 60-year period.   In 1960, therefore, 84% of all jobs held by Americans involved minimal independent judgment and decision-making.

This was truly an era in which to prosper a company had to have capital and to own natural resources; but really didn’t need much in the way of uniquely talented employees. Rather they needed lots and lots of competent and compliant ones.

From 1960, however, the economy started morphing in a fashion that required more and more workers to express meaningful judgment and decision-making. The growth in these jobs accelerated during 1960-2010 at a rate four times that of 1910-1960 – so much so that by 2010, the proportion of creative jobs had more than doubled to comprise 33% of the workforce.

This came as no surprise to Peter Drucker, who repeatedly predicted fundamental changes in the U.S. economy during the 20th century. As early as 1959 he was arguing argued that the economy was changing from one in which the key assets were strong legs, arms and backs to one in which the most important muscle was the one between an employee’s ears. These knowledge workers would be different, he suggested, they would not be able to distance themselves from their day-to-day work because their work was a product of their brain — they were their work. This meant that they needed to be treated more like volunteers than employees, an amazingly prescient observation.

The growing creativity of work of the past five decades has shown up in stock values as well. In 1960, just eight of the top 50 market capitalization companies owed their position to creative talent. Predictably, perhaps, the largest was IBM, which in 1960 stood at #4. But there were also Eastman Kodak (#11), P&G (#15), General Foods (#19), Coca Cola (#34), American Home Products (#40), Campbell Soup (#48), and RCA (#49). They were still outliers; far more common were firms that owed their position to their control of natural resources, such as oil or minerals, or real estate.

But these heralds were soon joined by many more and moved from being a small minority to the dominant force in the economy, comprising 28 of the top 50 companies.

It is hard to think about this transformation as anything but a positive thing. Twenty of every 100 American workers who would have had a routine-intensive job a century ago have a creativity-intensive one now. But it has come with a cost to the capitalists who own these companies. Competent and compliant workers also meant cheap workers. Workers who must demonstrate meaningful independent judgment and decision-making don’t come cheap and they certainly don’t exhibit compliance as one of their leading characteristics.

Capitalists used to spend their time battling unionized labor for the spoils of their joint economic pie — and generally capitalists were successful. Now their battle is with the high-end talent upon whom capital is entirely dependent to make the decisions that will make the company they own profitable or not. That talent has a hell of a lot more bargaining power than organized labor ever had. (And labor itself is now essentially friendless.)

Drucker was right on two fronts: talent-laden knowledge workers would become a dominant force and they would have to be treated with kid gloves as if they were volunteers. But it is unclear whether Drucker realized that they would need to be treated as ultra-highly paid volunteers and become in the 21st Century capitalists’ principal economic adversary.

The Reason Your Team Won’t Take Risks

Most senior managers agree that taking risks is important for innovation, but in far too many cases, they don’t act like they believe this. For example, one global organization, where one of us conducted a culture survey several years ago, considered itself to be highly supportive of developing new products, services, and practices. Yet when several hundred professionals were asked what would happen if they developed and tried “new and untested ideas,” only 17% said that such behavior would be rewarded or approved – 47% said that the reaction from their superiors would be “unpredictable.”

In other words, the reality in many organizations today is that despite the public emphasis on innovation, the underlying culture may be strongly risk-averse. As one senior manager in a large financial institution said to one of us (only partially tongue-in-cheek), “The key to success here is to never make the same mistake once.”

Unfortunately, this kind of attitude is anathema to successful innovation, which does indeed require a tolerance for risk-taking and learning from periodic failure. So how can you break out of this mode and create an environment that is more conducive to innovation? In our experience, one of the starting points is to be more explicit about what risk-taking really means, and what is acceptable and what is not. Here are four tactics for doing this:

Publicly define a smart risk. The better innovation companies distinguish the areas where risk is encouraged, and where it is not. One of our clients, for example, makes it clear that there should be minimal “execution risk” regarding customer commitments and financial results, but encourages “discovery risk” in developing new solutions to customer problems. These guardrails define the “safe zone” for innovation, and they should include specific parameters such as time (must show progress after x months) or financial impact (has the potential to generate xx revenue or costs no more than xxx).

Use the right words to encourage the right culture. Language drives behavior and creates a mindset around what’s acceptable and what’s not. For example, using terms like “experiment” or “scouting mission,” instead of “successful vs. unsuccessful project,” will signal a more open attitude toward risk. Centering innovation activities on the concept of “exploration” eases the tension associated with trying new things. That’s why Amazon’s Jeff Bezos encourages an “explorer mentality” rather than a “conqueror mentality” in his teams, so that their focus is on forging new paths rather than just doing better than their competitors. The beverage company Pernod Ricard established a division called the “Breakthrough Innovation Group” to experiment with new ideas. The group has a similar spirit to a Silicon Valley start-up, in that it brings an entrepreneurial, exploring mindset into the larger company.

Keep it nimble and small. Size matters, and when it comes to innovation risk, smaller – and faster – experiments are often better. Tesla keeps teams small, so they maintain an entrepreneurial mindset with a higher tolerance toward risk than older firms in the automotive industry that rely on larger teams. A similar example comes from the Defense Advanced Research Projects Agency (DARPA). Unlike other government agencies, it is a lean organization with only two management layers, which enables them to move ideas and decisions with speed, because as they say, “urgency inspires greater genius.” DARPA also employs small teams on projects that move quickly and have clear autonomy – which has led to incredible innovations.

Establish clear phases and criteria for funding projects. If you currently have risky (and expensive) innovations in your pipeline, stop providing blank checks. Instead, fund each project in clearly defined phases. If it passes one phase, give it additional funding. At Google, teams have timelines of three to four months to prove a concept’s viability. If the idea they’re working on doesn’t prove itself sufficiently in that timeframe, teams are disbanded. Teams are expanded only if ideas have demonstrable potential.

Successful innovation is never guaranteed – it always entails a certain amount of risk. If employees don’t understand the types and amounts of risks that are acceptable, they might not be willing to get into the innovation game. In the long term, that could put your company at even greater risk.

How to Clone Your Best Decision-Makers

Any company’s decisions lie on a spectrum. On one end are the small, everyday decisions that add up to a lot of value over time. Amazon, Capital One, and others have already figured out how to automate many of these, like whether to recommend product B to a customer who buys product A or what spending limit is appropriate for customers with certain characteristics.

On the other end of the spectrum are big, infrequent strategic decisions, such as where to locate the next $20 billion manufacturing facility. Companies assemble all the data and technology they can find to help with such decisions, including analytic tools such as Monte Carlo simulations. But the choice ultimately depends on senior executives’ judgment.

In the middle of the spectrum, however, lies a vast and largely unexplored territory. These decisions — both relatively frequent and individually important, requiring the exercise of judgment and the application of experience — represent a potential gold mine for the companies that get there first with advanced analytics.

Imagine, for example, a property-and-casualty company that specializes in insuring multinational corporations. For every customer, it might have to make risk-assessment decisions about hundreds of facilities around the world. Armies of underwriters make these decisions, each underwriter more or less experienced and each one weighing and sequencing the dozens of variables differently.

Now imagine that you employ advanced analytics to codify the approach of the best, most experienced underwriters. You build an analytic model that captures their decision logic. The armies of underwriters then use that model in making their decisions.  This is not so much crunching data as simulating a human process.

What happens? The need for human knowledge and judgment hasn’t disappeared — you still require skilled, experienced employees. But you have changed the game, using machines to replicate best human practice. The decision process now leads to results that are:

Generally better. The incorporation of expert knowledge makes for more accurate, higher-quality decisions.
More consistent. You have reduced the variability of decision outcomes.
More scalable. You can add underwriters as your business grows and bring them up to speed more quickly.

In addition, you have suddenly increased your organization’s test-and-learn capability. Every outcome for every insured facility feeds back into the modeling process, so the model gets better and better. So do the decisions that rely on it.

Using analytics in this way is no small matter. You’ll find that decision processes are affected. And not only do you need to build the technological capabilities, you’ll also need to ensure that your people adopt and use the new tools. The human element can sidetrack otherwise promising experiments.

We know from extensive research that decisions matter. Companies that make better decisions, make them faster, and implement them effectively turn in better financial performance than rivals and peers. Focused application of analytic tools can help companies make better, quicker decisions — particularly in that broad middle range — and improve their performance accordingly.

Predictive Analytics in Practice

An HBR Insight Center




Predict What Employees Will Do Without Freaking Them Out
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Learn from Your Analytics Failures
A Predictive Analytics Primer

The Silent Killer of New Products: Lazy Pricing

72% of all new products don’t meet their revenue targets. And a quarter of companies, according to the same survey, confess that not one of their new offerings met its profitability goals.

This new (and alarming) data comes from pricing consulting giant Simon-Kucher & Partners, which conducts its survey every other year with the Professional Pricing Society, a professional association. The 2014 survey polled approximately 1,600 executives and managers from over 40 countries and across a range of industries. (About two-thirds were in B2B businesses.)

I talked to Georg Tacke(co-CEO of Simon-Kucher) and Madhavan Ramanujam (Partner at the Silicon Valley Office of Simon-Kucher) about what’s causing this high failure rate – and how some companies manage to improve their batting average. Not surprisingly, they advocate bringing marketing and monetizing concerns much further forward in the R&D process.

What follows is an edited version of our conversation.

 

HBR: Is new products’ high rate of failure really a pricing problem, or does it reflect a more fundamental innovation problem?

Georg: We believe that there is a more fundamental problem. Of course, the pricing is always what signals the problem, but behind that it is how the innovation process is set up. However, our experience has been that [when a product fails] it’s not a technology problem or a pure R&D problem — it is really around marketing, customer segments, and of course pricing.

Madhavan: This is very consistent in our experience working with both startups and large companies. They build a product hoping to monetize, but not knowing whether they will be able to. What allows a company to extract full value is having a clear pricing plan from the get-go, not waiting until the end and then saying, “Oops, we need a price!”

Your survey also details how hard some companies are finding it to raise prices. For instance, you found that only a third of all planned price increases actually get implemented, and for every 5% price increase attempted, only about 1.9% is achieved. Why are companies having such a hard time raising prices?

Georg: It’s partly internal, and partly external. Internally, most companies are only thinking about it at one point in the innovation process – usually right before launch. Our survey showed that 80% of companies fall into this trap.

Externally, the reasons vary by industry. Pricing pressure is more intense in retail, less so in areas like top-branded luxury goods or highly differentiated machinery. If you are undifferentiated, then it’s a no-brainer that the pressure on your prices is going go be even higher.

Geographically, we observed that countries like Japan have some of the highest pricing pressures. When there are lots of companies whose goals are to go for high [sales] volume and high market share, that creates a price war.

One of the odder findings in your survey is that 58% of companies say they are currently in a price war – but 89% of those blame their competitors, not themselves, for starting it. Why does pricing feel so out of executives’ hands?

Madhavan: For many years, CEOs and executives have focused on improving the bottom line through cost cutting, finding efficiencies in operations and the supply chain. Companies have gotten better and better at that. Pricing is also a highly impactful driver of revenue, but companies probably spent the least amount of time on it. Often it’s the most misunderstood driver in a corporate boardroom. It’s not something that gets a lot of attention in business school, relatively speaking. However, it is also one of the easiest things to change and companies tend to be more reactionary [than strategic] about it.

Let’s talk about the outliers – the top 10% of companies who, you found, actually could introduce new products and raise prices. What are they doing differently?

Madhavan: The #1 success factor really for us is the C-level involvement. Having senior leaders participate in pricing discussions is a must. They don’t need to be part of every discussion, but CEOs do need to make pricing and new product development their priority.

The second factor is focusing on pricing being considered from the very conception of an idea. Many companies go through an innovation process where there is a lot of focus on R&D and then right before they are about to launch the product, that is when pricing is considered. [Instead,] think about pricing in the R&D stage. What do customers value, what might they value? If you ask someone, “Do you want this feature?” they might say yes—but if you ask them, “Would you pay two dollars for it?” it’s a totally different conversation. And how to charge for the product is far more important than the price itself. For instance, will it be a subscription or a transaction? Will it be bundled with something else?

Third, the companies with the most pricing power use technological tools to measure value and willingness to pay in a systematic way. They let evidence and facts drive innovation processes. Our study found that the top 10% of companies use pricing software and technology 40% more often than the bottom 90%.

What’s an example of a company that really does do a good job of thinking about price at the R&D stage?

Georg: BMW has been very successful in this area. They do all their research and innovation in one building, and all the functions – finance, marketing, engineering – they either come from their offices to that building, or they are already physically located there. Having such a building sends a strong signal that all these different functions are committed to the innovation process.

Companies that are not as strong, they start with a business case that details the four pillars of their new product — value, cost, price, and volume – but then the development team works in isolation. They start adding features, perhaps, because the competition has these features. That leads to higher costs, which then affects the price, which then affects the volume projections. By the end of the process, the four elements don’t fit together any more.

The important thing is to have those synchronized throughout the whole process. The most effective companies ask their teams to sign off on those four elements throughout the process, at different milestones, to make sure they are still synchronized.

To Succeed in Germany, Uber Needs to Grow Up

How should Uber go about conquering the world? Currently, it seems to operate on the “invade first, ask questions later” model.

This became a problem for the company last week, when the taxi-hailing company found its app blocked by a German court due to a violation of the country’s Passenger Transportation Act.  While the decision is only temporary until a full hearing takes place, the case highlights Uber’s mounting legal difficulties in Europe.

Uber has vowed to disregard the court’s ruling, but the company’s own reasoning is full of holes. Uber has to contend with far more than just a foreign legal system. The German cab market already exhibits many of the consumer benefits for which Uber deems itself a unique solution – and many deficiencies of the U.S. market simply don’t exist in Germany (and most of the markets in Europe in which it has entered).

One thing is for sure after the recent developments in Germany: Uber’s current stance – “My way or the highway” won’t fly.  Here are several assumptions Uber must reconsider if it hopes to succeed in Europe:

German riders need our product. In the United States, cab systems in major cities feature outdated clunkers as cars, and cabs literally disappear when it starts raining. Here, Uber can be put to good use. As a resident of Washington, D.C., where cabbies still resent the introduction of metered fares, I know of the major shortcomings of standard cab service, which basically amount to a Soviet-style approach in terms of product diversity – and service reliability. Uber has helped me out of a pinch many a time when I had to make sure that dinner guests could get a ride back to their hotel and there were no cabs to be found. In the U.S., Uber’s introduction of higher quality, up-market cars and a vastly improved notion of service is a definite benefit to American consumers.

Contrast that with the basic situation in Germany. Taxi service works like clockwork. When you need a cab, you call one citywide number, and you can reliably expect a cab in front of your door within 5-10 minutes. Pretty much the same timeline as Uber. There also are basically no clunkers on the road – most operators buy Mercedes cars for their cabs, mainly for reasons of better durability. Riding in style is hardly what the Germans are lacking. From a consumer perspective, that implies a less immediate need for Uber, although it will find its place in the market.

National laws don’t apply to us. The most breathtaking element of the Uber standard operating formula is to argue, as the company’s top executives regularly do, that no laws apply to the company. Why? Because – get this – the sharing economy wasn’t invented yet when the relevant laws and regulations for taxicabs were written. Ayn Rand must feel like resurrecting herself in excitement.

Uber must follow nationally established laws and regulations. Saying it’s an app and therefore it’s different begs disbelief. Most nations have established rules to introduce a taxi service. That, by the way, is exactly what Uber offers, no matter how much the company tries to spin itself away from that and toward the fact that it’s an innovative new app. (German taxis offer app-based service too these days, in addition to order by phone). Uber can file applications, and once it meets the standards and tests others have to meet, it can start operating.

When companies argue that they are preternaturally above the law in other countries, it demonstrates exactly the type of hyper-arrogance that much of the world by now has come to expect from U.S. businesses. It ultimately neither helps Uber’s, nor America’s, principal causes.

We’re good for the entrepreneurial ecosystem. According to the breathless apostles of the sharing economy, it will do wonders to promote micro entrepreneurship. The basic hoax behind this claim, as far as the field of car sharing is concerned, has been exposed in plenty of news stories. Fleet drivers are still essentially at-will employees and can be easily terminated. Never mind that operating a taxi system, to Uber’s likely dismay, is still only a very early example of the sharing economy.

The taxi business in Germany is plenty entrepreneurial. Many operators are family-owned businesses – and hence represent a true blue case of entrepreneurship. Uber will thus detract from, not really add to, that equation.

Mind you, none of the arguments presented above are a case against Uber. It will find its place in the market, whether in the U.S., Germany, or elsewhere. But it needs to observe global differences, contradictions, and obligations.

The free world definitely needs constant innovation to find a suitable way to a prosperous future. But we also need a better balance between the need to innovate and the need to have everybody play by the same rules.

A Man’s College Degree Does Have Value: to His Wife

Although a man’s educational level has no impact on his own happiness, a woman married to a man with at least a college degree is about 5% more likely to be very happy with her marriage, according to an analysis of the General Social Survey, funded by the U.S. National Science Foundation. “There seems to be an inherent quality of a man having a college degree that makes a woman happier in marriage,” write economists Bruce T. Elmslie of the University of New Hampshire and Edinaldo Tebaldi of Bryant University. Men, by contrast, seem to have little interest in the educational level of their wives.

The Creative Benefits of Boredom

In a past life, I used to be required to participate in quarterly sales meetings. These meetings followed a typical format: fly everyone in the company to an amazing destination, then lock them inside a hotel ballroom for 10 hours a day and force them to listen to speeches from sales leadership, as well as marketing, research, and legal departments (usually with a motivational speaker to close it all out). Try as they might, these meetings were boring. The real shame was that they were intended to rally troops and get the sales organization excited about new initiatives, as well as inspire them to think up new and better ways to increase sales in the field. The only saving grace: the late-night dinners. After 10 hours of being talked at, my colleagues and I would escape the hotel, find a local restaurant and talk to each other. Despite our best efforts, these dinner conversations were always about work – and good thing too. These chats were filled with new ideas for dealing with problem clients or increasing sales of new products. Late-night dinners became the source of the new and exciting our meetings were supposed to elicit.

Boredom at work (and meetings) is something nearly all of us feel at times, but admitting that boredom to coworkers or managers is likely something few of us have ever done. It turns out, however, that a certain level of boredom might actually enhance the creative quality of our work. That’s the implications of two recently published papers focused on the link between feeling bored and getting creative.

In the first paper, researchers Sandi Mann and Rebekah Cadman, both at the University of Central Lancashire, explained the creativity-boosting power of boredom in two rounds of studies. In both rounds, participants were either assigned the boring task of copying numbers from a phone book or assigned to a control group, which skipped the phone book assignment. All participants were then asked to generate as many uses as they could for a pair of plastic cups. This is a common test of divergent thinking—a vital element for creative output that concerns ones ability to generate lots of ideas. Mann and Cadman found that the participants who had intentionally led to boredom through the phone book task had generated significantly more uses for the pair of plastic cups.

Next, Mann and Cadman wanted to see what would happen when they really bored people out of their minds. So in a second round of their study, they created three groups—one control group, one phone-number-copying group, and a third group given the even duller task of simply reading the phone book. All three groups completed another task requiring creativity. In this case, the most bored group – the completely passive group of phone-book-readers – scored as the most creative, even out-scoring those assigned to the same phone book copying task from the first study. The findings suggest that boredom felt during passive activities, liking reading reports or attending tedious meetings, heightens the “daydreaming effect” on creativity—the more passive the boredom, the more likely the daydreaming and the more creative you could be afterward.

In another paper, this one from Karen Gasper and Brianna Middlewood at Penn State University, founding a similar effect using a different mundane task and a different type of creativity test. In their study, Gasper and Middlewood assigned participants to watch a video clip designed to “prime” participants by eliciting feelings of relaxation, elatement, distress, or boredom—depending on which video was watched. (Participants were told, however, that the clip was random.) They then had their subjects take what’s known as a remote associates test, where participants are given three seemingly unrelated words (for example: string, cottage, and goat) and asked to think of a fourth word that links the three (in this example: cheese). Remote associates tests are commonly used to measure convergent thinking, a different but complimentary element of the creative process that concerns ones ability to figure out the single, correct idea for a situation.

Just as in the Mann and Cadman study, participants in the bored category of this study outperformed the participants in the other three categories. Gasper and Middlewood suggest that boredom boosts creativity because of how people prefer to alleviate it. Boredom, they suggest, motivates people to approach new and rewarding activities. In other words, an idle mind will seek a toy. (Anyone who has taken a long car ride with a young child has surely experienced some version of this phenomenon.)

Taken together, these studies suggest that the boredom so commonly felt at work could actually be leveraged to help us get our work done better…or at least get work that requires creativity done better. When we need to dream up new projects or programs (divergent thinking), perhaps we should start by spending some focused time on humdrum activities such as answering emails, making copies, or entering data. Afterward, as in the Mann and Cadman study, we may be better able to think up more (and more creative) possibilities to explore. Likewise, if we need to closely examine a problem and produce a concise, effective solution (convergent thinking), perhaps we should schedule that task after a particularly lifeless staff meeting. By engaging in uninteresting activities before problem-solving ones, we may be able to elicit the type of thinking we need to find creative solutions.