Marina Gorbis's Blog, page 1493

Big Data’s Biggest Challenge? Convincing People NOT to Trust Their Judgment

Here’s a simple rule for the second machine age we’re in now: as the amount of data goes up, the importance of human judgment should go down.

The previous statement reads like heresy, doesn’t it? Management education today is largely about educating for judgment—developing future leaders’ pattern-matching abilities, usually via exposure to a lot of case studies and other examples, so that they’ll be able to confidently navigate the business landscape. And whether or not we’re in b-school, we’re told to trust our guts and instincts, and that (especially after we gain experience) we can make accurate assessments in a blink.

This is the most harmful misconception in the business world today (maybe in the world full stop). As I’ve written here before, human intuition is real, but it’s also really faulty. Human parole boards do much worse than simple formulas at determining which prisoners should be let back on the streets. Highly trained pathologists don’t do as good a job as image analysis software at diagnosing breast cancer. Purchasing professionals do worse than a straightforward algorithm predicting which suppliers will perform well. America’s top legal scholars were outperformed by a data-driven decision rule at predicting a year’s worth of Supreme Court case votes.

I could go on and on, but I’ll leave the final word here to psychologist Paul Meehl, who started the research on human “experts” versus algorithms almost 60 years ago. At the end of his career, he summarized, “There is no controversy in social science which shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one. When you are pushing over 100 investigations, predicting everything from the outcome of football games to the diagnosis of liver disease, and when you can hardly come up with a half dozen studies showing even a weak tendency in favor of the clinician, it is time to draw a practical conclusion.”

The practical conclusion is that we should turn many of our decisions, predictions, diagnoses, and judgments—both the trivial and the consequential—over to the algorithms. There’s just no controversy any more about whether doing so will give us better results.

When presented with this evidence, a contemporary expert’s typical response is something like “I know how important data and analysis are. That’s why I take them into account when I’m making my decisions.” This sounds right, but it’s actually just about 180 degrees wrong. Here again, the research is clear: When experts apply their judgment to the output of a data-driven algorithm or mathematical model (in other words, when they second-guess it), they generally do worse than the algorithm alone would. As sociologist Chris Snijders puts it, “What you usually see is [that] the judgment of the aided experts is somewhere in between the model and the unaided expert. So the experts get better if you give them the model. But still the model by itself performs better.”

Things get a lot better when we flip this sequence around and have the expert provide input to the model, instead of vice versa. When experts’ subjective opinions are quantified and added to an algorithm, its quality usually goes up. So pathologists’ estimates of how advanced a cancer is could be included as an input to the image-analysis software, the forecasts of legal scholars about how the Supremes will vote on an upcoming case will improve the model’s predictive ability, and so on.  As Ian Ayres puts it in his great book Supercrunchers, “Instead of having the statistics as a servant to expert choice, the expert becomes a servant of the statistical machine.”

Of course, this is not going to be an easy switch to make in most organizations. Most of the people making decisions today believe they’re pretty good at it, certainly better than a soulless and stripped-down algorithm, and they also believe that taking away much of their decision-making authority will reduce their power and their value. The first of these two perceptions is clearly wrong; the second one a lot less so.

So how, if at all, will this great inversion of experts and algorithms come about? How will our organizations, economies, and societies get better results by being more truly data-driven? It’s going to take transparency, time, and consequences: transparency to make clear how much worse “expert” judgment is, time to let this news diffuse and sink in, and consequences so that we care enough about bad decisions to go through the wrenching change needed to make better ones.

We’ve had all three of these in the case of parole boards. As Ayres puts it, “In the last twenty-five years, eighteen states have replaced their parole systems with sentencing guidelines. And those states that retain parole have shifted their systems to rely increasingly on [algorithmic] risk assessments of recidivism.”

The consequences of bad parole decisions are hugely consequential to voters, so parole boards where human judgment rules are thankfully on their way out. In the business world it will be competition, especially from truly data-driven rivals, that brings the consequences to inferior decision-makers. I don’t know how quickly it’ll happen, but I’m very confident that data-dominated firms are going to take market share, customers, and profits away from those who are still relying too heavily on their human experts.

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Who’s Hiring (and Who Isn’t) in Five Charts

Five years after the start of the worst six months for the U.S. labor market since the Great Depression, we learned Friday that 203,000 new jobs were created in November and the unemployment rate dropped to 7%. Discussion in the immediate aftermath of the news centered on whether the report marked more of the ho-hum same or a sign that, after three years of puttering along, the economy might finally be preparing for a return to something approaching prosperity.

We won’t know who’s right about that for months, maybe even years. So let’s look back instead. First, briefly, back to November 2008. U.S. employers had been shedding jobs for a few months already, but in November it turned into a mass defenestration: 775,000 jobs lost. And it went on like that for five more months. March 2009 was the worst, at 830,000 jobs lost. (These numbers, as with all those that I’ll cite here, are adjusted to iron out seasonal factors such as the customary rise in retail employment before Christmas and bust soon after.) The total for the six-month period: 4.5 million jobs lost. For the entire two-year-long contraction: 8.6 million. And we still haven’t gotten those jobs back. At 136.8 million jobs in November, civilian nonfarm employment in the U.S. is almost 1.3 million below its January 2008 all-time peak.

That’s not true of every industry, though. A few kept setting new employment highs even during the recession; others have kept declining even during the recovery. Cyclical fluctuations have a big impact on employment, and the worst recession in 75 years has an especially big impact. But over time it still pales next to secular shifts in the economy. And secular shifts make for cool charts! To start, here’s how things have gone for the main broad job categories (and three smaller ones of interest: finance, construction, and information) since 1980:

The most remarkable line in the chart is education and health services, which just keeps rising and rising, paying no mind whatsoever to the rest of the economy. This is mostly about health care, which accounts for 70% of the jobs in the category and has been adding them much faster than the educational sector. It’s also an understatement, as employment at public schools and government-owned hospitals shows up in the government category.

The government employment line is interesting, too. The number of government jobs peaked in April 2009 at almost 22.7 million, and while it seems to have stopped declining in the past few months, it isn’t really rising, either. This has been mostly a phenomenon of local governments, which account for 64% of government employment in the U.S. and were hit hardest by the decline in tax revenue in the aftermath of the housing crash and recession. Federal government employment, if you don’t count the once-in-a-decade binges of Census hiring (the funny little spikes in the government line), actually peaked back in the late 1980s. To repeat, the U.S. government has fewer employees now than it did in 1989. This doesn’t count non-civilians such as soldiers, CIA agents, and NSA snoops, but there’s no way that even big increases in employment at the latter two would make up for the 500,000+ decline in active-duty military personnel since 1990.

Beyond that, the chart mainly shows the already flogged-to-death contrast between rising service industries and declining manufacturing. Yes, manufacturing employment has been rebounding since bottoming out in 2009, but it’s from an awfully low base. For a more dramatic version of this, here’s the once economy-dominating auto industry plotted against an especially fast-rising category: home health care services:

The average hourly wage in the home-health-care sector, in case you were wondering, is $18.90. Among motor vehicle and parts workers it’s $24.06. That’s actually not as big a gap as I expected, but it does fit the oft-decried model of better-paid fields losing ground to worse-paid ones. Still, not every tale of changing fortunes in the workplace has such a discouraging ending. The average hourly wage in management and technical consulting is $37.44; in legal services it’s $37.07. And look who’s winning that jobs race:

As for the headline, yeah, yeah: most of the people in “management and technical consulting” jobs probably don’t have MBAs. But the chart does pretty dramatically illustrate the tough times the legal profession has been going through for the past decade. And while Clay Christensen, Dina Wang, and Derek van Bever say the consultants are next, it’s not showing up so far in the jobs numbers.

One sector that was booming up until the crisis and then tanked spectacularly was of course the financial-real-estate complex. One thing that’s a little surprising to see, in the first chart of this post, is that finance isn’t one of the really big job categories. True, there’s a bunch of real-estate-related jobs outside the category (construction, home-improvement retailers), but for all its impact finance itself just isn’t that big an employer. As for what’s been happening within the sector, credit intermediation (banks and other lenders) took a really big hit and seems to have started shedding jobs again, real estate has held up a bit better, and “securities, commodity contracts, investments” (Wall Street, more or less) has held up best of all. The dot-com bust appears to have hit it harder than a global financial crisis did:

The average hourly wage in that last sector is $48.82, by the way. Which is a lot higher than the $27.49 hourly wage at newspaper, book, and directory publishers (which includes magazines). We all know it hasn’t been going well for old media, as my final chart clearly shows. What’s interesting, though, is that information industries in general haven’t really been contributing to job growth. The growth in software and Internet publishing is nice, but they just don’t account for all that much employment, yet:

Talent and the New World of Hiring

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Get More Value Out of Social Media Brand-Chatter

It’s becoming commonplace for consumer companies to listen to what their customers are saying on social media, but the big question is: What do they do with the results? In a lot of cases, managers merely circulate them within the marketing department—after marking them with a prominent “FWIW.”

That’s because they don’t know what this information could be worth.

Companies don’t realize that with proper care and handling, insights harvested from social listening can become as robust a source of strategic inspiration as any must-have diagnostics on the dashboard.

Social listening is inexpensive too, in the sense that it has a high insight-to-dollar ratio. That’s because you don’t have to survey or interview anyone—unsolicited comments from engaged customers are already out there, waiting to be analyzed. And social-media data are continuous—you don’t have to depend on quarterly or annual consumer surveys that are out of date before they’re even analyzed.

To tap social listening’s potential as a source of strategic inspiration, think like a market researcher and follow these sensible steps:

Make sure the quality of your social-listening data is good. Like all data, the information you glean from social media should be subject to market-research protocols for reliability and validity. Ask the same kinds of tough questions you’d ask about any research project. Are the data drawn from the entire social-media landscape? Is the sampling of comments statistically sound? Is the system of data classification, in terms of topics, themes, and sentiments, accurate? Does your automated coding allow for idiomatic meanings, as in “This brand is the s—t”? The insights you get from social media are only as good as the data set you create.

Don’t make your social-media data stand alone. Information from social listening must be correlated with other streams of data that the company is using. For example, in an analysis we performed for a transport company, we found that complaints shared on daily Twitter feeds tracked 90% with the content of customer-service comments registered by phone or mail. Linkages like this go a long way toward speeding the adoption of social-media data as a valid strategic-insight source.

Sometimes the correlations are low between what you think you know and what social listening reveals. But that doesn’t mean you should jettison the listening data; it just means you need to consider both sets of findings simultaneously to decipher the true story.

In collaboration with a beer manufacturer, we conducted a brand-positioning analysis of three leading brands. Conventional wisdom at the company dictated that differentiation based on taste was not an option. Studies had demonstrated, time and again, that consumers could not pick out their favorite beer in blind taste tests. This finding had underpinned positioning and communication decisions for years.

But online comments revealed a different picture: Consumers went deeply into stories about the taste and sensory experiences of not just the beers they loved, but also of those “watered-down, hangover-inducing” beers they disdained. It turned out consumers thought they could pick their favorite beers out of a lineup. And perception, not reality, was what mattered in this space. The social-listening data allowed a marriage between the quantitative and the qualitative. Customer stories illustrated the insights, and the raw numbers (thousands of online statements) validated the insight, allowing the company’s conventional wisdom—and the branding programs guided by it—to slowly change.

Think about “impact” and not just ROI. Marketing managers tend to take too narrow a view of social listening, seeing it merely as a way to measure the return on investment of specific marketing campaigns. For example, an electric-toothbrush maker that had launched a campaign to woo “non-electric” brushers was dismayed to learn that the resulting burst of social-media activity came mostly from existing users. It branded the campaign a flop and moved on.

In so doing, the company overlooked the value of what it had found on social-media sites. Users were sharing positive stories, advocating electric brushing, and in some cases expressing their love of the company’s brand. The company was getting a rare unfiltered look at how consumers were living the impact of the company’s strategies and brands.

Be sure your social-listening analyses make their way out of the marketing-research department and into the wider organization, including leadership circles. Don’t let the information stay bottled up in the departments that collected and “own” the data. That means establishing a common analytical currency and language throughout the company so that managers can take action and be held accountable. One company we worked with created a Center for Digital Excellence to coordinate data on a vast brand portfolio. The company tied the digital indicators to bonus compensations, signaling C-level commitment to the program. It’s that kind of high-level integration that enables companies to focus efforts and resources effectively, creating value for the firm.

It’s not simple to turn large volumes of unstructured data into analyzable formats and insights. But it can be done. And it must be done—social listening is too valuable to be relegated to the “for what it’s worth” category.

Happy Workaholics Need Boundaries, Not Balance

Success is typically a function of our passion for work and accomplishment—my clients and students are generally “happy workaholics” who love what they do and wish there were more hours in the day to get things done. (I view myself this way as well.) The concept of life/work balance isn’t that helpful for us, because there’s always more work to do, we’re eager to do it, and we wouldn’t have it any other way. In some cases, particularly in junior roles early in our careers, this tendency can be exploited by a dysfunctional culture or an uncaring manager, and at those times we need to protect ourselves to avoid burnout. But as we advance professionally we’re less subject to those external forces, and we need to protect ourselves primarily from our own internal drive.

Here’s one way to think about protecting yourself. Years ago my colleague Michael Gilbert suggested that we substitute “boundaries” for “balance”: while balance requires an unsteady equilibrium among the various demands on our time and energy, boundaries offer a sustainable means of keeping things in their proper place. Gilbert drew upon his training as a biologist in his definition of healthy boundaries: “Just as functional membranes (letting the right things through and keeping the wrong things out) facilitate the healthy interaction of the cells of our bodies, so do functional personal boundaries facilitate the healthy interaction of the various parts of our lives. Bad boundaries lead to either being overwhelmed or withdrawal. Good boundaries lead to wholeness and synergy.”

What does this look like in practice? What types of boundaries do we need?

Temporal boundaries designate certain times exclusively for family, friends, exercise, and other non-work pursuits. Note that I’m talking not about balance but about boundaries; the amount of undisturbed time we preserve for certain activities will vary and may be quite small, but what matters is that we create and maintain a functional boundary around that time.

Physical boundaries ensure that we get out of our offices and workplaces at regular intervals and create actual distance between us and our work (which includes not only the office itself but also all our professional tools and artifacts–laptops, tablets, phones, papers, everything.) Again, the question is not about balancing the two worlds, but establishing boundaries to create the needed separation.

Cognitive boundaries help us resist the temptation to think about work and focus our attention on the people or activity at hand.  This is by no means an easy task, particularly given that so much in our work environment is designed to capture our attention (email alerts, message reminders, innumerable blinking lights and flashing icons). Recognizing when our attention is being held hostage by work and turning it elsewhere requires persistent, dedicated effort, but it yields substantial rewards, in part because our focused attention is one of our greatest resources. (And one reason I often recommend meditation is an improved ability to control where we direct our attention.)

This subtle shift – eschewing balance and establishing boundaries – isn’t easy work, but it’s worthwhile in trying to protect us from ourselves.

Many of my executive coaching clients and MBA students at Stanford are going through a transition that involves a step up to the next level in some way. They’re on the cusp of a big promotion, or they’ve launched a startup, or their company just hit some major milestone. Very few, if any, of these people would say that they’ve “made it”; they’re still overcoming challenges in pursuit of ambitious goals. And yet their current success has created a meaningful inflection point in their careers; things are going to be different from now on. The nature of this difference varies greatly from one person to another, but I see a set of common themes that I think of as “the problems of success.” You can read my first post here.

The Customer Support Hierarchy of Needs

Almost five years ago, I was sitting in the conference room of one of the world’s largest insurance companies, trying to push the idea of social customer relationship management to their corporate marketing team. I showed them the power of Twitter and Facebook, and painted pictures of how they could get closer than ever to their customers with these then-new touchpoints.

Roughly 4 hours and 45 slides later, the CMO stood up, shook my hand, and told me how he realized going social and “being there” for his customers was important. And then he added that he just didn’t have the bandwidth for it. He explained why his company was just not ready to go social, and why he believed it would be far too risky to allow his customer service onto public forums or leave his brand open for user generated debate.

About six months later, a tweet from an angry customer went viral and brought the insurance giant’s stock prices down by 8%. In a mere two weeks!

Today, being on social media or providing customers an awesome experience at just about every touch point is not really a choice that businesses have anymore. In fact, delivering exceptional customer experiences is steadily becoming the new status quo. You need to be exceptional today just to keep your head above water.

This begs the question: Why did the CMO turn his back on going social, despite understanding the importance of it? Why didn’t they want to cover every base, be exceptional?

In retrospect, the answer was so simple, so literally basic, I could only hold it against myself.

In 1972, Clayton Alderfer, an American psychologist, proposed the “ERG model” as an improvisation on Maslow’s Hierarchy of Needs. Alderfer categorized needs into a simpler continuum. The most basic needs of a person are Existential, which then lead into Relatedness and finally to Growth needs. People do not aspire for a higher level of needs when the lower levels are not met.

If you view a company’s processes and maturity through the lens of Clayton’s ERG model, the insurance CMO’s decision becomes kind of obvious. He simply could not realize the importance of a higher level of support needs, like getting proactive on social media, before the more basic needs of his customer service were met.

If you looked at how different organizations handled their customer support, the maturity of their operations, and the problems they were trying to solve, companies fall into 4 distinct strata — from a state of chaos, all the way up to the point where their support is perfectly aligned with their business goals. And as you move up the chain towards alignment, the number of companies in each strata steadily falls. I call this the Great Pyramid of Support Needs.

After helping over a hundred startups better focus on their customers, I’ve realized that Freshdesk isn’t really fighting for mindshare against sophisticated ticketing and support workflow tools in this market. We’re fighting against email inboxes and sticky notes. But there is a way up the pyramid — if you’re willing to take the right steps.

Ground Zero: From Chaos to Control. Most small businesses and startups often end up using email to support customers. Even larger businesses are tied with archaic issue tracking tools that do little to streamline support requests. So when the customer base grows and requests get more complicated, things quickly spiral into chaos.

Back then, the insurance giant was struggling to just keep up with their daily conversations, with zero visibility into their support processes. They relied on home-grown scripts from the “dot com” era, and every attempt to move away was struck down with cries of legacy data and sunken costs.

When your business is struggling to make sense of customers in this overbearing and noisy environment, the last thing you’re probably worrying about is adding another channel — no matter how critical it is. So, in a way, what the CMO said back then made sense.

Of course, what he should have done at this point is focus on getting his customer conversations under control. Just publishing a go-to email address, ticketing incoming queries and tracking conversations, could have cut down the confusion. And only then could he have evolved his team to the next stage.

Getting up: Moving from Reactive to Proactive. Luckily, a good chunk of businesses are able to streamline customer queries with the right processes, workflows, and tools. But just reacting to customer issues as they come in is hardly sufficient. This customer experience impact report from RightNow, for example, shows that 89% of customers discontinue transactions when they experience unsatisfactory support. Companies need to anticipate issues and start reaching out proactively to customers — even before the problem crops up.

But most customers wouldn’t hesitate a second to go on some social network to lament in public. Rows of call center agents patiently guiding customers through their scripted responses and canned email responses aren’t going to cut it, when your customers are on so many different channels.

And that’s what keeps many support teams at this stage awake at night — they need to figure out how to move from a reactive to a proactive support process.

It is only when businesses choose to make this transition that they start worrying about bringing in more communication channels between them and their customers. In most cases, their customers have already taken to Twitter and Facebook, so they realize it’s time they went there too.

The Run: Scaling Up and Ensuring Your Support Keeps Up. This is the toughest stage to be in. You have different products and a stellar team supporting in different channels and happy customers. But you’re also growing, and keeping your head over the water only gets tougher from here.

The difference between delivering exceptional experiences to a few dozen customers and continuing that level of service across tens of thousands of customers isn’t trivial. And to do that across channels and different products is a colossal feat. The structures and tools that made sense as a small business do not make sense at scale, and ideologies like “personalized service” start taking the back seat.

And as you acquire customers across geographies, your processes need to adapt to changing time zones and languages. Most companies at this stage start scrambling on their knees to get their support in order because most existing communication structures start to fall flat.

One solution is creating a rich, updated knowledge base like Google’s FAQs that can help streamline support issues by helping customers help themselves.

Successful businesses invest in scalable support channels right from the start. Apple, for instance, uses its wide customer base to its advantage through active user-driven communities. Customers help other customers by answering questions, sharing tips and suggesting workarounds.

As Apple has shown, an engaged community can drive the brand forward, even as the support load gets offloaded between customers. Every customer query becomes a useful resource for hundreds more.

Beyond Support: Aligning the Business with Customers. Quality starts at “conformance” and “zero defects”, and settles at “exceeding expectations.” Very few businesses have reached the peak of the pyramid, when their support structures are perfectly aligned with their business goals and customers. But that is not to say the peak is unattainable. Companies like Zappos have built their entire brand around the customer by obsessively keeping their service quality aligned with the business.

Since companies at this stage already have great processes and tools in place, their biggest threat is having their support representatives lose steam. The relationship between employee satisfaction and productivity is old, established and re-established. Apply that to support agents, the conclusion is obvious — if they aren’t happy, they aren’t about to make any customer happy. And customers can be very unforgiving about these things.

At this level, businesses need to invest on giving support agents more ownership at their jobs, and align business level KPIs like customer satisfaction, response time and accuracy to individual performance through game mechanics, and tangible or intangible rewards.

Solving your customers’ problems is every business’ duty and most do it acceptably well. But then again, most do it acceptably well. At this stage, what makes each support team different is going that extra mile. “Good” is hardly enough — businesses must be doing everything they can to get that “Wow.”

The Brief and Fascinating History of What You’re Wearing and Where It Gets Made

Here, Try SomeNixon and Kimchi: How the Garment Industry Came to Bangladesh Planet Money

I admit it: The reference to Nixon and kimchi in the headline got me to read it, but this piece on how Bangladesh came to be a world center for apparel manufacturing held my interest. Back in the 1970s, the newly formed country of Bangladesh needed something —anything — to build an economy on, so Bangladeshi businessmen looked to South Korea, which had climbed out of poverty by manufacturing textiles. As people from both countries collaborated to build a textile industry in Bangladesh, most of the culture clash seemed to focus on food: Kimchi made the Bangladeshis vomit, and the South Koreans found the Bangladeshi food repellent. (Nixon's role in the story has to do with global trade limits, which favored Bangladesh once South Korea had hit an export quota.)

The happy ending — there are now more than 4,000 garment factories in Bangladesh — has a cautionary lining, though: A factory collapse that killed more than 1,000 workers this year shows that rapid growth has left many workplaces crowded and unsafe. 

On a related note, Planet Money, curious about how T-shirts get made, recently pulled together a fascinating investigation by making its very own T-shirt. You'd better believe that Bangladesh is involved. —Andy O'Connell

Justin Bieber's Involved, of CourseThe Rise and Fall of BlackBerry: An Oral HistoryBusinessweek

For years, every CEO had a BlackBerry. Then, with the introduction of the iPhone, they didn't. While we're all familiar with the decline of Research in Motion, this oral history contains first-hand accounts of RIM's glory days of innovation, followed by inglorious missteps. It's worth remembering the revolutionary idea that started the whole thing: that people — executives in particular — would want to check their email away from the office, a notion that seemed preposterous until BlackBerrys were put into the hands of the likes of Michael Dell, one of the first people to order one. For a time, having a BlackBerry was the ultimate status symbol in business, as well as the ultimate addiction: One former global account manager recalls a CTO referring to the blinking red light as "digital heroin." Then came ill-advised decisions — like making a flip phone when market research showed that no one wanted flip phones — and an accretion of layers of management that operated by consensus. Oh, and an offer from then-up-and-coming star Justin Bieber to rep BlackBerry, an offer that was rebuffed by the marketing department.

"They said, 'This kid is a fad. He’s not going to last,'" recalls former senior business development manager Vincent Washington. "I said at the meeting: 'This kid might outlive RIM.' Everyone laughed."

My Patent Portfolio Is Bigger Than Your Patent PortfolioGoogle's Growing Patent Stockpile MIT Technology Review

Google, which is on pace to collect some 1,800 patents this year, appears to be committed to amassing one of the world's largest patent portfolios. The inventions, Antonio Regalado writes in Technology Review, range from automated cars to balloon-based data networks to images of keypads that can be projected onto a user's hand. Some of them have been filed by the company's founders, Sergey Brin and Larry Page.

Google apparently believes it needs a lot of patents so that other companies will hesitate to sue, lest Google sue back, Regalado says. The company seems to be following the philosophy of Apple, which suffered a $100 million loss in an intellectual-property fight over the iPod in the mid-2000s and now believes in patenting everything. In public, however, Google disparages patent claims. A couple of years ago, when the first major patent lawsuits over smart phones were filed, then-CEO Eric Schmidt criticized certain competitors for relying on legal action out of an inability to "respond through innovations." And the company's top counsel, David Drummond, says a typical smart phone could be covered by as many as a quarter of a million patents, but like most patents they are "largely questionable" and for the most part "dubious." —Andy O'Connell

Too Good to Be True? Fist-Bumping While Rome Burns: The Seduction of Silicon Valley Disruption SalonSalon

First there was the GoldieBlox/Beastie Boys dispute over this terribly clever ad that just happened to result in bit of a legal mess. Then the FDA got all "You can't do that" on 23andme, a home DNA testing start-up that seems to have either ignored or moved too slowly on requests from the federal agency. All this, of course, happened during an almost bacchanal time for Silicon Valley and amid a dismal economy for pretty much everyone else. So Salon staff writer Andrew Leonard felt a little badly when he zipped around in a pink-mustached Uber vehicle to attend a food-networking event centered around making one's own bitters. "This is perhaps the one thing that I didn’t expect from the future: my inability to distinguish it from satire," he writes. But digging into the reasons why the above two controversies happened, Leonard points to how the ethos of the Valley — "Don’t ask permission, move fast, break things" — is finally percolating into everyone's lives:  Because we're in a place where (unlike the first dot-com boom) the power of technology is not in its potential but in its reality. 

So should we fight it — as is becoming more and more common — or play along? "There are definitely times when I witness the baroque excesses of the Bay Area in 2013… and it all feels like the blind, unconscious decadence of a great empire just before its final descent into madness and irrevocable decline," Leonard writes. "And then I take a breath and wonder if it is still all just getting started."

Take the Money and Run How IBM Bypasses Bureaucratic Purgatory Fortune

If you work at IBM and you have a great idea for an internal IT innovation, you can crowdfund it — with IBM's cash. At most big companies, would-be innovators have to submit their proposals to review boards and then wait months for funding. But at IBM, employees like Ryan Hutton can pitch ideas on the year-old iFundIT site, which is modeled on Kickstarter and is designed to bring out employees’ creativity. Hutton's idea was to build a cloud-based app that would give any employee real-time data about how his or her own apps were being used within IBM. Hutton’s proposal attracted 40 fans on six continents and soon passed the $25,000 threshold at which the company funds a project. Not bad for a 24-year-old who joined IBM straight out of college. —Andy O'Connell 

BONUS BITSFamily Matters

You're Interviewing, and Pregnant (LinkedIn)
How Parental Leave Rights Differ Around the World (The Guardian)
Nine-to-Fiver or Workaholic? Either Way, It’s Your Parents’ Fault (Quartz)

Cherry-Pick Profitable Customers by Understanding Adverse Selection

Executives have valuable lessons to learn from the botched rollout of the Affordable Care Act (ACA), and not just the fairly obvious point that you want to test your website carefully before you go live. Website functionality problems will pass, but their existence now could contribute to a longer-term and more serious danger to the ACA, a danger that all companies want to avoid.

The difficulties of signing up for insurance on healthcare.gov will deter some people from acquiring insurance. One worry is that people who are sickly, and who know that insurance is very valuable to them, will persevere in signing up while relatively healthy people will be particularly put off by the website hassles. This could tilt the risk pool of insured people towards a higher risk/higher cost group. Known to economists as “adverse selection”, the worst-case scenario is that this could effectively kill the ACA through what is known as a “death spiral.”

Just as bad risks lead to higher prices which lead to worse risks which lead to higher prices and so on in health insurance pools, some businesses have found that servicing high cost customers leads to higher prices, which lead to higher cost customers and so on. These businesses have learned the hard way about product death spirals.

A great example of this was American Airlines’ attempt to lock in its best customers when it introduced the AAirpass in 1981. Price at $250,000, the AAirpass offered unlimited first-class travel on the airline for life. For an additional $150,000, the buyer could bring a companion on any flight she took. To the most frequent of frequent fliers, it turns out that it is not all that hard to run through a few hundred thousand dollars of tickets at first-class prices. So, while the AAirpass purchasers were frequent fliers, they ended up being such frequent fliers that they saved a lot of money relative to paying for each flight. Some AAirpass holders flew so much in one month that buying the tickets would have cost $125,000.

Bob Crandall, American’s CEO for much of the life of the AAirpass, admitted, “We thought originally it would be something that firms would buy for top employees. It soon became apparent that the public was smarter than we were.”

So what did American Airlines do? They raised the AAirpass price. But then only people who used it even more than the original group bought it. The cost of serving the customers went up just as quickly as the price went up because the pool of customers just got more and more expensive. After several price increases topping out at $1 million for an AAirpass, they gave in to the inevitable death spiral and stopped offering the deal.

There’s a more positive side to adverse selection, though, if you can find a way to cherry pick the most profitable customers from competitors that cannot fine-tune their products enough. In fact, this strategy led Capital One’s credit card business to rise from a third-rate player to a market leader.

You may know Capital One as the company with the silly Viking ads. But the story of Capital One, now a huge provider of credit cards and other financial services, began as a lesson in using adverse selection to one’s own advantage. Capital One was founded in 1988, when Richard Fairbank convinced a small regional bank to experiment with its credit card unit. At that time, pretty much all credit cards issued in the United States had the same interest rate on unpaid balances. Annual fees were comparable across cards. Fairbank believed that he could generate higher profits by tailoring the fees and interest rates to the cardholder’s risk of default.

After several unsuccessful experiments, Capital One struck gold (and their competitors were stuck with adverse selection) when it offered the first balance transfer program. Capital One would pay off the person’s credit card debt and charge the new customer little to no interest for the first year (after which interest rates increased to market rate).

One important thing to know about the credit card industry is that the most profitable customers are those who carry balances and do not default. Balance transfers, at least in 1988, appealed to credit card customers who had both of these attributes. A customer would not have a balance to transfer if she did not carry one on her credit card, and wouldn’t bother transferring a balance she did not intend to pay off. As a result, Capital One was able to cherry-pick the most profitable customers from the other cards. Customers who paid their balances in full every month and those who were likely to default did not find Capital One’s balance transfers attractive. But those customers are unprofitable and the other banks were stuck with them — adverse selection at work.

There are probably some entrepreneurial health insurers out there trying to figure out how to cherry pick the good risks who aren’t bothering to sign up for health insurance on healthcare.gov. But the ACA has made that difficult. Rules such as the individual mandate and the fact that insurers cannot base prices on preexisting conditions are meant to specifically deter cherry picking and avoid a death spiral. Most businesses, though, are freer than health insurance companies to learn from the examples set by American Airlines and Capital One. Executives at those businesses can think strategically about how to cherry-pick the most profitable customers and how, at the very least, not to be particularly attractive to the most costly customers.

Four Ways to Scale Digital Capabilities Beyond Your Team

Digital today is part of everyone’s job — and many enterprise organizations are adopting strategic mobile, social, and cloud initiatives to educate and empower employees. But these organizations still face a daunting challenge in distributing digital expertise: how do you develop digital competency more broadly across a large organization?

Here are four ways I’ve seen this problem tackled effectively in my 17 years leading and consulting to digital teams across organizations large and small.

1. Make sure your guidelines align to business objectives

Bringing distributed groups up to speed with digital strategy puts you in the guidelines business. A Center of Excellence model, where an internal unit leads and convenes efforts, can be effective for crafting a digital strategy, driving innovation, and developing guidelines. Examples include: What are the standards for mobile user experience? What platforms are approved or recommended for social media? It’s easy to limit these guidelines to a general digital/mobile/social/video 101 that offers widely applicable, useful advice. But these efforts deliver significantly higher value to the organization when the guidelines are tailored to specific business objectives with tangible examples, like “video of this length performs better for conversion” or “this social content strategy is more effective for sharing to this segment.” General digital literacy programs are important in the enterprise but the bar for a Center of Excellence is higher – this group needs to tie the digital learning to the business benefit.

2. Develop complementary pathways to learning

Consider two paths to learning, with both playing a part in getting digital capability to scale.

The first is on-the-job learning — you have a project that needs resources, and people learn as they go with targeted, just-in-time training to advance the project. Types of new skills might include: shooting low-fi, short-form video; mastering the basics of audio editing; or entering content and metadata into a CMS. Learning this way may well add to the project timeline, but has the benefit of being assimilated “in the field.”

The second is dedicated training. I think of this not only as formal training, whether in-person, conference sessions, or Lynda-style videos, but the kind of focused peer-to-peer training that happens at brown bag lunches or on quiet afternoons. This kind of skill building is ideal when there is an entirely new methodology to be learned, like Agile, or an opportunity to take a skill to at the next level, like honing web analytics through the Google Analytics Academy MOOC.

Think of digital learning as a stairway: The treads are the on-the-job learning, and the risers are the dedicated training that take skills to a next level. The key to remember is that it’s an uneven stairway — on-the-job generally is more immediately practical than the dedicated training but could take longer, and the dedicated training can introduce entirely new skills and systems, but assimilation of skills will vary.

3. Reward digital knowledge sharing, not hoarding

The saying “culture eats strategy for lunch” is never truer than when applied to knowledge sharing. Does your organization actively reward people who are conveners and promoters of digital learning in others? Make your organization more effective by hiring people with the ability to explain the tools, value, and methods of digital strategy to people who otherwise may not use or fully understand them. And find ways to reward those behaviors, like spot bonuses, high profile projects, or formal recognition programs. You can also identify a knowledge sharing goal as a Key Performance Indicator (KPI) of project success, alongside on time and on budget. Ask, “How did people working on this project advance their digital capability?” Finally, remind the entire team that successful enablement is its own reward — the more digital skills are distributed, the more the digital team can focus on higher-value, forward-looking work. Digital leaders must make it clear that the days of the webmaster holding the keys to digital kingdom are long gone, and digital knowledge sharing is a driver of individual and collective success.

4. Clean up your language

It’s easy for many — OK, for me — to become so immersed in and enamored of the technical world that language becomes jargon. Make sure both your presentations and hallway conversations meet a high bar for clarity. Ask third-party listeners with less specialized knowledge to offer frank feedback by email after a talk — my solicited listeners have offered terrific guidance on assumptions or acronyms that can seem off-putting. Expressions like COPE (create once publish everywhere) or native application may be appropriate internal shorthand, but merit explanation. While it’s easy to lapse into jargon, using plain English and being explicit in tying digital’s benefit to the business will help people understand and engage with your content.

Family Businesses Shouldn’t Hunt for Superstar CEOs

It’s a dilemma that faces family businesses all too frequently.

We saw it recently when we worked with a $4 billion global manufacturing business in Hong Kong. The company was managed by the founder, who turned it over to his son when he retired. The two men had created distribution channels, built a supply chain, entered profitable new markets–and, just as importantly, held the family together, ensuring that family members were well taken care of and that family disagreements didn’t harm the business.

Now with the passing of the founder’s son, the third generation–a collection of cousins who are geographically dispersed, prone to disagreements, and lacking the experience necessary to run such a large and complex business–are trying to figure out what to do.

It’s a tough situation, and the third generation decided to look outside the family to find a successor CEO. They wanted someone who could reset the strategy; someone to grow the business again; someone who could broker an agreement between the factions of the family who favor reinvesting for growth versus those who favor high dividends.

They called this idealized new CEO “Mr. Wonderful.”

A worldwide search turned up two leading candidates–both of them superstars. But each person turned down the job. Neither thought the business was ready for a non-family CEO.

These rejections were traumatic, but they have proven to be a tremendous gift. No one could have done everything the third-generation cousins wanted. The candidates most likely to accept such a role would either be incompetent in not understanding the complexity involved (thus dooming the business to failure), or, even worse, they would see an opportunity to assume control and push the family aside.

The situation this family faces is all too common. They approached the problem of succession believing that what worked successfully in the past would work for their generation. It wasn’t until outsiders began rejecting the CEO position that the family realized that even the most wonderful person could not have managed a business that had now grown so complex. Certain systems and structures had to be put in place first. This was work that only the family owners could do.

Businesses don’t need to have billions of dollars in revenue before hitting this point–we see it happen to family companies of various sizes. What should you do if you find yourself in the situation of wanting to bring in an outside CEO? First you’ve got to make some changes yourselves:

Step up as owners. Even if you’re used to your parents running the show, it’s time to realize that you’re the owners now, and have the right–and the responsibility–to develop your identity as owners before an outside executive can come in. Part of the problem is that as members of the next generation, many of you have experienced “learned helplessness.”  When, over years or decades, you’ve been shut out of decision making, you may not know how to call the shots, whether the patriarch or matriarch is still living–or not. So your first step is often the most difficult: assume psychological ownership.

Choose your ownership path. Once you’ve taken on the mantle of being owners, then you’ve all got to get on the same page about where the next CEO should drive the business. Know your agenda. Whether your goal is growth, liquidity, a turnaround, or employing more family members, your desired path dramatically affects what kind of person that you’re looking for, and the background and experience needed. The new guy can’t hit the ground running unless you reach a consensus about where you expect the business to go.

Fight the CEO’s fight. Clean up the messes that would hobble the new CEO. If there are family members all over the business that don’t belong there, get them out now. The worst thing that could happen is that Aunt Mary’s son Johnnie is high up in the business, and he’s incompetent. If you leave it to the new CEO to have to remove Johnnie, the leader will lose Aunt Mary’s 15 percent of the vote on the board. That’s a new CEO’s worst nightmare. You will also need to clean up the compensation system. One way patriarchs and matriarchs dominate family business systems for so long is that they buy people off. If someone has a special compensation deal, get rid of it.

Change the power structure. When you have a dominant patriarch or matriarch, the power structure typically gets overly centralized with the strong man or woman in the center sitting on the shareholders’ council, on the board, and running the business. It is impossible to have a successful non-family member CEO succession until this power dynamic changes. Appropriate checks and balances must be put in place. At the very least, you must clearly define and delineate roles for yourselves, the board, and the executive team. One client family reinvigorated their board in order to fill the gaps and correct the blind spots that they knew their CEO had. This strengthened the new guy and convinced the patriarch that it was safe to move aside.

Editor’s Note: Some names, locations, and other identifying details in this post have been changed to protect client confidentiality.

An Economist Finds That Many NFL Players’ Infractions Go Undetected

Officials in the U.S. National Football League detect just 60% of on-field infractions such as holding, according to a mathematical study by economist Carl Kitchens of the University of Mississippi. In analyzing play-by-play data for every regular-season game in the 2009–2010 and 2010–2011 NFL seasons, Kitchens found that being near an official greatly increases a player’s chances of being caught breaking a rule. In conclusion, he says, “there is plenty of reason for coaches to be screaming up and down the sidelines at officials for missed calls that potentially affect the outcome of games.”