Marina Gorbis's Blog, page 1366

LEGO’s Girl Problem Starts with Management

This summer, LEGO launched a minor revolution. It introduced professional women – scientists, no less – into its latest toy line aimed at girls. The new figurines – called “minifigs” by Lego die-hards – feature a female palaeontologist, an astronomer, and a chemist. They sold out on the first day.

This, after years of mediocre “pink” products that did little to grow Lego’s share of the girls’ toy aisle.

Why did it take until 2014 for the world’s second-largest toy maker to offer girls (and their toy-buying parents) products they might actually want? (After all, even Barbie has been an astronaut since 1965.)

Perhaps it has something to do with the profile of LEGO’s management team, comprised almost entirely of men. The three-person board of the privately-held company is all men, led by CEO Jørgen Vig Knudstorp. The 21-person corporate management team has 20 men and one woman – and she’s in an internally-facing staff role, not connected to the customer base or product development. When your leadership isn’t gender-balanced, it’s tough to have a balanced customer base. The new ‘Research Institute’ range was proposed by geoscientist Ellen Kooijman on one of the company’s crowd-sourcing sites. But it begs the question, is there really no one inside the company who might have come up with the radical idea of having women scientists feature in a 21st century toy company’s line?

The debate has been raging about toys and gender for decades. Should toys be gender neutral, or targeted differently at boys and girls? Lego’s somewhat tumultuous journey here will be familiar to anyone in a company struggling to tap into the female half of the market.

Family-owned LEGO toys used to be staunchly gender neutral – as self-professed Lego geek David Pickett exhaustively demonstrates. The early advertisements featured both boys and girls playing with identical toys. When minifigs were first introduced in the late 70s – the era of androngyny – gender was downplayed, and the 80s were a golden age for the company. But between the late ‘80s and early ‘00s, the company launched a stream of product lines aimed at girls, none particularly successful and most heavily anchored in pink. These weren’t toys that boys and girls could play with – the company was now making one set of toys for boys (which were often more interesting and challenging to build) and one set of pink, simplified products for girls, including a jewelry line and dollhouses. As Pickett points out, many of these pieces weren’t even compatible with the majority of Legos (i.e., the boy Legos) – and interchangeability is the whole value proposition of the Lego system.

Things finally got so bad that in 2004, Lego almost went bust. The first non-family CEO took over and turned the company around. LEGO rebounded through disciplined management – which included cutting back the unpopular line of pink toys, and making its products more macho. Lego’s customers were 91% male by 2012, when the company released girl-targeting Lego Friends after “four years of research.” What are Lego Friends? Essentially a gaggle of girls who live in Heartland, wear a lot of pastels, and hang out in a salon or at a pool. And, like Lego’s unsuccessful line of pink toys from the 90s, these figures are much less functional than the boys’ toys. In a sad and metaphorical twist, the male minifigs can drive cars, run, and hold tools. The female minidolls can’t move their hands. They can only sit, stand, or bend over.

And yet the company multiplied sales to girls threefold. So why argue with success?

For the same reason that any male-dominated company’s ‘pink’ strategies are limited in both scope and impact. You can build a female niche, you can make money off it – but wouldn’t the company make far more money if it doubled its existing market size, rather than incrementally improving a strategy that is not hugely popular with a wide swathe of public opinion, parents, and educators?

Moreover, the girls who want to play with dolls and accessories are probably not Lego’s target market. Doesn’t Barbie have a lock on those girls? Why not create something for the “other” girls entirely ignored by the majority of the tyrannically pink toy aisle? (In business, this is what we call a market opportunity.) Perhaps because the male-dominated LEGO company doesn’t see girls as a massive market containing a multiplicity of profitable niches – they see ‘girls’ as a single niche market in and of themselves. This is what the phone companies like Siemens and Nokia used to do with their range of pink ladies’ phones before Apple blew them out of the water with a gender ‘bilingual’ iPhone that integrated the preferences of both genders to make their market 50/50 gender balanced. That’s where the gold mine lies.

So let the immediate, sold-out market response to the timid introduction of the three new figurines be a message to the gentlemen at the table. Be bold! Innovate! Think outside last century’s box. Invite some of these innovative female Lego-lovers onto your board or into your top team.

Don’t hold your breath, though. Despite its first-day sold-out success, LEGO has decided not to continue the Research Institute line. It was only a “limited edition.” So girls, back to the pool. The guys in this boardroom don’t seem to want to give you any ideas… let alone seats at the table.

LEGO’s corporate mantra is “only the best is good enough.” In 2014, as the most ambitious, educated, and employed generation of girls the world has ever seen heads back to school, I suspect most girls might not agree that the company is making their own grade.

People’s Creative Output Depends on the Initial Stimulus

In a series of experiments, the novelty of people’s creative output was affected by the novelty of the raw materials they were initially exposed to, says Justin M. Berg of The Wharton School. For example, students who were asked to come up with product ideas for a university bookstore tended to produce ideas that were rated higher in novelty (3.82 versus 3.05 on a 7-point novelty scale) if they were first shown a fishing pole rather than a whiteboard. Conversely, participants’ output tended to be more useful and less novel if they initially saw less-novel items, Berg says.

How Cities Are Using Analytics to Improve Public Health

From clean water supplies to the polio vaccine, the most effective public health interventions are typically preventative policies that help stop a crisis before it starts. But predicting the next public health crisis has historically been a challenge, and even interventions like chlorinating water or distributing a vaccine are in many ways reactive. Thanks to predictive analytics, we are piloting new ways to predict public health challenges, so we can intervene and stop them before they ever begin.

We can use predictive analytics to leverage seemingly unrelated data to predict who is most susceptible to birth complications or chronic diseases or where and when a virulent outbreak is most likely to occur. With this information, public health officials should be able to respond before the issue manifests itself – providing the right prenatal treatments to mitigate birth complications, identifying those most likely to be exposed to lead or finding food establishments most at risk for violations. With this information, data becomes actionable. Predictive analytics has the potential to transform both how government operates and how resources are allocated, thereby improving the public’s health.

While the greatest benefits have yet to be realized, at the Chicago Department of Public Health (CDPH), we are already leveraging data and history to make smarter, more targeted decisions. Today, we are piloting predictive analytic models within our food protection, tobacco control policy, and lead inspection programs.

Recently, CDPH and the Department of Innovation and Technology engaged with local partners to identify various data related to food establishments and their locations – building code violations, sourcing of food, registered complaints, lighting in the alley behind the food establishment, near-by construction, social media reports, sanitation code violations, neighborhood population density, complaint histories of other establishments with the same owner and more.

The model produced a risk score for every food establishment, with higher risk scores associated with a greater likelihood of identifying critical violations. Based on the results of our pilot and additional stakeholder input, we are evaluating the model and continue to make adjustments as needed. Once it is proven successful, we plan to utilize the model to help prioritize our inspections, and by doing so, help improve food safety.

To be clear, this new system is not replacing our current program. We continue to inspect every food establishment following our current schedule, ensuring the entire food supply remains safe and healthy for our residents and tourists. But predictive analytics is allowing us to better concentrate our efforts on those establishments more likely to have challenges. In time, this system will help us work more closely with restaurateurs so they can improve their business and decrease complaints. In short, businesses and their customers will both be happier and healthier.

Building on the work of the food protection predictive model, we developed another key partnership with the Eric & Wendy Schmidt Data Science for Social Good Fellowship at University of Chicago (DSSG) to develop a model to improve our lead inspection program.

Exposure to lead can seriously affect a child’s health, causing brain and neurological injury, slowed growth and development, and hearing and speech difficulties. The consequence of these health effects can be seen in educational attainment where learning and behavior problems are often the cause of lower IQ, attention deficit and school underperformance. Furthermore, we’ve seen a decrease in federal funding over the past several years for our inspectors to go out and identify homes with lead based paint and clearing them. But thanks to data science, we are now engaging on a project where we can apply predictive analytics to identify which homes are most likely to have the greatest risk of causing lead poisoning in children – based on home inspection records, assessor value, past history of blood lead level testing, census data and more.

Predictive models may help determine the allocation of resources and prioritize home inspections in high lead poisoning risk areas (an active approach), instead of waiting for reports of children’s elevated blood lead levels to trigger an inspection (the current passive approach). An active predictive approach shortens the amount of time and money spent in mitigation by concentrating efforts on those homes that have the greatest risk of causing lead poisoning in children.

Incorporating predictive models into the electronic medical record interface will serve to alert health care providers of lead poisoning risk levels to their pediatric and pregnant patient populations so that preventive approaches and reminders for ordering blood lead level lab tests or contacting patients lost to follow-up visits can be done.

There is a great opportunity in public health to use analytics to promote data-driven policies. We need to use our data better, share it with the public and our partners, and then leverage that data to create better policies, systems and environmental changes.

Public institutions should increasingly employ predictive analytics to help advance their efforts to protect the health of their residents. Furthermore, large, complex data sets should be analyzed using predictive analysis for improved pattern recognition, especially from diverse data sources and types, ultimately leading to significant public health action. For the Chicago Department of Public Health, predictive analytics is not the future, it is already here.

Why the Apple Watch Is a Gift to the Swiss Watch Industry

The launch of the Apple Watch this week has raised questions about its impact on the Swiss watch industry. Contrary to Apple designer Jony Ive’s remarks that the Swiss watch could be in trouble, there are several reasons why the Swiss have nothing to fear from Apple’s success.

First, the Apple Watch makes wearing a watch relevant to a new generation of future watch collectors. I often ask other professors around the world how many of their students wear watches. The answer is always the same: “very few.” For many young adults who have grown up using their cell phone to tell time, the idea of wearing a watch is the equivalent of sending a telegraph or storing data on a floppy disk.

The Apple Watch introduces the concept of wearing a watch to many of Apple’s 18 to 35 target market. If it takes off, it is likely that these buyers will eventually consider purchasing other types of watches for events later in life. Talk to any Swiss watch executive today and they will tell you many of their best clients started out collecting Swatches in the 1980s, but eventually started purchasing more expensive brands such as Rolex, Blancpain, Breguet, or Audemars Piguet later in life. Like the Swatch, it is quite possible that the Apple Watch could spark a new generation of watch aficionados and collectors.

A similar phenomenon has recently occurred in the book industry. When Amazon introduced its Kindle eBook reader in 2007, many analysts predicted it marked the end of traditional bookselling. However, over the last five years independent bookstores have seen a resurgence in sales and in the number of stores, all the while selling traditional printed books. One of many reasons for this revival is that booksellers have benefited from continued demand for children’s books, which remain near the top of the fastest growing segments in the publishing industry. When parents and grandparents buy books to read to children at bedtime, they introduce the printed book to a new generation of potential users. As these children have grown up, data show they have been less likely to abandon the printed book in favor of the Kindle. In fact, most readers are happy to read both.

Second, the Apple Watch is likely to be a complement rather than a competitor to the Swiss watch. The Apple Watch is chock full of technological wonders that would be the envy of Dick Tracy, while Swiss watches are primarily luxury goods and status symbols. Apple is confident it will be able to reinvent its core technology every 6 to 12 months before competitors like Samsung attempt to render it obsolete. Swiss watchmakers, on the other hand, see themselves as craftspeople producing wearable art meant to be passed down from generation to generation.

The Swiss stopped competing for technological watchmaking supremacy in the 1980s when Japanese watch manufacturers like Casio and Seiko began producing far cheaper and more accurate quartz watches compared to their handmade mechanical timepieces. Within a decade of inventing the first quartz watch, the Swiss saw their export volume decrease from 45% to 10% of watches produced globally. By 1983, two-thirds of all watch industry jobs in Switzerland had vanished and over half of all watchmaking companies in Switzerland had gone bankrupt.

Thanks to the efforts of individuals like former Swatch Group chairman Nicolas G. Hayek and LVMH watch president Jean-Claude Biver (who oversees Hublot, Tag Heuer and Zenith), the Swiss watch industry cleverly repositioned its mechanical wonders as luxury goods. Unlike the $350 price tag suggested for a new Apple Watch, most of the Swiss watch industry’s meteoric growth over the last two decades has come from watches priced well over $10,000. The Swiss watch industry no longer competes on the same dimensions that will drive Apple Watch sales.

Third, Apple and Swiss watchmakers have this in common: they are deeply committed to connecting their product with the consumer on a personal level. During Tuesday’s launch event, Apple CEO Tim Cook touted the Apple Watch as the “most personal device we’ve ever created.” The beauty of the Apple Watch is that it can track people’s micro-movements and provide instant data to help wearers make sense of how they engage with the world around them. Similarly, while conducting research on the re-emergence of the Swiss watch industry, I interviewed a prominent Swiss watch CEO who said, “Your watch is part of you. The watch is you. It shows the type of personality you have: Are you elegant?, unique?, rich?, arrogant?, sporty?… all these elements are transmitted through your watch.”

The Swiss watch industry can be confident that a sufficient number of well-to-do and tech-savvy Apple Watch wearers will continue to pine for the highest end handmade timepieces.

The Apple Watch may keep perfect time, but it is not timeless.

3 Reasons to Kill Influencer Marketing

Marketers like to repeat the quote, “I know I waste half of my ad budget, I just don’t know which half.”  No one knows who first said it—it’s been attributed to a number of people—but the fact that it gets repeated so often is testament to how strongly it resonates.

So it shouldn’t be surprising that marketers like the idea of “influentials,” seemingly ordinary people who determine what others think, do and buy.  A recent study of 1300 marketers found that 74% of them planned to invest in influencer marketing over the next 12 months.

However, there’s good reason to believe that it’s all a waste of time and effort.  While the idea of influentials may be intuitively convincing, there is very little, if any, evidence that they actually can improve performance—or even exist at all.  So before you embark on another influencer campaign, consider that these three reasons why it’s a waste of time and money.

1. It’s the wrong metaphor. Malcolm Gladwell is probably the person most responsible for the massive interest in influencer marketing.  It was he who, in his blockbuster book, The Tipping Point, laid out his now famous “Law of the Few,” which he stated as:

The success of any kind of social epidemic is heavily dependent on the involvement of people with a particular and rare set of social gifts.

The idea of influentials makes intuitive sense because we all know people like the ones Gladwell described in his book:  “Connectors” who seem to know everyone, “mavens” who possess deep domain knowledge and “salesmen” who have the gift of gab.  We’ve seen how they’ve influenced us, so it seems plausible that they play a role in spreading ideas.

Yet social epidemics aren’t local phenomena.  They are long viral chains.  Just because someone might be good at getting an idea across, doesn’t mean that others are more likely to share the idea.  And if an idea doesn’t get shared, it doesn’t travel far.

A more accurate metaphor would be a wave at a stadium.  What “special traits” would it take to affect thousands of people throwing their arms up in sequence?  Could a 400 pound man do it?  If Jack Nicholson refused to stand up at a Lakers game, would a wave stop in its tracks?  Not likely.  Collective behavior requires a collective.

2. Science finds little evidence to support influencer marketing. While Gladwell’s book certainly did much to popularize the notion of influentials, the idea is not exactly new.  In fact, it goes back to research done by Katz and Lazarsfeld, two prominent sociologists, in the 1940’s and 50’s.  Yet even in their original study, they found that influence was highly contextual.

Recent research raises even more serious questions about the influentials hypothesis.  In one study of e-mails, it was found that highly connected people weren’t necessary to produce a viral cascade.  In another, based on Twitter, it was found that they aren’t even sufficient.  So called “influentials” are only slightly more likely to produce viral chains.

Duncan Watts, a researcher at Microsoft who co-created one of the most important models of how social networks function, says, “The influentials hypothesis is a theory that can be made to fit the facts once they are known, but it has little predictive power.  It is at best a convenient fiction; at worst a misleading model.  The real world is much more complicated.”

The empirical evidence is clear:  It’s time to debunk the myths about influentials.  Unless someone, somewhere, can produce evidence that these “special” people can further our marketing campaigns more efficiently than other approaches, we shouldn’t waste money chasing them.

 3. Recent events should remind us how precarious influence is.

So far, we’ve seen that the idea of influentials isn’t as intuitively appealing as it first seems.  We’ve also seen that scientific evidence contradicts the viability of influencer marketing.  Yet intuition is always fallible and scientific studies, even if rigorously and carefully undertaken, can be wrong.  Real life doesn’t always align with what happens in controlled experiments.

But there is another reason to doubt the idea of influentials: recent events and common sense.  We’ve seen powerful social epidemics erupt in the Arab Spring, the Euromaidan protests in Ukraine and the 2004 Orange Revolution that preceded it.  Small, loosely connected groups overthrew powerful regimes.

Now, it hardly makes sense that Hosni Mubarak and Viktor Yanukovych, who controlled the media and the major organs of power, lacked influence or access to influential people.  Yet they were powerless to stop the street protests that eventually brought about their downfall.

It is, of course, possible that the protestors were driven by people with “rare social gifts” that trumped the dictators’ more traditional influence, but then why did those gifts fail them in the aftermath?  In Egypt, the Muslim Brotherhood, not the largely liberal protesters, prevailed in the sunsequent elections.  In Ukraine, the Pora movement never evolved into a political force.

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The fundamental problem with influencer marketing is not that some people aren’t more influential than others, but that there is little, if any, evidence that influencer strategies—other than celebrity endorsement—are viable.  Yet all is not lost.  There is a way to consistently increase the likelihood of viral chains.

In 2001, Jonah Peretti had an e-mail exchange with Nike that went viral on the Web.  He was fascinated and, a year later, he met Duncan Watts at a conference.  The two struck up a friendship and then a collaboration.  They did a number of projects together that had promising results, which they published in Harvard Business Review.

Their approach, which they called big seed marketing, does not rely on identifying a small number of special people, but rather on harnessing the power of a large number of ordinary people.  By reaching a mass audience, and encouraging them to share, you increase the likelihood that a viral chain emerges and, even if it doesn’t, you still improve performance.

Peretti went on to co-found Huffington Post, which was sold to AOL for $315 million in 2011.  His second company, Buzzfeed, is now valued at $850 million.  In his extended interview with Felix Salmon, he credits not influentials, but “a constellation of connected things” for making his articles go viral so consistently.

So if you want things to spread, forget about special people with “rare qualities.”  Be interesting, reach as many people as you can and encourage them to share.

 

Your Company’s Energy Data Is an Untapped Resource

Most companies are unprepared for the emerging revolution in predictive energy analytics. In fact, many readers’ eyes will have already glazed over at the preceding sentence, with the natural initial reaction that energy-related data isn’t relevant to their jobs.

But what happens when every single light fixture in all of your company’s facilities becomes a networked mini-computer with an array of sensors? Who at your company will be put in charge of turning buildings operations from a cost center to a revenue center? These examples are not hypothetical capabilities; these are now real options for companies. And yet few corporate managers are asking such questions, much less taking advantage.

Cost Savings

Chances are, energy-related spending has a significant impact on your company’s profitability. There are over five million commercial and industrial facilities in the U.S. alone, according to the US. Energy Information Administration, with a combined annual energy cost of over $200 billion. The U.S. EPA estimates that around 30% of that energy is used inefficiently or unnecessarily. And many companies also face additional energy-related costs from their commercial vehicles, of which there are over 12 million in operation in the U.S. according to IHS, incurring fuel costs in the billions annually.

So there are some big potential savings out there to be gained, but for most companies the responsibility for capturing them is relegated to facilities and fleet managers. Furthermore, many of these managers are focused more on productivity and safety goals than energy savings, nor are they allocated budgets to acquire new energy-saving systems even when paybacks would be compelling. And of course, few such managers have a background in information technology.

But as computing and networking costs have fallen over the past few decades, it has opened up a host of new ways that data and IT could be applied to drive significant cost savings in company-owned buildings and vehicle fleets. Startups like First Fuel and Retroficiency are able to perform “virtual energy audits” by combining energy meter data with other basic data about a building (age, location, etc.) to analyze and identify potential energy savings opportunities. Many Fortune 500 companies have also invested in “energy dashboards” such as those offered by Gridium and EnerNOC, among numerous others, which give them an ongoing look at where energy is being consumed in their buildings, and thus predict ways to reduce usage.

Many companies use telematics (IT for vehicles) to track their fleets for safety and operational purposes, and some startups are now using these capabilities to also help drive fuel savings. XLHybrids, for instance, not only retrofits delivery vehicles with hybrid drivetrains for direct fuel savings, they also provide remote analysis to help predict better driving patterns to further reduce fuel consumption. Transportation giants like FedEx and UPS already use software-based optimization of fleet routes with cost savings in mind.

Operational Improvements

The benefits of tracking energy usage aren’t limited just to energy savings. Because energy usage is an integral part of all corporate facilities and operations, the data can be repurposed for other operational improvements.

Take lighting, for example. Boston-based Digital Lumens offers fixtures for commercial and industrial buildings that take advantage of the inherent controllability of solid-state lighting, by embedding intelligence and sensors and adjusting consumption based upon daylight levels, occupancy, and other inputs to drive energy savings of 90% or more. But along the way to achieving these direct energy cost reductions, many of their customers find additional benefits from having a network of data-gathering mini-computers all over their facilities. For example, manufacturers and warehouse operators who’ve installed Digital Lumens systems have the ability to generate “heat maps” showing which locations in their facilities get the most traffic, which allows the facilities managers to reposition equipment or goods so that less time is wasted by workers moving around unnecessarily. And now retailers are starting to leverage the same information to better position higher-margin product where traffic is highest within their stores.

Another use of energy data is in predictive maintenance. When a critical piece of equipment breaks in a commercial setting, it can have a significant financial impact. If the refrigerator compressor breaks in a restaurant, for instance, it can force a halt to operations of the entire facility. But often, long before such equipment fully stops working, the early signs of a problem can be discerned in its energy usage signal. Startups like Powerhouse Dynamics and Panoramic Power are finding that their small-commercial customers get as much value out of such fault-detection and predictive maintenance as the get out of the overall energy monitoring services their systems are designed to provide.

Don’t have a capital budget for energy savings projects? Well, other companies like SCIenergy and Noesis are now using predictive analytics to help underwrite energy-efficiency loans and even more creative financing which helps companies capture savings from day one, in some cases even guaranteeing system performance.

New Sources of Revenue

What really has the potential to radically change how corporate managers view predictive energy analytics, however, is how it can be used to turn existing “cost centers” into sources of new, high-margin revenue.

Electric utilities must keep the grid balanced at all times, and this challenge is only growing more acute. They can expensively purchase power from other sources at times of high demand, but it’s often better for them to avoid such peaks by reducing consumption when needed. Thus, many such utilities are willing to pay commercial customers to participate in so-called “demand response” or “frequency regulation” programs in which customers periodically reduce their electricity usage so the utility doesn’t have to bring another power plant online.

Imagine a big box retail store in the future: It has solar panels on the roof. A large-scale battery in the basement. Plus an intelligent load-control software system that deploys the battery’s power as needed, and also adjusts the air conditioning, lighting, and other energy-consuming devices in the building in incremental ways so that when such loads are shifted around minute to minute, no one in the building feels any impact on comfort or operations. The combination of these systems would not only reduce the facility’s bill from the local electric utility, it would also enable the building to automatically participate in that utility’s demand response program and generate revenue.

Does this sound like a pipe dream? Seattle-based Powerit Solutions offers such intelligent automation today, and they already control 800 megawatts of load in the marketplace.

Unfortunately, most corporations aren’t making the necessary investments in energy data analytics — they’re not providing budgets or the cross-functional teams to identify the available cost savings, much less the new revenue opportunities. To be done right, integrating such solutions into the enterprise requires not just knowledge about buildings, but also IT and financial leadership. The effective “facilities management” team of the future will have all of these capabilities. Leading companies across all industries will have to start viewing energy data analytics as a core shareholder value activity, prioritizing it accordingly.

(Disclosure: Black Coral Capital, where I am a partner, is an investor in Digital Lumens, Noesis, and Powerit.)

Why Your Brain Hates Performance Reviews

Your Brain Will Thank You Kill Your Performance Ratingsstrategy+business

No one likes performance reviews, and one reason is that we've seemingly locked ourselves into a doctrine of numerical rankings. Your company might not be as extreme as Jack Welch’s GE, which famously relied on forced rankings to cull weaker managers (a system still in use by more than half of Fortune 500 firms as of 2012), but chances are your company has given you a number that puts you in a specific spot on the employee continuum. There must be a better way, and strategy+business turns to neuroscience to figure out what that is.



In general, there are two explanations for why PM doesn't work: One, merely being ranked provokes a "fight or flight” response, which gets in the way of "thoughtful, reflective conversations" (but is great for when you're being chased by wild animals, which probably isn't exactly what your manager is going for). Two, a ranking assumes that people are fixed — either good at something or not — and incapable of change, though we know that's not true. The article's authors say we should start with the opposite assumption: that we all can grow and change. Then we should get rid of the numerical rankings. After all, "only one person typically feels neurologically rewarded by the PM exercise. It’s not the high performer, but the senior executive who oversees the ranking system."

Shoot the MoonMedicine's Manhattan Project: Can The World's Richest Doctor Fix Health Care?Forbes

I have truly had my fill of let’s-use-technology-to-fix-U.S.-health-care stories, but Matthew Herper’s opinionated, cranky piece about a super-wealthy physician with gigantic ambitions to do just that was the one more article on the subject that I did have room for. I admit I have a weakness for stories about larger-than-life characters, and Patrick Soon-Shiong, inventor of a cancer drug, has a compelling bio as a scientist and businessman. I can’t do it justice here, but he earned a phenomenal amount of money making bold moves and annoying a lot of people.



Over the past few years, Soon-Shiong has been putting together a company that he hopes will be positioned to take us into health care’s tech-heavy future, with computers sequencing genomes, helping doctors monitor hundreds of patients at a time, and providing up-to-date information on which treatments are best. Although the potential effectiveness of this patchwork company with its byzantine structure and visionary but exasperating leader is still unclear, Soon-Shiong shouldn’t be underestimated, Herper writes. “It seems very likely, based on a review of his claims, plans, and investments, that he will succeed at something.” —Andy O’Connell

No LimitsThe Trans-Everything CEONew York Magazine

The highest-paid female CEO in America, earning $38 million last year, is Martine Rothblatt, the founder of United Therapeutics, a pharmaceutical company. Previously, Rothblatt founded Sirius Satellite Radio, but back then, Rothblatt lived as a man. She had sex-reassignment surgery in 1994 and, soon after, published a "slim manifesto that insisted on an overhaul of ‘dimorphic’ (her word) gender categories." Rothblatt's gender both doesn't matter at all and matters entirely to everything she does. Her ambitions are inextricably tied to what's closest to her as well as the seemingly impossible. United Therapeutics, which was founded after her daughter was diagnosed with a rare disease, aims to do nothing less than use “blue-sky technology to extend life.” Rothblatt is also into artificial intelligence and has created a robot version of her wife. In the end, New York Magazine's Lisa Miller provides us with a CEO profile unlike any other, but not necessarily one to gawk at. Instead, it's a success story that has no limits when it comes to work-life balance, business savvy, ambition, and, bottom line, what it means to be human.

Not Snow White, but Close The Awful Reign of the Red DeliciousThe Atlantic

Curse you, Red Delicious apples. You are not delicious, as your name would suggest. You're mealy and mushy, and you have terrible-tasting skin. And I'm not the only one who feels this way: It's thought of as the "largest compost-maker in the U.S.," and, in the near future, between 60% and 65% of Washington State's Red Delicious crop will be shipped overseas. So how did the apple become the most frequently produced in the U.S. despite its general terribleness? Sarah Yager tells the fascinating little tale of the fruit, which she describes as uniquely American: the "confident intrusion on inhabited soil, opportunity won in a contest of merit, success achieved through hard work, integrity pulverized in the machinery of capitalism." Essentially, a few astute farmers created the apple, it was heavily marketed as the epitome of what an apple should be, people bought it, and then it was genetically altered to the point where it was stunning to look at but miserable to eat. So now the U.S. is shipping them primarily to Southeast Asia, where "success of the Red Delicious relies on targeting shoppers in places where the fruit is unfamiliar." I would like to apologize on behalf of my country, Southeast Asia.

A Golden AgeZoe Quinn’s Depression QuestThe New Yorker

Zoe Quinn suffers from depression, but fortunately she found her passion: inventing video games. Not long ago, she collaborated on a game that was designed to “get at the nasty heart” of what it’s like to live with depression. This seemed “a powerful use of the medium,” she says. A few users have responded positively, but most of her mail is hate mail. Some users think depression is an inappropriate video-game subject; others contend that the game’s quality is poor. Recently she was “doxed,” meaning that her personal details were made public. Afterward, the abusive e-mails and tweets so intensified that she stopped living at home. Yeah, there’s an ex-boyfriend mixed up in this somewhere, and it’s a little hard to tell from Simon Parkin’s New Yorker account what’s real and what isn’t, but the moral of the story, once again, seems to be that the internet has brought forth upon our world a Golden Age of hate. —Andy O’Connell

BONUS BITSAbout That Apple Event...

How Apple Just Reset Your Privacy (Medium)
Seeing Through the Illusion: Understanding Apple’s Mastery of the Media (9to5Mac)
How Apple Pushes Entire Industries Forward (HBR)

To Close a Deal, Find a Champion

In Greek mythology, Charon is the ferryman who guides souls across the river Styx to the underworld. Those who do not employ his services are forced to wander the shores—lost for a hundred years.

Dealmakers who try to “go it alone” can expect to suffer a similar fate. Closing a major deal with a Fortune 2000 company is seldom straightforward. Large organizations are so complicated and diffuse that frequently even the people working there are unclear about what is required to make something of consequence happen. Without a guide, time and effort are squandered and the deal goes nowhere.

In a previous article, I wrote about the triangle of players involved in getting a major project green-lighted: Champions, Blockers, and Decision Makers. Here, I will show how to identify and win over a suitable champion—the crucial sponsor who can help you navigate the labyrinth of opinion, prestige, and politics between you and the approval of your project.

Although champions are not the ultimate decision makers, and they rarely have substantial power within their organization, they have four things that make them irreplaceable in developing and closing the deal: credibility, connections, company intelligence, and motivation.

These were all true of Charlie, a champion I met in 2004 just as the tech world was beginning to show signs of life after the dot com implosion. At the time, Charlie was an internet security specialist at IBM, and I was running business development .

Zone Labs was an upstart internet security software developer aspiring to disrupt established giants such as Symantec, McAfee, Check Point Software, and Cisco. It had a free consumer product that was downloaded by millions of users as well as a modestly successful premium product that sold online for $49. These assets resulted in roughly $2 million in annual revenue—not bad for a young enterprise. Yet, the real money in the space was being made selling to large IT organizations with significant security concerns. Zone Labs needed a name-brand customer.

Charlie came into our line of sight following a routine product inquiry from IBM. After a few meetings, it was clear that he was a champion who could bring Zone Labs and IBM together. He had credibility—he was a well-respected staffer and a 25-year veteran at IBM. He had connections—he had a deep understanding of how IBM functioned and he knew scores of people in the organization. He also had company intelligence. We learned through Charlie that there was a major undertaking underway at IBM to determine which security-related products they would invest in for the following year. The existing short list was comprised exclusively of large, well-established companies and their marquee products.

Credibility, connections, and company intelligence are the table stakes—they are the attributes that all suitable champions possess. So, how does a dealmaker align with someone in command of these assets and capture their interest? That’s where the fourth element comes in: motivation.

Zone needed a major name to establish its credibility in the marketplace and set it up as a viable alternative to the usual suspects. But what did Charlie need? Figuring out a champion’s motivation can be difficult, but the exercise is compulsory. Why? Because understanding human nature is a primary part of doing the deal. In many cases, it is more closely linked to getting the green light than even financing and business fundamentals.

The champions that I have known, Charlie and numerous others, have been motivated by various (but often overlapping) objectives that can be boiled down to five key words:

Innovation. Some champions are visionary types with deep domain focus. They want to explore, experiment, and break new ground. I call these champions “the dreamers” because they are motivated by progress and exploration.
Advantage. Other champions hope to use the deal to improve their company’s competitive position within an industry. These champions are “the lions,” because they are motivated by a desire to advance their company’s competitive position and aggressively dominate an emerging trend or market.
Advancement. These champions strive to improve their own career prospects. As “the climbers,” they are motivated by opportunities to solidify their position within an organization or gain a lead over a rival individual or business unit.
Respect. Many champions are seasoned professionals who feel underutilized by their organization. I call these individuals “the loyalists” because they are the heart of every company—valued for their know-how but universally under-celebrated. These champions are motivated by status: they want someone to pay attention to them and value their experience and input.
Order. Many, many other champions simply want the numbers to work. Deeply rigorous, “the Vulcans” are motivated by logic, evidence, and proof of concept.

Like most champions, Charlie had a mix of motivations. During his lengthy career at IBM he had been consistently passed over for the big jobs (making him a loyalist). Although he knew his potential for advancement was limited, he had a deep desire to gain prestige. Charlie also cared deeply about his work and was looking to bring in new business that would offer IBM something above and beyond what the bigger security players had in-hand. He wanted to help create a competitive advantage (like a lion).

Understanding his underlying motivations, and knowing that he could help us navigate the mammoth IBM organization, we embraced Charlie as our champion. He got us on the short list (ostensibly to use us as a lever against the more established players). He identified the decision-makers and told us who the deal-blockers would be and why. And he was our inside man—delivering the details about what our competitors were doing. With his help we put together a solid story about why this little company, Zone Labs, was the future and could be trusted with a mission-critical component of a 300,000-person organization.

In this particular case, the decision-makers were a small group of IT professionals headed by Chris Matthews, the then-CIO of IBM. Matthews personally hosted regular visits from the CEOs of Cisco, Symantec, and others who lobbied for IBM’s business. We knew that, if Zone Labs prevailed, its product would need to sit on the computer of every employee next to the products of these competitors and would require additional support effort above and beyond what they were already managing.

In the end, Zone Labs beat out all the usual suspects and got a $1 million+ purchase order, plus a follow-on order for another $1 million for the next-generation of the product which, at the time, hadn’t yet been released. The company also made a strong connection with the service side of IBM, which became a major distributor of its product. Charlie earned lasting prestige within IBM and was credited with finding an unlikely yet extremely high-potential data security solution.

Champions are only one, crucial side of the deal triangle —you also have to align the deal’s blockers and the decision makers. All three must be managed with an understanding that people make decisions based on personal and professional motivations that are often hidden to the rest of the world. But once you get a champion in your corner, you’ve made a major breakthrough. You’re ready to enter the ring.

Get Your Team to Stop Second-Guessing Decisions

Not long ago, the division head of a multinational manufacturing company had a problem. After receiving an aggressive 12% annual growth target, she met with her team, and after a lot of research, meetings, and debates, they built a strategic plan around 17 key initiatives ranging from the overhaul of a production facility in Nebraska to fully integrating a new distributor in Nigeria. Everyone on the team was on board with the plan, and they were optimistic about its outcome. But just a weeks before the start of the fiscal year, they began to doubt their abilities to execute so many initiatives at once and they started second-guessing their overall direction.

What happened? Was it a case of performance anxiety? Was the plan truly flawed? Was the team just being lazy or disloyal?

None of the above. The team lost commitment because they suffered from a very common problem called planner’s remorse.

Just like the buyer’s remorse people feel after buying a car or a house, planner’s remorse is the natural sense of regret we sometimes feel after buying into a plan. It is the same psychological phenomenon that gives fiancés cold feet and causes negotiators to back out of deals during the cooling-off period. Immediately after deciding to pursue a course of action, we often experience a kind of euphoria because switching gears from contemplation to action activates a different part of our brains. But the bliss is fleeting. After a few days, our brains very predictably return to normal and euphoria gives way to remorse.

The big difference between buyer’s remorse and planner’s remorse is that the performance of a car or a house doesn’t change based on how we feel after we buy it. But the performance of a plan does change based on the attitude of the people implementing it. If a management team is wishy-washy about a strategic plan, they can rob themselves of the psychological commitment they’ll need to execute effectively, as well as the enthusiasm they’ll need to get everyone else onboard.

It turns out, however, that our brains are wired in a way that allows us to transform planner’s remorse into planner’s resolve.

A series of experiments (PDF) by Gergana Nenkov and Peter Gollwitzer showed that deliberation has very different effects on our minds depending on whether it happens before a decision or after a decision. On the positive side, all those discussions about the benefits and costs of pursuing different alternatives in the planning stages help us to arrive at more rational decisions. But deliberation can also erode our commitment to the eventual plan we decided on by planting seeds of doubt that blossom into planner’s remorse later on.

Thankfully, there is a way to avoid the negative effects of extensive planning. Nenkov and Gollwitzer found that if you take part in the same debates after creating the initial plan, you can actually increase your commitment. After you’ve decided on a course of action, discussing pros and cons forces our minds to defend the decision. Believe it or not, our defensiveness helps us achieve our goals by fostering grit and perseverance.

This phenomenon explains why I find myself so often facilitating what can only be described as post-planning planning sessions. On the surface they seem totally redundant. After all, the team has already held a strategic planning session, so what’s the point of another session? But what Nenkov and Gollwitzer show us is that the timing of that post-session session is as crucial as the content. When it happens shortly before we need to execute on the plan, it can provide an adrenaline shot to the team’s commitment.

Of course there is always the risk of succumbing to the irrational bias of “escalation of commitment.” What if that defensiveness makes the team too committed to the plan? Focusing the post-planning planning session on the first 90 days instead of the entire year helps minimize that risk because it forces the team to recheck their assumptions in another three months. But escalation of commitment is still a possibility.

On the other hand, would it really be all that bad if your biggest concern this year was too much commitment from your team?

How Business Schools Can Help Reduce Inequality

No institution is more responsible for educating the CEOs of American corporations than Harvard Business School – inculcating in them a set of ideas and principles that have resulted in a pay gap between CEOs and ordinary workers that’s gone from 20-to-1 fifty years ago to almost 300-to-1 today.

A survey, released on September 6, of 1,947 Harvard Business School alumni showed them far more hopeful about the future competitiveness of American firms than about the future of American workers. But, as the authors of the survey conclude, such a divergence is unsustainable. Without a large and growing middle class, Americans won’t have the purchasing power to keep U.S. corporations profitable, and global demand won’t fill the gap. Moreover, the widening gap eventually will lead to political and social instability. As the authors put it, “any leader with a long view understands that business has a profound stake in the prosperity of the average American.”

Unfortunately, the authors neglected to include a discussion about how Harvard Business School should change what it teaches future CEOs with regard to this “profound stake.” Now, I realize that HBS has made some changes over the years in response to earlier crises, but they have not gone far enough with courses that critically examine the goals of the modern corporation and the role that top executives play in achieving them.

A half-century ago, CEOs typically managed companies for the benefit of all their stakeholders – not just shareholders, but also their employees, communities, and the nation as a whole. “The job of management,” proclaimed Frank Abrams, chairman of Standard Oil of New Jersey, in a 1951 address, “is to maintain an equitable and working balance among the claims of the various directly affected interest groups … stockholders, employees, customers, and the public at large. Business managers are gaining professional status partly because they see in their work the basic responsibilities [to the public] that other professional men have long recognized as theirs.” This view was a common view among chief executives of the time.

Fortune magazine urged CEOs to become “industrial statesmen.” And to a large extent, that’s what they became. For thirty years after World War II, as American corporations prospered, so did the American middle class. Wages rose and benefits increased. American companies and American citizens achieved a virtuous cycle of higher profits accompanied by more and better jobs.

But starting in the late 1970s, a new vision of the corporation and the role of CEOs emerged – prodded by corporate “raiders,” hostile takeovers, junk bonds, and leveraged buyouts. Shareholders began to predominate over other stakeholders. And CEOs began to view their primary role as driving up share prices. To do this, they had to cut costs – especially payrolls, which constituted their largest expense. Corporate statesmen were replaced by something more like corporate butchers, with their nearly exclusive focus being to “cut out the fat” and “cut to the bone.”

In consequence, the compensation packages of CEOs and other top executives soared, as did share prices. But ordinary workers lost jobs and wages, and many communities were abandoned. Almost all the gains from growth went to the top.

The results were touted as being “efficient,” because resources were theoretically shifted to “higher and better uses,” to use the dry language of economics. But the human costs of this transformation have been substantial, and the efficiency benefits have not been widely shared. Most workers today are no better off than they were thirty years ago, adjusted for inflation. Most are less economically secure.

So it would seem worthwhile for the faculty and students of Harvard Business School, as well as those at every other major business school in America, to assess this transformation, and ask whether maximizing shareholder value – a convenient goal now that so many CEOs are paid with stock options – continues to be the proper goal for the modern corporation. Can an enterprise be truly successful in a society becoming ever more divided between a few highly successful people at the top and a far larger number who are not thriving?

For years, some of the nation’s most talented young people have flocked to Harvard Business School and other elite graduate schools of business in order to take up positions at the top rungs of American corporations, or on Wall Street, or management consulting. Their educations represent a substantial social investment; and their intellectual and creative capacities, a precious national and global resource.

But given that so few in our society – or even in other advanced nations – have shared in the benefits of what our largest corporations and Wall Street entities have achieved, it must be asked whether the social return on such an investment has been worth it, and whether these graduates are making the most of their capacities in terms of their potential for improving human well-being. These questions also merit careful examination at Harvard and other elite universities. If the answer is not a resounding yes, perhaps we should ask whether these investments and talents should be directed toward “higher and better” uses.