Marina Gorbis's Blog, page 797

The Art of the Elevator Pitch

Stuart McCall/Getty Images

Long before your favorite movie made it to a theater near you, it was presented in a pitch meeting. Hollywood screenwriters typically get three to five minutes to propose an idea, but it takes only around 45 seconds for producers to know if they want to invest. Specifically, producers are listening for a logline: one or two sentences that explain what the movie is about. If there is no logline, more often than not, there is no sale.

A winning pitch starts with a winning logline — a valuable lesson for innovators in any field. The most valuable innovations offer novel solutions to challenging problems. But without the support of investors, even the best ideas might never get off the ground. To influence the people who can turn your idea into a reality, you need to deliver your pitch in an exciting and straightforward way. All this starts with the logline — an art that screenwriters have mastered.

When asked what their movie is about, successful screenwriters have a ready answer that is clear, concise, and engaging. Business leaders are asked a version of this same question throughout their careers:

What is your presentation about?
What does your startup or product do?
What’s your idea?

If you can answer in one compelling sentence, you can hook your audience. According to molecular biologist John Medina of the University of Washington School of Medicine, the human brain craves meaning before details. When a listener doesn’t understand the overarching idea being presented in a pitch, they have a hard time digesting the information. A logline will help you paint the big picture for your audience.

You and Your Team Series
Public Speaking








Assessment: What’s Feeding Your Fear of Public Speaking?


Nancy Duarte and Tomas Chamorro-Premuzic


6 Ways to Look More Confident During a Presentation


Kasia Wezowski



How to Land Your First Paid Speaking Gig


Dorie Clark

In Hollywood cinema, one of the greatest loglines of all time belongs to the iconic thriller that kept kids out of the ocean during the summer of 1975:

A police chief, with a phobia for open water, battles a gigantic shark with an appetite for swimmers and boat captains, in spite of a greedy town council who demands that the beach stay open.

What makes it work? The logline for Jaws identifies the key elements of the story: the hero, his weakness, his conflict, and the hurdles he must overcome — all in one sentence. It depicts the overarching storyline in an interesting, straightforward way, rather than focusing on details that might seem meaningless without the context of the bigger picture.

Business leaders can use loglines in a similar manner to clearly explain a complex idea. If mastered, this can be a powerful and influential tool. But communicating your point in a simple, digestible way is hard. It’s actually easier to add clutter to business presentations than it is to eliminate unnecessary details and condense. Though mastering the logline is challenging, there are steps you can take to do so.

Keep it short. In his book Leading, venture capital investor Michael Moritz tells the story of two Stanford graduate students who walked into his office at Sequoia Capital and delivered the most concise business plan he had ever heard. Sergey Brin and Larry Page told Moritz: “Google organizes the world’s information and makes it universally accessible.” In 10 words, that logline led to Google’s first major round of funding. Moritz said the pitch was clear and had a sense of purpose.

A logline should be easy to say and easy to remember. As an exercise, challenge yourself to keep it under 140 characters, short enough to post on the old version of Twitter (before the platform allowed 280 characters per tweet). At 77 characters, the Google pitch makes the grade.

Identify one thing you want your audience to remember. Steve Jobs was a genius at identifying the one thing he wanted us to remember about a new product. In 2001 it was that the original iPod allowed you to carry “1,000 songs in your pocket.” In 2008 it was that the MacBook Air was “the world’s thinnest notebook.” Apple still uses this strategy today. Executives repeat a one-sentence description when presenting new products. This same logline goes on to appear on the Apple website and in the company’s press releases.

The “one thing” should cater to the needs of your audience. A sales professional for a large tech company recently told me a logline that he uses to address the needs of his audience — IT buyers: “Our product will reduce your company’s cell phone bill by 80%.” With one sentence, his customers want to know more because his logline solves a specific problem and will make them look like heroes to their bosses. Above all, the logline is easy to remember and gives people a story they can take to other decision makers in their organizations.

Make sure your team is on the same page. Every person who speaks on behalf of your company or sells your product should deliver the same logline. For example, I worked with top leaders at SanDisk, the flash memory company, to prepare them for a major financial analyst conference. Seven executives delivered five hours of presentations. I suggested that — before going into nitty-gritty financial details — each person should deliver the same logline at the beginning of their presentations, and then end their presentations by repeating it once more. As a group we decided on the logline: “In the coming decade, flash will be bigger than you think.”

The logline was meant to stir up excitement for all the products flash memory would enable, like iPads, laptops, smartphones, and cloud services. As the conference concluded, the first financial blog post that appeared carried the headline: “Flash will be bigger than you think.” Loglines attract attention; consistent loglines are memorable and repeatable.

If you can’t communicate your pitch in one short sentence, don’t give up. Sometimes the language will come to you immediately, other times it might take more practice. Be patient. Once you master the logline, you will be able to easily clarify your ideas and help the audience retain, remember, and act on them.

How to Gauge the Effectiveness of Employee Wellness Programs

thenakedsnail/Getty Images

Tracking the data on the effectiveness of employee well-being programs can feel like you’re watching a Ping-Pong match. While more than 60% of U.S. businesses offer such programs, research on their effectiveness has been mixed. There are findings that point to positive gains in both cost savings and productivity measures, while other studies, including the recent report from the National Bureau of Economic Research, find that programs can make for good recruitment tools but won’t do much to lower costs or improve health. The result: persistent questions on whether well-being programs deliver meaningful value and, if so, which ones do.

I contend that what lies at the heart of the inconsistent results is not the programs per se but rather how we define and ultimately measure “well-being.” WebMD Health Services has developed a measurement system that addresses this deficiency.

The wellness and well-being industry has traditionally focused on assessing the impact of programs on lowering specific health risks (smoking, stress, and weight, for example) with little acknowledgment of the interplay between those risks and how, taken together, they provide a more relevant definition of well-being and a more accurate reflection of value.

Insight Center



The Future of Health Care
Sponsored by Medtronic

Creating better outcomes at reduced cost.

Ask employees what happens when they succeed at a weight-loss effort. They may feel healthier, but they also report that they sleep better, have more energy, experience more positivity, and find that they can get off blood pressure medication. In short, weight loss may have improved sleep, minimized depression and anxiety, and conferred some clinical benefits as well. The impact went beyond the one specific metric.

But, as an industry, we neither define nor measure well-being in a comprehensive way. There is no substantive, relevant approach to assessing well-being and no models to measure the impact that an improvement in one risk can have on other health risks. For example, a change in a person’s risk for stress may also change the person’s risk for inadequate sleep; a shift in physical activity risk may shift the person’s risk for stress. In short, our definitions and measurement tools have gotten in the way of capturing the true value of well-being programs for employees and their employers, and traditional metrics, such as ROI, don’t always reflect whether the program is relevant to the employee.

Working with our team of statisticians and researchers, WebMD Health Services decided to build another model to evaluate the impact of our telephone health-coaching program, a one-on-one offering that employers often include as a part of their overall well-being program to support employees in making health behavior changes. We created a novel algorithm based on an employee’s modifiable risk factors and preventive screenings that serves as a proxy for health status and enables us to stratify health risks into categories that measure acuity, that is, the level of severity of a condition or a measure of overall health status.

With this holistic model, we stratify a range of health risks into high, moderate, and low acuity, and assess the impact of reducing the risk of an employee developing a chronic condition or worsening one they already have. Two distinct employee populations were evaluated: a group of 82,681 people who had lifestyle strategies to maintain overall health and well-being and minimize the risk of developing a preventable condition, and a group of 28,941 people who were managing at least one of five diagnosed chronic conditions — heart failure, coronary artery disease, chronic obstructive pulmonary disease, diabetes, and asthma.

For the lifestyle group, the algorithm included 12 modifiable health risks such as poor diet, smoking, and overweight/obesity, and 11 preventive screenings such as mammography and colonoscopy. Each risk was given a weight relative to all other risks based on future projected health care costs and the likelihood that it would contribute to future chronic conditions.

For employees with chronic conditions, we looked at medical and pharmacy claims. Acuity scores were calculated using a weighted algorithm consisting of four normalized indices: future predicted costs, potential hospitalizations and emergency department visits, current gaps in care, and the risk of developing related conditions.

Not surprisingly, the higher the acuity levels, the higher the direct medical costs. For employees focused on lifestyle changes, those with higher acuity levels cost employers an estimated $5,598, on average, versus $4,018 for people in the low-acuity group — a difference of $1,580 per person. In the condition management group, the mean health care cost per person was $25,046 in the high-acuity group and $6,302 in the low-acuity group.

Coaching made a difference. For healthy employees receiving coaching for changing their lifestyle behaviors, 23% moved from high acuity levels to moderate levels and 43% from moderate to low. Looking at a subset of 1,000 employees, the change in medical costs after 12 months of lifestyle coaching was estimated at $195 per participant, based on lower acuity ratings.

For people receiving condition management coaching, 39% of employees with high acuity levels moved down to moderate and 12% moved from moderate to low, for an annual cost savings of $1.1 million overall, or $1,113 per participant.

Despite the limitations of traditional measurement models and program evaluations, employers continue to invest in well-being programs, and with good reason. Making well-being a business priority can improve the lives of employees; infuse workplace culture with greater positivity, energy, and commitment; reduce health care costs; and potentially help transform health in the United States and elsewhere. A new measurement model can more accurately measure wellness programs’ impact.

Research: When Boards Broaden Their Definition of Diversity, Women and People of Color Lose Out

FotografiaBasica/Getty Images

Over the last several years, competing notions of “diversity” have emerged. In many corners, the traditional definition, focused on demographic diversity, has been eclipsed by a new concept centered on experiential or cognitive differences. Deloitte, a provider of advisory services to firms around the globe, including 85% of the Fortune 500, encapsulates the trend, noting, “Up to now, diversity initiatives have focused primarily on fairness for legally protected populations. But organizations now have an opportunity to harness a more powerful and nuanced kind of diversity: diversity of thought.” Similarly, Korn Ferry, a global management consulting firm, urges firms to reorient their recruiting efforts to emphasize “diverse perspectives, experiences, and contributions.”

This conceptual shift has had real-world consequences extending to the very apex of firms — the board of directors. We have been studying corporate governance for nearly two decades. Through a combination of interviews with board directors and analysis of statements and documents, our work has uncovered a clear shift in how corporate boards approach diversity. Whereas a mandate of diversity once inspired attention to demographic differences, including gender, race, and ethnicity, it now increasingly prioritizes differences of functional and industry experience.

This shift coincides not only with general trends but also with the investment industry’s embrace of a revamped definition of diversity. More than 50 institutional investors, controlling more than $22 trillion of financial capital, have joined as signatories to a set of governance principles mandating that “boards should be composed of directors having a mix of direct industry expertise and experience and skills relevant to the company’s current and future strategy. In addition, a well-composed board should also embody and encourage diversity, including diversity of thought and background.” Many financial intermediaries, ratings agencies, and even the business press now embrace this as the standard by which to assess board diversity practices. Meanwhile, key regulatory bodies, such as the U.S. Securities and Exchange Commission, accept an even broader interpretation of diversity. While mandating disclosure of boards’ diversity efforts, the SEC recognizes “any differences in the manner in which the nominating committee evaluates nominees for director.”

Our research shows this conceptual remaking has heralded a fundamental change in board treatment of diversity. Attention once oriented toward underrepresented groups (women and racial and ethnic minorities) is increasingly centered on technical attributes, such as experience and skills.

Mandatory proxy disclosures offer plain evidence of this shift. Among the largest U.S. firms last year, less than 45% attended to traditional measures of diversity (for example, gender) in their proxy disclosures. Director and recruiter interviews confirm evidence procured from company disclosures.

One result has been a noticeable slowdown in the rate of appointment of women and other minorities to board seats. Globally, women hold only 15% of all corporate board seats, a mere increase of 2% since 2015. Among large U.S. companies (S&P 1500), women hold just 16% of seats — fewer seats than are held by directors named John, Robert, and William. Moreover, among the premier Fortune 500, women’s share of board seats actually declined by two percentage points in 2016. Overseas, similar trends are noted. In the United Kingdom, for example, women made up 29% of hires to UK boards in 2017, down from 32.1% in 2014 and 31.6% in 2012, according to Egon Zehnder. The Alliance for Board Diversity’s multiyear study of Fortune 500 companies found that Hispanic/Latina women have lost board seats, Asian/Pacific Islanders represent only 3.1%, and African-American males have had an increase of only 1%. More broadly, the number of top firms with even a single racial or ethnic minority director has declined over the last 10 years.

In plain terms, broadening the definition of diversity has allowed boards to claim inroads regarding experience-based diversity at the expense of demographic diversity.

A redesign of the director selection process would allow meaningful success on both scores — achievement of diversity along dimensions of experience and greater minority representation. Its cornerstone involves purposefully seeking out underrepresented groups and focusing on the benefits of identity-based diversity to boardroom dynamics, an initiative which would be complementary to the current experience-based emphasis. To that end, a redesigned process should include anti-bias training for the nominating committee and other board members involved in the selection process; employment of wider recruitment networks to tap a broader range of candidates and director qualities; and, ideally, a blind review process wherein demographic identifiers are removed (name, gender, age, and so on).

Our research shows firms can and have applied many of these new approaches to important effect. Bank of New York Mellon is illustrative. The company has instituted a specific diversity initiative dedicated to minority inclusion. Its director nomination processes incorporate both experiential and identity-based differences, each critical to governing effectiveness. Of the current 12 directors, one is Latino, another African American, and three are women. Ten of the 12 directors are new following the enactment of the policy. Individually, these directors were identified through expansive networks, and their selection is reflective of a nominating committee dedicated to preventing bias.

Ultimately, board selection is what defines the board’s effectiveness, whether in terms of monitoring, strategy, or overall accountability. Moreover, the long-term financial success of firms is enhanced when boards better reflect the demographically diverse diverse nature of customer groups, communities, and other stakeholders.

Better board selection also contributes to diversity outcomes throughout the organization. For example, evidence shows gender equality at the top contributes to equality at lower managerial levels, together with lessened pay gaps. Likewise, board diversity along traditional lines contributes to a lower level of gender and racial/ethnic segregation of nonmanagerial workers.

In sum, while experiential breadth is clearly important, recent definitional broadening of diversity overlooks the many unique advantages afforded by talented women and other underrepresented minorities. Their distinctive perspectives, insights, and networks advance many of the most meaningful organizational outcomes. The broadening of diversity diminishes attention to these critical advantages, and companies’ ability to capitalize on them.

How One CMO Revamped Her Role

Don Farrall/Getty Images

When she first saw the email, she thought she was about to be fired. About 30 minutes prior to her weekly one-on-one with the CEO, the chief marketing officer at a multibillion global financial services firm received a cryptic email from him with the subject line “The Trouble with CMOs.” The email contained a link to a 2017 article one of us wrote for HBR, and suggested the CMO read it before their meeting. Uh-oh. She went into the meeting prepared for the worst. As she sat across from the CEO, he asked her whether she thought she had the right role to maximize marketing’s impact. With trepidation, she answered honestly and indicated that she didn’t.

What came next was a pleasant surprise. The CEO, grinning, agreed with her. He said he didn’t want to be the type of CEO who failed to design the CMO role correctly. The two executives quickly agreed the CMO role needed to be changed significantly, and they went on to discuss what success would look like. At one point, the CEO indicated that he would know that the right shift had occurred when the CFO, chief strategy officer, and other C-level leaders were seeking the CMO’s advice on strategic business problems. “He gave me not only the gift of alignment but also the barometer against which I could measure my own progress,” said the CMO, who asked that neither she nor her company be identified by name. “I needed to earn the right to be invited into key, firm-level strategic decisions.”

Like many of the CMOs described in the 2017 article, she was initially hired to focus on downstream commercialization roles, such as overseeing advertising. She and the CEO now agreed that she should play an upstream, strategic, enterprise-wide role. That sounds great in theory, but actually changing the role would be the hard part. The CMO knew that the transformation wasn’t just about her; it was about the entire department — 230 people, over whom she had limited desire to hire or fire large numbers to shift the mix of skills. This would require an action plan, time, and organizational commitment.

The first thing she did was define success. The shift in role would come from influence rather than authority. The type of work that her department focused on and the way in which the team engaged with other functions would have to evolve.

The CMO began by meeting with her direct reports. She explained the new vision for the marketing function: To drive the company toward more growth with more accountability. She then convened an all-department call, explaining that the CEO was inviting the department to step up and have greater impact.

In follow-up meetings with her direct reports, they identified high-impact work — and killed off less-important tasks to free up resources. Soon afterward, the CMO held an offsite for direct reports where each person talked about their primary priority. The goal was to get feedback and strengthen the thinking to maximize the potential impact on growth.

In the following months, the CMO began to look for opportunities where her team could proactively step “into the gap” — to take a leadership role on an opportunity that none of the other functions picked up. As an example, the CEO had been discussing the importance of driving more agile decision making. While addressing this opportunity wasn’t technically assigned to anybody in the C-suite, the CMO engaged her team and they stepped forward with recommendations on how to leverage internal communications to inform, educate, and lead the organization in agile thinking. As another example, the CMO directly challenged her team to identify disruptions that would deliver on the department’s and firm’s goals — to bring a more demand-centric perspective to key business challenges. What may seem like a small shift was significant. Neither of these examples was technically her team’s responsibility, and they represented a shift in thinking, in ownership (that transcended responsibility), and, ultimately, in impact.

She also began paying more attention to projects that would drive future growth. The firm had outdated segmentation frameworks and loyalty programs, weak customer contact management, and suboptimized client identity management, so she championed and launched a customer relationship management initiative to revamp them. Leaders from across the company are engaged and already benefiting from this effort, and soon the CMO will be able to run final cost savings and increased share-of-wallet calculations.

The CEO’s invitation to the CMO to expand her influence was primarily focused on strategic decisions. As the CEO observed the CMO and her team having greater influence, he began supporting the shift by setting the expectation that the CMO and her team participate in key strategic decisions — and offering opinions that went beyond marketing. In one example, the company pursued a “strategy refresh” project for the firm’s retail business, and the CEO ensured the CMO was deeply involved.

Finally, the CMO began insisting that marketing deliver greater financial accountability. Now, almost every project requires defined financial deliverables. The CMO says: “I now put marketing spend decisions on the same playing field as other spend decisions. We’re working on a model for the ROI of a technology dollar, versus a marketing dollar, versus a sales dollar, so we can make trade-off decisions. The language of finance and accountability has become a bit intoxicating, given the tools that marketers have today that we didn’t have 20+ years ago. In fact, I can’t remember the last time I talked about ‘advertising’ or showed creative to leaders.”

Nearly a year after the initial conversation between the CEO and the CMO, the role of the company’s marketing employees has changed. They have different strategic priorities; they are held accountable for new, shared metrics; they spend more time with cross-functional peers. And they have significantly more impact. For example, there are now regular meetings with the CFO to review firm growth and discuss the effectiveness and efficiency of marketing investment at a granular level.

The CMO indicated that the bigger impact that is occurring required three things to happen. First, the CEO’s vision and support were essential. Without his belief, coaching, and support where needed, the CMO’s actions could have been misinterpreted as “power grabbing.” Second, the shift required the marketing department’s leaders and associates to buy in. This wasn’t about the CMO changing as much as it was about the department changing. Finally, the other C-level functions needed to be supportive, which they were. They see that more impact from marketing benefits everybody.

This CMO’s story highlights that a bigger role doesn’t have to come from assigned responsibility. And while this is a win for the CMO and her team, it is a bigger win for the CEO and the firm. Net revenues are up 48%, client assets are up 39%, and earnings per share are up 80% from a year ago. It’s proof that right-sizing the role and impact of marketing can pay off.

Research: Why Ratings on Everything from Wine to Amazon Products Improve Over Time

perets/Getty Images

Ratings play an enormous role in our lives. Ratings made by critics, judges, and evaluators determine a range of outcomes, from the seemingly trivial (which wine you pick for dinner or which products you buy from Amazon) to the more consequential (which athletes win Olympic gold or which students get into top universities).

But how reliable are these ratings? How well do they hold up over time?

We thought about this when we learned about the speculation over wine rating inflation. When Robert Parker introduced his 100-point rating system for wine decades ago, the highest score he gave that year was 91 points. Now many wines each year receive perfect scores from his publication, the Wine Advocate. Similarly, in 2000 just 15% of wines rated by Wine Spectator received a score above 90. By 2015 the frequency of those scores had more than doubled: Nearly a third of all wines reviewed now receive a score above 90.

We wanted to know what was going on here, and whether people have a bias toward giving higher ratings over time.

Ratings Rise with Experience

In eight studies, recently published in Psychological Science, we captured more than 12,000 sequential evaluations to see whether ratings changed as the rater gained more experience. The evaluations covered much territory: judges’ scores on the TV show Dancing with the Stars, student grades from university professors, and ratings for short stories and photographs by college students. We also analyzed thousands of Amazon product reviews by devoted reviewers.

In one study, we analyzed 5,511 scores from the same panel of judges on Dancing with the Stars. As 20 seasons passed, we found that the more evaluations judges made, the higher ratings they gave. This was true even if we controlled for other factors, such as whether professional partners were actually improving or whether more-skilled dancers appeared in later seasons.

We followed that with a different study that examined student grades over a 10-year period in 991 courses that were taught several times by the same professors. As with the dance competition judges, the more times an instructor taught a course, the higher grades they gave. Again, we wondered if other factors could account for these results, including whether, over time, students were getting better, all course grades were increasing, courses that awarded higher grades were more likely to be offered again, and professors were improving their teaching. Despite controlling for these possibilities, we found the same results: When professors taught the same course many times, they tended to give higher grades.

To rule out alternative explanations, we also tested for this pattern in a controlled experiment in which people evaluated short stories. We asked 168 college undergraduates to rate one short story per day for 10 days. By the end of the study, all participants had rated the same 10 stories, but they each saw them in a different randomized order. Randomizing allowed us to isolate the influence of order (day 1, day 2, and so on) on evaluations. In other words, does making more evaluations, regardless of what people are evaluating, make ratings go up? As before, we found that the more stories a person rated, the higher ratings they gave. Consequently, the 10th story was rated higher, on average, than the first.

Across the board, we found the same result.

More Evaluating Makes Evaluation Feel Easier

Why might ratings rise over time?

We wondered if the process of evaluating might feel easier the more you do it, which may influence how positively you rate something. In a follow-up study, we asked 362 people in an online panel to rate one randomly selected story per day over 10 days. We also asked them such daily questions as, How easy was it to evaluate each story? As the days progressed, participants said that they found it easier and more enjoyable to rate each story. These feelings, in turn, led to improved evaluations for stories over time.

The findings suggest that biased evaluations are the result of a misattribution process: If something feels easier to evaluate, people believe that it must actually be better. In other words, they misattribute their own feelings about evaluation (it feels easier to make an evaluation) onto their assessment of the actual merits (this thing must deserve a higher rating). This was true even though each person’s sequence of stories was randomized.

When we asked if they thought their ratings were getting any higher over time, however, participants disagreed that they were. The outcome suggested that most people are unaware such bias might influence their judgments.

Product Ratings, Promotions, and Performance Feedback: How Trustworthy Are They?

Why do our findings matter for managers and organizations? One practical implication speaks to organizations that seek customer reviews. In a supplementary study, we found that reviewers on Amazon give higher product ratings the more reviews they give. For example, if someone makes an evaluation for the first or second time, she might give a lower star rating — regardless of the product — than if this is her 20th evaluation. If crowdsourced information is a key feature of an organization’s business model and a driver of consumer choice, biases like this would be important for business leaders to consider and for consumers to be aware of.

Our recent findings also raise an exciting, open question for managers: How might this bias in evaluations affect hiring, promotion, and performance reviews? Despite attempts to make accurate and fair assessments, our findings suggest that evaluation processes will benefit candidates interviewed by a recruiter who has been making evaluations for longer time periods. We are studying this next and seeking organizations to partner with.

We are also interested to see whether similar results would play out in promotion decisions and sequential annual 360-degree feedback processes. If true, the impact of these biases could be widespread and affect much of the current and prospective labor force.

Finding ways to mitigate this bias, such as making hiring assessments, performance reviews, and promotions more accurate, is something we are also eager to look at. We noted in our studies that most people seemed unaware that, over time, bias influenced decisions. One possible remedy is simply to make people aware of this potential influence on their decisions. There are other situational variables that we are also trying to better understand.

There are limitations to our studies worth noting. Despite the bias we found in all of the contexts we studied, many other factors contribute to evaluation decisions. Positive bias over time is just one. Second, there is some evidence that, under certain conditions, evaluations may also become more negative over time. The factors and conditions under which evaluations get more positive or more negative, however, is still an unanswered question.

Perhaps the next time you post on Yelp or spend time interviewing candidates, you’ll consider how many evaluations you have already completed and how your current assessment might drift more positively. Doing so could help you assess more accurately.

Conversely, when you depend on others’ numerical evaluations, keep in mind that the rating not only reflects the inherent product quality but may also be higher due to more-experienced raters. Indeed, it may be worthwhile to buy that older, lower-scored bottle of wine.

How Companies Can Tap Into Talent Clusters

Bill Kerr, a professor at Harvard Business School, studies the increasing importance of talent clusters in our age of rapid technological advances. He argues that while talent and industries have always had a tendency to cluster, today’s trend towards San Francisco, Boston, London and a handful of other cities is different. Companies need to react and tap into those talent pools, but moving the company to one isn’t always an option. Kerr talks about the three main ways companies can access talent. He’s the author of the HBR article “Navigating Talent Hot Spots,” as well as the book The Gift of Global Talent: How Migration Shapes Business, Economy & Society.

Download this podcast

How to Use Facebook’s Settings to Have More-Productive Conversations

HBR Staff

The past year has served as a wake-up call for many Facebook users. Between the Cambridge Analytica scandal, Mark Zuckerberg’s congressional testimony and the advent of Europe’s General Data Protection Regulation (GDPR), we have fresh insight into how much Facebook knows about us—knowledge that has inspired many people to re-think what they share on Facebook, how they manage their Facebook settings, or even whether they want to use social media at all.

While Facebook’s algorithm uses our data to show us content and ads that it thinks is more likely to be of interest to us, it can also distort our view of the world by limiting our view to the people and perspectives we find most appealing or otherwise engaging. That algorithm is also the reason that some Facebook threads unfold as civil, respectful (but perhaps insufficiently representative) conversations among like-minded souls, while others turn into all-out brawls that can be both personally distressing and professionally problematic. You are at the mercy of Facebook’s algorithm when it comes to determining which conversations appear in your newsfeed at any given moment.

But it doesn’t have to be that way. By taking control of your Facebook experience with lists and privacy settings, you can override the algorithm with your own explicit preferences, and forge your own balance between breadth and intimacy. Most importantly, if you start thinking more explicitly about what you want from Facebook at a given moment—Am I looking for a representative source of business intelligence? A few restorative moments? A trusted circle with whom I can discuss a complex issue?—you can make Facebook a more useful and less manipulative part of your online experience.

Use lists to create safe spaces

We all have times when we want to venture online for industry news, professional support or simple entertainment—without facing the risk of a major conflict or distraction. This is where Facebook lists come in handy. Lists allow you to create a digital safe space: a circle of people you pay attention to, or speak with, when you don’t want to do battle with the big world.

You may need more than one of these safe spaces. Perhaps you want one for talking about industry news and business strategy (if only to avoid boring your non-work friends), another for talking politics, and yet another for talking about your kids or your pets or your triathlon training. If you have a specific subject that you like to talk about regularly, but it’s a subject that can trigger either boredom or controversy, it’s worth thinking about giving this subject its own safe space in your online existence.

Facebook makes it incredibly easy to create these spaces: Just create a Facebook list for each circle, and put all the friends you want into that circle on the list. (Those lists are only visible to you, and not to the people you put on it, so feel free to create a list called Fellow Crazy Marathon Runners.) You now have a list that can help shape your conversations in two ways: by giving you more control over what you see, and by giving new options for privacy settings that affect who sees what you post.

Lists help you manage your attention and energy by letting you control what you see and when you see it. Add each list to your shortcuts (which appear on the left side of your Facebook page), so you can see posts only from those list members when you just don’t have the energy or inclination to look at the cacophony of your main news feed. I’ve written before about how to use lists to be more professionally personable on Facebook–that advice becomes all the more relevant in a highly charged political environment. This works better than creating separate Facebook groups because your friends don’t have to join, and because this will include all their regular posts–not just ones they deliberately post to a particular group’s page.

Use privacy settings to limit your exposure

Once you have your lists in place, you can use those lists in the privacy settings for your individual posts.  When you’re posting to Facebook, use the visibility drop-down to determine who can see that post: everyone (public), friends, or a specific list or group.

Use Facebook’s restricted list to ensure that only your real friends see your friends-only posts (just put any less-than-true friends on the restricted list), or use narrower lists to share political rants just with those friends who have similar views. Use your “industry news” list to share your views on the latest acquisition deal in your field (remembering to exercise judgement in what you say, because anyone can take a screenshot of anything, so you never really know where it will end up.)  Or, if you want to share a political post with colleagues and you’re willing to engage your crazy uncle in an argument it, but don’t want your colleagues to see the argument, post your message twice–once to a family list and again to your colleague list.  (Note that just because someone is on a list doesn’t mean they will see posts you share with that list; it just means they can: Facebook’s mysterious algorithm will still show those posts to some list members but not others.)

And, finally, remember that creating this kind of safe space for yourself comes with its own dangers: even without your deliberate efforts to filter out offensive content, social media algorithms give us a filtered view of society as a whole. When you’re living in a filter bubble it’s easy to think that everybody shares your worldview—a misapprehension that can have personal, social and professional repercussions. The safe spaces you create can be helpful in maintaining your emotional well-being and your professional connections, but don’t retreat into them so completely that you never hear anything else.

Facebook’s lists and privacy settings offer a way to take control of your experience and make Facebook more useful and less aggravating for you personally. It’s a step we all need to take if we want to reclaim our relationships, our careers and our democracy from the tyranny of the algorithm.

How Mount Sinai Health System Fosters Collaboration to Fight Cancer

Alice Mollon/Getty Images

Precision cancer medicine — sequencing a patient’s DNA in order to customize cancer treatments — shows promise, but is very much in its infancy. It’s still not nearly precise enough to launch a winning battle against many forms of cancer. To dramatically advance in this field, clinicians, medical researchers, and computer scientists must substantially deepen their collaboration.

This is a challenge for most academic medical centers, which are typically hierarchical, segmented organizations. The hospital stands apart from the medical school, and for employees of each to join forces, the chains of command must approve. Granting researchers access to extraordinary computational brainpower, essential for some of today’s medical research, often requires even more authorization.

The Mount Sinai Health System is organized differently from most, as one integrated institution. Doctors from the seven Mount Sinai hospitals work side by side with researchers from the Icahn School of Medicine at Mount Sinai. Indeed, many clinicians also have Sinai research labs. If a clinician and a researcher devise a viable idea to solve a medical problem, they are free to join forces and pursue the project. This makes it possible to rapidly bring a finding from the lab bench to the patient bedside.

Insight Center



The Future of Health Care
Sponsored by Medtronic

Creating better outcomes at reduced cost.

By taking advantage of Mount Sinai’s collaborative freedom, we are using advanced computer analytics to effectively treat some of the most challenging cancers, those that affect the blood and bone marrow. Cells from these cancers are highly heterogeneous, arising from different genetic drivers, each of which may or may not respond to a particular medicine. While solid tumors, like lung cancers, often respond well to treatments selected based on an analysis of the tumor’s DNA, such analysis cannot provide the information needed to effectively target blood cancers such as multiple myeloma. These kinds of liquid cancers are so complex and difficult to treat — virtually all patients relapse — that DNA analysis has yet to make a dent in treatment.

To unmask the genetic anomalies that are specific to multiple myeloma and that could guide treatment, our teams at the Icahn School of Medicine at Mount Sinai collaborated to map both DNA and RNA signatures of multiple myeloma tumors. RNA is the ultimate messenger of the genetic instructions a cell needs to manufacture proteins. Only information transcribed from DNA into RNA will ultimately impact the structure of proteins, including the mutations that lie behind cancers. So it is critical to understand the RNA of a complex cancer.

As we planned to crack and classify the RNA profile of multiple myeloma, it quickly became clear that the task would require a large amount of dedicated computing power. Our Institute for Next Generation Healthcare created a dedicated server for the multiple myeloma research, named CRUSHER, at an off-campus Mount Sinai computer lab, and a customized software program, named DAPHNE, to get the job done. With this high-power computing capability we were able not only to decipher the RNA of multiple myeloma cancer cells but also to link mutations affecting protein structure to disease patterns, and identify novel associations between clinical traits and genomic markers. Together, our myeloma specialists, genomics scientists, and drug repurposing experts used the DAPHNE software to integrate DNA and RNA sequencing, as well as clinical data, to identify non-myeloma drugs that might be repurposed to help patients whose disease had relapsed after receiving standard therapy approved for multiple myeloma.

Preliminary trials are highly encouraging. In one patient, the RNA analysis revealed abnormal activation of a molecular pathway that enables the transmission of a signal from a receptor on the surface of a cell to the DNA in the cell’s nucleus. After he was treated with a drug approved for other cancers that inhibits this pathway, his myeloma went into remission. In another case, RNA and DNA analysis identified oral drugs approved for breast cancer and chronic leukemia that should be effective against myeloma. A 70-year-old painter whose myeloma had relapsed after standard therapy went into remission following treatment with the new combination and has returned to painting.

RNA analysis turned out to be far more helpful than DNA sequencing in determining the most effective drugs for each patient. Of the 21 evaluable patients in our study, 11 received a personalized drug based upon RNA profiling, two received a drug based on both RNA and DNA, and eight received a drug based on DNA. This is remarkable because the vast majority of diagnostic companies and oncologists using genetic analysis are studying only patient DNA, not RNA.

The merging of high technology with medical research is still in its early stages while we strive to build our understanding of disease. As we embrace a new paradigm — treating cancers based on multiple genetic drivers rather than histology (cell structure) — academic medical centers should loosen their hierarchies and clear the way for computer scientists to deepen their collaborative efforts with oncologists, pathologists, and geneticists to look beyond DNA sequencing. This is how we will generate the knowledge that will achieve major progress in the war against the most difficult cancers.

How Mount Sinai Heath System Fosters Collaboration to Fight Cancer

Alice Mollon/Getty Images

Precision cancer medicine — sequencing a patient’s DNA in order to customize cancer treatments — shows promise, but is very much in its infancy. It’s still not nearly precise enough to launch a winning battle against many forms of cancer. To dramatically advance in this field, clinicians, medical researchers, and computer scientists must substantially deepen their collaboration.

This is a challenge for most academic medical centers, which are typically hierarchical, segmented organizations. The hospital stands apart from the medical school, and for employees of each to join forces, the chains of command must approve. Granting researchers access to extraordinary computational brainpower, essential for some of today’s medical research, often requires even more authorization.

The Mount Sinai Health System is organized differently from most, as one integrated institution. Doctors from the seven Mount Sinai hospitals work side by side with researchers from the Icahn School of Medicine at Mount Sinai. Indeed, many clinicians also have Sinai research labs. If a clinician and a researcher devise a viable idea to solve a medical problem, they are free to join forces and pursue the project. This makes it possible to rapidly bring a finding from the lab bench to the patient bedside.

Insight Center



The Future of Health Care
Sponsored by Medtronic

Creating better outcomes at reduced cost.

By taking advantage of Mount Sinai’s collaborative freedom, we are using advanced computer analytics to effectively treat some of the most challenging cancers, those that affect the blood and bone marrow. Cells from these cancers are highly heterogeneous, arising from different genetic drivers, each of which may or may not respond to a particular medicine. While solid tumors, like lung cancers, often respond well to treatments selected based on an analysis of the tumor’s DNA, such analysis cannot provide the information needed to effectively target blood cancers such as multiple myeloma. These kinds of liquid cancers are so complex and difficult to treat — virtually all patients relapse — that DNA analysis has yet to make a dent in treatment.

To unmask the genetic anomalies that are specific to multiple myeloma and that could guide treatment, our teams at the Icahn School of Medicine at Mount Sinai collaborated to map both DNA and RNA signatures of multiple myeloma tumors. RNA is the ultimate messenger of the genetic instructions a cell needs to manufacture proteins. Only information transcribed from DNA into RNA will ultimately impact the structure of proteins, including the mutations that lie behind cancers. So it is critical to understand the RNA of a complex cancer.

As we planned to crack and classify the RNA profile of multiple myeloma, it quickly became clear that the task would require a large amount of dedicated computing power. Our Institute for Next Generation Healthcare created a dedicated server for the multiple myeloma research, named CRUSHER, at an off-campus Mount Sinai computer lab, and a customized software program, named DAPHNE, to get the job done. With this high-power computing capability we were able not only to decipher the RNA of multiple myeloma cancer cells but also to link mutations affecting protein structure to disease patterns, and identify novel associations between clinical traits and genomic markers. Together, our myeloma specialists, genomics scientists, and drug repurposing experts used the DAPHNE software to integrate DNA and RNA sequencing, as well as clinical data, to identify non-myeloma drugs that might be repurposed to help patients whose disease had relapsed after receiving standard therapy approved for multiple myeloma.

Preliminary trials are highly encouraging. In one patient, the RNA analysis revealed abnormal activation of a molecular pathway that enables the transmission of a signal from a receptor on the surface of a cell to the DNA in the cell’s nucleus. After he was treated with a drug approved for other cancers that inhibits this pathway, his myeloma went into remission. In another case, RNA and DNA analysis identified oral drugs approved for breast cancer and chronic leukemia that should be effective against myeloma. A 70-year-old painter whose myeloma had relapsed after standard therapy went into remission following treatment with the new combination and has returned to painting.

RNA analysis turned out to be far more helpful than DNA sequencing in determining the most effective drugs for each patient. Of the 21 evaluable patients in our study, 11 received a personalized drug based upon RNA profiling, two received a drug based on both RNA and DNA, and eight received a drug based on DNA. This is remarkable because the vast majority of diagnostic companies and oncologists using genetic analysis are studying only patient DNA, not RNA.

The merging of high technology with medical research is still in its early stages while we strive to build our understanding of disease. As we embrace a new paradigm — treating cancers based on multiple genetic drivers rather than histology (cell structure) — academic medical centers should loosen their hierarchies and clear the way for computer scientists to deepen their collaborative efforts with oncologists, pathologists, and geneticists to look beyond DNA sequencing. This is how we will generate the knowledge that will achieve major progress in the war against the most difficult cancers.

One Reason Mergers Fail: The Two Cultures Aren’t Compatible

Yongyuan Dai/Getty Images

Amazon’s 2017 acquisition of Whole Foods was met with a lot of fanfare. The deal would allow Amazon to grow beyond e-commerce and sell groceries in hundreds of stores while collecting significant shopper data. Meanwhile, Whole Foods could lower its prices (organic avocados for just $1.69!) and scale up after its recent declines in sales and market share. In the words of Whole Foods CEO John Mackey, the partnership was “love at first sight.”

A year later, such optimism seems hard to find at Whole Foods. Stories of employees literally crying on the job over Amazon’s changes have begun circulating. Scorecards measuring compliance with a new inventory system are used to punish and sometimes terminate workers. A group of Whole Foods employees have recently taken steps to explore unionizing. Even customers — the stakeholders that Amazon values the most — have been angry over poorly stocked stores.

So where did the love go?

Amazon and Whole Foods’ relationship problems were completely predictable. The two companies may have seen value in capitalizing on each other’s strengths, but they failed to investigate their cultural compatibility beforehand. They now stand on a fault line where tensions often erupt in mergers. This fault line is what we call tightness versus looseness. When tight and loose cultures merge, there is a good chance that they will clash.

Tight company cultures value consistency and routine. They have little tolerance for rebellious behavior, and use strict rules and processes to uphold cultural traditions. Loose cultures are much more fluid. They generally eschew rules, encourage new ideas, and value discretion. Tight cultures have an efficient orderliness and reassuring predictability, but are less adaptable. Loose cultures tend to be open and creative, but are more disorganized. People in loose cultures prefer visionary, collaborative leaders: those who advocate for change and empower their workers, like Whole Foods’ Mackey. People in tight cultures desire leaders who embody independence, extreme confidence, and top-down decision making. Amazon CEO Jeff Bezos, who is known to expect unwavering discipline from his workers, personifies this leadership style.

Amazon’s culture is a tight one, characterized by structure and precision. Rooted firmly in the manufacturing industry, Amazon has defined processes to maximize its efficiency. Employees operate within a hierarchy and are well aware of the guidelines that dictate their behavior. According to Amazon’s leadership principles, leaders are instructed to “hire and develop the best” and “insist on the highest standards.” Performance is subject to constant measurement and review — employees can anonymously report each other to higher-ups through an internal phone system. Behavior is even more tightly regulated at Amazon’s warehouses, where target goals and surveillance keep production on schedule. This rule-bound culture ensures that all employees understand the company’s objectives and are consistently working to achieve them.

Whole Foods, on the other hand, has a much looser culture. The unique blend of idealism, high profit margins, and rapid growth that came with operating the first certified organic national supermarket in the U.S. provided the founders with considerable latitude in introducing innovative and unorthodox management methods. Prior to the Amazon merger, the company had an egalitarian structure organized around self-managed teams. This structure granted individual employees significant decision-making power. Face-to-face interactions between workers, vendors, and customers were the norm. Managers could operate their stores with autonomy and tailor products to customer preferences. “Empowerment must be much, much more than a mere slogan,” Mackey wrote in a 2010 blog post. “It should be within the very DNA of the organization.” Such decentralization and lack of structure, however, might have ultimately contributed to company-wide inefficiencies that drove up prices.

To understand more about how mergers between tight and loose cultures work, we collected data on over 4,500 international mergers from 32 different countries between 1989 and 2013. The study took into consideration factors such as deal size, monetary stakes, industry, geographic distance, and cultural compatibility. We found that mergers with more-pronounced tight-loose divides performed worse overall. On average, the acquiring companies in mergers with tight-loose differences saw their return on assets decrease by 0.6 percentage points three years after the merger, or $200 million in net income per year. Those with especially large cultural mismatches saw their yearly net income drop by over $600 million.

Fortunately, when diagnosed early, the tight-loose clashes that crop up in mergers can be handled productively. To increase their chances of achieving cultural harmony, companies should do a few things.

Prepare to negotiate culture. In addition to negotiating price and other financial terms, organizations discussing a merger need to negotiate culture. Leaders should start by conducting a cultural assessment to understand how people, practices, and management reflect tightness or looseness in both companies. They should determine the pros and cons of their current levels of tight-loose, as well as the opportunities and threats posed by merging cultures. How might sacrificing some discretion for structure, or vice versa, enhance or harm each organization? Above all, they should identify areas for compromise: Tighter organizations need to identify domains where they can embrace greater looseness, and looser organizations need to think about how they can welcome some tight features. We call these flexible tightness and structured looseness, respectively.

Construct a prenup. Once merging organizations better understand the strengths and weaknesses of their company cultures, they should develop a cultural integration plan that articulates which domains will be loose and which will be tight. Mutual input about how each company will change — and a formal contract documenting those changes — can help ensure long-term success. When Disney bought Pixar in 2006, Disney CEO Robert Iger agreed to a set of ground rules for safeguarding Pixar’s looser culture. For example, Pixar employees weren’t required to sign employment contracts with Disney, were free to choose the titles on their business cards, could decorate their cubicles and offices as they wished, and could continue their annual paper airplane contest. 

Get buy-in. Everyone across both organizations needs to be informed about the integration plan. Simply explaining what the changes will be is not enough; people need to know why they will be implemented. Communicating openly and gaining broad acceptance for changes will help minimize the threat people feel from new ways of doing business. People in tight organizations might feel their control is being threatened. People in loose organizations might feel their autonomy is being threatened. Leaders need to be culturally ambidextrous — or demonstrate the value of being both tight and loose, and work to address employees’ underlying fear of change.

Embrace trial and error. Finally, organizations need to be prepared to reevaluate their original integration strategy. No matter how foolproof the plan may seem, issues are bound to arise. Amazon’s increased standardization and employee surveillance at Whole Foods had positive business outcomes — prices dropped as much as 40% on certain items — but it was also hard on the company culture. Amazon now has an opportunity to learn from these results, and possibly incorporate some of the looser cultural elements that Whole Foods employees value. For example, Amazon could create a better balance between the time people spend on logging inventory and organizing store shelves and the time they spend interacting with customers. Likewise, there may be more domains where Whole Foods can relinquish some of its unstructured business practices. For example, using Amazon’s expertise in data science and logistics, Whole Foods has an opportunity to gain better customer insights and provide its clientele with services that are not only personal but also customized and consistent.

Negotiating tight and loose in organizations takes work, but patience and a willingness to make sacrifices can help merging organizations overcome some of the most difficult challenges. How will the Amazon–Whole Foods partnership pan out? It’s too soon to say, but spending more time on integrating their cultures could help.