Marina Gorbis's Blog, page 773

Set the Conditions for Anyone on Your Team to Be Creative

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One of the most damaging myths about creativity is that there is a specific “creative personality” that some people have and others don’t. Yet in decades of creativity research, no such trait has ever been identified. The truth is that anybody can be creative, given the right opportunities and context.

If you don’t believe me, take the least creative person in your office out for lunch — someone who doesn’t seem to have a creative bone in their body. Chances are, you’ll find some secret passion, pursued outside of office hours, into which they pour their creative energies. They just aren’t applying those energies to their day jobs.

The secret to unlocking creativity is not to look for more creative people, but to unlock more creativity from the people who already work for you. The same body of creativity research that finds no distinct “creative personality” is incredibly consistent about what leads to creative work, and they are all things you can implement within your team. Here’s what you need to do:

Cultivate Expertise

One of the things that creativity researchers have consistently found for decades is that expertise is absolutely essential for producing top-notch creative work — and the expertise needs to be specific to a particular field or domain. So the first step to being creative is to become an expert in a particular area.

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You Can Teach Someone to Be More Creative


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The reason expertise is so important is that you need to be an expert in a specific field to understand what the important problems are and what would constitute an important new solution. Einstein, for instance, studied physics intensely for years to understand the basic physical model for time and space before he understood that there was an inherent flaw in that model.

So how do you cultivate expertise? Performance expert Anders Ericsson has studied that problem for decades and found that the crucial element is deliberate practice. You need to identify the components of a skill, offer coaching, and encourage employees to work on weak areas. That goes far beyond the intermittent training that most organizations do.

For example, one skill that Amazon has identified as crucial to performance is writing. Employees need to constantly write six-page memos, even for introducing small product features throughout their careers at the company. They consistently receive coaching and feedback and need to write good memos in order to advance within the company.

Any company can replicate Amazon’s memo-writing policy. What’s not so easily replicated is the intense commitment to cultivating writing expertise that the company has prioritized for years.

Encourage Exploration

While deep expertise in a given field is absolutely essential for real creativity, it is not sufficient. Look at any great body of creative work and you’ll find a crucial insight that came from outside the original domain. It is often a seemingly random piece of insight that transforms ordinary work into something very different. For example, it was a random visit to a museum that inspired Picasso’s African period. Charles Darwin spent years studying fossils and thinking about evolution until he came across a 40 year-old economics essay by Thomas Malthus that led to his theory of natural selection. The philosophy of David Hume helped lead Einstein to special relativity.

More recently, a team of researchers analyzing 17.9 million scientific papers found that the most highly cited work is far more likely to come from a team of experts in one field working with a specialist in something very different. It is that combination of expertise, exploration, and collaboration that leads to truly breakthrough ideas.

That is how Google’s “20% time” policy is able to act as a human-powered search engine for new ideas. By allowing employees to work on projects unrelated to their formal job descriptions 20% of the time, people with varied experiences and expertise can combine their efforts in a way that would be extremely unlikely in a planned company initiative.

Empower Your People with Technology

In Walter Isaacson’s recent biography of Leonardo da Vinci, he recounts how the medieval master would study nature, from anatomy to geological formations, to guide his art. Now Leonardo was clearly a genius of historical proportions, but think about how much more efficient he would have been with a decent search engine.

One of the most overlooked aspects of innovation is how much technology can enhance productivity. Part of the reason is because it makes the two factors noted above, acquiring domain expertise and exploring adjacencies, so much easier. However, another reason is because it frees up time to allow for more experimentation.

You can see this at work at Pixar, which was originally a technology company that began shooting short films to demonstrate the capabilities of its original product, animation software. However, as they were experimenting with the technology, they also found themselves experimenting with storytelling, and those experiments led them to become one of the most highly acclaimed studios in history.

As Pixar founder Ed Catmull put it in his memoir, Creativity Inc., “Every one of our films, when we start off, they suck…Our job is to take it from something that sucks to something that doesn’t suck. That’s the hard part.” It is that kind of continual iteration that technology makes possible, and that makes truly great creative work possible.

Reward Persistence

Far too often, we think of creativity as an initial, brilliant spark followed by a straightforward period of execution, but as Catmull’s comment above shows, that’s not true in the least. In his book, he calls early ideas “ugly babies” and stresses the need to protect them from being judged too quickly. Yet most organizations do just the opposite. Any idea that doesn’t show immediate promise is typically killed quickly and without remorse.

One firm that has been able to buck this trend is IBM. Its research division routinely pursues seemingly outlandish ideas long before they are commercially viable. For example, a team at IBM successfully performed the first quantum teleportation in 1993, when the company was in dire financial straits, with absolutely no financial benefit.

However, the research wasn’t particularly expensive, and the company has continued to support the work for the last 25 years. Today, it is a leader in quantum computing — a market potentially worth billions — because it stuck with it. That’s why IBM, despite its ups and downs, remains a highly profitable company while so many of its former rivals are long gone.

Kevin Ashton, who first came up with the idea for RFID chips, wrote in his book, How to Fly a Horse, “Creation is a long journey, where most turns are wrong and most ends are dead. The most important thing creators do is work. The most important thing they don’t do is quit.”

Yet all too often, organizations do quit. They expect their “babies” to be beautiful from the start. They see creation as an event rather than a process, don’t invest in expertise or exploration, and refuse to tolerate wrong turns and dead ends. Is it any wonder that so few are able to produce anything truly new and different?

What Great Data Analysts Do — and Why Every Organization Needs Them

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The top trophy hire in data science is elusive, and it’s no surprise: a “full-stack” data scientist has mastery of machine learning, statistics, and analytics. When teams can’t get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Which of those skills gets the pedestal?

Today’s fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning the darlings of the job market. Alternative challengers for the alpha spot come from statistics, thanks to a century-long reputation for rigor and mathematical superiority. What about analysts?

Analytics as a second-class citizen

If your primary skill is analytics (or data-mining or business intelligence), chances are that your self-confidence has taken a beating as machine learning and statistics have become prized within companies, the job market, and the media.

But what the uninitiated rarely grasp is that the three professions under the data science umbrella are completely different from one another. They may use some of the same methods and equations, but that’s where the similarity ends. Far from being a lesser version of the other data science breeds, good analysts are a prerequisite for effectiveness in your data endeavors. It’s dangerous to have them quit on you, but that’s exactly what they’ll do if you under-appreciate them.

Instead of asking an analyst to develop their statistics or machine learning skills, consider encouraging them to seek the heights of their own discipline first. In data science, excellence in one area beats mediocrity in two. So, let’s examine what it means to be truly excellent in each of the data science disciplines, what value they bring, and which personality traits are required to survive each job. Doing so will help explain why analysts are valuable, and how organizations should use them.

Excellence in statistics: rigor

Statisticians are specialists in coming to conclusions beyond your data safely — they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand.

The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion.

Excellence in machine learning: performance

You might be an applied machine learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial-and-error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric — it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must.

The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demanded.

Wide versus deep

What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise.

Statisticians and machine learning engineers are narrow-and-deep workers — the shape of a rabbit hole, incidentally — so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.

Excellence in analytics: speed

The best analysts are lightning-fast coders who can surf vast datasets quickly, encountering and surfacing potential insights faster than those other specialists can say “whiteboard.” Their semi-sloppy coding style baffles traditional software engineers — but leaves them in the dust. Speed is their highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights.

The result is that the business gets a finger on its pulse and eyes on previously-unknown unknowns. This generates the inspiration that helps decision-makers select valuable quests to send statisticians and ML engineers on, saving them from mathematically-impressive excavations of useless rabbit holes.

Sloppy nonsense or stellar storytelling?

“But,” object the statisticians, “most of their so-called insights are nonsense.” By that they mean the results of their exploration may reflect only noise. Perhaps, but there’s more to the story.

Analysts are data storytellers. Their mandate is to summarize interesting facts and to use data for inspiration. In some organizations those facts and that inspiration become input for human decision-makers. But in more sophisticated data operations, data-driven inspiration gets flagged for proper statistical follow-up.

Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data (and prevent your audience from doing it, too). To this end, one way to spot a good analyst is that they use softened, hedging language. For example, not “we conclude” but “we are inspired to wonder”. They also discourage leaders’ overconfidence by emphasizing a multitude of possible interpretations for every insight.

As long as analysts stick to the facts — saying only “This is what is here.” — and don’t take themselves too seriously, the worst crime they could commit is wasting someone’s time when they run it by them.

While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.

Of the three breeds, analysts are the most likely heirs to the decision throne. Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain. Failure to do so is a red flag. As their curiosity pushes them to develop a sense for the business, expect their output to shift from a jumble of false alarms to a sensibly-curated set of insights that decision-makers are more likely to care about.

Analytics for decision-making

To avoid wasted time, analysts should lay out the story they’re tempted to tell and poke it the from several angles with follow-up investigations to see if it holds water before bringing it to decision-makers. The decision-maker should then function as a filter between exploratory data analytics and statistical rigor. If someone with decision responsibility finds the analyst’s exploration promising for a decision they have to make, they then can sign off on a statistician spending the time to do a more rigorous analysis. (This process indicates why just telling analysts to get better at statistics misses the point in an important way. Not only are the two activities separate, but another person sits in between them, meaning it’s not necessarily any more efficient for one person to do both things.)

Analytics for machine learning and AI

Machine learning specialists put a bunch of potential data inputs through algorithms, tweak the settings, and keep iterating until the right outputs are produced. While it may sound like there’s no role for analytics here, in practice a business often has far too many potential ingredients to shove into the blender all at once. One way to filter down to a promising set of inputs to try is domain expertise — ask a human with opinions about how things might work. Another way is through analytics. To use the analogy of cooking, the machine learning engineer is great at tinkering in the kitchen, but right now they’re standing in front of a huge, dark warehouse full of potential ingredients. They could either start grabbing them haphazardly and dragging them back to their kitchens, or they could send a sprinter armed with a flashlight through the warehouse first. Your analyst is the sprinter; their ability to quickly help you see and summarize what-is-here is a superpower for your process.

The dangers of under-appreciating analysts

An excellent analyst is not a shoddy version of the machine learning engineer; their coding style is optimized for speed — on purpose. Nor are they a bad statistician, since they don’t deal at all with uncertainty, they deal with facts. The primary job of the analyst is to say: “Here’s what’s in our data. It’s not my job to talk about what it means, but perhaps it will inspire the decision-maker to pursue the question with a statistician.”

If you overemphasize hiring and rewarding skills in machine learning and statistics, you’ll lose your analysts. Who will help you figure out which problems are worth solving then? You’ll be left with a group of miserable experts who keep being asked to work on useless projects or analytics tasks they didn’t sign up for. Your data will lie around useless.

When in doubt, hire analysts before other roles. Appreciate them and reward them. Encourage them to grow to the heights of their chosen career (and not someone else’s). Of the cast of characters mentioned in this story, the only ones that every business needs are decision-makers and analysts. The others you’ll only be able to use when you when you know exactly what you need them for. Start with analytics and be proud of your newfound ability to open your eyes to the rich and beautiful information in front of you. Data-driven inspiration is a powerful thing.

The Right Way to Solve Complex Business Problems

Corey Phelps, a strategy professor at McGill University, says great problem solvers are hard to find. Even seasoned professionals at the highest levels of organizations regularly fail to identify the real problem and instead jump to exploring solutions. Phelps identifies the common traps and outlines a research-proven method to solve problems effectively. He’s the coauthor of the book, Cracked it! How to solve big problems and sell solutions like top strategy consultants.

Download this podcast

What GM’S Layoffs Reveal About the Digitalization of the Auto Industry

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News that General Motors plans to cut up to 14,800 jobs in the U.S. and Canada was initially reported as a conventional business-cycle adjustment — a “trimming of the sails.” The main causes of the cuts were understood to be slowing demand in the U.S. and China, slumping demand for sedans, and the need to reduce over-capacity in North America.

Then the story turned political, as President Trump lashed out at GM while some observers framed the news as a blow to the president’s promises to bring jobs back to the U.S. heartland.

And then others focused on the community disruption of plant closings in the Rust Belt and how it might be mitigated.

While all of those perspectives are relevant, the most revealing aspect of GM’s announcement may well be what the layoffs say about broader technology trends. GM’s layoffs are not just incremental but existential, in that sense: They are about accelerating the staffing changes mandated by the company’s aggressive transition from analog to digital products and from gasoline to electric power. As such, the new layoffs (and associated future hirings) are likely an augury of much more disruption coming — in the auto sector, for sure, but also in firms all across the economy.

Central to GM’s announcement is, in our view, what we call the “digitalization of everything.”  By that, we mean that GM’s layoffs significantly reflect the talent and workforce strains associated with the diffusion of digital and electronic technologies into nearly every industry, business, and workplace in America.

Specifically, the advent of consumer electronics, IT, electric and battery powered drivetrains, and — soon — autonomy in the automotive industry are placing excruciating new demands on its workforce, and forcing painful change. Where once the auto-sector workforce was anchored by workers responsible for mechanical and machine-maintenance roles, the need for electrical skills is now growing exponentially due to the increasing electrical and electronic content of the car. Likewise, where mechanical engineers once predominated, the original equipment manufacturers (OEMs) are increasingly looking for software engineers, energy management experts, and data scientists able to build electric and self-driving vehicles.

Our recent analysis of the digital content of hundreds of occupations in the American economy shows that the digital content of auto work has soared in the last 15 years, with huge implications for workforce development in the sector. The mean digitalization score of workers in the advanced manufacturing sector, of which auto is a part, surged 60%, from 24 to 39 since 2002. This has reoriented the occupational mix of the industry, changing its hiring needs and layoff decisions.  As of 2016, for example, the fastest growing occupations in the auto sector were computer network support specialists and software developers while two of the fastest shrinking were drilling and boring machine operators and sheet metal workers. Similar patterns of cutting and hiring are visible in last week’s announcement.

 

Nor is that all: Look for more of the same in the future — from GM, and from all other companies in the sector.  According to our calculations, employing task-level work assessments provided by the McKinsey Global Institute, nearly 65% of all auto-sector jobs have task-level automation potentials of at least 70% in the next 10 or 15 years, meaning they are potentially susceptible to significant work changes, if not termination. With that said, as one of GM’s statements last week noted, “GM’s transformation also includes adding technology and engineering jobs to support the future of mobility, such as new jobs in electrification and autonomous vehicles.”

 

In that vein, last week’s layoffs surely were a response to changing near-term market conditions. But beyond that, the cuts went much deeper, to respond to massive, technology-driven changes in the nature of the work at hand.

As to what needs to be “done” about these transitions, the proper response almost certainly bears no resemblance to any of the ideas President Trump offered last week.  Trump is fuming at the plant closures, and appears to want to reverse the actions GM is taking to stay ahead of emerging technology and skills changes. To that end, Trump called on GM to close one of its plants in China.  And he threatened to strip the company of modest federal incentives to stimulate electric car production. However, that would only hurt GM’s and America’s competitiveness by hindering the company’s plans to invest more in the technology and people needed to produce electric and self-driving cars as those become viable products.

What should be done instead? As a nation, we should be embracing transformative technology and its widespread deployment whether it be electrification and hyper-efficient batteries in the auto sector or automation and AI more broadly. Likewise, we should be increasing our investments in education and workforce training (and re-training), with a focus on digital skills. Only in that way will workers be able to ride out the coming waves of tech-driven staffing changes. And finally, the nation needs to do much more to provide basic supports for people and places struggling with the harsh impacts of labor market change. To be sure, workers must adapt, but firms, governments, and regions all have a responsibility to help.

All of which is to say: GM’s announcement of layoffs last week is much more than a routine course-adjustment by a company alert to market softening after a good run.  Rather, it’s a wake-up call about the labor market implications of the “digitalization of everything.”

To Retain Employees, Focus on Inclusion — Not Just Diversity

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To retain talent, most organizations offer the typical things: free coffee and tea in the break room, competitive benefits, generous raises and bonuses, and employee recognition programs. But none of that works for an employee who doesn’t feel comfortable in his or her work environment. Picture, for example, a Muslim who prays in his car because he doesn’t want to advertise his religion, a mother who doesn’t put up pictures of her children so that coworkers won’t question her commitment to the job, or a gay executive who is unsure whether he can bring his partner to company functions.

Employees who differ from most of their colleagues in religion, gender, sexual orientation, socio-economic background, and generation often hide important parts of themselves at work for fear of negative consequences. We in the diversity and inclusion community call this “identity cover,” and it makes it difficult to know how they feel and what they want, which makes them vulnerable to leaving their organizations.

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Most business leaders understand the diversity part of diversity and inclusion. They get that having a diverse workforce is important to customers and critical to succeeding in a global market. It’s the inclusion part that eludes them — creating an environment where people can be who they are, that values their unique talents and perspectives, and makes them want to stay.

The key to inclusion is understanding who your employees really are. Three of the most effective ways to find out are survey assessments, focus groups, and one-on-one conversations. To be effective, however, they must be approached in a way that accounts for the fact that people — particularly those in underrepresented groups — can be more difficult to get to know than we think. Here are some best practices for getting to the heart of who your employees really are:

Segment employee engagement survey results by minority groups.

Many organizations conduct employee engagement surveys, but most neglect to segment the data they collect by criteria such as gender, ethnicity, generation, geography, tenure, and role in the organization. By only looking at the total numbers, employers miss out on opportunities to identify issues among smaller groups that could be leading to attrition, as the views of the majority overpower those of minorities.

In 2015, for example, women constituted 52% of the new associate class at global law firm Baker McKenzie, but only 23% of the firm’s 1,510 partners. To find out what was keeping women from advancing to senior roles, I asked our researchers to segment the results of a firm-wide engagement survey to examine responses from women lawyers. Based on that data, we learned that many of the firm’s women associates didn’t want to be partner nearly as much as their male counterparts.

That prompted us to launch a follow-up survey to find out why, which revealed four things that would make partnership more attractive to women: more flexibility about face time and working hours, better access to high-profile engagements, greater commitment to the firm’s diversity targets, and more women role models. Those four things became the basis for an action plan that included, for example, a firm-wide flexible work program that promoted remote working. By 2018, the percentage of women promoted to partner had risen to 40%, up from 26% in 2015.

Use independent facilitators to conduct focus groups.

Focus groups are another way to gain deeper insight into what employees care about and the issues that may be causing frustration and burnout.

One company-wide employee engagement survey conducted by a $15 billion food company showed that the employees in the Canada office had much lower work-life integration satisfaction scores than those in other countries. After conducting a series of focus groups to find out why, we discovered that many employees were receiving emails from their managers on weekends and feeling obligated to respond even when their managers told them not to until Monday.

We also learned that the leaders in that office were often tied up in meetings all week and used the weekends to catch up on email. When we asked the employees for solutions, they suggested banning emails on weekends and not having any meetings on Fridays so that managers could use that time to catch up on correspondence. After the office implemented these new policies, employees reported being happier and less stressed when they survey was conducted a year later.

These groups are best facilitated by an outside company or trusted diversity and inclusion professionals who don’t have a vested interest in the outcome so that employees can speak freely.

Get personal in one-on-one discussions.

A one-on-one discussion with a manager can be the most powerful tool for finding out what an employee cares about. But for these conversations to be effective, the manager needs to have an open-door policy and exude a “tell me anything” persona. One way for managers to prove they are trustworthy is by sharing their own thoughts and feelings when they are tired, sad, or struggling with an issue. It helps show they’re human.

Michael Santa Maria, who chairs Baker & McKenzie’s North America International Commercial and Trade Practice, tells his employees repeatedly that he wants them to succeed both at home and at work, and he takes an active interest in their families. His philosophy is that it’s hard to succeed at work when things at home are failing, and he openly talks about his family to his staff and even clients. The fact that he is open about his personal life makes his staff more comfortable sharing their own personal details — even those that may differ from the majority of their colleagues.

The road to retention

In an ideal world, all leaders would be adept at understanding their employees and making sure they didn’t lose any through neglect or ignorance. In the real world, however, most aren’t tuned into the factors that can get in the way of knowing what’s important to employees both individually and collectively. Tools such as segmented engagement surveys, focus groups, and personal conversations can guide management in taking the actions that will help keep their talent engaged and committed to the organization. The first step in retaining more employees is to use these tools.

The Business Case for an Employee Communication App - SPONSOR CONTENT FROM STAFFBASE

Internal communication is more important than ever. Companies need to communicate strategically and frequently with employees, sharing messages that are relevant and accessible. Businesses that don’t communicate in ways that resonate with their workforce find themselves paying the price in high turnover.

In the past, internal communications strategies that focused on efforts such as low-readership employee magazines or in-house intranet content often earned a reputation for draining resources without having much impact. But investing in an internal communications strategy that leverages mobile app technology can play an integral role in reaching employees, connecting strategy with operations and helping companies thrive, according to new research by Staffbase.

Employee apps are being adopted as a mobile solution for reaching employees via their personal smartphones/tablets—often with push notifications. An app can be an effective and secure channel for connecting dispersed workers who lack corporate email addresses or regular access to desktop computers, such as non-desk and/or remote workers, as well as freelancers, non-contract employees, temps, etc. As more and more people work this way, an employee app can provide them with easy access to corporate information and workplace tools, targeted for practicality and relevance, and promoting alignment and engagement.

Businesses Are at an Internal Communication Crossroads

Communication professionals try to utilize every available resource to reach their audience during times of change, to be a powerful voice in their lives, and to foster connections—but too often they aren’t getting through. Reach and relevance have become the greatest weaknesses of internal communications. However, new mobile communication technology can help companies reach employees directly when it matters most, targeting them with timely and vital information.

Surveys show that companies that optimize internal communication improve efficiency, compliance, and customer satisfaction, and have reduced risk and turnover. Research by Staffbase shows that a mobile employee app dramatically improves internal communication, and it identified four areas where companies can see concrete ROI.

Enabling Middle Management Communication Improves Work

Middle managers and operational leaders often hold the information that influences day-to-day business operations and guides employees in their jobs. An app allows communication professionals to identify influencers in that group and empowers them to distribute job-critical information.

Staffbase research found that effective delivery of safety information can lower the incidence of work accidents and reduce costs of occupational injuries and illnesses by 31 percent. The research also found that when apps are used by operational leaders to provide training and guidance for frontline workers, customer satisfaction (measured by Net Promoter Score) can rise at least 10 percent. For every 10 percent increase in customer satisfaction, companies increase revenue by 7%.

In 2017, German transport company Reinert Logistics implemented an employee app with their workforce, of which 83% of employees are non-desk workers. The company enabled operational managers to share safety videos and provide easy access to tools that helped with daily tasks, thus reducing incidents resulting in damages to company assets by 15%.

Reduce Wasted Time and Boost Revenue Per Employee

Studies by IDC, the Working Council of CIOs and Reuters show that employees spend on average 20% of their hours searching for information necessary to do their jobs effectively. While knowledge and non-desk workers differ in the kinds of information they need and at what frequency, the relevance of this issue for non-desk workers cannot be underestimated. A company using an employee app can modestly expect to reduce time wasted on these activities by 15%, increasing revenue per employee by 5%.

Troy Griggsby, Communications and Brand Manager at US Auto Logistics (80% non-desk workers) helped launch their employee app in 2017. Several months after implementation he reported, “The employee directory alone is pure gold. We’ve learned that some of the things that seem like small inconveniences of outdated communication are really decisive in how well employees can do their jobs. The employee app addresses a lot of inefficiencies that hinder the day-to-day activities that keep our business running smoothly.”

When Employee Engagement Rises, Turnover Plummets

Gallup’s most recent State of the American Workplace survey revealed that only 33% of US employees said that they were committed to their work. The same research found that the turnover rate for disengaged employees is 10 percent higher than engaged employees. Mobile employee apps reach all employees through a medium that is central to their everyday communication habits, and integrate features designed to keep employees engaged. Turnover risk drops significantly for engaged employees, a Gallup poll shows that engaged employees are 27% more likely to report excellent performance.

The research by Staffbase found that by capitalizing on the reach of mobile devices and creating relevant experiences and opportunities for reciprocity, mobile employee apps dramatically boost employee engagement. Companies utilizing an employee app were found to drive engagement levels to above 75%, which can save them anywhere from 25 to 65% on turnover costs.

Addressing Change-Related Stress Increases Performance

Technology has given everyone a voice, but companies are struggling to make themselves heard and understood. This is especially problematic when successful change depends on the cooperation of the workforce. Gartner reports that employees experiencing change-related stress perform 5% worse than average. Staffbase research found that through better reach and feedback opportunities, an employee app could reduce change-related stress by at least 2%, lowering related revenue loss by 41.2%.

Mergers and acquisitions involve complex organizational change, with fail rates from 50 to 75% according to research at Northwestern School of Education and Social Policy. Global refractory leader RHI Magnesita, with 14,000 employees (60% non-desk), went live with an employee app on day one of their 2017 merger. The app played a crucial role in maintaining performance during this period. The company finished 2017 with 11% revenue growth and their app was recognized with several awards for outstanding corporate and workplace communication.

Decision Makers Are Providing Proof

Transforming internal communication to meet the demands of the changing business landscape and those of modern employees is complex. Research on internal communication continues to offer new insights, but powerful evidence of the necessity of a communication transformation also exists in the growing number of companies investing in 21st-century strategies. Their success stories were the catalysts for the full report on the value of an investment in mobile internal communication.

Read more here.

What Big Consumer Brands Can Do to Compete in a Digital Economy

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No industry is failing faster than retail. Just last month, the 125-year-old Sears—once the world’s largest retailer—filed for bankruptcy. The public has more or less come to expect the shuttering of stores such as Macy’s, Sears, Toys ‘R’ Us, Kmart, Kohl’s, J.C. Penney, and Barnes & Noble. The ones that manage to escape are discount chains—such as T. J. Maxx and Marshalls—which compete aggressively on price.

Price competition hurts. It also hurts the brands sold inside the stores, which in part explains why consumer product giants like Procter & Gamble are seeing their sales stagnate for products like Tide detergent, Gillette razors, Pampers diapers, and Crest toothpaste.

A recent report by the consultancy BCG documented a general decline in sales among consumer packaged goods (CPG) companies in the United States during 2017, with mid-sized and large companies losing market share and small companies increasing theirs. Consultancy Catalina also revealed that 90 of the 100 top brands had all lost market share. In dollar terms, small players—defined as those with sales less than $1 billion—grabbed approximately $15 billion in sales from their larger peers between 2012 and 2017.

Shoppers now purchase more online, making fewer trips to stores and seeing fewer in-store promotions. A small but trendy razor club with a hip logo, Harry’s, attracts more Instagram followers and product subscriptions through its website than a fully stocked Gillette aisle in a supermarket ever could. And so Harry’s has been growing 35% year-on-year between 2014 to 2016, three times faster than the industry average, commanding 9% of all online razor sales.

Whereas the Gillette aisle in the local supermarket targets exactly one neighborhood, Harry’s website reaches millions. Harry’s bolsters the subscription habits of its recurring consumers, whereas Gillette relies on in-store impulse buying. When someone buys a razor in a store, Gillette has no clue who’s buying what and when; Harry’s knows it all.

Newcomers like Harry’s still represent only a fraction of the overall market, but they’ve captured the majority of the growth in that time—a defining feature of disruptive innovation. That’s why P&G has been restructuring for 20 years “without much to show for it,” according to one former finance manager. No matter how well P&G reorganizes itself, it can’t reverse the decline from $83 billion in sales in 2008 to $65 billion in 2017 without learning some new tricks.

I came across a report by the Wall Street Journal about Johnson & Johnson and how the sluggish sales of its baby shampoo have forced the company to make a desperate bet. J&J will take out the yellow dye from its iconic golden-hued baby shampoo and make it clear. Whether this will help J&J in the long run, no one knows. But the sluggish sales reflect the same dynamics that P&G’s been facing: market share lost to smaller brands, over-reliance on traditional retailers, and an inability to create direct relationships with consumers at a time when e-commerce is exploding.

Last year, P&G went to war to clean up the online ad market and used its pull as the world’s biggest advertiser to squeeze more information about the effectiveness of digital ads out of Google and Facebook. It slashed digital ad expenditures by more than $200 million and issued an ultimatum for tech firms to become more transparent. That’s a good start. But it also needs a new vision.

Activist investor Nelson Peltz, who sit on P&G’s board, has such a vision. He argues that P&G “must acknowledge that others will inevitably come up with new ideas, new opportunities for growth, and new products that are on-trend with consumers.” He also suggests that “P&G must be proficient at acquiring small, mid-size, and local brands and using its R&D and marketing clout to take them to the next level.”

What Peltz suggested is exactly what Xiaomi is already doing.

By March 2016, Chinese smartphone maker Xiaomi had invested in some 55 startups, generating products from power banks to air purifiers. Of these companies, 29 were incubated from the ground up by Xiaomi, and four were already unicorns worth over $1 billion. What Xiaomi offers to startups is a combination of funding and incubation by “taking non-controlling shares” and “leaving maximum interests to the startups” so that “they are much more incentivized and willing to fight on the front line,” explained Liu De, co-founder and vice president of Xiaomi.

Startups typically get access to Xiaomi’s brand and distribution—its online channel, its app, and its 300 offline stores. As a result, Xiaomi is poised as not only a low-cost phone maker but also an emerging powerhouse among the makers of the all-important “connected home devices.” “Our ecosystem even gives customers unusual new products that they never knew existed,” said Wang Xiang, Xiaomi’s senior vice president, referring to the company’s Bluetooth speaker, internet-enabled rice cooker, and air purifier (the first affordable one in China)—products that the company claims are not only best in class but also cost less than their existing counterparts.

P&G should follow suit. It can no longer be a mere “industrial corporation with a future based on technology” but rather must become a house of startup brands that runs pop-up stores, makes home deliveries, celebrates communities with parties, fosters subscription models, and curates compelling product personas, all while gathering comprehensive consumer data to guide new product innovation. In effect, it becomes a sort of coherent conglomerate. It makes a lot of bets, owns numerous largely separate businesses, but uses a few key centralized capabilities like branding and retail distribution to provide each of its subsidiaries with something independent CPG companies can’t match.

This structure would better enable P&G to copy what scrappy upstarts like Harry’s or Warby Parker do. These startups, despite being manufacturers, digitalize all customer touchpoints (like Netflix), control the user experience by forward-integrating into the brick-and-mortar realm (like Amazon buying Whole Foods), and then run data analytics to optimize merchandise mix and inform product innovation (like Alibaba’s Taobao).

So, are such steps too bold for a $235-billion (market cap) company from Cincinnati? The 180-year-old house of brands hasn’t managed to survive this long without making bold steps in the past.

When the first boxes of Tide went on sale in 1946, it was the first synthetic detergent that could deep-clean clothing “without making colors dull or dingy.” Before Tide, all soaps were “naturally” made by heating animal or vegetable fats with water and an alkali base. The benefits of a synthetic detergent that makes “white clothes look whiter” were so apparent that within just three years, Tide outstripped all other brands on the market and became the number one detergent. In the wake of Tide’s introduction, P&G would “no longer be a soap company” but “would become an industrial corporation with its future based on technology,” its technical staff body tripling that of the pre-Tide year of 1945.

But inside P&G, managers had feared the new products might cannibalize their beloved Ivory soap. Only Chairman William Cooper Procter, the last family manager of P&G, remained a staunch supporter of synthetic detergents. In a memorable remark addressed to his staff, he said, “this [synthetic detergent] may ruin the soap business. But if anybody is going to ruin the soap business, it had better be Procter & Gamble.”

P&G made bold moves to save itself in the past. Now it just needs to rediscover that chairman’s mentality. And that might well go for any consumer brand, not least P&G, looking to escape the utter ruins of the retail wasteland.

How to Collaborate with People You Don’t Like

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A few months ago, a former client — let’s call her Kacie— called me to check in. I had supported her through her transition when she had joined a prestigious global financial services firm several months prior. Given how deliberately and thoughtfully she’d gone through the process, I expected that our conversation would be about her early wins.

Instead, Kacie confessed that she had a simple but serious problem: she wasn’t getting along well with a peer-level executive — let’s call her Marta. The two had gotten off on the wrong foot, and as time passed things weren’t getting any better. Kacie told me that it was becoming painfully clear that her inability to get along with Marta was going to impede her success, and possibly derail her career at the company.

As Kacie and I explored the situation, she told me that Marta was seen as a highly talented, accomplished, and well-liked executive — she wasn’t toxic or difficult. But Kacie admitted that she didn’t really like Marta. They had different styles, and Marta rubbed her the wrong way.

Over a series of conversations, Kacie and I worked through the situation. She revisited the stakeholder map she had created in her first few weeks in the role, which clearly showed that Marta’s collaboration and partnership were essential for getting the business results Kacie wanted. In assessing the relationship more honestly, Kacie came to realize that she had been failing to reach out to Marta. She had not made her new colleague feel like her input and perspectives were valuable, had been leaving her and her team off communications, and had more or less been trying to avoid her.

Kacie developed a handful of useful strategies for working better with Marta. While none were particularly easy or comfortable, these are ideas and insights that almost anyone can use when they have to work with someone they just don’t like.

Reflect on the cause of tension and how you are responding to it. The first step is both acceptance and reflection. Remind yourself: You won’t get along with everyone but there is potential value in every interaction with others. You can and should learn from almost everyone you meet, and the responsibility for making that happen lies with you even if the relationship is not an easy one. Take an honest look at what is causing the tension and what role you play in creating it. It may be that your reaction to the situation is at the core of the problem (and you can’t control anything other than your reaction). Kacie had to recognize that Marta’s “unlikability” may really have been about Kacie herself.

Work harder to understand the other person’s perspective. Few people get out of bed in the morning with the goal of making your life miserable. Make time to think deliberately about the other person’s point of view, especially if that person is essential to your success. Ask yourself: Why is this person acting this way? What might be motivating them? How do they see me? What might they want and need from me? Kacie began to think differently about Marta as she came to appreciate that her colleague had goals and motivations as valid as her own and that their goals were not inherently in conflict.

Become a problem solver rather than a critic or competitor. To work better together, it’s important to shift from a competitive stance to a collaborative one. One tactic is to “give” the other person the problem. Rather than trying to work through or around the other person, engage them directly. Kacie invited Marta out to lunch and was open with her: “I don’t feel like we are working together as effectively as we could. What do you think? Do you have any ideas for how we can work better together?” If you ask people to show you their cards, and demonstrate vulnerability in the process, they will often reveal a few of their own.

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Ask more questions. In tense situations, many of us try to “tell” our way through it. We might become overly assertive, which usually makes the situation worse. Instead, try asking questions — ideally open-ended ones intended to create conversation. Put aside your own agenda, ask good questions, and have the patience to truly listen to the other person’s answers.

Enhance your awareness of your interpersonal style. It’s easy to chalk up conflicts to poor “chemistry” with another person but everyone has different styles and often being aware of those differences can help. Over lunch, Marta and Kacie discovered that they had both completed the Myers-Briggs earlier in their careers, so they shared their profiles. Kacie is both a clear introvert and a very strong sensing type: she prefers to have time to work through issues alone and quietly, and to draw conclusions from a broad base of data. Marta, on the other hand, is an extrovert and a strong intuitive type, comfortable reacting immediately, focusing on the big picture, and solving problems by talking them through with others. Given these differences in style and preference, Kacie and Marta were bound to find interacting with each other uncomfortable. But once they identified their differences, they realized that their styles could be quite complementary if they adapted and accommodated their approaches.

Ask for help. Asking for help can reboot a difficult relationship because it shows that you value the other person’s intelligence and experience. Over their lunch, Kacie grew confident enough to say to Marta, “You’ve been around here longer than I have. I feel like I’m starting to figure things out, but I’d love your help.” Then she asked questions like: “What should I be doing more or less of? Am I missing anything or failing to connect with anyone I really should? What do you wish someone had told you when you first started working here?”

Kacie and Marta’s relationship significantly improved. During my last call with Kacie, she told me that she and Marta communicate frequently in-person and via text and Slack, and they regularly take part in each other’s team meetings. Each quarter they bring their whole teams together to assess progress and seek opportunities to learn and improve their processes. While Marta and Kacie aren’t necessarily friends and don’t spend a lot of time together outside the office, they’re much better colleagues, and they like each other more than they initially suspected.

Kacie’s success in turning around her relationship with Marta was in part because she acted while “the cement was still wet.” Her negative dynamic with Marta hadn’t yet hardened so Kacie was able to increase her self-awareness, adapt her style, and reach out. It is possible to collaborate effectively with people you don’t like, but you have to take the lead.

To Retain New Hires, Spend More Time Onboarding Them

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This year, the unemployment rate in the U.S. hit a 49-year low of 3.7%. The demand for companies to retain top talent is intensifying. One report suggests that employee retention is the number one issue on the minds of CEOs today — not just in the U.S., but around the world. And yet, companies often spend very little time onboarding new hires. With up to 20% of staff turnover occurring within the first 45 days of employment, a standardized onboarding process is essential.

In my experience as a consultant for Fortune 500 companies, I’ve found that the most effective organizations onboard new hires for the duration of their first year — their most vulnerable period — and focus on three key dimensions: the organizational, the technical, and the social. By using this integrated approach, they enable their employees to stay, and to thrive.

Organizational Onboarding

Teach them how things work. The first and most common part of onboarding is teaching new employees the information they need to function day in and day out: where to park their car and get an ID card, how to navigate the building, how to enroll in health benefits and educate themselves on regulations and policies. Beyond this, it’s also important to teach them your workplace “language.” There’s almost always a litany of cryptic acronyms that company’s use for key processes or roles — decoding them can be one of the most distressing challenges for new hires. The more a new hire has to awkwardly ask, “Sorry, I’m new…what does SSRP stand for?” the more they feel like an outsider. Simple tools, like glossaries of terms, go a long way.

Help them assimilate. Organizations must be intentional about helping new hires adapt to organizational values and norms, especially during that first year. At key intervals — three, six, and nine months — hiring managers should formally engage them in conversations about the organization’s history and brand, how performance is measured and rewarded, and how growth opportunities arise. You should also encourage organizational “heroes,” or people held up as exemplary, to connect with new hires and share personal stories that demonstrate valued behaviors.

Technical Onboarding

Define what good looks like. Just because someone is hired for their capabilities and experiences, doesn’t mean they know how to deploy them at your company. New hires with deep areas of expertise can become insecure when they suddenly feel like beginners. They may even resort to citing past successes as a way to prove their competence, which can alienate them more and exhaust their colleagues — who might get tired of hearing a new team member start each sentence with, “In my last job.” To avoid this dilemma, communicate clearly from day one. Provide your new hire with a job description that includes well-defined accountabilities and any boundaries around authority or available resources they should be aware of. Clearly outline their decision rights to help them understand where their autonomy begins and ends. It’s also valuable to schedule weekly coaching sessions to check in and ensure they have opportunities to make meaningful contributions as soon as possible.

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Set up early wins. Giving new hires clear goals is another powerful strategy because it allows you to share realistic expectations. An astounding 60% of companies report that they do not set short-term goals for new hires. A good way to start is to assign tasks with an expectation that they be completed at the three, six, and nine-month marks. Start with targets you are confident your new hires can meet. If all goes well, gradually increase the level of responsibility associated with each task. This will help build trust and show them that you are paying attention. Through this process, you can openly discuss gaps in their skill set and work to close them. During check-ins, encourage them to share their growth areas and discourage them from “faking it.” New hires that feel grounded in their contribution and understand how it fits into the larger organization gain confidence and feel loyal faster.

Social Onboarding

Build a sense of community.  Recent research reveals that 40% of adults report feeling lonely . This sense of isolation is amplified for new hires — who often feel like a stranger in a foreign land — and can increase their chances of leaving a job.

Building relationships during their first year can help new hires feel less isolated and more confident. New hires, in partnership with their manager, should identify 7-10 people — superiors, peers, direct reports, and internal and external customers — whose success they will contribute to, or who will contribute to their success. The new hire should then craft plans to connect with each stakeholder, one-on-one, during their first year. This can be a short meeting over coffee or lunch — an opportunity to learn and ask for guidance. In addition to stakeholder cultivation, building social capital with teammates on a daily basis helps build camaraderie and trust. When new hires feel accepted and welcomed, they are less likely to feel like the new kid on the block.

If you want to retain the talent you spend good money to acquire, make sure a new hire’s first year is positive and productive. Organizations with a standardized onboarding process experience 62% greater new hire productivity, along with 50% greater new hire retention. Those that invest time and effort in their new employees reap the benefits. If you want to be an employer of choice for top talent, make sure a new hire’s organizational, technical, and social needs are well met.

Should Dual-Class Shares Be Banned?

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Voices against dual-class shares, which violate the principles of corporate democracy and the precept of “one share one vote,” have increased over time. An influential 50-member Investor Stewardship Group (ISG), overseeing $22 trillion in assets, demands a total elimination of dual-class stock. Council of Institutional Investors (CII), representing managers of $25 trillion assets, recently demanded limiting any company’s dual-class share structure to seven years. Yet this year,  Hong Kong and Singapore stock exchanges, which initially barred the listing of dual-class shares, went the opposite way, by allowing their listing.

Who is right? Influential institutional investors or Hong Kong and Singapore stock exchanges? Should dual-class stock be totally eliminated or, at least, have a mandatory sunset clause?

In our view, while the proposal to ban dual-class shares raises important issues, its implementation would do more harm than good, given the challenges from the digital revolution and the growing imperative for established firms to transform their business models.

For readers who haven’t been following this debate, here’s a quick primer. Companies with dual-class shares have two designations of common stock, typically A shares and B shares, with one class having more powerful voting rights than the other. Holding the more powerful shares allows some group of shareholders—often the founders—to control boardroom decisions even as economic interest in the firm is dispersed more widely. Some of the largest companies of recent times by market capitalization, such as Facebook, Alphabet, and Alibaba, carry dual class-shares. So do some older, family-controlled firms, such as Ford Motor Co. and The New York Times Co.

The use of dual-shares has been growing recently: One-fifth of companies that listed on U.S. stock exchanges last year had dual-class shares. Curiously, Warren Buffet strongly demands the elimination of dual-class shares, but Berkshire Hathaway continues to maintain two classes of voting stock. While most dual-class companies have superior Class B shares, which provide ten times more voting power than the inferior Class A shares, other companies such as Alphabet, Under Armour, Blue Apron, and Snapchat have taken this practice to an extreme by offering common shares with zero voting rights. Yet, investors price Alphabet’s Class C stock, which carries no voting rights, almost no differently than Alphabet’s Class B stock, which carries voting rights. Investors’ continued clamor for inferior-voting shares, even those with zero voting rights, suggests there must be some economic reason for their existence.

Academic research remains divided on the merits of dual-class shares. On one hand, some studies find lower stock returns for dual-class firms as compared to single-class firms, lower trading prices compared to fundamentals, and higher management entrenchment, executive compensation, and value-destroying acquisitions. Other studies, however, show that dual-class structure might be optimal in certain scenarios.  Firms with growth opportunities as well as the need for external equity financing often convert to dual-class shares. Aggressive-growth and family-controlled dual-class companies display higher long-term shareholder returns. MSCI’s recent analysis shows that unequal voting stocks outperformed the market over the period from November 2007 to August 2017.

While media companies, such as The New York Times Co., Comcast, DISH Network, AMC holdings, Liberty Media, News Corporation, and Viacom have traditionally had dual-class shares — arguably to maintain news independence — a more important recent development is the widespread adoption of dual-class structure by technology companies. Almost 50% of recent technology listings have a dual-class status. We explored reasons for the growing use of the dual-class structure in an HBS case study among technology companies. Our nickel summary is that their growing popularity is due to the increasing importance of intangible investments, the rise of activist investors, and the decline of other protection mechanisms available to existing management such as staggered boards and poison pills. A dual-class structure, offering immunity against proxy contests initiated by short-term investors, could be optimal if it enables founder-managers to ignore pressures from the capital markets and avoid myopic actions such as cutting research and development and delaying corporate restructuring.

So, in our view, outright ban on dual-class shares would not be costless. For example, one principal reason for decline in the number of initial public offerings is the increasing reluctance of technology companies to list their stock, which is largely caused by rising shareholder activism. At the margin, a ban on dual-class stock would encourage more technology companies to remain private, or motivate listed technology companies to go private, eliminating common investors’ chance to buy even the inferior voting stock. This growing possibility is likely why Hong Kong and Singapore stock exchanges have reversed their earlier stance and allowed dual-class shares.

But how can one argue against a mandatory sunset clause? This clause automatically converts a superior voting share to a low-vote class at a fixed time after IPO. For example, Groupon’s and Texas Roadhouse’s shares converted after five years of listing, and Fitbit, Kayak, and Yelp carry clauses for automatic conversions. This structure allows a dual-class structure for a defined period early in a company’s life to allow founders to pursue risky and bold initiatives without subjecting themselves to the pressures from short-term investors. Research shows that the benefits of dual-class structure dissipate over time. The costs of letting management entrench itself eventually outweigh the benefits of shielding the company from short-term investors. This argument is supported by the decline in the valuation premium of dual-class companies over comparable single-class companies as firms grow older.

In our view, a sunset clause would be ideal if there exists a fixed, predetermined time after which all companies become mature enough to need no further changes in their business models. However, we cannot completely endorse this idea for two reasons. First, the firm age at which sunset clause should kick is far from clear. We calculated the years after IPO when a growth company becomes a mature company in its lifecycle for the stocks listed on U.S. stock exchanges. We find that this age-to-maturity has been declining. In the late 1980s, we estimate it was ten years. It declined to 7.6 years in 1990s and has further declined to five years in the 21st century. The period to maturity differs based on the firm’s technology and business model. So, a one-size-fits-all policy would not work.

Second, and more important, we are unsure whether any of the today’s companies can bask in their established business models forever, given the increasing pace of creative destruction and the emerging competition from digital companies. Well-established companies, such as Ford, Caterpillar, Walmart, Macy’s, Sears, Boeing, General Electric, and John Deere, Duke Energy, and Thompson Reuters, are facing disruptions that require complete overhaul of their business models. Many large and mature companies, such as Amazon, Alphabet, IBM, and Apple, have had to reinvent themselves multiple times over. To the extent that a dual-class structure facilitates a company’s transformation, the assumption that a company predictably reaches a permanent business-model stage, and therefore does not need further transformation, would be detrimental to the nation’s innovation and shareholder value. For example, Pepsi’s transformation from a purveyor of sugary drinks toward healthy snacks would have been hindered had hedge funds succeeded in their demands.

In sum, instead of recommending a total ban on dual-class shares, or even a mandatory sunset clause, we recommend a more flexible shareholding structure. Companies with dual-class structures could be required, after a period of predetermined years, to gain approval from a majority of all shareholders to continue the dual-class structure. Furthermore, single-class firms should be given an option to convert to dual-class shares through a shareholder vote, in order to carry out significant transformations, instead of having to completely delist in order to achieve that goal.