Human Potential & Leadership


Seth Godin's Blog -

The math has changed.

It used to be, you paid money to run an ad. A little piece of media, bought and paid for. The audience came with the slot.

Today, of course, the ad is free to run. Post your post, upload your video. Free.

What to measure, then?

Well, one thing to measure is attention. How many likes or shares or views did it get?

But if you're going to optimize for attention, not trust or results or contribution, then you're on a very dangerous road.

It's pretty easy to get attention by running down the street naked (until everyone else does it). But that's not going to accomplish your goals.

When Oreo gets attention for a tweet or Halotop for a horrible ad, they're pulling a stunt, not contributing to their mission.

Yes, the alternative is more difficult. It doesn't come with a quick hit or big numbers. But it understands what it's for. An effective ad is far more valuable than a much-noticed one.


Decision making, after the fact

Seth Godin's Blog -

Critics are eager to pick apart complex decisions made by others.

Prime Ministers, CEOs, even football coaches are apparently serially incompetent. If they had only listened to folks who knew precisely what they should have done, they would have been far better off.

Of course, these critics have a great deal of trouble making less-complex decisions in their own lives. They carry the wrong credit cards, buy the wrong stocks, invest in the wrong piece of real estate.

Some of them even have trouble deciding what to eat for dinner.

Complex decision making is a skill—it can be learned, and some people are significantly better at it than others. It involves instinct, without a doubt, but also the ability to gather information that seems irrelevant, to ignore information that seems urgent, to patiently consider not just the short term but the long term implications.

The loudest critics have poor track records in every one of these areas.

Mostly, making good decisions involves beginning with a commitment to make a decision. That's the hard part. Choosing the best possible path is only possible after you've established that you've got the guts and the commitment to make a decision.


How Retailers Use Personalized Prices to Test What You’re Willing to Pay

Harvard Business Review -

Klaus Meinhardt/Getty Images

Have you ever looked up flights or hotels on an app on your phone, only to open your laptop and see different prices?

That’s exactly what happened to me recently. I was using Orbitz’s iPhone app to research a vacation package to New York City. Settling on a hotel, I accessed Orbitz’s website on my laptop to book the package. That’s odd, I thought, realizing that the package on my laptop — identical flights, hotel, room type — was $117 more (6.5% more) than the price on Orbitz’s app. A quick scan found that prices of identical vacation packages often differ between Orbitz’s app and website.

I then did a side-by-side app test of the same package with a friend who was sitting next to me. Her Orbitz app price was $50 (2.8%) more than my app price. Amazingly, Orbitz knew something that I regularly give my friend good-natured grief about: She overpays for almost everything.

When I shared my results with Expedia (the parent company of Orbitz), its spokeswoman explained that the pricing differences I found between the app and website can be due to the fact that its suppliers allow different prices to be offered to mobile customers as well as members (no fee to join) who are logged in.

With regard to the side-by-side app comparisons, Orbitz attributed the price differences to the A/B tests that it employs or other anomalies that occur when setting millions of prices that regularly change due to dynamic pricing. Orbitz told me that it does not offer different prices based on device, browser type, or number or type of searches.

The bottom line, though, is that based on a few characteristics (app or web, signed in as a member or not), a rudimentary type of personalized pricing is occurring: Some customers are receiving different prices than others.

The reason why retailers try to offer a personalized price goes back to the downward sloping demand curve highlighted in Economics 101. This fundamental concept illustrates that, for most products, some customers are willing to pay more than others. To exploit that, pricing managers employ techniques that try to discern — and charge — the exact price that each customer is willing to pay. Outsize profits can be extracted from “top of the demand curve” customers, who value the product highly. Meanwhile, if discounts can be discreetly offered to customers with a lower willingness to pay, additional sales (and profit) are reaped. The result is a more profitable customer base, with some shoppers paying more than others.

Personalized pricing can be found at most auto dealerships. The goal of salespeople is to determine how much each customer is willing to pay for a car through individualized negotiation. Prices are tailored by noting each customer’s characteristics and observing their actions. How shoppers dress, the car they currently drive, and answers to seemingly innocuous questions (Where do you live? What do you do for a living?) provide clues. Salespeople also observe actions, such as the other cars people are looking at and how they behave in negotiations (passive or aggressive). Evaluating each shopper’s characteristics and actions creates a pricing profile. Think of a profile as a polygraph test that suggests the highest amount each shopper will pay.

Web retailers can similarly profile their shoppers. Just as someone’s clothing can provide pricing clues, so can the manner in which a customer accesses an online store. Is a shopper using a laptop, app, desktop, or internet on their smartphone? What operating system are they using? Where are they located? A customer’s actions also provide pricing clues: What other products are they looking at? How many times have they visited the site? Much like car salespeople, web retailers can electronically evaluate the characteristics and actions of each shopper to create a profile that generates a personalized price.

A key question is whether personalized pricing, on the web or in-store, is ethical. Efforts to tailor prices may inadvertently lead to unfair results. A study by ProPublica found that the Princeton Review’s strategy of levying different prices based on zip code resulted in Asians being twice as likely to be charged a higher price. In a similar vein, a classic economics study on car negotiation found that the markup on final prices for black women was triple the prices offered to white men.

Whether personalized pricing catches on with web retailers is now up to consumers. Will shoppers be comfortable knowing that the prices they are offered may be higher than those presented to others? Will buyers relish “electronically bargaining” to outwit sellers? Retailers first “negotiate” with each customer by personalizing prices based on their profile. In response, savvy shoppers will “bargain” by checking prices on different devices, clearing caches, using the app, conducting multiple searches, asking friends in different cities to see what price they’re quoted, and so on. Or will they become fed up and steer clear of web retailers that price profile? Amazon is on the record as stating that all of its customers see the same prices — will other retailers be so clear-cut?

As the fate of electronic price profiling shakes out, one issue is clear: It is truly a caveat emptor environment for shoppers who use the web.

You Don’t Find Your Purpose — You Build It

Harvard Business Review -

Damien Gavios/EyeEm/Getty Images

“How do I find my purpose?”

Ever since Daniel Gulati, Oliver Segovia, and I published Passion & Purpose six years ago, I’ve received hundreds of questions — from younger and older people alike — about purpose. We’re all looking for purpose. Most of us feel that we’ve never found it, we’ve lost it, or in some way we’re falling short.

But in the midst of all this angst, I think we’re also suffering from what I see as fundamental misconceptions about purpose — neatly encapsulated by the question I receive most frequently: “How do I find my purpose?” Challenging these misconceptions could help us all develop a more rounded vision of purpose.

Misconception #1: Purpose is only a thing you find.

On social media, I often see an inspiring quotation attributed to Mark Twain: “The two most important days in your life are the day you are born and the day you find out why.” It neatly articulates what I’ll call the “Hollywood version” of purpose. Like Neo in The Matrix or Rey in Star Wars, we’re all just moving through life waiting until fate delivers a higher calling to us.

Make no mistake: That can happen, at least in some form. I recently saw Scott Harrison of Charity Water speak, and in many ways his story was about how he found a higher purpose after a period of wandering. But I think it’s rarer than most people think. For the average 20-year-old in college or 40-year-old in an unfulfilling job, searching for the silver bullet to give life meaning is more likely to end in frustration than fulfillment.

You and Your Team Series Making Work More Meaningful

In achieving professional purpose, most of us have to focus as much on making our work meaningful as in taking meaning from it. Put differently, purpose is a thing you build, not a thing you find. Almost any work can possess remarkable purpose. School bus drivers bear enormous responsibility — caring for and keeping safe dozens of children — and are an essential part of assuring our children receive the education they need and deserve. Nurses play an essential role not simply in treating people’s medical conditions but also in guiding them through some of life’s most difficult times. Cashiers can be a friendly, uplifting interaction in someone’s day — often desperately needed — or a forgettable or regrettable one. But in each of these instances, purpose is often primarily derived from focusing on what’s so meaningful and purposeful about the job and on doing it in such a way that that meaning is enhanced and takes center stage. Sure, some jobs more naturally lend themselves to senses of meaning, but many require at least some deliberate effort to invest them with the purpose we seek.

Misconception #2: Purpose is a single thing.

The second misconception I often hear is that purpose can be articulated as a single thing. Some people genuinely do seem to have an overwhelming purpose in their lives. Mother Teresa lived her life to serve the poor. Samuel Johnson poured every part of himself into his writing. Marie Curie devoted her energy to her work.

And yet even these luminaries had other sources of purpose in their lives. Mother Teresa served the poor as part of what she believed was a higher calling. Curie, the Nobel prize–winning scientist, was also a devoted wife and mother (she wrote a biography of her husband Pierre, and one of her daughters, Irene, won her own Nobel prize). And Johnson, beyond his writing, was known to be a great humanitarian in his community, often caring personally for the poor.

Most of us will have multiple sources of purpose in our lives. For me, I find purpose in my children, my marriage, my faith, my writing, my work, and my community. For almost everyone, there’s no one thing we can find. It’s not purpose but purposes we are looking for — the multiple sources of meaning that help us find value in our work and lives. Professional commitments are only one component of this meaning, and often our work isn’t central to our purpose but a means to helping others, including our families and communities. Acknowledging these multiple sources of purpose takes the pressure off of finding a single thing to give our lives meaning.

Misconception #3: Purpose is stable over time.

It’s common now for people to have multiple careers in their lifetimes. I know one individual, for example, who recently left a successful private equity career to found a startup. I know two more who recently left business careers to run for elective office. And whether or not we switch professional commitments, most of us will experience personal phases in which our sources of meaning change — childhood, young adulthood, parenthood, and empty-nesting, to name a few.

This evolution in our sources of purpose isn’t flaky or demonstrative of a lack of commitment, but natural and good. Just as we all find meaning in multiple places, the sources of that meaning can and do change over time. My focus and sense of purpose at 20 was dramatically different in many ways than it is now, and the same could be said of almost anyone you meet.

How do you find your purpose? That’s the wrong question to ask. We should be looking to endow everything we do with purpose, to allow for the multiple sources of meaning that will naturally develop in our lives, and to be comfortable with those changing over time. Unpacking what we mean by “purpose” can allow us to better understand its presence and role in our lives.

How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist

Harvard Business Review -


Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson’s adage applies well to AI adoption: The future is already here, it’s just not evenly distributed.

The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities. And spotting those opportunities doesn’t require a PhD in statistics or even the ability to write code. (It will, spoiler alert, require a brief trip back to high school algebra.)

Having an intuition for how machine learning algorithms work – even in the most general sense – is becoming an important business skill. Machine learning scientists can’t work in a vacuum; business stakeholders should help them identify problems worth solving and allocate subject matter experts to distill their knowledge into labels for data sets, provide feedback on output, and set the objectives for algorithmic success.

As Andrew Ng has written: “Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B).”

But how does this work? Think back to high school math — I promise this will be brief — when you first learned the equation for a straight line: y = mx + b. Algebraic equations like this represent the relationship between two variables, x and y. In high school algebra, you’d be told what m and b are, be given an input value for x, and then be asked to plug them into the equation to solve for y. In this case, you start with the equation and then calculate particular values.

Supervised learning reverses this process, solving for m and b, given a set of x’s and y’s. In supervised learning, you start with many particulars — the data — and infer the general equation. And the learning part means you can update the equation as you see more x’s and y’s, changing the slope of the line to better fit the data. The equation almost never identifies the relationship between each x and y with 100% accuracy, but the generalization is powerful because later on you can use it to do algebra on new data. Once you’ve found a slope that captures a relationship between x and y reliably, if you are given a new x value, you can make an educated guess about the corresponding value of y.

As you might imagine, many exciting machine learning problems can’t be reduced to a simple equation like y = mx + b. But at their essence, supervised machine learning algorithms are also solving for complex versions of m, based on labeled values for x and y, so they can predict future y’s from future x’s. If you’ve ever taken a statistics course or worked with predictive analytics, this should all sound familiar: It’s the idea behind linear regression, one of the simpler forms of supervised learning.

To return to Ng’s formulation, supervised learning requires you to have examples of both the input data and the response, both the x’s and the y’s. If you have both of those, supervised learning lets you come up with an equation that approximates that relationship, so in the future you can guess y values for any new value of x.

So the question of how to identify AI opportunities starts with asking: What are some outcomes worth guessing? And do we have the data necessary to do supervised learning?

For example, let’s say a data scientist is tasked with predicting real estate prices for a neighborhood. After analyzing the data, she finds that housing price (y) is tightly correlated to size of house (x). So, she’d use many data points containing both houses’ size and price, use statistics to estimate the slope (m), and then use the equation y = mx + b to predict the price for a given house based on its size. This is linear regression, and it remains incredibly powerful.

Organizations use similar techniques to predict future product sales, investment portfolio risk, or customer churn. Again, the statistics behind different algorithms vary in complexity. Some techniques output simple point predictions (We think y will happen!) and others output a range of possible predictions with affiliated confidence rates (There’s a 70% chance y will happen, but if we change one assumption, our confidence falls to 60%).

These are all examples of prediction problems, but supervised learning is also used for classification.

Classification tasks clump data into buckets. Here a data scientist looks for features in data that are reliable proxies for categories she wants to separate: If data has feature x, it goes into bucket one; if not, it goes into bucket two. You can still think of this as using x’s to predict y’s, but in this case y isn’t a number but a type.

Organizations use classification algorithms to filter spam, diagnose abnormalities on X-rays, identify relevant documents for a lawsuit, sort résumés for a job, or segment customers. But classification gains its true power when the number of classes increases. Classification can be extended beyond binary choices like “Is it spam or not?” to include lots of different buckets. Perception tasks, like training a computer to recognize objects in images, are also classification tasks, they just have many output classes (for example, the various animal species names) instead of just Bucket 1 and Bucket 2. This makes supervised learning systems look smarter than they are, as we assume their ability to learn concepts mirrors our own. In fact, they’re just bucketing data into buckets 1, 2, 3…n, according to the “m” learned for the function.

So far, this all feels rather abstract. How can you bring it down to earth and learn how to identify these mathematical structures in your everyday work?

There are a few ways you can determine whether a task presents a good supervised learning opportunity.

First, write down what you do in your job. Break apart your activities into: things you do daily or regularly versus things you do sporadically; things that have become second nature versus things that require patient deliberation or lots of thought; and things that are part of a process versus things you do on your own.

For those tasks that you perform regularly, on your own, and that feel automatic, identify how many others in your organization do similar tasks and how many people have done this historically.

Examine the nature of the task. Does it include predicting something or bucketing something into categories?

Ask yourself: If 10 colleagues in your organization performed the task, would they all agree on the answer? If humans can’t agree something is true or false, computers can’t reliably transform judgment calls into statistical patterns.

How long have people in the organization been doing something similar to this task? If it’s been a long time, has the organization kept a record of successfully completed tasks? If yes, this could be used as a training data set for your supervised learning algorithm. If no, you may need to start collecting this data today, and then you can keep a human in the loop to train the algorithm over time.

Next, sit down with a data science team and tell them about the task. Walk them through your thought process and tell them what aspects of information you focus on when you complete your task. This will help them determine if automation is feasible and tease out the aspects of the data that will be most predictive of the desired output.

Ask yourself, if this were automated, how might that change the products we offer to our customers? Ask, what is the worst thing that could happen to the business if this were to be automated? And finally, ask, what is the worst thing that could happen to the business if the algorithm outputs the wrong answer or an answer with a 65% or 70% accuracy rate? What is the accuracy threshold the business requires to go ahead and automate this task?

Succeeding with supervised learning entails a shift in the perspective on how work gets done. It entails using past work — all that human judgment and subject matter expertise — to create an algorithm that applies that expertise to future work. When used well, this makes employees more productive and creates new value. But it starts with identifying problems worth solving and thinking about them in terms of inputs and outputs, x’s and y’s.

What will you do with your surplus?

Seth Godin's Blog -

If you have a safe place to sleep, reasonable health and food in the fridge, you're probably living with surplus. You have enough breathing room to devote an hour to watching TV, or having an argument you don't need to have, or simply messing around online. You have time and leverage and technology and trust.

For many people, this surplus is bigger than any human on Earth could have imagined just a hundred years ago.

What will you spend it on?

If you're not drowning, you're a lifeguard.


You Have The Power

Steven Pressfield Online -

June 12, 1993, presented me with a question.

Go anchor or go springboard?

Let the day pull me deeper than the Mariana Trench or propel me beyond Hubble’s view?

I flip flopped for years.

Today I’m still in Hubble’s view, and it’s been years since I hung with the bottom feeders, but I look around me and see the same struggles.

I’ve written in the past that one key piece of advice that I’ve had for young artists is that they enroll in business classes, so that they can protect themselves from the wolves.

There’s something else: Be prepared to fight. There are people who will try to hurt you—and they don’t all reside in Hollywood.

Be prepared to fight today. Be prepared to fight tomorrow. Be prepared to fight every day that follows.

And if you cross paths with a rabid wolf, don’t let it steal your soul.

Hold on tight and fight.

Fight for your ideas.

Fight for your work.

Fight for your mind.

Fight for your body.

Fight for everything you hold dear.

At the time it might feel like they have the power, but remember your own power and use it.

Your mind. Your body. Your soul.

You have the power.

Everyday People Who Led Momentous Change

Harvard Business Review -

Nancy Koehn, a Harvard Business School historian, tells the life stories of three influential leaders: the abolitionist Frederick Douglass, the pacifist Dietrich Bonhoeffer, and the ecologist Rachel Carson. They all overcame personal challenges to achieve and inspire social change. In Koehn’s new book, Forged in Crisis: The Power of Courageous Leadership in Turbulent Times, she argues that tomorrow’s leaders of social change will come from the business world.

Download this podcast

In a Distracted World, Solitude Is a Competitive Advantage

Harvard Business Review -

Huber & Starke/Getty Images

“Always remember: Your focus determines your reality.” Jedi Master Qui-Gon Jinn shares this advice with Anakin Skywalker in Star Wars, but in our hyper-distracted work world, it’s advice that we all need to hear.

Technology has undoubtedly ushered in progress in a myriad of ways. But this same force has also led to work environments that inundate people with a relentless stream of emails, meetings, and distractions. In 2010, Eric Schmidt, then the CEO of Google, shared a concern with the world: “Every two days, we create as much information as we did from the dawn of civilization until 2003. I spend most of my time assuming the world is not ready for the technology revolution that will be happening soon.” Are we able to process the volume of information, stimuli, and various distractions coming at us each and every day?

A significant volume of research has outlined the problem with this onslaught of information. Research by the University of London reveals that our IQ drops by five to 15 points when we are multitasking. In his book, Your Brain at Work, David Rock explains that performance can decrease by up to 50% when a person focuses on two mental tasks at once. And research led by legendary Stanford University professor Clifford Nass concluded that distractions reduce the brain’s ability to filter out irrelevancy in its working memory.

You and Your Team Series Staying Focused

There is no silver bullet to solving the complex problems ushered in by the information age. But there are some good places to start, and one of them is counterintuitive: solitude. Having the discipline to step back from the noise of the world is essential to staying focused. This is even more important in a highly politicized society that constantly incites our emotions, causing the cognitive effects of distractions to linger. In our book, Lead Yourself First, Ray Kethledge and I define solitude as a state of mind, a space in which to focus one’s own thoughts without distraction — and where the mind can work through a problem on its own.

The ability to focus is a competitive advantage in the world today. Here are some thoughts on how to stay focused at work:

Build periods of solitude into your schedule. Treat it as you would any meeting or an appointment. If you don’t schedule and commit to solitude, something else will fill the space. One need not be Henry David Thoreau here; 15-minute pockets of solitude are very effective. If we spend our entire workday sitting in meetings and answering emails, it leaves little space in our minds to do the hard thinking that is essential to good decision making and leadership.

Analyze where your time is best spent. Most of us have meetings that we can afford to miss, and most of us underutilize our energy because we have not allocated time to reflect and be rigorous about our priorities.

Starve your distractions. Social media, YouTube, and the limitless possibilities of the internet hang over our heads. They tempt us to click links that take us to another five-minute video or article. Acknowledge the ways that the internet lures you in, and then intervene by logging out of your social media accounts and blocking certain websites during work hours — especially the ones you use for a quick distraction “when you have 10 minutes to kill.”

Don’t be too busy to learn how to be less busy. One of the biggest reasons we struggle to focus is because we fill our schedules with too many commitments and we consistently prioritize urgent tasks over important ones. Leadership development and training opportunities exist to enhance your ability to understand yourself better, to reflect, and to grow. Don’t let the tempo of work get in the way of good development opportunities (once in a while).

Create a “stop doing” list. There are only so many hours in a day. As your to-do list grows, you cannot keep accumulating more tasks. Solitude gives you the space to reflect on where your time is best spent, which provides you with the clarity to decide which meetings you should stop attending, which committees you should step down from, and which invitations you should politely decline. This is something that Jim Collins, author of Good to Great, has been advising people to do for many years.

The volume of our communication, and our unfettered access to information and other people, have made it more difficult than ever to focus. Despite this reality, there is another truth: Opportunities to focus are still all around us. But we must recognize them and believe that the benefit of focus, for yourself and the people you lead, is worth making it a priority in your life. In other words, before you can lead others, the first person you must lead is yourself.

Why Hospitals Need Better Data Science

Harvard Business Review -

katyau/Getty Images

Airlines are arguably more operationally complex, asset-intensive, and regulated than hospitals, yet the best performers are doing a better job by far than most hospitals at keeping costs low and make a decent profit while delivering what their customers expect. Southwest Airlines, for example, has figured out how to do well the two operational things that matter most: Keep more planes in the sky more often, and fill each of them up more, and more often, than anyone else. Similarly, winners in other complex, asset-intensive, service-based industries — Amazon, well-run airports, UPS, and FedEx — have figured out how to over-deliver on their promise while staying streamlined and affordable.

These examples are relevant to health care for two reasons.

First, hospital operations are in many ways like airline and airport operations and transportation services. There are many steps in the service operation (check-in, baggage, the security line, gates), high variability at each step (weather delays, congestion, mechanical issues), multiple connected segments in the user journey — and all these operations involve people, not just machines. In mathematical terms, hospital operations, like airlines and transportation, consist of hundreds of mini-processes, each of which is more stochastic and less deterministic than, say, the steps in assembling a car.

And second, hospitals today face the same cost and revenue pressure that retail, transportation, and airlines have faced for years. As Southwest, Amazon, FedEx, and UPS have demonstrated, to remain viable, industries that are asset-intensive and service-based must streamline operations and do more with less. Health care providers can’t keep spending their way out of trouble by investing in more and more infrastructure; instead, they must optimize their use of the assets currently in place.

Insight Center

To do this, providers need to consistently make excellent operational decisions, as these other industries have. Ultimately, they need to create an operational “air traffic control” for their hospitals — a centralized command-and-control capability that is predictive, learns continually, and uses optimization algorithms and artificial intelligence to deliver prescriptive recommendations throughout the system. Dozens of health care organizations are now streamlining operations by using platforms from providers including LeanTaaS, Intelligent InSites, Qgenda, Optum, and IBM Watson Health. What these solutions have in common is the ability to mine and process large quantities of data to deliver recommendations to administrative and clinical end users.

Improving hospital operational efficiency through data science boils down to applying predictive analytics to improve planning and execution of key care-delivery processes, chief among them resource utilization (including infusion chairs, operating rooms, imaging equipment, and inpatient beds), staff schedules, and patient admittance and discharge. When this is done right, providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience. Here are a few examples:

Increasing OR utilization. For a resource that brings in more than 60% of admissions and 65% of revenue at most hospitals, current block-scheduling techniques fall far short in optimizing operating-room time and in improving patient access, surgeon satisfaction, and care quality. Current techniques — phone calls, faxes, and emails — make block-schedule changes cumbersome, error prone, and slow. Using predictive analytics, mobile technologies, and cloud computing, providers are mining utilization patterns to dramatically improve OR scheduling.

For example, mobile apps now allow surgeons and their schedulers to request the block time they need with one click. At UCHealth in Colorado, scheduling apps allow patients to get treated faster (surgeons release their unneeded blocks 10% sooner than with manual techniques), surgeons gain better control and access (the median number of blocks released by surgeon per month has increased by 47%), and overall utilization (and revenue) increases. With these tools, UCHealth increased per-OR revenue by 4%, which translates into an additional $15 million in revenue annually.

Slashing infusion center wait times. Infusion scheduling is an extremely complex mathematical problem. Even for a 30-chair center, avoiding the 10 AM to 2 PM “rush hour” in a patient-centric way requires picking one of a googol (10100 ) of possible solutions. Faced with this challenge, NewYork-Presbyterian Hospital applied predictive analytics and machine learning to optimize its schedule templates, resulting in a 50% drop in patient wait times. In addition to improving longer-term patient scheduling, these technologies help schedulers manage an infusion center’s day-to-day uncertainty — last-minute add-ons, late cancellations, and no-shows — as well as optimize nurses’ workloads and the timing of breaks.

Streamlining ED operations. Emergency departments are famous for bottlenecks, whether because patients are waiting for lab results or imaging backed up in queues or because the department is understaffed. Analytics-driven software that can determining the most efficient order of ED activities, dramatically reducing patient wait times. When a new patient needs an X-ray and a blood draw, knowing the most efficient sequence can save patients time and make smarter use of ED resources. Software can now reveal historic holdups (maybe there’s a repeated Wednesday EKG staffing crunch that needs fixing) and show providers in real time each patient’s journey through the department and wait times. This allows providers to eliminate recurring bottlenecks and call for staff or immediately reroute patient traffic to improve efficiency. Emory University Hospital, for example, used predictive analytics to forecast patient demand for each category of lab test by time of day and day of week. In so doing, the provider reduced average patient wait times from one hour to 15 minutes, which reduced ED bottlenecks proportionally.

ED to inpatient-bed transfer. Predictive tools can also allow providers to forecast the likelihood that a patient will need to be admitted, and provide an immediate estimate of which unit or units can accommodate them. With this information, the hospitalist and ED physician can quickly agree on a likely onboarding flow, which can be made visible to everyone across the onboarding chain. This data-driven approach also helps providers prioritize which beds should be cleaned first, which units should accelerate discharge, and which patients should be moved to a discharge lounge. Using a centralized, data-driven patient logistics system, Sharp HealthCare in San Diego reduced its admit order-to-occupy time by more three hours.

Accelerated discharge planning. To optimize discharge planning, case managers and social workers need to be able to foresee and prevent discharge delays. Electronic health records or other internal system often gather data on “avoidable discharge delays” — patients who in the last month, quarter, or year were delayed because of insurance verification problems or lack of transportation, destination, or post-discharge care. This data is a gold mine for providers; with the proper analytics tools, within an hour of a patient arriving and completing their paperwork, a provider can predict with fairly high accuracy who among its hundreds of patients is most likely to run into trouble during discharge. By using such tools, case managers and social workers can create a shortlist of high-priority patients whose discharge planning they can start as soon as the patient is admitted. Using discharge analytics software, MedStar Georgetown University Hospital in Washington, DC, for example, increased its daily discharge volume by 21%, reduced length of stay by half a day, and increased morning discharges to 24% of all daily discharges.

Making excellent operational decisions consistently, hundreds of times per day, demands sophisticated data science. Used correctly, analytics tools can lower health care costs, reduce wait times, increase patient access, and unlock capacity with the infrastructure that’s already in place.

A publishing master class

Seth Godin's Blog -

Announcing a two-day workshop in my office for 8 people.

I define publishing as the work of investing in intellectual property and monetizing it by bringing it to people who want to pay for it. The world of publishing is changing fast, and I'd like to help a few publishers make a difference.

Publishing can include music, books, conferences and other experiences and content. The ideas may change, but the work of publishing at scale has much in common across all fields.

The form to apply, with dates and details, is right here.

Here's a quick FAQ:

Who's it for? Thoughtful leaders who are committed to publishing in a new way, making a difference and contributing to our culture by bringing out work that matters (and supporting those who make it). We're particularly looking for a mix of people with experiences and dreams that fall outside the mainstream in terms of background, posture or credentials. I think publishing is a profession, and I'd like to help others that do as well.

How much does it cost? I'm not charging a fee. Running a workshop is a powerful exercise, and I'll probably learn as much as you will. You'll need to pay your way here and find a place to stay, so I figure you'll have some skin in the game. Not everything is about making a profit. Maybe we'll even change a few lives.

Can you do it remotely, or turn it into something bigger? Not right now, sorry.

What do you know about publishing? Well, I've been publishing books, software, music, courses and even action figures for more than thirty years. Here are some highlights. This seminar follows on from the SAMBA, the FeMBA, the Agenda session and other intensives I've hosted over the years.

If you're interested, please apply right away. The deadline is really soon, and we never admit the last four people who apply to anything we do.


How Office Politics Corrupt the Search for High-Potential Employees

Harvard Business Review -

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Few topics have captivated talent management discussions more intensely than potential. The obsession with predicting who may be a future star or the next top leader has influenced academic research and human resources practices alike. But how good are we at evaluating human potential? The answer is, it’s mixed. On the one hand, science has given us robust tools and powerful theories to quantify the key indicators of future career success, job performance, and leadership effectiveness. On the other hand, in the real world of work, organizational practices lag behind, with 40% of designated “HiPos” — high-potential employees — not doing well in the future and at least one in two leaders disappointing, derailing, or failing to drive high levels of engagement and team performance.

The main reason underlying this bleak state of affairs is that HiPo nominations are contaminated by organizational politics. To be more precise, there are six dynamics that prevent organizations from identifying, promoting, and developing the right people for leadership roles, namely:

The politics of intuition. The foremost reason for the failure of HiPo identification programs is that without science it is virtually impossible to assess potential. People love and trust their intuition, but when our evaluations are based on intuitive judgments, they are ripe for being led astray by biases and political undercurrents. Most organizations rely on the leader’s subjective opinion to identify the relative potential of candidates, but leaders usually aren’t able to measure performance objectively, and even when they are, they tend to focus on past performance, which is not a good indicator of future performance when the context and role change. For instance, the majority of solid individual contributors — technical experts — are not good people managers, nor do they enjoy managing others.

You and Your Team Series Office Politics

The politics of self-interest. Those in charge of spotting potential are usually more interested in their own career than others’, and they tend to perceive a personal cost to promoting people who are a key asset, particularly when those people are better than the spotters. For instance, Ben (not his real name) leads the R&D division of a private pharma business. He is data-driven and sets clear objectives for his team. However, when asked to nominate his rising stars he makes a conscious decision to exclude his top performer, Sophie. Sophie is an extraordinary resource for Ben, but designating her as a HiPo would increase the likelihood that she takes on a more senior role and leaves his team — which may force Ben to do all the work himself. Furthermore, Ben rather enjoys Sophie’s company, so he doesn’t want her to leave — even if her replacement is equally productive, he will probably miss Sophie. And what if this promotion really helps Sophie’s career take off, to the point of eclipsing Ben’s?

The politics of avoidance. At times, politics may even drive managers to nominate faux-pos (fake HiPos) purely to avoid uncomfortable situations. For example, Jane is a midlevel manager in the marketing department of a large software firm. She is generally liked by her team, but she struggles to communicate critical or negative feedback to her reports. Jordan, an ambitious graphic designer on her team, is aware of Jane’s softness, and confronts her about a promotion and pay rise. Although Jane is not particularly impressed with Jordan’s work — ranking him below some of her other reports — she does not want conflict, so she decides to grant Jordan what he requests and keep him happy. Unsurprisingly, Jordan’s colleagues regard him as manipulative and pushy, but they decide to not raise the issue in the hope that Jane appreciates their attitude and that their achievements speak for themselves. Sadly, their strategy is far less effective than Jordan’s, which causes more of Jane’s reports to bully her into a promotion.

The politics of favoritism. Leaders tend to have asymmetrical information on different employees, and this personal limitation is addressed simply by picking the more familiar candidate — after all, “better the devil you know.” For example, Samantha was the HR business partner for the largest division of a food products giant. When Samantha was recommended for promotion to be the next head of HR for the business, opposition came from the regional general manager (GM), who said that since he was new to the region, he would need more time to “assess” Samantha’s potential and get to know her better. However, instead of taking the time to evaluate Samantha, the GM decided to nominate Mohan, the head of compliance who had worked with him for several years. Sometimes, employees’ past achievements can be meaningless when they haven’t been followed closely by those in charge of judging potential.

The politics of ageism. While rarely discussed, age is also a factor underlying the politics of potential. For instance, most leaders are grappling with the process of identifying candidates who will lead digital initiatives. The founder of a diversified business group wanted to set up a division that would work across all the businesses on digital initiatives in India. All the other business units were headed by leaders in their fifties who had worked in the business for the last two decades or more. Many of them had started their careers with the founder and had proved their loyalty over the years. The digital division was a strategic initiative recommended by the consulting firm that had worked with the business for the last year. They had recommended a panel of three leaders who had been in their early thirties. The conclusion was that it was best to hire “a more experienced leader” (someone also in their fifties) from outside since the internal candidates would probably not be accepted by the other business heads. Of course, ageism can also go the other way. For example, if you are over 40, it is very unlikely that you’ll be considered for a HiPo program, and the rise of technology has made managers and leaders younger and less experienced.

The politics of gender. As PwC’s seminal “leaking pipeline” report shows, a large number of women quit when they are experienced, mid-career, and at the level of manager/Senior manager. Decision makers (usually male-dominated groups) often ignore female HiPos. A tech services company we know was planning to deprioritize its retail vertical, because it always made losses. The board suggested that they try a female leader instead. Within two years, Sarah turned retail into the fastest-growing and most profitable division. And yet the board still underestimated her contribution, saying that the situation was so bad before she started that it could only have improved, even by osmosis. Her colleagues trivialized her achievements by saying that women tend to excel in shopping and any shopping-related task but are not well-equipped for leading strategically. A year later, when the new senior roles were announced, Sarah found her name missing from the list. Shortly afterwards, she left the company and launched a successful retail store business as founder and CEO.

In short, the politics of potential can prevent organizations from upgrading their leadership talent and make data-driven decisions an anomaly rather than the norm. Too many times we have seen the CEO’s favorite candidate be put through a formal assessment simply as a way of confirming a decision that has already been made in advance, not for merit. Helping the wrong people get to leadership roles is detrimental not only to those who have strong potential but also to the entire organization.

Netflix and Why the Future of Streaming Looks Like Old-School TV

Harvard Business Review -

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Netflix hit the industry with some bombshell moves this month. First, it announced that it plans to spend $8 billion on original content next year (including on 80 new movies). This is far more than any other online player. Obviously, this is great news for its 100 million-odd customers worldwide.

What isn’t so great for customers is the other news. Netflix will raise the price of its standard plan by a dollar a month and its premium plan by two dollars a month. With these increases it is slowly edging toward the $15-a-month plan offered by its competitor HBO.

This means Netflix isn’t just your Blockbuster replacement anymore. Makers of original content — including the likes of Disney — are moving away from Netflix. Instead, Netflix looks like an old-school TV network; if one had to predict what the industry will look like in five years, one might say there will be a set of online channels with the expectation that consumers will subscribe to all or most of them. At $15 a month each, that suggests consumers will take four or five of them instead of a normal monthly cable bill. Netflix is investing to be the “must subscribe” channel in that world.

There are obvious differences between the new TV industry and the old. On-demand is one, as is the “born global” approach of Netflix. This makes it easier to do what Netflix was built for: experiment with the long tail rather than go for mass-market hits. While it might be inconceivable for an old-style network to green-light a series that appeals to 0.5% of its viewers, for Netflix, if that series is the reason that 0.5% choose to subscribe, that is enough to justify it.

One great feature of subscriber revenue is that it makes life very comfortable. As Netflix contemplated its $1-a-month rise, I would guess board members had an easy time calculating in their heads the extra $100 million a month, or $1.2 billion a year, that would bring in. And they had every reason to believe that growth would continue, as subscribers are quite sticky. Netflix looks a lot less significant on people’s credit card statements than the traditional cable bill. Small amounts like that can go unnoticed for years. A little while ago, a professor colleague of mine recounted that in finally reviewing a credit card statement, he noticed a recurring AOL charge. He must have signed up for it a decade or more ago and forgotten to cancel!

Subscriber revenue is nice, but it has a flip side. When your business has reached its peak, letting it go can be hard. This is precisely the challenge that old networks and cable TV are facing. To be sure, every person and their dog can see where the industry is going. But if networks and cable were to jump to online and on-demand right now, they would accelerate the numbers cutting the cord. The drive to hang on another year or two and wring more from those sticky customers is just too tempting.

Netflix is banking on that. And if Netflix is right, it won’t end well for the old guard. When the traditional players really start to struggle, Netflix and others will be able to scoop up all that old content for a song.

That said, the same flip side will come for Netflix. It is already happening. I have a kid in college who still uses my Netflix account. There is certainly no economic reason for me to cut the parental cord now, but looking forward, I don’t see when it will ever happen. I have vague hopes that at some point they might be embarrassed to still be on their parent’s account. (Although my 30-something editor informs me that he still uses his parents’ account, too.)

You can see the issue for Netflix. It doesn’t have the luxury that internet providers have, that as soon as a kid leaves home they need their own subscription. So its subscription business does not grow with population. That is perhaps why it raised the price of its top plan by $2, which allows more simultaneous streams — but that increase hardly offsets the loss of a new paying customer.

Without new customers being born that Netflix has to compete for, there may come a time when the company runs of out of subscriber growth. Then the circle will come back around for it and, like today’s TV incumbents, it won’t be able to take advantage of new channels without cannibalizing its hard-won legacy customers.

Processing negative reviews

Seth Godin's Blog -

Assumption: Some people love what you do. They love your product, your service, the way you do your work (if that's not true, this post isn't for you. You have a more significant problem to work on first).

So, how to understand it when someone hates what you do? When they post a one-star review, or cross the street to avoid your shop, or generally are unhappy with the very same thing that other people love?

It's not for them.

They want something you don't offer. Or they want to buy it from someone who isn't you. Or they don't understand what it's for or how or why you do it.

Some of these things you can address by telling a story more clearly, some you can't.

Either way, right now, they're telling you one thing: It's not for them.

Okay, thanks for letting us know.


How to Tell Your Boss That You’re Not Engaged at Work

Harvard Business Review -

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Many people think of employee engagement as a relatively new idea, but scientists have been studying it for years. William Kahn first introduced the term in 1990, defining it as “the degree of psychological identification employees experience with their job role or work persona.” He noticed that organizations tended to overlook the influence that everyday experiences have on people’s work motivation, focusing instead on their talents, skills, and expertise. Although such qualities are no doubt critical, they are not sufficient to account for the wide range of subjective experiences employees have at work.

For instance, two people with similar skills and backgrounds may be working for the same company, in the same team, and have very similar roles — yet one of them may be totally immersed, enthused, and fulfilled, while the other is fed up, bored, and alienated. As a consequence, the former will perform better, stay longer in the organization, and be a positive influence on other employees, while the latter will underperform, have a negative impact on others, and quit. The difference between these two states (and people) is caused by engagement.

You and Your Team Series Communication

In line, research shows that higher engagement in its various forms tends to predict a range of positive organizational outcomes, such as individual job performance, team effectiveness, and customer satisfaction ratings. Meanwhile, lower engagement has been linked to a range of problematic outcomes, such as increased turnover, absenteeism, and stress. Despite the organizational benefits of engagement, global estimates indicate that most employees are not fully engaged at work — particularly in developed economies, where employees’ expectations are highest. In the U.S. alone, this translates into a productivity loss of about $500 billion a year.

One of the main drivers of employee disengagement is bad leadership, which on its own can be expected to account for as much as 30% of the variability in engagement levels. However, leaders are often unaware of this, not least because upward negative feedback is rare. Indeed, it is very unusual for employees to feel that they can honestly and openly criticize their bosses without paying the consequences. Even telling your boss that you are not engaged makes for uncomfortable conversation, yet the alternative — not saying anything — is arguably worse for everyone.

To address this issue, here are four ways you may want to communicate your dissatisfaction with work, in the hope that your manager may be able (and willing) to help:

  • “I need your help to reach my full potential.” This line highlights the known difference between maximum performance, what you can do, and typical performance, what you usually do. When people are highly motivated, both are very similar. But given the high disengagement base rate, for most people typical performance is merely a fraction of what they are capable of doing. Using this line will remind your boss that employee engagement is not a philosophical or metaphysical notion. On the contrary, there is a clear ROI on engagement, which is to align people’s potential with their actual performance.
  • “I need a new challenge.” This line captures the importance of learning as a driver of engagement. When people are put in roles that enable them to master new skills and solve challenging problems, they will feel more valuable and fulfilled. In contrast, having employees cruise in autopilot mode will turn their jobs into boring and meaningless routines and make them feel useless. Unfortunately, employees and managers are equally prone to optimizing work for efficiency and making everything as reliable and predictable as possible. For employees, this frees up mental resources and relieves anxieties; there are obviously practical benefits to having an easy job. For managers, it’s a way to de-risk employees’ performance, ensuring they do what is expected as efficiently as possible, making Frederick Taylor — the father of management consulting and work efficiency — proud. This is also why finding a role that leverages only your strengths is unlikely to work in the longer term. When everything is easy, where will you find motivating challenges and learning opportunities to grow?
  • “I’m not sure if this role is the right fit for me.” This line focuses on a critical determinant of engagement, and in turn job performance, namely person-job fit. According to this notion, people will be more satisfied and perform better when they are in roles that align with their values, interests, styles, and abilities. In that sense, talent is little more than personality in the right place. If this approach can enable a conversation with your manager about what your preferences and drivers are, it could help them rethink where you would fit — and perform — best. This conversation should also provide you with an opportunity to describe what aspects of your current job you enjoy more, in the hope that you can increase those activities and transition away from others you dislike. Accordingly, research shows that people who gain enough of their manager’s trust to craft a job in this way tend to find work more meaningful and engaging.
  • “I find my work exhausting — can you help me?” This final line is a gentle reminder that managers are largely responsible for the motivation levels of their employees and teams. All motivation is ultimately self-motivation, but it is a manager’s job to help employees avoid draining and demotivating work situations — where exhausting barriers outweigh exciting challenges. Furthermore, even when managers hire people who seem dispositionally driven and capable of pushing themselves, they cannot expect this drive to be maintained all the time. For instance, studies show there is a common “honeymoon effect” for engagement, where most people are quite excited about their jobs during the first year, only to disengage later on. Competent managers will try to understand what makes each employee tick and what turns them off in order to develop their role in a way that makes sense and provides meaning. And when meaning isn’t enough, there are always traditional incentives — including financial rewards, recognition, promotion, and flexibility.

To be clear, none of these approaches is guaranteed to work, for a few reasons. First, managers may dismiss them or blame employees for their own problems. Second, even when managers are interested in helping, they may be unable to; some jobs are hard to sell, alternative options may be limited, and the wider organizational context may be toxic or problematic. Third, even if these approaches seem unthreatening and subtle, not least because they acknowledge that engagement is also the employee’s responsibility, some managers may get offended and interpret them as criticism or negative feedback.

If you think your manager may have a negative reaction or may be unwilling or unable to help, don’t go into this conversation without a Plan B in place, whether that’s a job offer already in hand or the understanding that you may need to move on. While people are rarely fired for being disengaged (unless they also perform or behave badly), raising this issue could harm your reputation with your manager. But the risks of staying in a job where you’re disengaged could ultimately be even worse.

Using Technology to Improve Rural Health Care

Harvard Business Review -

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Providing health care in rural regions presents unique challenges. For some patients, the closest doctor may be a three-hour drive. Clinicians seeking an expert consult may find there’s no appropriate specialist within 100 miles. And vast distance can hinder the dissemination of best practices and coordination of care. At Sanford Health, one of the largest rural health-care-delivery systems, we’ve tackled this challenge by leveraging an array of technologies to provide high-value care to a population of around 2 million, dispersed across 300,000 square miles in the Dakotas. We’ve adopted a single electronic medical record (EMR) platform, embraced telehealth technologies, developed enterprise-wide departments, and committed to data transparency.

EMR platform. So far, we have rolled out our integrated EMR platform to 45 hospitals and more than 300 clinics. Key to its success in rural care delivery is that we can rapidly disseminate common decision-support tools across the entire network. For example, in order to improve hypertension control across our population, we built in decision support for our rooming nurses. Anywhere in our system, whether a patient is at the orthopedist or the allergist, if the blood pressure of a patient receiving medication for hypertension is found to be elevated, the software prompts the nurses to make sure the patient follows up with their primary care provider. By catching patients with high blood pressure wherever they might be in the system, hypertension control rates for our patients remain over 90%.

In addition, we’ve programmed our EMR to integrate uniform, evidenced-based treatment guidelines into every provider’s workflow, decreasing unnecessary variation and allowing nurses to work at the top of their licenses. Our standard treatment regimen for hypertension, based on JNC8 guidelines, is pushed to all providers, standardizing which medications to use, educational materials to provide, and when to follow up. As a result, our time to optimal control of hypertension fell from 110 days to 40. Hard copies of guidelines that once gathered dust on a shelf have been made into consistent action items, delivered through the EMR at the point of care for every appropriate patient, every time, anywhere in our system.

Enterprise departments. To further assure that our far-flung patients get the best and most consistent care, we created multidisciplinary teams gathered around one specialty (such as pediatrics) or one disease (such as breast cancer) to determine standards of care. For example, we assembled the best of our physicians who treat breast cancer patients — specialists in oncology, radiology, reconstructive surgery, and other areas — to create standards for screening, treatment, quality, patient safety, and patient experience. This team determined that 3D mammography should be the standard of care, and we have prioritized making that available to all Sanford patients. With the implementation of 3D mammography, patient recall rates have fallen, while cancer detection has increased.

Insight Center

Telemedicine. Connecting specialists with distant patients is one of the biggest challenges in rural care. Sanford has hundreds of physicians who have racked up many hours of “windshield time” doing outreach to our more remote communities. To improve access, we now have specialists do telemedicine consults with critical-access hospitals so that patients can receive care close to home while their doctors can still get, for example, an infectious-disease consult.

We also use telehealth to improve urgent care. With our telestroke program, when a rurally based physician suspects that a patient is having a stroke, they can immediately videoconference with a Sanford neurologist for a consult. Getting clot-busting medication to a stroke patient before transport to a hospital is time-sensitive and can prevent long-term sequela. For some patients, we believe our telestroke program was the difference between disability and a full recovery.

In designing these programs, we thought first about how best to improve care, and only then how to get reimbursed. Payment models haven’t yet caught up with telemedicine, but we believe that patients will be willing to pay something for these services, helping to offset our costs. Many of our patients who live remotely must take significant time off work to come to our clinic locations. We think many patients would rather pay $49 for a video visit for, say, a diabetes follow-up than be away from work or home for several hours to make the visit in person. We are now working to make it possible for all of our primary care providers to offer scheduled video visits for any patient appointment.

Data transparency. Finally, we have made our quality data transparent, which helps best practices flow through our system and ensures that our patients receive the same quality care whether they are in Sioux Falls, South Dakota (population 175,000), or Canby, Minnesota (population 1,700). To this end, our primary care physicians use tested metrics available through Minnesota Community Measurement for measuring and reporting on quality in the ambulatory-care setting, and any provider can see any other provider’s data. The resulting peer pressure inspires everyone to do their best work.

As important, if the data shows one region has developed a best practice that’s having a major impact on quality, it’s readily apparent and efforts to share the approach can immediately begin. For example, we had a high-performing clinic that reached the National Colorectal Cancer Roundtable’s “80% by 2018” patient screening goal. We identified its best practices — such as improved workflows, outreach through our “My Chart” patient portal, and celebrating top performers — and are now applying them in all clinics. We now have 10 clinics that are above the 80% goal and have improved our enterprise performance overall by screening an additional 14,200 patients in the last two years.

These initiatives and others have helped Sanford Health improve and become a model system for rural health. But my take is that these same tactics just might be of value to providers and patients in other settings, including the most urban delivery systems in the country.

The Case for Trash-Talking at Work, According to Research

Harvard Business Review -


In 2000, British Airways sponsored the construction of the London Eye, a giant Ferris wheel in the heart of London. When the builders encountered some technical difficulties while trying to erect the wheel, Richard Branson, the mercurial founder of rival airline Virgin Atlantic Airways, seized the opportunity. He arranged for a blimp to fly over the London Eye with a giant banner that read ‘‘BA can’t get it up!”

Though executives are acutely attuned to the role of competition in the workplace, far less attention has been paid to the role of competitive communication — trash-talking. Our research has studied what happens when individuals, managers, and CEOs mock their competitors and how it can influence motivation and performance.

Trash-talking is pervasive in organizations. When we surveyed office employees at Fortune 500 companies, we found that 57% of the employees reported that trash-talking occurs on a monthly basis.

Trash-talking is competitive incivility. More precisely, we define trash-talking as boastful comments about the self or insulting comments about an opponent that are delivered by a competitor before or during competition. For example, when Dan Akerson, former CEO of General Motors, announced that his company would be launching a new car that would compete against Mercedes-Benz’s C-Class line, he said “They call it C class because it’s very average.”

To understand the interpersonal consequences of trash-talking, we conducted two pilot studies and six controlled experiments. In our experiments, we randomly assigned participants to interact with neutral opponents or trash-talkers through an instant messaging platform. We then assessed their perceptions, performance, and behavior.

Our main finding was that trash-talking increases the psychological stakes of competition. Whereas we might naturally see each other as mere competitors, when we start trash-talking each other, we come to see each other as rivals. As a result, we found that trash-talking boosted motivation and productivity. Specifically, targets of trash-talking worked harder and accomplished far more in a competition — even when they worked for the same economic stakes.

Trash-talking, however, can also harm the target. Because trash-talking boosts motivation and the drive to defeat an opponent, it can also promote the use of unethical behavior. We found that when individuals were paired with trash-talking opponents instead of neutral opponents, they were almost twice as likely to cheat on a performance task involving unscrambling anagrams. Competitors become so focused on winning that they become more likely to cut corners on their path to victory.

Furthermore, we found that trash-talking undermines creativity. Individuals who are targets of trash-talking are highly motivated, but they are also distracted. After exposing individuals to trash-talking, we assessed their creative insight. We found that 52% of competitors exhibited divergent thinking when interacting with a neutral opponent, but only 37% of competitors demonstrated divergent thinking when interacting with trash-talking opponents. If the task at hand required careful thought, they were simply less capable of accomplishing creative work than they were before the trash-talking episode.

As a manager, does this mean you should trash-talk your employees to make them more productive? No, don’t do it. We contrasted the effects of trash-talking in competitive interactions with the effects of incivility in cooperative interactions. We found that when you trash-talk an opponent, your opponent performs better. However, when you make uncivil remarks to a teammate, your teammate performs worse. In both cases, when you are a target of aggression, you become motivated to punish. Targets can retaliate in competition by performing better, whereas targets retaliate in cooperation by sabotaging performance.

As managers, should you ever expose your employees to trash-talking messages from rival firms? Perhaps. If you share trash-talking messages that were delivered by a competitor, you could boost your employees’ motivation. But keep in mind that this boost in motivation will help most when the nature of the work requires persistence — for mechanical tasks. In this regard, trash-talking can be used as a motivational tool. However, these same trash-talking messages may prove disruptive for creative tasks, and they may prove to be particularly harmful when you need to make sure your employees do not cut ethical corners.

And what about trash-talking a competitor? In this case, you should be concerned about boosting your competitor’s motivation and transforming a mere competitor into an intense rival. But there are potential benefits. In addition to potentially disrupting their focus, the act of trash-talking a competitor may help you bond with your team, as you face off against a common enemy.

Finally, leaders should be careful to model the organizational culture that they aspire to create. What you say and what you post on social media can profoundly shape how your organization and others view the competitive landscape. You should be mindful and monitor how your employees communicate. And when you find trash-talking in your workplace, you should make sure the competitive flames fueled by trash-talking do not get fanned into flames that go wild.

Why You Can Focus in a Coffee Shop but Not in Your Open Office

Harvard Business Review -

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A few years ago, during a media interview for one of my books, my interviewer said something I still ponder often. Ranting about the level of distraction in his open office, he said, “That’s why I have a membership at the coworking space across the street — so I can focus.”

While I fully support the backlash against open offices, the comment struck me as odd. After all, coworking spaces also typically use an open office layout.

But I recently came across a series of studies examining the effect of sound on the brain that reveals why his strategy works.

From previous research, we know that workers’ primary problem with open or cubicle-filled offices is the unwanted noise.


But new research shows that it may not be the sound itself that distracts us…it may be who is making it. In fact, some level of office banter in the background might actually benefit our ability to do creative tasks, provided we don’t get drawn into the conversation. Instead of total silence, the ideal work environment for creative work has a little bit of background noise. That’s why you might focus really well in a noisy coffee shop, but barely be able to concentrate in a noisy office.

One study, published in the Journal of Consumer Research, found that the right level of ambient noise triggers our minds to think more creatively. The researchers, led by Ravi Mehta of the University of Illinois Urbana-Champaign, examined various levels of noise on participants as they completed tests of creative thinking.

Participants were randomized into four groups and everyone was asked to complete a Remote Associates Test (a commonly used measurement that judges creative thinking by asking test-takers to find the relationship between a series of words that, as first glance, appear unrelated). Depending on the group, participants were exposed to various noise levels in the background, from total silence to 50 decibels, 70 decibels, and 85 decibels. The differences between most of the groups were statistically insignificant; however, the participants in the 70 decibels group (those exposed to a level of noise similar to background chatter in a coffee shop) significantly outperformed the other groups. Since the effects were small, this may suggest that our creative thinking doesn’t differ that much in response to total silence and 85 decibels of background noise — the equivalent of a loud garbage disposal or a quiet motorcycle. Since none of us presumably want to work next to a garbage disposal or motorcycle, I found this surprising.

But since the results at 70 decibels were significant, the study also suggests that the right level of background noise — not too loud and not total silence — may actually boost one’s creative thinking ability. The right level of background noise may disrupt our normal patterns of thinking just enough to allow our imaginations to wander, without making it impossible to focus. This type of “distracted focus” appears to be the optimal state for working on creative tasks. As the authors write, “Getting into a relatively noisy environment may trigger the brain to think abstractly, and thus generate creative ideas.”

In another study, researchers used frontal lobe electroencephalographic (EEG) machines to study the brain waves of participants as they completed tests of creativity while exposed to various sound environments. The researchers found statistically significant changes in creativity scores and a connection between those scores and certain brain waves. As in the previous study, a certain level of white noise proved the ideal background sound for creative tasks.

So why do so many of us hate our open offices? The quiet chatter of colleagues and the gentle thrum of the HVAC should help us focus. The problem may be that, in our offices, we can’t stop ourselves from getting drawn into others’ conversations or from being interrupted while we’re trying to focus. Indeed, the EEG researchers found that face-to-face interactions, conversations, and other disruptions negatively affect the creative process. By contrast, a coworking space or a coffee shop provides a certain level of ambient noise while also providing freedom from interruptions.

Taken together, the lesson here is that the ideal space for focused work is not about freedom from noise, but about freedom from interruption. Finding a space you can hide away in, regardless of how noisy it is, may be the best strategy for making sure you get the important work done.

The Female Carries the Mystery

Steven Pressfield Online -


I’m re-reading one of my favorite books on writing, Blake Snyder’s Save the Cat! Goes To the Movies.

Blake Snyder (who died tragically at age 51 in 2009) was a screenwriter who did a lot of thinking about what makes a story work and what makes it not work. His first book, Save the Cat!, is a classic.

Bogey and Bacall in “The Big Sleep”

One of Blake Snyder’s writer-friendly inventions is what he called “BS2,” the Blake Snyder Beat Sheet.

The beat sheet broke a story—any story from the Iliad to La La Land—down into about sixteen “beats,” e.g. Opening Image, Theme Stated, Catalyst, Break into Two, etc.

I’ve been thinking about this a lot in the light of my ongoing “Reports from the Trenches” struggles.

I’m asking myself,

What am I learning through this process of rebuilding a story that has crashed?

How can I help others in the same straits?

What’s the Big Takeaway?

When you and I say that we “write instinctively,” what we mean is we trust our gut. That’s how we shape and flesh out our story. We might feel something like, “The story should be told by Character X, and not in memory but in the present.” Or, “Something’s missing in the middle. We need more with Characters Y and Z.”

What Blake Snyder was trying to do with his Beat Sheet (and what any good editor does, or what I myself am trying to do now with my Trenches project) is to formulize that process. Blake read a million novels and watched a million movies, and he concluded that the ones that work all follow certain timeless story principles or guidelines.

Sean Young in “Blade Runner” 1982

All stories that work have a similar shape, Blake believed. The specific one you or I might be working on at the moment will have its own unique shape. But it will cohere, in pretty predictable fashion, around the perennial “beats” of a narrative structure that has existed since our days of telling stories around the fire in the cave.

I agree.

Every story fits into a genre and every genre has conventions.

Here’s one I learned (I never knew this before) over the past five and half months beating my head against the wall on my police procedural/supernatural thriller.


            The female carries the mystery.


(Sara Paretsky’s wonderful V.I. Warshawski notwithstanding, I’m speaking in the old-school idiom where the detective—Sam Spade, Philip Marlowe, Rick Deckard—is a male.)

The above convention helped me enormously in reworking the story I was stuck on. I applied it and it worked.

What exactly do I mean by “the female carries the mystery?”

I mean that in a traditional detective story (which is what a police procedural is, even it’s set in the future like Blade Runner or Blade Runner 2049), the detective protagonist is usually following three threads as he drives the narrative forward:


  1. Solve the crime/bring the villain to justice.
  2. Unravel some inner personal conflict of his own.
  3. Unearth the secret(s) of the female lead, with whom he has become emotionally involved.


There’s always a woman, and the woman always has a secret.

Evelyn Mulwray (Faye Dunaway) in Chinatown.

Ruth Wonderly (Mary Astor) in The Maltese Falcon.

Helen Grayle (Charlotte Rampling) in Farewell, My Lovely.

The female can be a femme fatale or a damsel in distress.

Barbara Stanwyck in Double Indemnity, Kathleen Turner in Body Heat, Lauren Bacall in The Big Sleep.

But the bottom line for the male detective/cop/lover is


Unravel the woman’s secret (“She’s my sister, she’s my daughter!”) and you solve the crime.


What I’m trying to say is that genre matters.

Conventions count.

Ryan Gosling in “Blade Runner 2049”

The story principles that work in other stories will work in yours and mine too.



Maybe the reason ours is not working is that we’re either violating a convention or we don’t even know it exists and so we’ve left it out.


I like the way Blake Snyder thinks because he looks to timeless storytelling principles and tries not to ignore them or to blow them off but to respect them and enlist them in our own story’s cause.

I haven’t seen Blade Runner 2049 yet, but for sure the female’s secret in the 1982 original (Rachel [Sean Young] is a replicant and in desperate need of help because of that) is central to that plot and to Rick Deckard’s (Harrison Ford) actions throughout.

On leveling up

Seth Godin's Blog -

I got a note yesterday from a recent grad of the altMBA. He said, "I have to say that the value I have gained from this group far exceeds anything I could give back, and please know that it is rippling out and will affect many more than just the people that went through the program. Thank you..."

We put together this short video about the impact that this 30-day workshop is having on the thousands of people who have gone through it. I'll be talking a little bit about how and why we made it via Facebook Live today at 10 am NY time.

The next available session is in January. Tomorrow is the last day for First Priority applications. The application takes about fifteen minutes.

There are no tests.

If you're ready for us, we're ready for you.



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