Category Archives: Psychology

Measuring Employee Engagement Accurately

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Measuring Employee Engagement Accurately

GUEST POST from David Burkus

Employee engagement has been a hot topic for several decades. And for good reason. Business teams with highly engaged employees have a 59 percent lower turnover rate than those with less engaged staff. Highly engaged teams are 17 percent more productive. Engaged teams receive 10 percent higher customer reviews. And yes, businesses with engaged employees have higher profit margins than non-engaged competitors.

But getting employees to feel engaged is no small feat. Even how to measure employee engagement can be a difficult question to answer for many leaders. But there are good reasons to try. Measuring employee engagement helps identify cultural strengths for the organization. Done well measuring employee engagement builds trust through the company. And measuring employee engagement helps understand and respond to potential trends, both in the organization and across the industry.

In this article, we’ll outline how to measure employee engagement through the most commonly used method and offer the strengths and weaknesses of each method.

Surveys

The first method used to measure employee engagement is surveys. And this is also the most commonly used method as well—mostly for commercial reasons. After the Gallup Organization launched their original Q12 survey of engagement, dozens of competing companies with competing surveys sprung up all promising a different and better way to measure employee engagement. Most of these surveys present a series of statements and ask participants to rate how much they agree or disagree on a 5- or 7-point “Likert” scale. Some include a few open-ended questions as well.

The biggest strength of the survey method is that it scales easily. For an organization with hundreds or thousands of employees, emailing out a survey invitation and letting the system do the rest of the work saves a lot of time. In addition, surveys allow for objective comparisons between teams and divisions, or between the company and an industry benchmark. But while the comparisons may be objective, the data itself may not be. That’s the biggest weakness of surveys, they most often rely on self-reported data. And as a result, those taking the survey may not be completely honest, either because they want to feel more engaged or because they don’t trust the survey to be truly anonymous.

Proxies

The second method used to measure employee engagement is proxies—meaning other metrics that serve as a proxy for engagement. Because we know that employee engagement correlates to other measurements, we can assume a certain level of engagement based off those measurements. For example, productivity has a strong correlation to employee engagement when looking at teams or entire organizations. So, if productivity is high, it’s safe to assume employee engagement isn’t low. Likewise, absenteeism and turnover tend to rise as employee engagement falls, so changes over time on those metrics point to changes over time in engagement. (And comparisons between engagement in departments/teams can sometimes be made based on these proxies.)

The big strength of proxies is that they’re usually measurements that are already being captured. Larger organizations are already tracking productivity, turnover, and more and so the data are already there. The weaknesses of proxy measurements, however, are that they’re not a perfect correlation. It’s possible to be productive but not engaged, and there are often other reasons certain roles have higher turnover than others beyond employee engagement. In addition, some of these proxies are lagging indicators—if turnover is increasing than engagement has already fallen—and so they don’t provide leaders a chance to respond as fast.

Interviews

The third method used to measure employee engagement is interviews. And this method is the least common one but it’s growing in usage. Sometimes these are called “stay” interviews, in contrast to the exit interviews that are common practice in organizations. The idea is to regularly interview employees who are staying about how the company (and leaders) are doing and how things could be improved. While the questions used should provide some structure, the open-ended nature allows leaders to discover potentially unknown areas for improvement.

The biggest strength of stay interviews is that they’re a useful method for team leaders who may not have senior leader support for measuring engagement. Conducting stay interviews with ones’ team doesn’t require senior leadership approval or data from Human Resources. So, it’s available to leaders at all levels. And while that’s true, the weakness of stay interviews is that they’re hard to scale. Training thousands of managers on conducting a stay interview isn’t as easy as emailing out a survey. Moreover, because different managers would conduct these interviews differently, cross-comparison would be subject to bias. Stay interviews are a powerful way to measure engagement on a team, but they’re most potent when they’re used by managers who truly want the feedback their team provides (and not merely because they were told to conduct interviews).

Conclusion

While all three methods are a way to measure employee engagement, it’s not enough to merely measure. We measure things so we can improve them. So once the measurement is done, leaders need to have a plan in place make progress. That plan should include sharing out the results of the measurement and sharing the lessons learned from analyzing those results. In addition, leaders should share what changes are planned based on those lessons. And while it doesn’t need to be shared, it’s worth thinking ahead of time how the effects of those changes will be themselves be measured.

Done well, these measurements and the resulting plans will create an environment where everyone can do their best work ever.

Image credit: Pixabay

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The Hard Problem of Consciousness is Not That Hard

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The Hard Problem of Consciousness is Not That Hard

GUEST POST from Geoffrey A. Moore

We human beings like to believe we are special—and we are, but not as special as we might like to think. One manifestation of our need to be exceptional is the way we privilege our experience of consciousness. This has led to a raft of philosophizing which can be organized around David Chalmers’ formulation of “the hard problem.”

In case this is a new phrase for you, here is some context from our friends at Wikipedia:

“… even when we have explained the performance of all the cognitive and behavioral functions in the vicinity of experience—perceptual discrimination, categorization, internal access, verbal report—there may still remain a further unanswered question: Why is the performance of these functions accompanied by experience?”

— David Chalmers, Facing up to the problem of consciousness

The problem of consciousness, Chalmers argues, is two problems: the easy problems and the hard problem. The easy problems may include how sensory systems work, how such data is processed in the brain, how that data influences behavior or verbal reports, the neural basis of thought and emotion, and so on. The hard problem is the problem of why and how those processes are accompanied by experience.3 It may further include the question of why these processes are accompanied by that particular experience rather than another experience.

The key word here is experience. It emerges out of cognitive processes, but it is not completely reducible to them. For anyone who has read much in the field of complexity, this should not come as a surprise. All complex systems share the phenomenon of higher orders of organization emerging out of lower orders, as seen in the frequently used example of how cells, tissues, organs, and organisms all interrelate. Experience is just the next level.

The notion that explaining experience is a hard problem comes from locating it at the wrong level of emergence. Materialists place it too low—they argue it is reducible to physical phenomena, which is simply another way of denying that emergence is a meaningful construct. Shakespeare is reducible to quantum effects? Good luck with that.

Most people’s problems with explaining experience, on the other hand, is that they place it too high. They want to use their own personal experience as a grounding point. The problem is that our personal experience of consciousness is deeply inflected by our immersion in language, but it is clear that experience precedes language acquisition, as we see in our infants as well as our pets. Philosophers call such experiences qualia, and they attribute all sorts of ineluctable and mysterious qualities to them. But there is a much better way to understand what qualia really are—namely, the pre-linguistic mind’s predecessor to ideas. That is, they are representations of reality that confer strategic advantage to the organism that can host and act upon them.

Experience in this context is the ability to detect, attend to, learn from, and respond to signals from our environment, whether they be externally or internally generated. Experiences are what we remember. That is why they are so important to us.

Now, as language-enabled humans, we verbalize these experiences constantly, which is what leads us to locate them higher up in the order of emergence, after language itself has emerged. Of course, we do have experiences with language directly—lots of them. But we need to acknowledge that our identity as experiencers is not dependent upon, indeed precedes our acquisition of, language capability.

With this framework in mind, let’s revisit some of the formulations of the hard problem to see if we can’t nip them in the bud.

  • The hard problem of consciousness is the problem of explaining why and how we have qualia or phenomenal experiences. Our explanation is that qualia are mental abstractions of phenomenal experiences that, when remembered and acted upon, confer strategic advantage to organisms under conditions of natural and sexual selection. Prior to the emergence of brains, “remembering and acting upon” is a function of chemical signals activating organisms to alter their behavior and, over time, to privilege tendencies that reinforce survival. Once brain emerges, chemical signaling is supplemented by electrical signaling to the same ends. There is no magic here, only a change of medium.
  • Annaka Harris poses the hard problem as the question of “how experience arise[s] out of non-sentient matter.” The answer to this question is, “level by level.” First sentience has to emerge from non-sentience. That happens with the emergence of life at the cellular level. Then sentience has to spread beyond the cell. That happens when chemical signaling enables cellular communication. Then sentience has to speed up to enable mobile life. That happens when electrical signaling enabled by nerves supplements chemical signaling enabled by circulatory systems. Then signaling has to complexify into meta-signaling, the aggregation of signals into qualia, remembered as experiences. Again, no miracles required.
  • Others, such as Daniel Dennett and Patricia Churchland believe that the hard problem is really more of a collection of easy problems, and will be solved through further analysis of the brain and behavior. If so, it will be through the lens of emergence, not through the mechanics of reductive materialism.
  • Consciousness is an ambiguous term. It can be used to mean self-consciousness, awareness, the state of being awake, and so on. Chalmers uses Thomas Nagel’s definition of consciousness: the feeling of what it is like to be something. Consciousness, in this sense, is synonymous with experience. Now we are in the language-inflected zone where we are going to get consciousness wrong because we are entangling it in levels of emergence that come later. Specifically, to experience anything as like anything else is not possible without the intervention of language. That is, likeness is not a qualia, it is a language-enabled idea. Thus, when Thomas Nagel famously asked, “What is it like to be a bat?” he is posing a question that has meaning only for humans, never for bats.

Going back to the first sentence above, self-consciousness is another concept that has been language-inflected in that only human beings have selves. Selves, in other words, are creations of language. More specifically, our selves are characters embedded in narratives, and use both the narratives and the character profiles to organize our lives. This is a completely language-dependent undertaking and thus not available to pets or infants. Our infants are self-sentient, but it is not until the little darlings learn language, hear stories, then hear stories about themselves, that they become conscious of their own selves as separate and distinct from other selves.

On the other hand, if we use the definitions of consciousness as synonymous with awareness or being awake, then we are exactly at the right level because both those capabilities are the symptoms of, and thus synonymous with, the emergence of consciousness.

  • Chalmers argues that experience is more than the sum of its parts. In other words, experience is irreducible. Yes, but let’s not be mysterious here. Experience emerges from the sum of its parts, just like any other layer of reality emergences from its component elements. To say something is irreducible does not mean that it is unexplainable.
  • Wolfgang Fasching argues that the hard problem is not about qualia, but about pure what-it-is-like-ness of experience in Nagel’s sense, about the very givenness of any phenomenal contents itself:

Today there is a strong tendency to simply equate consciousness with qualia. Yet there is clearly something not quite right about this. The “itchiness of itches” and the “hurtfulness of pain” are qualities we are conscious of. So, philosophy of mind tends to treat consciousness as if it consisted simply of the contents of consciousness (the phenomenal qualities), while it really is precisely consciousness of contents, the very givenness of whatever is subjectively given. And therefore, the problem of consciousness does not pertain so much to some alleged “mysterious, nonpublic objects”, i.e. objects that seem to be only “visible” to the respective subject, but rather to the nature of “seeing” itself (and in today’s philosophy of mind astonishingly little is said about the latter).

Once again, we are melding consciousness and language together when, to be accurate, we must continue to keep them separate. In this case, the dangerous phrase is “the nature of seeing.” There is nothing mysterious about seeing in the non-metaphorical sense, but that is not how the word is being used here. Instead, “seeing” is standing for “understanding” or “getting” or “grokking” (if you are nerdy enough to know Robert Heinlein’s Stranger in a Strange Land). Now, I think it is reasonable to assert that animals “grok” if by that we mean that they can reliably respond to environmental signals with strategic behaviors. But anything more than that requires the intervention of language, and that ends up locating consciousness per se at the wrong level of emergence.

OK, that’s enough from me. I don’t think I’ve exhausted the topic, so let me close by saying…

That’s what I think, what do you think?

Image Credit: Pixabay

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The Malcolm Gladwell Trap

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The Malcolm Gladwell Trap

GUEST POST from Greg Satell

A few years ago I bought a book that I was really excited about. It’s one of those books that created a lot of buzz and it was highly recommended by someone I respect. The author’s pedigree included Harvard, Stanford, McKinsey and a career as a successful entrepreneur and CEO.

Yet about halfway in I noticed that he was choosing facts to fit his story and ignoring critical truths that would indicate otherwise, much like Malcolm Gladwell’s often does in his books. Once I noticed a few of these glaring oversights I found myself not being able to fully trust anything the author wrote and set the book aside.

Stories are important and facts matter. When we begin to believe in false stories, we begin to make decisions based on them. When these decisions go awry, we’re likely to blame other factors, such as ourselves, those around us or other elements of context and not the false story. That’s how many businesses fail. They make decisions based on the wrong stories.

Don’t Believe Everything You Think

Go to just about any innovation conference and you will find some pundit on stage telling a story about a famous failure, usually Blockbuster, Kodak or Xerox. In each case, the reason given for the failure is colossal incompetence by senior management: Blockbuster didn’t recognize the Netflix threat. Kodak invented, but then failed to market, a digital camera. Xerox PARC developed technology, but not products.

In each case, the main assertion is demonstrably untrue. Blockbuster did develop and successfully execute a digital strategy, but its CEO left the company due a dispute and the strategy was reversed. Kodak’s EasyShare line of digital cameras were top sellers, but couldn’t replace the massive profits the company made developing film. The development of the laser printer at Xerox PARC actually saved the company.

None of this is very hard to uncover. Still, the author fell for two of these bogus myths (Kodak and Xerox), even after obviously doing significant research for the book. Most probably, he just saw something that fit with his narrative and never bothered to question whether it was true or not, because he was to busy validating what he already knew to be true.

This type of behavior is so common that there is a name for it: confirmation bias. We naturally seek out information that confirms our existing beliefs. It takes significant effort to challenge our own assumptions, so we rarely do. To overcome that is hard enough. Yet that’s only part of the problem.

Majorities Don’t Just Rule, They Also Influence

In the 1950’s, Solomon Asch undertook a pathbreaking series of conformity studies. What he found was that in small groups, people will conform to a majority opinion. The idea that people have a tendency toward conformity is nothing new, but that they would give obviously wrong answers to simple and unambiguous questions was indeed shocking.

Now think about how hard it is for a more complex idea to take hold across a broad spectrum of people, each with their own biases and opinions. The truth is that majorities don’t just rule, they also influence. More recent research suggests that the effect applies not only to people we know well, but that we are also influenced even by second and third degree relationships.

We tend to accept the beliefs of people around us as normal. So if everybody believes that the leaders of Blockbuster, Kodak and Xerox were simply dullards who were oblivious to what was going on around them, then we are very likely to accept that as the truth. Combine this group effect with confirmation bias, it becomes very hard to see things differently.

That’s why it’s important to step back and ask hard questions. Why did these companies fail? Did foolish and lazy people somehow rise to the top of successful organizations, or did smart people make bad decisions? Was there something else to the story? Given the same set of facts, would we act any differently?

The Inevitable Paradigm Shift

The use of the term “paradigm shift” has become so common that most people are unaware that it started out having a very specific meaning. The idea of a paradigm shift was first established by Thomas Kuhn in his book The Structure of Scientific Revolutions, to describe how scientific breakthroughs come to the fore.

It starts with an established model, the kind we learn in school or during initial training for a career. Models become established because they are effective and the more proficient we become at applying a good model, the better we perform. The leaders in any given field owe much of their success to these models.

Yet no model is perfect and eventually anomalies show up. Initially, these are regarded as “special cases” and are worked around. However, as the number of special cases proliferate, the model becomes increasingly untenable and a crisis ensues. At this point, a fundamental change in assumptions has to take place if things are to move forward.

The problem is that most people who are established in the field believe in the traditional model, because that’s what most people around them believe. So they seek out facts to confirm these beliefs. Few are willing to challenge what “everybody knows” and those who do are often put at great professional and reputational risk.

Why We Fail To Adapt

Now we can begin to see why not only businesses, but whole industries get disrupted. We tend to defend, rather than question, our existing beliefs and those around us often reinforce them. To make matters worse, by this time the idea has become so well established that we will often incur switching costs if we abandon it. That’s why we fail to adapt.

Yet not everybody shares our experiences. Others, who have not grown up with the conventional wisdom, often do not have the same assumptions. They also don’t have an existing peer group that will enforce those assumptions. So for them, the flaws are much easier to see, as are the opportunities to doing things another way.

Of course, none of this has to happen. As I describe in Mapping Innovation, some companies, such as IBM and Procter & Gamble, have survived for over a century because they are always actively looking for new problems to solve, which forces them to look for new ideas and insights. It compels them to question what they think they know.

Getting stories right is hard work. You have to force yourself. However, we all have an obligation to get it right. For me, that means relentlessly checking every fact with experts, even for things that I know most people won’t notice. Inevitably, I get things wrong—sometimes terribly wrong— and need to be corrected. That’s always humbling.

I do it because I know stories are powerful. They take on a life of their own. Getting them right takes effort. As my friend Whitney Johnson points out, the best way to avoid disruption is to first disrupt yourself.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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Let Yourself Draw Inspiration from Others

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Let Yourself Draw Inspiration from Others

GUEST POST from Mike Shipulski

When you try something new, check to see who has done something similar. Decompose their design approach. What were they trying to achieve? What outcome were they looking for? Who were their target customers? Do this for at least three existing designs – three real examples that are for sale today.

Here’s a rule to live by: When trying something new, don’t start from scratch.

What you are trying to achieve is unique, but has some commonality with existing solutions. The outcome you are looking for is unique, but it’s similar to outcomes others have tried to achieve. Your target customers are unique, but some of their characteristics are similar to the customers of the solutions you’ll decompose.

Here’s another rule: There are no “clean sheet” sheet designs, so don’t try to make one.

There was an old game show called Name That Tune, where contestants would try to guess the name of a song by hearing just a few notes. The player wins when they can name the tune with the *fewest* notes. And it’s the same with new designs – you want to provide a novel customer experience using the fewest new notes.

A rule: Reuse what you can, until you can’t.

Because the customer is the one who decides if your new offering offers them new value, the novel elements of your design don’t have to look drastically different in a side-by-side comparison way. But the novel elements of your offering do have to make a significant difference in the customer’s life. With that said, however, it can be helpful if the design element responsible for the novel goodness is visually different from the existing alternatives. But if that’s not the case, you can add a non-functional element to the novelty-generating element to make it visible to the customer. For example, you could add color, or some type of fingerprint, to the novel element of the design so that customers can see what creates the novelty for them. Then, of course, you market the heck out of the new color or fingerprint.

A rule: It’s better to make a difference in a customer’s life than, well, anything else.

Don’t be shy about learning from what other companies have done well. That’s not to say you should violate their patents, but it’s a compliment when you adopt some of their best stuff. Learn from them and twist it. Understand what they did and abstract it. See the best in two designs and combine them. See the goodness in one domain and bring it to another.

Doing something for the first time is difficult, why not get inspiration from others and make it easier?

Image credit: Unsplash

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Productive Disagreement Requires Trust

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Productive Disagreement Requires Trust

GUEST POST from Mike Shipulski

When there’s disagreement between words and behavior, believe the behavior. This is especially true when the words deny the behavior.

When there’s disagreement between the data and the decision, the data is innocent.

When there’s agreement that there’s insufficient data but a decision must be made, there should be no disagreement that the decision is judgment-based.

When there’s disagreement on the fact that there’s no data to support the decision, that’s a problem.

When there’s disagreement on the path forward, it’s helpful to have agreement on the process to decide.

When there’s disagreement among professionals, there is no place for argument.

When there’s disagreement, there is respect for the individual and a healthy disrespect for the ideas.

When there’s disagreement, the decisions are better.

When there’s disagreement, there’s independent thinking.

When there’s disagreement, there is learning.

When there’s disagreement, there is vulnerability.

When there’s disagreement, there is courage.

When there’s disagreement, there is trust.

Image credit: Pixabay

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Will Artificial Intelligence Make Us Stupid?

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Will Artificial Intelligence Make Us Stupid?

GUEST POST from Shep Hyken

I was just at an industry conference focusing on AI (Artificial Intelligence). Someone commented, “AI is going to make us stupid.” Elaborating on that statement, the commenter’s reasoning was that it takes thinking and problem-solving out of the process. We will be given the answer and won’t have to know anything else.

I can see his point, but there is another way of looking at this. In the form of a question, “Did calculators make us dumb?”

I remembered getting a calculator and was excited that I could do long division by just pushing the buttons on the calculator. Even though it gave me the correct answer, I still had to know what to do with it. It didn’t make me dumb. It made me more efficient.

I liken this to my school days when the teacher said we could bring our books and notes to the final exam. Specifically, I remember my college algebra teacher saying, “I don’t care if you memorize formulas or not. What I care about is that you know how to use the formulas. So, on your way out of today’s class, you will receive a sheet with all the formulas you need to solve the problems on the test.”

Believe me when I tell you that having the formulas didn’t make taking the test easier. However, it did make studying easier. I didn’t have to spend time memorizing formulas. Instead, I focused on how to use the information to efficiently get the correct answer.

Shep Hyken Artificial Intelligence Cartoon

So, how does this apply to customer service? Many people think that AI will be used to replace customer support agents – and even salespeople. They believe all customer questions can be answered digitally with AI-infused technology. That may work for basic questions. For higher-level questions and problems, we still need experts. But there is much more.

AI can’t build relationships. Humans can. So, imagine the customer service agent or salesperson using AI to help them solve problems and get the best answers for their customers. But rather than just reciting the information in front of them, they put their personality into the responses. They communicate the information in a way their customers understand and can relate to. They answer additional and clarifying questions. They can even make suggestions outside of the original intent of the customer’s call. This mixes the best of both worlds: almost instantly accessible, accurate information with a live person’s relationship- and credibility-building skills. That’s a winning combination.

No, AI won’t make us dumb unless we let it. Instead, AI will help us be more efficient and effective. And it could even make us appear to be smarter!

Image Credits: Shep Hyken, Pixabay

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Meeting Expectations Versus Managing Hope

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Meeting Expectations Versus Managing Hope

GUEST POST from Shep Hyken

At a recent customer service presentation, the speaker who preceded me said that we must do better than simply meeting our customers’ expectations, and he shared some stories of truly amazing service experiences. Then it was my turn to speak. I didn’t want to contradict him, but I needed the audience to understand that it is impossible to go above and beyond with customers at every interaction. Sometimes meeting expectations is a perfect experience.

In my customer service keynote speeches, I talk about ‘Managing the Moment’. The idea comes from Jan Carlson, and if you’ve been following me, you will recognize this concept. Every interaction customers have with you or your company gives them the opportunity to form an impression. Understanding this simple idea is a good start to developing and/or maintaining your customer service and CX strategy.

I believe you must manage expectations, and if you are even the tiniest bit above average in doing what customers expect, your customers will love you, give you high ratings, and refer you to their colleagues and friends. The key to being successful with this idea is to be consistent. You want customers to say things like, “They always are knowledgeable,” or “They are always so helpful.” The word always followed by something positive, typically an expectation is what you’re going for.

Shep Hyken Expectations Cartoon

So back to the idea of just meeting expectations. Some people confuse expectations with hope. Here’s what I mean by this. If I call someone for help and leave a message, I expect them to call me back, and I hope they will return the call sooner rather than later.

Let’s say I’m called back within an hour. I’m pleasantly surprised because the person met my expectation of the callback and did it in the timeframe I hoped they would – maybe even a little sooner.

Most customers won’t analyze the experience quite this way, but it is exactly what they want – or hope for. They will, however, notice that the call was returned quickly and may say, “Thanks for calling me back so quickly.” The returned call was expected. The comment about “quickly” indicates their expectations were met or slightly exceeded. And if you do that every time, the customer will use the always when they talk about you and describe the experience by saying, “They always call me back quickly.”

Let’s flip this around. I believe most customers hope for a great experience, but not necessarily an over-the-top or above-and-beyond experience. And based on their typical experience with service laggards, they, unfortunately, don’t have high expectations. So, whenever you meet or just ever so slightly exceed what your customers hope for, you’ve created a positive experience that gets them to say, “I’ll be back!”

Image Credits: Shep Hyken

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Why Not Now?

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Why Not Now?

GUEST POST from Mike Shipulski

If you are anxious, you’re worried about what might happen. You’re living in the future. If you are sad or angry, you’re reacting to what happened. You’re living in the past. Nothing can be accomplished when living in the past because the die is cast. And nothing can be accomplished when living in the future because it’s all in your head. The only time we have is now.

The only time to start is now. Even if your project is a short one, you’re in a day-for-day slip with your completion date for every day you don’t start. And this is doubly true for long projects. If you’re living in the past, you block yourself from starting because the last project was difficult, you didn’t have the resources or it didn’t come out as expected, and you want to protect yourself from a rerun. If you’re living in the past, you block yourself from starting because you don’t know how it will turn out, you don’t have all the answers, you don’t have sufficient resources, and you don’t know what you don’t know. Acknowledge the problems with the past and potential problems with the future, and start anyway.

Starting starts with starting.

The only time to say something is now. If you’re living in the past, you block yourself from saying something controversial or thought-provoking because you remember how it went the last time someone did that. If you’re living in the future, you prevent yourself from saying something radical because, well, you weren’t paying attention and missed your opportunity to change history. Acknowledge that there may be some blowback for your insightful comments, live in the now and say them anyway. And live in the now so you can pay attention and use your sharp wit to create the future.

If you don’t say something, nothing is ever said.

The only time to help is now. Living in the past, you block yourself from understanding the significance of the situation because you see it through old lenses. Living in the future, you block yourself from helping because you worry if the helping will help or worry the helping will get in the way of your future commitments. If someone needs help, help them now. They will understand that the outcome is uncertain, and they’re okay with that. In fact, they will be happy you recognized their troubling situation and made time to check in with them. When you live in the now, people appreciate it. The time to help is now.

When no one helps, no one is helped.

When you find yourself living in the past, close your eyes, recognize your anger or sadness, and focus on your breath for ten seconds. And if that doesn’t work, put your hand on your chest and do it again. And if that doesn’t work, tell yourself your sadness is temporary and do it again. This is a fail-safe way to bring yourself into the now. Then, sitting in the now, start that project, say what must be said, and help people.

And when you find yourself living in the future, close your eyes, recognize your anxiety, and focus on your breath for ten seconds. And if that doesn’t work, put your hand on your chest and do it again. And if that doesn’t work, tell yourself your anxiety is temporary and repeat. This will bring you into the now. Then, sitting in the now, start that project, say what must be said, and help people.

The only time to shape the future is now.

Image credit: Pixabay

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The Real Reasons Employees Stay Or Leave

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Hint: It’s about more than money

The Real Reasons Employees Stay Or Leave

GUEST POST from Shep Hyken

What if every great employee you (or your company) hired never left? Of course, that’s unrealistic … or is it? Joey Coleman is one of the brightest authors and speakers on the planet. His first book, Never Lose a Customer Again, is one of the very best books I’ve read on how to keep your customers coming back. He’s now taken some of the same ideas that worked for customer retention and written a second book, just as brilliant, Never Lose an Employee Again.

Coleman studied and researched organizations worldwide, and he found that 50% of hourly employees quit before their 100-day anniversary. For non-hourly or salaried employees, it’s 20%. I interviewed Coleman on Amazing Business Radio to learn how we can keep good employees.

“How we onboard employees and make them feel part of our community can differentiate whether they will be long-time employees or leave almost as fast as they came,” Coleman said. “The first 100 days are the most important time in the entire relationship with an employee because this is where the foundation is laid.”

So, why do employees leave? Contrary to popular belief, the No. 1 reason an employee leaves to work elsewhere is not money. In the traditional exit interview, where an employee talks to their employer face-to-face, money is the easiest and safest excuse for an exit. The true reasons for leaving are more telling—and can help prevent an employee from going, even if offered more money somewhere else. Coleman cites the Work Institute employee retention study, sharing the top five reasons employees leave:

  1. No clear career path — This is the top reason employees leave. Nearly one-quarter (24%) don’t see future opportunities in the organization. Most employees want to advance their careers and learn new skills. Laying out a potential path for an employee from the very beginning of their employment with you can have long-term benefits.
  2. Stress or lack of resources — Not providing employees with the tools they need or giving them too heavy of a workload can impact their emotional health, which could lead them to find work at another company.
  3. Health and family matters — As much as an employee may love working with your organization, personal health, a sick child or an aging parent can interfere with their ability to work. Regarding the latter, Coleman says, “Just as some employers provide daycare for young children, some employers in the future will also provide an eldercare program.”
  4. Work/life balance — The job has to fit the employee’s lifestyle. Something as seemingly insignificant as a long commute can negatively impact the employee’s personal life so much that they leave.
  5. Money — Almost one in 10 (9%) leave because of money. That means nine out of 10 leave for other reasons, often within our control.

After reading the reasons listed above, here is Coleman’s top advice:

  • Affirm the employee made the right decision to come to work at your organization — The concept of affirm is one of the eight phases of the first 100 days Coleman covers in his book. There is a scientifically proven emotional reaction in which a new employee begins to doubt their decision to accept your job offer. It is called “new hire’s remorse,” which happens between when they accept the job offer and their first day. Reaffirm your new employee’s decision to accept your job offer. Establish a personal and emotional connection even before their first day.
  • On-boarding must be practiced at a higher level — Don’t just onboard the first day or two (or even a week or two). Coleman says, “If you’re not painting a clear path for your people but expecting them to manage and figure out their careers on their own, then you deserve to lose them.” The amount of time you spend with employees over the first 100 days directly correlates to how long they will stay.
  • The employee’s personal life is important — Notice that three of the five reasons people leave the organization are personal. Coleman says, “You need to know what’s going on between 5 p.m. and 9 a.m. as much as you are interested in what’s happening between 9 a.m. and 5 p.m. What are your people doing and dealing with when they are not at work?”

I’ve often said that you won’t have a business without customers. Coleman makes the case that the same applies to employees. Much of what gets customers to come back is a great customer experience. You can’t deliver a great CX without a great employee experience on the inside of your organization. Coleman says, “People think that customer experience and employee experience are two different silos. The better way to look at this is that they are two sides of the same coin. We must work on both!”

This article originally appeared on Forbes.com

Image Credits: Shep Hyken

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Three Cognitive Biases That Can Kill Innovation

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Three Cognitive Biases That Can Kill Innovation

GUEST POST from Greg Satell

Probably the biggest myth about innovation is that it’s about ideas. It’s not. It’s about solving problems. The truth is that nobody cares about what ideas you have, they care about the problems you can solve for them. So don’t worry about coming up with a brilliant idea. If you find a meaningful problem, the ideas will come.

The problem with ideas is that so many of them are bad. Remember New Coke? It seemed like a great idea at first. The new formula tested well among consumers and even had some initial success in the market. Yet what the marketers missed is that many had an emotional attachment to the old formula and created a huge backlash.

Our minds tend to play tricks on us. We think we’ve done our homework and that we base our ideas on solid insights, but often that’s not the case. We see what we want to see and then protect our ideas by ignoring or explaining away facts that don’t fit the pattern. In particular, we need to learn to identify and avoid these three cognitive biases that kill innovation.

1. Availability Bias

It’s easy to see where the marketers at Coke went wrong. They had done extensive market testing and the results came back wildly positive. People consistently preferred the new Coke formula over the old one. The emotional ties that people had to the old formula, however, were harder to see.

Psychologists call these types of errors availability bias. We tend to base our judgments on the information that is most easily available, such as market testing, and neglect other factors, such as emotional bonds. Often the most important factors are the ones that you don’t see and therefore don’t figure into your decision making.

The way to limit availability bias is to push yourself to get uncomfortable facts in front of you. In his new book, Farsighted, Steven Johnson notes two techniques that can help. The first, called pre-mortems, asks you to imagine that the project has failed and figure out why it happened. The second, called red teaming sets up an independent team to find holes in the idea.

Amazon’s innovation process is specifically set up to overcome availability bias. Project managers are required to write a 6-page memo at the start of every project, which includes a press release of both positive and negative reactions. Through a series of meetings, other stakeholders do their best to poke holes in the idea. None of this guarantees success, but Amazon’s track record is exceptionally good.

2. Confirmation Bias

Availability bias isn’t the only way we come to believe things that aren’t true. The machinery in our brains is naturally geared towards making quick judgments. We tend to lock onto the first information we see (called priming) and that affects how we see subsequent data (framing). Sometimes, we just get bad information from a seemingly trustworthy, but unreliable source.

In any case, once we come to believe something, we will tend to look for information that confirms it and discount contrary evidence. We will also interpret new information differently according to our preexisting beliefs. When presented with a relatively ambiguous set of facts, we are likely to see them as supporting out position.

This dynamic plays out in groups as well. We tend to want to form an easy consensus with those around us. Dissent and conflict are uncomfortable. In one study that asked participants to solve a murder mystery, the more diverse teams came up with better answers, but reported doubt and discomfort. The more homogeneous teams performed worse, but were more confident.

Imagine yourself sitting in a New Coke planning meeting. How much courage would it have taken to challenge the consensus view? How much confidence would you have in your dissent? What repercussions would you be willing to risk? We’d all like to think that we’d speak up, but would we?

3. The Semmelweis Effect

In 1847, a young doctor named Ignaz Semmelweis had a major breakthrough. Working in a maternity ward, he discovered that a regime of hand washing could dramatically lower the incidence of childbed fever. Unfortunately, instead of being lauded for his accomplishment, he was castigated and considered a quack. The germ theory of disease didn’t take hold until decades later.

The phenomenon is now known as the Semmelweis effect, the tendency for professionals in a particular field to reject new knowledge that contradicts established beliefs. The Semmelweis effect is, essentially, confirmation bias on a massive scale. It is simply very hard for people to discard ideas that they feel have served them well.

However, look deeper into the Semmelweis story and you will find a second effect that is just as damaging. When the young doctor found that his discovery met some initial resistance, he railed against the establishment instead of collecting more evidence and formatting and communicating his data more clearly. He thought it just should have been obvious.

Compare that to the story of Jim Allison, who discovered cancer immunotherapy. At first, pharmaceutical companies refused to invest in Jim’s idea. Yet unlike Semmelweis, he kept working to gather more data and convince others that his idea could work. Unlike Semmelweis, who ended up dying in an insane asylum, Jim won the Nobel Prize.

We all have a tendency to reject those who reject our ideas. Truly great innovators like Jim Allison, however, just look at that as another problem to solve.

Don’t Believe Everything You Think

When I’m in the late stages of writing a book, I always start sending out sections to be fact checked by experts and others who have first-person knowledge of events. In some cases, these are people I have interviewed extensively, but in others sending out the fact checks is my first contact with them.

I’m always amazed how generous people are with their time, willing in some cases to go through material thoroughly just to help me get the story straight. Nevertheless, whenever something comes back wrong, I always feel defensive. I know I shouldn’t, but I do. When told that I’m wrong, I just have the urge to push back.

But I don’t. I fight that urge because I know how dangerous it is to believe everything you think, which is why I go to so much effort to send out the fact checks in the first place. That’s why, instead of publishing work that’s riddled with errors and misinterpretations, my books have held up even after being read thousands of times. I’d rather feel embarrassed at my desk than in the real world.

The truth is that our most fervently held beliefs are often wrong. That’s why we need to make the effort to overcome the flawed machinery in our minds. Whether that is through a formal process like pre-mortems and red teams, or simply seeking out a fresh pair of eyes, we need to avoid believing everything we think.

That’s much easier said than done, but if you want to innovate consistently, that’s what it takes.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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