Marina Gorbis's Blog, page 1325

Accomplish More by Committing to Less

Believing that more is always more is a dangerous assumption.

There’s a cost to complexity. Every time you commit to something new, you not only commit to doing the work itself, but also remembering to do the work, dealing with the administrative overhead, and to getting it all done in the time constraints involved.

The unfortunate result of taking on everything that comes your way is that you end up spend more of your time managing the work and less time investing in truly immersing yourself in what’s most important and satisfying. Many people in large organizations spend a huge amount of their time going to meetings to talk about doing work, writing e-mails to communicate about work, and worrying about how they’re going to get work done — yet rarely making meaningful progress on a weekly basis. They see saying “yes” to everything and having a constantly growing to-do list as at best a marker of success, and at worst something that can’t be avoided anyway.

But the people creating the most value for their organizations take a different approach. They start with having radical clarity on the meaningful work that will create results. Then when something new comes up, they stop and evaluate the new item versus what they already know is most important before saying “yes.” Sizing up new opportunities — from a simple request for a meeting to a large request for a project — isn’t about being insubordinate or unhelpful. Instead, it’s about recognizing new activities for what they are: a request for time resources that if not managed properly could pose a serious risk to the stellar execution of the most significant priorities.

It’s simple math. Each additional project divides your time into smaller and smaller pieces so that you have less of it to devote to anything. Whereas if you reduce your number of responsibilities, you have more time to devote to each one. That means on an individual level, you want to strike the ideal balance between the number of projects and the time you need to excel in them. The same principle holds true on a department and company-wide level. Promising fewer new projects, new products, and even new customers gives everyone the capacity to deliver breakthrough results on what remains.

You and Your Team



Getting More Work Done


How to be more productive at work.

The best way to break out of this vicious cycle of over-commitment and underperformance is to very carefully manage what you agree to do. You can actually do more if you take on less. Here are a few steps you can take to prevent overloading your plate:

Create a pause. Whenever possible, avoid agreeing to new commitments on the spot. Instead slow down the decision making process to give yourself the space to make a reasoned choice. First ask clarifying questions. For example, if someone requests that you take on a presentation, say, “That sounds interesting. What did you have in mind?” Confirm the topic, format, and formality, as well, so you can ascertain how much prep work it will require. Then, ask for some time to review your commitments and get back to them with an answer: “I’ll need some time to review my current commitments. Would it be reasonable for me to get back to you tomorrow?” People want to be “reasonable” so they’ll typically say “yes.” If this correspondence happens via e-mail, you may not need to ask for the time to come to an answer — just take it.

Say “no” early and often. If you immediately know that you don’t have the capacity to take on a project, say “no” as soon as possible. The longer you wait, the harder it will be for you to decline the request and the more frustrated the other person will be when they receive your reply. A simple, “This sounds amazing but unfortunately I’m already at capacity right now,” can suffice.

Think through the project. If you want to take on the project, stop to think through what you’d need to do in order to complete it. For a presentation, that might include talking to key stakeholders, doing research, putting together the slide deck, and rehearsing. For a much larger project, the commitment may be more extensive and less clear. Map out what you know and then make rough estimates of the amount of time you think the steps might take.

Review your calendar. Once you’ve thought through the commitment, review your calendar. This allows you to see where you have — or don’t have — open space in your schedule. In the case of the presentation, if you see that your calendar has open time, then you can commit to the project with confidence and block out time for it on your schedule. If your calendar has no time free between now and the day of the event, and the presentation would require prep, you have a few options. The first is to simply decline, based on the fact that you don’t have any available time in your schedule to take on anything new. The second option would be to consider renegotiating your current commitments so that you could take on the new project. Evaluate the new request versus your current projects. Is taking this new project on worth dropping or delaying something else? If you’re not sure, you can ask your manager: “I was asked to do a presentation for XYZ. That would mean that I’ll need to take some time away from project ABC. Would you like me to adjust my priorities in this manner to accommodate the new request, or would you prefer that I not take on the presentation?” Using one of these strategies allows you to take on a reasonable amount of commitments and stay out of time debt.

Adjust your commitments. If you take on something new that will impact other projects, make people aware of what they can or can’t expect from you. They may not prefer that you made another initiative a priority, but if you’re aligned with your boss and your goals, you’re making the right choice. Also, if you let people know what to expect as soon as possible, they’re less likely to be upset. This gives you the opportunity to work with them on creating a new timeline or on delegating work to someone else with more availability.

Once you’re clear on your commitments, get them on the calendar. That way you know you have time and space for the work you’ve just committed to do. With this honesty in your scheduling, you can do the work and do it well. Give yourself hours at a time — even whole days to immerse yourself in excellence. When you’re not trying to eek out 20 or 30 minutes here and there between e-mails and meetings to move forward important initiatives, you can accomplish work of real value — and enjoy the process.

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An Important Data Lesson from an Inconsequential Football Scandal

As “Deflategate” rattles the National Football League in the run-up to this year’s Super Bowl, data analysts have swooped in, including Warren Sharp, one of many self-styled football analysts who blog about the topic. In a Slate article he analyzes the fumbling rate of the New England Patriots — the team accused of purposefully underinflating footballs to gain an advantage. The headline to his analysis calls the Patriots’ fumble rate compared to the rest of the league “nearly impossible.”

Sharp, you might think, found the smoking gun — a statistic that proves that the Patriots cheated. Only a patient reader who persists to the last paragraph will see that Sharp ultimately admits that New England’s spectacular performance on the metric could be explained in any number of ways, including legitimate ones like perfecting ball security techniques or practicing prevention.

In short, the data say the Patriots are excellent at preventing fumbles. It says nothing about why.

This distinction represents one of big data analysis’ most under-appreciated problems: talking about reverse causation. In reverse causation problems, we know the result and we work backwards to understand the causes.

Reverse causation investigations have the opposite structure from A/B tests, in which we vary known causes, and observe how the variations affect an outcome. If the number of visitors to your website jumped after you changed the image on your Facebook page, you conclude that the new photo is the reason for the traffic surge. (Note: Good A/B test construction can help you see most likely causes; bad A/B test construction creates its own set of problems.).

By contrast, the biggest obstacle to solving reverse causation is the infinite number of possible causes that might influence the known outcome. This is compounded by the fact that we want to assign a cause. So when some data is plucked out of a large set that fits a narrative we may have already constructed, it’s very tempting to simply assign causation when it doesn’t exist.

Most of the time, though, the data offer hints, but no proof. Sharp’s article on the Patriots is one such case. When reading this style of data journalism, pay attention to the structure of the statistical argument. Here is how I summarize Sharp’s:

New England is an outlier in the plays-per-fumbles-lost metric, performing far better than any other team (1.8x above the NFL team average).


Different ways of visualizing and re-formulating the metric yield the same conclusion that New England is the outlier.


There is a “dome effect.” Teams whose home stadiums are indoors typically suffer 10 fewer fumbles than the outdoors teams. New England is a non-dome team that surpasses most dome teams on plays-per-total-fumbles. If dome teams are removed from the analysis, New England is a statistical outlier.


Assuming that the distribution of the metric by team is a bell curve, the chance that New England could have achieved such an extraordinary level of play per fumbles lost is extremely remote.


Therefore, it is “nearly impossible” for any team to possess such an ability to prevent fumbles … unless the team is cheating.

Points 1 to 4 are essentially slightly different reiterations of the known outcome. It is point five in which a connection is established between that outcome and its cause(s). But the causal link is tenuous at best. However suggestive, the data does not prove intent or guilt. It simply describes a statistical phenomenon.

Indeed, digging in on the Patriots data shows that they may not be much of an outlier. In the “dome” analysis, Sharp switched from looking at fumbles lost to total fumbles (which includes recovered fumbles). Other football data analysts have concluded (more than halfway down the page) that fumble recovery is mostly random, so plays per total fumbles is the more useful metric.

Given this new measure, the Patriots are not an outlier, as they’re second to the Atlanta Falcons in fumble performance. Only when Sharp removed all dome teams (the Falcons being one) could he argue that the Patriots were an outlier.

Sharp showed that it is almost impossible for an average team to attain such a low fumble rate, but we have no data that proves the Patriots or any particular team couldn’t achieve it in a legal way. And in fact, the dome analysis suggests there are legitimate methods to perform equally or slightly better than the Patriots did — just look at the Falcons. Unless you want to allege the Falcons also tampered with footballs. (Others have also since refuted this fumbles-prove-malicious-behavior narrative and corrected what seems to be a major flaw in Sharp’s approach: eliminating dome teams from analysis, intead of dome games. When that change is made, the Patriots seem to perform well, but not strangely well; not even the best).

To his credit, Sharp did not argue point five. Nevertheless, many readers and incurious reporters made this causal leap. Sharp helped them along by using a loaded phrase “nearly impossible” to sell the story.

And that’s the reverse causation problem we face. Big data is exposing all kinds of outliers and trends we hadn’t seen before and we’re assigning causes somewhat recklessly, because it makes a good story, or helps confirm our biases. You see this all the time in your Twitter stream: “7 Charts that Explain This.” Or “The One Chart that Tells You Why Something Is Happening.” We’re getting better and better at analyzing and visualizing big data to spot coincidences, outliers and trends. It’s getting easier and easier to convince ourselves of specific narratives without any real data to support them.

Most good statistical analysis will be narratively unsatisfying, loaded down with “we don’t know,” “it depends,” and “the data can’t prove that.”

You can see how this can become a big problem for companies wanting to exploit the big data they’re amassing. If you think about most practical data problems, they often concern reverse causation. The sales of a particular product suddenly plunged; what caused it? The number of measles cases spiked up in a neighborhood; how did it happen? People with a certain brand of phone tend to shop at certain stores; why is that? In cases like these, we know the outcome, and we often don’t know the cause.

The possibility of any number of causes tempts us to retrofit a narrative but we must resist it. The astute analyst is one who figures out how to bring a manageable structure to this work. See this post by statistician Andrew Gelman for further thoughts.

In the mean time, maintain a healthy skepticism the next time someone suggests they’ve found causation in the reverse. Their claims may be overblown.

(Editor’s Note: This article is an edited version of a post that originally appeared on the author’s blog.)

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Building a Software Start-Up Inside GE

Is your company ready to compete in a world of smart, connected products? For some time now we’ve been living into a smarter world filled with Big Data and analytics, and a more connected one that’s been described as “the internet of things.” In this world, customers expect their suppliers to surround their products with data services and digitally enhanced experiences. This means that many organizations and their leaders are running as fast as they can to quickly build their software capabilities. How can these companies overcome the inevitable leadership, organizational, and cultural challenges involved?

General Electric turns out to be an excellent case in point. It made a massive investment (more than $1 billion) to build a software “Center of Excellence” in San Ramon, California to manage the data explosion created by the increasing intelligence of its industrial machines. CEO Jeff Immelt declared in 2011 that GE needed to become a software and analytics company or risk seeing its hardware products become commodities as information-based competitors took over. As Marco Annunziata, Chief Economist at GE, told me, “We’re no longer selling customers just a jet engine, a locomotive, or a wind turbine; we’re bringing data and actionable solutions along with the hardware to reduce costs and improve performance.” So GE has hired 1,000 software engineers and data scientists to provide enhanced software and analytical skills across GE’s many businesses. GE is now approaching $1 billion in new revenue annually from their expanded software and data activities. Here’s a brief account of how GE quickly scaled up a sizable software start-up within a big, successful conglomerate.

Getting Started

After Jeff Immelt threw down the gauntlet for building a global software center, GE faced significant physical, organizational, and cultural challenges. Who would lead it? Where would it be located? How would it be organized and how would it relate to GE’s existing businesses? How would it integrate into the culture of the larger organization? Would the traditional host organization reject the new software center as an alien entity?

The first step was to hire someone to run it. Bill Ruh was selected in 2011. Key selection criteria included experience in innovative software and service (versus product) development, and an ability to manage a start-up in a very large, complex company. Bill and his team set out to develop a system that could bring all GE machines onto one efficient cloud-connected platform. In a departure from GE’s traditional control systems, the Center was not set up as its own business unit with its own P&L, but rather was funded by a $1 billion investment by Jeff Immelt and became part of GE Global Research.

The next question was where to establish the software center. Though technology would have allowed for a significantly virtual enterprise, it was important to Ruh to have a physical building where people could actually be located together. It was also important to tap into the start-up software culture of Silicon Valley. Together, these constituted radical moves for an industrial company headquartered on the East Coast. San Ramon was selected because it was close to Silicon Valley and had expansion potential. GE started on one floor of a large office building in 2012 and has grown to take over all five floors. The interiors look like Google’s spare, open office plan with concrete floors and airy workspaces (unlike other GE facilities). A design studio is geared for collaboration and innovation work with customers and partners.

Then they needed to decide what kinds of people to hire. Matt Denesuk, Chief Data Scientist, told me, “Who you hire depends on your strategy. We said we were going to build a technology platform in the ‘cloud’ that would provide data plumbing, high-value analytics, and modeling content, so we hired the appropriate skills in software engineering, user experience, and data science. We decided we wouldn’t hire people for some other skills, such as systems integration and change management, and would use partners for that, instead.”

Hiring for Growth

The biggest challenge was growing fast. Melody Ivory, a User Experience Product Manager, told me, “I was about employee number 30 in February 2012. By June of 2012 we were close to 100. By the end of the year we were 500 people. There was an explosion of demand. We were a service organization in a big corporation, so we had ready-made customers. We didn’t have enough people to respond, and we couldn’t scale up fast enough. We were a startup, and like a startup, we grabbed quality people, were hands on, and wore many hats. We grew faster than we thought we would. Yet we still aren’t as large as we need to be to meet the demand from the businesses.”

Jennifer Waldo, Head of Global Human Resources, GE Software Center, was at the epicenter of GE’s recruiting challenge. She told me how difficult it was. “GE didn’t have brand recognition in software. 90% of the people we recruited didn’t know a GE software group existed. And the market for software talent was hot hot hot. There were three competing offers for each user experience expert or data scientist. We were competing with the cool, Silicon Valley tech companies, yet at the same time we needed to find people who would fit in GE’s culture.” To hit the aggressive growth targets (750 by the end of 2013 and 1000 by November 2014) Waldo had to rewrite some GE rules. “We hired a talent acquisition leader from the software industry, someone who really understood technology. And we ‘insourced’ the recruiting activity, hiring recruiters who knew where our target candidates hung out and what appealed to them. We focused on passive candidates, people that weren’t necessarily looking but could be a strong fit for GE. It also required us to amend our compensation practices to be competitive in the technology space.”

I spoke to several GE Software employees, all filled with enthusiasm about their ability to make a difference by combining GE’s industrial domain knowledge with the speed and innovation of a software start-up. In making the appeal to potential candidates, recruiters created a value proposition which emphasized GE’s brand, including GE’s reputation for leadership development; a compelling vision of the “Industrial Internet” as the next big thing; and the opportunity to work on meaningful challenges in fields such as healthcare and energy.”

Integrating with the Mother Ship

One challenge, as you might expect, was introducing a software center that disrupted the existing GE’s power structure, which resides in its business units, such as Aviation, Healthcare, Power and Water, and Transportation. I spoke to Ganesh Bell, Chief Digital Officer and General Manager, Software and Analytics, at GE Power and Water, who sits at the intersection between GE’s Software Center of Excellence and one of its biggest business units, to understand how a corporate startup can work effectively with existing units:

“It started with positioning. We created an expanded vision of customer partnerships with big, market-driven outcomes that the company could rally behind. And we took a stand on what the future holds: driving Industrial Internet solutions. If it were just about software, it wouldn’t fly, but this is a much bigger, customer-driven play. Second, we are incubating new software talent and [creating] software DNA. We have separate funding, and we set it up so that the revenue we generate from software-led solutions is recognized in the businesses. Our performance measures are aligned on driving additional revenue in the businesses. And third, we embraced the fast approach to innovation (“FastWorks”), which was already being driven across the company.”

 It’s working. Bell told me that a cross-functional GE team, working with customers like E.ON, used a software product (“PowerUp”), which is driving more output per wind turbine – a 4% improvement at E.ON.

Despite the vision and potential, there have been other bumps along the way. Many software developers at GE were concerned about reliability and security, which led some of them to resist moving some of the capabilities to the “cloud.” Incorporating rapid programming practices (“Agile” and “Extreme Programming”) to bring significant time-to-market and productivity benefits also required new and different skill sets than what were traditionally found within GE. And there were challenges moving people off old technology and onto the new.

The Silicon Valley software ethic of running experiments to fail fast and learn has been a cultural challenge for GE, where failure has been frowned upon. Successful companies like GE tend to protect the business and perpetuate formulas that have worked well in the past. An ingrained industrial mindset keeps things “within the yellow lines,” focused on controlling operations or managing safety. Streamlining a process with Lean Six Sigma fits this mental model: you focus your product development on making things perfect before releasing them to the market. However, the mindset of software development and Silicon Valley is quite different – you can try something and back it out if it doesn’t work. You have a hypothesis, you try it, and learn. It requires systematic leaps of faith, risk-taking, and potential failure as organic parts of the approach.

To overcome the resistance, Ruh and his team started by working with those businesses within GE that could change quickly. When others started to see the rapid transformation of their peer businesses, they couldn’t move fast enough to get on board. In a way, peer pressure created a domino effect across GE. To quote Annunziata:

“We have moved very quickly from a little resistance and skepticism to seeing the value embraced at all levels. We’ve brought a lot of people to San Ramon who have experience outside of GE and who interact in a less structured way, like a startup. To get these two cultures to work together, first we had a strong commitment from the top, from CEO Jeff Immelt. Second, there has been a cooperative attitude from the people in San Ramon, who are bringing new expertise and pushing the businesses, but listening. And the third success factor has been setting the right priorities, especially choosing opportunities that take advantage of the scale of our company, taking innovations from one place to others.”

The software-enabled revolutions we see in our daily lives, such as navigation using Google Maps or taxi service through Uber, are shaking up the industrial world, too. Machines are getting smarter and smarter. Industrial companies that don’t rapidly scale their software and data capabilities to leverage their hardware will be left behind. GE is one example of a big, industrial company showing how you can quickly build software capabilities using an internal start-up model. Going this route may mean breaking some rules, but ultimately, that’s a fair cost to pay in order to survive and grow.

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3 Questions to Get the Most Out of Your Company’s Data

Our world is sentient. Websites watch where we look. Mobile applications keep track of our response times. Companies learn which buttons we like to press and which we don’t.  With cameras, microphones, and thermometers, the human race is giving inanimate objects everywhere eyes, ears, and skin. And with all this observation, we’ve created a massive new layer of information.

Jonah Peretti, the CEO of Buzzfeed, knows that this layer of information can be used to test, learn, and iterate in rapid cycles. In this world, you can know, with some level of certainty, the way to craft the exact right title for an article — whether it’s investigative journalism or a cat video. “This isn’t possible in print, broadcast, or traditional films, which may be why the media industry is such a dysfunctional place,” Peretti has said. “Executives make huge bets based on gut, it’s hugely expensive to take risks, and most projects fail.”

But what if you could automate your gut decisions? What if machine intuition is better than human intuition? Trends like the pervasiveness of mobile, the near-infinite storage and computer power of the cloud, and new methods of analyzing masses of data are not just important in themselves. They’re important because they’re allowing businesses to develop new business models that better serve customers. Models that revolve around capturing information and putting it to work. In their simplest form, these businesses rely on the information that’s already there to provide automated intuition. In their most complex form, they flip age-old processes upside down to put a new type of information at the center of the business.

Consider entertainment. Buy a DVD, and the creator of the content learns practically nothing about you. That’s why film producers have no choice but to go with their gut. Netflix, on the other hand, sees every button you push, every movie you like, every TV series you finish — and every one you don’t. From its 40 million subscribers, it’s built an incredible understanding of entertainment preferences. This database has given rise to hit show after hit show, from House of Cards to Orange Is the New Black to Marco Polo (which was popular with audiences, if not with critics) According to Netflix’s own calculations, this data-driven original content is much more valuable for them than the content it licenses, because more people spend more time watching it — despite the high production costs of a series like Marco Polo.

Increasingly, we observe that the companies harnessing machine insight to augment or replace human intuition tend to be native to the internet. From Amazon with its pricing algorithms to Nest with its learning thermostats and smart smoke detectors, these companies have been born in an era where it’s natural to digitally track every interaction with a customer.

But companies from the industrial era are also figuring out how to capture digital insights and feed them back into their business to build advantage. General Electric has made machine-generated insight a priority. Over the last few years, the industrial giant has built an enormous software group dedicated to leveraging all the data they can extract from their sensor-laden hardware. The company realized that it would be much cheaper, for instance, to improve the uptime of turbines by sensing upcoming failures and rescheduling maintenance appointments than by investing in ever-more expensive parts that might break less frequently. Because GE is delivering machine uptime via 1’s and 0’s, instead of via improvements in metallurgy, it’s able to deliver that uptime at a far lower cost than its competitors.

Similarly, 30-year-old software company Intuit has invested extensively in putting its online Mint product at the center of its users’ lives. Many people only think of Intuit once a year, when they use its TurboTax software. But Mint, its product for personal budgeting and bill paying, is becoming the beating heart of its users’ daily financial lives. Mint collects users’ financial information in one place, learns their patterns, provides recommendations for better ways to save, offers investment options, and integrates with TurboTax. Mint has allowed Intuit, a tax software company, to slowly add more and more services otherwise provided by banks. The data that it already uses to help streamline your tax submission is invaluable for the algorithms that will ultimately replace the intuition and experience of financial advisors.

Every business can benefit from using digital intuition to compete. The key is determining how to catch the wave. We believe that three questions can help you position your business for success in the era of automated insights:

What’s your customer’s job-to-be-done?
In a perfect world, what information would help you complete that job?
If you had this information, what inside your business would need to change?

What’s your customer’s job-to-be-done?

Customers don’t buy products just for the hell of it. Customers buy products because a job arises in their lives for which they need a solution. The job of “I need to get this document from here to there with perfect certainty” is one that has existed for millennia. In Ancient Rome, the job required Caesar to hire his best charioteer to ride to the front lines of battle. Fifteen years ago, when that job arose, the name of FedEx popped into peoples’ minds. Today, we hire encrypted email services. But the job is the same. Knowing the job-to-be-done that your product is being hired to complete is the only way to be sure that the improvements you’re making are going to deliver the experiences your customers desire.

The key to General Electric’s evolution was the realization that it was no longer a provider of industrial machinery. It was part of an ecosystem that delivered productivity for their customers. Its products were hired along with a plethora of systems integrators, aftermarket services vendors, and other industrial machinery companies, to help businesses do things like fly planes, generate power, and pull oil out of the ground. And any innovation in service of helping their customers operate more efficiently would be welcomed.

This might seem obvious, but it’s often missed. Managers typically focus on how to improve their products and services the way they’ve always been improved. They invest in annual R&D cycles aimed at continuously improving features as opposed to exploring how the ever growing sea of information at their fingertips can be used to help them fulfill the job that arises in their customers lives.

In a perfect world, what information would help you complete that job?

The next question you have to ask is which information would help you complete that job better. What information could provide you with all the insight you need to make the best decisions on behalf of your customers?

In the case of Uber, the company knew from day one that its job was to make it as convenient as possible for customers to get from point A to point B. The key information required was where people were located when they needed a ride. For cab companies everywhere, that’s always been the holy grail of information; unfortunately, none of them had done a particularly good job of capturing it. Instead, cab drivers were forced to rely on intuition and an understanding of the city to head to areas most likely to support fares. But the fact that taxis were already on the street gave them an advantage over car services that need to come to customers from central garages only after they’ve been summoned. Uber used information about both where the cars were and where the customers were to blend both models into one: allowing customers to simply push a button and be connected with an available, nearby, driver.

Many of us already have access to the information we need. It’s stored in our systems, or our customers are willing to offer it to us. Some of us will need to go out and get it — whether through a partnership, public databases, or the development of new offerings. But one way or another, the key is recognizing the job we’ve been hired to do and figuring out what’s the ideal information that would help us complete that job best.

If you had this information, what inside your business would need to change?

The final question is the hardest to answer. Even when we have the ideal information that would let us generate insight and displace our reliance on human intuition, we still need to identify what we would change as a result to capitalize on it.

Typically, this question forces us to think about developing new business models. Certainly, Intuit, Netflix, and General Electric all had to build models that relied less on human experts than they did on algorithms. Such a model typically reduces the need for employees and reduces prices to customers. Often, this is an unpalatable option for executives. But the reality is that the more we can use machine intuition to improve our existing services and lower prices, the more attractive our offerings will be to more customers — in turn further improving our machine-generated insights. Find yourself in a virtuous cycle like this, and the more likely it is that your businesses will flourish. And when businesses flourish and grow, it’s more likely we can support employment and profitability over the long term.

The reality is that we’re in a time of transition. Machine-based intuition is changing the way companies work. The question is: who in your industry will figure it out first?

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Manage Your Team’s Attention

What’s your scarcest resource at work?

Most people answer, without hesitation, that it’s time. It certainly is finite, but I would argue that time isn’t actually your scarcest resource. After all, everyone has the same amount of time, and yet individual differences in productivity can be enormous.

A better answer might be your attention — your personal capacity to attend to the right things for the right amount of time. As Nobel Laureate Herbert Simon first suggested 40 years ago, when information is plentiful, attention becomes the scarce resource.

So perhaps the biggest challenge we face as individuals at work, and as leaders, is attention management.  This means being thoughtful and disciplined about how we split our time between different activities, and also about how we encourage others to focus on the right things. How?

First, consider yourself as an individual contributor. If attention is your scarcest resource, the first thing you need to do is discipline yourself to avoid interruptions. So if you are working on something that needs real focus — say, writing a report — then switch your phone to silent, and close down Outlook and Facebook. This is obvious stuff, but it’s amazing how often we don’t do it, and how easily we get sidetracked.

Second, and more difficult, is figuring out when to stop gathering information. When I was a doctoral student, the cost of acquiring information was high — I had to go to the library and make my own copies of annual reports or academic papers. Today, such costs have shrunk dramatically, but the net result of easy access to information is that we often keep on collecting information long after we have enough to make a decision or write a report. How can we avoid this “analysis paralysis”? The best approach is to develop your hypothesis or argument early on, so that your search is focused on supporting or refuting that argument. If that doesn’t work, just give yourself a deadline. One rule I use when working with collaborators is to ensure I have something to send over to them by the end of the day: this helps me avoid getting into an open-ended search process.

Third, even though we live in an era of ubiquitous information, we should not be afraid to bring our intuition and emotion to the table. It is tempting to seek evidence to support every argument we make, but the most successful business leaders — from Jack Welch to Steve Jobs to Jeff Bezos — have always sought to combine rational and intuitive thinking. An ounce of real insight is worth a pound of data.

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Finally, when we have plentiful access to information, we also need to find time for reflection. Think of this as a low-tech version of meditation or mindfulness: it simply means creating breaks in the day, perhaps during a commute or while exercising, where you make sense of the stimuli you have been bombarded with, and where your ideas are allowed to gestate. When I am feeling distracted, a half-hour swim is the best way I know for clearing my mind and clarifying my next work priorities.

Now, consider your role as a manager. Remember, your team is as easily distracted as you are. Your team members are also highly sensitive to stimuli and cues that come from above. If you start talking about, say, an impending cost-cutting initiative, you are manipulating your team’s attention, whether you like it or not. Changes to job titles, to the layout of the office, to the agenda of the weekly meeting, to decisions about who gets promoted — all of these are attention “cues” that collectively shape people’s views of what is important, thereby shaping how they behave. (This idea was first developed by Tom Davenport and John Beck in The Attention Economy).

If you recast your role as the manager of your team’s attention, there are a couple of simple pieces of advice to follow. First, keep the message simple and clear. If you emphasize different things each week, people will become confused, and will learn to tune out. But if you come back to the same message time and time again, the effect on your team’s behavior is likely to be substantial. For example, most mining companies start every meeting with a “safety share” (a story about a recent safety-related incident) — it’s a simple, but effective way of keeping safety top-of-mind.

Second, be clear on what the default focus of attention is, so that you can be strategic about how to shift your team away from it. Here’s an example: a global software company was losing out on opportunities in Asia because every decision ended up prioritizing the needs of the European business (its historical home base). The CEO moved himself (temporarily) to Asia; global team meeting times alternated between morning in Europe and afternoon in Asia; the Chair of the meeting alternated between the two locations; the agenda always included region-specific as well as global concerns. By manipulating these relatively symbolic cues, rather than changing the entire reward system or reporting structure, there was a marked shift in behavior towards a greater focus on Asia but without a loss of attention to Europe.

Our key job as managers is to make efficient use of scarce resources. In the industrial era, the scarce resources were capital and labor. In the knowledge era, we have become accustomed to thinking of knowledge and information as the scarce resources we need to harness. But increasingly, information is ubiquitous and knowledge is shared widely across companies. In such a world, the scarce resource is our own and our employees attention. We need to become smarter about how we manage it.

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What HoloLens Has That Google Glass Didn’t

Courtesy of Microsoft

I admit that when Microsoft unveiled its holographic computing engine at its Windows 10 event last week, I didn’t pay much attention. Despite some positive press, it felt too much like Google Glass (and skepticism of that platform looks increasingly warranted) and reminded me of many past gee-whiz announcements from Microsoft. (This one comes to mind.)

James McQuivey, principal analyst and vice president at Forrester, suggested that executives should sit up and pay attention to HoloLens. I wasn’t convinced, but when he wrote to me that “holograms are coming fast and are here to stay; ignore them at your distinct, proximate peril” I reconsidered my dismissiveness.

McQuivey, the author of Digital Disruption, agreed to answer this skeptic’s questions about HoloLens, why it matters, and why executives should pay attention. The conversation follows:

HBR: Executives don’t have a lot of time to think about things that are just hype. Is there any reason for them to pay attention to HoloLens?

McQuivey: Yes. As an executive, you care about this because in Forrester’s Technographics survey data, there are 7.2 million adults in the US that have the ideal combination of attributes that makes them early candidates for HoloLens. They like technology, they have an Xbox, they have children, and they have an annual household income of more than $100,000. If Microsoft can persuade even half of them to jump in, that’s 3.6 million consumers, or 45% of the people who bought a Kinect at launch who will try a HoloLens by 2016. And going into 2017, just two years from now, the momentum they will have generated will force executives at your company to sidestep drones, self-driving cars, and robots to focus on this technology. They’ll see by then how it changes the way your customers interact with the products, services, and information that you provide them.

As one former digital agency executive told me after watching Microsoft’s event, “If I were managing a brand, I’d head right to my digital agency and say, ‘What is our plan for holograms?’” She said this even though she admitted she wouldn’t expect them to have an answer. But they would have to have a plan for generating an answer.

Skeptics will say this is Google Glass — largely regarded as a failure — just a couple of years later. What’s different?

Holographic interactions like what Microsoft is suggesting are where every other company from Oculus to Google were ultimately heading, but they haven’t gotten there. The endgame for enhancing our lives with digital visual tools is not virtual reality, it’s this mixed reality. If the glasses can help you accomplish tasks that matter to you, ones you already do, then it’s not just fun, it’s useful. By introducing the idea of holographic computing and baking it into every Windows 10 device from launch forward, Microsoft is offering developers, marketers, and ultimately consumers, completely new ways to do what they already want to do.

Isn’t that what Google promised with Glass, though? I’m not convinced.

Glass was a study in contradictions. On the one hand it was proposing a future where we would have ubiquitous computing on the go. Definitely a thing of the future. But on the other hand, the experience merely put a short list of functions that you can easily do on your phone in front of your face. The apps and functions were too limited, less powerful than your phone, certainly. That’s what made Google easy to ridicule. If you spend all that money to accomplish such a narrow list of tasks, and potentially look silly doing it, then you are not in the future, you’re not even in the present. You might as well be on a feature phone in 2005.

In order to introduce a new technology, you do have to start by helping people accomplish things that they already know they want, as Google was trying. But it has to make those things dramatically easier, more enjoyable, and more useful, which Glass did not. To get to the future from that point, you can then swiftly lead them on to do things they didn’t already know they wanted to do but now seem obvious. That’s precisely how holographic computing will rapidly infiltrate and then take over computing just as touch interfaces did starting with the iPhone.

Doesn’t this face the same ethics and social challenges as Glass? What’s the HoloLens equivalent of the Glasshole?

There is a subtlety here that executives should understand. Glass was only ever intended to be an on-the-go experience. Because Glass couldn’t actually “see” what you are doing, it can’t really be helpful except as a tool for grabbing information when you’re out and about. This is precisely what made Glass vulnerable to the ethical and social issues that dogged it. HoloLens, on the other hand, is not yet designed to be used outdoors. Instead, it’s a tool for doing what you need to do at home and work more effectively. It can only do that because it sees what you see, understands three-dimensional objects and surfaces, and can create virtual experiences for entertainment and productivity purposes in the places where you do most of those things. This opens up hours worth of opportunities for companies to serve customers in those places, privately, where other people won’t judge your eyewear or choices.

So what are some of the killer apps for this kind of holographic interface?

Retail, travel, automotive, financial services, all of these will be obvious fits. When Ikea builds a holographic catalog so that you can drop furniture into your bedroom and see what it would look like, even walk around it, you know you have a game changer. When Allrecipes.com can point to specific cupboards in your kitchen and tell you to retrieve the cocoa, and count out as you measure out tablespoons, you have another game changer. Even in the enterprise, where it’s likely HoloLens will be more useful more quickly, there will be holographic apps for technicians that do maintenance on jets on the tarmac, collaborative 3D design environments for architects, and special headsets for dentists that guide them through tricky extractions. But ultimately, I disagree with the premise of the question: Like the early web, this technology will not generate a killer app but will instead make smaller breakthroughs with existing applications throughout a wider range of industries and companies. Unlike the early web, it will not take a decade for that diffusion to occur.

Why won’t it take that long?

All the pieces are in place. Consumers are ready for new technology — Apple sold 80 million iPads in its first two years, compared to 1 million iPods in its first two years. Studying barriers to consumer adoption has been my passion since before my doctoral studies and I now find myself with very little to study given how rapidly the barriers are falling. But the technology itself is moving faster than before. Connectivity is ubiquitous; batteries are amazing; graphical processing units are powerful yet cheap;even the original technology Microsoft built just for HoloLens, the Holographic Processing Unit (HPU), could be built to higher levels of power at lower cost and in shorter time than such chips have ever been built before. In short, the world of technology has been digitally disrupted not just in one area, but in every area. Combine all of those innovations into a single area of focus, as Microsoft has done, and boom, you find yourself five years into the future.

I’m not sure I’d want to be the executive sticking his neck out saying this is the next big thing, given the relative failures with similar products.

This is why companies are constantly catching up. They were afraid to embrace social media and are still struggling to get up to speed on mobile. Think of it this way: A HoloLens video Microsoft posted on YouTube has been viewed more than 12 million times. Many of those viewers are the people below you in your company, and your customers. And then there are your executive peers and your board of directors. They carry around their iPads with some pride, but they are uncertain of where to put their attention next. All of these people will be looking for the executive that can stand up and offer a plan for preparing for a future of holographic computing.

So what’s that plan?

You’ll immediately face reasonable questions from inside the company like: Have we as a brand spent the time to understand what customers really need? Have we used the shift to web, social, and mobile to become experts in rapid product development techniques? The answer is likely no, not completely, or only somewhat. That’s why preparing for holographic computing isn’t really about building holograms, not in 2015, and for many not even in 2016. Instead, you will prepare for holographic computing by finally bringing your company culture, policies and practices into alignment with a customer-first strategy for innovation.

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How Doctors (or Anyone) Can Craft a More Persuasive Message

The recent measles outbreak starting in Disneyland, California, provides a sobering reminder to doctors that even when the message they’re delivering is a compelling one — backed by strong evidence — attempts to influence and persuade others can sometimes still come up short.

Over the past few years I’ve worked with Britain’s NHS on numerous programs designed to change people’s perceptions and behaviors in relation to health protection. Our work has uncovered some fundamental insights about persuasive messaging.

There are lots of reasons why well-crafted messages fail to persuade, but one of the most common is because the communicator focuses too much on constructing the content of the message rather than choosing the right messenger. The distinction between the messenger and the message is an important one. In today’s information-overloaded world, in which we’re exposed to lots of conflicting messages, people will often act more on the basis of who is communicating the message rather than the actual message itself.

So if the deliverer can be as important as the message itself, what are the characteristics of persuasive messengers? And what steps can doctors and other would-be persuaders take to increase the chances that their important messages land successfully?

Persuasion researchers have long known that the most effective messengers have three key attributes: expertise, trustworthiness, and similarity. Let’s take a closer look at each.

Expertise. When people feel uncertain, they typically look to experts to guide their decisions. In one study, when subjects were asked to make a series of unfamiliar financial decisions they were much more likely to choose options that were accompanied by advice from a prominent economist. Brain scans showed that in the presence of expert advice, the areas of the participants’ brains linked to critical thinking and counter-arguing flat-lined. This runs counter to the traditional assumptions about expert advice: that people listen to advice, integrate it with their own information, and then come to a decision. If that were true, the researchers would have seen activity in brain regions that guide decisions. Instead they found that when people receive expert advice, that processing activity goes away.

A clear lesson emerges when structuring a persuasive message: Because people frequently disengage their critical thinking and counter-arguing powers in the presence of expert advice, communicators who can legitimately lay claim to relevant expertise should always make that expertise clear early on. This doesn’t require making boastful claims (factors that will likely disengage rather than engage one’s audience) but instead using “authority cues” that convey your expertise. For example, studies have shown patients were more receptive to messages from medical professionals who prominently displayed their medical diplomas in their offices or who wore a stethoscope when delivering a recommendation.

Messengers who see that their messages are falling on deaf ears should ask themselves whether they’re taking steps to credentialize themselves before delivering their message. For those who already do a good job of telegraphing their expertise, the following two points will also be important.

Trustworthiness. In ambiguous, uncertain, or controversial situations where multiple answers vie for believability, it can be tempting for a messenger to conceal any small doubts or uncertainties about their message by sweeping them under the carpet, believing they could be detrimental to success. However, evidence suggests that signaling small uncertainties or doubts immediately before the delivery of the strongest argument actually has valuable trust-raising qualities. Sequencing is the key lesson here. Start your message with a small weakness or drawback, then use the word “but” before delivering your strongest message. A doctor who says, “No vaccine in the world is without the occasional adverse event, but this vaccine is extremely safe and has been used to protect millions of children,” strengthens her trustworthiness and credibility. But notice how reaction to the message feels different if the weakness follows, rather than precedes, the strength.

Similarity. We’re more likely to believe people who are like us. So another way that a messenger can increase the persuasiveness of their message is to show how they share similarities with their audience. For example, a doctor who wishes to advocate vaccinating children with the measles, mumps, and rubella vaccine might find it useful to point out that not only are they a medical professional, but they are also a parent. Sometimes, of course, it can be challenging to signal both expertise and similarity. In these instances, entirely different messengers may be required. African healthcare professionals leading a public health program designed to increase condom use to reduce the transmission of STDs and HIV quickly realized that while they possessed expertise, they had little in common with the audiences they were targeting. However, by recruiting local hairdressers to deliver their message through the “Get Braids Not AIDS” campaign, the impact of their message rose significantly.

There is a clear lesson here. Even though you may be the best qualified person to deliver your message, you may not be the most effective messenger.

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The Ideal Work Schedule, as Determined by Circadian Rhythms

Humans have a well-defined internal clock that shapes our energy levels throughout the day: our circadian process, which is often referred to as a circadian rhythm because it tends to be very regular. If you’ve ever had jetlag, then you know how persistent circadian rhythms can be. This natural — and hardwired — ebb and flow in our ability to feel alert or sleepy has important implications for you and your employees.

Although managers expect their employees to be at their best at all hours of the workday, it’s an unrealistic expectation. Employees may want to be their best at all hours, but their natural circadian rhythms will not always align with this desire. On average, after the workday begins, employees take a few hours to reach their peak levels of alertness and energy — and that peak does not last long. Not long after lunch, those levels begin to decline, hitting a low at around 3pm. We often blame this on lunch, but in reality this is just a natural part of the circadian process. After the 3pm dip, alertness tends to increase again until hitting a second peak at approximately 6pm. Following this, alertness tends to then decline for the rest of the evening and throughout the early morning hours until hitting the very lowest point at approximately 3:30am. After hitting that all-time low, alertness tends to increase for the rest of the morning until hitting the first peak shortly after noon the next day. A very large body of research highlights this pattern, although of course there is individual variability around that pattern, which I’ll discuss shortly.

Managers who want to maximize their employees’ performance should consider this circadian rhythm when setting assignments, deadlines, and expectations. This requires taking a realistic view of human energy regulation, and appreciating the fact that the same employee will be more effective at some times of the day than others. Similarly, employees should take their own circadian rhythms into account when planning their own day. The most important tasks should be conducted when people are at or near their peaks in alertness (within an hour or so of noon and 6pm). The least important tasks should be scheduled for times in which alertness is lower (very early in the morning, around 3pm, and late at night).

Naps can be a good way to regulate energy as well, providing some short-term recovery that can increase alertness. A large body of evidence links naps to increases in task performance. However, even tired and sleep-deprived employees may find it difficult to nap if they work against their circadian rhythms. Fortunately, there is a nice complementary fit; naps are best scheduled for the low point of alertness in the circadian rhythm. Thus, smart managers and employees will schedule naps around 3pm, when they are less useful for important tasks anyway, such that they will be even more alert later on during the natural high points in their circadian rhythm.

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Unfortunately, we often get this wrong. Many employees are flooded with writing and responding to emails throughout their entire morning, which takes them up through lunch. They return from lunch having already used up most of their first peak in alertness, and then begin important tasks requiring deep cognitive processing just as they start to move toward the 3pm dip in alertness and energy. We often put employees in a position where they must meet an end-of-workday deadline, so they persist in this important task throughout the 3pm dip. Then, as they are starting to approach the second peak of alertness, the typical workday ends. For workaholics, they may simply take a dinner break, which occupies some of their peak alertness time, and then work throughout the evening and night as their alertness and cognitive performance decline for the entire duration. And in the worst-case scenario, the employee burns the midnight oil and persists well into the worst circadian dip of the entire cycle, with bleary eyes straining just to stay awake while working on an important task at 3:30am. All of these examples represent common mismatches between an optimal strategy and what people actually do.

As I briefly noted above, there are of course individual differences in circadian rhythms. The typical pattern is indeed very common, and the general shape of the curve describes almost everyone. However, some people have a circadian rhythm that is shifted in one direction or the other. People referred to as “larks” (or morning people) tend to have peaks and troughs in alertness that are earlier than the average person, and “owls” (or night owls) are shifted in the opposite direction. Most people tend to experience such shifts across their lifetimes, such that they are larks as very young children, owls as adolescents, and then larks again as they become senior citizens. But beyond this pattern, people of any age can be larks or owls.

These differences in circadian rhythms (referred to as chronotypes) present some challenges and some benefits. The biggest challenge is matching patterns of activity to individual circadian rhythms. A lark working a late schedule or an owl working an early schedule is a chronotype mismatch that is difficult to deal with. Such employees suffer low alertness and energy, struggling to stay awake even if they really care about the task. Some of my own research indicates that circadian mismatches increase the prevalence of unethical behavior, simply because victims lack the energy to resist temptations. This is bad enough for an employee who is working alone. In the context of groups, finding a good time for a team composed of some larks and some owls to be at optimal effectiveness may be difficult. However, it does also provide opportunities. For organizations or tasks that require around-the-clock work, if managers can optimally match employees with different chronotypes to work different shifts, the work can be handed off among employees who are all working at or near their circadian peaks. This requires knowing the chronotype of each employee and using that information when developing work schedules.

Flextime provides an opportunity for employees to match their work schedules to their own circadian rhythms. However, managers often destroy this opportunity to capture value by punishing employees for using schedules that match an owl’s rhythm. In my own research, I found that supervisors tend to assume that employees who start and finish work late (versus early) are less conscientious and lower in performance, even if their behavior and performance is exactly the same as someone working an early riser’s schedule. Managers must see past their own biases if they want to optimize schedules in order to match the most important activities to the natural energy cycles of employees. Managers who do this will have energized, thriving employees rather than sleepy, droopy employees struggling to stay awake. Your most important tasks deserve employees who are working when they’re at their best.

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A Step-by-Step Plan to Improve CMO-COO Collaboration

A traveler arrives in a foreign country and attempts to use his credit card to make a purchase. It is the same card he used to buy the plane ticket and book the hotel. While this would seem sufficient to alert the bank to his impending trip abroad, “transaction denied” flashes across the terminal screen, leaving this longtime customer stranded, fuming, and no longer so loyal.

This all-too-familiar story illustrates the challenge facing companies — especially customer-facing companies. Customer journeys today are a complex series of interactions across multiple channels and platforms, where each point of contact has the potential to encourage the sale or derail it entirely. Coordinating the infrastructure, technology, and messaging in a way that appears seamless and fluid to the customer is, to be blunt, a logistical nightmare.

But getting it right pays off. Delivering great journeys can boost revenues 10 to 15 percent, lower service costs 10 to 20 percent, and increase employee engagement 20 to 30 percent. (For further convincing, see the HBR article, “The Truth About Customer Experience.”) Delivering a consistent experience on the most common customer journeys is an important predictor of overall customer experience and loyalty. We have also found that improving customer experience from average to “wow” is worth a 30 to 50 percent improvement in “likelihood to remain/renew” and “likelihood to buy another product.”

Despite these opportunities, companies have been slow to respond to the customer journey imperative in an organized way. Executives focus on optimizing discrete touchpoints rather than improving the complete customer experience. This is like treating a symptom without bothering to find the cure.

The CMO and COO are the natural partners for turning this around. As Jo Coombs, Managing Director at OgilvyOne, London, observes, “I don’t think it can just be one or the other. If it’s all about the operations then you lose sight of the customer. If it’s all about the customer, then you may not have the infrastructure and back-end to support what you’re trying to do.”

While the CMO and COO have a good track record of collaborating in certain areas, a certain tension has long defined the relationship. Here are our recommendations for how CMOs and COOs can develop a more collaborative working relationship:

1. Develop a shared vocabulary and shared metrics.

When CMOs and COOs talk about the customer decision journey, that language needs to be translated into metrics and key performance indicators (KPIs) that more accurately measure progress. For example, a call center may pride itself on completing X percentage of service calls within 30 seconds, but that’s not a valuable metric for determining overall customer satisfaction – or what the customer then does after that service interaction. The better metric would measure the percentage of calls that were made and required no additional follow up.

Rather than measuring marketing KPIs or operations KPIs, focus instead on the more customer-oriented journey KPIs, such as lifetime margin. On the operations side, metrics should not focus on how quickly the call center can address customer issues, but rather on how successful the call center is at eliminating follow-up calls or how successfully it isolates the root causes of customer complaints.

Once the drivers of the costs of the journey are understood, marketing can work with operations to address them. For instance, at a major bank, the operations group reviewed data on the cost to serve customers and found that certain types of customer acquisition efforts yielded less profitable customers. Based on this data, operations recommended marketing cease actively trying to acquire these customers. This required a change in the media buy metrics to focus on “likely margin” versus “likely sales.”

2. Build a structure for collaboration.

Moving towards a more collaborative approach represents a new way of doing things, which will at first feel strange. The CMO and COO can wield a lot of influence by setting up a regular call, for example, devoted to a specific customer journey, such as the onboarding process. They can also take deliberate actions to involve the other in processes where generally the “other function” has little to no involvement. The CMO can bring the COO into the marketing planning process early on, for example, to help ensure that the company can in fact deliver on the marketing promises it is making to its customers. This as approach helps both CMO and COO become invested in the successful implementation of the plan.

For any change to stick, the CMO and COO need to have joint accountability and create incentives that reward collaboration. Consider how Dutch energy company Essent is redesigning the customer experience. Marketing takes the lead on defining how best to serve customer needs and what sort of service this requires. They then set the initial minimum cost that operations can work with to deliver it. Operations and marketing meet, discuss, and iterate what the final budget will be and what the ultimate goal will be. What makes this process work is that they share both the responsibility and the rewards for meeting the targets. In fact, between 30 and 50 percent of their bonus compensation is based on reaching their joint targets.

3. Work together on a few customer journeys that matter.

Complex analytics can reveal often hundreds of opportunities to improve customer journeys, but the CMO and COO can help prioritize them based on impact. To do that, there is no substitute for a team of marketing and operations people physically walking through a specific journey.

That happened at a car rental company, which had identified the need to get their customers from the front desk to the front seat faster. When sales managers saw a rush of customers, they could put more people on the front desk. But the crews responsible for prepping the cars had no contact with the front desk. Whether it was slow or busy out front, it was always business as usual back in the garage. When the marketing/operations team walked through each of the stages of this particular journey, they quickly developed a greater appreciation for the entire process. They also were able to identify critical changes, one of which was to install a simple system — a light in the garage activated by a switch at the front desk that signaled surges in demand — so that prep crews could respond accordingly.

Importantly, fixing customer journeys is about a mentality rather than a one-off solution. And that means setting up processes to respond rapidly. The CMO and COO should work together to develop, in advance, processes and protocols for addressing negative feedback quickly. The insurance company E-surance, for example, uses a “control tower” system to monitor what is happening in real time. When looking at quote and bind ratios (i.e. the percentage of people who commit once they’ve received a rate quote), they could see those segments where the ratios were poor and immediately shut down the digital advertising in those markets. Operations set up the analytics and marketing was right there to act on what those analytics revealed.

It’s worth bearing in mind that this is more than a “marketing and operations” show. Delivering on journeys requires many different parts of the organization to come together, such as working with the CIO on the technology implications of developing journeys, and providing the CFO with hard ROI data on customer journey investments.

4. See the customer journey all the way through.

Marketing people are good at shaping emotions for customers, but operations folks, not so much. Yet that’s the department that typically takes over once marketing gets customers in the door. That post-purchase onboarding process, often designed to be low cost/high volume, has the potential to reinforce the connection with the product – or conversely, foster buyer’s remorse. Operations plays a key role in making sure those new customers stay loyal.

For example, one financial services company applies a marketing lens to its customer onboarding for many months after signing up a new customer. A personal welcome letter is sent with the goal of both deepening the relationship while still getting necessary paperwork completed. Similarly, bills and call center script guidelines reinforce the same personal tone that’s been established. This focus on solidifying a personal relationship with customers has become a key differentiator for the company and a great asset for marketing and sales.

At its most fundamental, mastering the customer journey is about doing what’s best for your customers, which includes being there whenever they happen to need you. Doing what is best for the customer, as it turns out, is also often what is best for the company. We recognize that the recommendations we are making here represent a real shift in the traditional roles and responsibilities of marketing and operations. But there is tremendous upside for those CMOs and COOs who pool their talents and resources to focus on the customer journey, adjusting systems, processes and, most importantly, mindset, to ensure that each and every customer journey is a rewarding one.

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Being Experienced Doesn’t Automatically Make You a Great Mentor

Coaching and mentoring is more popular than ever — and for good reason. As individuals progress in their jobs and careers, they’re constantly challenged to build their skills and act outside their comfort zones. Timid executives are called upon to learn to deliver motivational speeches; conflict-avoidant managers need to learn to deliver bad news; and mild-mannered job seekers need to pitch and promote themselves at networking events.

And mentoring doesn’t just happen in traditional corporate settings. It also abounds in educational, religious, athletic, and nonprofit worlds as well, where deeply experienced individuals become coaches and mentors to help others with less experience get on the fast track to success.

Or at least that’s the hope. The reality, I’m afraid, is much more of a mixed bag, as often coaching does more harm than good. The problem lies in trusting that experience alone can make you a good coach. What I’ve observed watching coaches and mentors — and even coaching young professionals and executives myself — is that there can actually be a liability of experience when it comes to coaching. As we take on the roles of coaches or mentors, we must be aware of how our experience can hold others back, or else it can lead to negative results and frustrating relationships.

The first liability of experience has to do with emotion. As coaches and mentors, we’re typically chosen for our experience — which is a good thing. But that experience can also be a liability when it means we’re far removed from the actual experience of learning challenging new skills. People can really struggle when trying to master new skills. They can feel anxious, self-conscious, embarrassed, and even frustrated and angry. It can take a delicate touch and keen insight to give the right advice, intervene in a timely manner, offer the right words of wisdom and encouragement, and really understand how to nurture a trainee’s sense of confidence. But if you haven’t been there in a while and you can’t empathize with your trainees’ experience, you can miss this emotional side of skill building, which is a critical part of the process.

Moreover, as a deeply experienced mentor, you can also fail on an emotional front by not being able or willing to express your own vulnerabilities around learning challenging new tasks. In a mentor or coaching relationship, individuals want their experienced counterpart to be able to empathize with their experiences. They want to hear that you have struggled — and that you understand what they’re going through. This helps build a connection between you and “normalize” the challenges they’re experiencing. But as a mentor, if you’re too fixated on expressing an image of expertise and not exposing your own vulnerabilities, you can miss a critical opportunity to connect with your clients.

In addition to these emotional barriers, experienced mentors and coaches often have unrealistic expectations about how learning typically occurs. Since they are so far removed from the experience of learning new skills, these individuals generally forget that success isn’t immediate. When learning a challenging new skill, people often have very uneven results. For example, someone I worked with from a different country learning to interview in the United States was very successful in her initial attempts, and as her mentor, I was extremely pleased — for her and, I have to admit, for myself! But as she inevitably struggled in subsequent attempts, I found myself frustrated, noting in my mind these next attempts as “failures” and wondering why she was now having issues when the early attempts had been so positive. I’m certain now that this frustration and disappointed “leaked” into my conversations; this didn’t help the person I was working with and created a tense relationship.

Although they may be fleeting, it’s critical to celebrate these early achievements when they happen. Many mentors (myself included) often focus on “what’s next” without stopping to actually celebrate and appreciate how much someone has accomplished, even if that accomplishment is a small step. This failure to celebrate small wins can cause mentors to miss a valuable opportunities to build their trainee’s self-confidence, which is often just as critical as the more traditional skill building process.

Finally, a last trap that experienced mentors can fall into is that of unfair comparisons. Who among us has not compared one employee to another, one student to another, or even one child to another? Comparisons are a temptation we often can’t avoid, but in the case of coaching, they can be damaging. When we compare one employee to another — or even to ourselves — we can end up missing evidence of actual progress, marking one person as a “failure” and another as a “success,” when the reality might be that the first person has actually made progress in other, more subtle ways. By falling in the trap of comparison, we are potentially missing important details in the experiences of the people we’re actually trying to help.

As mentors, we all want to use our deep experience and expertise to help the people we’re working with. And by paying attention to the potential downside of this experience and managing it appropriately, we can deliver on that promise.

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