Author Archives: Gerry McGovern

The psychology of cheap

My old landline phone gave out (yes, I still have one), and when I lifted it off my desk, I had to remove two wire connections. As I did that, I had a strong impulse to push the old wires off the back of my desk and let them fall (my desk faces a wall). Then, I remembered how I had finally decided to clean up the mess of wires behind my desk a month ago. I was forced to do it because I had some technical issues, and figuring out where each wire was connected proved not only difficult but also awkward. So, before adding the new phone, I forced myself to spend the extra 2–3 minutes required to tidy things up.

About five years ago, I had a discussion with a web manager who was quite stressed about the fact that they had a website with 500 pages of content. It was simply too much for them to keep up-to-date. I met the same manager recently and asked them how they were getting on. Everything was great now.
“How many pages do you have on the site now?” I asked.
“Thousands” was the reply.
“Thousands? But the last time I met you, you were stressed out because you felt you couldn’t manage 500.”
“Yes, but now that there are thousands of pages, nobody expects me to manage anything. I used to constantly fight with people, telling them that they can’t publish this and they can’t publish that. But now, I just go with the flow and put up whatever content I’m asked to put up.”

This should have been a “once upon a time” story. Instead, it’s a 2019 story. Content management has always been an oxymoron. It was never really about management. It has always been about cheap publishing and storage.

“Working on a data lake,” Steve Peters stated, replying to my last article on digital glut. “Asked the customer what data they needed from the source. They were too lazy to perform due diligence so they said “all of it”. Not the first time and not the last time. It’s been my experience that 20% of the tables in a database hold 95% of the relevant data. But OK, if you want to pay for the other 3TB of useless data—OK.”

Artificial intelligence learns by analysing existing data. What sort of AI is going to evolve that’s fed on 90% junk data?

Digital is cheap but so too was oil. Cheap takes from the future to serve the present.

Cheap storage. Cheap processing power. Cheap energy. It’s all great. We don’t have to think. We just dump our content onto the website and let search engines figure it out. Why not leave stuff always on? I mean, what’s the harm if billions of people waste a little bit of energy every day?

Cheap makes us lazy. Cheap makes us short-term thinkers. Cheap makes us mindless producers and consumers. We can do better if we spend just a little more time thinking about what we’re about to do. If we spend just that much more time thinking about the future impact rather than present convenience, we can do a lot better.

Digital is garbage

Digital is mainly garbage. 90% of data is never accessed again 90 days after it is first stored. 80% of downloaded apps are never used again after 90 days. 90% of data has been created in the last two years. Over the years, we found that we had to delete 90% of a typical website to make it useful. Even the information that is used is usually full of garbage. I’ve rarely come across 1,000 words that can’t be edited down to 200 and made more effective.

And images, all those utterly stupid, useless stock images strewn across the Web. In every organization, there must be a Department of Useless Images.
“Hey, I’m looking for a useless image for an article I’m writing about digital garbage.”
“How about some old computers piled in a heap, or some network images, or someone screaming at their computer, or lots of ones and zeros?”

A Microsoft executive once told me that there were an estimated 15 million pages on Microsoft.com, four million of which had never been accessed. That’s basically the population of Ireland of stuff that nobody has ever looked at. Why?

Digital is a huge accelerant of global warming. It encourages the production of stuff on an unimaginable scale. And most of this stuff is garbage. It’s one thing to use energy to create something useful that will be used again and again. It’s simply appalling behaviour to use energy to create something that becomes garbage a few moments after its creation.

Modern technology facilitates the production of cheap, disposable crap. In Ireland, festival-goers leave behind a wasteland of single-use tents. Fast Fashion is the number two global polluter, right after the oil industry. People are buying an excessive number of clothes, hardly wearing them, and then dumping them. But of course, they don’t degrade because they’re made of polymers and all that crap.

Digital is like sugar: it’s so sweet and irresistible, so easy to create, so easy to store, so easy to ignore. So cheap. So everywhere. Digital is the ultimate ‘just do it’ mantra. Think of all that useless content that organizations create. Why? Because that’s what management wants. The senior marketing manager wants the bling stock images because they look cool. Marketers find these sugar-coated smiley faces irresistible. And stock images are so cheap now. Why not pump them everywhere? Who cares if they slow down the site and waste energy? Who cares if it takes longer to scroll and find things?

All this stuff we create. Why? Do we ever ask why? We never have time because 90% of it is spent creating garbage. We never have time to do it right. We only have time to do it wrong. We can’t resist the tools of creation. Maintain? Who wants to maintain and continuously improve when you can just create something and then throw it away? And of course, creating new crap is how you get ahead in 90% of organizations.

If digital was a digestive system, it would have no capacity to poop. Every PC and smartphone is like a mini-garbage truck brimming over with useless junk. Think before you create. Before you take that picture, write that content or code. Are you doing something genuinely useful?

The Technology God is fake

In the grand delusion that is Brexit, the grandest delusion of all is the Brexiteers’ fawning adoration of the Technology God. According to Brexiteers, the Technology God will banish all problems, particularly those associated with the border on the island of Ireland. Grand Boffo Johnson ascended the mountain, and the Technology God conveyed the message that there existed no need for a border because the Technology God would solve everything. No evidence, no detail required, just faith in the Technology God.

Years ago, I encountered a senior UK civil servant who told me his colleagues had a type of technology PTSD. “Whenever they hear the word ‘technology’ being uttered by a Minister,” he said, “their instinct is to crawl under the table.”

I know I should be battle-hardened at this stage, but I am still astounded by the toxic mixture of arrogance and ignorance that exists at the senior-management level concerning technology. The Technology God is everywhere and is one of the core reasons we continue to see declines in productivity and living standards despite the massive investment in technology.

In government, disastrous technology-related decisions follow a relentless monotonous pattern. In Ireland, for example, we have a technology-driven national ID card fiasco that is “mandatory but not compulsory” (yes, an actual politician uttered those idiotic words). We have a broadband plan, which though noble in its intent, seems to be managed by the slowest of slow minds.

When I visited Canada last year, they were still embroiled in the Phoenix payroll system fiasco. Phoenix (what a name!) has been described by Canada’s top auditor as an “incomprehensive failure.” But it’s not at all incomprehensible; rather, it’s totally and absolutely comprehensible and predictable because such technology disasters happen year in, year out on a global scale. They are caused by bad, delusional managers who are total, relentless suckers for the fairy-tale IT sales executives bearing gifts from the Technology God.

The Technology God will get rid of workers and cut so many costs, there won’t be any costs left to cut. The Technology God will be so easy to use that it will be a dream. Bugs will be banned, ordered out, vaporized. Poof!

“But Prime Minister, there is not a border on earth where technology has made everything friction-free thus making the border invisible.”

“Blobberdash! Smelly socks and horsehair! The Technology God solves all problems!”

“But Prime Minister…”

“Enough! Away! Who brought me this fool who talks dangerous sense? The Technology God. Hi ho, away!”

Pull your hair out. Throw your hands in the air. But remember, it is not you who are insane. Rather, it’s those senior managers and politicians who lose all sense of rational thinking, when it comes to technology.

Technology is powerful, but it is not all-powerful, and the Technology God does not exist. Though possibly dangerous and career-threatening, we must patiently and persistently put technology in its proper context. We must continue to point out that making truly easy-to-use technology takes considerably more resources than most budgets allow, that getting things into a reasonable state tends to take twice as long as we think, that investing in continuous improvement and maintenance is the way to go, and, the irony of ironies, that without quality people, there cannot be quality technology.

Understanding digital speed

In the physical world, beyond a certain point, speed becomes perilous and destructive. One crazy driver can wreak havoc. The greater the speed, the worse the crash. Thus, much of our road infrastructure is concerned with managing speed.

We have created a digital world where so much has got faster and faster. And yet we pay very little attention to managing digital speed. The implications of road rage are clear. Yet, we have vastly more digital rage, as digital allows us to communicate at speeds and scales that were previously unimaginable. Our thoughts become digital actions in a flash and can create flash floods of misunderstanding, anger and retribution.

We’ve been given this wonderful high-powered network and all these fancy tools, and so many are putting the boot down in our digital Ferraris. “Productivity and collaboration are two sides of the same coin,” Kevin Kwok writes. Kwok believes that the objective of tools such and Slack and Dropbox is to become the central nervous system for organizations, but that they have not achieved that objective.

Let’s say Slack was a toolkit for building houses. It’s not enough. You still need the right mix of skills. You still need to decide what type of houses you need and when and where and at what price. You still need planning permission, etc. You can’t just throw a bunch of smart people a toolkit and tell them to go collaborate.

Years ago, I had a conversation with an executive who was about to retire. He told me that when he was being appointed as a manager for the first time, he was sent on a course called “Managing your filing cabinet”. When the organization introduced computers, they stopped giving that course. He worried about how employees were going to be able to manage hundreds of ‘filing cabinets’ on their computers without any training or guidance.

Collaboration is an essential skill of the digital economy. And yet, in my experience, how to collaborate productively is hardly ever taught either in universities or in the workplace. You’re just expected to know. But people don’t always know because while non-productive collaboration is a no-brainer, productive collaboration is really hard. It requires a whole range of communication, organizational and social skills.

Learning to write simply and clearly is not easy. Learn to speak effectively in remote meetings is not easy. Learning to organize files and other digital stuff in a way that they will be easy to find later by yourself and by your colleagues is not easy. Learning to work in a multidisciplinary, culturally diverse teams is not easy.

A fool with a tool is still a fool, as the saying goes. Organizations have been willing fools to the Technology God. For decades, they have bought the idea that all you need is this brand-new digital tool. As they watched a constant stream of catastrophic IT implementations, they never learned. As they wondered why global productivity had slowed so dramatically, they never asked if maybe just buying the cool new tool was not in fact part of the problem.

Collaboration is like water. It’s wonderful. It’s life giving. But you can drown in it. When you speed up collaboration, communication and content creation, you get to a point beyond which serious floods and crashes become inevitable. Just like everything else, we need to manage speed, we need to manage collaboration.

The Arc of Collaboration

Survey Monkey: when support overcomes poor design decisions

I have been using Survey Monkey software for more than ten years. Over that time I have seen it become more and more cumbersome and complicated. I used to be able to put surveys together without thinking. It was so simple and smooth. I have a lot of colleagues who have used Survey Monkey over the years and I never used to get any questions from them. Then, the questions started coming: How do you do this? How do you do that?

For example:

“How do clear the responses from survey X?”

“It’s in the “More” option at the far right of Survey X”

“No, it’s not.”

“What do you mean?”

“The only options are “share, copy and delete.”

“That’s really confusing.”

And then we set up a call, and then I realize they’re in the stupid “Dashboard” section which for some reason doesn’t allow you to clear responses. And on and on and on, up to a point that I now find it a real challenge using Survey Monkey, when ten years ago I found it so simple and wonderfully easy.

Of course, this is not new. Many products become more complicated over time. Sometimes, that’s because of enhanced functionality but it’s also because new features and redesigns are how designers and managers stamp their mark on a product.

The thing that keeps me using Survey Monkey is its exceptional support. Their support is designed to make my life easier not theirs. It starts at the very beginning. When I have a problem, I don’t have to “open a ticket” because that’s not what I want to do: I want to solve my problem as quickly and easily as possible. Instead, Survey Monkey let me send an email to support. And they reply really quickly with friendly, helpful advice. This is not an accident. It’s part of their culture.

According to a study by TalentLMS, SurveyMonkey consistently delivers excellent customer support by:

  • Investing a lot of time and energy in product training.
  • Keeping staff in the loop by providing ongoing training.
  • Communicating clearly how important customer service is for the business.
  • Making data central to decision-making. Use lots of different data sources to build insight.
  • Employing nice, friendly people and using emotional intelligence to de-escalate tough situations.

“We actually train specifically on emotional intelligence and how to build and leverage those skills during tougher customer interactions,” Dan Henig, Vice President, Customer Operations at SurveyMonkey explains. “At the end of the day, customers are rarely angry with an individual, but rather with the situation they’re struggling to resolve.” In an age of technology, automation and AI, being professionally friendly can be a key competitive differentiator.

A characteristic of successful companies today is that they embrace technology AND human potential. Combine emotional intelligence with data and you are likely to see better insights and better decisions. Great people and great technology deliver great products and services. A friendly, emotionally intelligent, knowledgeable, technology-supported professional has all the tools and skills required to deliver a truly exceptional customer experience.

Lessons from SurveyMonkey

The patterns evident in Top Tasks research

In 2014, we completed our largest ever Top Tasks identification project for the European Union. It was in 28 countries and 24 languages. Almost 107,000 voted. After 30 voters, the top three tasks had emerged. Yes, the top three tasks after we closed the survey with 106,792 voters were the same as the top three tasks at 30 voters. (The top three tasks were: EU law; Research and innovation; Funding and grants.)

We’ve been carrying out Top Tasks identification projects for about 15 years now. Over 400,000 people have voted in over 100 countries and in more than 30 languages. Certain patterns have remained consistent. We get the same basic voting patterns whether we are trying to understand what people in Oslo want from urban transport; what health policy professionals want in India; what consumers want in Brazil; what people in Liverpool want from their council; what Bot developers want; what matters to managers when it comes to Artificial Intelligence; what people buying cars want; what IKEA, Rolls-Royce or BBC employees want. The same patterns repeat again and again.

To get statistically reliable data, we aim for about 400 voters. However, we know that at about 25 voters, the top three tasks will begin emerging. The more voters we get, the more stable the overall list becomes.

A typical tasklist will have between 50 and 80 tasks. We tend to define Top Tasks as those who get the first 50% of the vote. That is typically about 15 tasks. We want those 15 tasks to be as stable as possible, so that’s why we aim for 400 voters. Over multiple surveys, we have seen that at about 400 voters we get stability in the first 15-20 tasks.

For simplicity, let’s say people were asked to vote on 100 tasks. The top 5 tasks will get an average of 25% of the vote, with the bottom 50 tasks also getting 25%. In other words, the top 5 tasks get as much of the vote as the bottom 50. After 400 people have voted, the chances of a task from the bottom 50 of the vote becoming a top task are infinitesimal, as are the chances of a top task dropping into the bottom 50 tasks.

We have carried out more than 500 Top Tasks Identification surveys. To my knowledge, we have never found a situation where there are more than 8 tasks in the first 25% of the vote. In every environment of human endeavor we have surveyed, there are a small set of things that really matter to people, and a large set of stuff that doesn’t matter so much.

The same stuff matters everywhere. If you’re living in a city in Norway, Canada, the Netherlands, or the UK, you care about roads, schools, libraries, rubbish collection. When it comes to public health, health professionals in Nigeria care about the same things as their peers in India.

Again and again, the same stuff ends up at the bottom of the list. The stuff that the organization often cares most about. We quite often find inverse relationships. That which people care most about, the organization is doing least about. That which people care least about, the organization is doing most about. The ego and vanity of organizations is one of the most universal and persistent patterns of all.

Fallible data

Data is not fact and fact is often just a hypothesis anyway. We humans design how data is created and we humans are the ones who interpret data and draw conclusions from it. Therefore, data will always be inherently fallible. Unless we approach it with a sense of humility and a willingness to acknowledge our opinions as hypotheses to be tested, we will end up using data to entrench the gut instinct behaviors we claim we want it to replace.

To many, data is the new god. It can do no wrong. “The data says” has a certainty and absoluteness to it. “Data doesn’t say anything,” explains Andrea Jones-Rooy in an excellent article for Quartz magazine. “Humans say things. They say what they notice or look for in data—data that only exists in the first place because humans chose to collect it, and they collected it using human-made tools.”

For those involved in customer or user experience, or in the creation of content, or in the digital design world in general, data can be a powerful advocate. Again and again, I meet web professionals who struggle with organizational politics and ego. The opinions of other stakeholders often demand the creation of more and more digital stuff, much of it unnecessary, and some of it counterproductive to a good customer experience.

Many of these stakeholders are in senior management, so if your response to them is your opinion, it is their opinion that will invariably win. Or even if you win, your career will lose because, whether we like it or not, pleasing our superiors by agreeing with and implementing their opinions, is still the safest way to promotion. Those who champion the customer are often seen as awkward, obstructive, stubborn. Not a great career move.

Data can take the heat for you. You can use data to convince others. In the best scenario, data can help you change minds and opinions, so that stakeholders develop new opinions based on data. That’s a genuine win-win situation.

However, once you enter the arena of data, you have to be careful. Some will see your behavior as a claim to have a higher truth, to have more robust facts. Some will challenge your data. You need to be able ensure that your data is as valid as possible, while at the same time clearly outlining its weaknesses and limitations.

“Data is an imperfect approximation of some aspect of the world at a certain time and place,” Jones-Rooy explains. “It’s what results when humans want to know something about something, try to measure it, and then combine those measurements in particular ways.”

According to Jones-Rooy, we can introduce imperfections into data in number of ways. Random errors occur as a result of faulty equipment or human mistakes. A systematic error results from “using data from Twitter posts to understand public sentiment about a particular issue is flawed because most of us don’t tweet—and those who do don’t always post their true feelings,” Jones-Rooy states.

You may choose the wrong things to measure. You might measure how long people spend on your website, thinking the longer they spend the better the experience, whereas it may reflect time wasted because of confusing navigation and low quality search results and content. Errors of exclusion happen when you exclude a particular segment of the population and then assume that the data applies to them too.

I’m a data scientist who is skeptical about data

The three elements in understanding your customers

Let’s say you’re trying to deliver an excellent customer experience for a health website or app. You start by truly knowing your audience. Let’s say, for example, that you have statistically reliable data that tells you that mental wellbeing, which includes stress reduction, mindfulness and positive thinking, is particularly important to those under 24. That those who care about mental wellbeing also care strongly about harmful habit reduction, quitting (smoking, alcohol, drugs), and diet, food, nutrition (healthy eating, intolerances, weight). That men and less likely than women to state that they care about mental wellbeing. And that plain language and simple straightforward content is of particular importance to those who care about mental wellbeing.

You have developed a useful picture of the people you need to serve by combining three elements of understanding:

  1. Their tasks (mental wellbeing, harmful habit reduction)
  2. Their experience they wish for as they seek to complete these tasks (plain language)
  3. Their profile or category (age, gender)

Why are men less likely than women to state that mental wellbeing is a top task for them? What is the other data saying? In many countries, suicide is the number one killer of young men, and men are many times more likely to commit suicide than women. However, it is also true that women are often more likely than men to attempt suicide.

To build a true picture of your customer, use multiple sources of data. Never depend on one source.

Digital is primarily an interactive medium. There are over 60,000 searches every second on Google. That’s activity initiated by a person. There are many more links clicked on, pages scrolled, forms filled out, content read, listened to, viewed. People are out there on the Web doing stuff. What you must first establish is what it is they’re trying to do. They may not be doing what you want them to do, but they’re doing something, and if you don’t understand what they want to do then you have very little chance of delivering them an excellent customer experience.

You now know, for example, that young men are reluctant to acknowledge mental health as a top task, even though other data is showing that they are silently suffering from mental health issues. If they’re not searching for mental health stuff, then what are they searching for? Could it be that young men at risk are searching for help on harmful habit reduction? Can you validate that? Because if you can, then that could be a conversation starter about mental health.

But hold on. You must first answer the question. If they searched about stopping smoking, then it is absolutely essential that you first address how to stop smoking. Only after you have clearly answered the question might you then provide some links such as the following:

Feeling stressed? Learn how to reduce stress

Steps you can take now to improve your mental wellbeing and health

It all begins with understanding the task, the reason the person is online, the purpose of their search. Sure, there may be an underlying purpose to that search. Quitting smoking may not be feasible until that young person can find better ways to manage and reduce stress. But you got to start where people are at if you ever want them to get to someplace else.

Are your customers low or high information?

Low information people tend to be highly emotional, impulsive and habitual. They hero worship. They blindly trust their instincts and fiercely distrust everything and everyone else. Much traditional marketing and advertising was designed to pull the emotional triggers of low information people and get them to hero worship brands. This reached its perfection where low information consumers met low information products. These are impulsive, addictive or habitual products that most of us don’t—or don’t want to—think too much about. They include snacks, fast foods, alcohol, cigarettes, sugary drinks.

We tend to see this low information hero worshipping marketing and communication as the only marketing and communication. This is because it is mass marketing. We have seen it everywhere for most of our lives.

As our societies have evolved, we have seen a steady rise in high information consumers and high information products. Better education, computers and smartphones have, by and large, made many of us smarter and more skeptical.

People tend to be a mix of low and high information consumers, depending on the product or service. There are people who are low information when it comes to their health, for example, but who will be very high information about choosing a camera. Most of us are a complex often contradictory mix.

Someone once said that we are not rational animals but rather rationalizing animals. Sure, emotion drives people but it is not the only driver. Emotion alone didn’t build the space shuttle or the iPhone. We are led to believe that fake news is sweeping the world and that facts don’t matter. Fake news always swept the world, and if facts historically mattered why have so many people believed in religion down through the ages?

Objective journalism was never that objective. Many of the world’s greatest dictators (Hitler, Stalin, Mao) were journalists. Governments regularly lied to their citizens. The ‘upper class’ were—and still are—consummate liars. Brands like Coca Cola turned an addictive, obesity and diabetes-causing, sugar-saturated fizzy drink into a must-have brand for the cool generation.

What has changed is our ability and willingness to identify fake news. There is a growing number of people who are prepared to wade through high information messiness, who wish to analyze, compare and evaluate. After all, if everyone had all the answers, we wouldn’t need to search so much. There are about 60,000 searches every second on Google.

Many have stopped trusting politicians, brands and elites not because the elites have become less trustworthy but rather because we have become more knowledgeable and aware of the lies and fake news that are constantly being served to us.  

In this world of contradictions and opposites, many organizations still play the low information game, even when they have high information customers, and even when they have positive facts to present. Carousels pumped with fake news. Effusively smiling fakes pretending to be customers. Content so empty that it hardly even deserves to be called fake.

If you know your customers are the low information type, then using traditional, mass marketing branding is still the perfect tactic. If, on the other hand, they’re high information customers, you need to show them that you’re useful. You need to give them the facts because that’s what they want.  

Mental strain of delivering excellent service

People tend to avoid feeling empathy because it requires too much mental effort, according to a study published by researchers at Penn State University and the University of Toronto, in June 2019. 

“Across all of the experiments, participants on average chose the empathy scenarios 35% of the time, showing a strong preference for the scenarios that didn’t require empathy,” according to a Science Daily report on the study. “There also weren’t any financial costs for feeling empathy in the study because no one was asked to donate time or money to support child refugees or anyone else featured in the photos.”

Participants even avoided feeling empathy in situations involving joy. It seemed like too much effort to participate in the happiness of others. However, when participants were told that they were good at empathy they were then more likely to engage in empathy scenarios and to report that the mental effort was not strenuous. “If we can shift people’s motivations toward engaging in empathy, then that could be good news for society as a whole,” study author, Darly Cameron said. “It could encourage people to reach out to groups who need help, such as immigrants, refugees and the victims of natural disasters.”

Another study found that “in order for the performance of black service providers to be rated equivalent to whites, blacks had to amplify and fake positive emotions to override those negative racial stereotypes. In other words, to be seen as good as white employees, black employees need to perform more “emotional labor”, a concept introduced by sociologist Arlie Hochschild.”

“Though putting on a smile might seem like a small price to pay to get ahead at work, research shows that keeping up a friendly façade is a path to job burnout, a state of complete exhaustion linked to a desire to quit and health issues. Recognizing this situation is a first step to improving conditions for black employees and customers alike.”

Are we as digital professionals designing a service-based world where huge numbers of mentally stressed service workers are forced to fake-smile their way through the day while they beg for ratings from their rich, pampered customers? When we talk about empathy, are we including, or even thinking about service workers? I stopped using personas years ago because I found, again and again, that they were so very often artificial, happy-smiling fakes that much more reflected the designer’s perfect marriage partner than an actual customer.

Empathy should never trump evidence. We need much more logical, evidence-based thinking. Another study found that people with autism have lower levels of empathy but concluded that that may not be a bad thing. It wrote about “selective” empathy where people feel empathy much easier for people like them. “Autism has been linked to higher levels of logical thinking and rational decision making,” the study authors noted. “Autistic people have also been shown to make fairer social decisions.” (Of course, there is no better example than climate change activist, Greta Thunberg, who has been diagnosed with Asperger’s.)

It’s easy to feel empathy for people like you. One of the most critical and most common of design mistakes is the invention of personas of people made in your image.

Study: Empathy is hard work: People choose to avoid empathy because of its cognitive costs

Black employees in the service industry pay an emotional tax at work

Autism is linked to lower levels of empathy – but that may not be a bad thing