Long Form Articles Sindustry

The COVID-19 Sindustry 90-Day Forecast

Sindustry, noun

  1. A contraction of the words Sin and Industry
  2. Any particular branch of economic or commercial activity with a strongly negative public image or stigma; the activity may be legal, illegal, or partially legal depending on the circumstances or geographic location

Why should you care about gambling, firearms, and multi-level marketing when millions of people are filing for unemployment benefits? How about drug dealers, prostitutes, and hackers? Operating on the fringes of society in normal times, they’re even less visible now. But what you don’t see can hurt (and help) you. This concise summary of 23 unique “sin industries” will show you how they are adapting to COVID-19 – either showing you opportunities for innovation or warning you to real and present dangers to your life and property. Only four of 23 sindustries profiled are likely to struggle, and nearly half (11 or 23) will probably do quite well.

In my own professional career, I’ve been involved (either directly or indirectly) in the development and launch of hundreds of new products and services. In many cases, what appear like an “innovation” in a “legitimate” industry was long-standing practice in the Sin Industries, or Sindustries for short. What industry originally popularized ecommerce? Pornography. What groups originally invested in cryptocurrencies? Organized Crime. You get the idea.In all the coverage of the economic impact of COVID-19 / Coronavirus on the global economic, analysts rightfully focus on the most critical targets: Healthcare, medical devices, transportation, food, employment, and energy. But what about those industries people aren’t talking about? What about those industries you won’t read about in an MBA case study?

It shouldn’t surprise us that these industries tend to be so innovative – not just resilient, but what Nassim Taleb would term anti-fragile. Whether legal, illegal, or a mix of both hardly matters. They operate largely out of the spotlight, but they are accustomed to massive shocks to their businesses – whether from government regulation, police actions, or civic-minded individuals and groups.

To brand all these industries “Sinful” is a bit unfair – only a small number are fully illegal. However, the stigma surrounding them can be just as powerful. That’s how I picked the list: Negative public opinion, whether it’s deserved or not.

Why am I doing this? Two important reasons.

First, if history is a guide, we can expect the Sindustries to adapt and innovate more quickly than their more respectable counterparts – they possess stronger survival instincts. They’ll find the new opportunities that those other leaders can exploit…if they’re willing to listen.

The second reason is more ominous. In times of crisis and chaos, we are vulnerable. The media – both traditional and social – are rightfully fixated on the urgency of hospital beds, ventilators, economic impact, and “bending the curve”. While we’re distracted, criminals are taking advantage. If we don’t protect ourselves against more than the virus, we are in more danger than we realize.

It’s time to overcome our distaste for some (many?) of these industries.

How to read the forecast.

I’ve highlighted 23 Sindustries below. Each one features a brief summary of the biggest factors impacting them during the pandemic, and their likely responses. The first set of Sindustries are likely to benefit in the short term, the second will see mixed results, and the third will struggle.

PART 1: Sindustries likely to BENEFIT from the disruption.

Sindustry 90-Day Forecast
Firearms Assertive governmental actions (seen as threats to personal freedoms) will boost gun sales in the United States, though probably only to the small percentage of people who already own firearms.
Pornography The pornography industry is already well-ahead of any other rival in terms of digital delivery of content. With more time at home, consumption will increase, even if new content production slows.
Burglary and Fencing With many businesses closed, burglars will be able to steal unmonitored equipment and supplies because struggling businesses may not be able to afford to maintain security measures. Local police will be too busy responding to pandemic-related calls to do much to prevent them.
Hackers With an increased number of people working from home (and using less-secure technology platforms), hackers will be able to exploit many more vulnerabilities.
Industrial Espionage Rapidly changing situations and confused communication inside large organizations can open the door for watchful parties to exploit gaps in security or employees’ willingness to help.
Counterfeiters Always quick to see value, counterfeit products will crop up to meet the demand for medical supplies, PPE, chemicals/sanitation products, etc.
Organized Crime Much of organized crime already takes place in cyberspace. Lax security from newly WFH (Work From Home) employees will lead to a boom in theft business. Furthermore, organized crime tends to adapt quickly to changing conditions.
Personal Injury Lawyers While sidelined for the moment, personal injury law firms will exploit the confusion to bring suit in multiple areas (employee law, health and safety, ineffective medical care, etc.)
Marijuana Dispensaries Often classified as “essential businesses” these suppliers should see an increase in business as people spend more time at home and suffer from anxiety.
Sex Toys and Lifelike Dolls A newfound need to “socially distance” will likely lead to an increase in the purchase and use of sexual stimulation devices, also leading to innovations in more lifelike options if the aversion to physical closeness persists.
Cults and Extremist Groups Extremist organizations flourish in crisis situations as they can easily adapt fear and panic, redirecting it to their own memberships.

PART 2: Sindustries likely to have both BENEFITS & DRAWBACKS from the disruption.

Sindustry 90-Day Forecast
Alcohol Liquor store sales will spike; sales to bars and restaurants dry up. Supply chains will struggle, especially in beer (cans are more expensive than kegs.)
Tobacco Respiratory diseases will should make smoked tobacco products (including vaping) less appealing, but quitting is difficult, and the impact should be modest (if at all). Longer quarantines may lead to increased sales in other forms of chewed and trans-dermal products.
Prostitution/Escort Prostitution and escort services will be much more difficult in the short term. Practitioners will attempt to switch to “virtual parties” to maintain income. Some percentage will not return to the “in-person” version after the pandemic because they find success with virtual meetings and experience lower risks.
MLM Businesses Many multi-level-marketing businesses rely on personal connections and in-person persuasion techniques. The survivors will be digital-first business models and those that focus on “health” products.
Street Gangs Street gangs operating in the open are exposed when few others are out on the streets. Their operations (as well as violence) should be lessened during quarantines. Many tech-savvy gang members will turn to cybercrime as an alternative to street violence.
Drug Dealers Person to person transportation will be severely challenged in the short term. Additionally, supply chains from overseas and across borders will suffer due to closed borders. “Home brews” (dangerous in many cases) will fill part of the gap.
Pawn Shops / Payday Lenders Government stimulus packages will blunt the need for short-term, high-interest loans in many areas. However, no amount of stimulus will be able to completely address individual / family issues.
Cryptocurrencies / Non-Fiat Currencies In a crisis, consumers tend to flock to “safe havens” rather than (what they perceive to be) risky new ventures. However, the crisis may accelerate some governments to create their own digital currencies, normalizing the behavior and paving the way for broader adoption.

PART 3: Sindustries likely to STRUGGLE with the disruption.

Sindustry 90-Day Forecast
Gambling In-person gambling, both in tribal and corporate casinos, will nearly cease – especially given the population demographics (older) of those most at risk. Online gambling (both legal and illegal) will replace some, but not all, of the gap.
Home Party Businesses In the short term, home party businesses will struggle. Online / virtual parties are much less personal and do not allow for the same level of socializing and peer pressure.
Online Dating & Sex Person to person apps (e.g. Tinder) will suffer in the short term. Virtual “hookups” and dating are not as compelling.
Street Performers and Panhandlers With foot and automobile traffic severely curtailed, those who make their living asking for handouts from passersby will struggle to earn enough.

Critical Takeaways:

  1. Star in your own peep show. Look for ways to make the “virtual” experience more compelling – audio is a must and video is quickly becoming one. But think about real-time user feedback and interactions. Adult “peep shows” provide rich user interaction and experiences, and they should be the “bar” to strive for.
  2. Lock the doors. Just because “everyone is pulling together” and that the biggest fear on your mind is getting sick, doesn’t mean criminals aren’t looking for opportunities. That means both physical security (surveillance of shuttered businesses), cybersecurity (password management, video platform vulnerabilities, etc.), liability protection (follow employee policies, etc.), and product validation (guard against counterfeiting).
  3. It’s time for some experimentation. Times of change open peoples’ minds to new experiences and new ways of doing things. Don’t miss the opportunity to try a “crazy idea” that you’ve been thinking or revisiting an idea that didn’t gain traction in the past.

What Shoshana Zuboff and Mark Zuckerberg both get wrong about privacy, and how you can fix it.

The debate over privacy rights has devolved into a polarized and unproductive shouting match between two opposing points of view. On one side is Silicon Valley, who believes that the benefits of innovation trump any quaint notions of “private” lives. Privacy rights get in our way! On the other is the New York elite – The New York Times Editorial Board and Shoshana Zuboff, who believe our very lives are being threatened in an act of corporate violation. We are here to save you!

Both perspectives are wrong. They’re not wrong because they don’t have valid arguments; they are wrong because they both forgot to ask consumers, citizens, patients, and employees what they think about their own private lives. To put it simply: Zuckerberg and Zuboff think they know better than you do about how you should think about your own privacy. That’s why the so-called “debate” over privacy will never result in meaningful progress.

It’s long-past time for a new approach. To do that, we’ll turn the question of privacy on its head and explore the roots of the complex set of tradeoffs everyday people make when they device whether or not to share private information. When we’re done, we will have a framework that will not only help you make better decisions, but also a way to predict how others might make similar choices.

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How did privacy become not about people?

Privacy concerns? Uber completes over 40 million rides per month in the United States.

Who would have predicted 10 years ago that we would, routinely, get into a stranger’s car and trust that we would arrive safely?

Privacy concerns? Amazon has sold 100 million Alexa-enabled devices.

Would anyone have known 10 years ago that we would, routinely, allow a listening device into our homes so that we could order pizzas and play music?

Privacy concerns? The Mayo Clinic’s Biobank has over 50,000 participants.

Was it reasonable to guess that 10 years ago that we would, routinely, share our health information with a private massive database?

Ten years ago, each of these statements would have seemed ridiculous. Sure, people may be willing to share what they had for dinner on Facebook, but they would never submit to such blatant intrusions of their privacy.

And yet, here we are.

That’s not to say that the privacy situation has not become the subject of intense debate. The New York Times has been publishing articles throughout 2019 in an ongoing series titled “The Privacy Project”. Surveillance Capitalism was one of the top non-fiction books of the past year. Security expert Norton reported that in 2019 alone, 4 billion records were breached.

What does that all mean? We can no longer argue that we aren’t aware of these intrusions, nor can we argue that we don’t know the risks.

And yet, here we are.

The best-selling devices worldwide included Echo Dot, Fire TV Stick with Alexa Voice Remote and Echo Show 5. (Amazon Press Release, Holiday Shopping Season, 2019)

In fact, over two years of intense and overwhelmingly negative media coverage hasn’t made a dent in the growth of the personal information economy – the so-called “internet of all of us”. If that’s true, then business owners, organizational leaders, healthcare experts, and politicians who see consumer, employee, or patient data as critical to their business models must ask themselves a difficult question:

Why is it that people are aware of data gathering, and understand its risks, do they keep clicking “accept”? Will they stop? What happens if they do?

Are people privacy lions or are they privacy sheep?

If we ask the New York Times, they might argue that consumers are only just understanding the true costs, and that once they do, they’ll push back hard. Perhaps they’re right.

If we ask Silicon Valley, they might argue that consumers say they’ll do all sorts of things, but they know better. If consumers want it, they’ll take the risk. Perhaps they’re right.

When I began my research, I saw privacy the same way Shoshana Zuboff and Mark Zuckerberg see it – as a simple paradox born of the personal data economy. The problem was that this simplistic approach didn’t work. It failed again and again to explain the decisions people made when it came to their private lives.

Simple narratives may make good stories, and they may sell books, but they clearly weren’t the truth.

The privacy debate couldn’t lead me to the insights that would help me answer critical business questions. I needed a more practical approach.

  • Will employees consent to listening devices if those devices can prevent harassment and abuse?
  • Are customers likely to adopt a freemium software offering if it aggregates and remarkets their data?
  • Should patients accept a lower cost health plan option if it requires ongoing monitoring?

To get there, I spent the past three years working to understand how individual privacy and the data economy interact. And today, I intend to share with you what I’ve learned. As I do, I’ll ask you to confront the same questions I did during my exploration. And finally, I’ll show you how that broader perspective will pay off for both your personal life, as well as your business.

But before we begin, a warning: You may not like everything you hear, and the questions won’t be easy. And when we’re finished, you won’t have better answers, but you will be able to ask better questions.

Let’s get started.

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Perspective #1: Deep History

Private Lives in The World Until Yesterday - What Can We Learn from Traditional Societies?

When you met your colleague or friend this morning, was the first thing you talked about the color, odor, and volume of your last urination? (I hope not.) But tribespeople in New Guinea do. And it makes sense. If you relied on the person next to you for your life and safety, you would be very interested in their elimination habits. It’s probably the best indication we have of overall health absent modern measurement techniques.

It is only since the industrial revolution that we substituted processes, infrastructure, and technology instead of other people for our survival. Privacy is a very recent invention.

But no system can address all of our needs. We continue to rely on our family, friends, and neighbors to help us navigate the uncertainty and scariness of daily life.

Nextdoor user interface

Nextdoor is a good modern example of this tradeoff. Yes, you could call it a “system”, but it is more accurate to think of it as a way for busy neighbors to help each other figure out what’s happening near them. To share in this way requires releasing some level of personal anonymity for the feedback other people can provide – much like a modern version of a tribe.

But let’s not be theoretical, let’s get personal. My first question for you relates to the deep historical perspective I’ve just described:

Would you rather rely on other people for your personal well-being or would you rather rely on systems, infrastructure, and technology?

Privacy question 1: Would you rather rely on other people for your personal well-being or would you rather rely on systems, infrastructure, and technology?

Make a mark on the continuum between those two extremes where you personally fall. There is no right or wrong answer. And don’t overthink it. Your first impression is what your unconscious brain is telling you is right for you.

Now, let’s go. We have nine more to go!

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Perspective #2: Global Privacy Law

Our second perspective is one that many of us is familiar with – the legal and regulatory approach. Critics may argue that privacy law simply hasn’t caught up with technology and marketing, and that government is always bumbling and slow. But I’m not so sure that’s fair.

Specifically, the new California Consumer Privacy Act went from idea to implementation in 18 months. That’s pretty fast. If we hop a plane to the European Union, we see the ongoing implementation of GDPR – the first large-scale attempt to address data privacy and the rights of individuals. Leave Paris and land in Beijing, and you have privacy rights – unless the entity who wants to know sf the government, in which the public need outweighs your personal right. More on that later.

Global data privacy laws

My intent isn’t to debate the specifics of any legal or regulatory framework, but rather to show that privacy “rights” depend entirely on where you are.

More than that, all laws have consequences. Here are just a few examples:

  • Can your credit card company still offer effective fraud protection when criminals can hide behind data anonymity?
  • Will hackers in another country care that you live in California?
  • Should abusers on social media be able to hide behind aliases?

There aren’t easy answers. This complexity leads to the second question I will ask you to consider:

Is it better for the law to define privacy for you, or is it better to guarantee transparency and allow people to decide for themselves what they share or do not share?

Is it better for the law to define privacy for you, or is it better to guarantee transparency and allow people to decide for themselves what they share or do not share?

Mark your spot on the continuum.

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Perspective #3: Free

New laws aren’t emerging for no reason. The fastest-growing category of new products, services, and business models rely on information about you as the primary “product”.

To understand how we got here, we need to put ourselves back in the early days of the internet. Business leaders aren’t dumb. They learned from the experience of my hometown university – the University of Minnesota – who, in 1993, decided to charge a small fee for the implementation of its Gopher protocol. Remember Gopher? Most people don’t. We all use HTTP now for a simple reason: It was free.

It’s easy to see why: Who would pay for something that hadn’t really proved much tangible value beyond the military and academic communities? It had to be free to get people to adopt it.

The Gopher server interface

But once people get a taste of “free” it’s hard to get them to pay anything else. Marketing people (like me) understand that better than most. Someone had to pay the bill, and advertisers were the only ones who would.

In fact, “Free” is Google’s entire business model. Facebook’s too. Even Netflix – the poster child for getting people to pay for content – is considering an ad-supported model to compete with new streaming services on price (no matter what executives say they’re not considering). For many business, not-for-profit, and healthcare organizations, “freemium” is (or will be) part of the business model.

But just because it has been that way, doesn’t mean it needs to stay that way. You may feel that pandoras box has already been opened, but I’ll remind you that no one thought that free Google would take off in 1998. We all survived “free, ad-supported” television. There is no reason to think paid Google won’t work in 2020.

The age of ad-supported television

Here’s your question:

Is it better to pay for services so that you can restrict the use of data, or is it better to use free services and accept the release of your data for advertising and other data mining purposes?

Is it better to pay for services so that you can restrict the use of data, or is it better to use free services and accept the release of your data for advertising and other data mining purposes

Go ahead and put yourself on the continuum.

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Perspective #4: Safety & Security

Let’s stick with Google for a moment. Last month in Milwaukee, Wisconsin, Google complied with a request from federal law enforcement for any device using Google services within a 30,000 square meter geofence. They were looking to solve a spate of arsons in 2018 and 2019.

Knowing that most people (including, presumably, the criminals) carry smartphones, and that most of those smartphones use some Google service, and that those devices track time stamps and geo-locations, police could then ask for more detail on those accounts that match specific areas and times of interest.

Anonymized Google Data

But it’s funny, if you search for “Milwaukee Geofence” on Google, this is what you get: A new feature built into costly Milwaukee brand power tools to help owners track them down when they’re stolen. Power tools, as anyone in construction knows, are both expensive and portable (for obvious reasons) and also makes them easy and profitable targets for thieves.

Milwaukee OneKey

Neither instance is as simple as it appears.

In the first case, most people get a little uncomfortable about the idea of a “dragnet”, but police are only asking for additional data on specific devices at the scene of a crime … and with a judge’s consent. If they get through that hurdle, defense attorneys could challenge the admissibility of that evidence in US court.

The second case seems more straightforward. The power tools are simply a matter of tracking property. However, let’s say this drill is in the worker’s toolbox and he puts that toolbox in his trunk. Could his employer use that information to track that employee on his way to a marijuana dispensary? Wouldn’t that tracking be warranted because of the safety risk of using marijuana on the job?

What’s more important: Protecting privacy or preventing harm?

I’ll bet we could spend the better part of a day arguing the details, but I won’t give you that kind of time. Here’s your question:

It is better to allow law enforcement complete access into our lives in order to protect us, or should law enforcement only respond after a crime has been committed?

Struggling with that one? This isn’t going to get easier.

It is better to allow law enforcement complete access into our lives in order to protect us, or should law enforcement only respond after a crime has been committed?

Mark your spot.

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Perspective #5: Privacy Technology

Speaking of not getting easier, now let’s have a discussion of large semi-prime number factorization. I’m kidding, we won’t. Just suffice to say that the biggest security issue today isn’t quantum computers breaking RSA encryption, it’s us. We are the problem.

According to PC Magazine, 35% of people never change their passwords. That’s closer to 80% for so-called IoT devices. We respond to phishing emails. We write passwords on Post-It notes. We toss our health records in the recycler. Security is a cat and mouse game, and unlike Tom and Jerry cartoons, the cat usually wins.

PC Magazine 35 percent of people never change their passwords

We can try two-factor authentication. Our phones can incorporate biometrics. Online banking can insist on redundancy. We lock ourselves in faraday cages for goodness sake. But convenience is a more powerful motivator than any of them. We want one-click purchasing and instant answers. That’s the real reason no security system is foolproof. We won’t stand for it.

One click versus two clicks

From a technical perspective, privacy requires constant vigilance. And that is the essence of my next question for you:

Is privacy more important than convenience?

Is privacy more important than convenience?

Go ahead. What answer comes to mind?

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Perspective #6: Media & Information Flow

Now is a good time to circle back with the New York Times. I’d recommend reading the entire opinion series on the Privacy Project, but be warned. This series falls victim to the oldest truth in media: If it bleeds, it leads.

I pulled out some keywords and phrases from a long piece from December 19, right before the Holiday last year. You can see them below. To be fair, the authors are describing what they believe is a serious problem, but I want you to notice something about the words: They are universally negative – there is no discussion of the positive side of data sharing, no balanced perspective.

New York Times Privacy Project Dec 19 2019 word cloud

This is not to say that the matter isn’t serious, but this perspective hints at a powerful dilemma in a modern society – exemplified by the founders of the internet, Wikileaks, and paradoxically, the New York Times itself.

It is that the free flow of information is critical for the functioning of society. Without it, we cannot hold people and systems accountable. The paradox is obvious: In a world where the most important information is about you, it’s hard to have it both ways.

information wants to be free

Where do you stand?

Should information be free, or should information be restricted?

Should information be free, or should information be restricted?

Wow. This is starting to get uncomfortable, isn’t it? You ain’t seen nothing yet. Let’s talk about religion.

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Perspective #7: Faith

All major religious traditions address privacy in one way or another. And while I am not – nor do I claim to be – a scholar on comparative religions, it’s not difficult to find an opinion from all the world’s major faiths:

  • Judaism addresses the twin issues of consent and modesty, while stepping back from the perspective that privacy is a right.
  • Islam takes a stronger view regarding the inviolability of the private life.
  • Hindu scholars hold a more nuanced view – privacy exists, but they recognize that the concept is difficult to pin down.
  • Confucian and Taoist traditions seem to favor a different view – that family and society play a stronger role than individual self-determination.

Map of World Religions

But perhaps no religious tradition addresses the polarity of privacy more than Christianity. To see that, we only need look at two passages from the Gospel of Matthew. I could have picked many others, but we can clearly see the tension between private faith and public faith. (below, emphasis mine).

Matthew 6:6
But you, when you pray, enter into your inner chamber, and having shut your door, pray to your Father who is in secret, and your Father who sees in secret will reward you openly.

Matthew 28:19
Go, and make disciples of all nations, baptizing them in the name of the Father and of the Son and of the Holy Spirit.

We may hear plenty about how people are turning away from religion, but that does not mean they are turning away from faith, and it certainly does not mean that our concept of right and wrong aren’t strongly influenced by religious traditions – even if we, ourselves, don’t consider ourselves “faithful.”

How do you see it?

Do you agree that your private life is akin to only your inner relationship with your creator (or with yourself)? Or do you see yourself as having a duty to live openly to serve as an ambassador and example for others?

Do you agree that your private life is akin to only your inner relationship with your creator (or with yourself)? Or do you see yourself as having a duty to live openly to serve as an ambassador and example for others?

Mark the spot on the continuum that feels right for you.

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Perspective #8: The Greater Good

Let’s expand on that last question from a different perspective. As we’ve already discussed, since the industrial revolution, we’ve relied on systems and technology (rather than other people) to achieve things no small group of individuals could do on their own.

But we’re running up against the wall. In order to break through and understand the toughest and most intractable problems – cancer, climate change, and racism to name just three – we may need to rethink the importance of other people in our collective lives.

Speaking of personal, let’s get personal.

I am part of the Mayo Clinic’s bio bank. It is a massive data collection program with the mission to build an ever-expanding database of people’s personal lives and habits – and how those variables impact our health and the course of disease. The goal is profound: To create next generation of medicine.

For me, the decision to participate was easy: Cancer took my dad at 61. I would give very much for someone else to be spared that. I might even be inclined to support a mandatory program of data sharing.

Donald Voiovich

In that, the Chinese government might agree with me.

In China, the government is engaging in perhaps the largest data collection exercise in history – they call it social credit – and their goal is to use data in the most ancient of Chinese objectives: to create a more harmonious society. Don’t visit your aging parents enough? You might not get a small business loan. That’s oversimplifying, but it’s the basic idea.

China social credit system

To do that, China tracks all manner of information – from everything you buy to everywhere you go to everything about your health, and so much more. Where some in the West view this as Orwellian, and that the government is only doing this to maintain “control”, China’s experience reminds us that “the greater good” is in the eye of the beholder.

With those two examples in mind, here is your next question:

What is more important to you, protecting your individual privacy or the contributing to the greater good?

What is more important to you, protecting your individual privacy or the contributing to the greater good?

Make your mark.

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Perspective #9: The Psychology of Privacy

It’s funny, isn’t it, that in all this time we haven’t talked about the study of the human mind itself – and what psychologists can tell us about the balance between a private and public life.

When most people think of “privacy”, they tend to think of it in very simple terms – as in being away from other people or having your thoughts, actions, or identity shielded from others.

But psychologists say it’s more productive to think of privacy as a “boundary control process” through which we control whom we interact with, how we interact with them, and when and where these interactions occur.

But even psychologists admit that our need for privacy is a combination of nature and nurture. In other words, our introversion and extroversion, the context situation we’re in, the culture to which we belong and identify, and our biological heritage. On that last point, we only need to look at our closest living relatives to see that we seem to be more social than solitary.

But even individual chimpanzees have different preferences. How about you?

What’s more important to your emotional well-being in general: Privacy or socializing?

What’s more important to your emotional well-being in general: Privacy or socializing?

Make your selection.

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Perspective #10: Economics

We’ve ended at what I feel is the most honest, and useful, aspect of privacy: That privacy is, fundamentally, an economic question. There are benefits to be gained by sharing information, whether those are social, psychological, legal, religious, or monetary. There are also costs. From an economic point of view, it’s a simple question: Do the benefits outweigh the costs?

To explore this is just a bit more depth, let’s compare the idea of privacy as a right to the idea of privacy as an asset.

As a right, privacy is something to which you are entitled. The downside is that rights are given to you by where you live. You don’t control them. Inalienable rights in one country don’t translate to inalienable rights in another.

Privacy as a right

As an asset, privacy is yours to use as you see fit – to incur the benefits and the costs. However, that control comes with responsibility. Ignorance is not bliss, and the powerful will tend to take advantage of the powerless.

Privacy as a asset

With those definitions in mind, here is your final question:

Do you see privacy more as an inalienable right, or more as an asset to be utilized?

Do you see privacy more as an inalienable right, or more as an asset to be utilized?

Make your final selection.

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Predicting the Future of Privacy?

Do you feel a little mentally exhausted? Confused? Frustrated? The first time we truly broaden our perspective, it will seem challenging. Privacy is complicated. It’s high time we respected privacy for the tough series of tradeoffs that it is.

Let me help you make the past 15 minutes actionable for you. What you see below is my privacy profile based on the same questions I just had you answer. Now you’ll see the power of this perspective to help me make an everyday decision about my privacy.

Here’s the question:

Should I purchase an Alexa-enabled virtual assistant for my home?

I could use any (or all) of my answers to the 10 questions to help me decide, but my research has taught me that middle-of-the-road opinions don’t drive much action. Strong opinions do. In my case, my three strongest opinions refer to security, the greater good, and economics.

From a security perspective, I know that the more people who are involved in the process, the less secure information is. At this point, we know that Amazon is using humans to “train” its artificial intelligence. The likelihood of a breech for someone (not necessarily me) might be remote, but the likelihood of it happening eventually to someone is basically 100%. It’s not irrational to be wary of fat tail risks like this one.

Secondly, I am not sure that by buying one of these voice assistants that I am contributing to the greater good. If, perhaps, the voice assistant was tracking the tone of my voice to help create a database of anxiety or conflict disorders, perhaps. But for ordering toilet paper online? Not so much.

Jason Voiovich - filled in provacy profile with Alexa - choice 2

Finally, the economic tradeoff. The risk I could incur, and the lack of any greater benefit, means that the “price” I pay for the Alexa-enabled device isn’t worth the “costs” I incur – even if it were free of charge.

Jason Voiovich - filled in provacy profile with Alexa - choice 3

So, no. An Amazon Alexa-enabled device isn’t the right decision for me, at this time.

But that doesn’t mean that you, using your own chart, may not come to a different conclusion. Your chart is your decision-making machine.

That’s great for you in a personal context, but how can this exercise help you answer critical business questions?

We can use these questions to build a survey of our patients, employees, or customers, and learn how they view privacy in aggregate. Using regression analysis, we can discover which of the 10 answers in strongest in a given context. Finally, we can use that analysis to better predict the answers to critical questions, like the ones I showed you at the beginning of our discussion.

  • Will employees consent to listening devices if those devices can prevent harassment and abuse?
  • Are customers likely to adopt a freemium software offering if it aggregates and remarkets their data?
  • Should patients accept a lower cost health plan option if it requires ongoing monitoring?

But the real question for you is:

What different decisions might you make if you knew this information about how your critical stakeholders view privacy? I can’t answer these questions for you, but I know who can.

Because not knowing how your customers feel about privacy – in this next “personal” information age – is as irresponsible as failing to test your product before you launch it.

More than ever, understanding privacy is a business imperative.

Now you have a tool to start asking better questions.


This isn’t the first time I’ve tackled the privacy issue, although I think the piece you just read is the furthest along in my thinking on the topic. Interested in past work? You can see how much I struggled with the issues, and also get a sense for how I ended up where I did. Here goes:

“Alexa, play some music” isn’t the only time Amazon is listening to you.
Using Google Maps costs more than you think.
Data Exchange Networks, AI interrogators, and corporate espionage (Chapter 2 of the Dr. Thomas story)
Your “smart” TV is a dumb idea
Messing with data: The 10-step subversive instruction manual to hit the tech companies where it (really) hurts.
What if someone offered $6,495 for your private data? Would you sell?
You don’t have a right to privacy. You have something better.
In America, your digital freedoms are what the tech companies say they are.


About Jason Voiovich

Jason’s arrival in marketing was doomed from birth. He was born into a family of artists, immigrants, and entrepreneurs. Frankly, it’s lucky he didn’t end up as a circus performer. He’s sure he would have fallen off the tightrope by now. His father was an advertising creative director. One of his grandfathers manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (true!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting.

Jason Voiovich and grandpa

I think this photo explains a lot about why things didn’t turn out the way I’d hoped.

He took bizarre ideas like those into the University of Wisconsin, the University of Minnesota, and MIT’s Sloan School of Management. It should surprise no one that they are all embarrassed to have let him in.

These days, instead of trying to invent novelty snack dispensers, he has dedicated his career to discovering why people do what they do – because that’s the only way we’ll tackle our biggest challenges and accomplish the next great thing.

Marketer in Chief Marketing Ethics Rehumanizing Consumerism

What if Warren Buffet is wrong about reputation?

It’s always nice to have the Oracle of Omaha give a ringing endorsement of your profession. Among many things, I consider myself a professional reputation manager. Many of you reading share that vocation. We take it on faith and intuition that reputation matters. Good ethics is good business, right? We all know what happened to Enron, right?

People like us have built our careers helping organizations manage and shape their perception in the marketplace with the understanding that – while it may be challenging to measure – those actions produce results.

But what if reputation didn’t matter?

What if corporate reputation didn’t connect with any objective measure of performance? Worse, what if it paid to be bad? Let’s set aside the existential questions for a moment (Should you be good for its own sake? Do you believe in a higher power? Do other people have the inalienable right to be treated well?). As a professional reputation manager, I am interested in a more fundamental question:

Can I measure the impact of reputation on corporate performance?

It turns out, I can. And as much as it hurts to say, reputation doesn’t seem to make a difference.

Buffet might be wrong.

I hope you’re bristling right now, wondering how I could possibly make such a stupid and thoughtless claim. Haven’t I spent the last five years writing about Rehumanizing Marketing? Haven’t I railed against abuse of power from Big Data? Wouldn’t I be the last person to suggest that being a good corporate citizen doesn’t matter to bottom line performance? This simply doesn’t make sense. Of course, reputation matters.

Part of growing as a professional (and as a person) is being honest with yourself in light of new evidence. And one of the hardest things to do is question the basic assumptions that underpin your worldview.

If you’ll let me, I’m going to ask you to follow along.

Here’s what prompted the discussion – and resulting exploration – that helped me ask myself a difficult question: The Reputation Institute recently released their list of the Top 100 organizations. Netflix topped the list this year, followed in sequence by a notable list of public and private companies. While you could question the specifics of their scoring process, the Reputation Institute is full of smart people. They have as good a process as any at ranking something as nebulous as “reputation.”

Despite that tacit endorsement, I usually ignore bullshit rankings like these. (I ignore college rankings for the same reason. They all suck.) But two recent email subject lines caught my eye:

“We live in a reputation economy.”

“Strong corporate responsibility increases purchase intent by 8.0%”

If you notice carefully, they’re making a claim that reputation links to revenue in some way. That’s a concrete claim. That’s testable. So, I tested it.

Does reputation correlate with revenue? No.

First, a few basics about what you’re looking at. The X-axis represents the Top 100 companies, by reputation rank, in descending order from left (#1) to right (#100). The Y-axis charts the change in revenue over the past three annual reporting periods. The scatter plot is the result. Here’s what you should notice:

  1. There aren’t 100 dots – Reliable data on private companies is not easily available. The analysis includes only publicly traded companies with at least three years of history.
  2. The dots don’t represent revenue, they represent change in revenue – If a better reputation leads to more revenue, and you try to compare actual dollars from Amazon and Tractor Supply Company, the former will dwarf the latter and skew your results. Changes in revenue help level the playing field.
  3. We’re measuring change over three years, not over one year – We’re also assuming that one year is too short for “reputation building” efforts. As Buffet reminds us, reputation building takes time. A lot can change in one year, but three years helps give efforts time to materialize.

If you’re curious, a few of the key statistics are:

  • Correlation: -0.040
  • Mean: 21.37% 3-year growth
  • Standard Deviation: 37.4%

If it’s been a while since you took your last statistics class, here’s what they mean:

If reputation and revenue performance were positively correlated, we would expect the fitted line to slope downward, meaning that better reputation leads to better revenue performance. Instead, we notice that the line is, basically, flat. (The -0.040 correlation confirms what is obvious just by looking.) There is no correlation between reputation and three-year trailing revenue performance. In other words, you can’t say that a better reputation (at least by the Reputation Institute’s scale) will lead to higher revenue growth.

[Fun fact: If you track one-year revenue instead of three-year revenue, the correlation is much more negative, meaning revenue performance is better the worse your reputation is.]

But it’s not just that the line is flat – the standard deviation tells you how wide the variance is. In other words, you can get wildly different results in revenue performance. It’s telling us that other factors are at play – as well as random chance – in determining those results. The wide variance and non-existent correlation suggest that reputation is not likely to be among them.

Hey, wait a second. Revenue is a trailing indicator. Reputation is about future performance.

I tend to agree, and that’s why I used three years of prior revenue data, but I see the point. While reputation doesn’t seem to correlate with past revenue, it could correlate with future revenue. Luckily, we have a way to begin to test that: Market Capitalization. In simple terms, market cap is the value of one share of stock multiplied by all shares outstanding (adjusted for weird shit finance people do.) It’s a useful measure because it tracks what market believes the value of the company will be in the future. There are lots of factors that influence valuation, but we would assume reputation would be among them.

Perhaps reputation accounts for 20% of the variability. Or 10%. Or something we can measure.

So, does it?

Does reputation correlate with market valuation? No.

Same chart as before, but this time the dots represent changes in market capitalization over the past three years. And again, the statistics:

  • Correlation: 0.064
  • Mean: 6.59% 3-year change in market capitalization
  • Standard Deviation: 52.5%

Notice anything? It’s a very similar chart. In fact, changes in market capitalization are even more variable than revenue. What’s more, if you remove the meteoric rise of value in Netflix stock (#1), the correlation turns negative.

Different metric, same conclusion: Reputation does not correlate with future value either.

After looking at two unique ways to correlate reputation with performance, I’m left scratching my head at what I could be missing.

Perhaps the dynamics of smaller companies (or individuals) are fundamentally different than larger, publicly traded organizations? Maybe, but fractal self-similarity tells us we should expect similar patterns at different scales. (I know, that’s nerdy, but that math works.)

Perhaps reputation matters in some other way: Shorter time scales, much longer time scales, only for specific products or services … or maybe reputation matters most in a recession? Maybe, but I worry we’re simply finding new ways to run the data until we find an answer we like.

Perhaps reputation matters more to the bottom line than to the top line. In other words, a good reputation might make it easier to attract and retain the best talent, hence making the company more profitable. Maybe, but profitability is notoriously difficult to compare from one organization to another (see previous note about the weird shit finance and accounting people do). I don’t think we can cherry pick.

Speaking of cherry picking, bad reputations could impact individual companies in strongly negative ways even if it doesn’t matter in general. Maybe, but that’s the foundation of statistical inference. As a manager, if I can’t predict whether a reputation will help or hurt my organization’s performance, I can’t deal with it effectively.

Perhaps reputation helps guide the “social conversation” around your brand, leading to positive feelings and a stronger intent to patronize the organization. Reputation Institute seems to think so. Maybe, but if that were true, it should show up in actual revenue. It doesn’t.

Perhaps “ranking lists” are simply ways to sell consulting services to ego driven CMOs who want their organization nearer the top of the list. Maybe, but that’s just my inner jaded Gen-Xer showing. I don’t think these lists are designed purely for profiteering, although I’m not naïve enough to think that has no bearing.

Perhaps there is some other metric I’m too stupid to see. Now there’s a better answer. Other scholars have found positive correlations, negative correlations, U-shaped correlations, and no correlations. Here’s my problem with all that: When lots of smart people look at the same thing and find wildly different results (and most often, no results), that points to a simpler answer:

Reputation doesn’t matter to objective organizational performance.

What the fuck?

I have to be honest; I expected some amount of correlation. I wanted to believe that doing the right thing meant getting the right result – perhaps not immediately, but in the end. I wanted to believe that attention on social media mattered. That taking a stand mattered. That employee ratings mattered. That companies that treated customers like data points and flouted the rule of law would get what was coming to them. That what I was researching and writing about mattered.

Actually, I think it’s time to bring back those bigger questions:

Should you be good for its own sake?

Do you believe in a higher power?

Do other people have the inalienable right to be treated well? 

To measure reputation as you would measure dollars cheapens reputation. It shackles “good in the world” to some temporal and fleeting notion of material gain. That may seem too spiritual for an email from a self-described Honey Badger, but hear me out.

If reputation doesn’t impact corporate performance, that doesn’t mean a good reputation isn’t a worthy goal, but rather that you can do good without fear of how it will impact your business performance. Patagonia wants to save the planet? Go for it! Starbucks wants to have a conversation about race? Let’s have it! Nike wants to take a stand for athletes? Just do it!

If none of these actions impact corporate performance, it actually clarifies and purifies their intentions.

Under that assumption, you can know a company believes in something because believing in that thing won’t matter to their revenue or stock prices. They’re doing it because they truly want to. (If they only care about their shareholders, that’s obvious too.) You can decide how to align your values (and your purchases) without the confusion around intentions.

[Fun note: My Jewish friends might argue the intentions are subordinate to actions. I see their point. I tend to like clarity of purpose.]

Some organizations (and some people) may look at these data and conclude that they can break the law with impunity. I like that clarity as well. We cannot rely on “public shaming” or “cancel culture” to adjust their behavior. If they broke the law, they need to be prosecuted.

I began this process with certainty: Reputation matters. During the middle of this process, I learned it did not. But in the end of this process, I discovered clarity. Organizations should do good because it’s the right thing to do.

The money doesn’t matter.


Writing Update

Many of you follow me because of the many threads of writing I’ve pursued over the past five years. Most of them, at their core, are founded in an evolving notion of ethics in the practice of marketing. In that time, I couldn’t help but wonder why corporate leaders would listen to me politely … and then continue to act in ways that were the exact opposite of that ethical foundation. That includes plenty of organizations you’ve heard of – data privacy breaches, audience exploitation, you name it. They’re all “legal” in the strict sense of the word. In my opinion, they’re unethical.

Why do they keep doing it? I suspect they’ve known for a long time what I just learned: Reputation (and the positive and negative actions and perceptions that create it) don’t make a difference in the only measures they care about.

It’s freeing, really.

Disconnecting the idea of Rehumanizing Marketing from a business perspective to an ethical (and personal) perspective allows me to sidestep the issue of revenue performance. Instead of speaking to the corporation or organization (who must, by law, care for its shareholders first and everyone else only to the extent the law requires), I can speak directly to the CMOs themselves. I can appeal to them as people – not in the way their pastor or spiritual guide might, but as a peer. We have the ability to use our positions to create change in the world, and we can tell our CEOs that the social media “noise” is just that: noise. As long we produce revenue, we’re free to pursue a bigger mission and ask a very different question:

What could you do in the world if your corporate reputation didn’t impact your company?

I think a large number of CMOs (and even CEOs) would relish that question. It’s liberating and exciting. For those who wouldn’t, I have a different idea. For those who realize reputation doesn’t matter, and then decide to push the ethical boundaries, how far can you go before you cross from ethical trouble to legal trouble? How to you break rules strategically? The market seems to reward ethical rule-breaking as “innovation” and punish legal rule-breaking with fines and restrictions (a la Uber, in both cases.)

I have some ideas I’m working through on that angle.

In other news, I’ve decided not to let the idea behind “Marketer in Chief” die on the vine. I get it that the major publishers can’t find a good home for it right now. That’s okay. I’m interested in the idea, and I don’t really care that they’re not. That said, I intend to pivot the project into a weekly interview series (me and a presidential historian) for the 45 weeks leading up to the next election in 2020. I have a few such folks interested already. I’ll create a separate blog and Twitter feed, and I intend to do some minor publicity work to make sure the political journalists know about it.

This can’t be any worse than the “public dialog” we’re going to have in the run-up to the 2020 election.

And speaking of reputation and its impact, I have two entries forthcoming in the Palgrave Encyclopedia of Public Affairs. I’ll send an update when they make it through editorial review.



Long Tail. Fat Risk. Why You May Want to Rethink Your “Platform” Strategy. Right Now.

Platforms existed before Chris Anderson’s 2006 book The Long Tail, but no other single work did more to bring the concept of exploiting micro-markets to the mass consciousness. The core idea is simple: Although blockbusters and bestsellers get all the attention and show disproportionate sales volume on a per unit basis, items that sell fewer units individually can present a large opportunity cumulatively. The top selling items represent the “fat head” and the lesser selling items make up the “long tail.” Internet-enabled platforms allow entrepreneurs to find so-called long tail opportunities in a cost-effective manner ­– difficult or impossible in the days before technology made them accessible. But the real winners have been the platforms themselves: Amazon, Google, and Facebook have made billions on the long tail idea, providing the infrastructure to aggregate these small opportunities. It should come as no surprise that the real interest from the investment community isn’t in the fat head nor in the long tail, but rather in the platform itself. That’s why Airbnb and Uber can have unicorn valuations while they bleed cash. But the investors and the entrepreneurs are missing something critical: Long tails are, in fact, fat tails. Their wide statistical distributions hide systemic and unknowable risks – risks that will lead to their inevitable collapse. We’re already seeing those risks appear with Amazon (counterfeiting), Google (zombie search results), and Facebook (psychological manipulation) – among many other examples. The only reason a full implosion hasn’t happened yet is that laws in many countries protect platform companies from the liability stemming from these risks. But that’s changing. And when it does, all platforms will be exposed. 

Entrepreneurs and investors may want to rethink the wisdom of platform strategies in light of new evidence of the true risks. However, if they’re willing to jump in the pool, it makes sense to know how deep it is.



Neither the “long tail” nor the “fat tail” are new ideas. Both ideas come from statistics – specifically, they are easy words laypeople (non-mathematicians) can use to visualize a statistical distribution. Let’s do that.

Imagine a bell curve – the “normal” distribution you learned about in high school – and notice how the curve stretches off to either direction, getting skinnier as it goes. In practical terms, that means the further out you go, the less likely a result will be. That’s why you don’t see many people greater than 8-feet tall. We tend to see distributions like this one in the natural world, most often in biology, chemistry, and physics. In fact, that’s probably where you learned about it.


Our World in Data is a treasure trove of fun visualizations like this one.


Now imagine that same bell curve, but then pull it up it a little at each end and stretch it at the top. In simple terms, this means that events closer to the center and farther away from the center are more likely than in a normal distribution. It’s the middle that’s skinny. (Look up Pareto and log-normal distributions if you want to geek out.) We see this sort of pattern in certain natural phenomenon (earthquakes, for example), but especially in financial markets. On one side of the graph, you can get very unlikely positive events – a startup skyrocketing in value just after you bought it. On the other side of the graph, you can get the 2008 financial crisis. The implications are very simple, but profound: Although the likelihood of either event happening at any one specific time is vanishingly small, the likelihood that event will happen eventually is (essentially) 100%.

Standard & Poor’s (of S&P500 fame) provides this data of monthly returns over the past 70 years or so. Notice how the “normal” distribution doesn’t match up well to actual events.


You can read all about this sort of thing in Taleb’s The Black Swan, so I won’t belabor the point. What’s important to understand is that we don’t just see fat tails in financial markets. We see them in marketing. All the time. You simply may not recognize them because they look a little different.



Misinterpretation of mathematics is, perhaps, marketing’s greatest “gift” to the world. Oh, and one more thing: Whenever a marketing person offers you a multi-colored, smooth chart representing data, be suspicious. It’s probably bullshit.


What we see here is simply one-half of the distribution we saw previously, because in marketing, it doesn’t make sense to have “negative” sales of something. (Or does it? More on that in a minute.) To get specific, we see fat tailed distributions in the following common situations:

  • Popularity of search terms on Google
  • Sales of individual products on Amazon
  • Frequency of posts from certain individuals on Facebook or Twitter

If you’ve ever plotted “sales by customer” for your business, you’ve likely seen the same sort of chart. It’s not a normal distribution; it’s usually a Pareto distribution. They resulting chart features a fat tail, not a long tail.

This may seem like a nit, but language we use to describe our world is critical to how we choose to act on it. Chris Anderson wanted to make a distinction between the “fat head” and the “long tail” – because that’s what it looks like, right? Could you see a book titled “The Fat Tail” selling quite so well? Anderson’s publishers were smart not to be accurate.  Long tends to be a positive word. Fat tends to be a negative word.

However, that lack of accuracy hides a nasty surprise: Anderson’s “long tail” isn’t a long tail at all. It’s a fat tail. That’s why there is so much opportunity for aggregation of niche demand. A statistical long tail wouldn’t be as interesting, nor would it be as profitable. To paraphrase the central point: With great opportunity comes great risk. Platforms that exploit these fat tails underestimate systemic risk. They have exploded in value because they take advantage of the upside. They will implode, eventually, because of the inherent downside – a downside they are ignoring.

Why is that? We could argue that our language (long tail instead of fat tail) and our visualization (only showing the positive side of the curve) hide that risk. But more than that, most entrepreneurs aren’t thinking of the downside when they craft a platform strategy because they haven’t seen a major meltdown in a platform company. Even those who understand (and accept) fat tail risks point out that legislation protects platforms from liability, mooting those risks. In a risk-free environment, why not take advantage?

Reasonable. Stupid, but seemingly reasonable.

Laws can change. When they do, every platform is exposed. And if you don’t have the financial and legal resources to protect yourself, your platform implodes. We’ll use the example of a medical device connectivity platform (cloud-based infrastructure that connects pacemakers, heart monitors, and other medical equipment, and stores/synthesizes their data), to help us illustrate the problem, as well possible solutions.


Stop misinterpreting negative results as a cost of doing business. Start visualizing both sides of the distribution of possible events.

What can these geeky statistical artifacts tell us about risk in a platform strategy? We’ve already seen how marketing has fumbled the distinction between a “fat tail” and a “long tail,” but what does it really matter? All sales are positive events, aren’t they?

Perhaps not.

When we see all sales (or all content, or all search results, or all data collection) as positive, it means we only visualize one half of the graph – the right-skewed side distribution, always trailing off in the positive direction. The tail might be “fat,” but a fatter tail only means that there is more hidden opportunity than a “long tail” would predict. That’s why Google, Facebook, and Amazon make so much money.

Isn’t it funny, however, that the previous graphs (from biology and finance, respectively) showed full curves with both a right and a left distribution? Hmm. What would happen if we graphed that in a marketing context? Let’s try.



This graph is a thought experiment, showing sales of legitimate products as “positive” events and the sales of counterfeit products as “negative” events on an ecommerce platform such as Amazon or Alibaba.


Consider the implications of this visual thought experiment. We plot legitimate products and sales on the right side of the graph, and counterfeit (illegitimate) product sales on the left side of the graph. All sales are “positive” in revenue terms, but the impacts are not. A counterfeit sale could be graphed as the equivalent of a reduction in sales of the legitimate product. (If you like, think of this as the “Upside Down” of normal commerce.)

Near the tall head of the (now mirrored) graph, we find counterfeit versions of high-value products – knockoffs of popular movies, electronics, and fashion items. Because of their popularity, platform companies have a (relatively) easy time finding and removing them. It’s a game of whack-a-mole, but algorithms tend to be good assistants to human fraud-prevention experts. That’s why the chart seems skewed near the center – there are sales in that section (especially on less-regulated platforms such as Alibaba), but less than you’d expect.

Now look further to the left. The tail is fat. In fact, it’s fatter than you’d expect it to be. That’s because fraud and counterfeiting are difficult to catch. The sellers tend to be smaller and have fewer resources to help bring counterfeits to the platform’s attention. Reactions tend to be slower, and they rely on customers to help catch them.

Case in point: #CopyPasteCris. This is a recent case of sophisticated plagiarism on the Amazon Kindle Store involving one “author” allegedly lifting portions of books from dozens of legitimate romance authors. It was not the authors nor the platform that caught the issue, it was a careful reader who uncovered the problem.

That’s not all. Where there’s smoke, there’s fire:

Why don’t the platforms pay more attention? We’ll cover more about the root cause of that in the next section. But for now, it’s enough to know that platforms consider bad actions and bad actors as a part of the business model – costs to be minimized, but costs that never can be eliminated. There is a tricky balance: These platforms rely on independent entities (sellers, reviewers, video producers, etc.) to provide the bulk of their “content” – for free. If platforms needed to produce (aka pay for) all of their content; they could not exist in their current form.

Cracking down too hard on the bad actors also makes it more difficult for legitimate content providers, but not doing enough erodes trust in the platform itself. While we can sympathize with their challenge, it isn’t the risks in the center of the distribution (the obvious ones we can see) that are the major problem. Those occur with enough frequency that we simply need to watch for them. It’s the events in the fat tail that are the problem. Most simply not have happened yet because they are so infrequent. But as we know from the inevitability of the mathematics, those nasty unknowable surprises will happen, given enough time and a large enough sample set. And the platforms are only getting bigger.

For those who want to build their own platforms, the knowledge that risks are (a) hidden, (b) unknowable, and (c) inevitable is quite sobering. Despite that, many of those risks can be mitigated. The first step is awareness. Our visual thought experiment can help us identify categories of risk for our medical device connectivity platform.


Risks are easier to spot when you know where (and how) to look for them. The best way to spot risks in a platform strategy is by using a fat tail visualization.


Essentially, this is a scenario planning tool that uses a statistical distribution of events (or the thought model of one) to help illustrate the risk profile of its platform strategy. Traditional scenario planning processes can be quite valuable, but they struggle with the critical input question – how do you decide what scenarios to consider? (Expert facilitators can help overcome some of these problems, but cannot eliminate them, especially in fat tail situations.) Using this visual technique illuminates the possibility that fat tail (unknowable, unpredictable) risks can occur, even if you can’t see them. You can read more on scenario planning processes, or consider hiring an expert facilitator, but let’s use a simple series of three questions to begin the risk assessment process, helping us brainstorm for possible risks:

Question 1: How could a high-frequency positive outcome become a negative outcome?

This technique helps us mirror the “tall head” opportunities and discover risks. This is a good place to start because the risks here tend to be knowable and predictable – at the very least, they’re much more likely to happen given their relative frequency. In our medical platform example, collecting large amounts of data, without a clear hypothesis or study design, could lead to overtreatment. PSA screening for men and breast cancer screening for women tend to fall into this category.

Question 2: How could low-frequency positive outcomes become negative outcomes?

This technique helps us expose some fat tail risks by looking at the positive side of the fat tail for clues. For example, the medical device connectivity platform team may have written a case study on a thankful story from a user who – through the tracking of heart rate data – caught a hidden defect that would have been unexpectedly fatal. Question 2 asks us to mirror that unexpected event – what if the device on the platform missed a similar defect in someone else?

Question 3: What downside risks are we seeing in other (unrelated) platforms?

A “Black Swan” that occurs on another platform need not be “unexpected” for you. Question 3 asks us to look for nasty surprises happening on other platforms. For example, could a version of #CopyPasteCris (plagiarism on the Amazon Kindle Store) happen to our medical device platform? Could bad actors looking to commit insurance fraud manipulate data to lower their rates? It’s possible.

Before we move on, let’s be crystal clear: You cannot predict all risks in a fat tail. However, that does not mean you shouldn’t try; there is value in the effort. With even limited information and only a subset of all possible risks, you can make better decisions and put preventative measures in place.


Stop believing your platform is immune to risks. Start planning for the day when artificial protections are removed.

Doesn’t it seem odd that a fancy library is one of the world’s most valuable companies? That’s not to say that Google isn’t good at what it does, and not to say that information isn’t the arguably the world’s most valuable currency, and not to say that Google doesn’t offer any other value, but is organizing information really that valuable?

There seems to be little downside in the business model. If Google search links to incorrect, fraudulent, or offensive content, you can’t sue Google. Same with Gmail when it goes down. Same with Google Maps if it guides you to the wrong place. They’re all free. (Even the paid versions limit liability.) Yes, advertisers (who pay Google’s bills) might throw a fit over racist content on YouTube, but what other options do they have? Platform companies have matured to the point where they’ve eliminated all of their meaningful competition in a winner-take-all positive feedback loop.

Heads, Google wins. Tails, you lose.

Most people have come to accept the platform mantra of caveat emptor without much thought. They do not stop to realize that the only reason they can (largely) pay lip service to the fat tail problems in their business model is that our laws allow them to do that.

More specifically, the legal and regulatory framework in many countries shields platform companies from liability. In the United States, Section 230 of the Communications Decency Act of 1996 protects Internet Service Providers from liability for people who commit all manner of crimes using their network. We have allowed that protection to extend to Amazon, Facebook, and Google – and basically every other open platform. In other words, those companies can effectively transfer fat tail risks (which would be significant costs to other business models) to their users and advertisers.

In fact, platforms have a disincentive to do too much.

That same law that shields intermediaries (ISPs/libraries/platforms) does not shield them if they become involved in the content itself. You may remember the story of, a website that often-featured advertisements for prostitution. The platform was shielded, but when the court found evidence that the company was more actively involved, the shield vanished. But it’s not just about obvious issues such as prostitution; platforms that edit content for language can run into trouble – technically, they are no longer libraries; now they are newspapers – and hence subject to libel laws.

But all that is likely to change.

The same politicians who helped put those protections in place are now seeing the need to remove them. In the United States, Senators are different ideologically as Ted Crux (R-Texas) and Elizabeth Warren (D-Massachusetts) are both pushing for major overhauls to Section 230 legislation. Just last week, President Trump’s team leaked that it was working on an executive order to do just that.

Platform companies seem to have forgotten that they operate under a set of laws – laws that can change. Laws that are changing. Most entrepreneurs and investors excited about platform strategies aren’t paying attention to politics. They should.

What can an aspiring platform company – in this case, a medical device connectivity platform – do to plan ahead for the day when these laws may change? It’s useful to abstract the problem. Platforms are simply conduits for data – product sales, videos, social media posts, rental listings – they are all data of one type or another as far as the platform is concerned. In that case, we can use data management practices to help understand what precisely might be targeted by new legislation.

  1. Data collection: Our medical device connectivity platform gathers data from devices over a cloud infrastructure. In simpler terms, your customer’s pacemaker connects to her home’s Wi-Fi network and send that data to the platform’s central server. Mitigating risks from new laws could involve restricting collection only to those devices that meet certain security criteria. That would help prevent bad actors from flooding your servers with low-quality or potentially malicious data.
  2. Data processing: This is a key risk area for platforms – if they process data, they can be held liable for the results of that processing (see previous note on the difference between a “library” and a “newspaper”). Our medical platform could mitigate this risk by asking for explicit consent for any data processing rather than relying on implied consent. For example, the platform would ask the user to approve the process of analyzing heart rate data and sending it to your doctor.
  3. Data storage: Any time an asset resides somewhere, you need to secure it. That’s why there are locks on car doors and security guards next to bank vaults. The laws surrounding liability for data breaches are tricky. Mitigation strategies include the “nuclear option” (not storing data at all for any longer than absolutely necessary) to the “bulkhead strategy” (splitting up key parts of the data into different databases with non-connected redundancies).
  4. Data access: To grow quickly (“scale” in Silicon Valley’s language) most platforms try to remain as open as possible. The rationale is that restrictions on access impedes adoption by making it more difficult to use the platform. By now, you should see the inherent risk in that thought process – it is only valid because of a transient legal framework. HIPAA provides a better framework for data access, and it would be natural fit for our medical platform company example. However, this is one case where other platforms would be wise to follow those rules versus Facebook’s rules.

It bears repeating again: There is no way to predict all risks in advance, especially in a fat tail scenario. However, building a system that better balances “openness” with “security” will help prepare your platform for inevitable (and soon arriving) changes in legislation that will change your operating environment.


Stop trying to capture the entire market. Start strengthening your position with a barbell strategy.

Let’s return to the mathematics for our risk mitigation strategy. What makes a platform so appealing is its ability to scale. By aggregating a few large chunks of demand, and many smaller ones, a single platform can dominate an entire market category. If we convert marketing language into mathematics, the total available market (TAM) is defined by the integral of the curve – the area underneath it.

This is the classic way to represent a platform TAM. After reading this far, we now know better. We need to look at the full picture. When we draw the full curve, the TAM is what’s left over when you subtract the bad side (left integral) from the good side (right integral).

Simply understanding that you need to subtract the negative market from the positive market in your valuation of your platform strategy is a revelation in itself, but that doesn’t tell you much about what to do to maximize your profitability while mitigating as many risks as possible.

Let’s visualize how we might do that.


Instead of trying to capture the entire market, we’re slicing off part of the “head” and a part of the “tail,” sacrificing large portions of the fat tail as well as the “middle market” to other platforms. While it may seem against some Silicon Valley religious text to say “no” to any part of the potential market for a platform, this approach better focuses our attention in three ways:

  1. Capturing all of the demand forces us to think about the platform more than we think about the content on the platform. In other words, traditional platform strategies concentrate more on their technology (and how cool it is) rather than their users and use cases.
  2. Focusing on the tall head ensures we can capture those few uses cases that generate disproportionate revenues. It also makes sure we stay focused on those needs.
  3. Focusing on a complementary portion of the fat tail ensures we don’t miss those ancillary use cases that support the tall head. Instead of the entire fat tail, we’re cherry picking those use cases that relate to our top customers’ needs.

The idea here is to employ a use case-centered platform strategy versus a market-centered platform strategy. As we’ve seen, we are exposed to risks in a platform strategy from both known, unknown, and unknowable events. The more we understand the use cases and customers, the more we can understand (and then mitigate) a much larger portion of the risk profile.

In finance, they call this a “barbell” strategy because that’s what it looks like. Your portfolio consists of only ultra-low risk assets and hedges on one side, and ultra-high risk and high-return investments on the other, with nothing in between. There’s plenty of math to show why this works, but I like the simpler reason: Because you only are concerned with extremes, you pay better attention. Focusing on everything in the “muddy middle” or the “total market” mean you can’t pay attention to everything. You’ll miss stuff. It’s inevitable.

Our medical device connectivity platform example makes the illustration of the strategy clearer.

By focusing on the “hospital to home” heart monitoring use case, we select only those devices that fit the “tall head” and their related devices in the “fat tail.” We eliminate the muddy middle (and most of the tail) entirely. This strategy need not preclude growth. Our platform could scale by expanding the set of devices and data collection those people use (perhaps common exercise equipment and Apple Watches on one side, and physical therapy specialty equipment on the other). In other words, instead of trying to collect “all” health device data on one platform, mitigating the risks involves refocusing on the users and their needs.

Its platform technology can focus on specific types of data (heart rate and related health issues) instead of all types of health data. This level of specialization reduces the risk of “lowest common denominator” protections and knowledge. The resulting barbell approach gives direction to which areas of the fat tail to look for opportunities. While it cannot eliminate risk, by choosing the risks you take, you convert an unknown or unknowable risk into a knowable one. You can better mitigate a risk you understand.

Yes. This means you have to make choices. Mitigating fat tail risk with a barbell strategy means you must cede portions of your total market. It’s no longer an open platform that does everything for everybody.

You’re also far less likely to get hit by a metaphorical bus you didn’t see coming around the corner.

That seems like a fair trade.



Platform strategies aggregate consumer demand in a way no other business model can match. They provide countless opportunities for small businesses to exploit niche opportunities that would be too small for large companies to pay attention to, therefore satisfying a wider range of needs and wants. The ability to make money enabling thousands (or billions) of transactions is the pied piper of Silicon Valley. However, those entrepreneurs and investors are underestimating the inherent risk in large data sets in four specific ways:

  1. They mistake a “fat tail” for a “long tail.” This simple error obscures the risk of major negative events. Formal scenario planning can help mitigate the knowable risks, but it can do nothing to address unknowable risks (aka Black Swans).
  2. They misinterpret emerging threats as costs of doing business – counterfeiting, fraud, and other crimes transfer the risk from the platform to the entrepreneur and the consumer, ultimately providing limited incentives for the platform provider to act to correct them.
  3. They believe legal and regulatory frameworks are unchanging features of the market landscape. They forget that critical aspects of the law protect them, artificially, from liability. Those laws can be changed at any time.
  4. They succumb to greed of capturing the entire market. A barbell strategy (taking the highest volume on one end and the most complex and niche at the other, while ignoring the middle ground) helps hedge against fat tail risks.

We’re enamored with platforms the same way we were enamored with the housing market in 2007. There is nowhere to go but up! The reason we’re still excited about platforms is that we haven’t seen one of them implode. Fifty years ago, no one thought the original “platform” company (Sears) would implode. And yet, here we are. It may seem unthinkable that Facebook, Amazon, or Google will collapse. It’s not. It is only a matter of time.

You should be careful using their strategy as a role model for yours.


Marketing Education

Innovations Diffuse, but People Adopt Behaviors: How to Use Misinterpretations of Diffusion of Innovations to Your Advantage.

Playing “Oregon Trail” as a kid made me grateful for cross-country transportation innovations.


Everett Rogers was not an engineer. That might seem odd to most technologists. Since 1962, they’ve used his groundbreaking idea – The Diffusion of Innovations – to track the introduction and spread of revolutionary technologies through a population. Iconic examples of the so-called S-curve permeate techno-lore: Refrigerators, color televisions, microwave ovens, dishwashers, personal computers, mobile phones, and the Internet itself. Individual entrepreneurs use it to justify hope in the inevitability of all innovation and the eventual success of their idea, product, or service. They’re reading it incorrectly. Rogers was a professor of Communication Studies – exploring how people accept change in societies. He never claimed all innovation would succeed. In fact, innovation only seems inevitable in retrospect. Many innovations never achieve 100 percent of their potential market. But more to the point, technologists are not paying attention to a simple point of language: Innovation is not the same as a single idea, product, or service. The innovation may succeed. You may not. Therefore, while the core ideas behind the Diffusion of Innovations may be helpful for innovators in general, they offer little value to help ensure the adoption of your specific product.

Let’s use Diffusion of Innovations as a starting point to something more useful for individual entrepreneurs.



The Diffusion of Innovations is a representation of the natural process of the spread of innovations throughout a population. The word “natural” is intentional here – the resulting diffusion results in a so-called normal distribution over time – aka, a bell curve. Rogers divided this bell curve into five segments. The largest two groups (the “early” and “late” majorities) are each one standard deviation from the center and represent about two-thirds of the entire population. On either “tail” are innovators and early adopters on the left and laggards on the right. The critical point comes from Rogers’ background as a communications scholar: People use different decision-making processes at different stages of adoption. Early Adopters are more apt to seek out relative advantages and have a lower threshold to trying something new. Laggards, much less so.

Diffusion of Innovations

You can look at the diffusion process in two ways – the first as a bell curve distribution, and the second as a cumulative measure. The first view defines the behavioral categories. The second view creates the S-curve.

This idea has been around so long because it seems to match the world around us. While the time scale of diffusion may change for any particular technology (making the curve shallower or steeper), the “S-curve” we see on charts that track these data seem remarkable consistent.

But not always.

Technology Adoption Evidence

Our World In Data is a fun tool to use. When you do, it seems like it confirms Rogers’ theory – we see plenty of what look like S-curves. We also see an acceleration in technology adoption in recent years (steeper curves). But look more closely. The actual data tell a more complex story the Diffusion of Innovations would suggest.

A funny that happens when you begin to test your assumptions with actual evidence. While the data collected here are incomplete, we notice a few critical features:

Feature #1: Diffusion doesn’t always follow a smooth line.

Sometimes, due to non-market forces (World War II comes to mind), diffusion can slow or stop altogether. Those seeking to introduce an innovation would be wise to mind the non-market as those factors may dramatically change the path and the rate of diffusion.

Feature #2: Not all innovations reach 100% of a population. In fact, most do not.

These charts track technology adoption, but any “innovation” will do. Sometimes, the innovation is a product that some people will never afford. Sometimes, that innovation conflicts too strongly with a cultural norm. Sometimes, that innovation doesn’t catch on for no good reason (the Linux desktop is one of many examples). The bottom line is that defining the “population” is a critical step in understanding the shape of the diffusion curve. Only in rare cases is that number “100%” of a population.

Feature #3: Some innovations are overtaken (prematurely) during their diffusion cycle.

Innovations such as eBook readers and cell phones (both overtaken by smartphones) are good examples. As the pace of change in many areas accelerates, new products, services, or ideas may stunt the growth curves of others. What’s more, all charts like these fall victim to survivorship bias – in other words, we only see the winners. The chart would look very different with the losers included. Innovators would be wise to watch for disruptive forces outside their direct competitive set.

Innovators use Rogers’ work, as well as extensions of his ideas such as Moore’s 1991 book Crossing the Chasm, to help them choose promising avenues for investment, produce reliable sales / voter / opinion forecasts, and (most importantly) choose communication strategies most likely to work at different stages of the diffusion cycle.

But there is an important distinction to make here: An innovation is not a single product. That may seem obvious on its face, but many leaders fail to make the logical leap. To put it more simply: Buying your first “microwave” is not the same as buying a “Whirlpool brand microwave”. The Diffusion of Innovations theory may provide some guidance on features and messages most likely to resonate at a given point in time, but the theory provides little in the way of actionable advice to a specific entrepreneur within a broader innovation category.

Let’s do that now.

We’ll use Diffusion of Innovations as a starting point to help provide actionable guidance for a manufacturer of a “Keto” snack bar product line. In other words, we’ll start with the general advice of Rogers’ theory and work down to specific advice for an individual brand. Why do we need to do that? Diffusion of Innovations helps explain what happens, but not why it happens. Like many great theories, we understand the results long before we understand the underlying mechanisms. Sixty years later, that’s finally changing.


Stop using the words diffusion of innovations. Start using the words adoption of behaviors.

How we use words can shift their meaning, and by extension, how we act on them. This is especially true with theories that have entered the general public consciousness. Diffusion of Innovations is no exception. Let’s have a look at each word, what it means precisely, and what words we might choose instead to help individual innovators.

Diffuse vs Adopt

The word “diffusion” and its root “diffuse” weren’t communication terms originally – they were physics terms. To diffuse is a natural process so long as the correct conditions are present. For example, high density gasses will diffuse (spread out) into a fixed volume of low-density gas as soon as a barrier between them is removed. Diffusion (at least in physics) is inevitable.

Diffusion of Innovations, in contrast to the gasses example above, are not inevitable. Many so-called innovations fail to “catch on” – especially in the light of what the word “innovation” truly means (more on that in a moment). By using the word diffusion, we are making an unconscious assumption that our product, service or idea has a certainty to it – we simply need to remove the “barrier” between our innovation and the available market. That assumption is a fatal error for entrepreneurs.

A better word to use is adoption.

Instead of an inevitable process, adoption is a conscious choice by individual people. Adoption is by no means inevitable. Yes, the Diffusion of Innovation theory provides general guidance for the mindset of groups of people at different lifecycle stages, but that doesn’t tell us much about how to change the behavior just one person. We’ll get to a better idea for that process later. At this point, it’s better to recognize that the process of adoption takes active work on your part as the entrepreneur.

But even more problematic is our general understanding of the word innovation.

Innovation vs Behavior

This is a very simple definition of innovation, but it is a powerful one. Notice something about it? The “product” (aka “technology”) is third in the list. Processes and modes of thinking also are innovations. In other words, the idea of representative democracy is just as valid an “innovation” as the technology of the microwave oven.

Many technologists conflate the words innovation and technology to mean the same thing. But technology is a sub-item within innovation. It may seem like a philosophical point, but it’s not: Innovations cannot exist without a human acting on them. To put it another way and paraphrase the well-known saying: If an innovation falls in the forest, it simply doesn’t exist.

Therefore, innovation, at its heart, is behavior.

That’s especially important when we begin to combine the words “adoption” and “behavior”. Innovations do not diffuse. It only appears that way on the outside looking in. Individuals adopt a new behavior. The first is inevitable. The second is not.

Think of our example of the Keto snack bar. The snack bar was an innovation at one point in the past – but not so much today. That behavior (eating a pre-packaged snack bar) has reached nearly 100% of its target population. No, the innovation here is a process and idea behavior. The process is the Ketogenic diet, and the idea is the concept of protein balance in human macronutrient consumption. There may be some technology in creating a shelf-stable “Keto” snack bar, but that technology isn’t critical to the behavior change process (although it may be a critical part of the process of whether you could produce the product at all).

Let’s use some data to help us illustrate the point:

Keto popularity

We’re using our standard comparison formula, benchmarking “Ketogenic diets” against terms that help us highlight changes. Over the past three years, Keto (the blue line) is moving up steadily while the benchmark items remain flat or cyclical.

For our Keto snack bar entrepreneur, the true behavior to adopt is the Ketogenic diet. Given that, what conclusions could our innovator draw?

  • Keto’s so-called “diffusion” won’t help any particular product other than providing a larger market. In other words, just because Keto is “hot” doesn’t mean that people will buy a Keto snack bar. We can see that in the chart – the top-ranked Google Keto product company fails to get any “boost” from general interest in Ketogenic diets.
  • The innovation of Keto (as a scientific concept) isn’t as important as the change in behavior. Just because people are talking about Keto does not mean they have changed behavior (in fact, many of those who talk about it the most don’t actually follow it – or even know quite what it is). Our entrepreneur should focus on finding evidence of behavior change. Search behavior is one way to do that, but diet survey data is better.
  • To that point, what if people are interested, but have not yet changed behavior? Shouldn’t our Keto snack bar entrepreneur work to educate the market on Keto to help boost its own market? No. Not if they don’t want to run out of money. The “education” process is slow and thankless. And per point #1, just because people follow the Keto diet, doesn’t mean they will purchase your product. Our entrepreneur should watch the market, follow the adoption path, and focus on linking the prior behavior before the Keto diet (snack bar consumption) with the new behavior change.

Words matter. Diffusion of Innovations happens at the population level. Change of behaviors happens at the individual level. You always sell one to one.


Stop planning to address a market opportunity. Start making the most of feedback loops.

One of the most persistent criticisms of Rogers’ work is that it assumes a sender-receiver model of communication. A technology diffuses into a population, the population does not diffuse into the technology. To put it more simply, the people buying microwave ovens didn’t change the microwave oven as a technology.

That’s ridiculous, of course, and even more so now with software-driven products. The true innovation process is iterative and dynamic, with the innovation itself morphing as it is adopted by larger groups of people. Much of the bias in Rogers’ thought process comes from the dominant view of the communications process at the time – a mechanistic view of “sender-channel-receiver” born of telephone communications in the 1940s and 1950s.

This process isn’t limited by the type of innovation, but it is most visible in “idea” or “policy” innovations. Family leave policies at modern corporations are a clear example. The early innovation was guaranteed family leave for mothers for a certain amount of time. That was extended early on to adoptive mothers. Then to fathers (albeit for a shorter period of time). Then to fathers for a longer period of time. Then to same-sex couples. And so on. As the original innovation “diffused” within the corporate population (restated: as individual corporations adopted the policies), and people used them, the original innovation changed both in scope and intent.

Behavior patterns also change as technologies as they are adopted: Smartphones are an obvious example.

Technology Evolution Smartphones

Innovations evolve over time based on user behavior. In the case of smartphones, their evolution followed other innovations and supplanted (or stalled) others.

The early behavior “making a call outside of a fixed location” morphed into “use a handheld and mobile internet access device” not only because of technology improvements (of which there were many), and because of price reductions (giving more people access), but most importantly because of behavior changes from people adopting cell phones in greater numbers. It was the true genius of innovators such as Steve Jobs to recognize that the technology didn’t drive behavior, behavior drove technology. The faster the innovator can respond and implement adopter feedback, the more successful that innovator will be in the marketplace.

How do multidirectional feedback loops help our Keto snack bar entrepreneur?

The basic science of the Ketogenic diet evolves (as all science does) as more people adopt the behavior change (the Ketogenic diet) and researchers learn more about the biological processes behind it. That translates specifically into the quantity and proportion of macronutrients you should eat, and at what times of the day they are needed. As you could imagine, how much to eat and when to eat it would have a material impact on our Keto snack bar product.

An important reminder: This isn’t the behavior change process difference of an “early adopter” versus a “laggard” in Rogers’ framework. This is a feedback loop within the innovation itself. Specifically, the Keto snack bar entrepreneur must be ready to adjust their product in four unique ways:

  1. Formulations: As the science evolves, the basic nutritional requirements may (will) change, and therefore, the product itself may need to change. This is more than a “brand extension” strategy (e.g. new flavors and package configurations).
  2. Key messaging: Again, this is different than “early adopter” versus “laggard” messaging strategies, as language around “Keto” changes, packaging and marketing language also will need to evolve. Consumers (whatever their “lifecycle stage”) are likely to be confused by changes in dietary advice.
  3. Buying channels: Only specialty retailers carried “Keto” products five years ago – now, nearly all of them do. Selling products in mass retail channels versus specialty retail channels all present different challenges. Generally, new channels open over the course of the adoption curve as larger retailers see the opportunity, but with “long tail” channels such as Amazon, large retail can begin selling products very early in their adoption process.
  4. Pricing models: Subscription models are a parallel innovation that has changed the way supplements and diet products are sold. Staying true to any diet (Keto included) is easier with consistent availability and delivery of food options. A subscription for snack bars can help our entrepreneur prevent people from “abandoning” the behavior.

Doesn’t that sound like 4Ps (Product, Price, Place, and Promotion) of the marketing mix? It should. It is the core principals of Marketing 101 that are better guidance for our entrepreneur than the Diffusion of Innovations framework. Entrepreneurs must maintain flexibility in their marketing mix to respond to innovation/adoptee feedback cycles, many of which can happen with blinding speed and unpredictability.

Following an innovation is like weather in Minnesota. If you don’t like it, wait a few minutes. It’ll change.


Stop equating innovation with a single technology. Start accepting that multiple behaviors compete to solve the same underlying need.

To this point, even though we’ve used different words (behavior in place of innovation and adoption in place of diffusion), we’re still making the assumption that people adopt one behavior to satisfy one need.

  • How do you get around? Adopt the automobile.
  • How do you keep food cold and fresh? Adopt the refrigerator.
  • How do you stay connected on the go? Adopt the smartphone.

One need. One solution.

But our language shift has another benefit. When we stop thinking about technology and start thinking about behaviors, we open our mind to the flaw in the one-to-one logic: There is more than one way to address a need. That may seem obvious on the surface, but it gets a bit trickier unless we use an example. The “automobile” is as good as any, but you could use any technology, product, service, or idea. Simply think about the need being addressed rather than the innovation solution. When you do, the picture gets more complex:

Technology Behaviors - Transportation

A funny thing happens when you look at the need and not the technology. People adopt multiple behaviors to solve the same problem. (In this chart, we’re ignoring “walking” which was the dominant mode of transportation until humans domesticated the horse.)

Charting the diffusion of a single innovation is ridiculous. The automobile is just one mode of transportation for a small set of the total number of transportation situations. The aircraft is the fastest way to cross a large country, but perhaps not the most fun. By contrast, a scooter or bike might be the slowest way to do the same job, but it might be much more enjoyable (for the right person). Harvard researcher Clay Christensen puts it even more simply: Customers hire products to do jobs for them. In this case, people are hiring the automobile to get them from place to place … but that’s not the only “product” they could “hire” to do the job. When you change the focus of the chart from one innovation to one underlying need, Christensen’s point becomes obvious.

In marketing, we would say that transportation has a broad competitive frame, but nearly all underlying needs have a variety of ways you could address them. At a high level, we can simplify those options into three major categories:

  1. One option can overtake another option. Horses are lovely animals, but they’re not a serious option for urban transportation. (Deep wilderness is another matter.)
  2. One option can coordinate with another option. People may use a car to get to the “park and ride” in the suburbs and a light rail train to get to work in the city.
  3. One option can coexist with another option. People can use any or all of the transportation options on our chart. Using one doesn’t mean we cannot use another one at a different time – even for the same purpose. We may ride our bike to the grocery store one day, and then take our car the next.

But do all innovations follow that pattern? Nearly every household in the United States owns a refrigerator. The only thing left to do now is build better and better refrigerators. Right?

Perhaps not.

The diffusion of innovation is really just an adoption of a behavior to satisfy a need – in this case, keeping food fresher for longer. But what if there were a different way to do that? We’re not talking about food replicators from Star Trek or anything so 24th century as that. What about Uber Eats? Do we still need the original innovation (the refrigerator) if a new innovation presents itself that’s more useful in some way? What’s fresher than “at your door in 15 minutes?” It may seem like a silly premise: People will keep refrigerators and adopt real-time food delivery. Who is going to stop having a refrigerator in their house?

Consider this: By some estimates, Americans waste over 30-60% of everything they put in their refrigerators. Think nothing of the immorality of wasting food when so many people live with food insecurity, that level of waste costs the average household over $1,600 each year. Is the ability to store food for a longer time really a useful innovation? What if people started thinking that real-time delivery was a more useful behavior than storing cold food in a huge box in their kitchen (the largest single appliance in most homes), and perhaps do something else with that space … and save $1,600 every year to boot?

We shouldn’t need to do the same mental exercise for smartphones. The pattern is self-evident.

Let’s return to our Keto snack bar company. Understanding their challenge in a broader sense of behaviors and needs provides better guidance than “following the wave” of diffusion. Ketogenic diets compete with a variety of other diets and trends to solve the same underlying needs: weight loss, fitness, health, and wellness. Atkins, vegetarianism, veganism, organic, local, raw, calorie counting, and countless other legitimate (and illegitimate) diets all attempt to address the same need. In Christensen’s language, people are hiring the diet to help them lose weight. If the diet fails to deliver, they’ll fire that diet and hire another one. Or they mail keep one diet and hire aspects of another one. The needs are simple. The behaviors adopted to address those needs can be complex.

How should our snack bar entrepreneur think about that complexity? Some of the diets we mentioned coordinate well with Ketogenic diets (Atkins); some won’t make much of a difference if a person adopted aspects of both (local or raw), and some could conflict (calorie counting). As the Keto snack bar innovator, what can this competition of ideas tell us about product development strategies?

  • Coordinate: You may be able to convince Atkins followers to eat your snack bar with some subtle shifts in messaging.
  • Neutral: Convincing “raw food” adherents might be challenging depending on your snack bar formulation, so you may want to consider a new offering that uses only raw ingredients. Also, you could consider licensing production in high-density urban cores where “local food” adherents congregate.
  • Compete: Calorie counting doesn’t fit well with Ketogenic diets (calorie densities in fats). A possible solution could involve offering a smaller snack bar size, therefore reducing the calories per bar and increasing the chances a calorie counter might try it.

This isn’t a matter of trying to ride three horses, but rather it is a deliberate diversification strategy. Adoption of complex behaviors is not predictable by some bell curve – your intuition already tells you this. (How many diets do you see come and go? With the same person?) As an innovator, you would be wise to accept this reality and plan for multiple possible paths to the same end goal for the consumer.


Stop trying to predict adoption with lifecycle phases. Start using q.

The bell curve shown in the Diffusion of Innovations theory simply gives us a macro view to the adoption process. It is not showing us what’s happening at the person to person level. In fact, Rogers’ admitted the mechanism of adoption largely is a mystery.

It’s not.

There is an old saying in marketing: If word of mouth was perfectly efficient, there would be no need for advertising. The number one source for information about a new product or service, and the most reliable form of persuasion, and the biggest predictor of your behavior, is your social group. This truism is not the result of modern social media, although these networks give us a powerful and measurable tool.

We’ve all had this experience. You signed up for Netflix once a certain number of your friends, coworkers, or peers signed up. Just one person usually isn’t enough to trigger your behavior. But there is a certain number, we’ll call it q, that tends to tip the scales in your decision-making process. The research shows this adoption process isn’t necessarily tied to the “phases” in Diffusion of Innovations. Even if you would consider yourself a “laggard,” if enough people in your social circle adopt a new kitchen gadget (hot pots come to mind), you’re likely to do it too. Social pressure is that powerful.

Complex Contagion

On the left is a simplified representation of Centola and Macy’s original work on Complex Contagions. They argue that high-risk adoption behaviors (e.g. a new diet) requires confirmation from multiple sources in your social network – a “wide bridge” of support. Dean Eckles from MIT expands and revises these conclusions with theoretical models combining short and long social ties. The math isn’t as important as the conclusion: Humans are social monkeys, and social ties are the mechanism of adoption.

Put simply, this is the idea behind “virality” in the spread of ideas and behaviors. There’s plenty of math behind this (explore graph theory and complex contagions if you’re interested in learning more), but here are the basic ideas:

  • Adopting a behavior change (read: innovation) is costly to the early adopter, and less costly to someone who waits.
  • Behavior changes lack credibility until enough people in your social network adopt them.
  • Simple “awareness” of a behavior change option usually is not sufficient.
  • There is a difference between “uncontested” and “contested” behavior changes. Uncontested behavior changes don’t have an alternative other than the status quo – antilock brakes on cars are an example. Contested behavior changes have competition – diet changes are almost always contested by other diet options beyond the status quo.

The variable q simply represents the fraction of people in your social network who have adopted a new behavior. When q gets large enough, you are highly likely to switch as well. The threshold simply is lower for uncontested changes (q can be smaller). Centola and Macy’s work finally starts to address the mechanisms behind behavior change in a way that we can act on them as innovators.

That takes us back to our Keto snack bar example. Instead of using a “lifecycle stage” with its generic population percentages, it is more useful to think about the total addressable market (TAM) for the Keto snack bar as a complex, contested contagion where the q variable (the fraction of those people in a social network) must be high in order to “convert” a new buyer to adopt the Keto diet.

In this case, our Keto snack bar entrepreneur would be well advised to concentrate efforts on distinct social networks of existing Keto adherents, following the spread of converts in their social networks. In other words, instead of attempting to influence q, the entrepreneur simply is discovering concentrations of people and taking advantage of the progression of the contagion at work.

This is not the same as the oft-discussed tactical approach of “creating a video and hope it goes viral,” or an “influencer strategy” on Instagram – this is a hyper-focused strategy based on individual social networks. Later in the process of mass contagion (when the behavior spreads to enough “nodes” in the graph) the entrepreneur can pursue mass advertising channels and big-name influencers. But in the beginning, better (and more cost-effective) tactics include:

  • Targeted social network advertising based on defined interests and engagement history. Specifically, you want to target those people who have adopted the Keto diet, not simply those discussing it. You may find these people at Keto-focused fitness centers, nutritionist client bases, or the scientists themselves.
  • Once you find those people (nodes), focus on incentives for them to choose your snack bar. Those could include special offers, but they are more likely to include in-depth information about how this snack bar fits with their lifestyle choice.
  • The spread of the snack bar, then, will spread along with conversions to the Ketogenic diet from people in the original node’s social network. In other words, it’s a piggy-back strategy.

This flies in the face of “big advertising” and “mass awareness” campaigns. When you’re talking about behavior change (aka innovation), simple awareness is not enough. People change behavior because people around them change behavior. Our Keto snack bar entrepreneur will spend less on advertising, and they will spend more time mapping individual influence networks.

When you’re trying to innovate, individual people and their personal networks matter more than mass markets, so-called influencers, and brand awareness.



Rogers’ work on the Diffusion of Innovations, as well as follow-on work by dozens of other scholars and practitioners, has been groundbreaking work. It helped remove the spread of innovations from the realm of alchemy and into a testable social science. However, in the intervening years, we have learned much more about how to apply Rogers’ original work – like most great theories, its best value isn’t in its precise conclusions, but rather in the new questions it forces us to ask. It is to that end we make the following suggestions to individual entrepreneurs hoping to succeed with their own idea, product, or service:

  1. At the population level, innovations appear to diffuse. However, that masks the underlying reality. All diffusion is the result of an individual person adopting a new behavior in place of an old one. Concentrating on the individual person’s adoption process – and the mechanisms to influence it – will be more effective than generalized lifecycle phases.
  2. Innovations do not remain static as they diffuse into a given population. They evolve. They always evolve. Smart innovators must remain flexible, ideally designing their complete marketing mix (the “product” included) to follow those evolutionary paths.
  3. An innovation may never reach 100% of its target audience. In other words, contrary to popular examples, many innovations fail to fully diffuse. That’s largely due to other innovations competing for attention and adoption. The original innovation may be useful, but it will lose out to another more useful innovation – or it may be adopted alongside another innovation. Smart entrepreneurs will understand that usefulness is the key in satisfying the underlying problem or opportunity for that person and plan multiple winning strategies based on customer needs, not technologies.
  4. Lifecycle phases are poor predictors of behavior change. Specifically, the rate of change is not obvious from the bell curve. A better predictor is a person’s social network and how many of those people have adopted the change. Smart entrepreneurs will focus on surrounding target audiences and take advantage of the social pressure from those who already have adopted the change.

The aim here is not to discredit Diffusion of Innovations, but rather to build on it to help individual entrepreneurs see the difference between the success of an innovation at the population level – and their idea, product, or service at the individual level.

To give yourself the best chance, focus on the need first, the behavior second, and your offering third.


Marketing Education

Learn the true influencers for CRISPR using Brand24 (not Gartner)

Need more details? Check out my full article here.

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Decoding the Future of Bitcoin Using Google Trends and Live Coindesk Data

Need more details? Check out my full article here.

Marketing Education

Using Google Trends to Build Your Own Hype Cycle (Today’s Example: CRISPR)

Need more details? Check out my full article here.

Marketing Education

Five Reasons You Should NOT Use ANY Gartner Hype Cycle

Need more details? Check out my full article here.

Marketing Education

Unhyping the Hype Cycle: Five Secrets to Building an Attention Dashboard for Any Innovation

Photo by Jongsun Lee on Unsplash. SQUIRREL!!! added by the author.

If we could only predict the next boom in cryptocurrency. Or when gene therapies might show up at your local Walgreens. Or when planes will fly themselves. Timing any of these correctly will make individuals rich and shareholders giddy. Timing them wrong will bankrupt investors and sink promising product launches. The most popular model for understanding technology trends such as these, and cashing in when the time is right, is the Gartner Hype Cycle. If ever there were applications where its usefulness should apply, it would be cryptocurrency, gene therapy, and self-flying planes. Unfortunately, the Gartner Hype Cycle can’t help us understand any of these trends. The only enlightenment it can offer is the simple truism that “hype exists.” Sorry Gartner, but PT Barnum proved that theory 100 years ago.

Let’s see how we can do better.


Anyone who has sat through a corporate PowerPoint presentation or Investor Pitch Contest has seen the Gartner Hype Cycle. As testament to Gartner’s excellent marketing, most corporate followers can recite it by heart: After a trigger event, hype grows quickly, peaks, crashes just as quickly, and then slowly reaches a balanced equilibrium. It’s so reasonable and intuitive that most of us rarely question its basic premise.

Since 2009, Gartner has published a so-called “Hype Cycle” for all manner of markets, technologies, and trends. In fact, the model has become so popular that “analysts” now publish nearly 60 different hype cycle charts each year – from Supply Chains to Digital Marketing to Government Technology. Here’s the gotcha: When we apply even the most modest level of scrutiny to it, each assumption that underpins the Gartner Hype Cycle falls apart.

Great marketing. Bad science.

But what’s the alternative? In the early stages of any new technology, “hype” seems to be all there is to go on. Entrepreneurs need some mechanism to understand if a hot topic has potential, or if it’s just hot air. Investors need to know when to jump into the water, and when to sit on the beach.

What follows is the answer.

In five easy steps, we’ll dismantle the flawed logic of the Hype Cycle and replace it with a bespoke Attention Dashboard – a set of building blocks you can use to generate meaningful insights on your own. No fancy charts. No breathless anticipation of an analyst’s opinion. No massive checks written to consultants. Less branding and marketing spin. More data and useful conclusions.

To help illustrate the process, we’ll use an emerging technology clearly in its commercial infancy: clustered regularly interspaced short palindromic repeats, or CRISPR, for short.

Step 1: Stop using the word hype. Start using the word attention.

There is something about hype that seems both unseemly and unethical at the same time. Hype is disconnected for substance, truth, and reality. Hype is a deception. And the worst criticism of all: Hype is marketing.

Definition of Hype and Attention

Source: New Oxford American Dictionary, edited for clarity. Hype and Attention mean much the same thing, but hype is an emotional trigger on our lizard brain. Attention is a neutral (even positive) word, allowing us to engage our rational mind.

While not wholly false, when we use the word hype (with all its emotional baggage), we ignore its usefulness. Whether positive or negative, hype draws attention to a product, service, or idea, helping to break through the background noise of daily life and shine a spotlight on it – if ever so brief and fleeting that light may shine. Why are businesses, politicians, and advocacy groups so willing to risk the criticism that comes from hype? Because the biggest risk to achieving your goals isn’t being criticized, it’s being ignored.

Hype or Attention?

Let’s make a helpful edit to this Gartner Hype Cycle chart. If we’re interested in the promise of CRISPR, it’s more useful to think of it as “attention” rather than “hype.” Attention draws interest. Hype turns people off.

Gartner was smart not to brand its “Hype Cycle” as the “Attention Cycle” – the word hype carries more emotional energy, making you feel as though you are privy to forbidden knowledge of how the world works. But while it may be smart branding, and while it may drive business for consultants, using the word hype leads to poor decision-making. Most decent people want to avoid hype, but they’re just fine with attention – especially when they believe in what’s getting the attention.

As an entrepreneur or investor exploring commercial opportunities for CRISPR, the word “hype” clouds our thinking. Using that word implies that the promises of the technology won’t materialize – that CRISPR is doomed to the dustbin of promising, yet ultimately failed, therapies. But that’s an oversimplification. Some specific therapies may not pan out. Others may provide only marginal improvement over current therapies. Still others may be revolutionary, transforming our approach to entire branches of medicine. Like many emerging technologies, CRISPR is pluripotent, meaning that it can morph into many specific applications as it develops. In fact, many versions of the base CRISPR science already are diverging to meet different therapeutic needs. Each of those will have different commercialization pathways. Some may end up meeting the literal definition of hype, but many will not.

What you call something matters. Choose your words carefully.

Step 2: Start focusing on what you want. Stop chasing squirrels.

It may seem tempting to track a wide variety of “emerging technologies” on the same chart, but it doesn’t help you understand them. In fact, it doesn’t help you understand any of them. You’re like a dog let loose in the backyard with 20 squirrels running in every direction. Unless you can pick one, you’re not likely to catch any of them.

It’s the same dynamic with emerging technologies. They’re all squirrels. They all dart around the marketplace at random. If you’re trying to catch “Smart Dust,” “Connected Home,” or “Biochips” by chasing them all, you’re just like our poor dog – exhausted and frustrated. Yes, those technologies may be “emerging,” but each one of them follows different market forces, varying user applications, and independent technology development paths. Plotting them all on the same curve implies they all will follow the same “hype” (read: attention) path, and more importantly, that none will fail or that none morph into something else entirely. That’s a ridiculous assertion on its face. We have examples of many technologies failing to ever reach the “plateau” in anything resembling their initial form. “Cold Fusion” immediately comes to mind, but give yourself 30 seconds, and you could come up with a dozen other examples.


Technology Topic Clusters versus Hype Cycles

Another helpful edit – let’s track related, not unrelated, technologies. But wait … where’s CRISPR on the “Emerging Technologies” list? By almost any measure, CRISPR is “emerging,” yet we don’t see it. The Gartner Hype Cycle is simply a random list of whatever they want you to notice, not what’s important to you.

By definition, emerging technologies change quickly as they mature. In other words, we have non-comparable data points plotted on the same graph. It’s not about comparing apples and oranges. It’s about apples and orangutans.

Instead, let’s zero in on what matters to us. We’re interested in tracking attention specifically on “CRISPR” as a therapeutic technology. To do that, we’ll want to track “CRISPR” as a term (obviously), but also a few other related terms in a topic cluster:

  • Gene Therapy: CRISPR falls within this general category. Tracking this topic will ensure you catch other, related developments in the field as they emerge.
  • Pfizer: While not specifically focused on CRISPR, Pfizer is a major player in pharmaceuticals, and is likely to play a major role in the future of the technology. Including it ensures we don’t ignore organizations who can (singlehandedly) drive the conversation.
  • CRISPR Therapeutics: In addition to a major player, you’ll want to include a smaller organization solely focused on the topic area. You’re watching to see if their fortunes rise and fall with changes in attention … or if they do not.
  • Coronary Artery Bypass Surgery: This is an excellent benchmark – a well-understood and mature healthcare topic. No, it’s not used to treat the same conditions as CRISPR might treat, but it provides a reference point for attention. Attention in “Coronary Artery Bypass Surgery” is stable over time, whereas attention in CRISPR ebbs and flows. Comparing two topics in the field helps us understand if attention in CRISPR is high because attention in all healthcare is high, or if the variation is unique to CRISPR.

You can use the same formula with your own topic of interest: (1) Choose the specific term; (2) Add the generic technology/topic; (3) Add a major player in the field; (4) Add a minor player or startup; and finally, (5) Remember to include a benchmark for comparison. Think of it as a study group with “controls” included. It’s good research design. Consulting firms tend to struggle with good science.  Now, you know how to do better.

If you’re not careful, you’ll end up chasing squirrels … and never catch one.

Step 3: Start quantifying attention. Stop fretting over hype.

The Gartner Hype Cycle uses “expectations” as its dependent variable (on the Y-axis) as simply another word for hype. At first glance, it seems intuitive – expectations can rise and fall over time. But take a more careful look at the chart – is the expectation at the “trigger” at the same level as the “trough”? Not exactly. The trigger is more accurately a representation of “no expectations” and the trough is a representation of “negative expectations” – or at least lower than the initial condition (that’s what “disillusionment” means.) At the very least, we should draw the chart like this:

Sentiment in the Gartner Hype Cycle

Attempting to correct the scale (and the zero point on the Y-axis) highlights a more pressing issue: The measurement of “hype” as a variable at all. What is hype, exactly? How do you measure it? Is it a measure of the number of articles written within a certain amount of time? Is it a measure of the tone or sentiment of those articles? Is it the authority of the publications? Or the authority of the authors? Or the size of their Twitter following? Without a standard quantification for your variable, you don’t really have one.

Quantifying Hype 

Whenever you see a chart, be wary of undefined terms or vague ranges masking as quantified variables. In the first case, we’re given no explanation for “expectations” on the Y-axis other than “it goes up and down.” The time series ranges in the legend don’t correspond with any formula – they’re just an analyst’s guess. Ignore them.

In years past, if we wanted to quantify attention, we needed to make educated guesses based on counts of media articles, exposure to advertising, or some other indirect measure. Today, we can use actual behavioral data to track the pattern of changes in attention. Google Trends is a good place to start (and it’s free), and it’s a powerful tool if you know how to use it. In simple terms, Google Trends shows you the change in search volume on a topic (or topics) over a period of time. It will not show actual search volume (in number of searches), but it will show you much more important information in addition to change over time: relative search volume. If you compare multiple terms (up to five) on the same chart, you can see how they compare to each other. This is particularly useful when you benchmark a new term against one you understand quite well.

CRISPR Attention 2016 to 2019

Hmm. Notice something about the chart once we use actual data from Google Trends? Do you see anything like the curve represented in the Gartner Hype Cycle? No? More on that later.

The blue line represents the topic “CRISPR” over a three-year period. As you can see, interest spikes and wanes over time, but remains high as it relates to the terms and topics we’ve chosen as controls. From this data alone, what conclusions could we draw? Pfizer (the company) and Coronary Artery Bypass Surgery (the topic) draw more consistent attention than CRISPR, although CRISPR occasionally spikes. Attention from CRISPR seems to drive a bit of interest in gene therapy more generally, but it does not create a “halo effect” on any specific startup company. If I were an angel investor, I might tentatively conclude to hedge my bets on several CRISPR-related startups rather than just one. What would have learned from a “static position” on the Gartner Hype Cycle (had CRISPR even been on it)? Not very much. And what you would have learned would have been wrong.

Is Google Trends the only source for attention data? Certainly not. Engagement on social media (likes, comments, and shares) is another one. As is checking the prices for keywords in a pay-per-click auction (Google, Amazon, and LinkedIn all use these real-time systems.) What you want to avoid are “counting” metrics such as “the number of media articles written on a topic.” The media might be paying attention, but what you really want to know is if everyone else is paying attention.

Bottom line: Never settle for pure intuition without bothering to confirm it with data … especially if the data is accessible, accurate, and affordable.

Step 4: Stop imposing arbitrary phases. Start fixing time as a truly independent variable.

Time is the usual suspect for the independent variable, appearing reliably on the X-axis of most charts. But notice something about this one. Time isn’t the variable; phase is the variable – e.g. “Plateau of Productivity.” Be careful, time and phase are not interchangeable. Using one in place of the other isn’t unheard of, certainly. The more-familiar S-curve of Diffusion of Innovations uses product lifecycle stages instead of absolute time measurements as well. However, swapping time for phases is a mental trick that can fool you if you’re not careful.

Gartner uses branding not actual time

Never confuse phases for actual passage of time. When time is an independent variable, it can dramatically impact the shape of the “curve” in the plotted data – compressing or extending it. When you’re making a decision, the phase isn’t as important as the velocity through that phase. The only way you know that is by plotting actual time.

We’re used to seeing time on the X-axis. Quantum mechanics aside, time is a fixed, periodic variable. It progresses in an orderly fashion, and at the same rate, from the past into the future. It’s so obvious that we don’t stop to think about it. When you exchange time for phases, you risk being tricked into thinking that phases act in the same way as time: Linear, with fixed intervals, and progressing with consistent velocity. Emerging technologies never evolve in an orderly fashion – they are riddled with fits and starts, sometimes accelerating quickly and sometimes languishing for years. By swapping time (predictable) for phase (unpredictable), we hide the most critical information.

Any time you think about trends, a snapshot in time isn’t as important as the velocity of the movement and what triggers those movements into motion. That’s how investment decisions are made. The arbitrary, non-measurable phases in the Gartner Hype Cycle are not truly independent variables at all.

To see for ourselves, we only need to go back to our example:

Google Trends for CRISPR

Instead of “one” trigger to “start off” the “hype cycle,” we notice at least two spikes in attention in a reasonably stable volume of attention over time. The better question to ask is what triggered those changes?  Although we cannot know what will cause attention to spike in the future (sorry, Gartner), we would be wise to see what drove attention in the past. Luckily, our chart gives us some clues – two spikes in particular: One on or about May 1, 2018 and another in December of that same year.

Sticking with Google (although you could use any news aggregation tool), a time-limited search quickly reveals the answers:

Spike #1: Scientists can use CRISPR to edit genes. Should they?

Spike #2: Marc Thiessen: Gene editing is here. It’s an enormous threat

In the first case, it was a national news program (60 Minutes) that brought the technology to a wide audience. Were there stories about CRISPR in the media before? Of course, but none quite captured the broad-based public attention that 60 Minutes could generate. In an era of “micro-influencers” it seems that big media can still drive big interest.

In the second case, a Chinese scientist claimed to do what the 60 Minutes program hinted at – using CRISPR technology to edit human infant genomes. Whether he actually did it or not is a matter of debate, but news outlets pounced. As with other emerging technologies, the negative attention of a “worst case scenario” can drive interest. It’s notable that positive developments (potential therapies for debilitating disease) failed to result in a similar spike in attention.

Again, if I were that angel investor, I might reconsider my micro-influencer strategy and be prepared to fund my startup to go for the national news. I might also focus on those companies who could court negative attention – either by doing what people fear (or shouldn’t fear in this hypothetical investor’s opinion) or by preventing it.

Beware of the “phase trick” on any chart. Keep time independent of value judgements.

Step 5: Stop believing in generic cycles. Start discovering true attention drivers.

We may admit that we can’t quantify hype, that we can’t predict timing, and that we must stick with comparable data points, but the shape of the Gartner Hype Cycle curve still seems to match our everyday experience. After a trigger, hype rises quickly, peaks with a hysteria, crashes hard, and finally, slowly regains a reasonable middle ground. It all seems so intuitive.

Like so much of our intuition, it turns out that we’re wrong.

When scholars attempted to replicate the hype cycle (here, here, and here as just three of many examples) the resulting curves looked nothing like the intuitive story the curve attempts to tell us. In one particular example, researchers used actual quantitative data (gasp!) to plot the actual “hype cycles” for three emerging technologies: Internet Telephony, Gene Therapy, and High-Temperature Superconductivity. Here’s what they found:

Hype Cycle - Actual Data

See a pattern? No? Precisely. When scientists put the Hype Cycle – or any hypothesis for that matter – to the test, they’re trying to disprove it. Having an idea is just fine, but that’s just the first step. You must test it, empirically, to decide if your idea actually represents something about the natural world. If it does, you don’t accept it, but rather you continue to try to find its flaws. If it fails, you discard it. That’s how science works. Clearly, that’s not how consulting works. Here’s what one group of researchers said in the introduction to their 2013 paper (emphasis mine):

Given the model’s proclaimed capacity to forecast technological development, an important consideration for organizations in formulating marketing strategies, this paper provides a critical review of the hype cycle model by seeking evidence from Gartner’s own technology databases for the manifestation of hype cycles. The results of our empirical work show incongruences connected with the reports of Gartner, which motivates us to consider possible future directions, whereby the notion of hype or hyped dynamics (though not necessarily the hype cycle model itself) can be captured in existing life cycle models through the identification of peak, disappointment, and recovery patterns.

Let’s translate academic English into standard English: Generic hype cycles are bullshit.

To better understand changes in attention and tone surrounding CRISPR, we must understand who to pay attention to, and who we safely can ignore. We could stick with Google here and set up a “News Alert,”, but it’s more efficient to use a media tracking tool. Brand24 is a functional, low-cost option, and we will use it here. Why use an analysis tool rather than a simple raw news feed from Google? A few reasons:

  1. Media attention shifts quickly – you may have very few results for many weeks, and then a flood of them in one afternoon. You want a tool that can help you see the trend line.
  2. Influence on attention tends to aggregate – as you already know, certain media outlets, companies, universities, and individuals disproportionately impact what gets attention and when. You need a tool that can show you the “hidden” influencers in the network.
  3. Attention tends to multiply iterative and duplicate results – not all attention is created equal. The 60 Minutes piece, for example, spawned thousands of “reactions” from less-influential sources. While important in aggregate (to know what’s getting attention), the flood of information can overwhelm your analysis, leading you to follow the wagging tail, not the happy dog. Your need a tool that can analyze the origin of the topic in real time.

Let’s use Brand24 to collect some basic data over the past 30 days:

CRISPR data from Brand24

Huh, the “influencers” aren’t who we thought they’d be.

Again, our intuition fails us on multiple levels. Is Twitter the biggest source of attention (aka “news”) on CRISPR? No. It’s YouTube. Are the traditional media outlets driving the conversation? No, not recently. It’s the science-explainer YouTube channel Seeker. Although MIT Technology Review clocks in at the number three position, the usual “big media” suspects aren’t present.

What about tone and sub-topics? When you examine the most influential sources in the recent conversation, you notice something unmistakable – they’re all explainers. In other words, although there has been plenty of media coverage, the public remains largely ignorant of the underlying technology. As that angel investor, I would be wary of sentiment and opinions from the general public – they’re interested, but they don’t know enough (yet) to buy a commercially-available product. That investor might be wise to stick to investing in startups offering technical products to an informed, scientific audience – at least until public knowledge grows.

Attention doesn’t follow a set pattern. Take the time to learn what drives changes in attention for your specific topic.


The Gartner Hype Cycle is a brilliant piece of marketing, and it’s a boon to consultants everywhere, but it’s not going to help you make decisions. Specifically, it fails in five key areas:

  1. It chooses a loaded, emotional word (hype) instead of a variable you can objectively quantify (attention). Instead of the word hype, use the word attention to eliminate negative bias. You can always track positive and negative attention (sentiment) separately if that’s meaningful.
  2. It fails to quantify attention – simply using the word “hype” distracts us from recognizing this error. Instead, use a quantitative measure of behavioral data, such as the number of web searches on a given set of topics.
  3. It fails to stabilize time as the independent variable, using branded “phases” to mask the reality of changes over time. Instead, use actual time. Specifically, choose a time scale that makes sense for your area of interest. For fashion trends, that might be a few months. For medical devices, that might be a few years.
  4. It fails to measure the correct data, grouping non-comparable trends and technologies onto the same graph until it’s impossible to see the meaningful pattern behind any one of them. Instead, choose your topic cluster (and comparable / benchmark topics) carefully and intentionally.
  5. It fails to properly identify the shape of the curve itself, relying on our “intuition” and a “reasonable-sounding story” to trick us into not looking deeper for the real patterns. Instead, use your own data to plot the “attention cycle” unique to your area of interest. When you use your own data, you’ll begin to discover the true influencers and possible triggers. Hint: They never follow a predictable pattern. You’ll need your own data.

In other words, use this:

Attention Dashboard for CRISPR

Real, actionable, accurate, and reliable data on a topic that matters to you.

Not this:

Issues with the Gartner Hype Cycle

The next time you’re in a meeting and someone whips out a version of this chart, feel free to respond with this one. You’re welcome 😉

As we’ve demonstrated, you don’t need expensive consultants or sophisticated software to build an “Attention Dashboard” for the topic that interests you. In a few easy steps and for less than $50, we were able to draw useful conclusions about the past, present, and future of CRISPR – far more than we could learn by studying the so-called Hype Cycle, or by listening to consultants wax philosophical to the tune of thousands of dollars per hour.

If all else fails, you’re safe to bet against anyone who still believes in hype.