Long Form Articles

Five data lies that need to die … now streaming on Netflix

Let’s rewind the clock 25 years. Back then, the trendy company was Walmart and the trendy topic was supply chain management. You couldn’t throw a rock in the business section of the Wall Street Journal without hitting a journalist waxing philosophical about how the company was “reinventing retail” through ruthless supply chain efficiency. But it didn’t take long before those articles turned negative. By the early 2000s, Walmart was “destroying Main Street” and bullying suppliers.

Leaders who followed the pundits’ whipsawing advice – that supply chain would solve all their problems, or that ruthless supply chain management led to unsustainable relationships – largely wasted time and money. What could your small business take from Walmart’s strategy? Probably very little, but it made for a good story.

Trendy companies and fashionable opinions come and go, but the pattern remains the same: The stories are meant to tell good stories to drive increased readership. They rarely provide sound and actionable advice.

“Netflix” is simply the latest trendy company and “data” is simply the latest fashionable topic. The innumerable stories about the transformative power of the Netflix algorithm may make for good reading, but they aren’t necessarily good advice about how to use data.

Let’s have a look at the recent punditry and unmask the storytelling masquerading as advice.

Data Lie #1: Our company (or strategy, or marketing, or product) is data driven.

In this column on the Neil Patel website (who should know better), the author explains the multiple ways Netflix uses the data it gathers from its 130 million subscribers to refine suggestions for other content you might like to watch, viewer engagement levels (when you watch, and for how long), and even to predict attrition rates. What’s more, Netflix can use detailed viewer history (including stop points) to improve content development by providing valuable, real-time feedback to content creators.

That’s all fine. Here’s where it goes wrong. The column then quotes a few Netflix data geeks who – no surprise – were willing to highlight their successes: “Orange is the New Black” and “House of Cards” as Netflix content investments, and “The Dark Knight” as a licensing coup. At the time, with only a $7.99 per month subscriber fee, Netflix was “smart about their decisions” and “took full advantage of their analytics.”

The implication, of course, is that other companies simply ignore their data and make decisions based on gut instinct. If you would only be “data driven” like Netflix, then you also would have that success. By that logic, if I were to wear Michael Jordan’s shoes, I would be able to dunk like Mike. (Trust me, new shoes would not be enough.)

Data is simply another asset. With 130 million subscribers and data on billions of television hours watched, if Netflix were not using its assets appropriately, its shareholders should fire its management team. It is no different than a farmer leaving the tractor in the barn and plowing the field with a shovel. Additionally, most businesses (especially small businesses) do not have access to the rich stores of “big data” that would allow for that scale of sophisticated analysis. A concrete contractor with a dozen active customers isn’t likely to see many benefits from an even a historical statistical analysis. The key asset for that type of business is its relationships, not its data.

If you read the comments from Netflix officials carefully, even they understand the limits of their own data, huge though it may be. Data helps make content decisions; it does not drive them.

Netflix is not a data driven company; Netflix is an entertainment driven company.


Data Lie #2: Data can predict the future.

In this article in Forbes, the author falls victim to classic hindsight bias. He fawns over the decision by Netflix executives to invest $100 million in “House of Cards” with no script, no pilot, and no plan ­– relying on its “algorithms” that predicted success based on Kevin Spacey’s appeal, remnant fans of the British series, and the “subject matter.” He then decides to spin the wheel of cognitive biases again, this time landing on “cherry picking” with a similar process selecting Sandra Bullock’s “Bird Box” thriller.

The implication is that an “algorithm” can make terrific decisions just as well as Hollywood executives could. Creativity isn’t necessary. All you need is enough data.

When you look in the rearview mirror, of course you can find examples of success. And because Netflix is notoriously secretive with its viewership data, of course you only will see the successful experiments. But without seeing the failures the algorithm predicted, you cannot make the claim the algorithm can predict the future. A robustly thoughtful article would have asked to compare investments in multiple films using the same algorithm. It would then compare those results to expert analysis, as well compare them to random guessing.

Bluntly, betting on star power, fans of a genre, and timely subject matter is not magic. Hollywood executives have been doing it for 100 years. If you read Netflix executive’s actual statements, they say as much.

No, Netflix algorithms cannot predict a show’s success. Netflix uses its data as an input to executive decisions, as any smart company would.


Data Lie #3: Data should make my decisions.

Finally, a real statistician to help us chew through this one! Roger Peng does a better job explaining the Wall Street Journal article than its authors do. Peng describes the situation Netflix faced when advertising “Grace and Frankie” starring Jane Fonda and Lily Tomlin. In its testing, Netflix discovered that more people clicked on the promotional image when it included only Tomlin, and not Fonda.

Apparently, the data team argued its case, but Netflix executives decided to use the poorer-performing image because it did not want to alienate its relationship with a big star. And if Fonda felt miffed, data be damned, she could go to Disney’s upcoming streaming service instead.

The implication is that “data” and not “egos” should make the decisions because egos are flawed, subjective measures.

But here is where WSJ falls flat and Peng shines. Peng calls out the flaws in the “data makes the decision” assumption that permeates the WSJ article. The data is unlikely to be able to account for all of the variables. Like any good analysis, it makes a narrow conclusion based on a wide sample of data. In this case, it sampled large segment of viewers with a choice between two promotional images. Yes, more people clicked on one image than another – probably a statistically significant amount – but was it really Jane Fonda that made the difference? Was it something else about the photo? What kind of regression analysis determined that it was Fonda, and not some other factor, that drove the choice?

Critically, even if the data are clear, the designers of the experiment are human. That means humans decide what counts in the analysis, and to what degree, even if they are unconscious about it. (Don’t get me started on so-called “learning” algorithms. They often do as much to amplify biases than dispel them.) What’s more, the data scientists are unlikely (Peng’s argument, and I agree) to have included a factor for the Net Present Value (NPV) of the ongoing relationship with Jane Fonda, because, as we’ve already seen in Data Lie #2, data cannot predict the future.

In the end, Netflix executives themselves say the decision is 70/30 (70 percent experience and instinct, and 30 percent data). It seems that they understand the data better than the WSJ does. There are always limits to data, even with billions of hours of viewership data.

No, data doesn’t make Netflix creative decisions. People do. Data helps.


Data Lie #4: Data are objective.

By now, we should be seeing a pattern. Data might be neutral, but human use of it (and interpretation of it) are not. The more we believe data is objective, the more blind we are to its biases.

Case in point: Netflix tends to recommend shows with black characters to black people. Forbes contributor Adam Candeub switches from gushy to judgy quickly in his article about the apparent racism embedded in the Netflix recommendation algorithm. Netflix defends itself by saying that it does not collect data on race, and that the algorithm responds to user inputs.

Specifically, Netflix responded:

“We don’t ask members for their race, gender or ethnicity, so we cannot use this information to personalize their individual Netflix experience. The only information we use is a member’s viewing history.”

Candeub doesn’t buy it. Netflix collects physical address data and “can predict” race based on “their data,” that the advertising is “discriminatory,” and that Netflix is “hypocritical,” “evasive,” and “disingenuous.” The implication is that with access to so much data, Netflix has a responsibility to hold itself to a higher standard, share its data with others, and advertise in a race/gender neutral manner.

You can agree with Candeub or you can agree with Netflix. It doesn’t matter to the central point: Data are never neutral. The more data you have, the less neutral it is. Collecting more data is like collecting more of any other asset. What responsibilities to huge farms have to the food supply beyond mere profit? What responsibilities to huge news outlets have to the public discourse beyond mere profit? What responsibilities to huge hedge funds have to the financial system beyond mere profit?

To paraphrase: With great data comes great responsibility.

No, Netflix data are not objective because people are not objective.


Data Lie #5: Data are free and easy.

According to some estimates, up to a third of all internet traffic is attributable to one source: Netflix. It’s not hard to understand why, as Phil Nickinson explains in his article. He takes a simple and effective approach to helping the average person understand just how much bandwidth Netflix requires to send streaming high-definition video content to your home. His point is to help people not overextend their data plans and incur overage charges. But there is a bigger issue at play than simple bandwidth.

To this point, we’ve talked a lot about the data Netflix gets back from you as the consumer, but this last lie relates instead to data management and delivery. The insights Netflix gets back from you might be valuable, but from a bandwidth perspective, that metadata is tiny.

Why is this a big deal? Data doesn’t magically float through the air, arrange itself logically, backup itself in the ether, and make itself presentable to you in a useful form at a whim. No, data management is hugely complex. Transmitting data requires costly investments in unsexy telecommunications infrastructure (the real core of the Net Neutrality argument). Storing data requires vast data centers (you could make a compelling argument that data centers are Amazon’s core skill). Retrieving and presenting complex data in a useful way requires immensely powerful software (Oracle and SAP are masters of this).

Netflix makes significant investments in data science not because it has to. Every major organization has to.

No, all data costs. Good data costs a lot.


Netflix clearly understands its data, how to use it, and its limitations. It’s the pundits who don’t. Before you follow their advice down the rabbit hole, teach yourself the statistics and the data science. You’ll realize quickly how challenging, and how limiting, even “big” data truly is.

In the end, you’ll appreciate data. You’ll use data. But you won’t rely on data.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Agile Learning Audience Empowerment Audience Engagement Information Management Long Form Articles Rehumanizing Consumerism

Big Data promised less (and better) marketing. It hasn’t worked out that way.

Consumers: So, remind me again why I need to give up oodles of my private data?

Marketing: Well, not only do you get to use our awesome products for free (or for less than their true cost), but we also will use that data to stop bombarding you with irrelevant advertising.  It’s better for us because we can be more efficient, redirecting that money into developing better products and services instead of wasteful advertising spending. And it’s better for you because you see advertising that’s much more useful to you.

You’ve heard some version of this argument from marketing for the past 20 years. If consumers allow marketing to collect ever increasing amounts of data, they will use it to produce more targeted advertising. More targeted advertising is more efficient, meaning that (ideally) marketers should be producing less advertising, not more. As a consumer, you should be seeing fewer promotional messages, and the ones you do should be much better.

Who among you thinks that is true?

I certainly don’t.

Let me walk you through just one example.

My wife and I enjoy cooking at home. We patronize several grocery stores, delis, and kitchen supply outlets to find the just the right ingredients and tools to try new recipes. (A Thai coconut sweet potato soup was our latest win.) As you might guess, one of the stops on our shopping trips is Williams-Sonoma. We’ve purchased all manner of utensils and tools from them over the years, and we were one of the first members of their “email list” – allowing them to collect data on our purchases at the point of sale, whether that’s online or in store.

You would think that Williams-Sonoma would know us well enough through our extensive data trail to target advertising and offers precisely to our buying habits.

You would think that, and you would be wrong.

How do I know?

I ran an experiment.

From February 1 to March 31, 2019, I collected every email Williams-Sonoma sent to us. During that time, we made two purchases, and in both cases, provided our email address. The test is simple: Do the promotional email messages reflect our buying patterns? In other words, does Williams-Sonoma use the data we provide them to deliver better advertising?

Here is the data summary:

n=175 (number of emails)

d=59 (number of calendar days)

n/d=2.97 (emails per day)*

*This measure of central tendency isn’t hiding anything. Williams-Sonoma sent three emails per day, every day, for two months, save for a couple of exceptions.

What did the emails say? I created a word cloud to help visualize the subject lines. You can see that word cloud below.

The most immediate and obvious conclusion is the word “Percent” which relates to some sort of “percent off” offer, anywhere from 20 to 75 percent. This is a typical example:

LE CREUSET **Special Savings** & Recipes + up to 50% Off Spring Cookware Event

The rest of the data set is barely worth an analysis at all: Williams-Sonoma has an inventory of brands to sell us. They’re experimenting with different percentage offers, different levels of urgency (today only!), and different deadlines (Easter is coming!) to get us to bite.

We reviewed all the percentage offers, urgencies, and deadlines: We often buy at full price, because when you’re interested in a specific receipt, you don’t want to wait for a sale. (Wouldn’t you think they’d notice that we downloaded a specific recipe?) We reviewed all the brands featured. We have never bought any of them. (Wouldn’t you think they’d notice what we just bought?)

Here’s the rub: Williams-Sonoma does know all that. They have all of our purchase data, yet they have chosen not to use it.


It may seem like I’m picking on Williams-Sonoma, but I could just as easily have picked any number of brands. I suspect you could hunt through your inbox and find a dozen examples of bizarre, irrelevant marketing from brands you patronize as well.

But this was just one example. Other brands do better, don’t they? Perhaps the macro-trend is heading in the right direction, and brands such as Williams-Sonoma eventually will be out-competed by brands who are more efficient and can redirect that excess capital. Perhaps this is just a symptom of struggling retailers. If that were true, what might we expect the macro trends to look like?

First, we might expect that marketing spend would be growing at a rate at least equal to, but ideally lower than, population growth. In other words, the ratio of marketing dollars per person on the planet should be shrinking over time. Is that the case?

The chart below shows global marketing spend growing at 3.9% per year:

Source data

The next chart shows global population growth slowing over time, about 1.0% per year during the same period.

In other words, marketing is spending more per person each year, not less.

But wait, you say. Population growth is not necessarily an indicator of economic growth. It would be fairer to look at global GDP growth over the same period.

Great. Let’s do that.

Source data.

Over the same period, we see global GDP at an average of 3.6% per year. In other words, at an average of 3.9% per year, marketing is overrunning GDP growth by about 10%. And because North America and Western Europe are the largest marketing “markets,” and those regions are growing slower than Asian markets, the overshoot is even higher.

In other words, for all its data, marketing is becoming less efficient over time. Put simply: Big data is making marketing worse, not better.


How on earth can that be?

Let’s refute a number of possible alternative explanations.

Explanation #1: It takes a certain amount of time to realign marketing based on what it’s learning from Big Data. What’s more, that knowledge has yet to completely diffuse into the professional community.

Really? It’s been 10 years, and there is no evidence that the growth rate in marketing spend in bending downward. In fact, it’s accelerating. No, marketing knows what it should be doing, but it is not doing it for a much more obvious reason: There is no downside.

Email protection laws are barely enforced. GPDR is just finding its footing n in Europe, but enforcement has been spotty. A state-by-state patchwork of privacy laws in the United States isn’t likely to do much better. Enforcement takes resources. In other words, marketing has no incentive to be efficient.

Explanation #2: We’re looking at the wrong channels. Email (in the Williams-Sonoma example above) is an “owned” channel, meaning the company does not need to follow guidelines as it would on Google or Facebook. Email might be inefficient because it’s “free,” but when marketers are paying for advertising, they do better.

Really? A shift from tough-to-measure analog media to digital, data-driven media over this 10-year period should have resulted in more efficient performance. But look at the growth pattern in marketing spending over the past 10 years and compare it to GDP. You would expect better data to lead to more efficient use of resources as it does everywhere else in organizational operations, but that is not the case.

Explanation #3: You’re looking at average data, and averages can distort the picture. We should be examining the distribution (variance) in the data to truly determine marketing efficiency.

Really? Marketing success doesn’t follow a normal distribution (aka a “bell curve”), it operates on a power law distribution. In other words, a small number of marketing operations and tactics deliver a disproportionate amount of the success. The bottom line is that a vast majority of marketing operations and spending does not generate a positive return on invested capital (ROIC).

Explanation #4: Of course, we know that most marketing doesn’t meet an ROIC threshold. That’s because marketing is an investment in the future of the organization. We’re building a brand, not quarterly returns. Failure is necessary to the learning process.

Really? So, when precisely will “investment” turn into “returns” on that investment? The data over 10 years shows no appreciable return on marketing investment that outstrips economic growth. You may be able to cherry pick organizations or campaigns that deliver good results, but the overall impact is a negative ROIC over the long term.

Explanation #5: You’re aggregate analysis hides material differences in the performance of marketing by industry. Put simply, B2C is not B2B, and doesn’t need to spend as much. Consumer marketing might be more wasteful, but business to business marketing is much more efficient.

Really? My B2B friends, what happens when you count all selling expenses? That includes “marketing”, but it also includes “tradeshows” and “salespeople” and “executive time selling” and a whole host of other goodies you’re probably not counting in the marketing line on the balance sheet. When you do that, B2B is just as out of whack as B2C.


Sorry, marketing. I hate to poop in your sandbox, but none of these explanations hold up. As an organizational function, marketing is not delivering a positive return on investment.

Yes, there is plenty of industry scuttlebutt about how consumers are getting pissed off and opting out. Marketing frets over Netflix and Apple end-running traditional advertising channels by switching to ad-free subscription models. But marketing, I wouldn’t be as worried about consumer anger as I would be worried about the next conversation with your CFO.

The party ends the instant the global economy goes into recession. Marketing bemoans the “short-sightedness” of financial professionals when they look at ROIC instead of “brand health” in their calculations, but what are they supposed to think? The rates of growth don’t match, meaning marketing is delivering a lower return on investment, in aggregate, with each passing year.

A shotgun approach to email – per my example above – is simply the canary in the coal mine.

Ask yourself this question: If you needed to get better results with 80% of your current budget, could you do it? If the answer is “no,” you had better start working on a plan. It might be time to actually use all that “big data” you’ve been so excited about.

Because the day of reckoning is coming.

Good luck.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Long Form Articles

#CopyPasteCris and the fight to stop writing from devolving into content marketing

Nora Roberts: “Not a Rant, But a Promise” blog post, that went live (and viral) 2/23/19:

I’m getting one hell of an education on the sick, greedy, opportunistic culture that games Amazon’s absurdly weak system. And everything I learn enrages me.

There are black hat teams, working together, who routinely hire ghosts on the cheap, have them throw books together, push them out–many and fast–to make money, to smother out competition from those self-pubbed writers who do their own work. Those who do their own work can’t possibly keep up with the volume these teams produce by these fraudulent tactics.

They tutor others how to scam the system.

If you’re curious why one of the world’s most famous, most prolific, most talented, and best-selling romance authors would call out Amazon on her blog, we need to rewind the clock about two months.

Romance readers are voracious consumers of their favorite authors, with some readers finishing a book each day. It should come as little surprise that it was a zealous reader who noticed something odd about passages in several of Courtney Milan’s books in February 2019. That reader was the first to discover evidence that Milan may have been plagiarized by another author, Cristiane Serruya, a best-selling romance author from Brazil.

It didn’t take long for the dam to burst.

Other readers and authors started to look for evidence of plagiarism, and they found it. Lots of it. Including multiple titles from Nora Roberts, aka Not The Author You Want To Piss Off.

At first, Cristiane Serruya took to social media to defend herself, claiming that it was writers she hired on Fiverr that plagiarized the materials. (If you’re not familiar, Fiverr is sort of like an Uber for writers and other creative professionals. Sadly, it doesn’t have a great reputation for quality.)

No one was buying Serruya’s excuses.

If it’s your name on the cover, why wouldn’t you check work from subcontractors? You mean you don’t write all your own work? You’re trying to blame a freelance writer who you paid peanuts when you’re a best seller? How long has this been going on?

Within a few days, Cristiane Serruya shut down her social media accounts and went dark. Legal action is pending.

A few avid readers are keeping track of instances of plagiarism they find, and it’s ugly. As of this writing, @CaffeinatedFae counts at least 85 books, 36 authors, 3 articles, 3 websites, & 2 recipes as possible examples of plagiarism. Of course, allegations are allegations, not legal proof, and Cristiane Serruya will need to have her day in court (courts). But that doesn’t change the fact that this situation has damaged the credibility of the entire genre – and in some ways, publishing in general.


Readers are pissed off.

Authors are pissed off.

Publishers are pissed off.

They should be.

But Cristiane Serruya is not the problem.

Amazon is not the problem.

The romance industry is not the problem.

Content marketing is the problem.


Cristiane Serruya, aka #CopyPasteCris, is a symptom of a powerful trend in algorithmic, data-driven marketing.

In the past 10 years, and especially the past three, the frequency of content has far outpaced the quality of content on every platform. That is especially true on major social media platforms, but it’s also true on Amazon (books) as well as many traditional publishers. Writers are rewarded for publishing more, lower-quality content rather than less, higher-quality content. It’s tempting to think this is because of a desire to consume more content (the voraciousness of romance readers I referred to earlier), but it’s not.

Algorithms are driving those decisions.

However, algorithms are not acting on their own. Engineers are making the programming decisions, and marketing is telling the engineers what they want. The formula is quite simple: More content from one author begets more attention (clicks, engagement, book sales) for that one author in a winner-take-all positive feedback loop.

It’s really as simple as that. If you need to produce a lot of content to have success, and you don’t care (within reason) how good it is, you look for the cheapest way to do it. Should you go to Fiverr and have them write for you? If you want more revenue, that’s the cheapest and fastest way to do it. Do they plagiarize? What is the risk-benefit analysis? If Fiverr humans are unreliable, why not simply use AI to scramble the original text “just enough” to avoid copyright concerns? Do you really care?

The algorithms drive two parallel and opposite trends: An increase in quantity of writing and a corresponding decrease in the quality of writing. Yes, there is always a commercial imperative for writing, but this technology-driven business model is a powerful accelerant.


As a reader, you can’t miss it.

As a writer, it’s probably driving you nuts.

As a marketing professional, you wonder how you can stop the wildfire you started.

I happen to have a front row seat for all three of those. Not only do I see the behind-the-scenes operations of these content platforms, I have my own evidence.

Over the past six months, I have deployed two unique content strategies on the same set of platforms: Medium, LinkedIn, and my own blog. In one strategy, I published shorter, lower-quality content each day, sometimes multiple times per day. (It’s all my content, however. I’ve never used Fiverr or any other freelance writer for my work.) For the other strategy, I published more longer-form and researched weekly content.

The results have not surprised me as a marketing professional, and by now, they should not surprise you. The daily posts create a positive feedback loop where I attract more attention the more I publish – out of proportion to the scheduling ratio. In other words, I published according to a 7 to 1 schedule, and I saw a 50 to 1 result.

Sadly, to be commercially successful (at least for now), modern writing has become content marketing.

You can justify lower quality writing to yourself however you like – you use a “pillar content” strategy, it’s the only way to make money, love the player / hate the game, give people what they want, this is a business not an art form, it’s the brand that sells not the writing. Whatever. You do you.

I say, fuck that.

I opted out of the first strategy in disgust. In my life, writing (an art form) and marketing (selling) are related, but distinctly separate, activities.

Marketing should get out of the writing business, because I know what is bound to happen if it doesn’t.


The backlash has already started.

Smart publishers are just starting to figure out that readers are paying to escape the shit storm, and they are doing something about it.

Every major media site, including the New York Times, Wall Street Journal, the Economist, Medium, and even your local news organization, is implementing some form of pay wall or paid subscription service. Social media platforms are in a bigger pickle. Their business models are built on you providing the content. As content has become worse (how many times can I see the same memes on LinkedIn or the same GIFs on Instagram), users are less engaged and opting out.

No brand wants to pay “influencers” for the next “Fyre Festival.”

Yes, some marketers buck this trend. Joe Pulizzi, nearly single-handedly, created the content marketing genre 10 years ago. His creation, the Content Marketing Institute, maintains high standards through training and coaching. MSP-C, a content marketing agency based in Minneapolis, Minnesota, consistently delivers world-class writing for brands. You may have a “great example” yourself. That’s nice, but that’s cherry picking, and you know it.

Despite those examples, an innovation 10 years ago has become a perversion today.

The wheels are coming off the content marketing business model as brands stop paying the bills for shitty, iterative, click-bait, bot-friendly content. It’s a race to the bottom. If you’re involved in the business, you must have noticed that the prices per unit of content are dropping, that the attention per post is declining, and that sales per unit effort are stalling.

It’s not hard to understand why. Brands won’t pay as much for content marketing any longer because it’s not working.


I wish it wouldn’t have turned out this way.

Content marketing and algorithm-driven platforms initially helped underrepresented and unheard voices get attention and compete on a level playing field. But those with better marketing skills (and questionable ethics) quickly gamed the system and shut out those independent voices.

It’s like marketing broke their own toy by playing with it too hard.

Good. Maybe we’ll learn next time.

And that takes me back to why marketing should get out of the writing business. Writing is creativity. Marketing is selling. It is best that they cross paths only gingerly. Yes, there are rare instances of great marketing that is also great writing, but those are special because they are rare. Readers want to read writing from actual writers. Readers don’t want to read writing from marketers masquerading as something other than what they are in a thinly veiled excuse to sell them something. Frankly, this implosion of the content marketing industrial complex will refocus marketing on what it should be doing – and what an entire generation of practitioners have forgotten how to do – sell something. There is no shame in, nor a lack of creativity required, to achieve that goal.

I, for one, and happy to see content marketing die.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Long Form Articles

Data Exchange Networks, AI interrogators, and corporate espionage (Chapter 2 of the Dr. Thomas story)

What follows is the next chapter of the story in a possible future filled with Data Exchange Networks (DENs) that help us sell our private data. Our protagonist learns that not all security is created equal, and that breaches have consequences.

Haven’t read the first chapter? Read here first.

. . .

March 25, 2029

How long had it been? Lynn thought.

Twenty minutes? Two hours? Two days? It was hard to know. Her smartphone and watch were confiscated during her arrest this morning. She had no way to know how long she had been in this room – a small space by even her apartment’s standards. The walls on three sides were painted cinderblocks. What was the correct name for a color that peeled in places with the two prior colors peeking through in random blotches? The flaking concrete walls stood in contrast to the sleek mirror that faced her.

She looked terrible. She felt worse.

A streak of mascara scarred her face – the failed result of attempting to scratch her cheek. Her hands were cuffed to the top of a plain metal desk, giving Lynn about six inches of movement.  Both ankles were chained through a metal loop in the concrete floor. Her metal folding chair was manufactured in an era before luxuries such as “padding.” Her ass hurt.

Just this morning, everything seemed to be going so well.

Her experiment using nine photovoltaic paint samples was running in her lab – a simulation that would take about three hours – giving her plenty of time to teach her introductory physics class. But that was before all of this. Would her software shut down properly? Was the experiment ruined? Would she need to rerun it? Maybe one of her students shut it down for her?

No, Lynn thought. They were freshmen. This group had trouble making it to class on time.

And what must they think of her now?


Lynn replayed the scene in her head.

As she walked from her lab to the classroom, Lynn lamented her “junior professor” status at the University. Frankly, she was lucky to be a “professor” at all. Nine in ten “faculty” positions were now “adjunct” instructors – basically, gig researchers. And because none of them would teach this group, the “junior” professor was stuck with them. There were no “A students” in this class. Lynn was surprised they made it out of high school, and even more surprised they were in a good college. She thought darkly that the era of rich people getting their pretty children into good colleges clearly wasn’t over.

Just like the over-promoted high school students they were, they were nearly impossible to manage. But Lynn wasn’t so easily defeated. She decided to run the classic “pendulum” experiment to snap them back in line. This classroom was equipped with a 20-foot chain attached to an anchor on the arched ceiling. From the bottom of the chain, she attached a 15-foot weight.

Lynn remembered the look on Frat Boy’s face as she called him to the front of the class, carefully told him where to stand, and stretched the pendulum’s weight to the tip of his nose. He looked nervous. She knew he was in no real danger – and he would know that too, had he paid attention in class. But she didn’t let on. Lynn made a dramatic production of telling the young woman nearest the emergency phone to be ready to dial 911 in case “anything went wrong.”

Lynn warned him – in a deathly serious tone – not to move.

Frat Boy didn’t breathe as the pendulum released from the tip of his nose, swung in a wide arc toward the back of the room, hung for a moment in the air motionless, and then accelerated back towards his face at alarming speed. Frat boy flinched, but he didn’t move. About an inch from his face, the pendulum came to a slow stop and reversed direction.

“Good work,” Lynn remembered saying to him. “You may return to your seat.”

A slightly sweatier version of Frat Bot returned to his seat, quickly and quietly.

She finally had their attention.

Good, Lynn remembered. At least we can get through one class without disruption. What demonstration would she run next week to keep them in line?

It was at that moment of contemplation and success when the classroom door flung open.

“Lynn Thomas?” announced a police officer in a crisp black uniform. Her voice was firm. Unfriendly.


“You are under arrest. Officers, please take the suspect into custody.”


The next few moments were a blur. Jonathan Freeman (a mountain of a man, her eyes came up only to the nameplate above his badge) slipped handcuffs over both her wrists. She heard the first officer’s voice as she read what she assumed to be Miranda rights. She had heard the standard lines on Law & Order reruns, but they were a blur now.

She stammered a confused Yes and felt Officer Freeman nudge her toward the door.

As Lynn was being led out of the room, she caught a glimpse of Frat Boy’s face in open shock. He looked scared. She was too.

Students and faculty on the front lawn followed her with their eyes in silence as officers helped her into a waiting police cruiser. Ten minutes later, she was at the police station. After electronic fingerprints and retinal scans, another officer led her here.

The cinderblock room.

Murder? Is that what the officer said?


“Doctor Lynn Thomas?”

What Lynn thought was a mirror in front of her flashed on in an instant. Instead of her own face, she was now looking into the eyes of – she could swear – her college roommate.

“Uh, yes?”

“I am here to ask you a few questions. My name is Rachel. May I call you Lynn?”

Okay, this was weird. Rachel was her college roommate’s name too.

She hesitated.

“I’m sorry. You just remind me of someone I knew. Yes, Lynn is fine.”

Rachel smiled.

“I get that a lot. This must be very uncomfortable for you, Lynn. Is there anything I can do for you before we get started?”

“I’d love something to drink. And if it isn’t too much trouble, the wrist restraints are very uncomfortable.”

Rachel winced.

“Yes, I can see them cutting into your wrists. That looks like it hurts. She looked away and typed. I just put in a message to the detective in charge of this unit. He should see it shortly.”

Lynn relaxed a little. Rachel (her roommate Rachel) had always looked out for her.

“While we wait for him to respond, I was hoping you could help me clear up a few questions I have. Hopefully, this will all be over soon. Can you tell me where you were last night between 6 and 8 o’clock?”

Lynn started to feel, at least a little, at ease.

“That’s easy,” Lynn replied. It felt good to be using the logical part of her brain.

“I finish teaching a class a 5:30. That night, two students stayed after to talk about the upcoming test. I must have left around 6 and started walking back to my apartment. I stopped by a restaurant for some pho ga.”

“Yes, I have a purchase record her from the Orchid Restaurant for a bowl of soup and a mixed drink.”

Lynn felt a little embarrassed.

“It was a long day.”

“I understand, Lynn. I’m certainly not one to judge.”

Rachel smirked. Lynn couldn’t help it. She smirked too.

“I cross referenced that purchase with your smartwatch’s GPS locator. The two records matched. So, we can establish where you were from 6:12 to 7:38 pm.”

Okay, Lynn thought. This was good. She felt a surge of relief that she had selected that restaurant as one of her “places to try” from her personal data sale in February. Thank goodness! Had she not done that, maybe she would never had gone there. Maybe she would have gone straight home where she turns off GPS to protect her privacy.

“Do you remember taking a napkin out of the restaurant with you.”

Lynn thought for a moment.

“Yes, I did,” Lynn remembered. “I added too much siracha sauce to the pho and my nose was running. I wiped my nose with it as I was leaving, but I must have thrown that away.”

“You did,” Rachel replied, her tone noticeably cooler.

Lynn’s heart started to race.

“Officers recovered the napkin in a garbage can about five feet from where a young man was killed that night. The DNA on the napkin matches the DNA medical examiners recovered under his fingernails.”

Lynn couldn’t breathe.

“Are you familiar with an organization named Central Biopharma Specialties?”

“Uh,” Lynn stammered.

“Let me help. They conduct research on BRCA gene variants.”

“Uh, yes, I guess. I am involved in one of their research studies. What does that have to do with this?”

“We obtained a warrant for your DNA records they had on file as part of the study. We used that data to match your DNA to the napkin and to the DNA on the body of the deceased. Our GPS records place you within 10 feet of the scene within five minutes of the murder.”

Lynn could feel her heart beat. It was loud.

“We’re pulling the security footage. We believe it will show you confronting and strangling the victim.”


“Lynn,” Rachel paused and regained a measure of compassion in her tone. “It’s time for you to admit what you did.”


The door flung open.

A tall woman entered. She had severe features, a ramrod posture, and a brilliant crimson suit. She was terrifying, but oddly familiar.

“This interview is over,” the woman scowled to Rachel. “My client invokes her right to legal counsel.”

The screen immediately switched off.

This new woman reached into her breast pocket, took out a small stack of adhesive notes, and peered into the mirror. After a moment, she carefully placed three notes in separate locations on the mirror.”

“Okay, now that we’re not being watched, let me introduce myself. My name is Jessica Fulbright, and I am an attorney. Your attorney, to be precise.”

“But,” Lynn said. “I didn’t hire anyone. I haven’t seen or talked with anyone since I got here. Well, except for Rachel, the detective.”

Jessica smirked.

“Rachel isn’t a detective, she’s AI meant to put you at ease and conduct initial interrogations. Let me guess, the name ‘Rachel’ means something to you?”

“Um, yeah, Rachel was my college roommate. She even looked like her.”

“Hmm. Figures. I’ll bet the detectives pulled your old Facebook posts, ran a bit of a scrambling algorithm to obscure some of the details, and generated a persona tailored precisely to you. I’ve seen it before. Some law enforcement departments have found AI is more convincing than a human detective at building rapport and encouraging quick confessions.”

Lynn couldn’t believe what she was hearing.

“But … what? What’s going on here?”

Rachel slid a thin silver tablet out of her flawless Coach bag, touched the screen, and scanned it.

“Authorization Jessica Fulbright, practicing legal license XV1998, representing Dr. Lynn Thomas. Request file transfer.”

“Does the client accept representation?” came a firm voice from the tablet.

Jessica turned to Rachel and waited.

Stunned, Lynn didn’t move.

“Lynn, you need to authorize representation, otherwise I can’t see the charges and evidence.”

“But I didn’t hire you.”

“My son did, on your behalf. He is a student of yours. Steven Fulbright. He called me just after you were arrested. Apparently, he’s quite fond of you.”

Frat Boy.

Lynn never would have guessed. She had misjudged him.

“Okay, yes, I give authorization.”

Jessica nodded sharply and started to scan the screen quickly.

“Ah, I see what’s happening here.” Jessica began. “A young man was killed near the Orchid Restaurant last night. Surveillance cameras see you leaving the restaurant and crossing into the proximity of the crime scene just a few minutes before he was killed. See here?”

Jessica turned the screen to Lynn. It was her, walking down the street, stopping to wipe her nose, and tossing the napkin in the trash.

“Police recovered the napkin after they cross referenced nearby restaurant purchase records. Once they had that, they were able to match your DNA records to a trove of genetic information they purchased through a Dark Web broker. They didn’t need a judge to compel the biotech company to release your records because the data had already leaked. It’s solid police work. I would have picked you up too.”

“But,” Lynn stammered.

“Hold on, Lynn. I’m not finished. Ah, I see. The police weren’t able to recover any physical evidence from the deceased. Lynn, they can’t match you to the victim.”

“Wait! That’s not what Rachel … should I even call her Rachel … is she even a her … what the hell is going on!?”

Jessica looked sympathetic.

“Detectives can lie to you during interrogation. It’s a common technique to get a confession. They present just a little more evidence than they have hoping you’ll fill in the details. There’s a reason they tell you that you have a right to remain silent.”

A red light flashed on the tablet.

“What’s this?” Jessica said.

After a quick scan, a satisfied smile came across Jessica’s face.

“I knew it!” Jessica said. “New security footage just in from the deli across the street,” Jessica said. Her tone quickened. “Here’s you … blowing your nose … throwing away the napkin … and … getting on the train.”

A small red light on the restraints on her wrists turned green. The latch popped open. She couldn’t see them, but she felt the restraints on her ankles release in the same moment.

“I still don’t understand what’s going on?” Lynn exasperated.


The door opened and a giant man walked in. Lynn recognized him. Jonathan Freeman. The same one who arrested her what seemed like days ago.

“It means you’re free to go, although I’m hoping you won’t,” he said. “My name is Lieutenant Freeman. It’s nice to meet you, Lynn, and to see you again, Jessica.”

“You have some balls to walk in here and ask that after how you treated my client,” Jessica stood up and glowered straight at him. He might outweigh her by 100 pounds, but at 6-foot, 2-inches, they stood eye to eye.

“We couldn’t be too careful,” Jonathan said, nodding respectfully at Jessica and taking one of the (padded) chairs in the room. “In about 30 minutes, the NYT/WaPo will publish the details of a major genetic data breach. Ordinarily, it would take days or weeks for leaked information to turn up on the black market, but this case was different. We tracked a criminal organization who purchased 62 people’s records within just a few minutes. Along with the Google Maps data breach yesterday afternoon, organized crime members have been able to kill 13 people in the past 24 hours – each planting evidence of a different hacked victim. We needed to verify Dr. Thomas’ identity and alibis before we could release her.”

“What? Data breach? Google Maps? What is happening here?” Lynn couldn’t believe what she was hearing.

“Dr. Thomas, are you familiar the MENSA Data Exchange Network?”

“Lynn, stop. You are free to go. You don’t have to say anything more.” Jessica glared at the detective.

“She’s right, Lynn, you don’t. But before you go, let me explain the situation. You don’t need to say anything.”

Jessica looked at Lynn, questioning. Lynn nodded. She wanted to know.

“The MENSA DEN was the place the genetic hack originated. Once hackers got into their database, your own security measures were compromised as well. Our records indicate you accepted coupons from the Orchid Restaurant, and that you used Google Maps to allow advertising notifications for that restaurant.”

Lynn remembered the push notification on her phone on her way back from class. Had she not seen that … oh my God.

“It seems like you’ve been set up to take the fall for this. The victim’s name is Muhammed Farooqi, in the United States from Qatar.  Does that name mean anything to you?”

Lynn looked to Jessica. Jessica nodded.

“Um, yeah. He is the coordinator for a Qatar-based science group. I have been paid to try to recruit female science students using a private social network.”

“Muhammed Farooqi is not his real name, and unfortunately, the group isn’t real either. Do you know of anyone who might have a grudge against you or have a reason to hurt you?”

“This is unbelievable. What could I have that anyone wants? I’m barely making ends meet while I work on my startup. I mean, I just had the first successful experiment on photovoltaic paints. I’ve cracked the 80 percent efficiency barrier. I had a meeting with a venture capital team tomorrow. If I can replicate the results in my lab, they said they’d fund large-scale production.”

Lynn stopped herself. The paints she had left in the lab.

“I don’t understand half the words you said,” Jonathan sighed, smiling a bit nervously. “But I know one thing. Someone wants you gone, and we need to find out why. You’re not safe.”


Obviously, this is a work of fiction, and probably not a very good one. The more I experiment with fiction, the harder it gets. I was under the foolish impression that fiction would be easier than non-fiction. It’s not. Thanks for playing along.

That said, I think this dramatization raises some important questions:

  • Does a future of private data monetization make the impact of data breaches riskier? Less risky? Does it introduce new risks we haven’t considered?
  • Many people struggle to understand the underlying technology behind data collection and privacy today – is making data brokering more common better or worse for consumers?
  • Is it ethical to create data exchange networks where people do not have the expertise to use them and protect themselves?
  • Do data exchange networks introduce more vectors for hacking?
  • Will criminals use this data for more elaborate crimes, rather than simple phishing schemes and credit card fraud?
  • What about the next generation of corporate espionage?
  • Is it acceptable for the police to subpoena these records in the course of an investigation? How about when the data have been hacked and posted on so-called Dark Web sites?
  • Should the police be able to use AI interrogation techniques?

Fiction is just that, fiction, but I think it can help us understand real life in a way an explanation of the facts alone cannot. Simply understanding how genetic information is shared is one thing; seeing how it could positively (and negatively) impacts real people is another.

I don’t have good answers, but I’m getting better at asking good questions. That’s the place we need to start.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Long Form Articles Rehumanizing Consumerism

Your “smart” TV is a dumb idea

That Hisense 55-inch 4K LED flat screen Smart TV with built-in Roku for $349 sounds like a great deal, doesn’t it?

This isn’t some “Black Friday” special or a “scratch and dent” fire sale, this is the regular price. At some retailers, you might even get a special offer – I’ve seen this model sell for as low as $299. Want to go even lower? Best Buy’s Insignia-brand model retails for a bit cheaper. Prefer a big-name brand? Samsung, Sony, VIZIO, and others all offer similar models in the same price range.

But if you buy one of these today, you might be disappointed. A new wave of Smart TVs is on its way from Xiaomi and Huawei later this year that are reported to cut that price by more than half. That’s right, these new 55-inch 4K LED Smart TVs might start to approach the $150 mark. At some point in the future, we could see a scenario in which the Smart TV comes free as part of a package of “cable” or “streaming” services. Smartphones use that pricing strategy today. Free Smart TVs might arrive as early as 2020.

Pretty cool, huh? Wouldn’t you like to pick up a new 55-inch flat screen for the price of a nice dinner and bottle of wine? Low-cost manufacturers are already seeing success in the Indian market; if the reports are true, the rest of the world doesn’t have long to wait.

I can almost hear my dad…

If it seems too good to be true, it probably is. 

He’s right. I intend to show you just how much you’re paying for a Smart TV.


Let’s start with a basic rundown of the critical features most people look for in a new television:

  1. Screen size: We can almost stop the list right here. When surveyed, buyers talk about additional features, but the truth is that most people make their buying decision based on the measurement of the screen size (measured diagonally from one corner to the opposite corner). The bigger the better – up to a point. In practical terms, the television needs to fit in your car or truck (or you need to be comfortable paying a delivery fee) and it needs to fit on your wall. Those buyers will say they bought that monster screen so that they can stay home instead of going to a theater, but that’s usually not the case. Heavy entertainment users (most Americans) do both. The actual reason is quite simple: People buy “big” to impress their friends and neighbors.
  2. Screen resolution: This is the second most sought-after feature. Resolution is essentially a measure of picture quality. The most common measurement is the number of “pixels” on the screen, and here’s where it gets a little confusing:
  • 480p SD: Standard Definition, or 640 pixels wide by 480 pixels tall. You’ll have trouble still finding one of these models even if you wanted one.
  • 780p HD: This is the first so-called “High Definition” standard, with dimensions of 1280 pixels wide by 720 pixels tall. These are the “cheap” HD screens.
  • 1080p HD: Often (confusingly) called “Full HD” with dimensions of 1920 pixels wide by 1080 pixels tall. The average consumer can be forgiven for looking at the “1280 pixels” in the other HD standard and believing that was “more pixels than” the 1080p HD model. Marketers aren’t always clear on which dimension they’re referring to, as we’ll see.

Okay, watch what happens now. It’s a little marketing trick. Instead of using the vertical pixel dimension, marketing switched to using the horizontal pixel dimension. That’s not necessarily inaccurate, but it’s not very clear either.

  • 4K Ultra HD: If we were using the same standard, 4K would be called 2160p HD…or 1080p HD should really be called 2K. Confused? Most people are. But in practical terms, with dimensions of 3840 pixels wide by 2160 pixels tall, 4K is often clear enough to see nose hairs on your favorite actors.
  • 8K (Superlative TBD) HD: Still rare, these screens have dimensions of 7680 pixels wide by 4320 pixels tall. Get ready for a journey past the nose hairs and into the nasal cavity. How about “Nasal HD”? No?

The final confusing bit is the relationship between screen size and resolution. A smaller 4K screen will appear clearer to your eyes than a very large 4K screen. Same number of pixels, in a smaller surface area, equals sharper appearance. That’s why you can get away with 1080p HD on the smaller screens and they look just fine…but the larger screens appear to benefit more from the higher resolution (this is called “pixel density”). And yes, television wonks will wax philosophical about signal bandwidth, image contrast, and color quality, but most people can’t tell the difference. (Marketing loves the wonks. You should be suspicious.)

Everything else falls down the list quickly. Almost 80% of the purchase decision is made based on screen size and resolution. Other factors matter, but much less so. Different brands use minor differences in port counts, sound system choices, and mounting options in an attempt to separate themselves in your mind. But once your TV is mounted to your wall, size and resolution drive your enjoyment. Everything else is trivial.

I’ve spent time explaining the basics of television marketing to highlight an important problem: Both of the key driving factors in television purchase selection (screen size and resolution) have become commodities, but we’re still vulnerable as consumers to Smart TV marketing that tugs at our egos and confuses rational decision-making.

It gets worse.

This commoditization puts tremendous pressure on less-critical factors in the buying decision, encouraging manufacturers to resort to gimmicks (curved screens) and confusing marketing (blacker blacks) to drive sales.

What’s more, as retail prices continue to drop, the price you pay as a consumer for that new Smart TV barely covers the cost of the large screen, plastics, electronics, packaging, shipping, distribution, retailing, and marketing – if it covers it at all. At $150, it almost certainly does not.

But as the end consumer, why should you care? If a manufacturer wants to give me a Smart TV in exchange for a year of streaming service (that I would have bought anyway), why would I say no? The reality is that cheap Smart TVs are such a win for consumers, that we often don’t think much beyond the price.

We should start. Manufacturers are not in business to lose money. Profit has to come from somewhere. Let’s find out where.


I strategically failed to mention one more important features of modern televisions: Software. Specifically, Smart TV software.

Only 20 years ago, televisions didn’t use software in any meaningful sense. Yes, televisions have long since abandoned mechanical actuators to change channels (and therefore needed basic microprocessors), but the consumers saw little evidence of that software beyond crude on-screen displays. Software of that era simply needed to recognize whether the television was on or off, what channel you were on, your volume, and whether an external device was plugged in. In fact, most of the “software” came secondhand from your video game console, DVD player, cable box, or home audio system.

But televisions have come a long way, driven by competition from mobile devices. Manufacturers saw their share of home entertainment under threat from tablets and smartphones, as well as plug-in devices such as Roku, Apple TV, and Amazon Fire TV. Their flexibility (and competitive advantage) came from software, not necessarily better hardware.

Smart TV systems, in various forms, are the television makers’ answer to the iPad. You may not get all the iPad’s flexibility, but you get access to popular streaming services, a smattering of apps, and management of external devices (DVD players, Cable TV providers, on-demand content, and other gaming systems) – all on a huge, beautiful screen.

It’s not hard to understand why they would do that. Television manufacturers incur massive hardware development expenses, and then go through the trouble of getting the big screen into your living room, only to hand over the after-purchase revenue to someone else.

And that’s what this is all about: After-purchase revenue.


The television is the least of what you pay for in home entertainment.

With only a brief look at the average monthly bill for content coming through the television, we can see why television manufacturers might want to get in on that. Let’s start with the obvious costs and benefits – the ones you see on a monthly (or on-demand) bill:

  • Cable or Satellite Service: $107/month on average – People might complain about cable television, but they still buy it, often because it’s bundled with other services (phone or internet) or because that is the only way to get access to popular programming (live sports is a common example).
  • Streaming Service(s): $10-$15/month per subscription (many homes have two or more): Netflix, Amazon (part of a Prime membership), and Hulu are the big ones, but they’re not the only providers. YouTube also offers subscription services to avoid its advertising, and AT&T offers a plan as well.
  • Movies and Pay-Per-View Entertainment: $20-30/month: Want to watch the latest movie? Don’t want to buy the DVD? You can buy it through iTunes for $10-$15. Most homes order 2-3 additional offerings each month.

Yes, some people have “cut the cord” and use streaming services in place of cable and satellite services, but many households use both. If we do the quick math, that’s more than $150 per month in entertainment services for an average home. (And we’re not counting internet connectivity and mobile phone plans.) That’s almost $2,000 each year. Now compare that to the falling retail price of the average Smart TV and you’ll understand the appeal of after-purchase revenue.

The real money isn’t in selling you a Smart TV, it’s in selling you entertainment.

But again, as the consumer, why should you care how much the television manufacturer makes?

When you use your Smart TV to access Netflix, you’re not paying your bill through Samsung. The Smart TV is simply a portal to organize these services, and you know Samsung needs to make money somewhere. As the consumer, you get the benefit of less clutter, fewer external devices, and an easier user interface. You might even get a discount on some of those streaming services.

What’s not to like?


In the modern television ecosystem, you’re not consuming entertainment, you are the entertainment.

Here is the point in the story we need to introduce you to Samba TV. It’s not the only such provider of television viewer data, but it’s the big one you may have heard of, mainly from this article last July.

In short, if you enable the Samba Interactive TV function on your Smart TV (and about 90% of people do), the company can track your viewing habits, aggregate that data, and sell that data to advertisers. Content providers and advertisers can then use that data – not only in its aggregated form – but also to deliver individualized programming recommendations and targeted advertising. With Samba, television manufacturers (finally) get a cut of the aftermarket.

You can almost hear marketing directors squealing with joy.

Not almost.

Let’s allow one to tell you herself, as described in the New York Times.

Citi and JetBlue, which appear in some Samba TV marketing materials, said they stopped working with the company in 2016 but not before publicly endorsing its effectiveness. JetBlue hailed in a news release the increase in site visits driven by syncing its online ads with TV ads, while Christine DiLandro, a marketing director at Citi, joined Mr. Navin at an industry event at the end of 2015. In a video of the event, Ms. DiLandro described the ability to target people with digital ads after the company’s TV commercials aired as “a little magical.”

That’s why the Smart TV is such a big deal. By centralizing all of your entertainment consumption activity, you also centralize all of your behavioral data. And there is a bigger market for your television viewing data than you might think:

  1. Content Optimization and Ratings Data: The days of the Nielsen set-top monitoring boxes are now painfully quaint. Why settle for a sampling of television viewers when you can gather all of the data from every Smart TV-enabled system? Content providers know not only how many people watched, but at what points they stopped watching, and even at what points they were unengaged with the content. That last one is the most important. Lack of “engagement” isn’t simply taking a bathroom break; actual engagement is more subtle than that. If you’re playing with your kids, you’re not paying attention to the programming.
  2. Product Advertising: Advertisers want to know if you’ve viewed their ads as well as how engaged you were – just like content programmers. But advertisers want much more than that. Instead of delivering advertising and hoping you make a purchase at some undetermined point in the future, advertisers want you to make the purchase immediately. Ideally, right on the screen. That’s the “magical” part DiLandro referred to.
  3. Improving Facial Recognition and Voice Algorithms: You may have wondered how your Smart TV knows you’re watching it and how much you’re actually paying attention. Here’s a hint: Many (most) of these new Smart TVs have both cameras and microphones built in. When you’re watching a modern Smart TV, the Smart TV is also watching you. Older versions could only tell if “someone was in the room,” but newer models can also track where you’re looking on the screen. With newer voice recognition systems, they also can tell if you’re discussion the program or advertising … or talking about something else. They use this data to improve how content (both entertainment or advertising) should be optimized for maximum consumption and conversion.

This data is worth billions. And we just gave it away for a cheap flat screen TV.


At this point, it’s fair to think all that monitoring might seem a bit creepy, but it’s not as though they didn’t tell you they were doing it. You can adjust the privacy settings to disable those Smart TV functions if you don’t want them. And who cares if television manufacturers are making money off your data? They’re delivering better programming and targeted advertising. That sounds like a win-win. What’s more, monthly fees from cable and streaming services are expensive enough. At least the Smart TV is getting cheaper.

It’s hard to argue with that logic.

In fact, you could argue (and many have) that better entertainment and better advertising are small prices to pay for an enhanced experience. The average person in the United States spends about 8 hours in front of the television each day. That surprises you, doesn’t it? You may have thought that computers, tablets, and smartphones have eaten away at that number – and for some segments of the population, they have – but on the whole, people of all generations enjoy consuming content on a big, immersive screen.

And now, finally, the technology inside the television is catching up with technology of the screen itself. What’s wrong with that?


Let’s set aside the issue of providing consent, and how difficult it is to read and fully understand privacy policies. That’s a separate issue, but it’s under your control.

I am going to ask you more difficult questions:

Do you consent to a Smart TV monitoring your children?

What about your kids using your Smart TV when you’re not at home (or when you’re out of the room)? Is it okay for advertisers to ask your children to buy a product they see on the screen? Are you aware of (and use) the parental control settings? Do they work as you would expect them to work?

Well, you say, that’s the parent’s job. I don’t want (or need) some intrusive regulation telling me how to raise my kids.

Okay. Let’s ask another question.

Do you consent to a Smart TV monitoring you in your hotel room?

Yes, that same technology exists in nearly every hotel room, and because that Smart TV is not your property, you have little control over its privacy settings. Wiretapping is illegal. Using the Smart TV is not.

Well, you say, the Smart TV is the hotel’s property. They can do what they want. I don’t have to stay at that hotel, and I don’t need to use the Smart TV.

Okay. Let’s ask another question.

What about when they companies violate their own policies about sharing and protecting your data?

We’ve seen this before: Last year, the Federal Trade Commission fined VIZIO $2.2 million for selling data on 11 million viewers without their consent starting in 2014. Samba TV skirts this situation by paying television manufacturers to pre-install its software, but it doesn’t sell the data, it sells targeted ads. That seems like an awfully fine line to walk. If internal controls fail at the company, or its servers are hacked, your data is at risk.

Well, you say. Now you’re being silly. That doesn’t happen that often, and those companies get caught. You can’t prevent all the bad stuff from happening. And besides, I have nothing to hide, so I don’t care if people know what TV shows I watch.

I don’t have to agree with you to respect your point.

But I’m not done asking questions just yet.


Are you willing to risk espionage from foreign governments?

To help explain why it’s not unfeasible to use Smart TVs for espionage, we need to revisit the biggest computing story in 2018. No, it wasn’t the launch of the iPhone Xs, or some new AI technology debuting at CES, it was a story about a tiny microchip in an obscure supply chain for ultra-fast server hardware. If you’re an IT professional, this was big news. Most people missed it.

Here’s the short version: California-based manufacturer Supermicro was an important part of the supply chain for several companies, manufacturing circuit boards for high-end, ultra-fast servers used by companies such as Amazon, Apple, and other major corporations (as well as US government agencies) to process huge volumes of data. Allegedly, buried deep in the circuit board was a tiny microchip – a chip that wasn’t supposed to be there, and that the Chinese People’s Liberation Army forced Chinese-based subcontractors to install – that opened a “backdoor” into the server from a remote location.

If it’s true, that’s data espionage. Plain and simple.

The entire story is fascinating. You should read it. Fortunately, impacted companies and agencies discovered the problem and eliminated it (allegedly, they won’t admit it). Predictably, Supermicro vigorously denied the reports. The story is ongoing. In the end, however, it doesn’t matter if it happened precisely as Bloomberg reported it or not. The idea is exposed.

So, let me ask my question a different way. Consumers in the United States alone own about 150 million Smart TVs. What if only one percent of those devices had a “spy chip” installed? That’s 1.5 million potential surveillance devices.

That doesn’t account for the possibility of hacking the Smart TV software – much easier, and far more likely. Research from the team at Consumer Reports (published in 2018) shows Smart TV software was vulnerable to hacking.

They allowed researchers to pump the volume from a whisper to blaring levels, rapidly cycle through channels, open disturbing YouTube content, or kick the TV off the WiFi network.

Researchers (white hat hackers, in this case) couldn’t extract information using these methods solely through the Smart TV interface. But many people use the same WiFi network for their phones and tablets as their Smart TVs. That increases vulnerability to software intrusions that come from elsewhere – say, clicking on a phishing email.

Fortunately, while Smart TV software may be vulnerable, there’s no evidence that hardware tampering has happened or that anyone has found a “spy chip” in a consumer television.


But absence of evidence is not evidence of absence.

What if your Smart TV was, unwittingly, a listening device for a foreign government? It makes Russian tampering with Facebook advertising seem quaint by comparison.

This is a big fucking deal.


Holy shit, huh?

You didn’t think you’d need to consider geopolitics while browsing for Smart TVs on the sales floor of your local Best Buy.

Sadly, in today’s ultra-connected world, we need to broaden our perspective. But luckily, there are a few easy things you can do today to help mitigate invasions of your privacy, while still accessing the entertainment you want.

  1. Learn the privacy settings on your Smart TV. This isn’t as easy as managing the settings on an Apple iOS or Google Android device. There are many Smart TV versions out there, and even more manufacturer-specific settings. You’ll need to find yours and understand them. Fortunately, the good folks at Consumer Reports have provided a starting point.
  2. Remove any unwanted/unused apps from your Smart TV. Just like your smartphone, any app on your Smart TV might be collecting data, even if you’re not using it.
  3. Be careful of gaming platforms, especially with kids. Microsoft, Sony, and Nintendo have solid protections in place, but many Smart TV-accessible games may not. Know what you kids are doing.
  4. Speaking of kids, learn the parental controls on the Smart TV too. Your kids cannot know the technology better than you do. Sorry, you’ll need to learn.
  5. Find the camera and microphone on your Smart TV. They’re usually described in the instruction manual so that you do not cover them. Cover them.
  6. Unplug your Smart TV when you’re not using it.
  7. Or if you’re not going to do that, have your Smart TV on a different WiFi network than your other devices – especially “listening” devices such as Google Home and Amazon Alexa, or home security systems.
  8. Is it about time to contact your representatives about GDPR-style legislation? What’s it going to take?
  9. Consider purchasing a Smart TV brand based in a country with a lot to lose from pissing off your home country. South Korea and Japan fall into that category for the United States. China, not quite so much. Although supply chains are global, and many of these manufacturers use Chinese sub-contractors, another (friendly) government provides an extra layer of vigilance.


Managing your own privacy is part of modern life. The tech companies won’t do it. They barely think humans as anything more than moist computers with a checking account. The Smart TV manufacturers won’t do it. They’ve finally entered the data race, and they’re hardly going to stop now. The advertisers won’t do it. They’re addicted to data – however they can get it. The media can’t do it for you. They only report what’s already happened (and by then, it’s too late). Your government can’t do it either. Even GDPR has holes, and even tight regulation can’t protect you from bad actors who simply break the rules and hide.

No, protecting your privacy is up to you.

That’s the price of entertainment.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Agile Learning Long Form Articles Segmentation vs. Stereotyping

Is this the worst article you’ve read today?

The answers to most questions teach us very little.

Let me illustrate using a question I found that was designed to help shy people network with their peers more effectively – presumably in some version of introvert hell, aka the “mixer.”

“What great movie have you seen recently?”

What’s wrong with that question? Well, it’s certainly better than many common alternatives: Where are you from? What do you do? What’s your sign? When did you first notice that growth on your cheek?

(Don’t laugh. I heard someone ask that last question at a networking event. And no, it wasn’t me. And no again, I wasn’t the unlucky soul with the growth.)

The point is, of course, to “break the ice” and “get people talking,” but more often than not, this style of question tends to deliver weak answers that don’t tell you anything meaningful about the other person.

Why not?

Try answering the question yourself. What comes to mind? If you’re like most people, you’ve seen several movies in the recent past. But beyond its lack of specificity, but what does “great” mean in the context of this professional setting?

Let’s say you enjoyed the recent movie “A Star is Born” starring Lady Gaga and Bradley Cooper. How will this new professional acquaintance interpret your answer? Do co-stars Andrew Dice Clay and Dave Chappelle trigger an emotional reaction in the other person (even if they haven’t seen the movie)? What does the person think about romance movies? Will that person view you less professionally?

Is there a correct answer to that ice-breaker question?

No, there isn’t. To say that the best strategy is to “be honest” and to “not care what others think of you” is silly and unrealistic. Humans are social creatures; we continually assess our status within groups. We can’t help it.

This positively-worded and seemingly innocent question triggers a quick and almost subconscious response: What is the social setting? What do I know of the other person? What can I guess from body language and non-verbal cues? What am I hoping to achieve at this meeting? What is my status in relation to the other person?

We want to be “liked” in a social setting. Our true answers (and our true preferences) are confounded with our social preferences. In other words, you aren’t likely to learn anything meaningful about that person. You learned their facade. You learned what they wanted you to learn. And you’re nowhere closer to that professional relationship you hoped to spark.

Want to change that?

Simply flip the question: What was the worst movie you’ve seen recently?

We may not always be sure of what we want or like (we can have lots of “likes” depending on the circumstances), but we’re all experts on what we don’t want and don’t like. Try it sometime. I’ll bet you’ll find the answer comes quicker. It’s less guarded. Before the other person can put up their defenses, you’ve actually learned something important about the other person.

Let’s find out why negative is so positive.


If positive questions are so poor at yielding useful information, why are they so common in our everyday interactions? Why are customer service surveys filled with them? Why do we hire people based on the answers? Why do we choose to go on a date with someone who matches our preferences?

Simple. Positive questions are easier to ask.

A positive question yields a positive response. It may not be useful, but it feels good. What’s more, because a positive question tends to spawn more positive questions (aka follow up questions to clarify the intent of the initial question), they create more data. In this case, like so many others, more data is not better data, it’s actually poorer data.

A negative question, by contrast, yields a negative response. The answer is more useful, but it takes courage to face negativity.

It took me the better part of 20 years of my professional career to learn that. I’ve been asking some version of positive questions my entire advertising and marketing career. I’ve been so consistently frustrated with the results that I harbored a deep skepticism for social science research of all kinds.

More often than not – in fact, much more often than not ­– the information was useless.

Where did I learn to ask better questions? From the most negative people on the planet: Trial lawyers.


“Most people completely misunderstand the purpose of voir dire. It isn’t to select jurors, it’s to eliminate them. The only way to know them is to get them to talk. Most judgements we make about people are just plain wrong.”

– Jeremy Rose, Ph.D., consultant for the National Jury Project

We don’t need a detailed explanation of the jury selection process in the United States. A brief summary will do.

The term voir dire (VWAH-deer) was originally a French phrase, “to see to speak.” The process is designed to examine potential juror circumstances and biases that might impact their objective review of the facts in the case. Of course, no one can put aside all their biases, but voir dire hopes to expose the most blatant ones that might impact a defendant’s right to a fair trial.

A brief example: A defendant is accused of robbing a liquor store at gunpoint. Voir dire questioning likely would focus on the following lines of questioning: Have you ever been the victim of a violent crime? Do you own a liquor store? What are your opinions about guns?

Lawyers from each side (as well as the judge) are looking for answers that might bias a potential juror for or against the defendant. That dichotomy is the critical point: Because the legal system in the United States is an adversarial system, the defense lawyer will have an incentive to eliminate a juror from the pool who has been the victim of a violent crime as possibly biased against the defendant. The prosecuting attorney has the opposite incentive. In reality, the both attorneys tend to “strike” (eliminate) potential jurors on either end of the spectrum, leaving a group of people in the middle who are more likely to see the facts impartially.

Read that again carefully: The goal is not to select the correct juror (your adversary will want to eliminate that same person), your goal is to eliminate the wrong ones.

It’s a negative system. To be successful in a negative system, trial lawyers are good at asking negative questions. In fact, they’re some of the best at it.

No one wants to come out and say they feel the “sin” of a liquor store attracts trouble, and that the owners “got what was coming to them.” Could you imagine a positively-worded question being effective? What’s your opinion of liquor stores? That question wastes time – time the trial lawyer may not have. The jury pool might have 15-20 (or more) potential jurors, all of whom need to be questioned.

Instead, good trial lawyers go straight at the crux of the issue. Instead of What’s your opinion of liquor stores? they are more likely to ask, Do you feel that the liquor stores shouldn’t exist? The first question will require multiple follow up questions. The second question will deliver a useful answer immediately.

Trial lawyers don’t have the luxury of wandering lines of questioning nor to over-sensitivity to your feelings. When their questioning fails to uncover hidden bias, their client goes to jail. Or worse.


If this all seems a little abstract or outside the realm of your everyday experience, let’s try a more concrete example. Anyone who’s ever taken a customer satisfaction survey will recognize the following problem.

Below is a data set of results common in a “Net Promoter Score” survey – the answer to the question, Would you recommend a product or service to a friend or colleague? on a 0 to 10 scale.


10, 9, 9, 9, 10, 2, 3, 8, 7, 7, 8, 8, 10, 9, 8, 2, 10, 9, 8, 7

The average of this small data set (n=20) is 7.7


What comes next is a strategic question: Is it better to focus on respondents scoring 7 and 8 (to increase their scores to 9), or is it better to remove the very low results from the dataset? The answer isn’t immediately obvious. If we count the 7s and 8s, we have eight opportunities for modest improvement. There are only three very-low results (2s and 3s). It seems like we should focus on improving the experience of the respondents who provided “almost there” ratings.


Simple math gives the counterintuitive answer:


Positive strategy: Work to improve all of the 7s and 8s to 9s, all else equal.

New average (assuming success): 8.2


Negative strategy: Remove the 2s and 3s, all else equal.

New average (almost certain success): 8.6


In other words, we would do better to focus on removing the negative responses. Why is that? In the first case, we had to improve eight average scores by hoping to understand the preferences of many more people. In the second case, we need only focus on three negative responses – why those ratings were low, and correct the problems; or failing that, choose not to continue serving those customers.

You may be tempted to think that focusing on middling results somehow “focuses on your strengths” and that more satisfied customers somehow are “more profitable” than less satisfied ones. In the first case, focusing on strengths only works effectively for respondents who score 9 or 10. In the second case, notions of satisfaction are often poorly correlated with profitability.

I think you now understand why.

The math shows us what trial lawyers already know: The middle is messy. Focus on the edges, especially the worst edge. Be negative.


Okay, enough with the storytelling and abstract reasoning. Let’s put negative questions to work. If all that seems a little harsh for everyday conversation, don’t worry, I’ll teach you how to tone it down a notch.

I’ve hunted down some of the more uninformative questions I’ve found in several professional and personal surveys.

Let’s use our newfound skills to fix them, shall we?


Customer Satisfaction:

Positive question: What did you like best about your most recent visit to our restaurant?

Why it won’t work: Even when you provide a list of options, or ask people to rank them, you’re likely to see a muddy middle of preferences without any clear sense of the emotional attachment to those preferences. If the purpose of the survey is to prompt changes, what action will you take on these results? Likely, nothing. You’re doing well, why change anything?

Negative question: What was the worst thing about your most recent visit to our restaurant?

Why it works: Even if people liked their experience overall, everyone has something that bothered them. Maybe the table was too close to the door, the bathroom was cold, or the water tasted funny. You wouldn’t learn that from a positive preference question. However, the answer to the negative question provides clear direction: Move the table. Install a heater in the bathroom. Filter the water.


Human Resources:

Positive question: What work environment do you do best in?

Why it won’t work: You just walked by a soul-crushing “open office” floor plan, what do you think people are going to say? Exactly.

Negative question: What work environment have you learned will absolutely not work for you?

Why it works: People are adaptable creatures. Depending on the work tasks and corporate culture, plenty of work environments could be manageable. What you really want to know are the few situations that the candidate will find unacceptable. The answer to that question will eliminate a candidate from consideration before either party invests too much in the process.



Positive question: What candidate do you prefer in the upcoming primary election?

Why it won’t work: Seeing a pattern yet? The obvious follow up questions that try to gauge strength of commitment and likelihood to vote inevitably begin to add layers of assumptions onto layers of assumptions. No amount of fancy statistics will get people excited about a dull candidate in a crowded field of similar contenders.

Negative question: What candidate will you absolutely not support in the upcoming primary election?

Why it works: In a general election, strong negative opinions could be used to your advantage, but in a primary election (designed to winnow down a large group to a smaller one) the middling candidate that emerges usually isn’t the strongest. You learn more about voter preferences by examining what they dislike. Negative emotions motivate action (aka voting) more strongly than positive ones.



Positive question: What do you want in a partner?

Why it won’t work: Relationships are more complicated and situation-dependent than we like to admit. I’ve seen dating apps that try to use “regression analysis” to determine the unique combination of preferences that best “match” you with an ideal partner. In reality, their algorithms often aren’t much better than random guessing, for reasons you now understand.

Negative question: What is unacceptable to you in a partner?

Why it works: Is smoking off the table? How about a person of a different faith? How about anyone under 6-feet tall? Instead of preferences, define a (narrow) set of knock-out criteria. The resulting matches are still random guesses, but now you can focus on how well you connect with that person without the expectation that they share your preferences.


Positive questions are wonderful social lubricant, but that’s often all they are. You keep the conversation going, you learn what you already knew, or worse, you think you’ve learned something useful.

Negative questions are incisive and instructive. Handled correctly and politely, they also can make fun dinner conversation. But more to the point, they sharpen conversations, they expose what people may have kept hidden, and they point to clear actions.

Using them will take some practice, but if you want better answers, there is no “worse” way to learn.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Long Form Articles Marketing Ethics Rehumanizing Consumerism

Messing with data: The 10-step subversive instruction manual to hit the tech companies where it (really) hurts.

An apology means something to me.

You can find plenty of advice on how to say you’re sorry, but none I’ve found match the simple wisdom of Theresa Britz, the fiercely-lovable science teacher at my Catholic grade school. To her, an apology meant nothing without a commitment to changing your actions in the future. Without change, “I’m sorry” were simply words better off not said.

Britz used a more colorful phrase: “word vomit.”

Wow, I miss her.

I thought about her as I read a story on Slashgear about cameras installed on Delta and United Airlines entertainment systems. Of course, the cameras were “pre-installed” by the manufacturer to serve possible “future functions.” But don’t worry, they won’t use them to watch you in your seats without your knowledge. They’re “sorry” if you are bothered by this, but they have no plans to remove the cameras.

Delta and United Airlines vomited words. They mean nothing.

I thought about her as I read a story on CNET about a microphone Google installed in a version of its Nest Secure home security system without informing customers. I come from a product development background. One of the ways you save money is to purchase circuit boards (in bulk) with pre-installed components. You may want to use those functions later, and it’s easier to update software than hardware. But don’t worry, Google won’t turn on that microphone without your consent in an attempt to compete with Amazon’s Alexa or SimpliSafe’s security system. They’re “sorry” if you are bothered by this, but they have no plans to remove the microphones.

Google vomited words. They mean nothing.

I thought about her as I read a story in Mashable and the Wall Street Journal about how the mobile app Flo shared information with Facebook about women’s menstrual cycles. As every woman in my life has confirmed (I had to ask), “When was your last period?” is one of the first questions pre-menopausal women get every time they visit the doctor’s office. This app was a simple way to keep accurate health records. But don’t worry, Flo promises not to share information (any longer) with Facebook. They’re “sorry” if you are bothered by this, but they have no plans to compensate users nor to insist Facebook expunge that data.

Flo vomited words. They mean nothing.

As individuals, we are statistically insignificant data points to the tech companies, something less than human beings – at best, a source of cash to buy the next gadget or subscribe to the next subscription service. We’re irritating ants who should just do our jobs.

When you think of it that way, why should tech companies be truly sorry? To paraphrase the trickster god Loki, the boot does not apologize to the ant.

It’s about time we flipped the script.


To paraphrase the West African proverb: If you think you are too small to make a difference, try staying calm with a fire ant in your underpants.

To understand why you can be the proverbial fire ant in Mark Zuckerberg’s briefs, we need to acknowledge two critical realities of the modern data technological complex:

  1. Data is more valuable to a technology company than gold to an alchemist, more valuable than oil to an army, and more valuable than liquidity to a stock broker. Without data, the tech business model implodes.
  2. That data comes from you, as individuals, and you have a choice to provide it or not. Sharing your data may not seem like your choice, but it is. Modern life may be more difficult without Facebook, Google, Amazon, and Apple, but it is (very) possible.

If it doesn’t seem like consumers have their finger on the scales of the balance of power, it’s only because we have short memories. Let’s refresh them, shall we? In the beginning of the Silicon Valley revolution, technology companies begged for your loyalty by churning out ever-more-impressive products and services – the search engine, ecommerce, smartphones, and social networks. As time passed and adoption of these products peaked, Silicon Valley confused their ubiquitous use with a shift in the balance of power. This is a misinterpretation of the situation: Search engines, ecommerce sites, next-generation smartphones, and always-on social networks aren’t innovations any longer. They are commodities.

(And they know it.)

To keep the ants from biting, Silicon Valley has resorted to psychological tricks: Lengthy terms and conditions on tiny mobile screens, asking you to accept this choice under time pressure (downloading a ticket app as you’re walking into a theater), defaulting information sharing to “on” with the ability to change it buried in confusing menus of settings, obscuring the identity of “marketing partners” to hide the ways they sell your data to make more money, and turning lame announcements of iterative products into baited-breath rock star parties.

The party is over.

When Toyota builds a car with a defect, they apologize and issue a recall to fix it. When Facebook shares your private data with advertisers, it’s your fault for not checking the correct box. How long will it be before smaller tech companies, medical and health companies, and even your local grocery store sees what’s happening and thinks they can get away with victim blaming too?

Well, fuck that. I’ve had enough of cleaning up word vomit.

It’s time us ants remembered we can bite.


Biting back, aka Data Subversion, takes three distinct forms.

  1. Data denial: Refusing to provide any data not explicitly required to use a product or service.
  2. Data damning: Posting (and amplifying) discoveries of data misuse by mentioning not only specific companies, but also specific management
  3. Data distortion: Having fun with any data that is purely preferential or optional.

(Note: The last term also carries a specific technical definition in data science. If you have trouble with the difference, simply think of this last one as “having fun with data.”)

How do these three techniques impact data privacy offenders?

First, data denial shrinks the size and scope of their consumer/user database. Smaller, less-rich databases are less valuable and cannot easily be monetized by selling access to third parties, nor are they as rich a source of future product development.

Second, data damning exposes what many companies would prefer to keep private, causing public shame for individual management team members and redirecting corporate resources to address the issue. (For our purposes, management includes only those with a fiduciary corporate responsibility – officers of the company – not your everyday line manager. Sorry CXOs, with a great paycheck comes great accountability.)

Third, data distortion degrades the quality of the resulting database by introducing an overabundance of “outliers” and bogus preference data. If management isn’t observant enough (or simply lazy), they’ll use these oddball datasets and make poor decisions.



Before we proceed, the lawyers have advised me to issue a few ground rules and perfunctory statements. Put simply, when people read “subversion,” they interpret it to mean sabotage, theft, and destruction. That’s not what we’re talking about. This of this instead as non-violent resistance.

But to be more specific, here’s a list of techniques I consider completely off the table: hacking (either the black hat or white hat variety), spoofing (pretending to be someone else), providing false required data, phishing, doxing, stalking, harassment, or anything else explicitly illegal where you live.

Additionally, depending on your role or profession, you may be subject to additional constraints. Those people include, but are not limited to: fiduciaries, caregivers, physicians, lawyers, peace officers, elected/non-elected officials, as well as those subject to employee codes of conduct and other contractual agreements.

This list isn’t meant to cover everything. If you feel uncomfortable with anything on the list you’re about to read, don’t do it. That’s what personal freedom means. You choose.


Okay, done reading the disclaimers? Are you sure? Good. Let’s get specific.


1. Defeating the surveillance state for less than a penny.

Cameras have become ubiquitous … and quite good. Your average smart phone has a better camera than most full-bodied cameras built before 2000. In fact, their ubiquity has dropped the price to the point they can be installed just about anywhere – in phones, yes, but also laptop computers, DIY security systems, cars, street corners, airplane seats, and even light bulbs.

Defeating one is as easy as the humble sticky note. The 3M brand seems to last a little longer (better adhesive), but even the generic brand will do. My preference is the smaller 2 x 1.5 in size because they can cover the camera on my laptop without too much overhang. Yes, you can buy camera covers specifically designed for this purpose, as well as special “gadgets” designed to cover and uncover your camera on your smartphone, but don’t overpay.

I did the math: You can buy 1,200 yellow 3M notes for $9.19. That’s $0.008 per note, or less than a penny. Cameras might have gotten cheaper, but they’re not less than a penny.


2. Always keep a Yahoo email address.

How many times are you asked to provide an email address to get access to a special deal, use a coupon, or download an app? How many times have you done that only to find your important (or work) email box filled with spam and other advertising? It’s not just from the original offer, many companies sell your email address to other “marketing partners” as soon as possible.

To defeat them, I maintain a email address. After a few days, it’s completely unusable. My mailbox must have hundreds of thousands of messages by now (though I believe Yahoo purges them after a while). Never conduct any important business or sensitive transactions using this email address – it’s the most likely to get hacked or compromised – but it’s perfect for taking advantage of special offers without divulging anything more than necessary.

Bonus! Create a fun username that you can repeat (loudly) when asked at a store. I suggest something like “IKNOWYOUREGOINGTOSPAMME at YAHOO dot COM”.


3. Do your part to encourage humorous new product development

Perhaps the second-most common request from the tech companies (and pretty much every company these days) is the “quick” survey. What did you think of your last visit to Starbucks? How was your last Uber ride? Would you recommend Fidelity Investments to your friend or colleague?


I’m irritated, and I’m in marketing.

But don’t despair! You can have some fun. Remember, these are preference surveys, which means you can decide what you prefer to share. A suggestion: Imagine if you were answering the survey as your favorite Disney character. What would Mr. Incredible think of that last Uber ride? Have fun!


4. Make ‘em pay.

You may have noticed that when you search for a brand or a company on your favorite search engine, you often get two results. The first (usually at the top, as in this example) shows the icon for “Ad” next to the link. The one below it does not. There’s a difference. The company – Williams Sonoma in this case – paid Google to advertise its brand at the top of the screen. But Williams Sonoma is so well-known that its name would appear near the top regardless. Why would they pay? The marketing team is taking no chances.

If you click on the link marked “Ad,” Williams Sonoma will need to pay for that. There is no set amount (it’s a complex auction formula), but it can be anywhere from $0.50 to $10.00 depending on the keyword.

Here’s the question: Do you think the brand is taking you seriously as a customer? Awesome. Scroll down and click on the “organic” link. Not taking you seriously? Not respecting your privacy? Sending you spam? Click on the ad and imagine a cash register sound in your head.

(A note: Repeatedly doing this, or worse, using some software bot to do it, is called click fraud. Not cool, and also a violation of Google’s terms of service. Don’t do that. Have fun with your individual act of resistance.)


5. Buy your phone (and your laptop) a condom.

Using a modern smartphone is a lot like unprotected sex in the 1970s – lots of interesting partners, but plenty of unintended side-effects. (I’ll let you use your imagination.) Today’s smartphones can track all kinds of location data as well as listen for voices, turn on their cameras, and track biometrics. You consented to all that, right? Sure, you did.

Let’s use another example: Have you ever left your laptop locked in the trunk of your car – not powered down, but “asleep,” while you go about your business? With a simple device (no, I won’t link to it) thieves can scan car trunks and discover a laptop hiding inside. A professional can be in and out of your vehicle in under 60 seconds.

Preventing either scenario requires learning a teensy bit of physics. In order for most of these functions to work, your phone, your laptop, as well as thieves’ scanning devices require the unimpeded progress of electromagnetic radiation – not the kind that hurts you, of course – think “WiFi” signals. A so-called Faraday case will block any wireless signal coming in or out of your device. In other words, from an electronics perspective, it is invisible.


6. Channel James Veitch in your next chat session.

James Veitch is famous for replying to spam email. His videos and TED Talks are, without question, some of the most hysterical examples of modern comedy I’ve ever seen.

Who’s James Veitch? Okay, stop right here. Watch this video. Come back in a minute.

Now, I think you’ll know where I’m going with this. The next time you’re irritated with a company not respecting your intelligence and your privacy – and you see a “live chat” link – I think you’ll know what to do.

Bonus! Try chatting as your favorite literary character. Can you hold an entire chat as Jean Valjean? How about chatting only in Haiku? Ever dreamed of being Batman? Wonder Woman? Go nuts.


7. Give nosey apps more than they bargained for.

Apps always want more information. I used to use MyFitnessPal to track my basic nutrition, exercise routine, and weight. Then Under Armor bought the app, and immediately began asking for more data at every turn, connecting with its database to compare my progress to others, and (of course) sending a never-ending torrent of ads for Under Armor gear to “improve” my workouts.

These days, I use it only to track weight. Oh, and some other stuff. You see, when I imagine myself as Batman, I imagine the workout routines I must do to fight criminals with my bare hands. (I think I’d do way better than Ben Affleck, but I suspect not as well as Adam West.)

I also know a guy who lets his cat “weigh herself” on his internet-connected scale. The resulting graph is hysterical.

Bonus! Many apps – especially the less popular ones – have “offline” versions. Try those instead. You may discover they are almost as good for fewer privacy invasions and marketing partners.


8. Quack Quack Start!

Did you know that search companies (and your internet service provider, in some cases) can track you even in “Incognito” mode on your browser? Most people don’t, but it makes sense. Without information to sell advertisers, there is no one to pay for the work it takes to build and maintain a functioning search engine. In fact, Google is so good at it that advertising drives more than 80 percent of all corporate revenue (much more if you only count “search”). In other words, search engines are advertising engines. Nothing more. That you get some tangential benefit isn’t the point; it’s only needs to be good enough to keep you addicted.

But remember, search technology has commoditized. Privacy-first browser DuckDuckGo strips away the advertising part of the business model to create a more seamless search experience. They’re banking on being ahead of the curve as more of us start to take control of our data. You may want to give them a try.


9. Learn how to tag executives on LinkedIn.

Do you only check your account on LinkedIn when you need to find a new job? You may want to reconsider. Many corporate executives hang out there, keep their information updated, and respond to requests. In other words, if you want to send the Chief Marketing Officer of Nike a message, LinkedIn is the place to do it.

Be warned. Here’s where it gets personal.

Do you have an issue with something a company has done? Ready to post it on Twitter? Consider using LinkedIn instead. Email gets filtered (if you can find the correct email). Twitter is a dumpster fire, and most (smart) executives use someone in their communications department to manage their Twitter feed to avoid saying something, uh, damaging to the company. But LinkedIn is different. It’s a professional resume and reputation management tool.

Mad about the Flo app sharing your data with Facebook?

Well, here’s Founder and President Yuri Gurski’s public LinkedIn profile. You can send him a message (if you use the Sales Navigator tool), or better yet, write a post with your opinion and “tag” him in it using the @ symbol. LinkedIn will fill in the rest of the data and he’ll be notified. All his connections may see that tag as well. One post is enough. (More than that could be harassment, and you’ll likely be blocked.) Use this one wisely. You’re not hidden on LinkedIn either. Be respectful and ask good questions. Perhaps, something like this:

@ExecutiveName, I learned your app shares private health information with Facebook and other advertisers. I appreciate using the product, but what assurance can you give me that my privacy is protected under HIPAA regulations?

You get the idea.


10. Alexa Cat™ – Amazon, here’s a new product idea. You’re welcome.

Your Amazon device does not have the computing power, nor access to all of the information it needs, to function without an internet connection. It has just enough on-board processing power to listen for your voice, recognize its trigger word, and send that information back to a set of central servers to respond and complete your request. It can do all sorts of things – tell you the weather, play music, answer simple questions, and connect with all sorts of other third-party services.

Each time it does that, it creates a richer and richer database about you and people like you.

But like most appliances in your home, they’re not used most of the time. And if you’re like most people, you have a bored and lonely dog or cat waiting around at home for you to return from work. What if you could entertain your pet and have fun degrading Amazon’s database at the same time?

I have the solution for you! Create a recording of yourself making random requests – for the weather in Katmandu, playing Beethoven’s 9th symphony, answering questions about the highest mountain ranges in the world – anything you can think of. Set it to play loudly enough to ask Alexa to answer these nonsense questions while you’re away at work.

(A warning: Test it out first with you in the home. You want to make sure your dog doesn’t go berserk, or that you’re not inadvertently making purchases while you’re away, but assuming everything checks out…you can have loads of fun, and your pets will be happier.)

Bonus! Set up a camera and see what your cat or dog does as two robots (your recorded voice and Alexa’s servers) talk nonsense to each other. Be creative. You might just become a YouTube star!


It’s fair to ask if the actions of just one person will really hurt the tech companies in any meaningful way. In other words, if you keep your Amazon Echo busy all day while you’re at work with random requests to entertain your cat, will that really make a difference?

Yes. More than you think.

First, data denial practices get you in the habit of being conscious about your choices and make you aware of the value of your privacy. Second, data damning raises the issue for others who haven’t yet considered their own privacy. Third, data distortion is just good fun. We could all use a little more fun.

Personally, I have a bit of a mischievous and contrarian personality. I find it funny to entertain a cat with nonsense Amazon requests, but I recognize that for some of you, all this might sound a little icky.


Fighting back isn’t for the timid.

Perhaps you’ll be fortunate to have your government come to the rescue with GDPR-style legislation. Perhaps that legislation will be able to keep up with the pace of technological change. Perhaps new consumer-first and privacy-centric technologies will begin to take root among more than just a few activists.

I’m hopeful on all these fronts, but I’m not holding my breath. And I’m not waiting. Neither should you.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.


Long Form Articles Rehumanizing Consumerism

Science vs. scientism, keto vs. low-fat diets, and why you shouldn’t drink a teenager’s blood.

To paraphrase both Albert Einstein and Don Henley, the more data we collect, the less we seem to understand it.

Case in point: Do we really need the U.S. FDA to warn older people not to infuse themselves with youthful blood plasma in some vain attempt to slow the aging process? Really?

Apparently, yes. Yes, we do.

Let’s imagine the conversation between FDA Commissioner Scott Gottlieb and Head of Biologics Peter Marks ahead of issuing their public statement about a Silicon Valley startup doing just such a thing:

“You have to be fucking kidding me, Scott! I thought the sparkly vampire trend was over.”

“It’s not like this was the first time someone’s tried this, Peter. We can go back hundreds of years ­– thousands, even. Trying to cure aging with youthful ‘serum’ has a long history of quackery. Same quacks. Different day.”

“But seriously. $8,000 for one liter of plasma! And the blood comes from 16 to 25-year old people. What parent in their right mind is giving consent for the 16 and 17-year-old children?”

“Not any parent I’d like to meet.”

“And I can’t imagine these kids are earning anywhere close to $8,000 for their efforts.”

Scott opens his web browser and clicks on Ambrosia website.

“Hey, Peter. I’m looking at the website, it’s $12,000 for two liters. That’s 50 percent off the second IV bag! What a deal.”

“This isn’t funny, Scott.”

“I know, I know. But you have to admit, warning people about things like this is sort of like a sad game of Mad Libs. Swap out the word ‘plasma’ and we could just as well be talking about any number of quack treatments.”

“Ugh. Okay, let’s write the press release.”


Of course, I’m just imagining what that conversation might have sounded like, but I’ll bet I’m close. Here’s an excerpt from the actual statement:

“There is no proven clinical benefit of infusion of plasma from young donors to cure, mitigate, treat or prevent these conditions, and there are risks associated with the use of any plasma product.”

Go ahead and read the full statement if you like; it’s one of the most entertaining (and depressing) government publications you’ll ever read.

One more thing: Unfortunately, I am not fictionalizing the price. The FDA pushed Ambrosia out of business last week, so you won’t see much on their website if you visit them today … but that’s why we have the Wayback Machine. Sadly, $8,000 and $12,000 for one and two liters of youthful blood plasma were indeed the published rates.

In Ambrosia’s case, it seems that experiments in mice provided the inspiration for the therapy, as well as the resulting company to commercialize that therapy. Make note of the word choice: Inspiration. Inspiration, in and of itself, is not the problem. The problem is that inspiration is not a clinical trial. An idea, no matter how compelling, is not a fact.

The real question we need to ask ourselves is: Why we keep falling for things like this?

Our first instinct is to blame the easy targets – investors such as billionaire Peter Thiel – for enabling these startups with access to capital, media attention, and perceived legitimacy. But that’s a bit of a red herring. I can’t speak for Thiel’s motivations, but it’s not hard to understand the appeal: Investors are interested in the $8,000 per liter price tag. Even if there are only 10,000 people in the world who can afford a “treatment” regimen of, say, 12 infusions, that’s a billion-dollar market.

An investor is like a dog with a bone. Don’t blame the dog. We need to quit throwing him bones.

No, the issue is deeper than that. At the heart of the matter is our own misunderstanding of data, evidence, and the scientific method. Or put simply: We confuse science with scientism.


“Do whatever it takes to avoid fooling yourself into thinking something is true that is not, or that something is not true that is.”

– Neil deGrasse Tyson

Before we tease apart the difference between science and scientism, we need a refresher on the scientific method. Don’t worry, we won’t go all the way back to your 10th grade biology class, and you won’t need to dissect a fetal pig. (#nightmares)

At its core, the scientific method is a thought process – a logical progression from idea to repeatable process – in five steps.

  1. Start with a question or an observation. For example, younger people seem healthier than older people. Why is that? Why do people become less healthy as they age? Do they need to? You’ll notice this is not a conclusion, but rather it is a good question. And yes, sometimes we know things work before the scientific method tells us why they work. We’ll get to that with Ketogenic diets in the next section. Stay tuned.
  2. Form a hypothesis. That’s just a fancy name for an educated guess. What makes it different than a guess is that it can be falsified with evidence. Astrology isn’t science because you cannot disprove that it wasn’t an “alignment of Jupiter” that healed your heartburn. To follow our example, researchers could notice that older mice respond to infusions of plasma from younger mice, and that the same benefit might be measured in humans.
  3. Make a prediction. There’s an important reason you write this down – quantitatively and specifically – before you move on to step 4. Not only does this help you design a proper experiment, but it also helps to eliminate the human tendency to reframe your initial hypothesis to fit your future observations (aka hindsight bias). In our example of blood plasma infusions, we need to define precisely what effect we will measure. Is it better cardiovascular performance? Some measure of blood chemistry? Reduction of observable wrinkles in skin? What, exactly, does “better” mean?
  4. Test your prediction with controlled experiments. The gold standard in science is the double-blind, randomized controlled trial (DB-RCT). That means you have a test group and a control group. Participants are randomly assigned to the groups and not even the researchers know who is in which group (only a database number identifies them). This high standard isn’t always possible, and that’s often where we run into problems. Are there enough participants? Do they change their behavior because they are being observed? Are they truly following the protocol? Health, diet, and exercise studies in humans are rife with these sorts of issues. In our example, one group could get blood plasma from a “young” donor, another group from an “age-matched” donor, a third group could get a dyed saline solution, and a fourth group (a control), would get nothing. It’s expensive, time-consuming, and difficult – in other words, far more complex than I’m letting on.
  5. Analyze the data. Only statistical analysis can tell us if the results of a well-designed experiment are truly the result of the treatment and if those results are more than you would expect from random chance. This analysis still may not tell you if there was no other possible explanation for the findings, or if the effects will hold up over time, or for different races, cultures, genders, or lifestyles of people. I think you can see for yourself how complex a study of “youthful blood serum” might be.

In addition to those five basic steps, serious scientists carefully document their work so that others can attempt to replicate it. If they can’t, they may have simply gotten lucky. Non-corporate (and even some corporate) researchers submit their research findings for external peer review – a rigorous, if imperfect, process of allowing others to critique their work.

Yes, this is how the scientific method is supposed to work. And no, it doesn’t always work that way. But it is one of the best ways we have to understand our world at a deeper level.

By now, it should be obvious why the FDA took the action it did. Ambrosia skipped over the grueling work in the middle of the scientific method, jumping from “idea” to “marketing” that idea.

I like to call this shortened process “scientism.”


I hope you’ve noticed I am not a scientist. I learned the scientific method in high school and college as many of you did.

I am an advertiser. I learned the scientism method because it is a more effective method of mass persuasion. I am a fan of the former, but I am an expert in recognizing the latter. Let me tell you something you already know: Compared with scientism, true science doesn’t stand a chance in the court of public opinion.

I think it’s about time that changed.

I’m going to teach you the secrets to distinguish between the two, immunizing yourself from the persuasive effects of advertising and marketing.

The Top 10 Differences Between Science and Scientism

  1. Science uses many data points and hundreds of subjects – the more, the better. Scientism uses a handful of data points and limited numbers of subjects – whatever proves to be the minimum necessary to persuade you.
  2. Science draws narrow and specific conclusions based on large volumes of data. Scientism draws broad and general conclusions based on limited data.
  3. Science examines trends in that data over long periods of time before it accepts a conclusion as “true.” Scientism draws conclusions (and uses its findings) as quickly as possible.
  4. Science attempts to determine causation (treatment X caused effect Y). Scientism is quick to accept correlation alone (treatment X happened at the same time as effect Y, or X happened just before Y).
  5. Science draws tight lines of evidence between links in a logical chain. Scientism is comfortable with “reasonable-sounding” rhetorical leaps.
  6. Science always wants to use the most rigorous and controlled study designs. Scientism is comfortable drawing conclusions solely based on “observational” study designs.
  7. Science is comfortable with incomplete explanations and telling you what it doesn’t (yet) know. Scientism works best with tidy narratives.
  8. Data, as seen by science, is often messy and incomplete. In Scientism, data are either “smooth curves” or discarded altogether in favor of compelling personal anecdotes.
  9. The quality of science is based on its ability to predict future events. Scientism is always “right” because it focuses on retrospective examinations of past events.
  10. True science is often complicated and boring. Scientism gets people excited.

Perhaps the biggest difference is that scientism is so much easier, faster, and cheaper … and that is looks like science at first glance. But scientism is a façade, a mirage, and an illusion. Now that you know the difference, you can begin to spot scientism everywhere you look.


Second case in point: Ketogenic diets.

There’s one problem with the scientific method – a flaw that practitioners of scientism use to their advantage: Knowledge doesn’t always flow from idea to repeatable process. In fact, most of the time, we notice something works before we realize why it works. Humans are a practical species. If it works, we don’t wait for a scientist to tell us why it works. We use it now.

A hundred years ago, physicians began to notice that Ketogenic diets seemed to help patients suffering from brain seizures. (To oversimplify, a Ketogenic diet dramatically reduces carbohydrates in favor of fats and proteins, forcing the body into the biochemical process of ketosis, where it burns fats instead of sugars for energy.)

Clinicians didn’t have a good idea why they worked (although they had some educated guesses), but in the face of uncontrollable suffering, anything that seemed to demonstrate relief became something to try.

More recently, dietitians and clinicians frustrated with the lack of success of so-called “low fat” diets began to experiment with other alternatives. They knew about Ketogenic diets, began to try them with patients, and saw positive results.

Those successes formed the inspiration to start the process of the scientific method.

What have researchers found so far?

In the early days of research, many studies focused on small groups of people, for short periods of time, with poor experimental controls. (Humans are notoriously fussy research subjects.) But over time, a wide array of studies confirmed statistically-significant weight loss benefits as a result of Ketogenic diets.

You can read consumer-friendly summaries of this research at the National Institutes of Health, and the Harvard Medical School.

Here’s the issue: These studies also find that other diets also can be effective – for example, vegan diets, Mediterranean diets, and simple calorie restriction. They also are careful to point out that we do not know how these diets compare over long periods of time (most of the studies track patients for about one year), how they compare with people from different races or cultures, or how they compare with individual genetic/gut-biome profiles. Other well-designed studies point to a “Goldilocks’ zone” of appropriate carbohydrate, fat, and protein intake – too much of any nutrient in proportion to another seems to be linked with early mortality. Also, while seizure benefits of Ketogenic diets have been shown in mice, human trials are as yet inconclusive. (Mice are less difficult research subjects. They do what they’re told and eat what you feed them. Humans don’t.)

It is in this uncertainty that scientism flourishes.

Related case in point: The media coverage of Ketogenic diets.

The University of California San Francisco’s article “explaining” what we know and don’t know about Ketogenic diets is an example of what you might find as you try to research this diet for yourself. There are no links to actual research. The entire article spans a mere 697 words, about 10% of those words devoted to this prime example of scientism:

“For instance, Weiss himself has been on a low-carb high-fat (though not strictly ketogenic) diet for more than six months, and claims he does feel much better. But he’s clear about what he knows and what he doesn’t. He’s lost weight and his borderline pre-diabetes is gone.”

“I think I feel great,” he said. But that might be because he’s eating less processed food, sleeping better, or enjoying compliments on his new physique.”

I don’t blame the scientists for the sloppy article; the UCSF news center is simply giving the public what it wants. Most publication editors simply don’t allow for complexity, because nuanced arguments don’t get clicks. But think about it for a moment: This is an article from a university written with actual scientists – what is the average person supposed to conclude?

I also know several people who claim success with Ketogenic diets. Their successes are inspirational and deeply moving. But personal anecdotes are not science. Their successes may not translate to my success nor to your success. Worse, following their experience could be dangerous depending on your specific health situation.

I also have heard the story about how dairy farmers pissed off President Johnson in the 1960s by refusing to back him. He politically retaliated by encouraging a spurious link between eggs and cholesterol – as well as the first so-called “food pyramid” (with his grain farmer supporter products at the base). This episode helps explain the “high-carb, low-fat” diet trend that captivated public opinion for 50 years. But conspiracy and political revenge don’t prove Ketogenic diets work. They simply mean Johnson was a liar and an asshole.

If there is that much confusion coming from universities, your friends and neighbors, and even the Surgeon General of the United States, what do you think an advertiser is going to tell you?

You already know the answer.

I’ll bet you didn’t need a formal market research study to see the rapid growth in Ketogenic products. A new product (or an existing relabeled product) seems to reach the market almost every day, many promoted by so-called “influencers” who stand to benefit when you add the product to your shopping cart.

It was the same pattern in product marketing we saw with low-fat, Mediterranean, Atkins, and dozens of other diets.

They’re not interested in your health. They’re interested in cashing in.


To this point, we’ve focused on the culture of scientism rampant in health and diet advice. However, I suspect you’ve been screaming into your keyboard about all of the other places you see the same thing:

  • The positive (or negative) impacts of corporate mergers and acquisitions
  • Financial and stock market advice
  • Student graduation rates and education policy
  • The causes (and “solutions”) to poverty
  • Discussions about “Big Data” (home to scientism’s cousin “data-ism”)
  • Similar discussions about “Artificial Intelligence,” “Blockchain,” and the “Internet of Things”
  • New cryptocurrencies and ICOs (Initial Coin Offerings)
  • Just about anything your politicians are debating this week
  • Most of social media

Anyplace you find complexity, you will find scientism.

I am not an expert in drinking blood, Ketogenic diets, nor all the other places complexity pops up. In all those things, I’m just as foolish as everyone else. But I am an expert in persuasion, which makes me an expert in scientism.

That’s the one area I’m qualified to give advice. Here goes:

  • When you read a popular article talking about a “scientific” issue, check to see how well it matches the definitions of “science” or “scientism” based on the handy reference guide in this article.
  • Better yet, don’t read popular articles about science at all – instead, use Google Scholar or some other search engine to find the actual study written by the actual scientist. Read the “abstract” of the article. You’ll likely discover the “headline” from the popular media doesn’t match the research.
  • Those search engines are also a handy place to see how many other scholars have cited this author – an imperfect, but telling, way to judge credibility in academic circles.
  • Read anything you find imagining yourself as a detective solving a crime. One the first questions they ask is Cui bono? (Who benefits?) In other words, who paid for the study? What group stands to gain? Who stands to lose? What is someone trying to sell you?
  • Related to that last one: Be suspicious of any product claim tied to a major trend (Keto or otherwise). The advertiser is using “halo effect” to trick your subconscious brain into positive associations between their product and a hot trend.

If all else fails, skip the popular press and read Science Daily, a surprisingly readable summary of actual scientific research. You also can find glimmers of excellent reporting in the smallest of news outlets: Susan Perry is one to watch.

In the end, should you try a Ketogenic diet? It seems like it’s safe, and it’s likely to be good for you, but it’s not the only choice. You may do just as well on many other diets … or simply being more conscious of what you eat. But remember, non-expert talking here, you should check with an actual doctor.

And should you drink blood of teenagers? I feel safe in saying no. Just, no.


If you liked what you read (or even if you didn’t, and simply want to argue with me),please consider signing up for my email list. There’s a link on the side of this page if you’re reading on a desktop, or at the end of the article if you’re on a mobile device.

Once you sign up, you’ll get a glimpse into my thought process, get a chance to interact with me directly, and even get a chance to tell me what you think I should write about next. You won’t hear from me more than once a week unless you want to.

What do you say?


Finally, a public service announcement. It should go without saying, but I’ll say it anyway: Please don’t comment on this article with a link to some product you’re selling. I’ll report it or delete it. I have zero tolerance for that.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Long Form Articles Rehumanizing Consumerism

The Silicon Valley plan for Homo sapiens: Domestication

Who can afford to live forever?

You’ll notice that’s a different question than “Do you want to live forever?” or “Is it possible to live forever?” Human mortality is the great equalizer. No matter their money, their influence, or their intellect, Jeff Bezos, Sergey Brin, Sheryl Sandberg, and Tim Cook will someday pass from the scene, just like the rest of us.

But what if they didn’t?

What if the dream of Ponce de Leon’s Fountain of Youth isn’t simply an old man’s fantasy, but rather a simple engineering problem – a solvable problem – with technology (nearly) available today? Forget the drama of selecting a new headquarters location, designing a better search engine algorithm, consolidating social networks, and inventing new smartphones, the giants of tech are using their intellect and capital to tackle a much richer prize.

The next killer app is killing death.

I’m not the first to explore the topic. Popular articles written in the New York Times and multiple works from Yuval Noah Harari cast an eye toward a future of near-immortality – or at the very least, a much less painful aging process. Mortal humans watch in wonder (and hope) as these geniuses attempt to eradicate bladder cancer and osteoporosis as we once eradicated polio and smallpox. We may bristle at the intrusions to our privacy that the tech companies demand as they enrich themselves … but to cure the cancer that took my father? I’m human. Given the choice, I would make that devil’s bargain.

The bargain assumes, of course, that the tech elite would give this elixir of life to the rest of us, or that they would give it to us for a price we could afford. More on that in a moment.

With that backdrop in mind, let’s begin a thought experiment. (Be warned. You won’t like where it takes you.)

  1. Assume a small percentage of these so-called “creating death” experiments work. In the Silicon Valley ecosystem, that may mean a success rate of only two or three for every hundred startups. However, with the sheer volume of investments in play, as many as two to three dozen age-defying therapies could reach the market within the next decade.
  2. Let’s also assume that (at least in the beginning) these therapies will be prohibitively expensive. They likely will require some combination of implantable pharmacology, bionic enhancements, and genetic tinkering. Only the top 1% of all people on the planet will be able to afford them.
  3. Let’s finally assume these therapies do not eliminate death altogether in the next decade or so, but rather that they extend lifespans to two-three times what they are today. That means this new class of people who can afford the therapies (barring serious or catastrophic accidents) could live for 150 to 225 years in good health.

None of these three pre-conditions is out of the realm of possibility. The question becomes: What does it all mean?

To this point, journalists and academics have argued about the answer to this question using two major competing narratives. The first is that the tech elite will use their resources and intellect for the benefit of all humankind, creating a post-mortal utopia giving birth a new golden age of humanity. Yes, extending life will be expensive at first – the wealthy often fund early-stage progress – but these innovations will “diffuse” into the society at large as prices come down due to market forces.

The second narrative is a dystopian future in which the tech elite hoard the power of this new innovation. Cheating death isn’t simply a next-generation smartphone, after all … a greater lifespan is the ultimate competitive advantage. It’s the great unequalizer in human existence. They’ll use a combination of surveillance, incentives, and punishments to enslave the rest of us in some sort of neo-Orwellian nightmare.

I cannot predict the future. These two narratives seem like reasonable organizations of the facts as they appear today. Either may turn out to be true, partially true, false, or partially false. In that spirit of uncertainty, I would like to propose a third narrative – one that also can explain many of the facts we see at play, and one that fits a narrative that has played out successfully in the past.

The third narrative is human domestication.

Consider this: What does a healthy 180-year old (with the physical and mental fitness of today’s 40-year old) need with the rest of us?

This scenario is not as far-fetched as it may seem. Average life expectancy in the United States is declining for the first time in over one hundred years not due to a major war. However, the notion of “average” hides the reality buried in the numbers. Life expectancy is not declining, it is bifurcating. In other words, the privileged are living longer. Everyone else is dying earlier. This split in society between haves and have nots is widening psychologically and culturally as well. Elites in any society always have had a disdain (at worst) or paternalistic (at best) view of lower classes. The tech elite already view human behaviors as “data points” to be “nudged” to achieve more favorable outcomes. Within a decade, they may also live twice as long as the rest of us. Do you think that difference will make them more or less considerate of your “human” rights?

I come from a family of advertisers, refugees, immigrants, and entrepreneurs. I don’t hold a flowery view of human nature.

Now, consider this: What if those same tech elites got the idea that humans are simply another type of animal – an animal that could be manipulated with enough data into behaving in a way that suited their interests – perhaps in the same way early Homo sapiens saw wolves, wildebeest, and wild rice two millennia ago? What if just like those animals and plants, Homo sapiens were simply imperfect candidates that could be molded into something more useful and desirable over time for the benefit of a superior species?


There is another word for that process: Domestication.

Let’s pause for a quick refresher on the process of domestication. We can all name the most popular domesticated plants and animals on the planet – dogs, cats, cattle, chickens, hogs, goats, sheep, corn, wheat, rice, and barley (plus a few others in different parts of the world). What’s important to understand is that most species cannot be domesticated (humans have tried, and they have failed with several). Also, domestication is not the same as “taming.” A tame elephant in the zoo is not domesticated. The herd of cattle in the farm down the road is.

Melinda A. Zeder, Ph.D., curator emeritus at the Smithsonian Institution in Washington DC, highlights multiple conditions a species must meet in order to be a candidate for domestication. Those conditions include:

  1. Efficient Diet
  2. Quick Growth Rate
  3. Ability to Breed in Captivity
  4. Pleasant Disposition
  5. Tendency Not to Panic
  6. Social Structure

To be clear, Dr. Zeder did not consider Homo sapiens as a potential candidate for domestication. But this is my thought experiment, and I’m going to do just that. Let’s use these six criteria from the perspective of an enhanced, longer-lived new species of human: Homo technorati. If Homo sapiens are indeed a candidate, what evidence might we have that the process of domestication has already begun?

Criteria #1: Efficient Diet

Animals that eat plants are less expensive to keep in captivity. Carnivores are expensive – just ask any zookeeper. Domestication, ultimately, is an economic decision.

Many humans are moving in the direction of a plant-based diet for a variety of reasons – environmental protections, health benefits, and sustainable/local food chains. From 2014 to 2017, vegans grew from 1% to 6% of the US population. That doesn’t count people who consume less meat (a trend that also is increasing). But “diet” doesn’t mean simply “food” for Homo sapiens.

Let’s expand this narrow definition of “diet.” Homo sapiens require mental stimulation as well as physical sustenance. What’s the technical equivalent of a plant-based diet? Social media. Instead of the messy process of creating something new, Homo technorati have trained us to “farm” our own entertainment much like a farmer might tend a field. “Carnivorous” intellectual exercises (building or creating something utterly new) are rare. Plenty of people drink beer; very few brew it.

Criteria #2: Quick Growth Rate

The best domesticated animals grow quickly to maturity, ideally spawning multiple generations within a human lifespan. For example, a domesticated chicken will go from egg to dinner table in 8 to 12 weeks. The objective of much of domesticated animal husbandry is to reduce this time as much as practical.

When all humans live about the same number of years, the difference in “growth rate” isn’t relevant. But when Homo technorati begin to experience two to four generations of their Homo sapiens precursors in one lifetime, that changes the game. The more the age gap widens, the more chances Homo technorati will have to experiment.

Is this happening already? How many tech giants are sponsoring STEM programs in early childhood education? How many have started to hire for “technical skills” instead of “critical thinking” and liberal arts? Homo technorati only needs a small group of critical thinkers. By training technical skills at an earlier age, Homo technorati accelerates the time between birth and “usefulness” – much like a breeder reducing the time between “hatchling” and “egg layer.”

Criteria #3: Ability to Breed in Captivity

If you cannot get an animal (or plant) to breed in captivity, you’re stuck with “collecting” them from the wild. This adds a layer of inefficiency and difficulty that only makes sense when the animal commands an appropriate reward.

I wonder. Will Homo sapiens have a choice in breeding partners in the future? Younger people are having a difficult enough time navigating the complexities of dating in a social-media-always-connected world. Coming to their rescue are eHarmony,, Tinder, and countless others – all with the promise that “data” and “algorithms” will help you navigate complexity and find a partner. Meeting someone in a bar? Risky! Dating in college campus? Not enough time! Love with a coworker? Discouraged! A dating app will take care of that choice for you.

Criteria #4: Pleasant Disposition

This one should be self-evident: Honey badgers make lousy house pets.

Homo sapiens are different. To say most people are “nice” or most people are “mean” is an oversimplification. Our dispositions are situation dependent; our biochemistry, our experiences, and our social networks all play different roles based on ever-changing scenarios. Worse, every once in a while, you get a small number of people who will perform highly anti-social behaviors (riots, shootings, sabotage, etc.)

Homo technorati didn’t quite see this coming with the popularity of social media, but they do now. With small nudges in your “feed,” developers can adjust your mood – or at a minimum, polish off the roughest edges. And when everyone participates, AI can begin to spot anti-social (unpleasant) behavior well in advance … alerting social assistance in mild cases, and law enforcement in extreme ones.

Criteria #5: Tendency Not to Panic

There’s a reason police call it “rabbiting” when people flee the scene of a crime. Rabbits have been domesticated to a point, but their wild cousins survive so well by bolting at the first sign of danger.

The world is indeed a safer place for most people than it was a century ago. Put in more modern terms, it’s unquestionably safer to play Call of Duty than to actually heed the “call of duty” as a member of an active military. Electronic games like these (and their simpler phone-based cousins) are engineered to provide the correct dopamine boost to eliminate nervousness and boredom … as well as trigger purchase behavior. Who can stand in the security line at the airport without their Candy Crush game to amuse the children (or themselves)?

Criteria #6: Social Structure

Domesticators co-opt the natural herd mentality and social networks of the animal population to assert a new dominant member (the farmer) at the head of the group. Homo sapiens are no different in this regard, we simply haven’t had a clearly superior species (yet) to take control.

Tribes, city-states, nation-states, families, and religion where the organizing control factors for much of our history. For the past two decades, Homo technorati has been at work actively breaking down those barriers and replacing them with something new. Your tribe is who “you” decide it is. Your nation is simply a physical place, less important than your interests and affiliations. Your family could be anyone and they could be anywhere. Your religion … well, how about “spirituality” and “mindfulness” instead of an organized church? There’s an app for all those things. It seems like Homo technorati is allowing you to make your own choices, but in reality, they are simply switching your allegiance and membership from one group to their group.


Do you feel a little less comfortable considering yourself a truly “wild” species?

The process of domestication seems so reasonable, doesn’t it? It doesn’t even seem like it’s happening. If you (Homo sapiens) cede control of your life choices the long-lived and wise technical elite (Homo technorati), they will reward you with a safer existence free from stressors and uncertainty as well as predictable resources (aka Universal Income).

But there’s a problem with that. Over time, domesticated animals lose the capacity to revert to a “wild” state. Most dogs, chickens, cattle, and corn could not live without humans. Their brains have atrophied – actually shrunk in size over many generations – because little is intellectually required of domesticated animals. If humans are simply animals, why couldn’t that happen to us? At what point are we reduced to mindless entertainment and breeding to do the jobs machines and artificial intelligence are not (yet) capable of doing? At what point do Homo technorati and Homo sapiens become distinct species on different evolutionary paths.

Let’s hope we’re not delicious, huh?

Is this thought experiment hyperbole? Perhaps. But I find the idea of human domestication at least as likely as either a tech utopia or an Orwellian dystopia. On the path to the utopian singularity, you simply buckle in and enjoy the ride. There’s not much for the average person to do unless you’re one of those select few techno-elites. Keep buying new iPhones and always renew your Prime membership. They need your money (and will do better with it) than you will. On the second path to digital slavery, humans are unlikely to go down without a fight. It may be ugly, but there are enough contrarians in the world today to sound the alarm. Resistance is not futile. Remember, tech companies need your money. That’s your superpower.

But domestication? That’s the most dangerous scenario because it happens so silently. One day, your grandchildren wake up in a world where they cannot make a decision without asking Alexa for help.

Luckily, the easiest way to prevent domestication is to refuse to live by its conditions:

  • Don’t be so eager to spend time browsing social media. Go out an create something.
  • Take language, art, theater, and literature in school. Don’t accept that “learning to code” is the only way to get a job. Don’t specialize! Chickens are specialists. Look what it got them. No matter how good your coding skills, they’ll slaughter you when you can’t lay eggs anymore. Learn to think instead.
  • Get out there and meet people in person. Learn the subtle art of approaching people and making friends with respect and dignity.
  • Mess with the algorithms. If you’re being tracked at work with your badge, purposely walk in and out of the scanner at random times. Don’t be so predictable.
  • Volunteer, sacrifice, and do real stuff. Play fewer games.
  • See a problem in your community? Get involved. Don’t like politics? Change it. Mad at your church? Go back and fix it. Define your own tribe.

Be unpredictable. Be unreasonable. Be human.

But most of all, stay wild.


If you liked what you read (or even if you didn’t, and simply want to argue with me), please consider signing up for my email list. There’s a link on the side of this page if you’re reading on a desktop, or at the end of the article if you’re on a mobile device.

Once you sign up, you’ll get a glimpse into my thought process, get a chance to interact with me directly, and even get a chance to tell me what you think I should write about next. You won’t hear from me more than once a week unless you want to.

What do you say?


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher, but the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

Thank you! Gracias! 谢谢!

Your fellow human.

Audience Empowerment Information Management Long Form Articles Rehumanizing Consumerism

What if someone offered $6,495 for your private data? Would you sell?

What follows is a fictionalized vision of a possible future filled with Data Exchange Networks (DENs) designed to bring the process of private data collection out into the open.

. . .

February 5, 2029

As a fractional research scientist, Lynn Thomas uses her talents to aid a number of clients – from University labs who need an extra set of eyes on experimental design, to corporate R&D departments conducting optical glass experiments, to startups working on new protein-based sweeteners. In 2028, she managed six retainer clients (including one startup where she took equity instead of cash) and felt like she earned a good living. 2029 looks just as good.

But her experience working for an energetic founder infected her with the startup bug. Lynn has had her own idea for a new type of photovoltaic paint since she first read about the idea as a graduate student.

It’s time, she thought. She needs to put up or shut up.

The problem is money.

It’s always money with startups, and that’s especially true in the hard sciences. At this early proof of concept stage, she doesn’t need much money, but enough to purchase the synthesizing equipment, raw materials, and lab time. She figures about $4,000 will cover it ­– $5,000 to be safe. She’s too early for angel or venture capital funding. She’s also too early for legit crowdfunding sites. They want a promise of a deliverable at the end. She’s doing some early stage science. She has no idea if anything will come of her work. It’s too risky. She is on her own.

How will she do it? Take on another client? No. She’s already maxed out. And if she does, she won’t have the spare time she needs. Luckily, she has another option. Thirty years ago, she might have begged friends and family for the spare cash she needed to fund her startup.

In 2029, she has the option to sell her private data.


Lynn Thomas prides herself on her rational mind. It got her a scholarship to a private high school, internships at the National Institutes of Health, two master’s degrees paid for by corporate sponsors, and a Ph.D. from Oxford. Still, selling private data on a Data Exchange Network (DEN) still seems a bit sketchy. She had a friend who used one … that DEN ended up selling his data to a dating site, much to the chagrin of his partner. Other DENs are known for bombarding you with advertising. Most DENs don’t pay very well. It’s the last fact that’s the real problem.

But one does pay well: The MENSA DEN.

Perfect, she thought. MENSA made the decision ten years ago to begin cashing in on its membership base. However, they couldn’t simply sell member data. Not only was their data set not as detailed as they thought it might be, their average member was too smart to let them do it without getting paid. (Makes sense, huh? They are MENSA members.) So, MENSA cut a deal: You let us market your data to interested parties, and we will share the revenue with you. Members decide what to share (and what not to). A sophisticated auction market will determine the prices paid. It’s smart, fair, and rational.

Lynn was a MENSA member. That meant she could give the MENSA DEN a try. What did she have to lose?


“Siri, open the MENSA DEN,” Lynn said.

“Okay, Lynn. I found it,” the automated voice replied. “The MENSA DEN checked your records and confirmed that you have an active membership in the MENSA organization, but not a DEN account. They say you need to complete a profile before you can enter the marketplace. Do you want to proceed?”

“What kind of information do they want?”

“I’ll check. They say they want some basic demographic information, most of which you already provided in your organization membership. Specifically, they’re missing your current physical address, gender identifier, biological gender, and family status.”

Ugh. Lynn thought. That’s already more personal than she was hoping for. But she swallowed her discomfort and continued. Eye on the prize she thought.

“Ask them what security measures are in place.”

“Good question, Lynn. It seems like they anticipated that. I have a full encryption schematic you can view on the main screen. It’s similar to the one you and I use to communicate: Two-stage blockchain with polynomial and fractal encryption. It’s not perfect, but the task of breaking it would require a dedicated government-level quantum super-computer running for 82.5 hours. The risk of a breech seems reasonable.”

“Agreed. Let’s go. But set a reminder to change our MENSA DEN credential password every 60 hours or so.”

“Smart precaution. Done. I’ll now open the secure link.”

Lynn proceeded to share her physical address, her gender identifier (her/her’s), her biological gender (female), and family status (living alone, no children).

Deep breath, she thought. I’m in.


“Okay Lynn, the MENSA DEN found seven offers for you to consider. I’ve posted them to your mobile screen. Where would you like to start?”

Hmm, Lynn thought.

That’s more options than she imagined there might be. Siri asked a good question. Where do you start on a journey like this? You’re selling a part of yourself to the highest bidder. “Social media” seemed like the easiest place. Fewer people share personal details on those sites, especially since Facebook imploded. Today, most people use any number of “Virtual Reality” or “VR” social networks to meet up with friends around the world. You have to pay to use most of those. What could they want? Lynn thought.

“Let’s start with social media. I’m interested in what they’re offering,” Lynn finally responded.

“Good choice, Lynn. The first is a scientist-specific VR meetup group. They were founded in Kuwait and have been trouble attracting female members. Your profile fits their criteria and they are willing to bid $12.50 per month for you to log in at least three times for 30 minutes each during the month.”

Lynn did the quick math. $12.50 for 90 minutes was less than $10.00 per hour. More to the point, it would take 33 years to make the $5,000 she needed. But perhaps there was other value to be had. Maybe she could build relationships with other scientists and collaborators along the way?

“Siri, go ahead and counteroffer with $30.00 per month, same time commitment.”

“Understood. I’m submitting the bid now.”

There’s no way they’ll…

“Response received. They countered with $25.00 per month for four sessions. They’ll pay the first month in advance.”

Better. Not great, but better. Lynn considered for a moment.

“Go ahead and accept that offer. Let’s keep looking.”

“Okay, let’s move on to an easy one,” Siri responded. “I have 15 businesses in your area that will provide discounts for dinners, events, and performances if you allow them to track your physical location whenever you get within 10 miles of their facility. I’ve added the list to your mobile screen along with a map overlay of your typical travel patterns. Only six of them overlap.”

Lynn examined the map. Siri was right. Six of the 15 were in her daily routine. She touched the screen in four places.

“Let’s go with these four,” Lynn decided.

“Confirmed. Where to next?”

Another good question. So far, Lynn realized she only accepted offers for $25 (per month, yes, but only $25 today) and four dinner coupons. Not so good.

“Siri, let’s re-sort the list from largest potential revenue to smallest.”

“Okay, I finished re-sorting your list. The largest opportunities are in the health information category. I’ve taken the liberty of cross-referencing the opportunities list with your private genetic workup. The results are on the main screen.”

Lynn looked up. Ah, there we go. Here’s the bigger money. She examined the details on the screen.

The first opportunity was a breast cancer clinical study based on her unique BRCA variant gene for $3,250. She would be part of a control group, meaning she wouldn’t have to do anything other than keep doing what she was doing. And as a bonus, she would get to read the resulting research.

The second opportunity was a pharmacological study on a synthetic cannabis derivative. This one was a “double-blind” study, meaning she would not know what she was getting, and neither would the researchers. There was a link to a 32-page disclosure and waiver document. They were offering $2,750.

The third was a biofeedback device that used light therapy to lower cholesterol levels. Since she inherited a gene that correlated with high-LDL levels from her mother, the researchers would double the normal payout of $750 to $1,500. She would need to use the device as directed (and tracked via an IoT connection) for three months and complete twice-monthly blood tests.

This was a tough decision. If she said “yes” to all of them, she would have all the money she needed…and more. But they weren’t created equal, and none would accept counter offers. It was a “take it or leave it” situation.

“Okay Siri,” Lynn said after a long minute. “Let’s accept the gene study and the biofeedback device. I’m not comfortable with the risks in the cannabis study.”

“Understood. The contracts are accepted. You will receive detailed instructions via a VR-mail later this week. Should I give the cannabis study authors the reason for your rejection?”

“Sure, tell them I’m not comfortable with the risks of not knowing what I’m getting. They could have been more clear, up front, on protections.”

“Understood. Feedback submitted. If they answer your questions, are you willing to reconsider?”

“No, I don’t think so. Mute their responses.”

“Will do.”

Over the course of the next 20 minutes, Lynn walked through a number of other auctions and offers. Siri knew Lynn was a “gig worker” and removed any explicit job offers disguised as information sharing. Lynn did consider one that was essentially a beta test of new lab software … but she had enough on her plate. She instructed Siri save that one for 30 days.

One interesting organization wanted her complete purchase history of all food and beverage products for the past 18 months. They offered $300, but Lynn negotiated the initial offer and closed the auction at $445. What the heck? It was just “food” and not “all purchases,” so the risk was low. And besides, they offered to share research findings with her that we personalized to her habits. She didn’t need to lose any weight, but she has been working on improving her muscle density. Who knows? Maybe she’ll learn something useful.

Three religious organizations wanted her to donate her information so they could better profile target members. She turned them all down.

The political organizations were a different story. The two major parties wanted free information (another “no”), but science-focused interest groups wanted her research notes to write up case studies to teach young people about the scientific method. They had a grant from the National Science Foundation, and they were offering $425 per unpublished lab book. She was under NDA with two of her five projects that qualified, but she accepted the others.


“Ok, Siri. Where are we at?” Lynn asked.

“I calculate $6,495 in total accepted contracts, with $25 per month continuing until you cancel the VR meetup group participation with the Kuwaiti-based organization. Do you want to continue and expand your search?”

“No, that’s all for now. Go ahead an exit the MENSA DEN, but remind me to check back in 90 days.”

“Will do. Signing off.”

Lynn felt a sign of relief. She had more than enough capital to begin her work – almost 50% more than she needed. She remembered the advice of a graduate advisor: Always assume your research will take twice as long and cost twice as much. If you do, you’ll be covered. She didn’t quite get to twice her initial figure, but she felt good.

“Ok Siri, let’s go shopping for lab equipment…”


Obviously, this is a thought experiment. Lynn Thomas isn’t a real person (yet). It’s not 2029 (yet). Privacy isn’t explicitly for sale in this way (just yet…or is it?).

I have a message for entrepreneurs reading this and wondering how the brokerage service could earn trillions of dollars as the secure intermediary in these transactions: Why aren’t you working on it?

I have a message for consumers reading this and wishing they could finance their dreams using assets they already own…but would be willing to sell under the right circumstances: Why wouldn’t you?

And finally, I have a message for all those tech leaders who feel that consumers will continue to give away private information for free because of your “unicorn” technologies: They won’t.

Lynn’s world is coming. It’s about time we all caught up.


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 grandfather manufactured the first disposable coffee filters in pre-Castro Cuba. Another grandfather invented the bazooka. Yet another invented Neapolitan ice cream (really!). He was destined to advertise the first disposable ice cream grenade launcher. But the ice cream just kept melting!

He took bizarre ideas like these 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, Jason has dedicated his career to finding marketing’s north star, refocusing it on building healthy relationships between consumers and businesses, between patients and clinicians, and between citizens and organizations. That’s a tall order in a data-driven world. But it’s crucial, and here’s why: As technology advances, it becomes ordinary and expected. As relationships and trust expand, they become stronger and more resilient. Our next great leaps forward are just as likely to come from advances in humanity as they are advances in technology.

If you care about that mission as well, he invites you to connect with him on LinkedIn. If you’re interested in sharing your research, please take the extra step and reach out to him personally at jasonvoiovich (at) gmail (dot) com. For even more, please visit his blog at and sign up for his mailing list for original research, book news, & fresh insights.

Thank you! Gracias! 谢谢!

Your fellow human.


Photo license obtained: Shutterstock