Tag: Marketing

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

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

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

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

Introduction

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

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

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

But not always.

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

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

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

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

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

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

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

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

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

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

Let’s do that now.

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

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

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

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

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

A better word to use is adoption.

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

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

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

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

Therefore, innovation, at its heart, is behavior.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

One need. One solution.

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

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

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

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

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

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

Perhaps not.

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

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

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

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

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

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

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

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

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

It’s not.

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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Tags Diffusion of Innovations, Innovation, Marketing, Technology

Marketing Education

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

Post author By Jason T Voiovich
Post date July 26, 2019
No Comments on Learn the true influencers for CRISPR using Brand24 (not Gartner)

Need more details? Check out my full article here.

Tags Brand24, CRISPR, Influencers, Innovation, Marketing

Long Form Articles

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

Post author By Jason T Voiovich
Post date April 21, 2019

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.

Tags Business, Data, Journalism, Marketing, Netflix

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.

Post author By Jason T Voiovich
Post date April 8, 2019

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

Thank you! Gracias! 谢谢!

Your fellow human.

Tags Business, Data, Marketing, Media, Privacy

Long Form Articles

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

Post author By Jason T Voiovich
Post date April 1, 2019

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.

Wow.

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

Thank you! Gracias! 谢谢!

Your fellow human.

Tags Business, Marketing, Plagiarism, Publishing, Writing

Long Form Articles Rehumanizing Consumerism

Your “smart” TV is a dumb idea

Post author By Jason T Voiovich
Post date March 18, 2019

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:

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.
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:

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.
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.
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.

Yet.

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.

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.
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.
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.
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.
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.
Unplug your Smart TV when you’re not using it.
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.
Is it about time to contact your representatives about GDPR-style legislation? What’s it going to take?
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

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Your fellow human.

Tags Entertainment, Marketing, Privacy, Smart TV, Technology, Television