Monthly Archives: March 2014

SXSW observation: Prediction is the 5th stage of technology

tech hipster hand

As I watched a small heli-drone hover over a crowd outside the Austin Convention Center at SXSW, I thought: the evolution of technology will culminate not in gadgets, or data, or surveillance, but in predicting human behavior. This is not a moral declaration, but a statement of the inevitable. Like the five stages of Elizabeth Kübler-Ross’s structure for grieving, technology is passing through five intertwined steps of evolution:

1. Hardware came first — the wheel, the horse-and-buggy, the iPhone in your pocket, the physical “thing” that most people think of when they hear the word “technology.” Hard tools are human capacity expanders, from the leather shoes that allow us to run on hard surfaces to the mobile phones that connect us to the world. But hardware is only the bottom rung of technology’s ladder.

2. Software came second — the required knowledge system, in its broadest sense, to run any hardware. This includes human minds, as a construction worker must think to wield a hammer, and the programmable electronic strings that make tablets and DVRs run.

3. Sensors are third — defined as any input that collects data to drive hardware/software outputs. You must type into a typewriter to generate a letter. The gyro in your smartphone rotates its screen, keeping it vertical. Sensors are shrinking, dropping in cost, and rising in sophistication. Today, the Xbox can sense your location, motion and even heartbeat from across the room to run a video game. The Nest thermostat knows when you leave the home. Your iPhone dims the screen when you hold it close to your ear. Like the oblong telescreen built into Winston Smith’s 1984 wall, gadgets are watching you while you watch them, too. This has always been the case, as cars need gas pedals and steering wheels to be directed; sensors are simply, inevitably proliferating.

4. Data is next — any tool to work must input, collect, and store information to function. Note that data flows two ways, and as sensors/software/hardware scale in quantity and plummet in costs, the data that comes in from you will begin to outnumber anything that comes back out.

5. And prediction is final — because data will by necessity be used to predict behavior to make any tool more useful. People today — even tech leaders — often misunderstand technology to focus on gadgets or applications or data, which are “cool” and “new,” vs. the predictive knowledge all of these new systems combined will generate.

Because we hunger for our tools to provide more utility, and prediction is the fastest way for us to get what we want, prediction is where all of technology must lead.

How are observations proliferating?

On the SXSW stage, tech-trend observer Robert Scoble addressed how Google Glass, the little eyeglass gizmo with a screen/computer embedded on one side, is really a collection of sensors that observe you. “Glass,” he said, “is one of those products that you know is the future … and the real privacy problem is it is a sensor platform. It will know whether I’m sober or drunk. Will that data get sent to my employer, my insurance company, my wife? As these technologies shrink and disappear into our eyeglasses, our computer systems, Google will be watching what we think. And it is mind-blowing to think about the privacy problems of that.” 

Each day, people are already exposed to millions of interception points. At another SXSW presentation on UX Design, Alfred Lui of Seer Labs noted that the average U.S. consumer is interrupted 80 times a day by technology; by default, each system interruption may be backed by scores of hidden data observations. While designers focus on how to make the data around each technology bit helpful — “just being able to collect data does not make you useful,” Lui said, “you need to give data a purpose” — those growing interaction touch points create numerous ways any individual can be observed.

Why will all these observations morph into predictions?

Because forecasting action may be the highest utility of societal interaction. Governments (despite Snowden’s protestations and the associated debate around them) use data mining to predict and prevent terrorist threats, a societal benefit. 23andme, a genetics company that can test your profile based on a simple bit of saliva, is able to predict a person’s propensity to medical disease. The vendor floor at SXSW included headsup virtual-reality eyeglasses that monitor eye movements and a billboard display that tracked whether people walking by were men or women, young or old. Each of these inputs is used in its own way to monitor human behavior and predict something — a terror conspiracy, a health risk, what you will see, what digital ad you should be served. And marketers, the driving force that subsidizes almost all of today’s entertainment for consumers, will rush to collect new data threads that improve predictions that enable customized advertising matching desire with sale.

The sensors that watch us are shrinking and being built into every object. We will use these new gadgets to sense data that predicts our future. We will trade privacy for utility, if we find the exchange beneficial. As the great Kevin Kelly wrote in “What Technology Wants,” “progress is only half real. That is, material advances do occur, but they don’t mean very much. Only intangibles like meaningful happiness count.” In 5 years, your email will draft customized auto-replies in your own tone of voice, predicting what you would write when you’re out of the office based on your past emails. (Google has a patent on this.) In 15 years, you’ll get into a self-driving car that already knows where you want to go based on your daily habits. In 25 years, you may fall in love with a digital avatar that anticipates your every need.

Data exists to be observed; observations exist to form predictions; predictions are made because they improve happiness. Predictions are coming. It’s not an ethical debate. It’s an unstoppable technological evolution. We just can’t help ourselves.

Nice native ad, Samsung. Samsung? Are you there?


Before we discuss the wrinkles of native advertising and why Samsung slipped up in this now-famous Oscar shot, let’s drink a cup of Postum.

In 1895 a man named C.W. Post roasted some grain, ran hot water through it, and dubbed the resulting 10-calorie drink “Postum.” Post began an aggressive print campaign to convince the American public that coffee was sickening, causing everything from nervous jitters to poor athleticism, while Postum, by comparison, was “free from the evil effect of caffeine — the habit-forming drug.” Physicians or scientists didn’t write that editorial-sounding copy; the Postum Cereal Company did; but oh, how the public was convinced. By World War II, when coffee became rationed, sales were through the roof.

The Postum ads were a prescient taste of today’s hottest marketing trend — to disguise the source of a piece of advertising in a manner that tricks consumers into believing it has editorial value. U.S. marketers spent $1.5 billion on native advertising in 2012 and $1.9 billion in 2013 — a tiny fraction of the $74 billion spent annually on television spots, but rapidly eclipsing mobile. Going native with messaging can be done well, as Quartz shows with its high standards for sponsored content, or poorly, as The Atlantic found out when its readers rebelled against a barely-disguised Scientology advertorial. Dunkin Donuts pays to have its sign appear over the shoulder of a politician in Netflix’s “House of Cards.” The New York Times launched a sponsored content section with technology pieces paid for by Dell. Everyone is jumping in.

Why is native advertising suddenly popular? Advertorial has been reborn as a solution to falling CPMs in digital publishing, where programmatic media buying and vastly increasing ad inventory has killed publishers’ ability to make a profit from marketers. So-called “native advertising” can be sold by a website at a premium, because there are no standards for pricing editorial integration. Television, looking over its shoulder at digital, is ramping up native, too, especially in live events such as the Academy Awards that attract enthralled audiences willing to retweet the breaking fun. Marketers are willing to pay more for embedded ads, because they’re taking a bet “native” will grab more attention, and more hopefully, will be shared among the masses. And consumers seem to be taking it all in.

So what are the risks? We count three.

1. The mask can work too well. If you build a good-enough mask for your ad message, consumers won’t recognize your face. Samsung found this out when it spent a reported $20 million to integrate its mobile devices into the recent Oscar presentations. Ellen DeGeneres took a selfie with the Samsung phone, creating what may be the most retweeted image ever on Twitter — and yet Samsung is nowhere in the photo. It’s a wonderful shot of A-list celebrities. But if you ask 1,000 people what brand made that snap happen, we bet the majority would guess Apple.

2. The investment doesn’t work enough. The ROI calculation on native advertising is wildly volatile and often negative. For every Samsung selfie hit (provided people did know the product that made the content happen), there are thousands of sponsored posts that get minimal page views and are never propagated. The effective CPMs on such buys are difficult to calculate in advance, because the “viralness” of each attempt is a crapshoot. If you need evidence native advertising may be an inefficient marketing play, ask — why are publishers so intent on pushing this new format? Because they make more money from you when you buy it, that’s why.

3. The platform becomes too polluted. Finally, like town sheep overgrazing a commons until all the grass is dead, native advertising can despoil an ecosystem. In the 2000s, spam did this to email marketing. In the 1990s, telemarketing died due to consumer rebellion against too many 6 p.m. sales calls. When marketers intrude too far into any communications platform, the platform often wilts.

The great online encyclopedia Wikipedia is now worried about this third threat. This month Wikipedia ran banners asking its users to provide feedback on a proposed new rule that would require paid authors to disclose that their Wikipedia edits are, well, paid for. While this seems obvious, Wikipedia is addressing an epidemic in which authors are manipulating content to make their client organizations look better than hard facts might dictate. Manipulated content is by nature less accurate; this diminishes the utility of Wikipedia; and if false or fuzzied content spreads, could damage the site enough that users start to bail.

The crux of the ethical problem is that native advertising seeks to misrepresent the source of the ad message. Yes, most native ads are “disclosed,” but the very act of making content look like real entertainment or editorial is a disguise. If you show up at a party wearing a mask with the words “this is a mask” typed on top … it’s still a mask. The Scientology article that upset The Atlantic’s readers was clearly marked as sponsored content, but readers became confused — the advertorial mask worked too well — and rebelled thinking The Atlantic was endorsing the religion.

This confusion of source breaks a core human logic, because people make decisions based not only on data we receive but also on the reputation of who we believe sent it. There is simply too much information in the world for us to digest all inputs each day; but if our spouse yells “the house is on fire,” we will run for the extinguisher or the door. Sources matter in human communications because they are the core filters by which we judge value.

Native advertising misrepresents the source.

So is native all bad? Well, no. As marketers, we can only point out the risks vs. benefits of native. Yes, you might cause confusion, or mask your message so well your Samsung phone disappears in the “ad,” or even pollute an entire communications platform. (Facebook and Twitter, y’all be careful, you hear?) But you could also reach millions of people in a new way and jack up your sales. Native could work superbly, so why not test it? The upside evaluation should include the efficiency of the buy on a per-thousand basis, the potential passalong, and the likely greater recall of a persuasive concept that is digested as quasi-real.

So is native all good? Well, no. As consumers, we miss the days when marketers admitted they were just trying to sell us something.

Does native work? Let us know if you buy a Samsung phone.