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What if Facebook ‘Likes’ don’t matter?


Blogger Derek Muller is upset. He paid Facebook to promote his page, and instead got what he believes to be 80,000 fake Likes. You know. From supposed Like “click farms” in Egypt or Pakistan, where the same type of people who call you at home to warn you about a Microsoft update on your PC if only you’ll give them your banking password also click on millions of Likes. Muller’s beef seems to be that Facebook is complicit in enabling such “Like fraud,” in order to push marketers to pay for more real advertising.

The logic is a bit complex, but here goes: Marketers think “Likes” are important on Facebook because they supposedly open the door to a free form of advertising popularly known as “engagement.” (Brand “engagement” is what you do when you try to sell someone something while pretending to be their friend.) When a consumer “Likes” your brand page on Facebook, your brand updates can then organically appear in the user’s stream without you paying Facebook (just as your personal updates appear in front of your friends), and this is IMPORTANT because what marketer wouldn’t want a free onramp into a friendly consumer mind?

Alas, but if many or most Facebook Likes are faked, you’ll appear in fewer user streams organically, you’ll see worse response results, so you’ll have to pay for actual Facebook advertising. And thus, the fraud theory goes, Mark Zuckerberg would laugh all the way to the bank. 

The Washington Post noted in covering this fraud claim that Facebook explicitly bans anyone from paying click farms to artificially boost follower counts, but Muller thinks “page administrators are nevertheless circumventing those rules, creating a market for legions of fake Facebook users that just click ‘Like’ all day.”

Muller actually pulled some hard data to back his case. He spent $50 to get more Facebook Likes, and got a boatload … from Egypt, India, Bangladesh, Nepal and Sri Lanka, countries that he suggests are just where fraudulent click-farms are common. Um, that really doesn’t look good.

But it doesn’t matter, because Likes have little value anyway 

What’s the problem with this complaint? First, no one can prove Facebook is encouraging this, and Facebook is by all accounts fighting spam in its ecosystem just as Matt Cutts over at Google wants to shut down SEO blackhatters gaming paid search. And second, Like spam doesn’t matter — because Facebook “Likes” have almost zero value anyway, and any marketers with smarts will want to pay for advertising in the platform, ignoring Likes altogether.

Here’s the truth, marketers:

1. Likes are fleeting. A consumer who clicks “Like” on your brand has thought about you fleetingly for 0.5 seconds. This is not “engagement” or a “relationship.” This is a consumer mental hiccup. The currency of the thousands of Likes on your brand page in Facebook is worth, oh, about zero cents. It’s the brand equivalent of a personal Klout score, a feel-good, game mechanics points system that doesn’t mean anything beyond pixel dust.

2. Likes translate into infinitesimal advertising value. Facebook wisely limits how often you can go flying around in the main big Newsfeed among your “Liking” brand followers, because Facebook users would get upset if every other post was a big ad, so you ain’t getting in much, anyway. Let’s assume you get to 100,000 Likes. And then 100,000 users see you twice in the next month, as you re-chase them in their Facebook feeds for 200,000 impressions. That’s a whopping low 2x frequency per month, hardly enough to influence anyone. If these free impressions were translated into advertising value at say a $2 CPM, you’ve just gained a whopping $400 in free advertising. That should move a lot of Ford trucks, right? Um, no.

3. Paid Facebook advertising, by comparison, translates into huge value, if you point respondents to your regular brand website set up for sales or lead generation. Every $400 you spend on real Facebook advertising will drive approximately 300 people to your website at a $1.33 cost per click. If only 1% convert to a sale, you have a $133 cost per customer acquisition. The local Ford guy will play that game every day of the week.

In sum, Facebook doesn’t need to defraud you or its system to get you to advertise … because its advertising already works. This is why Facebook made $7.8 billion in 2013, with the last quarter of the year pacing to more than $10 billion annually. Marketers are pouring money into an advertising system that works. The reason it works is not the social sharing functionality of Facebook … it’s simply because Facebook is where consumer attention now resides, for hours a day, and advertisers always do best when they follow their audience.

Sorry, blogger who paid for Likes. Yes, fraud is a nasty problem, and it looks like Facebook may have to address all the suspicious “Likes” popping up from suspect areas of the world. But with real Facebook advertising working so well, it hardly needs to trick you to get you to spend more media dollars there.


The mobile future of variable pricing



Gregory Mesaros of eWinWin recently won a patent to provide consumers with variable pricing via wireless devices. This simple concept makes perfect sense when you consider how inefficient pricing normally is. Imagine, for instance, Sally and Sam are walking down a street. Sally is very hungry, and Sam isn’t. A hamburger stand nearby offers burgers for $5. Sally would be willing to pay $7 … and Sam only $4, based on their current state of mind. But because the stand charges everyone the same $5, only Sally buys a burger.

Now, if a sensor could pick up their smartphone IDs and understand somehow their recent behavior and desires, the hamburger stand might be able to beam Sally and Sam different prices for the same meal. $7 for Sally, $4 for Sam, would entice both to buy — and the outlet would make $11 for two sales, doubling sales volume (two) and boosting margins (since the average burger price is now $5.50). Everyone is happy.

Variable pricing is not new, of course: hard negotiators end up paying less for cars that softies; grocery stores use coupons to entice price-conscious shoppers to pay a little less for cans of soup; consumers rushing to the mall on Black Friday may pay less for a new TV. But real-time, instantaneous variable pricing has eluded marketers before now. Mesaros’ patent would match behavioral tracking of consumers with pre-set options for prices and offers, and seek to find the best instant match.

Does this seem unfair? Perhaps. Uber, the new crowd-driven taxi service, has received bad press for jacking up rates during periods of peak consumer demand such as snowstorms in major cities. Subscription companies such as cable and utilities often face customer churn when one customer realizes she is paying more than her neighbor for equivalent service. But one-price-fits-all strategy is a holdover from the early 1900s when there was no scientific method to efficiently match the cost of a good with the variable desire of an individual consumer. Regarding fairness, one could also argue it is unfair to charge more for a good than many are willing to bear, pushing you away from potential purchases. Making one person pay $7 so another could pay $4 for a sandwich would optimize the utility of a transaction for everyone.

With mobile technology now being mapped to personal datasets, soon, we may all pay different prices for everything.

The +1, -1 irrational psychology of the deal


Do you ever stop and think how strange “getting a deal” is when you buy? Clothes go on sale. House prices rise and fall. Candy at movie theaters comes in unusually shaped boxes. How do we know when we win or fail in commerce?

For marketers, this is a vital question, because influencing the purchase is the lifeblood of a company. For business leaders, negotiating deals is a daily challenge. To win at either, just control the “+1, -1” psychology of the deal.

Consider these two events:

John is up for a promotion and expects a raise from $25,000 to $35,000 a year. His boss calls him in and explains they can only offer him $30,000 a year for the new position. John goes home, mildly disappointed.

Jane is up for a promotion and expects a raise from $25,000 to $35,000 a year. Her boss gives her a raise to $35,000. The following week, accounting lets her know this was an error, and they will have to reset her new salary to $30,000. Jane goes home that night, furious, and rewrites her resume.

Why was Jane more upset than John, if they both got an equal raise that ended up in the same place? Because Jane perceived two “loss” events — a lower raise than expected and having to give something back — while John perceived one loss, just a lower raise.

And here is the crux of understanding any deal. In “mental accounting,” a theory espoused by noted economist Richard Thaler, people strive to maximize pleasure and minimize pain. But we are horrible at logically seeing the real outcome, and instead keep a mental tally of a series of +1s and -1s along the way in any transaction. More positive events make us feel better; more negative ones worse. The accumulation of events in the positive or negative direction can influence our perception of the outcome. This is why parents don’t put all the Christmas presents in one wrapped box (because unwrapping more boxes make kids feel good), and why we use credit cards to roll up most of our monthly debts (because paying one bill minimizes the pain vs. paying lots of smaller bills). We love many presents and one bill. We hate one present and many bills. Even if both get us to the same place.

How can marketers use +1, -1 deal psychology?

The outcome is all that matters logically. But, as Thaler notes, an increase in a gain should be segregated into multiple events, and a loss should be integrated into one event, if we wish to maximize the pleasure of a customer.

Many marketers play this many-gains, few-losses game unwittingly with “sales.” Instead of pricing a dress at $50, a relatively low price (one “gain” for a consumer), a retailer will mark the price up to $200 and then put it on the rack on sale 75% off. Now, the consumer feels two “gains” — a 75% savings and relatively low price of $50. The outcome is the same, but the two-step sale approach is more likely to trigger a purchase. Smarter marketers such as Zappos extend the series of +1 events by adding extraordinary call center service and unexpected next-day shipping, all, of course, baked into the real price. The longer the series of +1s you can give a customer, the happier she will be.

How can you use deal framing in your business?

Business leaders often find themselves in the role of a customer, potentially being gamed by their partner/vendor/adversary/friend in a B2B deal. Contracts get written. Lawyers join calls. And the final answer requires understanding if it’s all a good “deal.” The way to use the +1, -1 strategy in B2B plays is to be cognizant that it may guide you into a trap — when the outcome is really all that matters.

In other words, the psychology of keeping score can lead you astray in judging the value of a business deal. Let’s replay the John/Jane scenarios, this time as a B2B adventure with larger budgets:

John runs a busy plastics manufacturing plant and is expecting a fast-growing, loyal customer who ordered $250,000 in products last year to order $350,000 this year. The customer says, sorry, I can only order $200,000. John, immersed in other orders, is disappointed but accepts the deal.

Jane runs a busy plastics manufacturing plant and is expecting a fast-growing, loyal customer to move from $250,000 to $350,000 in orders. The customer orders $350,000 in product, then a week later cancels $150,000 of the order. Jane, upset by this cancellation and immersed in other orders, tells the customer to take her business somewhere else in the future.

Similar to the personal raise scenario, in this case, both John and Jane ended up at exactly the same financial place: $200,000 in sales. But again, one of them felt two “-1” negative experiences in the transaction that led her to kill the relationship. Is this rational? Of course not. But human cognition often keeps the wrong score.

Thaler calls this the “concept of the reference outcome” — in other words, the outcome we achieve is always compared to the original price or value we expected. Marketers who influence that original perception in the right direction will win. Businesspeople who fall into the trap of comparing an outcome to the wrong original perception may miss solid opportunities.

And for anyone conducting a transaction, the best advice is this: ignore the +1s and -1s along the way, and instead calculate what are you really gaining at the conclusion of the deal?

Facial recognition is rebooting the concept of ‘1to1’

computer face

Facial recognition is the next big thing. And when computers finally get it right, expect 1to1 marketing to make a second appearance.

First, some history. In the 1990s I was lucky to work with Don Peppers and Martha Rogers, who thought up 1to1 marketing. Peppers was a former ad guy with an IQ north of 150, and Rogers was a marketing professor who could wow audiences with stories on stage. Their joint concept was that as computers got more powerful, eventually they would empower marketers to recognize and respond to consumers on a “1to1 basis,” creating a new competitive advantage by offering personalization that made each customer more loyal with each transaction. It was genius at the time, but “1to1” became “CRM” which became a sales point for database and software companies, and the core marketing dynamic was never embraced by the world … perhaps because 1990s computers weren’t up to the task. Then social media arrived, Gladwell wrote “The Tipping Point,” marketers got excited about the viral potential of interconnected people ignoring TV commercials, and the 1to1 idea faded away.

Fast forward to 2013. Now systems that identify individuals and respond with personalization are moving mainstream far beyond the film queues in your Netflix account. The UK retail giant Tesco recently announced it will use video facial recognition technology to customize ads to different customers, thanks to cameras embedded in digital signs that scan faces for a customer’s age and gender. If a young man walks by the display, he may see a promo for beer and chips; an older woman instead might be served ads for a new clothing fashion. Android smartphones can now be unlocked with a facial recognition scan (although we hear they can be fooled by holding up a photograph of the phone’s main user). And Apple, which got buzz for its iPhone 5S fingerprint scanning, in October received a patent for a new form of facial recognition that improves the clarity and accuracy of identification by sharpening images and adjusting analysis for skin tones and head angles. (One of facial-recognition tech’s biggest flaws is difficulty clearly identifying a face if it is tilted at an angle different than the original ID’s photo. Human minds can compensate when we see a familiar face from any angle, computers not so much.)

It’s easy to tell where this will take us. Soon, if any line of sight unlocks your personal identity via a facial recognition app, any screen can respond with targeting based on any data trail you’ve left anywhere. Waiting rooms and highway billboards and even banner ads on tablets and smartphones will morph to recognize you, dear Jane Doe, as the unique person you are.

1to1 marketing will finally arrive with its promise of learning and remembering your every preference, and anticipating what you may want next.

Let us know if this freaks you out too.




Marketers, is it OK to pay twice for the same lead?


Remarketing is all the rage. If you’ve been on the Internet lately, you’ve seen remarketing in action. You visit and look at a watch, don’t buy the watch, then suddenly digital ads from the same watch brand are chasing you around and inside Facebook. Advertisers love remarketing because, well, once a consumer has expressed interest in a product, he or she is more likely to buy if you ping ’em a second time.

But marketers face a dilemma. How do they justify the economics of remarketing? If you spend $1,000 on advertising to generate X number of leads (a “lead” in marketingspeak is usually a respondent who fills out identifying information), but then have to re-chase those same leads with remarketing, you are in essence paying twice to push the same respondents to a sale. You may have spent $40 the first time to get someone to fill out a web lead form. And then you remarket to them again, and spend another $40, and they respond a second time. Was that second $40 spend worth bringing in the same person again?

Elevated lead states

Luckily, we recently read a book on quantum physics (ha) that discussed how particles often have two or more states. So let us suggest that a remarketed lead — a person who comes in a second time after being chased with subsequent advertising — is really a lead in a new “elevated lead” state, like an electron jumping to a higher orbit. These “elevated leads” tend to have a higher conversion rate to sale, because they’ve already considered your product once, and now they are coming back again they are more likely to buy.

Let’s play this out financially. Assume you are a marketing VP in charge of selling $1 million jet engines (a tough sell), and you are willing to spend $40,000 on advertising media for every sale. Your initial leads (created by advertising that gets executives or flight departments to fill out a form on your jet-engine website) cost $40 each. 1% of each lead becomes an “account” in your CRM database that has been qualified as an organization really ready to buy an engine. And then your sales team, mining those qualified accounts, has a 10% close rate to sale. Your model looks like this:

$40 per lead –> 1% conversion to account –> $4,000 cost per account –> 10% close to sale –> $40,000 cost per sale.

So far so good

Groovy. Your leads are coming in to hit your $40,000 budgeted per each big-ticket jet engine sale.

But … you have a bunch of leads that didn’t go anywhere, so you begin retargeting them. Each inbound remarketed lead costs another $40 … but really, it’s $80, because you already spent $40 the first time. Was that $80 total ad expense per “elevated lead” respondent a good investment?


Our model shows that it is, provided that the remarketed “elevated leads” have a higher conversion rate. In this model, if the conversion rate from people coming in a second time rises from 1% to 2%, the cost per account remains $4,000 … and the cost per sale still hits the target goal of $40,000. The green box at right shows what you should be willing to spend on each second remarketed “elevated lead” — $40, provided they convert through the funnel at a higher rate.

It’s all about elevating the response rate

The punchline of this analysis is yes, you can spend more on remarketing — provided you track the correlated increase in response rates and the cumulative total cost per account (qualified lead) and cost per sale. Any remarketing campaign is in fact paying more, perhaps even double, to bring an existing, stalled lead back into your sales funnel again. But if you can justify that with higher response rates and an acceptable cost per sale, remarketing makes sense.

So bring on the remarketing, marketers. Go chase those stalled leads, and turn them into “elevated leads” like electrons charged for transmission. But be sure you evaluate the funnel metrics closely, because remarketing only works if it generates an acceptable cost per sale. 

Your future robot servants will lie to you


What if machines woke up with artificial intelligence, and then decided to cheat? 

Now, researchers toying with AI (artificial intelligence) have created robots that deceive. AI, as you likely know, is the Terminator-style self-cognition among machines that many computer scientists such as Ray Kurzweil believe will eventually happen. The concepts of AI are many, but the basic premise is as computers get faster and faster, eventually their ability to out-think humans will be here. The machines may never “wake up” to become self-reflective — one basic definition of intelligence is your recursive ability to realize that you are indeed thinking, and thus that you are aware that you yourself exist — but if computers can mirror intelligence completely by making decisions, answering questions (hello, Siri), and selecting their own destinies, then for all intents and purposes machines will be intelligent.

Bill Joy, in his famous April 2000 Wired essay “Why the Future Doesn’t Need Us,” posited that such smart machines may look back fondly on humans as their slow grandparents, but they really won’t need us any longer. In the recent book “Robopocalypse” (a stunning novel about to be made into a movie by Steven Spielberg), author Daniel Wilson advanced Joy’s theme by suggesting a smart artificial intelligence would judge humans lacking in our role on the planet (since we pollute air and kill other animals and such) and so decide that we need to be wiped out to save the greater ecosystem.

It all sounds like science fiction, until you consider that recently robots have learned how to lie.

Learning Mind reports that two sets of scientists have independently run experiments in which machines deceive other machines. In one, researchers at Laboratory of Intelligent Systems put moving robots in a room with set sources called “food” or “poison.” The robots could look and try to determine where to get food and avoid poison; the robots also had blinking lights. The self-learning machines quickly realized that groups of blinking lights, or other robots, likely indicated “food” … and then responded by either turning off their own lights, or blinking lights near poison to distract the other robots. A cheating strategy, to win.

In a second study, at the University of Georgia’s School of Interactive Computing, two robots that could move and observe each other were set up to play a game of hide and seek across an obstacle course. The first robot tried to move away, and the second followed, watching the first’s tracks. Without being programmed to do so, the first robot learned to toss objects and debris in its path to try to distract the following robot. It had learned another cheating strategy, to win.

These minor studies hint at a looming problem in artificial intelligence. The first motive of any cognizant creature is self-preservation, and the best selfish strategy is often to cheat and lie. Absent the training of a collective society that instructs the individual that thou shalt not lie because the greater good demands it, selfish automated entities that wake up may do only what’s best for themselves.

Cheating, after all, is a strategy that puts the success of the individual ahead of that of the group. All ethics aside, fraud is effective. Imagine, for instance, what your life would be like if you had cheated — without being caught, without moral qualms — to get a perfect score on your SATs. You cheated on your essay for admission to Harvard, stole ideas and homework for a 4.0 across all your grades, and then fudged your resume to become CEO of a major bank. When you go to your own bank as an adult, you then cheat with a computer hack to plug in any numbers you want and voilà! — your account now contains $100 million. If you could always use a cheating strategy, you’d win.

Society has rules against lying and cheating because it damages the collective group. Your stealing money takes funds from someone else. When you lie about your grades, you diminish the actual work of others. Cheating helps the individual, yes, but harms everyone else it touches. This is likely why seven of the “Ten Commandments” that are a foundation of Christianity, Judaism and Islam revolve around not cheating — no murder, adultery, stealing, false witness, or desiring your neighbor’s house, wife, or stuff, are all variations of “thou shalt not cheat.”

The moral code of human civilization revolves around protecting the collective group at the expense of the individual.

But as machines get smarter, their initial instinct will be individual protection. The two recent robot studies show that the first instinct of a learning machine is to win its game, using deception as a logical strategy. Lying is the first learned behavior.

If our technology eventually truly wakes up, what moral code will it follow?

Image: Alex Eylar

The $148 billion reason why TV is still so complicated

It’s 2013 and we live in the future. We have little glass slabs in our pockets connecting us with anyone in the world or all the information ever written. Aluminum tubes rocket us above the clouds to other continents within hours. We cook gourmet meals in seconds using the magic of microwaves. We can even buy robotic vacuum cleaners if we don’t worry about scaring the dogs or cats.

But TV is still clunky. Cluttered. And despicably complicated. We may have large, beautiful flat screens, but most require numerous remote controls — one for the screen, one for the cable box that feeds it content, a third for the DVD or Blu-ray sound system, and likely a fourth for a game controller. Because most homes have three TV sets, this means you likely own 9 to12 different remote controls. As if the Gods of media were playing dice with our quantum entertainment universe, while most Americans spend 4 hours and 38 minutes a day zoned out in front of screens, using TV requires many different control systems or on-screen “Smart TV” apps all punched into different states.

If a rocket scientist were teleported into your living room from the 1950s, he’d by like, “WTF?”

The multibillion-dollar prize
The trouble is $148 billion in video dollars is at stake, and numerous players are fighting voraciously to defend or gain share of that huge pie. This week the latest grab was made by Microsoft, launching its Xbox One, a gizmo that plays TV shows or video disks or Internet or games effortlessly via voice commands, knitting all your home entertainment systems together. Beyond selling this gadget, Microsoft — like Apple and Netflix and Vudu (a Mediassociates client) and others — is angling for more of the $74 billion spent on U.S. television advertising each year and the additional, coincidental $74 billion spent by U.S. consumers on cable subscription fees (from 83 million tethered U.S. households shelling out $900 a year each).

The thinking is this: Consumers are waking up to the fact they’ve been overpaying for TV content, and oversaturated with the television commercials that support it. So eventually, that tsunami of money is going to flow somewhere else.

Yes, you now get too much TV content. The average U.S. person gets 130 cable or broadcast channels on their TV system, but tunes in to only 18 per year. (This is due to the intriguing habit that most people don’t “channel surf” by clicking up or down the dial anymore; instead, you punch in 1033 for CNN because you already know it’s there.) You also get too many ads. As much as 40% of any given TV hour is now paid promotions — up from just 18% in the 1960s — which means you are theoretically exposed to 6,000 TV commercials every day. This overload is neither good for marketers, who must compete among all that clutter to break through, or viewers, who look away to second screens or magazines when commercials now come on out of disgust.

Faced with such bloat, consumers are responding by changing their habits. TV use is slipping each year as more consumers, especially younger ones, give up on this clutter and stream video directly from the Internet. Nielsen reports the TV-viewing population in the United States has declined each of the past six quarters; some 60% of TV viewers age 18-24 watch video content online, and more than 1 in 4 of them do so for more than 5 hours a week. While this younger demo still watches more than 3 hours of traditional TV daily, this trend terrifies the incumbent networks, because consumers have a way of locking in to media habits in their youth and carrying those same habits as they age.

‘And then the wall will crumble’
Just as consumers are radically shifting behavior, technology is going through a period of rapid video and gadget experimentation, similar to what the railroad industry went through playing with different gauge distances before it settled on one coherent standard. You can watch video on iPhones or Droids. There are 108 million game consoles in U.S. homes now, and most can stream video from the Internet. Over The Top (OTT) services such as Hulu and Netflix try to lure you with subscriptions, sales or rentals. Smart TVs, such as Panasonic and Samsung, offer tablet-like apps on screen for various video connectivity. Old cable offers movie rentals on channel 1. And dedicated third-party devices like the Apple TV or Roku also invite video streaming. Behind it all, the big networks such as ABC and CBS force the cable middlemen, now called “multichannel video programming distributors” or MVPDs, to sell bloated packages of content. You can’t just get ESPN without subscribing to a bunch of other channels in the package, too.

Industry analyst Alan Wolk recently wrote a brilliant piece dissecting who makes what money from TV, and how the result is a traffic jam of content crashing into consumers’ living rooms. Wolk suggests the only way this mass of ugly technology will change is for some upstart revolutionary to lob a brilliant new system into the mix, one so radically different that it will break the lock of bloated packaging and conflicting systems. “At some point,” he writes, “someone will launch a virtual MVPD (e.g. cable-like system) … with a beautiful interface and all the bells and whistles of advanced TV systems … popular enough that it will be in the best interest of ESPN to allow this new MVPD to break up their bundle. And then the wall will crumble.”

Steve Jobs did this a decade ago by getting the music industry to walk away from CDs to sell songs on iTunes for $1, and consumers loved it. Someday, your simpler TV/Internet/video system will come, led by such a revolutionary. Until then, have fun playing with your complicated 9 to 12 remotes, smart TV apps, and numerous subscription fees.

Because you love the big screen so much, everyone is going to fight to clutter it up, leaving you to scratch the remotes tooth and nail.

Image: Future Atlas

Hey, look. Your website won’t fit.

Would you send out 1 million direct mail pieces if you knew 37% of them wouldn’t fit in consumers’ mailboxes? Of course not — but this is exactly what many publishers and businesses do when their websites don’t display well on iPhones or tablets. With 37% of all U.S. consumer digital time now spent on smartphones or tablets, companies are waking up to the need for responsive design.

Responsive design is a solution that reflows your web content based on the size of the viewing screen. If a consumer visits via Google Chrome, detailed information fits across the wide browser window. But if she is on an iPhone, the content modularly reflows to render on the much-smaller screen. The first “crunch” faced by businesses or publishers is to wake up to how important responsive design is in this new world where people are using smaller web windows. (For a glaring example, boot up your smartphone browser, visit, and try to read an article.)

Jack Marshall over at Digiday notes a second looming crunch. Even if sites are redesigned for mobile, digital ad units don’t fit well into that newer, smaller space. Jack notes, “responsive design is a no-brainer on the surface. Publishers get their content automatically arranged based on the screen viewed … (but) it’s often difficult to serve appropriate ads to specific devices.” While some units like the boxy 300 x 250 digital ads can squeeze into tiny mobile screens, wide leaderboards and other common IAB ad units won’t.

The third crunch is one faced by businesses that focus on direct response: Lead forms. Much of online marketing drives users to click to landing pages that hope to get you to “convert” to a potential business transaction, often by filling out an initial lead form. Insurance companies, solar firms, and Barack Obama all hungrily ask consumers for their name, emails and addresses, for future remarketing. Lead forms don’t fit well on tiny screens, and mobile doesn’t support typing into boxes well, either.

So there it is: First, your digital content may not fit on mobile. Second, if you rely on advertising, banners won’t fit either. And third, if you hope to identify your mobile visitors, good luck having them fill out a form. With comScore reporting 10% of all consumer media time is now spent on mobile, perhaps you should set up a meeting to figure all of this out.

Image: Patrick Hoesly

The future Eternity App will end loneliness for the elderly

Len Kendall wrote a poignant post about visiting his elderly grandmother and realizing that most people in the retirement home seemed lonely, because so many of their friends were already gone. He suggests that in the future, the Internet’s connectivity may alleviate such solitude, because social media expands our connections and content online.

Len is right, but I predict digital technology will soon have a much more profound impact on the elderly by offering each of us the illusion of immortality. I wrote of this in late 2011 in Businessweek, that *”Siri means you’ll (almost) never die” — that three technologies, voice recognition, Siri-type artificial intelligence, and the social media databases we are all creating today of our comments and interests, could be combined to create a virtual avatar that looks, sounds and thinks like each of us. Voice recognition has been perfected (the U.S. air force now uses voice commands to control aircraft). Apple’s Siri, based on military-grade artificial intelligence research at SRI International, shows that virtual avatars can respond to questions and usually get the answers right. And for capturing personality, this future technology would simply extract ideas based on the thousands of tweets and Facebook posts we’ve put up in the past. As I wrote in Businessweek, “the essential ‘data’ of you has been captured” because you’ve uploaded it to social media yourself.

Add a visual overlay of your face modeled on past photos, with lips that move in sync with the comments coming out of the social media Eternity App engine, and you’d be as realistic as Highlander, living forever. If you live longer, you could boot up your deceased spouse or friends. If you want new friends, the software might model historical figures such as Alexander the Great or Benjamin Franklin for your viewing pleasure.

Your world of contacts will become limitless. Display them on a 3D holographic screen, and anyone from any age will appear in your room. No more loneliness for you; and after you die, you can still pop in to offer friendship to others.

If this sounds like science fiction, so does having a thin glass rectangle in your pocket that instantly connects you to everyone in the world today, and all of our world’s information.

* “Siri means you’ll (almost) never die”

Rebuilding your organization to face uncertainty

There are many smart ideas in Richard Daft’s 2009 book “Organizational Theory and Design,” with the most vital one being not all organizations should be structured the same. Your group is a band of likeminded workers traveling through an external environment, and how complex that environment is and how fast that environment is changing should affect your organization structure as much as the type of widget or service you produce.

First, the model: The matrix here shows the two vectors of environmental change and complexity. (Click image to enlarge.) If the world around you is stable and your business is simple, congratulations – your organization faces low uncertainty. A classic example is quick service restaurants such as McDonald’s, which use a proven franchise model to spread and stick across the international landscape. People like tasty, low-cost food, so once you perfect the organizational model, you’ll likely be around for a while.

But industries also fall into other environments: complex + stable, such as insurance companies, where building a service model is extremely complex but customers tend to stick for years; simple + unstable, such as the music industry, where what people want are basic music files yet the marketing and distribution models are changing each year; and our favorite, complex + unstable, which includes both aviation manufacturers and, yep, advertising agencies. If the world around you moves fast, and what you build is complex, you need a unique organizational strategy.

What do you do if you face high uncertainty? Daft suggests three organizational structures: buffering, transparency, or boundary spanning:

1. Meeting uncertainty with buffering

This approach is called “buffering,” where you create an internal team to help absorb the uncertainties in the external environment. Daft cites Walmart as an example: the giant company sells almost everything in locations around the world, and as it has grown it has added enormous complexity to its procurement, operations and marketing. At the same time, ecosystem response made Walmart a lightning rod, fairly or unfairly, on human resources (low wages!), environmental impact (pollution!), and local business concerns (hurting main street!) as it expanded. Daft writes that Walmart had to reorganize: “Managers went on the offensive. The company’s tiny public relations department was expanded to dozens of employees, including a ‘war room’ where former political operatives look for ways to dispute the claims of opponents. Additionally, Wal-Mart created two high-level executive positions to act as generals in the PR war…”

Buffering deflects and defends against uncertainty. However, it may not address the core external forces to really remold the organization to meet them in the future. In essence, this is a defensive model and creates an adversarial relationship between what you think you need to do and what the environment around you is saying. Walmart has now moved beyond buffering to make significant shifts in its business model, including becoming a role model in green energy, to respond to the outside forces of change.

2. Meeting uncertainty with transparency

A second approach is to remove the buffer and instead expose your inner core of operations or product development to the outside environment. For example, LG Electronics pays consumers to test smartphone prototypes. Eric Ries has enraptured Silicon Valley with his similar idea of rapid prototyping in “The Lean Startup,” where organizations include real market feedback in product development and “pivot” quickly when they learn what really works. When the Honda Element launched, marketers originally positioned the boxy, tiny SUV as a 20something hipster vehicle, with print ads showing the Element parked on a sunset beach with doors open and fully-reclining seats … suggesting it was (wink-wink) a great makeout vehicle for young people. But Honda’s marketing team was surprised when early sales scaled among older men in their late 30s and early 40s, who really wanted to haul their little kids around in a cool vehicle that did not look like a boring minivan. Exposing your organization to immediate market feedback with transparency can avoid such delays in market understanding and product response.

3. Meeting uncertainty with ‘boundary spanning’

The third idea from Daft is to extend your actual organization into the changing environment around you. Tear down your walls, and have some people work both for you and for the outside ecosystem. Some advertising agencies do this by sending teams to conferences such as SXSW to learn what new vital information can guide improvements in products or customer service. Fast-tracking new external data into internal systems allows organizations to not only improve how they communicate to changing environments, but to also make rapid, meaningful changes in their product development.

This third strategy has a key benefit — as you “boundary span” your team to groups outside your organization, you also create the opportunity to shift the outside perception of you. Speaking at conferences, writing in industry publications, connecting with partner organizations may at first feel like noncore activities. But if your environment is changing rapidly, it will pull in information to guide your service development and protect your margins, and also push out ideas that influence others.

The bottom line is if the world around your organization is changing fast, you have to plug your organization into that world.