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Why Apple tests silly iPhone apps like Clips

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Two weeks ago Apple launched Clips, an app designed for cutesy video editing on your iPhone. Users can combine clips, add filters or emojis, and even use voice translation to quickly put subtitles on videos. The app is not yet embedded in the native iPhone camera app (you have to download it from Apple’s App Store) but expect that to come soon.

But why in the world is Apple doing this, when Snapchat and Instagram and thousands of other apps offer similar video editing and better sharing?

Because Apple needs to protect its iPhone. What many commentators have missed is Apple has morphed in the past five years into, well, an iPhone company. iPhones now make up nearly three-quarters of all Apple revenue. In business-speak, this is known as becoming “concentrated” — where one product line drives the majority of your business — and that is a scary place to be. Because what happens if that one thing starts to go awry?

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Apple’s risk factors

Now, consider the risk. In its annual report, Apple, like all public companies, discloses “risk factors” of things that could go wrong with its business. Public companies are required to share these risks, and if you want insight into the future of any business, it’s always smart to start with the challenges they face ahead. In its 2015 annual report, Apple writes:

Global markets for [Apple’s] products and services are highly competitive and subject to rapid technological change, and the Company may be unable to compete effectively in these markets … if the Company is unable to continue to develop and sell innovative new products with attractive margins … the Company’s ability to maintain a competitive advantage could be adversely affected.

And there’s the rub. Apple lives or dies now on iPhone sales, and the iPhone is becoming a commodity. The current largest model, the iPhone 7 Plus, has a 5.5-inch hi-res diagonal screen, 32GB base storage, and 12 megapixel cameras. Hm. The new Samsung Galaxy S8 has a 5.8-inch screen, 64GB storage, and 8 and 12 megapixel cameras. A space alien exploring our technology culture would be hard pressed to tell mobile hardware apart.

So Apple’s future is software

It sounds irrational to predict that the Cupertino technology giant that conquered the world with slick, Jony Ive-designed hardware will ever pivot to software, but that is exactly what Apple must do. Mobile devices housed in glass and aluminum frames are becoming, well, basic glass rectangles, and the nuances of an Apple iPhone vs. Samsung Galaxy vs. Sony Xperia vs. HTC One M9 are merging fast. The real differentiator of the future will be the images and sounds emerging from transparent panes.

Apple still has some hardware upside, but it is closing fast. In 2016, global smartphone sales were $428 billion, and by this year one-third of the world population now owns at least one mobile phone. Apple and others can push farther into the human population, and entice us all with biannual upgrades. And it’s trying with ever-fancier iPhone shapes.

There are rumors that Apple is building slicker augmented reality visuals into its future iPhones … or that iPhones may have wrap-around glass screens, eventually turning the entire device into a glowing orb that could be translucent, invisible (imagine the front-facing camera making the back of the glass phone “disappear”), or a portal into a 3D immersive world. But these visual tricks are already being tested by other brands’ hardware, such as the Nintendo 3DS which uses eye tracking to project a stereoscopic vision.

Eventually, all these gadgety panes of glass will become like windows in your wall — something that you expect to use, but that you don’t really value much at all. The shape of the portals into the new virtual worlds will start to become less valued, and the software powering those new digital images will be all that matters. We are on the verge of a multibillion-dollar mobile hardware industry collapsing as technology advances to the point where digital screens become as common as pieces of white office paper.

All of which is why Apple is testing silly iPhone apps like Clips.

See more of our point of view on this trend in this edition of Digiday.

Mary Meeker points to a hands-free, zero-screen future

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Wouldn’t it be ironic if in our rush to adopt media technology, we all decided to ditch computer hardware and screens altogether?

It’s starting to happen. Several years ago Disney Research created a Touche interface that turns any surface into a digital input device. By tracking the vibrations you make when you sit on a coach, or tap on a tabletop, or even splash your hand in a bathtub, Touche would signal electronic devices to take action. Lie down on the sofa, and your living room lights would dim. No keyboard required.

We thought of that innovation recently reading Mary Meeker’s influential “2016 Internet Trends” report. Meeker, one of the top analysts in the first 1990s Internet boom, is now a consultant for the VC firm Kleiner Perkins Caufield Byers, and her annual late-spring slide show on media trends is one of the most anticipated pieces of content in the marketing industry. This year’s report had some typical, predictable findings (mobile ad spend is still out of sync with mobile share of eyeballs!), but one intriguing new section on … hands-free device inputs.

Meeker expends several of her slides on voice-recognition trends: the use of technologies such as Apple’s Siri or Amazon’s Alexa to understand commands and respond with actions. Philips, for instance, now sells Hue “personal wireless lighting” bulbs that can be given individual names and controlled via voice, partnering with Siri on an iPhone. “Reading light, please dim” will now make your reading light dim. Home Depot sells Bluetooth wireless locks that open with a tap, no key required. Belkin offers electrical outlets that turn on triggered by motion, so your coffee maker can boot up when you stroll into the kitchen each morning.

Meeker notes that this trend toward hands-free, screens-free user interfaces on electronic devices is rising fast, thanks to a few factors:

  • Voice accuracy is improving. Google’s voice systems now clear 91% accuracy in recognition of tens of thousands of words. What used to be difficult, getting a gadget to understand a voice command, is now easy.
  • Consumers are tired of the plethora of touch-screen-oriented apps. While the typical U.S. smartphone user has 37 apps on her phone, she uses only 3 of them — Facebook, the Chrome mobile web browser, and YouTube — 80% of the time.
  • Simple tasks, after all, don’t need keyboards. Consumers are recognizing that voice just works better for short commands. 55% of voice searches are done while driving a car or “on the go,” with top commands including “navigate home,” “call Mom,” or “call Dad.” (Sadly, moms get twice as many calls from kids as dads, but that’s another story.)

The use of hands-free computing interfaces is rising fast; only 30% of U.S. consumers reported using voice commands with technology in 2013, while by 2015 that portion had jumped to 65%. With augmented vision devices such as Magic Leap soon replacing video displays, thanks to their ability to beam hi-def images of screens into the air like a Tony Stark Iron Man hologram, keyboards and computer monitors may become a thing of the past.

The irony of this rush to control the Internet of things via the air is some device-makers may put themselves out of business. When your couch controls your lights, and your TV screen floats in front of your augmented eyeglasses, will we need solid screens or keyboards at all?

The ‘Small World Theory’ of going viral

LL Bean Duck Boots

Why are L.L. Bean duck boots, a product that’s been around for 100 years, suddenly everywhere? The retailer will sell 500,000 pairs this year, up 3x from a few years ago. Kanye West just launched his own brand of the footwear. Marketers trying to “go viral” in today’s world of social media likely understand the basic dynamics of seeding conversations among influencers. But one model often neglected is how ideas that completely oppose each other — say, Hillary Clinton vs. Donald Trump, or rubberized Maine boots donning the feet of New York City hipsters — often collide in networks to surprising effects.

Let’s start first with how things spread in social networks. In 1990 John Guare wrote the play “Six Degrees of Separation,” later made into a movie with Will Smith, which theorized everyone in the world is connected via relationships in only six or fewer steps. Put the right idea in the right network connection, and that idea might spread to everyone. The theory was made more popular by Malcolm Gladwell’s writings and the movie game “Six Degrees of Kevin Bacon” (think of a film with Kevin in it, his other actor, and you’ll likely connect that second actor to any other actor if you’re clever, ’cause Kevin gets around…).

As social media emerged, this theory was one of several others — including Robin Dunbar’s rule of 150 relationships, Metcalfe’s law of network value, and Zipf’s law that things in series always follow in statistical diminishing value — that helped marketers understand how things spread virally online.

The mathematical formula for going viral

The idea of “going viral” actually has a basic mathematical model. Ideas, or “memes,” spread when the passalong rate exceeds the absorption rate of each next node, multiplied by the cycle time. This basic formula for “going viral” …

Viral spread = (Message generation rate — Absorption rate) *Cycle time

is used by companies such as Symantec and organizations like the Centers for Disease Control and Prevention to predict when digital or biological viruses will scale to the masses. 

But there is an important second part to network theory, which explains why ideas seem to replicate and also run into their polar opposites at the same time in human networks.

The Small World Theory

In June 1998, researchers Duncan Watts and Steven Strogatz published a letter in Nature called “Collective Dynamics of ‘Small-World’ Networks.” They analyzed biological, technical and social networks and found a paradox in almost all network connections: While individual “nodes,” such as humans on a computer, tend to cluster in groups of similar beings, even tightly knit network groups tend to have a few links that shoot out to another clustered group somewhere. It only takes a few of these distant links to collapse the overall network into a “small world,” where ideas or viruses or memes from one clustered population can rapidly spread to another. Imagine, a community is hit with a bad flu virus, and then just one person gets on a plane. Or you share a funny story about Donald Trump or Hillary Clinton, and suddenly one Facebook friend gets upset. Watts and Strogatz called this near-and-yet-far propagation the Small World Theory.

We see this dichotomy in our U.S. presidential race (Donald Trump vs. Bernie Sanders supporters applauding each other while yelling at others), in our broader media ecosystems (Fox News vs. MSNBC information bubbles, often reacting to what the other side says), and even in our global political tribes (Western liberalism vs. ISIS conservatism, a fight that The Atlantic recently reported is largely based on the perceived role of women in society). What one group considers normal is validated by others in close proximity, but the idea is shared across a long connection to another group who may despise the same idea.

For marketers, this Small World Theory has big implications, because occasionally an idea loved by one community can break through to another via these random long connections. Think of today’s fad for L.L. Bean duck boots, which are now running out of stock due to college-student demand; or the surge in beards among all U.S. males started by a few hipsters for a November “Movember” cancer-awareness stunt, harming razor blade sales. Small led to big, and somehow, big adapted.

Because ideas spread from close homogeneous groups first to different-interest groups second, a marketer must rethink her strategy to two stages. First, an idea should not only resonate among the product’s closest fans or prospects, but also be able to influence a different population at the second stage, when it is boosted via long-network links to groups outside the core audience. If both stages can be achieved, the marketing idea will then truly scale. The ideas with the greatest sticking power — today’s major religions — have followed this dynamic.

As James Gleick wrote in his masterful book “The Information,” “the network has a structure, and that structure stands upon a paradox. Everything is close, and everything is far, at the same time.” Networks are built to replicate the ideas we love among those nearest us, and at the same time, send our ideas into orbit among others who don’t understand how we think at all.

Who’s fighting for Black Friday?

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Our friend Edward Boches, a professor of advertising at Boston University, recently posed a question on Facebook: “Are we, in advertising, responsible for the real life version of Hunger Games?” He was alluding of course to the images of Black Friday shoppers battling for electronics, parents stealing boxes from the hands of other parents’ children, stampedes by doors, that type of thing. Boches linked to a 2014 column by Luke O’Neil in The Washington Post, who suggested Black Friday “brawl videos” are how rich people shame the poor: that is, wealthy people stay home, aghast at the consumerism we see among the less-fortunate, who race for sales stoked by the elite.

Well, no, this is not the case. Holiday retail sales in fact appeal to all demographics, with the price framing thought up by Richard Thaler in 1980 becoming a core motivator of human behavior. Black Friday shoppers closely mirror national averages for household income. What is different is the crowds on Thanksgiving Day and the Friday thereafter skew young.

But before we dig into Black Friday profiles, let’s see where this strange shopping holiday came from.

Army vs. Navy

The common legend is the day after Thanksgiving was the date in the calendar year when retailers went from being in the “red” – with expenses greater than profits – to making it into the “black” financially; hence “Black Friday.” But the History Channel recently reported the actual holiday name stems from a day of raucous shopping and shoplifting in Philadelphia in the 1960s, when an annual Saturday Army-Navy football game brought throngs of consumers into the city the day after Thanksgiving. Police staffed up to manage all the retail turmoil. This year, the National Retail Federation reported 135 million Americans planned to shop over Thanksgiving weekend. Many retailers began opening their doors to sales on Thanksgiving as well. Walmart and Target opened on Thursday this year at 6 p.m.; JCPenney at 3 p.m.; and the Family Dollar Store at the ungodly hour of 7 a.m. The encroachment of retail sales on turkey day seems unstoppable.

Who shops on Black Friday?

The crowds that come on Thanksgiving or the Friday after closely match national demographics for household income, countering O’Neil’s opinion that Black Friday shopping is a sport for the poor – but they do skew younger, being 86% more likely to be under age 30 than average shoppers, and slightly more female, according to a national study by CivicScience.  (Gallup found similar results in a 2012 poll, with 34% of adults age 18-29 being the predominant Black Friday shoppers.) These consumers are technologically savvy, with a majority using smart phones to check prices and coupons. And they’re most interested in buying electronics or clothes — items where seeing, touching and feeling seem intrinsic to the purchase decision.

But beyond the youthful rush at the mall, consumers may be pushing back on Thanksgiving-week sales. CivicScience found that 90% of U.S. consumers said they were not at all likely to shop on Thanksgiving Day, and 81% were unlikely to shop on Black Friday.

And all the rush to open physical doors earlier does not appear to be jacking brick-and-mortar sales. The National Retail Federation reported today that 103 million U.S. consumers shopped online over Thanksgiving weekend, beating the 102 million who showed up at malls. ComScore reports that digital sales last Thursday and Friday were up 20% over the prior year, compared to brick-and-mortar sales being slightly down.

The upshot is ironic: Retailers continue to push further into the holiday calendar, opening earlier and earlier on Thanksgiving Day, while the holiday shoppers are moving more to online outlets. Black Friday is not an event the preys on poorer people, but rather on the psychology of all consumers, enticing with perceived deals as the dark of winter approaches. The most interesting trend is older consumers, where wealth is concentrated, appear most likely to stay home, surfing for discounts by the warm light of their computers. Why hello, there, Cyber Monday.

 

It’s the day Marty arrives in Back to the Future. Did we get what the film promised?

Back to the Media Future Oct 2015

It seems like yesterday when the film series Back to the Future delighted 1980s moviegoers. But now, the date when Marty McFly arrives in the future is here: October 21, 2015. Did our modern world give Marty the media and technology he was promised?

Click on the infographic above to see. And keep your eyes out, Marty’s DeLorean will be arriving any second…

Find terrorists, forecast sales: A game of predictive analytics

GAMEBOARD FOR PRED ANALYTICS

Last week I spent some time with LeapFrog Solutions, a Washington D.C.-based think tank specializing in federal agency communication and change management. Next to me in a conference room filled on one side with glowing windows and the other with whiteboards was Bob Derby, LeapFrog VP of Strategic Communications. On the phone was John Verrico, president of the National Association of Government Communicators and chief of media relations for the U.S. Department of Homeland Security’s Science & Technology Directorate. Bob is one of those management consultants with nearly 30 years experience who helps organizations plan their entire staffing and missions, and John is a rare data-communication guru hybrid who has helped federal agencies use analytics to track down terrorists.

Our mission was to help draft an upcoming seminar on Predictive Analytics, or more precisely, how to help enormous federal agencies take best practices from Corporate America to turn “big data” into actionable steps to achieve program outcomes. Frankly, it’s a head-scratcher.

The topic is enormously vast—predictive analytics range from modeling credit scores and terrorist threats to machine learning, data mining, and marketing campaign forecasts. Modeling varies across industries, from pharmaceutical giants predicting drug adoption curves to insurance companies evaluating the future impact of global warming on policies for homes on the Gulf Coast. How in the world do you boil the vast data flowing around every organization into a system for valid forecasts?

Yet as we drew diagrams around the topic, a simple model for predicting outcomes from complexity started to emerge.

When New York City’s Citicorp building almost fell down

First: What are predictive analytics? 

In one of our favorite data stories, documented by Joel Morgenstern in The New Yorker, in 1978 William LeMessurier, lead designer of the Citicorp Center skyscraper in New York City, received a call from a young engineering student who had been tasked with evaluating the Citicorp building, what was then the seventh-tallest building in the world. The student said that he had modeled wind forces on the building and thought the columns at the bottom—placed in the center of each building side, allowing for the bottom corners of the building to jut out over space several stories in the air—had been put in the wrong spots. The building, the student thought, might fall down in high winds.

LeMessurier chuckled at the kid’s naivety, got off the phone, but later re-ran some numbers and found, to his horror, the building actually might buckle in hurricane-force winds if they were what sailors called “quartering winds,” coming from an angle and hitting two sides of the building at once. Hurricanes did hit Manhattan every 90 years or so. And thus began a secret race to reinforce the Citicorp building’s structure from the inside out, all due to an error in math.

The Citicorp story is an illustration of predictive analytics: You are trying to build something (here, a 59-story tower that won’t fall down), need to evaluate how internal systems you control (steel beams) support the outcome, but also need to forecast external factors (high winds) that put stresses on your system. Your data must follow a chain of logic from outside to inside, prediction to event to result. If you model it right, you can control the outcome.

A simplified ‘gameboard’ for prediction

During our call, Bob and I started doodling in notebooks and the whiteboard, and a lucid model for “Predictive Analytics” emerged. The first draft looked like this:

pred analytics sketch

It’s extremely simple, really. The Y vertical axis, at left, shows two major areas—the external environment of things you cannot control, and the internal systems that you can control. The X horizontal axis, at bottom, shows time in three groupings: predictions, the period of time before your campaign or activity when you need to anticipate outcomes; events, the things that happen as your campaign is running; and response, how you react (in marketingspeak, this is often called the “optimization” period of your campaign).

Thus, on one side of the board, the things you can and cannot control; on the other, your forecasts, events and response.

Now, within the board, analytics and systems are grouped into six areas:

1. Game. This is where you make predictions for external environmental factors beyond your control, but which if gamed out, could be anticipated. (We use “game,” from “game scenarios.”) Example, a contender for president could have gamed out that a populist billionaire such as Donald Trump might enter the race and springboard off the undercurrent of economic dissatisfaction and fear in this country. No politicians can control Donald Trump, of course, but his current success in the polls could have been predicted. Marketers can game out competitor moves. Business leaders can explore Michael Porter’s 5 Forces models of competitors, suppliers, customers, market entrants, and market substitutes. Government agencies can game out scenarios of news events that might raise or lower public opinions of their missions. The future is uncertain, but major environmental influences on campaigns can be pre-conceived.

2. Forecast. This is where you model the variables you can control, and forecast outcomes using various statistical methods. In marketing, this typically involves comparing financial inputs into a campaign into forecasts for impact in awareness, response, conversions, acquisitions, and ROI. Forecasts can be tied to benchmarks of prior similar campaigns (overall) and specific communication pathway performance, channel by channel, medium by medium. A $1 million investment, providing no shocking “game” influences from step 1 above, should lead to XX,XXX results flowing out.

3. Test. This is real-time event management by testing variables you can control. Messages, ad creative, media channels, influencer outreach, conversion pathways, all should be tested with different flavors, colors, images, media tactics, and path structures. Even the very audience you are trying to reach should be tested. A classic example comes from automotive; when Honda launched its boxy Element mini-SUV in 2001, it originally thought buyers would be young men in their 20s who wanted a cool beach-surfing-camping vehicle. Buyers turned out to be dads age 35+, who wanted a fun small SUV to carry kids around in without looking like a mom in a minivan. As test data flowed in, Honda rapidly pivoted its ads away from pictures of the Element on the beach with fold-down seats for fully reclining (wink, wink, young men) to more family-focused advertising creative.

honda element ad

4. Monitor. This step means setting up rapid-response monitoring systems, so you can react to the world’s events as they happen. Tied closely to Step 1 above, gamemonitoring systems may include ongoing analysis of competitor organization communications campaigns; tracking of news stories; or sentiment monitoring of consumer discussions about your mission on social media using tools such as Radian6. One great, simple approach to setting this up is to draw a “touchmap” of data flows from all major outside factors (audiences, organization outlets that touch audiences, competitors, sales systems, news events, market entrants) to your internal data systems. Then, draw little stop signs where you have gaps from data you might need to your internal flows. Then, fix the gaps.

5. Measure. This is a simple word for a vast construction, and we’ve written elsewhere detailed guides to campaign measurement methodologies. But at its core, measurement means evaluating your campaign from the audience perspective—how their attention is reached efficiently; how their awareness is increased; positive or negative shifts in sentiment, responses, conversions; and the economic cost per desired action. Measurement puts the data dictionary and data flows against your Step 4, Monitor. It is worth being clear here that measurement does not necessarily mean investing heavily in new technology systems. For most organizations we work with, clients have all the systems they need in place already; instead of more investment in data systems or software, what is often needed is simple counsel in connecting the dots.

The danger of measurement is the output can be overly complex, leading to dashboards that look like a mathematician lost his breakfast. We recommend setting up KPIs (key performance indicators) that use a tree-branch structure, similar to drawing your family genealogy. What 2 or 3 main factors are you really trying to evaluate (such as your 2 parents)? For a marketing campaign, these might be lift in intent to use our product (a core brand metric) and cost per customer acquisition (a core direct-response metric). Behind them, what supporting data elements lead to these KPIs (similar to your grandparents and great-grandparents)? Etc. By nesting your measurement findings into a hierarchical structure that leads to a few core outcomes, you can both measure real progress in the major terms and also explore the more minor variables that create the chain to these results.

6. React. This is the punchline, the moment when you react to how the market around you has moved. But instead of reaction being “reactionary,” if you’ve successfully staged the preceding steps, you will be able to react smoothly and calmly to redirect your campaign as outside forces and audience results emerge. If the stock market crashes tamping down demand for your product gizmo, you will have anticipated this. If breaking news tosses a PR crisis your way that damages your brand, you will have a plan B and subsequent plan C in place. And whatever breaks, you’ve pre-installed measurement systems to gather news in as quickly as possible and gamed out rapid response pathways to maximize your influence.

It’s a very simple gameboard, filled with, yes, lots of complex work. But this work does not have to be expensive. We recommend as you consider predictive analytics that, instead of investing in a million-dollar data system, you throw this model on a whiteboard in your office, break out some yellow Post-It notes, and see how simply you can cover all the bases.

Update: We’ll be speaking on Predictive Analytics for the Future Nov. 5 in Washington, D.C. We’ll post details on the event in this space soon. If you would like to attend this morning session on Predictive Analytics, email benk@mediassociates.com.

 

 

 

The pigeon-guided missile (building a marketing offer)

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One of the most important elements of any advertising campaign is not the brand or media channels or call center or landing page. It’s the offer. The typical U.S. consumer is bombarded with more than 6,600 TV ads and 375 minutes of radio commercials each month, so no matter how compelling your brand story, an offer is required to break through. So how do you construct one?

Our favorite models come from the work of psychologist B.F. Skinner and behavioral economist Richard Thaler. Let’s start with the story of Skinner’s pigeon-guided missile.

Birds in the front of a WWII missile?

You likely remember Skinner from a college Psych 101 class as the Harvard psychologist focused on human behavior, famous for the concept that dogs, if fed after a bell rings calling them to dinner, would start to salivate every time they heard the bell.  But did you know in World War II he trained pigeons to guide surface-to-surface missiles?

Skinner believed that all behavior was the result of such triggers, and complex actions were simply caused by the “chaining” of a series of trigger events. In the 1940s, he used these ideas to solve a vexing issue for the U.S. military: Missiles were expensive, but no autonomous guidance systems had yet been invented to fine-tune their direction as they flew through the air toward enemy boats once they had been fired. Skinner had the brilliant idea to set up three compartments at the front of a missile with lenses pointed in three directions, projected on three internal screens. A pigeon would be placed in each compartment, trained to peck on a screen if it saw the image of a battleship, and the peck would guide the missile to turn in that direction. Skinner received $25,000 in funding — about $420,000 in today’s dollars — but the military eventually stopped the project out of, er, disbelief.

To put a finer point on it, Skinner conceived there were two forms of behavioral triggers:

Respondent behavior, where an outside event makes a being respond. Pigeon sees image of battleship, and used to getting grain, it pecks. You smell toasted food from the oven, and you get a pang of hungry desire.

Operant behavior, a more complex trigger, in which a person performs an action started internally that is then reinforced or discouraged from the outside. Say, an executive smiles brightly in a meeting one day and everyone suddenly warms to her idea; she learns to smile repeatedly when she wishes to influence others.

Everything animals or people do, he suggested, came down to chains of respondent (outside) and operant (internal) triggers.

The components of a good offer

What’s interesting about this (rather crazy) missile idea is Skinner, when tasked with setting up the fastest response mechanism possible, guiding a missile, resorted to respondent behavior triggers from the outside — the simple visual cues that made pigeons peck. There were no complex chains of multiple trigger events. When a response was needed fast, he relied on simple triggers first.

Advertising offers work in a similar vein. While internal marketing departments tend to think deeply on their products (as is their job) and fall in love with the nuance of how they may be slightly different than competitors, outside customers who rarely think of you must make a decision in split seconds as to whether your story is enticing. The external trigger needs to be lightning fast — akin to the smell of warm toast, or an image they immediately recognize.  

Richard Thaler, the University of Chicago mind who invented the field of behavioral economics in his landmark 1979 paper “Toward a Positive Theory of Consumer Choice,”  created a model for building rapid-response triggers. In essence, Thaler suggested humans are irrational, busy, and apt to emotional judgments. Fairness is a classic cloud that causes people to make illogical decisions. Logically, if a business deal could earn you $10,000 in profit you should take it. But if you felt the deal should be worth $20k instead, you might walk away because the $10k offer feels “unfair.” On the flip side, if you thought $5k would be fair, you’d rush in to close the $10k deal, because you’ve somehow won extra. Thaler’s research found that the actual end result isn’t what matters for humans as much as the perception of whether we are victorious along the way.

Thaler suggests the best way to influence humans is to separate gains, minimize losses, and use a reference point to play to the human psychology that we all like to win more than we lose.

Combining Skinner with Thaler, here are the components of a good marketing offer:

  • Set a reference point to “frame” your value. Decisions on gains are often made compared to a fictional “reference point.” Thaler found most people are bad at judging value, so we all like to be told what the starting reference point is on a product or service. A man may not want a suit priced at $500 … but if the suit is on sale marked down from the (fictional) price of $1,000, he suddenly covets it, now “50% off.” So one way to influence people is to show how the cost of your product is far below a (fictional) higher price.
  • Set a time limit suggesting they’ll lose something. Thaler found that most people hate losing $100 more than gaining $100. We have a steep aversion to loss, because psychological it seems unfair. So set up a fictional loss, such as a time-constraint on an offer after which “savings” will disappear, to spur behavior.
  • Segregate gains suggesting they’ll win more. In several studies, Thaler found that if given a choice between winning a lottery two times for $50 and then $25 respectively, or winning just once for $75, the majority of people preferred to win twice. Again, numerically this makes no sense, but emotionally we feel better about the “winning events” than the actual value. So marketers can separate gains into distinctive categories — “get X savings and also Y free!” — for the same value to drive greater response.
  • Integrate and minimize losses. Unlike the lottery example above where people prefer to win smaller prizes more often than one big prize, if given a choice in pain, humans prefer to take a loss all at once. So make the actual cost (pain) of the product as simple and minimal as possible. Don’t charge for the coffee and the coffee cup and the lid and the sleeve separately, because even if they all add up to $1.50, people will be turned off by the multiple cost pain points. Don’t charge for the product and installation and service separately. Instead, lump all costs together, and if possible spread them out into low payments to make the “pain threshold of price” as low as possible. The best price point is simple, low, and nearly invisible. This is why Netflix charges one low fee per month for streaming video, and not a separate fee for every movie you want. This is also why Amazon.com has had huge victory with its “free shipping” that really comes bundled into an annual Prime membership fee. The loss, or price paid, comes once, but the benefit feels free every time you order something throughout the year.
  • Consider bundling or price obfuscation. All of the above can be combined to maximize gain segregation (the series of good things the consumer “wins”) and minimize loss (the financial cost the consumer “loses”) with bundling. Omaha Steaks is the classic example, where a package of bacon-wrapped filet steaks comes bundled with burgers, franks, French fries, vegetables and something called apple tartlets. All marked down from $173.00 to $79.99. Is this a good deal? We don’t know. But this complex bundle coupled with a simple price below the reference point sure sounds desirable.
  • Avoid complex service claims and focus on desire. Your long-term customer service may be incredible, and that’s great for building loyalty, but the “offer” in your ad must be a simple understandable hook that gets the consumer pigeon to peck. New response triggers must come from the outside and be easily understandable — an element of your product or service that is immediately desirable, and that consumers must feel if they don’t act now, they will lose.

In sum, marketing offers must be weighted toward Skinner’s “respondent behavior” — fast triggers from the outside that are immediately understandable — vs. “operant behavior,” the other more complex triggers learned over time. There is a difference between the elements of your product/service that build long-term loyalty and the sizzle that gets consumers to respond right now.

Like the enemy battleship screened before the pigeon, you have to make your offer immediately recognizable. Without a strong offer, you’ll miss the boat.

Media predictions for the far-forward future

woman hologram

Put down your smartphone app and think far, far ahead. Media prognosticators rarely do this, perhaps because advertising clients and digi-journalists gain more from toying with the latest Twitter group chat update than they do by conceptualizing the state of media 100 years from now … but a far-forward forecast could be worth the effort.

So let’s play the prediction game.

Before we start, here is our inspiration: the brilliant book “The Next 100 Years,” in which George Friedman examines the macro trends of history in attempt to predict world events through the coming century. It’s an amazing feat, reeking of intellectual arrogance, to try to foresee 100 years of future events … until the reader discovers that Friedman has a solid methodology.

Friedman bases far-forecasts on geopolitics — the combination of national resources, locations on the globe, culture, and economics — which has ongoing patterns that make shocking events, such as World War II or the terrorist attacks of 9/11, predictable. Individual players on the planet, even presidents or kings, typically have far less power than we imagine, and must play upon a chess board that is already set. The future, it seems, can be predicted, if you really examine the macro trends. For instance, looking backward, Friedman argues:

  • It was inevitable Europe would become a global power in the 1800s, because it needed supplies from Asia, and after Turkey cut supply routes over land Europe sought a route west to India by sea and thus learned to manage the oceans — controlling global commerce.
  • It was inevitable that the United States would win the cold war over Russia in the late 1900s, because allies to the United States could “sell in” to its vast consumer demand set, making U.S. friends rich, while Russian allies might get weapons but end up impoverished. 

And, looking forward, he suggests:

  • The United States will remain the leading world power in the 21st century, because it controls the world’s oceans, due to its fortuitous placement between both the Pacific and Atlantic oceans, thus controlling trade.
  • There will undoubtably be another horrible global war in the 21st century, given the tensions between the rich and poor and the continued belief of humans in their personal nation states.
  • And this war, like all others, will eventually end, and generate new technology systems from the wartime investment that lead to sources of clean power and communications we today can barely imagine, such as microwave energy beamed down from outer space — the most efficient way to capture energy from the battery in the center of our solar system, the sun.

Oceans, human antagonism, and sunlight are all constants, and they will define our future.

So what is the real far-future of media?

Here are our predictions: (1) Environment monitoring, driven by sensors around us; (2) virtual visual overlays that are constantly on, created by the inevitable shrinking of screens until they fit in your contacts; (3) ambient personalization, as you control what you see everywhere; and (4) societal upheaval as we relearn how to interact with other humans in a virtual world.

1. Sensors everywhere — Behind all the Nielsen updates on multiscreen use or Pew reports on social media fads, the truth is information flows only two ways — to us or from us. While media writers remain fascinated with toys, the biggest trend in information flow is the spread of connected sensors in all devices. The iPhone 6 has six sensors built into it — including proximity, motion sensor/accelerometer, ambient light, moisture, compass, and a gyroscope — coupled with GPS features that pinpoint the phone’s location. The Disney Research lab has created a Touche interface that can turn the surface of any object, such as a table or couch, into an input sensor by monitoring vibrations created by human contact. Philips has launched “design probes” that explore tattoos with sensors that disappear based on touch, and clothing that changes color based on your mood.

With sensors everywhere, you will be tracked. Tracking will require control, so humans will use that to personalize their environments (a benefit) while suppressing unwanted third-party oversight (a cost).

When you walk into a room in 2070, the room will know who you are.

2. Screens everywhere — Concurrently, the spread of screens is obvious. At SXSW Interactive last spring, the head of the Consumer Electronics Association, Gary Shapiro, said that within 10 years consumers will buy wall TVs — or whatever we will call high-resolution digital screens that fill an entire wall. Apple has a patent for holographic wall screens that project 3D images to both eyes of each user in the room, without them wearing googles, by monitoring the location of their heads. And Microsoft has just launched a HoloLens goggle prototype that overlays 3D images on reality with a wider field of vision than the (recently aborted) Google Glass. As big TVs grow into walls, little visual screens will also shrink into contacts.

With screens everywhere, you will see whatever image you wish to pull up.

3. A personalized universe — The great media prediction for the next 100 years is that humans will be able to retreat into completely personalized bubbles of vision, overlaying data about others in their contact lens, porting their images into virtual meeting rooms thousands of miles away, and pulling entertainment into the real world around them. Because if everything (from couch to table) senses you, things will recognize your preferences, creating demand for automated content that overlays your reality to meet your unique needs.

From a content creation perspective, this will unlock a gold mine of opportunity for film (hologram) producers, game designers, social media entrants, work/office productivity software, pornographers (always the earliest refiners of visual technology), religions (where belief systems could now be “seen” as reality), and yes, marketers (who will find a way to support this content with some form of advertising over there on the side). This information rush will become fuel for economic growth, with visual services an entire new platform for monetization.

4. Societal unrest — These media trends are our predictions, not Friedman’s, but he has a point that may refine ours: Every evolution in society comes with unintended consequences. The vast rise of visual screens and the concurrent measurement of human personal preferences on every surface device may unearth new social dynamics we cannot anticipate. Will people become more gregarious as they seamlessly are able to beam their avatars into the world? Or will humans retreat into dream bubbles, like those poor enclosed battery souls in the Neo Matrix, asleep in cocoons while they envision a fantasy of greatness?

We cannot predict that. But one thing is certain: The far-forward future contains much more than an iPhone.

Why Google beats Facebook in mobile conversions

facebook mobile

Adweek seemed surprised this week to report two findings — a whopping share of Facebook and Google ads are now being served on mobile, and yet for some reason, both lag behind desktop ads in conversion rates (the % of people who click on an ad who end up completing the desired action, such as filling out a lead form). So let’s break this down:

1. First, both Google and Facebook have much lower conversion rates on mobile than on desktops. Marin Software monitored $6 billion in ads, or about 3 billion ad clicks, and found that while Facebook received 63% of all ad clicks on smartphones and tablets, only 34% of its conversions happened there. Google did a little better, with 39% of all paid search clicks being on mobile and 31% of all purchases made there.

2. Should mobile marketers panic? Well, no. Duh. Mobile devices have small screens and awkward touch keypads, so conversion will be lower, of course. Have you ever tried filling out a web lead form or typing in credit card information on an iPhone? So the overall trend will be for consumers to explore ad information, if interested, on mobile, but then convert on desktops (or even by telephone or physical store) later. This explains why Facebook mobile ads have only a 0.3% conversion rate vs. its desktop ads converting on average at 1.1%.

3. Now, within this race, why did Google still outperform Facebook on mobile conversions? Modality. Google search ads are triggered by consumers who instigate a search for a particular product, so they are already leaning toward conversion. If you punch in “airline tickets to Florida” on your iPhone, odds are you may be thinking of making a travel purchase. Facebook ads, instead, are pushed out to target consumers who have expressed no immediate interest in buying the product — so even if they click, their mode may be one of cursory exploration vs. immediate consumption.

All of this is to say that mobile ads can work very well in reaching audiences with information about a product; marketers should also take heart that most conversions happen subsequently across different channels. 100% of television ads, for instance, have conversions elsewhere — web, phone, retail store visit — because no one buys anything by clicking on a TV ad. (You can’t.) Imagine the histrionic Adweek headline: “U.S. marketers spend $70 billion annually on TV with a 0% response rate! Why aren’t there conversions?” Um, yeah.

Break out the regression analysis

The real solution to cross-channel mobile is to use multichannel measurement, evaluating the responses from a cumulative mix of digital or traditional advertising media. (We do this for clients with a mix of software and statistical regression analysis*; it’s quite fun.) The real story may be those early ads on Facebook spark interest that bring people in to Google search later, just as a TV campaign can build lift across physical stores. All ads are connected. The data trails between them are complex, but can be measured.

So keep making mobile impressions, marketers. Your spouse didn’t marry you after your first impression. This is not to say that first impressions don’t count.

* If you are new to stats, “regression analysis” sounds complicated but the concept is simple: It models relationships between variables, such as X television schedule and Y Google searches, to find with relative certainty how they are connected. (Imagine you go out partying and the next morning have a wicked headache. If you model this with enough parties over a year,  you could say with relative certainty: partying in fact does cause headaches.) Regression analysis is useful in evaluating how different, unconnected media tactics — and outside events, such as major winter storms or competitor behavior — work together to influence responses to ad campaigns. Without this type of analysis, you might make a mistake of shutting off one media channel, such as TV, which in reality could be driving thousands of customers in elsewhere in your marketing ecosystem.

 

Understanding multiple device use: Meshing, shifting and stacking

woman smartphone 2

A young woman plops on the couch, turns on the TV, and as her favorite reality show casts a blue glow across the living room … she also boots up her smartphone to check on her friends in Facebook and … also swipes open her iPad to play Words with Friends.

All at the same time.

Marketers who want to reach a consumer on all devices concurrently often struggle with understanding how these touchpoints interconnect. The biggest challenge is consumers often use all these devices for different things. While TV is on for video entertainment, mobile devices are used more often for playing games or participating in social media.

Behind this is the fast-growing trend of, yes, people using mobile gadgets plus TV at exactly the same time. BI Intelligence just reported that 45% of all smartphone use by U.S. consumers age 16-44 is done with the TV on, as well as 37% of laptop use and 55% of tablet use. If mobile is ascendent, TV seems to be its constant companion.

To address this puzzle, we’ve searched for frameworks on how people actually use different screens at the same time — and found the best from Monique Leech, an analyst at global research firm Millward Brown. With a hat tip to Leech, here’s our own interpretation of her findings: there are three core ways people use multiple devices and each requires a unique marketing strategy.

Meshing: ‘Hey look, tennis is on ESPN. Let’s read tennis.com too!’

“Meshing” is when people use two or more devices to watch directly related content. For instance, when Jane Smith was watching the Super Bowl on TV a few years ago, she was surprised by a blackout in the stadium lights, and turned to Twitter on her handset to chat about it. Oreo famously leaped on this moment by tweeting “You can still dunk in the dark,” and Jane would have laughed. Marketers who want to leverage “meshing” behavior can either target integrated advertising content, such as a buy on a weekend sports event and a concurrent media buy on ESPN.com, or deploy “real-time marketing” responses on social media during major awards shows or sports events.

Alas, meshing is only part of the story, and typically not the dominant form of concurrent media use. Putting an ad on Tennis.com to match a pro tennis tournament on TV at the same time may not always be the best approach. The next behavior, “stacking,” explains why.

Stacking: ‘Hey look, Walking Dead is on TV. But let’s chat on Facebook too!’

“Stacking” behavior is different, and more common, in which, say, James Smith is watching “The Walking Dead” on television while simultaneously chatting with buddies on Facebook via his iPhone. Stacking means adding different content from one device to unrelated content on another media device, all at the same time. Numerous studies show this is the dominant form of concurrent device usage. Salesforce.com recently monitored 470 consumers for a month and found they spent an average of 3.3 hours on smartphones per day with the top activities being emailing, searching the Internet, or social networking. Tablet behavior was similar, with social networking and reading at top. What’s most interesting is so few reported watching TV-related content simultaneously on mobile gadgets, it didn’t make the list.

For marketers, this means you can’t just buy ads on CNN.com to align with viewers watching CNN on TV. Instead, you must explore audience targeting across content platforms at simultaneous times, to reach consumers on Facebook or in a game while they watch a show on television.

Shifting: ‘This content is fun, but I’ll pause now and continue it later.’

The third form of multiple device usage, “shifting,” is one of shifting from one gadget to another while pursuing related content. This could be as direct as watching part of a Netflix movie on a tablet and finishing it on TV, or more nuanced such as researching a trip to Italy on a smartphone and then completing the reservation via a computer browser window.

This “shifting” device behavior poses two challenges for marketers, in targeting and measurement. For targeting, it requires understanding how different media touchpoints may be used in sequence for a consumer to learn about, explore, consider, and then consummate a desired action — and for measurement, it means the combined impact of all these channels must be evaluated not in silos, but by their cumulative lift in results.

Three puzzles, not one

The punchline is each type of behavior poses unique challenges. You can try to intercept consumers who mesh their related content, but be aware they may actually be using different content at the same time. You can also try to reach consumers as they stack different content on different devices, but to do so you’ll need to be more clever in how you coordinate your ad messages. And for consumers who shift across devices pursing related content, you’ll need expert measurement systems to understand this pathway and how to influence it.