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?
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.]]>
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:
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?]]>
When advertising agencies brainstorm client solutions, pricing rarely comes up, because “price” is perceived as both dangerous and boring. Dangerous because get it wrong, and sales will plummet. Boring because, hey, who cares about pennies when we could be discussing brand positioning?
So when a friend recently asked us whether an airline-related client should adjust price, we dug into the research — and realized, yes, this lever is critical. Here are two frameworks for price strategy: One based on logic, the second tied to emotion.
Economic logic: The price elasticity of demand
The “price elasticity of demand” is a classic model that rekindles visions of boring Econ 101 classes, but it is fascinating when put in human terms. Think of this fancy phrase as how elastic, or stretchable, demand will be if you change the price. If you lower your price, will demand “stretch” up much higher, with many more people clamoring for your service? Or is demand inelastic or “unstretchable,” with shifts in price barely moving sales?
This elasticity concept is important for marketing, because it tells you whether you can justify a high price. Consider our friend’s question:
“I’m working for a travel-related service, and they charge about $80 for a unique [service offering X]. The client wants to know, should they lower the price by a few dollars to spur more sales?”
At first, the puzzle seems unanswerable. But the theory of price elasticity of demand has an answer: Demand will respond most to price changes if the product and service has (a) readily available substitutes or (b) if it is a big chunk of the buyer’s income. Demand fluctuates least if your offering is (a) unique and (b) a small part of the buyer’s income.
Consider milk and houses. Milk is an example of a product with many brand substitutes — if one brand charges $2.10 a gallon and the other brand in the store cooler costs only $1.90, consumers will readily shift from Acme Farm Milk to purchase the cheaper Beta Farm Milk. Same product perceptions; lots of substitutes; thus a price shift makes a change in demand for a given brand.
Houses are an example of something that’s a big chunk of your income. If you are moving to a new city and find one home priced at $500,000 and another similar house for $490,000, you’ll go for the lower price — even though the difference is only 2%. Same product perceptions; high share of your income; thus a price change also makes a quick shift in demand.
But let’s think now of this unique travel service. It’s only $80 and the service is unique. Should the marketer drop the price to say, $75? Nope. A small change in price would do zilch to stimulate demand. There are no substitutes, and it’s a small part of a frequent traveler’s annual budget. To back up our recommendation, we researched how airlines charge for other up-selling services and found that, indeed, travelers pay $38.1 billion annually in surcharge fees to U.S. carriers for things as odd as more legroom, booking by phone, changing flights, or bringing extra bags. Apparently, in the crush to get on a plane, people will pay something for almost anything that makes the trip easier.
Behavioral emotion: Playing with price framing
That’s the logical way to look at price changes. But, as our election debates show this year, consumers are often illogical and emotional, too. In 1980, Richard Thaler wrote the landmark paper on behavioral economics outlining how consumers often use a “mental accounting model” to decide if prices are good or bad. Thaler’s central argument was that shifting a price point is not the only way to stimulate demand; instead “framing” the perception of price could be more effective.
Consider, which offer is more appealing?
1. A dress that costs $60.
2. A dress that costs $70 marked down from $140 (50% off sale).
Thaler noted, in several studies, that choices such as No. 2 above are often preferred by consumers, when in reality, the second dress is just more expensive. His explanation: People are inherently bad at judging value, so use “reference points” see if they are getting a benefit or loss. Because in option 2, the dress is positioned as being far below the “real” price of $140, it feels like a better deal. This illogical-but-compelling mental accounting is why most retail stores offer goods “on sale,” or why candy at movie theaters that costs $5.00 comes in oddly shaped boxes. We feel great when we get something that looks larger than usual, or is bundled with other things, or is “marked down” in price, when the reality is each of these experiences is a bit of manipulation from a marketer creating an artificial reference point.
So there you have it: With logic, moving a price point makes sense if there are few substitutes or the total cost is a low overall risk to the buyer. With emotion, you can keep prices as is, and even increase them, by positioning the cost against a “reference point” that makes the buyer feel better about her or his mental accounting.
We all want to win. Prices are numbers that, if used carefully, can make every buyer feel a winner. Sorry if this sounds manipulative, but we have to run — there’s a great sale at the hardware store we want to hit on the way home.
Japanese roboticist Hiroshi Ishiguro unveils an android copy of himself at SXSW. Will marketing bots based on artificial intelligence help advertisers?
We love the idea of boosting a horror movie with an AI chatbot. Marketers looking to scale communications are watching Focus Features’ clever experiment in awe. Marketing bots are coming fast. But before we explain, let’s catch up on AI advances.
It’s been a whirlwind year so far for artificial intelligence. At SXSW Interactive in Austin this March, Japanese scientist Hiroshi Ishiguro presented an android that looked exactly like himself, capable of carrying on intelligent conversations in either English or Japanese thanks to Siri-like database matching, linguistic software and voice recognition. Days later, AlphaGo, an AI software developed by Google DeepMind, beat South Korean champion Lee Sedol at Go, a game multiple times more complicated than chess. And weeks later, Microsoft launched the silly Twitter bot Tay on the world, trying to demonstrate its AI could learn conversations by tweeting back and forth with users. Tay flamed out when online trolls taught it to say racist, violent things, forcing Microsoft to abort its Twitter experiment … but Tay did learn a human personality, albeit a mean one, quickly.
Artificial intelligence is no longer science fiction. It’s here to stay.
In marketing, AI algorithms and “bots” in recent years have earned a bad name, typically attributed to “black box” digital media buying systems that may opaquely distort ad campaigns with bad impressions, or bots that pretend to be human but are really designed to jack up clicks for inflating results. The media buyers who run today’s programmatic systems often invest a sizable portion of their time in monitoring digital ad campaigns for quality control—a war against bots, if you will. But now, some bots may be bringing benefits to marketing.
When marketing bots help promote a horror film
MIT Technology Review reports that some new mobile services such as Kik and Telegram have created “bot shops,” where AI virtual users provide everything from simple horoscopes (just fun) to helpful service and personal conversation. Focus Features used this AI-type system in Kik to promote the new film “Insidious: Chapter 3,” in a brilliant virtual conversation. In the movie, a girl named Quinn is stuck in bed, and needs to converse with the outside world. The Kik bot allowed you to do just that … with your personal conversation growing more and more intense.
Yikes. And well done. Just scanning those messages makes us feel like we’re inside the actual movie.
Marketers are watching this because one-on-one conversation agents could unlock value, in everything from explaining products to stamping out customer complaints. In call centers, human labor accounts for up to 85% of costs, while customers grow irate if hold times exceed a few minutes. Deploying bots in customer service could save companies millions while helping customers gain faster answers, in turn reducing customer churn.
When AI systems work well, they not only duplicate human intelligent conversation but do so at scale. Imagine a world where there was no more “on hold” time when you call a call center, and a friendly, Siri-type intelligence immediately took your complaint or order.
But can AI manage the real complexities of life?
But as the Tay debacle showed, AIs are still rough simulacrums at best, and prone to error, or worse, offense. The reason it took 20 years between IBM’s supercomputer Deep Blue beating Garry Kasparov in chess and AlphaGo whipping Sedol at Go this spring is chess, on average, has only 35 possible legal options per player move, while Go is far more complex, with approximately 250 game options to consider per player turn. AI can finally keep up with just 250 scenarios on a simple board. Real life has millions of possible turns in every human move. While marketers may rush soon to deploy AI bots to try to influence or serve customers more easily, they’ll need to tread carefully.
Microsoft CEO Satya Nadella told a conference of developers this spring, “We want to take the power of human language and apply it more pervasively to all of the computing interface and the computing interactions.” But to paraphrase Microsoft’s twitter AI-bot Tay, as she went off the deep end about Hillary Clinton, beware of “a lizard person hell-bent on destroying America.”]]>
In 2009 the U.S. Department of Defense’s research arm DARPA issued a seemingly impossible challenge: To celebrate the 40th anniversary of the creation of the Internet, it would station 10 huge, red weather balloons at random, undisclosed locations around the United States and offer a $40,000 prize to first team of people to find them all. The challenge was daunting by traditional intelligence-gathering standards. A roaming team of 10,000 people might take a year to find all the red balloons. DARPA built the puzzle to see if modern social-media networks might solve what government spies could not, and also to generate ideas on how social networks might help during natural disasters when mass communications or even 911 systems might fail.
The winning MIT team found all the balloons in less than 9 hours. How?
In his remarkable book “Social Physics,” MIT professor Alex Pentland explains he used theories of how ideas flow through people to create action to solve the puzzle. A traditional marketer might have realized the $40k prize wasn’t enough to get millions of people looking, so would have spent $4 million in national advertising with the $40k prize as an offer. Others would have tried PR, or pleas for a common good, or hiring thousands of students to work for pennies. Or maybe hacked weather satellites.
Pentland instead came up with a brilliant team-building idea — where he motivated individuals to influence others to search, and not just to win a prize. He divided the $40,000 prize into 10 prizes of $4,000 for each found balloon … but further split each individual balloon prize into the referring networks of all who found it. The actual person who found a balloon would get $2,000. The person who invited him or her to play would get $1,000. The person who invited that person to search would get $500. And the person above them $250 … etc. The chain looked like this:
Why did people then suddenly participate on the MIT team? In post-contest interviews, MIT found people thought if they invited others, they were doing their friends a favor … similar to sharing a lottery ticket. In other words, they weren’t incentivized to win a prize, but instead, to build a bigger network of participants.
The moored balloons were set up at 10 a.m. on Dec. 5, 2009, randomly located across the 3.8 million square miles of the United States. More than 4,000 search teams had signed up. But within hours, Pentland’s MIT group had enlisted 5,000 core volunteers who shared the network incentives to an average of 400 friends each, creating a network of 2 million people who … found all the balloons in 8 hours, 52 minutes and 41 seconds.
The underlying strategy is networks of peers are extremely influential; so to move people, marketers must learn to move the network connections. (Consider: If you play golf, you may wear a golf jacket you purchased after seeing an ad. But the reason you play golf is that you grew up with a father or college buddies surrounding you with the idea that golf is a fun game. The network of peers around you is what inspired you at the core.)
Pentland suggests that if you find ways to motivate people to share ideas across network connections, and not just respond, you are more likely to make an impact.
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.]]>
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 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…]]>
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:
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.
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, game, monitoring 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 firstname.lastname@example.org.
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:
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.]]>