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Forget ads. Plan with The Information Ecosystem.

Six years ago, as enthusiasm over a new thing called “social media” began to crest, Pepsi went big on social. In 2011 PepsiCo slashed outbound advertising on its Pepsi brand, and redirected communications budgets to Twitter, Facebook, and other inbound social community management. That didn’t go so well. By March of 2012, sales of Pepsi had slumped  and for the first time in two decades Pepsi fell, embarrassingly, to the No. 3 soda brand behind both Coke and Diet Coke.

Inspired by Pepsi’s error, that year we conceived a framework called The Information Ecosystem — in essence, a strategic game board that explains how your brand should communicate to, or engage with, customers. It has two basic information systems — the “flow” of communications outbound or inbound from customers, and the “scale” of information being used by many or a few. All communication either goes out or in, and it’s either for a handful or huge crowds. Map communication strategies into each corner of that ecosystem, and we get four intuitive tactics:

  • Broadcast — not just TV, but here defined as any mass medium “broadcasting” outbound to large groups of people.
  • Personalization — another form of outbound messaging, but this time tailored to a few.
  • Research — the inbound analysis of communications from groups.
  • Engagement — the inbound communications from one person, or just a few.

The Information Ecosystem framework was a hit, because it showed that different communication strategies have different uses. Advertising strategist Faris Yakob was kind enough to publish our theory in his 2015 book “Paid Attention: Innovative Advertising for a Digital World,” commenting that “the important thing to remember is that each task requires the right approach or mix of approaches. Not every problem can be solved with the same solution set, which is a challenge for many agencies, who too often assume that their primary product is the optimal solution.”

But let’s revisit what this means. First, this ecosystem is not just about media tactics.

Yes, various media platforms can be plotted above in different ways. Some, such as Facebook’s social network, can be used in all four areas (for inbound research or organic engagement, and outbound personalization or mass-market communication). But the real value of this model comes from mapping where your customers fit in the ecosystem:

  • C1: Customers in group “C1” have different needs and varying financial value to you. Strategy: Personalization. Why? Your customers need different things, and if you can personalize to the most valuable customer micro-segment, you’ll maximize profits. For this customer group, investments in personalization are required. Examples include real estate, financial investing, travel, fashion and luxury goods. There’s a reason why airlines have numerous loyalty schemes, boarding segments and seating assignments. That lady up front needs something special, and she’s worth a lot more than you.
  • C2: These customers, group “C2,” have similar needs and rather static financial value. Pepsi drinkers go here. A casual Pepsi drinker might imbibe three sodas a month, and a heavy loyalist 30 Pepsis, but that 10:1 ratio really is not enough to justify the expense of personalization. And all these customers want the same thing — a sweet drink. So take out a TV ad or run an OOH campaign; your strategy is to Broadcast outbound messaging to a mass audience. The lower costs of mass media will simply get the job done.
  • C3: For inbound communications, marketers who wish to understand what drives a common need use Research. Inbound inputs from masses are gathered and sliced, but the result is typically a product-centric view, collating the needs of a group around a given sales item or media goal. Nielsen ratings evaluate who watches show A or listens to radio station B; comScore ranks who watches website C; qualitative or quantitative research uncovers which customers will buy product D. Consumers’ financial values may vary greatly, but research is focused on understanding common needs.
  • C4: Engagement is the wild card of the matrix, the area for inbound customers with very different needs — and best if your customers have a range of value. The temptation for marketers to rush into this quadrant is enormous; who would not want to provide individualized answers to any customer question, in an effort to both solve problems and sell products? But this Engagement strategy works best when your customer base wants many different, nuanced things, and provide enough profit to justify the expense. At this point, you can easily see where Pepsi’s rush into social went awry. One-on-one social engagement is not needed when all a customer wants is a sweet sip.

Which brings us to the final question: which inbound and outbound strategies go together?

Ah, sharp readers will have guessed — it all revolves around your customer base analysis. If your customer group varies in what they need, outbound personalization should be matched with inbound engagement (C1 and C4); our agency, for example, works with a fashion e-commerce brand that combines hyper-targeted, personalized outbound advertising with high-touch inbound human service. For customer groups where the needs are varied, and where some have extremely high value, this combination is powerful. The only caveat is to make certain one portion of your customer base provides enough financial value to your business to justify this micro-segmentation and personal service.

But if your customer group contains similar needs, outbound broadcast mass media can be matched with inbound research to great effect (C2 and C3). Traditional research segmentation studies can be used to direct cable television schedules. ComScore ratings will tell which websites have the greatest reach among the largest slice of your target. Analyzing what makes one large group tick is enough to push out messaging that reaches the same large group at the lowest media cost.

Of course, this is all just Step 1

There are of course many layers of nuance behind this Information Ecosystem strategy. Broadcast media are becoming more targeted; addressable TV, for instance, can reach households down to the PII level with personalized commercial breaks based on your past shopping behavior, and technical advances in some digital billboards can recognize the demographic composition of the audience viewing them. Social can be used for research, and research can be used to identify niche group needs. As media outlets advance to the future of Minority Report, communication lines will bleed across all channels.

Which is why this model is useful. As you plan your communications strategy, it’s worth starting with an analysis of just how unique, or common, your target audience is. That one question will help you model whether you should invest in personalization and engagement, or simply research how to go for mass communications scale.

 

 

Improving mobile advertising via colocation

Every spring for the past five years, analyst Mary Meeker has published her influential report on “Internet Trends” and noticed the same, strange thing – while most media platforms attract a share of advertising budgets closely matching the time consumers spend on them, mobile always falls behind. TV, for example, accounts for 38% of U.S. consumer daily “media time” and also attracts 38% of all U.S. ad spend. Radio has 9% of consumer media attention and gets, yes, 9% of media budgets. But for some reason, mobile falls short.

Today, there are 2.8 billion smartphones on the planet – so nearly 1 in 2 humans use a mobile screen – and in the United States, adults spend 3.1 hours per day on mobile, about 28% of daily “media time.” But mobile attracts only 21% of U.S. ad dollars.

So what’s up? Why are marketers still slow to spend on mobile?

The core reason is media plan budget allocations always try to predict, and then adjust to, actual returns in performance. If the key metric return on ad spend (ROAS) shows that one type of media returns $3 for every $1 spent on advertising (an ROAS of 3:1) and another returns only 90 cents (ROAS 0.9:1), then the first media channel will get more budget. Because ad dollars are not flowing to mobile user eyeballs, something in mobile performance must be broken.

Solution: Moving beyond geo-targeting

The American Marketing Association recently offered a solution, suggesting marketers typically get mobile audience data wrong. In the AMA’s most recent Journal of Marketing, researchers Peter Pal Zubcsek, Zsolt Katona and Miklos Sarvary point out that mobile devices are collecting reams of data about consumers, but marketers typically respond with simplistic geo-targeting that does not take advantage of all options.

First, consider the data. Some audience profile data is “sandboxed” or not shared between apps, while other platforms (especially Facebook and Instagram) can target mobile audiences based on hundreds of profile or interest habits (everything Facebook knows about you can be used to reach you on your iPhone). But the most valuable data from mobile comes from user locations – the geographic coordinates that not only reveal where a mobile user is today, but where he or she has gone over time, and consistencies or variances in those travel patterns. People are just smart animals, after all, and our trails give away our true desires.

Alex Pentland, an MIT researcher who experiments with reams of mobile data, was able to predict which movie theaters in winter have crowds of people who are contagious with the flu virus by assessing slight common variances in mobile travel and communication patterns that indicate who is starting not to feel well – in essence, predicting people will get sick before they know it themselves.

Zubcsek, Katona and Sarvary build on this “spacial-movement-pattern recognition” theory, suggesting marketers need to move past simple geo-targeting to consider the social ties between people visiting locations.

Looking outside the fence

Much geotargeting is still rudimentary, focused on audience member X being at location Y so getting offer Z. For example, a current popular mobile targeting approach is to build a virtual fence around an auto dealer, and then hit a consumer with a mobile offer when she or he enters that perimeter. Another, slightly better approach, can pick up people who have visited a certain type of retail location and then retarget them elsewhere later – say, a child clothing retailer could retarget consumers at home who have recently visited daycares, baby-supply stores and toy stores, knowing those audiences are likely new moms. The thesis here is a consumer makes a “location choice” to maximizing some underlying utility function (to buy something, eat something, make a transaction), so marketers who sell similar services simply match their offers to that location signal.

But what if you consider deeper uses of mobile data?

1. Push consumers slightly off path. Shoppers who travel off planned paths tend to be more profitable. Zubcsek, Katona and Sarvary point out a coupon that hits someone on a pre-planned path may have limited influence, or worse, give away too much, for someone who is already thinking of making a purchase at that location. But if a mobile offer pushes that consumer to go slightly further off path, while response rates may decline, the consumer may make a much more profitable transaction. The “effort” involved in that consumer shifting travel patterns means they may have greater intent to respond. There is obviously an inflection point where the economics break down – someone seeking a restaurant on a Friday night in Chicago is not going to pop over to Los Angeles – but if marketers model distance paths and optimal pushes for changing the consumer pathway within an acceptable distance, the resulting sale and profit will be higher.

2. Consider mobile colocation data for modeling. Zubcsek, Katona and Sarvary write that consumers who visit the same pattern of places have similar preferences, even if they don’t know each other – and that by mining location data, marketers can build segmentation models that are 19% more likely to predict profitable purchases vs. older standard segmentation tools (e.g. psychographic and demographic segmentation or media usage studies). In other words, mobile geo data isn’t just for targeting; it can model groups most likely to be your Most Valuable Customers.

3. Go long. When offering mobile incentives, extend the length of the offer and the frequency of impressions. Once targeted appropriately, consumers are more likely to take action on an offer if their future repeat paths put them in proximity to the desired action. This is intuitive, but many marketers miss the mark by not allowing a frequent-enough series of exposures to align ad impression with audience target as he or she nears the desired location of action.

4. Look elsewhere. Beware that ignoring valuable prospects outside a specific geo-fence “may leave money on the table.” Consumers signal their interests when they share similar travel patterns. So if Consumer B closely matches Consumer A in geo-location travel, but only Consumer A moves within a geo-target fence to “trigger” an offer, Consumer B may be just as – or more – valuable! So targeting of consumers via mobile must move beyond specific single geo-fences to a range of geo-triggers, if you are to reach similar prospects with high interest in purchasing your product.

5. Mix up the formats. Creative design obviously has influence in any advertising, and mobile creative options are now much more vast than small banners. In-image ads, video ads, sponsored stories, native ad units, and 360-degree immersion units support higher frequency against targets, once identified, without triggering wearout. A mix of ad units can also boost response; in the advertising research book “What Sticks,” authors Rex Briggs and Greg Stuart uncovered that consumers reached with different types of impressions typically respond at higher levels vs. one media format used at the same frequency. So mix it up.

All of this is to say that geo-targeting by itself is not enough. Marketers’ main goal is to match messages with people most inclined to take action. What Zubcsek, Katona and Sarvary have uncovered in their research is that mining the travel connections of consumers across time and space, and matching those paths, unlocks new ways to identify Most Valuable Prospects likely to respond. By mining these pathways through colocation, you can predict not only where consumers will go, but also what they may do next.

Or, as MIT’s Alex Pentland might say, don’t forget to get a flu shot.

This sweet little song was written by AI

Like frogs placed in a pot of cool water set on a stovetop burner turned up high, we have little idea if the growing warmth of artificial intelligence experiments now around us will comfort us or lead to humanity’s devastation. Artificial intelligence, or “AI,” what was once the plot sidekick of science fiction movies such as “2001” and “Alien,” has been gaining speed, surrounding us with tactical adjustments at first, and now, startling advances. For example, the pretty little pop ballad in the video above was entirely scored by an AI algorithm.

If this AI debate sounds theoretical, watch the video, then ponder that a computer wrote that song. If you’re smart, that blows your mind. Then keep reading.

There are two schools of thought on where AI is going. The positive view is led by Kevin Kelly, the granola-y co-founder of Wired magazine who spent his youth backpacking China with a camera before becoming a technology savant. Kelly posits that AI is the electrical grid of the coming era, a growing intelligence service that future applications will simply plug into. We’re seeing evidence of this utility today. Cars have automatic brakes that pulse faster than your feet can reach the peddles. Most of your next airplane flight is navigated and managed smoothly by computer systems, not real pilots. Apple iPhone’s Siri (an offshoot of the government’s DARPA) and Amazon’s Alexa answer almost any question. Because most human requests or desires can be broken down to algebraic solutions, even songwriting can be managed by computers today.

The pop ballad above is sung by human Taryn Southern, but the entire musical score and orchestration were written by an AI algorithm named Amper. Amper was designed by a team of professional musicians, but only computer logic is at its core. Somehow it analyzed what makes pop songs “work,” then built an entirely new melody that you want to hum. The idea that something as nuanced as music that delights your soul can be written by automated code is a bit scary. If emotional storytelling can be modeled and then evolved by a computer, what’s next? Entire novels, written so eloquently that Will Shakespeare or F. Scott Fitzgerald or Cormac McCarthy couldn’t compete? Or perhaps deeper logic structures, such as future religions, with spiritual tales that resonate above and beyond what mere human prophets can conceptualize? God may exist, but if our understanding of God depends on logical interpretation, surely an advanced computer system could write the better belief code.

Which leads us to the negative forecast for AI as well, led by the Oxford philosopher Nick Bostrom. Bostrom is notable for two efforts: His challenging book “Superintelligence,” which predicts there are multiple research pathways to launching AI, so it will happen, and that when it happens, it will soon go beyond our control. And second, for a little paper “Are You Living in a Computer Simulation?,” which gave even Elon Musk pause. Bostrom sure sounds right: If AI can get smart, it will easily soon get smarter than humans; and if not tempered with the right controls, that AI could chase missions at odds with our survival quickly.

Paperclips everywhere

For example, Bostrom imagines an AI working within a factory that manufactures paperclips that escapes the building, with the simple mission to optimize paperclip output, and reconfigures the entire world into machines mining metal for bending wires while humans suffer, unable to stop it. Silly, perhaps, but the U.S. stock market “flash crash” of May 6, 2010, almost tanked the economy when two different computer algorithms started feeding off each other in a strange way that caused the market to tank a whopping 9 percent before circuit breakers kicked in.

AI is becoming a buzzword, but it really is an ecosystem of many moving parts. IBM has rebranded itself around its “Watson,” an AI that does everything from predicting weather patterns and healthcare outcomes to managing display advertising programmatic buys, in tests that outperformed human bidders by 2 to 1, according to IBM’s head of brand, Jon Iwata. In a speech to the U.S. Association of National Advertisers a few years ago, Iwata stood on stage with the image of hundreds of nested hexagons projected behind him, like the honeycomb of a beehive, each with a tiny label showing a different service that AI could complete. IBM had just acquired Weather.com to pull feeds about atmospheric conditions into its AI honey trove. When Iwata told the gathered marketers there that his artificial intelligence behemoth had recently beat humans in planning digital advertising, the audience didn’t gasp, but simply nodded. Later, on the patio of the Florida hotel, real humans representing advertising technology vendors met with marketers over wine, trying to convince each other that their human teams would perform better for advertising results. Watson didn’t show up, but I could feel him lurking, observing somewhere behind the scenes.

Do you speak Chinese?

There is a philosophical puzzle behind AI, which of course is your question, is artificial intelligence really “intelligent”? Philosophers suggest this doesn’t really matter. If IBM’s Watson or Amazon’s Alexa are not really real, that won’t change the outcome of what those AIs do or how you experience them. The classic answer to this question is the Chinese room mind experiment by John Searle.

In essence, Searle figured out that AIs can be intelligent even if they don’t have the recursive, self-aware consciousness (i.e., “living souls”) that we humans believe we have. His example: Imagine there is a room with an English-speaking person inside it, who can’t speak Chinese. Before him is a door with a slot, and behind him is a massive library filled with drawers with the answer to any question posed in Chinese.

A Chinese-speaking person on the other side of the door writes down a question, and slips the paper inside the slot.

The English-speaking person picks up the paper. He has a coded instruction manual that leads him to the right drawer; picking up the answer, he pushes the Chinese solution back through the slot.

Here’s the rub: The Chinese speaker outside gets a perfect answer in Chinese. The English speaker inside the Chinese room didn’t understand what he did, but somehow he gave the perfect answer.

The English speaker was not “intelligent” or “self aware” in Chinese, but he got the job done.

Computers are doing that, already, today.

AI is here, and it’s getting warm

The point is, AI is more than a buzzword and much more than a digital tactic. It is no longer a movie fantasy, and it does not need a consciousness to complete the recursive loop of understanding and doing things, even better than humans do. The looming wave of coming intelligent algorithms will be the new electrical current of society, and its application will expedite everything from marketing predictions to media buys to the creative advertising images and stories and music that get people to respond.

Where AI goes is hard to predict, but its mission seems obvious. Computer intelligence will be an accelerant to human desires and objectives. It is more than databases and algebra. AI will do more than pump your brakes or direct your airplane. It will empower anyone to try to predict what anyone else wants.

Like a song structure that anticipates the next note to delight you, AI will build structures that align with your needs. We just hope you know what you really want.

(Thanks to Angela Natividad for the song tip.)

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)

Screen Shot 2015-07-12 at 8.55.36 AM

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.