The 7 levels of loyalty programs (why emotion trumps logic)


One of the great ironies of marketing is that while organizations worry continually about customer loyalty, economists provide scant help in thinking through the levers of a loyalty program. The presumption of economic theory is that people conduct transactions rationally to maximum perceived value (profit) and minimize perceived pain (loss). So most marketers try to build loyalty by giving what they think is economic value (say, coupons or points programs) or using subscription agreements that maximize the pain of leaving.

But people aren’t purely rational. “Loyalty” goes far, far beyond the silly points programs or contractual switching costs that most marketers deploy. To understand the real levers, first consider that loyalty has two fundamental psychological levers: rationality and perceived fairness.

When faced with a decision, human beings logically seek to gain value and avoid pain; however, we are also emotional creatures worrying about fairness, attachment, and obligation. As behavioral economist Richard Thaler has noted, if pure economic incentives were all that mattered, no one would ever leave a good tip for a waiter or return a lost wallet found on the street. At many levels, we see fairness as a currency overlay, flowing outward to make others feel good if we think they deserve it and flowing in because, well, we demand it.

Fairness influences corporate behavior as well. Grocery chains, for example, often face surges in demand when bad winter storms approach and could easily charge $100 for the last roll of toilet paper or bottled water on the shelves; but they don’t, because such gouging just doesn’t seem fair. Businesspeople also react emotionally to perceptions of unfairness; a vendor proposal that could make you money might still be rejected if you thought the deal wasn’t “fair,” when in reality, the ROI on the potential investment is what really matters.

The root of fairness is a concept called “ethical altruism,” a dynamic in which humans are guided by their impact on others and not just themselves. But altruism isn’t all, either; you don’t have to be Ayn Rand to recognize that not everyone returns the wallet … especially if we saw the person dropping it was the same one who flipped us off in traffic 10 minutes before. Economic rationality and emotional fairness sit on either side of a loyalty scale.

So let us propose a simple hierarchy of loyalty program concepts that balance both logic and emotion, starting from least to most effective:

Level 7: Discounting. This includes coupons, savings, price framing, and price obscurity. This is the lowest form of loyalty inducement because (a) discounts are easily replicated and (b) by nature they erode the other perceived values of your service. In 2011 we predicted in Digiday that Groupon, a hyped social business that focuses on coupon marketing, would falter because aggressive discounting is not a sustainable model … and today its stock price, once $26, is languishing at $8 a share.

Level 6: Accrual of value-oriented benefits: This includes common points programs, such as airlines or hotel points, that are built up over time in exchange for repeat transactions. This is the second-lowest form of loyalty program because in reality, it’s just another method of price competition. Give away 10,000 hotel points and your customer at first may feel loyal; but your competitor can match those points, and it all becomes a pricing game. If your lover only stays because you buy her expensive jewelry every week, at some point, you might wonder what happens when another guy goes to a jewelry store too.

Level 5: Accrual of psychologically oriented benefits: This approach is similar to value accrual, but plays to the human ego with points or status measures that are purely mental. Today this is most common in social media. Twitter follower counts, Likes on Facebook, Klout influence scores, Boy Scout and military merit badges, certificates of diploma are all psychologically staged levels of perceived accomplishment that have no real value other than the fiction of stature they put in your head. This is why the Facebook layout has a little red button at top telling you how many friends recently commented about you — ping, your brain just got a mental high score, and in two hours, you’ll come back to check again.

Level 4: Entanglement for negative switching costs: Here, someone leaving has to incur a cost. If you break a cell phone contract, you pay money. If you fire your ad agency, you pay a kill fee. If you leave your spouse, you end up sleeping in a cheap motel. Making the switch costs you some pain. These switching costs are usually quietly established in the early stage of a customer relationship, and are triggered only when the customer decides to leave.

Level 3: Entanglement for positive switching costs: This, the positive twist on negative switching costs,  was the focus on Don Peppers’ 1990s “1to1 marketing” methodology, in which leaving means you give up something good that you can not find easily somewhere else. Peppers suggested that “personalization” of services to anticipate customers’ needs could create a new barrier to exit, since a consumer who spent time training a company to meet his or her expectations would not find the same value elsewhere. Examples include Netflix movie personalization that makes it easier to find a good film; teaching Pandora music channels that anticipate your preferences; and a personal accountant who recalls exactly how to expedite your taxes based on your prior years’ history.

Level 2: Complacency. Yet a higher form of loyalty inducement is to encourage customers to stop thinking about you altogether, since change requires a mental action. This doesn’t mean ambivalence, but rather, assurance so sound you don’t even come to mind. Complacency is the sleepy self-satisfaction that customers feel when they (rarely) think about your service, because they know they’ve made the right choice. Utilities, insurance providers and cable companies often focus on “unfocusing” their customers, since if a customer goes to sleep he or she will never switch to a competitor. While powerful, this is challenging to manage because it requires  (a) removing any disruption points in customer interactions with your organization, (b) having a surrounding competitive environment where no triggers for disloyalty emerge, and (c) deliberately walking away from a strong brand position in the customer’s mind. The risk is the ecosystem can change, and new competitors may enter to wake up your sleeping loyalists.

Level 1: Advocacy. Emotion almost always trumps logic in human decisions, and emotional feelings of unfairness about a product (“that bill was too high”) or lust toward a competitor’s product (“that new holographic iPhone looks so sexy”) can unravel any of the loyalty programs above. The solution is to remove the psychological space for unfairness or lust by filling the customer with a desire to be part of your brand. For example, a regional hospital in Connecticut has engaged hundreds of local cyclists to raise funds in an annual 100-mile “century ride” to support its cancer research; there is little chance any of those athletes or their friends will go to a competing hospital if they need cancer treatment, because they have become engaged as part of the brand mission. Building such advocacy requires brands to move beyond their core product or service to what consultant Scott Henderson calls “adjacency marketing,” or marketing to a popular, emotionally compelling issue adjacent to your brand proposition. This issue pulls customers forward, and your service by association becomes uncontested.

All of these levels of loyalty programs face challenges. Service disruptions, market entrants, new product designs, changes in consumer life stages, social persuasion, and the human desire to partake in novelty are all triggers that can make a loyalty program fall apart. But if you can combine emotional attachment and feelings of obligation with the perceived switching costs in your loyalty program, all adding more economic value than cost, then we might consider sticking around.

SXSW observation: Prediction is the 5th stage of technology

tech hipster hand

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

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

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

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

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

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

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

How are observations proliferating?

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

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

Why will all these observations morph into predictions?

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

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

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

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


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

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

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

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

So what are the risks? We count three.

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

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

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

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

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

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

Native advertising misrepresents the source.

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

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

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

Drunken tweets and Barbie covers: It’s all a meme game

meme seeding

A casual observer might think Western culture is going nuts. JC Penney tweets typos drunkenly during the Super Bowl. Actor Shia LaBeauf walks out of a film festival with a paper bag on his head. Miley Cyrus gives America erotic dancing on a major awards show and starts sticking her tongue out in every photo. And Sports Illustrated puts a Barbie doll on its annual swimsuit cover.

Yet this is all sanity. These are all careful attempts to game the limited attention span of consumers by seeding provocative memes. You’re being played, people.

A meme is a concept by evolutionary biologist Richard Dawkins that information passes through society similar to genes or viruses — evolving, striving to thrive and spread, but often failing. When memes succeed, millions of people change their behavior: Handshakes become fist bumps, baseball caps get worn backward instead of forwards, men begin pairing blue jeans with blazers, women begin wearing knee-high boots in winter. At a certain point, like an epidemic of the flu, the information “tips” into mass adoption. Marketers love this concept, of course, because it requires zero advertising budget — and the Holy Grail of any campaign is to get the world to buy your product out of simple collective desire.

The challenge for meme adoption, like the spread of anything, is physics. Friction slows momentum. There is a basic formula for how information goes viral, and for when it slows down, which is:

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

or more succinctly

V = (M-A) * C

As I wrote back in 2010, computer security companies such as Symantec use this formula to predict when a computer virus will spread or fade. In simple terms, if the message generation rate from node to node, or person to person, exceeds the absorption rate, the message will spread. But if the “absorption rate,” or percent of people in the passalong chain who get bored and stop sharing the message, exceeds those who do pass the message, the viral idea will stall. And cycle time, of course, is simply the speed of passage. Tweets that get buzz during the Super Bowl have a fast cycle time because millions of people are scanning Twitter for a fun idea at the same time; religion, one of the most successful memes in history, has a slower cycle time but is comparatively more stable.

Nice cuffed jeans, champ

Oh how marketers want to become the next meme. Do you wear a tie to business meetings? That’s a meme that’s stuck in your head, men. Women, do you wear eyeliner? Meme. Hipsters, have you started cuffing the bottoms of your jeans? Welcome to an emerging meme. Have you noticed that eyeglass frames are getting much bigger in the past two years, approaching 1950s plastic dimensions? Uh-huh. The pressure of collective adoption is changing your behavior without you even realizing it.

You, dear consumer, are a meme sucker.

The trouble that marketers face in trying to push their next product through this meme cycle is the absorption rate in society today is huge, and rises quickly as attention moves on to the next news item. There are simply too many messages competing for our attention for us to adopt and pass along every one. So the best way to get buzz is to create a message so shocking that it will jack up the message generation rate rapidly. And ideally, you’d do this during an event where mass attention is heightened — which will boost cycle time as well.

This is why JC Penney tweeted drunkenly during the Super Bowl (in what turned out to be a promotion for mittens). This is why Miley Cyrus shook her booty in a skin-toned suit to recast her persona during the MTV Video Music Awards. Shock + major period of mass attention = high potential for meme success.

These were not crazy mistakes of judgment, but calculated attempts to boost the meme propagation rate of a brand, to get everyone in society talking about an issue, with the frosting of controversy spread over the deep cake of commerce.

It works sometimes, but with so much meme competition, marketers will have to continue to raise the shock value.

Here’s to seeing Barbie next year covered only in body paint.

What if Facebook ‘Likes’ don’t matter?


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

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

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

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

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

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

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

Here’s the truth, marketers:

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

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

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

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

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


The mobile future of variable pricing



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

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

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

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

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

The +1, -1 irrational psychology of the deal


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

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

Consider these two events:

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

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

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

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

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

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

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

How can you use deal framing in your business?

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

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

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

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

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

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

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

How to mine the upcoming gold rush of wearable data

wearable wrist


Like early-1800s railroad engineers trying to figure out the optimal width for track gauges, gadget makers in the 2010s are frantically experimenting with wearable technology. Nike put a “+” sports sensor into sneakers. Google launched Glass heads-up eyeglass displays. Disney research labs has announced Touché, a technology that can turn any surface — clothing, water, your leather couch — into a touchscreen sensor. Reebok has headgear that tells football players when they’ve taken too big a hit. Wearable is the new gadget gold rush.

Some say it won’t happen. Humans will have to get comfortable walking around with tech, and there have been some stumbles in design (Bluetooth earpieces are still uncool, and Google Glass has yet to overcome the image of Robert Scoble wearing it in the shower). The convergence of humans with technology faces the barriers of evolutionary aversion, as we recoil from the Uncanny Valley of humanoid-looking creatures that aren’t really human, to the social barriers of us accepting our friends are with us but completely checked out.

But, like the locomotive, we believe you can’t stop this wearable train. ABI Research, which monitors such things, predicts seven types of wearable technology are coming: heads-up display glasses, cameras, clothing, healthcare monitoring, sports monitoring, 3-D motion detectors, and watches. Beyond wearable tech, others suggest human tattoos could have embedded computers and connectivity. And beyond our bodies, self-driving cars, self-monitoring homes (Google is buying Nest smart thermostats for $3.2 billion), and self-flying drones are all pushing the Internet of things to every device imaginable.

This is really nothing new. As we’ve noted before, humans have been part-cyborgs since the invention of leather shoes. You embed your body with technology every day in the form of clothes (expansions of your skin), cars (rolling metal exoskeletons), and memory (what is Google Search if not an expansion of our minds?). Eyeglasses, dental fillings, wristwatches, pacemakers, iPods, and houses with roofs are all extensions of our bodily defenses, senses and biological movements. Given our evolutionary adoption of mechanical and information systems to make us move and think better, it’s predictable that in 100 years we’ll have contact lens heads-up displays and Google whispering in our ears. It’s a no-brainer.

What does this mean for marketers?

The hidden question is not what the gadgets will look like; rather, it’s what the nimbus of data surrounding all these tiny embedded devices will do to human social behavior, politics, marketing and commerce. All of this technology will collect, share, and output trails of information. Marriages may falter and the NSA may get even more publicity based on these waves of personal data trails, but for now, let’s think of the implications for marketers.

Marketers exist to influence consumers, and wearable tech poses a threat to that mission: As data streams proliferate, consumer attention will be even more distracted, and the opportunities to intercept communications will become more fleeting and devalued. Just as the web has decimated newspaper advertising, and mobile threatens to undermine the web, wearable will be the next tsunami of vast inventory that squeezes content publishers’ advertising monetization. If you think the web is awash in bloated ad inventory, imagine the world where all of the 5,000 products each consumer owns begin gathering, sharing and broadcasting data.

Wearables will push communications inventory to near infinity. 5,000 products per consumer could broadcast 100,000 impressions a day, and no one will stomach that amount of intrusion. Unless marketers provide new utility in all of those wearable, touchable, object-driven media touchpoints, they will fail to gather attention. 

The 4 new data uses from wearable tech

While some wearable technology may have screens for standard ad impressions, we predict the vast majority will be too small — and consumers will rebel against traditional ads there even more than they do on today’s business card-sized iPhone screens. However, the data from wearables will provide four new dynamics that marketers might leverage to find an influence path in to consumers:

Anticipation — The most important form of data from wearables will be predictive modeling algorithms that anticipate what you want next, because for the first time humans will be tracked in the physical, real-time world. This predictive modeling will go far beyond current behavioral monitoring, CRM crunching, or RFM models (which are current crude marketing techniques that use a tiny portion of your past behavior to try to guess what you’ll want next). An iWatch or Google Glass or e-tattoo that is always on and tied to your search and social-sharing behavior can monitor your speed, pulse, movements, location, purchases, interests, needs, and relationships. Push this data stream forward, and for the first time, marketers will be able to immediately anticipate what consumers want. Google will know that you are hungry for sushi before you do.

Redirection — This is a current huge gap in all of today’s marketing: how to redirect consumers when they near the actual point of sale. When you walk into a mall, there is no promotional ping telling you to turn right instead of left. When you are at the grocery store, no AI simulation reminds you that your spouse wanted you to buy more milk. But with Internet-connected devices embedded into the fabric or skin of our bodies, marketers will be able to provide useful nudges that actually redirect purchase behavior when consumers go into the final shopping mode. This may sound Orwellian, but if played right, the utility could be enormous. Imagine walking down a crowded street in New York City and missing your old college friend walking the other way. Wearable tech could redirect you to see her … or similarly redirect you to find exactly the product you want, when you didn’t know it existed on the shelf directly behind you.

Cross-channel integration — Cross-channel marketing attribution is a buzzworthy concept today, but it’s mostly BS. (The trouble today is most attempts to track how different media channels work together to influence consumers measure just a fraction of the life of a human being. Online, marketers use software to track the paths consumers take to a web conversion, but this misses all offline touchpoints; offline, statistical regression analysis takes broad swaths of events to see if TV exposure lifts paid search results. All are clever, and all are amazingly rough models.) Wearables, however, could solve this. Gadgets in your clothes or skin could track exposures to all media as well as physical objects, environmental context, and the people around you. A map of every touchpoint around you would allow marketers to understand what really works, in what sequence. This also might sound creepy … until you realize it could remove unwanted ads and give you promotions for what you really want next.

Risk aversion — This may be the most interesting use of wearable technology data, because it would ameliorate consumer fears over privacy. Yes, people may freak out about the idea of all devices monitoring their behavior; the solution will be to use that data to give consumers real benefits that far outweigh any marketing intrusions. Contacts that help consumers avoid risks would be first. Imagine traffic alerts provided by Ford; food health counseling in your fridge from Pepsi; reminders for you to help your son study for next month’s SAT tests from a nearby college; a nudge at the mall to buy your wife a present before Valentine’s Day from Hallmark. Much of consumers’ impulse to buy is actually avoiding a risk of failure, the disutility of missing an opportunity. (This is why Black Friday is so popular: consumers don’t need what’s on sale at the mall, but they fear missing the opportunity of a sale.) Marketers who use the new, more personal touchpoints from wearable technology to minimize consumer disutility may be respected enough to be invited in for advertising messages as well.

Marketers will need to offer new, unexpected value in the looming landscape of millions of communication streams on every product, bit of clothing, and nearby wall. The inventory of media impressions is about to approach infinity, and the definition of media is about to be expanded to “everything.” If brands don’t offer real benefits in this wearable, touchable world of technology, they will be shut out.

Why Netflix walked away from personalization



In 2006 Netflix offered a $1 million prize for anyone who could improve its movie preference recommendations by 10%. Netflix, at the time, made most of its money sending DVDs in the mail to users’ homes (Internet streaming had yet to take off), and personalization offered two major advantages as customers built their ”movie queue” on the Netflix website. First, if the recommendations seemed to make sense, Netflix consumers would be happy as they searched for films online. And second, once the DVDs came in the mail, users might actually enjoy the movie — since a truly personalized prediction would be more likely to meet your taste than your guess based on a movie’s cover image and brief description. Happy ordering and happy watching built Netflix customer loyalty.

To spur improvement, Netflix did more than offer big bucks in the competition. It made public a dataset of 100 million-plus ratings on 17,000 movies, which included the customer rankings from 1 to 5 stars and the sequence in which customers watched films, and allowed competitors to play with the data. The cleverest part was a subset of the data was hidden blind, and Netflix would run the proposed new algorithms against that to see if the prediction models matched how customers really behaved in film rankings.

Mathematicians went wild. The competition was lauded by business pundits as an example of crowdsourcing genius. Because this was damned hard math, the project took years. And then in 2009, a team of mathematicians called “BellKor’s Pragmatic Chaos” actually cracked the code, achieved a 10% lift, and Netflix gave them the $1 million.

And then … Netflix never implemented the winning algorithm. Because personalization at that point no longer mattered.

What happened? Netflix at the time said the technical work of implementing the new personalization would be too costly for the anticipated return. These seems like a rather lame excuse, since bundling a new math model into a computer system surely doesn’t cost more than a bit of coding. Other observers noticed that, by 2010, Netflix’s business model had changed, moving away from DVDs-by-mail to instant streaming. When you can order any movie instantly online, personalization isn’t as valuable — since if the movie is a dog, you simply click over to another movie. Today, in 2014, Netflix’s online interface has a series of rows of film titles, and most of them aren’t personalized recommendations at all.

You learned my needs. But I don’t really care.

The deeper issue is that personalization is not as exciting as many once believed. In the 1990s, Don Peppers built a consulting business on the concept of “1to1 marketing,” where new computer systems would learn individual preferences and businesses would respond with customized offers. Don’s concept was that personalization would create an unbreakable competitive advantage — because once a consumer trained a company to anticipate her needs, she would be reluctant to go through the same process with a competitor. Don was observant enough to note that such customization wouldn’t be a fit for every business model — but companies that had customers with a wide range of needs (such as Netflix movie watchers) or a wide range in value (say, financial advisors courting investors) would benefit by deploying 1to1 personalization.

Despite the noble dream of giving customers more utility and companies more brand loyalty, personalization never took off. Amazon was really the best case study … but it struggles still to offer truly relevant personal recommendations on its website (the core challenges being it cannot easily recognize multiple users on the same Amazon account, or differentiate between your modality as you shop for your spouse one day and yourself the next). Twitter has a personalization engine behind its “Discovery” tab to push news or links to you based on your observed Twitter profile. That site section has so little utility, most Twitter users don’t use it. And Facebook, which arguably has the greatest trove of data on human personal interests, is really at the mercy of the advertisers who wish to target you; this is why you, guys, get ads for men’s underwear whether you really want them or not.

Why is personalization so difficult? Why is it so hard to anticipate what people want, and use that for business advantage? The challenge is personalization is at odds with a core driver of consumer purchase behavior — novelty. Consumers are constantly hungry for something new, something improved, something that will stimulate their endorphins in a manner unseen before. The iPhone 5S had marginal improvements over the prior models, but people lined up in droves for the new OSX, excited by — wait for it — a thinner Helvetica font. Most cable on-demand movie rentals are “new releases,” when logically the utility you derive from a film should have little to do with whether it was released in 2013 or 2003. Retail stores make a business of rotating in new fashions that don’t keep your body any warmer, but spring your desire to shop.

Psychologists have termed our love for newness “the novelty effect,” and it has both positive and negative implications. Humans have the highest stress response when first faced with a threat, likely a survival mechanism that spurs our fight-or-flight reflexes when a mountain lion appears over the hill (and which also explains our grumpiness on Monday mornings at work), so new things can make us angry or upset. But people also have the highest interest when a new person enters their life, a new service is launched, or a new technology is offered. Teachers have noticed, for instance, that when educational information is presented in a new medium — say, tablets — students’ test scores initially rise; the information could be the same as that presented in a history book, but the novelty of the new approach lifts interest and recall.

There is also an evolutionary bias toward novelty in human relationships, both emotional and sexual. A 2012 study by The Journal of Marital and Family Therapy found that at least one spouse in 41% of marriages admitted to marital infidelity. The reason, psychologists believe, is that physical and emotional excitement often diminish in long-term relationships. This could be tied to an ancient instinct for humans to be sexually promiscuous to spread their genes as randomly as possible, ensuring the survival of our species. Even with love, people will trade proven history for risky novelty.

And that is the trouble with personalization. Finding something new is likely at odds with our old interests — because newness by definition is a break from the past. It’s an interesting lesson for marketers now playing with big data. Yes, you can learn and model the past needs of your customers. But just as Netflix didn’t implement a 10% improvement in personalized recommendations because its analysts didn’t predict much value from it, you may find that launching a new product with different sheet metal or miracle fiber gets customers more excited tomorrow than a personalized version of what they wanted yesterday.

No one ever asked for a phone with a camera on it. But today, we can’t live without it.



Google patents a way to clone your mind

females in mirror

Imagine if software could automatically respond to a request using your own intellect while you were away on vacation. Not a, “thanks for your email, I’m out of the office.” But instead a detailed, “John, arg, mate, that’s a superb proposal, and I think the pink elephant-on-a-Mercedes is just the concept needed to win the account. Let’s do dinner at Emily’s next Thursday to nail this down!”

Two years ago we predicted in Businessweek that the convergence of three technologies — voice recognition, artificial intelligence simulation such as Siri, and social media datasets — would enable some savvy marketer to create an app that would simulate your personal response to any situation without you being there. Now, Google has patented a system for “automated generation of suggestions for personalized reactions” that does just this.

In essence, Google would pull data from all your social networks and email accounts to learn how you would respond, and then prepare detailed automatic replies for future events. Initially you would opt-in by clicking “approve” on the replies, but like email out-of-office notifications, eventually you could set your doppelgänger-intellect on autopilot. Mike Elgan over at Cult of Android suggests the most obvious application would be Google Glass (where responding via the eyeglass-frame computer is now cumbersome, and an expanded auto-reply would be most helpful), but other opportunities include managing waves of email without reading them or extending your social network persona while not really being there.

For instance, the Google patent notes,

“Many (people) use online social networking for both professional and personal uses. Each of these different types of use has its own unstated protocol for behavior. It is extremely important for the users to act in an adequate manner depending upon which social network on which they are operating. For example, it may be very important to say ‘congratulations’ to a friend when that friend announces that she/he has gotten a new job. This is a particular problem as many users subscribe to many social different social networks…”

The most startling aspect of Google’s system is it won’t just suggest replies, but also actions. Sure, you can set it to say “congratulations!” … but you could also have the system give your opinion, cast a vote on an initiative, or say go or no-go to a business decision. Add in voice simulation, such as that used by Roger Ebert after his throat cancer, and your persona could talk through your automatic replies.

As we wrote back in 2011, the social repercussions will be huge. Conference calls won’t require you being there — and the artificially intelligent version of you might even sound smarter. Or imagine a widow receiving a call from her deceased husband, in his own voice, opining on whether she should marry that new guy. Everyone could take actions without action, decide without thinking, and live long after they are dead. Google isn’t the only tech company chasing self-intelligent avatars; Apple has a patent that does exactly the same thing.

It’s heady stuff, this autonomous future. Yes, you may like Google’s self-driving cars. But do you want a self-driving you?