Monthly Archives: January 2014

The +1, -1 irrational psychology of the deal

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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

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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

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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.