Apple Watch isn’t a gadget. It’s a desperate loyalty program.

iPhone iPad unit sales trendsb

The most unremarked thing about Apple’s launch of its new Watch and larger iPhone 6 models is how these devices were required to save Steve Job’s company from the train wreck it approached in sales. Unit sales growth, as measured year over year, was plateauing for both the iPhone and the iPad — creating an enormously dangerous position for Apple as investors began sizing up Google, Samsung and even Microsoft mobile device alternatives. Investors want annual growth — because if you can’t beat 8% or 10% growth year over year, you might as well put your funds in a Vanguard index — and Apple was not delivering.

Yes, both the iPhone and iPad were smash hits, and total sales were inching upward. iPhone sales skyrocketed from 270,000 in the first quarter of shipment (3rd quarter calendar year 2007) to 35.2 million in the April-June period of 2014. But iPhone global unit sales growth, measured as units bought in one year over the prior year, had plummeted from 268% in 2008 to a measly 13% in 2013. iPad sales growth cooled even more rapidly, from 174% in 2011 over year prior to only 13% in 2013 — basically following the same  stall of the iPhone, but in only three years instead of the iPhone’s five.

Did you catch that? iPad sales growth stalled in almost half the time as that of the iPhone, despite the numerous tablet design upgrades. Growth was getting dangerously close to the 10% threshold where you might push your investment over to … anything. Yikes.

The scary thing for Apple is all of this was driven by a basic form fact: glass tablets are  becoming commodities, making the brand that designs them less meaningful. The iPhone had a good head start in the mobile-glass-pane business; the first iPhone model officially went on sale in June 2007, two full years before similar touchscreen Android-based  HTC (Hero) and Samsung (Moment) went to market. That explains why the iPhone held on for so long, because for the first two years no one was there to challenge it. But today, screens are everywhere. Amazon is practically giving away its Fire phone.

So, will the iWatch and larger iPhone 6 screens save Apple from stalling in a glass panel world? If design alone were its strategy, absolutely not. No matter how much we love Jony Ive, his miracles of design tend to fade quickly (Are you still enraptured by the parallax features of the iPhone 5 operating system? Didn’t think so.). So Apple is shifting strategy to one of multi-device and information-platform entanglement. The vectors of desire connecting you to your future Apple Watch (you won’t buy one now, but you know maybe you want one next year), the mobile systems’ new health and home control centers, and the larger units of the iPhone 6 models will accelerate Apple sales of commodity flat touchscreens in the near future. Apple wants you to regret leaving any one shiny piece of its ecosystem, and by combining the pieces together more tightly, your switching costs (what you give up if you leave) go up. The Apple Watch, really, is a simple loyalty strategy.

But only for the near future. The competitive cycle is accelerating. Smart watches can also be emulated, Samsung is good with glass, and Google has an advance on both map locations (required to make wrist devices most useful) and home intelligence systems (having already bought Nest, the innovative thermostat/alarm/home monitoring company).

The tagline for the first Apple iPhone was “this is just the beginning.” We wonder if Apple was referring to its competition.

 

 

 

5 things you can learn from Twitter’s new analytics

twitter dash 4

Whether you’re a brand curious about how your tweets are faring or an individual longing to gaze into your social-media navel, Twitter’s opening of its analytics dashboard this week is sure to excite you. Twitter first launched Google-esque analytics to its advertisers and verified users back in July, but now, anyone can go to the dashboard site to see how far and wide your tweets go.

For users new to Twitter analytics, the results are surprising:

  • First, a negative: Fewer people are exposed to your tweets than you might think. A Twitter account with about 10,000 followers will, on average, have each tweet seen by only 300 to 500 people. This intuitively makes sense, since obviously not everyone is using Twitter at all hours of the day, and your 9 a.m. missive about coffee is buried in the stream by the time another user logs in an hour later. Still, Twitter reach is less than expected.
  • Second, a positive: “Engagement rates” are through the roof. Twitter defines “engagement” as a user taking any action on your tweet, such as clicking on a link, favoriting it, retweeting it, or replying. While the standard interaction rate on normal banner ads is about 0.07%, Twitter interaction rates hover around 2.5%. This appears to be a decline from a few years ago (in 2011, Twitter boasted on its blog that brand interaction rates were 3-5%), but 2.5% is still amazing — users exposed to your tweet are basically 36 times more likely to take an action than if they saw a digital banner ad.
  • Third, click-through rates are also very high. Using my personal account @benkunz as an example, from Aug. 1-28 I sent out 109 tweets, of which 34 had links. The tweets with links generated a 1.70% click-through rate based on 330 clicks against 19,380 actual impressions. More than two-thirds of my Twitter interactions were clicks, and that CTR is 24 times higher than the average banner ad response rate. The caveat here, of course, is I’m not a brand trying to sell you anything, just a human trying to be interesting, but that response rate bodes well for any brand with an authentic, meaningful content stream on Twitter.
  • Fourth, if you want your tweets shared: News tied to the zeitgeist of Twitter chatter works. In our non-scientific-but-super-insightful study of what tweets have the highest interaction rates, missives related to current events usually have 2x or 3x the average Twitter interaction rate. In the past few weeks, our tweets about Matt Damon doing something innovative with the ice bucket challenge, Starbucks redesigning its stores, or Robin Williams being photographed as a mime in Central Park in 1975 had much higher interaction rates that our supposedly smart asides on life, liberty and the pursuit of advertising. News, on Twitter, sells.
  • Fifth, timing matters. We found the highest interaction rates on Wednesdays, Fridays and Saturdays, and the lowest on Sundays and Mondays. Apparently Twitter users back away from social media on Sundays and are too buried in work on Mondays to spend much time interacting.

Wisely, or perhaps sadly, Twitter only allows you to see analytics on your own  tweets. As of now, you can’t view the Twitter activity of Pepsi or Robert Scoble to parse how big brands or social-media celebrities are faring with their own Twitter messages. But if you want to examine your own tweets to see what makes people respond, the dashboard is an excellent resource.

Of course, the other option is you could continue to just be yourself online and share real, authentic insights and news not worrying about what other people think. Brands and humans, we’ll leave that up to you.

Why can’t advertisers get personalization right?

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Deep near the bottom of today’s NYTimes story on Facebook — “How Facebook Sold You Krill Oil” — a marketing manager for Reckitt Benckiser, a company that sells fish-oil pills, says “Facebook is a fantastic tool for doing personalized marketing at scale.” The NYT case study goes on to explain how the advertiser in question is able to pinpoint-target different female demographics, users of other fish-oil products, and even “lookalike” profiles of individuals with similar interests. The Facebook campaign worked and fish-oil sales went up.

Yet all this is outbound targeting from the marketer’s perspective, similar to really good archers being able to fire arrows into the chests of different consumers with different needs in a crowd. It’s not personalization.

True one-to-one personalization was described by Don Peppers in the 1990s as an iterative process in which consumers are identified, differentiated on both their financial value to a company and need from a company, interacted with, and then given customized services or communications. The most crucial step was to make all of this a feedback loop, a “learning relationship” in which a marketer grows ever more sophisticated about anticipating individual needs over time.

It is this anticipation of needs that creates loyalty, Peppers said, because once a consumer has trained one organization to anticipate her wants, she would face switching costs going somewhere else. The classic example of marketers who actually pull this off are local coffee shops who see you walk in and immediately warm your specific version of coffee and breakfast muffin without you asking. You skip the line, you save 3 minutes, you’re individually recognized, and so you don’t want to go anywhere else. One-to-one personalization, evolved as a learning relationship, becomes almost like a strong marriage — where all the prior history of learning enhances the bond between consumer and brand.

Now, let’s revisit advertising.

Today, I checked my ads for Facebook, and saw this: retargeting ads from a hotel chain I searched two weeks ago; a technology course apparently inspired by an online friend who works in Web services; a tiny chip-wafer thing I can attached to objects around the house to “never lose them again”; and an ad for a local plumber. Personalization score, from 1 to 100: About a 5. At one point, I was interested in the hotel.

Twitter was even worse. I got two ads for a Red Bull music festival; a local Applebee’s ad for a frozen fruit drink; and a promotion for small-business insurance. Personalization score: 3. Maybe, just maybe, we’ll go out tonight to a restaurant, but I don’t like frozen fruit drinks.

All of these ads express a knowledge of my personal needs about as sophisticated as the  direct mail lists that trigger our Pottery Barn catalogs. I am a target with some general color descriptors: So, go ahead, fire your arrow into me.

Product-focus creates a personalization failure

The problem that personalization faces is most marketing engines base it from the marketer’s point of view (since the guy with the ad budget is calling the shots). This creates a data collection model centered on a product, which inherently has vast gaps. A home services company will build a CRM system to include lots of information about an individual prospect’s interactions and a customer’s service records, but that data is only germane to the product. Jane Smith may have a forecast lifetime value of $20,000 in utility bills over 10 years … but the data systems don’t recognize that she is a mother with three daughters interested in mountain climbing and kayaking. The utility frankly doesn’t care.

All of this is driven by economics: individual companies want to store and analyze only data related to their product sales; social networks want to release only the data needed to target a product to a customer; and a truly customer-centric personalization campaign would require coordinating millions of potential product offers, likely from competing brands who have little incentive to sell services outside their own scope.

In simple terms, because the varied needs of a customer would require unified data and services that cut across brands, until an ecosystem of brands has an incentive to share data and revenue, personalization will not happen. What this gives us is ads on Facebook for fish-oil pills based on a rudimentary understanding that you are friends with someone who takes vitamins, but no anticipatory personalization that informs you of where to take your wife to dinner on the anniversary of your first college date.

The customer loses out

What would it take for true personalization to arrive? A few companies may come close. Amazon and Walmart, which house millions of product SKUs, have incentive to use data to anticipate your needs and the service offerings to potentially benefit from a vast range in sales. Twitter, which conceivably could parse your real conversations to build better profiles of your mind than simple Facebook stated interests, could truly personalize its #discover newsfeed to create content germane to your interests.

Facebook might do true personalization, if it could somehow depress ad offers that had nothing to do with your interests. Unfortunately, Facebook wants the billions from the unexpected plumber ad or computer technology course offering that sprays you despite your disinterest. Most Facebook ads are priced on a cost per click, rewarding Facebook if a user clicks on the ad, but not worrying about the 99.97% of users exposed who don’t want to respond. The economic model focuses on what advertisers catch, and ignores the adverse impact of what they spill.

Television is in the same boat. The typical U.S. consumer receives 6,600 spots per month, based on 4 hours and 34 minutes of TV viewing per day. You may want 3 of those products, putting your “response rate” in the 0.05% range, about the same as responses to online banner ads. If TV truly personalized ads, it might have to forego 99.95% of its advertising inventory — and lose billions of dollars a year by removing all that unwanted communications bloat.

Truth is, personalization will never arrive until a giant platform is able to match transparent data on individual needs with a vast consortium of products and services that can be personalized in promotions without friction from marketers demanding that their product appear next in line. Marketing would have to become a true marketplace, and product-makers would have to cede their product focus to a willingness to give consumers what they really want — even if that means the brand down the street wins instead of them.

Which is why, tomorrow, you’ll see more unpersonalized ads on Facebook for fish oil. Today’s economic incentives decree that unpersonalized ads are the way to go.

Photo credit: Ruurmo

Mobile pushes young adults to shop at the mall. But why?

mobile samsung tabwatch

 

Well, this is interesting. A recent survey found that mobile devices make young adults 2x more likely to buy something at a physical retail store — but only young adults. Why?

In May Gallup asked 1,505 U.S. adults if mobile devices such as smartphones had boosted their in-person retail store shopping. Most adults over age 30 were ambivalent, with about 20% saying they shopped more as a result of iPhones and tablets, a similar 20% saying less, and the remainder saying no change at all.

But young adults were different. 29% of adults 18-29 years old said mobile had increased their in-store shopping, while only 15% said mobile had decreased it. That’s nearly a 2-to-1 edge for mobile pushing youth to the mall, a whopping finding. What gives? Why would mobile communications push young people to stores to buy, while most other adults ignore them?

Here are four possibilities:

1. Younger adults might have less access to credit cards, so mobile necessarily pushes them to physical stores. Gallup floated this idea in explaining its study, but it’s unlikely — since 60% of college seniors now have a credit card vs. the 70% U.S. adult average, the slight difference would not account for the 2x response in youth to mobile influence.

2. Younger adults spend more time at the mall, so mobile is more likely to increase their shopping while there. Hm. Possible. A recent tracking study of malls around the United States found that 34% of visitors were adults age 18-24, while that same demo makes up only 15% of the U.S. population. If young people are already there, mobile would be likely to get them to spend in stores.

3. The primary use of mobile is to enable social behavior, and young adults are more likely to think of shopping as a social experience. Teens, for instance, go shopping 75% of the time with friends, and 64% of adults age 18-24 go to the mall with someone else vs. being alone (vs. 55% among all adults), according to a 2009 Arbitron study. This confluence of mobile-social-shopping behavior among youth and young adults would make mobile communications more likely to drive retail purchases.

4. Youth are more open to mobile or social communications related to commerce. This conjecture is hard to prove, but another recent Gallup study did find that young adults were more open to social media influencing their purchase decisions. 43% of Millennials said social networks spurred their commerce, vs. only 34% of Gen Xers, 26% of Baby Boomers, and 16% of the oldest adult demo that Gallup kindly calls Traditionalists. Social media is not exactly mobile, but it’s close enough we can surmise youth are also more open to mobile messaging that drives shopping.

We suggest the correct answers are 2, 3 and 4 above. Youth go to the mall. Mobile is social. And social influences young adult shopping. This is important news for marketers who are struggling to reach the future generation of shoppers, since humans tend to take their media habits with them as they age.

If you’re wondering why Amazon went to the trouble of launching its own smartphone, or Facebook is so interested in pushing ads into the main mobile Newsfeed, Gallup tells you why.

Google patent pulls personal data up from the crowd

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At SXSW this spring, Robert Scoble said the big news about wearable technology isn’t what it allows you to do (capture video via glasses or monitor health stats on your wrist) but rather the data it captures about you. Wearable tech is filled with sensors that watch what you do, where you go, and what you like. Google Glass, for instance, has a gyroscope, accelerometer, magnetic field detector, light sensor, location sensors, touchpad, camera/video input, sound input, and sensor tracking your eye movements so you can wink to take a picture. Privacy advocates freak out over what large data companies could do with all of this information, since your hand motions, heartbeats and eye movements can signal, for instance, whether you are lying.

But what happens when all that new data helps companies monitor groups in a room?

Google has received a patent that would upload preference data from mobile devices to allow environments to personalize the media played for crowds in a given venue. The patent, titled sexily “Collaborative Rejection of Media for Physical Establishments,” would pull wireless signals from a group of people in a setting, such as a film screening, concert theater, or disco, and use either direct input from individuals or the history of user preferences to modify the media presented. If a group of country music fans from Tennessee walk into a New York City bar, the tunes could flip automatically to Blake Shelton and Dierks Bentley. While the obvious use would be to customize music playlists in stores, restaurants or bars, this system could also tap the collective preferences of the group in a facility to tailor video content, ad messaging, even film plot lines. 

And not all votes would count. The most intriguing aspect of the Google patent is it recognizes that not all customers are created equal. In one scenario, “a customer having the premium status is afforded superior media file rejection.” If you’re walking next to an affluent businesswoman at an airport, a digital screen could size up both of you and flip to the ad message she is interested in, if she had greater financial value to the company pushing the message.

It’s an elegant concept, because it solves the problem of personalization in public spaces. When 20 people are in a room, it’s hard to know what image to push onto a screen or over the audio. If 3,000 people are at a concert, it’s cumbersome to interrupt them all to ask for feedback on the music set list. Now, Google can sort the media via monitoring signals from mobile gadgets (perhaps eye dilation or heart rhythms in the near future) to please the statistically most relevant people in the audience. And because all of this will be based on an invisible signal from all of your pockets, the implementation would not be as freaky as a large-screen ad retargeting only you, so consumers will be unlikely to rebel.

Cheers, mobile-device carriers. Soon at the bar, marketers will know all of your names.

 

Amazon’s product recognition kicks Apple’s glass

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What are we to make of the Amazon Fire Phone? Forget the design. It holds the killer marketing app — impulse-tapping product recognition.

The constant story of invention is when the Big New Idea comes along, at first nobody knows what to do with it. When Lord Kelvin heard of Guglielmo Marconi’s wireless telegraphy, a device to send signals magically through the “ether” via “waves,” he said, “wireless is all very well but I’d rather send a message by a boy on a pony.” Second, once the invention is established as useful, people invest wildly in the inventors. Third, and finally, the invention becomes a platform that morphs into a commodity that induces yawns.

Nobody today is rushing out to invest in radio towers.

Which brings us to modern mobile phones and tablets. Apple made us gaze in wonder at the first iPhone in 2007, when Robert Scoble emerged from the store with two (the limit then!) boxes in his hands. Now, shiny pretty screens are commodities, and soon will be ubiquitous. The new Amazon phone has a 13 megapixel camera and hi-def screen, but who cares? So does every other phone. Frankly, Amazon, your phone design is a yawn.

But. Oh, man. Amazon’s phone has a killer feature — product recognition. Wisely, the retailer giant is betting far beyond a  hardware design to a new, revolutionary system that connects you instantly to any product you capture via the phone’s camera or audio. The Amazon Fire phone uses a “Firefly” app that can recognize in a second more than 70 million products, or listen in on audio to pick out 240,000 movies or 160 television shows. Whatever is around you, if you like it, you can instantly bookmark it or buy it.

Miss, I like your dress. Snap. I just bought it for my wife.

Dude, great shoes! Snap. Now on order to my home.

That YouTube trailer looks like a great flick! Snap. Downloading to watch this weekend.

More than a simple dongle tying you to Amazon’s ginormous product ecosystem, this Fire phone is a new way to tap the Holy Grail of marketing, influencing consumers at the impulsive point of purchase. Amazon could seriously cannibalize other retailers — say, Walmart or Target or Macy’s or Nordstrom — by allowing you to quickly and easily price-shop by snapping a picture of any product on any shelf. Amazon, which has learned to thrive on razor-thin product margins, will undercut other retailers too keen on inflating price with rapid delivery of the same product.

Meanwhile, as Amazon makes purchasing easier than ever, it will collect an entire new ecosystem of data about you. It already knows your shopping habits and can infer from them deep data on your personal psychographics and behavior. But an Amazon phone will collect your location as you search for items, and can pinpoint how consumers are changing gears while at competitor locations. Imagine a heat-map of every consumer ordering shoes from inside every shoe store in the world, and then parsing which stores have the highest conversion rates or shopping-cart abandonments so Amazon marketers could adjust  pricing and product selection accordingly to compete more deeply with Dick’s Sporting Goods. As the data accumulates from consumer real-time, location-based, in-competitor-store transactions, Amazon will gain a data edge that no other retailer could match.

Or a final idea — we’ll give this one to you for free, Amazon marketers — is tying location to variable pricing. If Amazon were really clever/evil, it could adjust pricing from any phone inquiry to nudge you to buy from Amazon instead of the store you’re walking in, by slightly undercutting any store’s individual product price and using higher margins elsewhere to offset it. Say, you find shoes you like at Dick’s for $95, so Amazon could offer them to you as you check on your Fire phone for $90 instantly — and only you — while charging  other consumers who blindly click to the main Amazon.com website $97. If only 1 in 5 shoppers needs the price break, Amazon would still come out ahead.

It feels like a chess move that Garry Kasparov might make. Well, at least Amazon can’t get products to you in just a few hours. Something that crazy might require shipment by drones.

 

 

Amazon and Google’s billion-dollar race to kill the mall

drone eerie

So news broke yesterday that Amazon has posted job listings for its much-derided drone delivery service. What seemed a few months ago like a futuristic whim may be getting traction, at least if you are a software engineer or communications professional willing to join “Amazon Air” in San Francisco. Soon, Amazon will be dropping packages you order from the sky within a half-hour of your click.

Same-day delivery, whether by airborne robots or regular trucks, is gathering steam. FedEx now offers same-day delivery in 50 states for packages under 150 pounds. A company called SameDayDelivery (.com) professes to specialize in the service, with 50,000 vehicles and air freight racing from coast to coast, although you have to fill out a lead form to get a price quote. And Google Shopping Express will nab you goods from REI, Toys R Us, or Staples, provided you live in San Francisco or a few other special urban areas.

While the press focuses on the cool factor of heli-robots, the real question is — what would all this do to brick-and-mortar retail? Crush it, of course. If same-day delivery scales, the nightmares of the first Internet bubble in which pet food companies worried about Internet disintermediation will become reality. Simply put, when you can click your way to a particular product and have it on your doorstep or office desk within minutes, why would you drive to the mall? Same-day shipping will reboot all the “channel conflict” challenges of the late 1990s.

Most retailers would secretly love to kill their stores

Channel conflict, at core, means any company faces an internal conflict if, say, it can sell a shirt for $50 at the mall and the same shirt for $50 online. Both shirts must have the same price point, to keep customers using both channels, mall and online web store. Companies selling shirts keep both stores and websites running, because they know customers want both. But the shirt at the mall may incur $20 in retail rent and overhead costs, while the shirt shipped to the home may eat up only $2 in shipping. That 90% cost difference means this shirt-maker would love to push more products through the web, and fewer through malls, creating a “conflict” within its competing delivery “channels.”

Yep, you guessed it. While most goods can be delivered more cheaply via Internet orders than store stocking, retailers keep the real stores open because consumers love to touch and feel goods in physical space … and Internet orders usually take 2-3 days, killing impulse purchases.

But same-day delivery? If you could go to any website now and get that new watch or dress in 30 minutes, wouldn’t you be tempted? This near-instantaneous consumption revolution will push huge traffic to online retailers, and cannibalize the old-fashioned stores at your local mall. As Internet retailers push same-day delivery, consumers will flock around the portals that can remember their preferences the best. It’s no wonder Amazon and Google are investing in tests of this type of same-day service. Huge retail dollars, and the preceding online search or other ad revenue, are at stake. Physical stores will never go away entirely, but even a moderate shift from brick-and-mortar to cyber-insta-delivery would put billions of dollars into the winners of flying robots and speeding trucks. In 2012, only 5.4% of all U.S. retail sales were made online. Imagine what Google or Amazon could make by doubling that.

If you thought buying pet food online was a silly 1999 Internet bubble concept, perhaps you just had to wait a few years.

Facebook solves the mobile sandbox problem

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At its annual F8 conference this coming week, Facebook will announce it’s solved a vexing problem for marketers trying to reach consumers on mobile: The sandbox.

Mobile advertising, you see, to date has stunk. The prime reason is that data about consumers — the core of any advertising is the information that allows you to target someone — has been largely missing in mobile advertising. When you use your cell phone, there is large entity called your phone carrier between you and most marketers, and the data about who you really are (gender, age, income, habits) doesn’t get through that intermediary very easily. Now, of course, many apps can monitor your behavior and gather information about you. A weather app likely knows what cities a business traveler goes to, and a news app may be able to build a profile of you based on your content consumption.

But apps don’t talk to each other well, and all the data within each app has been “sandboxed.” This means that the vast majority of mobile advertising to date has been ludicrously un-targeted. Some mobile ad networks claim they can IP target, but that is based on cell tower location, and only good to a few miles. So, like the very early days of web advertising, mobile targeting hasn’t worked well, and mobile ad dollars have not followed.

Except for Facebook.

In 2013 Facebook began rocking mobile advertising with its own system, because of course Facebook is more than a social network — it is a data giant, with enormous profiles of who you are, who you are dating or married to, your friends, your interests, and behavior. If you are logged into Facebook, suddenly marketers have a dreamload of data about you. In Q4 2013, Facebook made more than half its total revenue from mobile advertising.

Facebook is smart, and realizes that its nexus as the main social media platform may not last forever — so it needs to build out new revenue streams. How? By using all that data elsewhere, outside of the Facebook system.

Observers say Facebook will announce at F8 a new mobile advertising platform that allows marketers to use Facebook data outside of Facebook on other mobile apps. This is revolutionary, because for the first time marketers can really target mobile based on robust profile information. Marketers will love this, not only for the targeting ability, but for scale — because now it won’t matter if the consumer is reading her Facebook Newsfeed or checking a weather app, she can be reached across thousands of mobile touchpoints.

The data that used to be sandboxed inside each single mobile app is now accessible everywhere, with Facebook owning the treasure trove. And with 37% of all U.S. consumer digital “media time” now spent on mobile devices,  ad dollars will pour into Facebook’s new mobile ad network.

Data is the future of Facebook

Beyond this tactical network, this signals in the future Facebook will be much more than a social network. It has become both the keys to the Internet (you can log on to most major sites with Facebook) and the safety deposit box for your personal information. Facebook is the new Experian, a vast trove of data that marketers can use almost anywhere. If wearable technology takes off, Facebook will be there. If consumers gain enough trust to start buying products through Facebook, the social network could rival Amazon.com in personalization and e-commerce. If Facebook wanted a slice of the $144 billion U.S. television market ($70B in advertising plus $74B in cable subscription fees), it could launch broadcast capabilities with revolutionary data targeting ability.

This is not silly conjecture. Facebook is smart, and somewhere in its boardrooms lies a master, multiyear plan of how it will expand its services carefully using its data bank to protect itself from the inevitable decline of its social network while jacking up its stock price. Networks can only increase in value if the size of the network increases. With Facebook’s direct social users capping out (there are only so many people on the planet), it needs to expand its nodes elsewhere.

Like 1970s Ham radio, social media fads don’t last for long. But the data Facebook has on you is forever. Look for it next month via relevant ads on your smartphone.

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

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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.