Category Archives: personalization

Media predictions for the far-forward future

woman hologram

Put down your smartphone app and think far, far ahead. Media prognosticators rarely do this, perhaps because advertising clients and digi-journalists gain more from toying with the latest Twitter group chat update than they do by conceptualizing the state of media 100 years from now … but a far-forward forecast could be worth the effort.

So let’s play the prediction game.

Before we start, here is our inspiration: the brilliant book “The Next 100 Years,” in which George Friedman examines the macro trends of history in attempt to predict world events through the coming century. It’s an amazing feat, reeking of intellectual arrogance, to try to foresee 100 years of future events … until the reader discovers that Friedman has a solid methodology.

Friedman bases far-forecasts on geopolitics — the combination of national resources, locations on the globe, culture, and economics — which has ongoing patterns that make shocking events, such as World War II or the terrorist attacks of 9/11, predictable. Individual players on the planet, even presidents or kings, typically have far less power than we imagine, and must play upon a chess board that is already set. The future, it seems, can be predicted, if you really examine the macro trends. For instance, looking backward, Friedman argues:

  • It was inevitable Europe would become a global power in the 1800s, because it needed supplies from Asia, and after Turkey cut supply routes over land Europe sought a route west to India by sea and thus learned to manage the oceans — controlling global commerce.
  • It was inevitable that the United States would win the cold war over Russia in the late 1900s, because allies to the United States could “sell in” to its vast consumer demand set, making U.S. friends rich, while Russian allies might get weapons but end up impoverished. 

And, looking forward, he suggests:

  • The United States will remain the leading world power in the 21st century, because it controls the world’s oceans, due to its fortuitous placement between both the Pacific and Atlantic oceans, thus controlling trade.
  • There will undoubtably be another horrible global war in the 21st century, given the tensions between the rich and poor and the continued belief of humans in their personal nation states.
  • And this war, like all others, will eventually end, and generate new technology systems from the wartime investment that lead to sources of clean power and communications we today can barely imagine, such as microwave energy beamed down from outer space — the most efficient way to capture energy from the battery in the center of our solar system, the sun.

Oceans, human antagonism, and sunlight are all constants, and they will define our future.

So what is the real far-future of media?

Here are our predictions: (1) Environment monitoring, driven by sensors around us; (2) virtual visual overlays that are constantly on, created by the inevitable shrinking of screens until they fit in your contacts; (3) ambient personalization, as you control what you see everywhere; and (4) societal upheaval as we relearn how to interact with other humans in a virtual world.

1. Sensors everywhere — Behind all the Nielsen updates on multiscreen use or Pew reports on social media fads, the truth is information flows only two ways — to us or from us. While media writers remain fascinated with toys, the biggest trend in information flow is the spread of connected sensors in all devices. The iPhone 6 has six sensors built into it — including proximity, motion sensor/accelerometer, ambient light, moisture, compass, and a gyroscope — coupled with GPS features that pinpoint the phone’s location. The Disney Research lab has created a Touche interface that can turn the surface of any object, such as a table or couch, into an input sensor by monitoring vibrations created by human contact. Philips has launched “design probes” that explore tattoos with sensors that disappear based on touch, and clothing that changes color based on your mood.

With sensors everywhere, you will be tracked. Tracking will require control, so humans will use that to personalize their environments (a benefit) while suppressing unwanted third-party oversight (a cost).

When you walk into a room in 2070, the room will know who you are.

2. Screens everywhere — Concurrently, the spread of screens is obvious. At SXSW Interactive last spring, the head of the Consumer Electronics Association, Gary Shapiro, said that within 10 years consumers will buy wall TVs — or whatever we will call high-resolution digital screens that fill an entire wall. Apple has a patent for holographic wall screens that project 3D images to both eyes of each user in the room, without them wearing googles, by monitoring the location of their heads. And Microsoft has just launched a HoloLens goggle prototype that overlays 3D images on reality with a wider field of vision than the (recently aborted) Google Glass. As big TVs grow into walls, little visual screens will also shrink into contacts.

With screens everywhere, you will see whatever image you wish to pull up.

3. A personalized universe — The great media prediction for the next 100 years is that humans will be able to retreat into completely personalized bubbles of vision, overlaying data about others in their contact lens, porting their images into virtual meeting rooms thousands of miles away, and pulling entertainment into the real world around them. Because if everything (from couch to table) senses you, things will recognize your preferences, creating demand for automated content that overlays your reality to meet your unique needs.

From a content creation perspective, this will unlock a gold mine of opportunity for film (hologram) producers, game designers, social media entrants, work/office productivity software, pornographers (always the earliest refiners of visual technology), religions (where belief systems could now be “seen” as reality), and yes, marketers (who will find a way to support this content with some form of advertising over there on the side). This information rush will become fuel for economic growth, with visual services an entire new platform for monetization.

4. Societal unrest — These media trends are our predictions, not Friedman’s, but he has a point that may refine ours: Every evolution in society comes with unintended consequences. The vast rise of visual screens and the concurrent measurement of human personal preferences on every surface device may unearth new social dynamics we cannot anticipate. Will people become more gregarious as they seamlessly are able to beam their avatars into the world? Or will humans retreat into dream bubbles, like those poor enclosed battery souls in the Neo Matrix, asleep in cocoons while they envision a fantasy of greatness?

We cannot predict that. But one thing is certain: The far-forward future contains much more than an iPhone.

Google patent pulls personal data up from the crowd

dance club dancers

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.


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.



The sad case of Twitter’s missing personalization

Way back in the 1990s I had the fortune to work with Don Peppers, who created the concept of “1to1 marketing” strategy. Don wrote a series of best-selling books describing a future world where information would allow nearly every service to be personalized — the idea being such customization, newly empowered by cheap databases, would build unbreakable switching costs. LIke a marriage where your spouse remembers you like chocolate in your coffee on Sunday mornings, you’d never leave a business that remembered so much for you that it became inconvenient to go somewhere else. Don’s 1to1 idea was adopted by software companies, renamed CRM for “customer relationship management,” and ended up a sales bullet point for The rise of social networks in which consumers talk to each other also took some steam out of the idea, since the business-to-customer 1to1 dynamic became less the focus of marketing strategy when CMOs were scared that the surrounding customer networks had grown out of control.

Which is sad. Here’s a case in point: Twitter. Go to and click on the “# Discover” link at top and you’ll get hit with stories that are popular right now among everybody — but not tailored to you. Imagine all the data Twitter has on us: Our tweets express all our interests, politics, purchases, hope and fears, and our links match up to homophily friendships that could further define exactly what we want. Twitter could build an unmatched predictive newsfeed for every individual, blowing The New York Times or Fox News out of the water with tales customized to our whims.

Except Twitter doesn’t. One top “Stories” recommendation for me today is a link to a Kentucky-Baylor basketball game. I don’t watch basketball. I have never mentioned basketball in any of my 24,410 tweets. Twitter, the game bores me to death, and a cursory analysis of my stream should show I’d much prefer a link to artificial intelligence advances, science fiction films, or chocolate recipes. (Don’t judge. That’s me.)

Perhaps for Twitter the cost of mining tweet data is too high (really?), or the small Twitter team is still underwater keeping the servers running (probable), or Twitter would rather go after very large advertisers such as Pepsi and AmEx who can spray everyone with the same message (ding! we have a winner!). So your news feed inside Twitter is completely off base.

Maybe Twitter just can’t see beyond the initial investment. Treating everyone the same is the easiest form of marketing, and 1to1 requires an upfront investment hurdle far before services reap the rewards of loyalty or increased use. The only companies really doing personalization are and Netflix, which must offer recommendations right before each sale, and so 1to1 prediction is a competitive requirement. Twitter is still just learning to accept ad payments from mass marketers, so the personalization concept may be too sophisticated at its early monetization stage.

Sad. Because, Twitter, this unpersonalized “# Discover” news stream stinks, and makes me spend yet more time on Google+. Which is exactly the point: If you treat everyone the same, customers don’t feel bad about leaving you.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Originally posted on Google+.

Why personalization fails

Personalization is everywhere, especially online, where companies retarget you with banner ads if you visit their web site, or bid on competitor terms that pop up on finance pages (see the nice play by Blockbuster, above), or even chase you if you don’t click on an ad by figuring out a lot about who you are. For instance, savvy digital media buyers can run a few hundred thousand banners on a section read heavily by CEOs and pay a small fortune — but then tag the CEOs’ computers to serve additional banners downstream, wherever those CEOs go online, at 90% savings. In essence, this “lifts” the audience from WSJ, aligns offer with the target, and slashes media costs.

The premise of all these tactics is personalization lifts response. But does it? More than a decade ago I worked with Don Peppers, the brilliant father of 1to1 marketing who helped launch the CRM craze in the 1990s (before the term Customer Relationship Management became an acronym for software baloney). Personalization assumes that an offer with higher relevance, based on your personal and unique needs, will grab your attention, convert you to a sale, and keep you as a loyal customer.

Yeah, 1to1 can work, but it’s only one aspect of three major prongs of competition — the others being price (or perceived value) and product (where innovation is hot). Wives love husbands, but some still chase younger boyfriend or girlfriend products over personalized marital service. Apple doesn’t give a damn about personalization, for instance, yet makes a fortune in profits off of hot product designs. (I’ve often thought the reason iTunes’ interface is so horribly cluttered is Apple has found confusion leads to more sales as we click on random songs/videos we didn’t know we wanted). Consumers want deals and cool product designs; personalization cannot address those aspects.

No one ever, ever, ever asked for a two-door minivan or a cell phone with a camera or a flat computer screen with no keyboard.

Personalization does not lead to market revolutions.

Another problem with personalization is entire industries make money off waste. The cable industry, for instance, pushes more than 166 :30 second spots to a typical U.S. consumer each day (based on 5 hours and 9 minutes of TV time and 16-18 spots per hour). If you could get only the personalized ads you wanted, you might put up with 10 or 20 spots — but the remaining 146 spots would vaporize and all the ad revenue with it. Media intermediaries make boatloads off of waste. True targeting on TV, the current king among consumer media consumption, would erase billions of dollars from the ad industry.

Finally, people are not unique data sets. We have modality. I’m constantly frustrated by offering me Legos or Oprah books when those recommendations are based on shopping I’ve done for others. Amazon, like Netflix and others who attempt personalization, needs to provide a modality dial. Tonight I may want food, or history, or a book on technology, or sex, or a spy film. I have no idea who I will be in a few hours.

So keep trying, marketers. We try for our clients too. But it’s hard, when your carefully crafted personal offer is sent to a moving target.

We are humans, and we contain multitudes.

Inspired by +Len Kendall

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

The long road to personalization

Our friends Bill Green and Alan Whitley at digital shop BFG sent us a Mashable article declaring demographics are dead. The column’s author, Jamie Beckland, raises excellent points that new forms of personal data are more effective for marketing … but stretches too far.

We jotted this email back.

Conceptually I agree that marketers continue to improve targeting, and that psychographics are better than demographics. But, as with any provocative article, this writer takes the case too far, because the theory can’t be implemented usually and demographics, while a broad categorization, are still an effective form of targeting. If you are a mom in your 40s, yes, I’ll run a morning news spot promoting a local hospital, because your demo makes sense, and no amount of psychographic profiling in the world can predict that yikes, you just found a lump in your breast.

Yes, there is waste in such approaches, but advertising is a game of what you catch, not what you spill.

I spent an early part of my career working with Don Peppers, the father of 1to1 marketing, who wrote a book in 1993 titled “The One to One Future” (and spawned the CRM craze of the 1990s which eventually became a term for software after marketers had difficulty implementing it). Don’s idea was that eventually marketing targeting would get so perfect, it would become 1to1 personalized relationships, a feedback loop with every customer. Brilliant idea, but very difficult to implement. When I read people like Joseph Jaffe now claim “the 30 second spot is dead,” I laugh a little, because it’s the same vision 20 years later. It is coming, but slowly, and we’re not there yet.

One of Don’s great thoughts was that “1to1 marketing” – or hypertargeting – works best in certain industries which have
a. Variance in what customers need or
b. Variance in the customer lifetime value to the business

This is why personalization has been implemented best by and Netflix (where Bill Green and I likely have very different “needs” in books and movies), and why differential treatment strategies are implemented by airlines and hotels (where a business frequent flier has 100x the value of a typical vacation traveler). In such industries, investment in customer data systems and corresponding hyper media targeting make sense.

But in other industries with mass appeal, demographic targeting is fine. Insurance is a classic example – State Farm and Geico spend millions on billboards, which is smart, because their products appeal to almost everyone and it is almost impossible to tell when any individual is going into play after a bad experience with their old insurance company.

Psychographics cannot predict customer modality, which is why Netflix personalization is still problematic. I don’t know what movie I want to watch next week, and I’m me.

In terms of the quote that a 1% response rate is bad so traditional advertising doesn’t work, that is ridiculous. As I said, advertising is a game of what you catch, not what you spill. If the math works out on a tiny response rate, at an acceptable cost per acquisition, marketers will throw money at the channel every day of the week. There are 3.5 billion women in the world and I married one – was my personal marketing effort for love and sex a bad campaign? No.

Finally, one major error in this type of prediction is it doesn’t look at how humans actually use media. Internet use is still less than 1 hour a day for most U.S. consumers. The typical U.S. consumer watches 5 hours and 9 minutes of television a day, which works out to exposure to 166 :30 second TV spots each day. People spend hours in their cars, looking at billboards. There are more marketers who want to push a message out than consumers who want to receive them; people still spend huge amounts of time letting mass media wash over them; and personalization just can’t work at that scale (who would possibly respond to half of those 166 TV offers even if they were exactly what you want?). It will be decades before media channels figure out how to implement personalization across such broad media touchpoints.

Personalization is coming and we’ll continue to improve our tools, but as with any idea, the theory is often better than the execution. Pinpoint targeting is a dream, but broad media hammer strokes still work, too. Our recommendation is to try to combine both tools, but certainly not to disregard either one.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Why 1to1 personalization hasn’t arrived (hint: media loses money)

The concept of 1to1 customer relationships, in which a marketer learns your needs and later gives you an offer tailored exactly to your whim, is tantalizing. Don Peppers espoused it back in the 1993 book “The One to One Future,” and brands as wide-ranging as Levi Strauss,, Zappos, PaineWebber, IBM and General Mills toyed with it. The idea was a clever counterpoint to the “Positioning” mass-communication strategies of the 1970s, and agencies and software companies, always ready to drink the Kool-Aid of customer focus, embraced 1to1 in the 1990s as much as they love social media hyperbole today.

Trouble is, the personalization idea never took off. No brand prepares your grocery list, picks out your clothing, or foresees what you’ll want for dinner at the restaurant Saturday night. The major impediment was not technology — true personalization requires vast inventories, efficient mass-customization of production assets, and brilliant algorithms, difficult but possible as Netflix has demonstrated with DVDs — but market incentives. Waste against the masses is usually a source of profit, and this is especially true in the media landscape.

Personalization kills media profits
Case in point: Cable television. With boxes in every home, you’d think advertising could be customized easily to every household based on your demographics, personal viewing history, even past shopping habits. It doesn’t seem hard to tie your cable box into an Experian data set to give dads with kids hitting mid-life 30-second spots for red convertibles (ahem), yet we’re not there yet. Experimenters such as Eyeview are beginning to combine audience data, advertiser assets and marketer products to personalize online video ads — in real time, showing snow or rain in the car spot based on your local weather. But that’s just a start. Personalization has become the Great Pumpkin of the ad universe, always almost here, and when it someday arrives it will be really, really big.

Personalization is a huge threat to old media empires. Truly targeting ads means you need fewer messages to get to your audience, and that efficiency is counter to what gives publishers and media giants money. Consider cable: The typical U.S. consumer watches 5 hours and 9 minutes of TV each day, enough to receive 166 30-second TV spots … and most of those are wildly off base. If advertising were truly targeted, you could receive only 10 ads a day for products you really want, and you might respond to 2 of them — enormous marketing success. But all the ad revenue from the 156 off-base spots would disappear. Online publishers, where personalization is much easier thanks to cookies that tag user computers, face similar threats as DSPs and ad exchanges begin allowing media buys that circumvent their high prices and audience control.

Most media is never seen. But advertisers still pay.
Put another way, the typical American subscribes to 130 television channels and yet “tunes” to only 18 of them (consumers no longer “surf” through channels and instead typically punch in 33 for CNN on their remote, “tuning” in to the channels they prefer). That 18 of 130 options means 86% of all programming, and its associated advertising, is never seen by each individual. The bloated waste of advertising is good for the media producers and transmitters, but not so good for advertisers who pay their bills.

Of course, consumers and advertisers want targeting. Media planners, direct marketers, and CMOs spend their careers trying to make their brands relevant; consumers rush to the malls each Black Friday looking for just the deal they crave. From the market efficiency view, personalization really is the Holy Grail — to spend production resources only against those consumers likely to respond. But it is worth noting that goal is diametrically opposed to what drives profits for the media intermediaries. Good luck, Eyeview, with those clever customized video ads; but don’t expect the marketplace forces to get behind your efficiency anytime soon.

(Bonus points: Don’t miss the 2000 press release for General Mills’ customized cereal.)

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Beyond ZOMG: cell phone tracking and Color’s new business model

German Green Party politician Malte Spitz is concerned about privacy. So to prove a point, he sued Deutsche Telekom to have them release six months of tracking information collected by his cell phone; Spitz then gave the data to Zeit Online, which made a freakily accurate animated graphic of Spitz’s exact travels over six months. The map shows Spitz as a dot moving from meeting to meeting, stopping to sleep … and by combining the phone data with publicly available information from his tweets and web activity, observers can determine much of what he was thinking and doing throughout each day.

Of course a normal reaction is ZOMG, cell towers are watching us. Once you get past this, the next reaction is to wonder, how could consumers be protected while putting such data to beneficial use?

In marketing, for instance, personalization is something consumers long for (see: the success of Netflix) but something marketers fail miserably at (mainly because each consumer has thousands of different modalities that manifest themselves almost randomly, e.g. you could be a hipster and father and lover and friend and cycling enthusiast and researcher of pharmaceutical meds, all at different times in one day). Truly accurate geolocation tracking, coupled with data feeds that flag or predict your consumption modality, would allow advertising to be tailored to what you really want, when you want it. Such personalization would fill the very basic marketing gap — blindingly obvious, once you think of it — of digital coupons at checkouts tailored to something consumers really want but haven’t purchased yet (as opposed to current couponing in U.S. grocery stores, which offers you discounts on products you already bought after you pay the clerk, doh).

Personalization, perhaps the real play for Color
Tailoring offers at the point of decision remains excruciatingly difficult, which is why marketers have failed to make so-called 1to1 personalization happen. Better tracking systems could finally make personalization possible.

App upstart Color could be a player. Robert Scoble noted this week that Color, the much-ballyhooed and confusingly designed photo app, launched to apparent failure … but has location technology behind it that could make future use more interesting (and worthy of the $41 million VC investment). Scoble wrote, “for instance, when you take a photo [Color] measures the audio profile of the room, captures the compass reading and other sensor readings, and pretty accurately knows other users in the room at the same time.” At face value Color creates a new type of spontaneous social network, useful for sharing pictures with strangers at a rock concert. But imagine deploying Color’s location sensors to reach groups of consumers passing through the mall on Black Friday, parsing which people are leaning to your retail location … and tying that back to past individual consumption histories to predict what they want, with photos of products and associated discounts popping up on their handsets. Unlike the social couponing aspects of Groupon, a geo-location discount from Color would not try to get someone in the door, but rather influence consumers for more sales once they are at the store. The leverage there is exponentially higher.

The data is out there, and businesses are just beginning to learn how to use it. The future of personalization may finally arrive. Of course, that also means not freaking you out with marketers who know when you’ve been sleeping and know when you’re awake.

(It’s worth playing with the cell phone data map here to see the tracking experience yourself.)

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Via Groovemonkey.

Japan shocks and the last-second decision

We’re at SXSW Interactive in Austin this weekend exploring 1,400 panels, and everyone this year is searching for the next big technology. It ain’t here. Last year it was Foursquare and location-based services, and a few years before it was Twitter; this time, nothing new has risen (except for me-too startups flaunting cloud-based web editing, Groupon coupon knockoffs, etc.).

So a suggestion: What about a service that uses consumer location and mobile to influence buyers right at the moment of decision? If you want to see a rare example of this last untapped land of marketing influence, check out mGive, an innovative mobile service that helps consumers give money instantly with a text-this-to-that on their cell phones. Many orgs raising funds for the devastation in Japan use it (you can find a list of reputable aid groups for Japan here.) What’s fascinating is the simple dynamic steers consumers to a close based on a snap judgment, a dynamic most marketers can’t achieve.

Think of the opportunity: A man in a wine store, confused over labels, about to approach the checkout. A woman buying soccer cleats for her daughter, looking at the rack of 100 Nike and Adidas models on the wall. Today there is no way to touch those people just as they reach for their wallets. To pull off a signal consumers might respond to would require vast integration — of LBS, store inventory, customer preference data, observation of consumer modality (“the woman is approaching the Nike shoe wall…”), push notification, pre-staged marketing offers or price framing, and perhaps even near field communication that turns mobile handsets into faux credit cards. But if you did that, you could personalize an offer just as Sally Smith reaches for the sneakers. Advertising could become truly relevant, helping a buyer nearing commitment make her confusing decision. We might raise even more money for Japan.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Wired data stalking and the demise of 1to1 personalization

Wired magazine’s UK edition pulled off a nice stunt by collecting publicly available data on its subscribers and printing customized covers that greet individuals with freakishly accurate tidbits about themselves: Their birthday, whom they live with, even colorful comments about a recent online spat with a friend. Yikes.

Yes, a clever troller (or buyer of an Experian list) can learn a lot about you. The more interesting question isn’t whether privacy is gone (it is, check your direct mail), but why similar ultra-personalization has never taken off as a marketing tactic. Don Peppers and Martha Rogers founded a consulting group in the 1990s devoted to advocating personalization based on 1to1 relationships — corporations learning to connect with individuals via data that recognized their personal interests. It was a visionary concept, where every behemoth of an organization could treat you as intimately as the owner of a local store. What happened? A few companies, such as Netflix, managed to make quasi-personalization work, but almost no marketer has nailed the 1to1 concept. Personal relationships between consumer and corporation gave way to networks of consumers talking among themselves; social media arose, and personalization was passed by as companies yearned for “viral” strategies to reach the masses, not individuals, ignoring them. If markets are efficient, and data collection has become easy, why aren’t you greeted at the mall with a digital sign saying, “Hello, Mr. Jones, welcome back, the shoes you like are on sale at the Smithswalk Outlet on Level 2”? Because 1to1 doesn’t sell as much volume as 1tomany (TV) or manytomany (viral success).

Beyond the corporate incentives, 1to1 recognition may never have been what people needed. Perhaps we don’t want unexpected personalization at all, because the serendipity of random product encounters creates desire tied to a whim. Like cotton candy or a high school crush, the sugary rush of blood that comes from longing something unexpected is oh so satisfying, mainly because the desire surprises us with novelty.

Or perhaps more simply, the aura of an unknown someone really knowing us, like a Wired UK magazine cover, just freaks us out.