Don’t Do It Just Because You Can

Don’t do it just because you can. No kidding. … Any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

Don’t do it just because you can. No kidding. By the way, I could have gone with Ben Parker’s “With great power comes great responsibility” line, but I didn’t, as it has become an over-quoted cliché. Plus, I’m not much of a fan of “Spiderman.” Actually, I’m kidding this time. (Not the “Spiderman” part, as I’m more of a fan of “Thor.”) But the real reason is any geek with moderate coding skills or any overzealous marketer with access to some data can do real damage to real human beings without any superpowers to speak of. Largely, we wouldn’t go so far as calling them permanent damages, but I must say that some marketing messages and practices are really annoying and invasive. Enough to classify them as “junk mail” or “spam.” Yeah, I said that, knowing full-well that those words are forbidden in the industry in which I built my career.

All jokes aside, I received a call from my mother a few years ago asking me if this “urgent” letter that says her car warranty will expire if she does not act “right now” (along with a few exclamation marks) is something to which she must respond immediately. Many of us by now are impervious to such fake urgencies or outrageous claims (like “You’ve just won $10,000,000!!!”). But I then realized that there still are plenty of folks who would spend their hard-earned dollars based on such misleading messages. What really made me mad, other than the fact that my own mother was involved in that case, was that someone must have actually targeted her based on her age, ethnicity, housing value and, of course, the make and model of her automobile. I’ve been doing this job for too long to be unaware of potential data variables and techniques that must have played a part so that my mother to receive a series of such letters. Basically, some jerk must have created a segment that could be named as “old and gullible.” Without a doubt, this is a classic example of what should not be done just because one can.

One might dismiss it as an isolated case of a questionable practice done by questionable individuals with questionable moral integrity, but can we honestly say that? I, who knows the ins and outs of direct marketing practices quite well, fell into traps more than a few times, where supposedly a one-time order mysteriously turns into a continuity program without my consent, followed by an extremely cumbersome canceling process. Further, when I receive calls or emails from shady merchants with dubious offers, I can very well assume my information changed hands in very suspicious ways, if not through outright illegal routes.

Even without the criminal elements, as data become more ubiquitous and targeting techniques become more precise, an accumulation of seemingly inoffensive actions by innocuous data geeks can cause a big ripple in the offline (i.e., “real”) world. I am sure many of my fellow marketers remember the news about this reputable retail chain a few years ago; that they accurately predicted pregnancy in households based on their product purchase patterns and sent customized marketing messages featuring pregnancy-related products accordingly. Subsequently it became a big controversy, as such a targeted message was the way one particular head of household found out his teenage daughter was indeed pregnant. An unintended consequence? You bet.

I actually saw the presentation of the instigating statisticians in a predictive analytics conference before the whole incident hit the wire. At the time, the presenters were unaware of the consequences of their actions, so they proudly shared employed methodologies with the audience. But when I heard about what they were actually trying to predict, I immediately turned my head to look at the lead statistician in my then-analytical team sitting next to me, and saw that she had a concerned look that I must have had on my face, as well. And our concern was definitely not about the techniques, as we knew how to do the same when provided with similar sets of data. It was about the human consequences that such a prediction could bring, not just to the eventual targets, but also to the predictors and their fellow analysts in the industry who would all be lumped together as evil scientists by the outsiders. In predictive analytics, there is a price for being wrong; and at times, there is a price to pay for being right, too. Like I said, we shouldn’t do things just because we can.

Analysts do not have superpowers individually, but when technology and ample amounts of data are conjoined, the results can be quite influential and powerful, much like the way bombs can be built with common materials available at any hardware store. Ironically, I have been evangelizing that the data and technology should be wielded together to make big and dumb data smaller and smarter all this time. But providing answers to decision-makers in ready-to-be used formats, hence “humanizing” the data, may have its downside, too. Simply, “easy to use” can easily be “easy to abuse.” After all, humans are fallible creatures with ample amounts of greed and ambition. Even without any obvious bad intentions, it is sometimes very difficult to contemplate all angles, especially about those sensitive and squeamish humans.

I talked about the social consequences of the data business last month (refer to “How to Be a Good Data Scientist“), and that is why I emphasized that anyone who is about to get into this data field must possess deep understandings of both technology and human nature. That little sensor in your stomach that tells you “Oh, I have a bad feeling about this” may not come to everyone naturally, but we all need to be equipped with those safeguards like angels on our shoulders.

Hindsight is always 20/20, but apparently, those smart analysts who did that pregnancy prediction only thought about the techniques and the bottom line, but did not consider all the human factors. And they should have. Or, if not them, their manager should have. Or their partners in the marketing department should have. Or their public relations people should have. Heck, “someone” in their organization should have, alright? Just like we do not casually approach a woman on the street who “seems” pregnant and say “You must be pregnant.” Only socially inept people would do that.

People consider certain matters extremely private, in case some data geeks didn’t realize that. If I might add, the same goes for ailments such as erectile dysfunction or constipation, or any other personal business related to body parts that are considered private. Unless you are a doctor in an examining room, don’t say things like “You look old, so you must have hard time having sex, right?” It is already bad enough that we can’t even watch golf tournaments on TV without those commercials that assume that golf fans need help in that department. (By the way, having “two” bathtubs “outside” the house at dusk don’t make any sense either, when the effect of the drug can last for hours for heaven’s sake. Maybe the man lost interest because the tubs were too damn heavy?)

While it may vary from culture to culture, we all have some understanding of social boundaries in casual settings. When you are talking to a complete stranger on a plane ride, for example, you know exactly how much information that you would feel comfortable sharing with that person. And when someone crosses the line, we call that person inappropriate, or “creepy.” Unfortunately, that creepy line is set differently for each person who we encounter (I am sure people like George Clooney or Scarlett Johansson have a really high threshold for what might be considered creepy), but I think we can all agree that such a shady area can be loosely defined at the least. Therefore, when we deal with large amounts of data affecting a great many people, imagine a rather large common area of such creepiness/shadiness, and do not ever cross it. In other words, when in doubt, don’t go for it.

Now, as a lifelong database marketer, I am not advocating some over-the-top privacy zealots either, as most of them do not understand the nature of data work and can’t tell the difference between informed (and mutually beneficial) messages and Big Brother-like nosiness. This targeting business is never about looking up an individual’s record one at a time, but more about finding correlations between users and products and doing some good match-making in mass numbers. In other words, we don’t care what questionable sites anyone visits, and honest data players would not steal or abuse information with bad intent. I heard about waiters who steal credit card numbers from their customers with some swiping devices, but would you condemn the entire restaurant industry for that? Yes, there are thieves in any part of the society, but not all data players are hackers, just like not all waiters are thieves. Statistically speaking, much like flying being the safest from of travel, I can even argue that handing over your physical credit card to a stranger is even more dangerous than entering the credit card number on a website. It looks much worse when things go wrong, as incidents like that affect a great many all at once, just like when a plane crashes.

Years back, I used to frequent a Japanese Restaurant near my office. The owner, who doubled as the head sushi chef, was not a nosy type. So he waited for more than a year to ask me what I did for living. He had never heard anything about database marketing, direct marketing or CRM (no “Big Data” on the horizon at that time). So I had to find a simple way to explain what I do. As a sushi chef with some local reputation, I presumed that he would know personal preferences of many frequently visiting customers (or “high-value customers,” as marketers call them). He may know exactly who likes what kind of fish and types of cuts, who doesn’t like raw shellfish, who is allergic to what, who has less of a tolerance for wasabi or who would indulge in exotic fish roes. When I asked this question, his answer was a simple “yes.” Any diligent sushi chef would care for his or her customers that much. And I said, “Now imagine that you can provide such customized services to millions of people, with the help of computers and collected data.” He immediately understood the benefits of using data and analytics, and murmured “Ah so …”

Now let’s turn the table for a second here. From the customer’s point of view, yes, it is very convenient for me that my favorite sushi chef knows exactly how I like my sushi. Same goes for the local coffee barista who knows how you take your coffee every morning. Such knowledge is clearly mutually beneficial. But what if those business owners or service providers start asking about my personal finances or about my grown daughter in a “creepy” way? I wouldn’t care if they carried the best yellowtail in town or served the best cup of coffee in the world. I would cease all my interaction with them immediately. Sorry, they’ve just crossed that creepy line.

Years ago, I had more than a few chances to sit closely with Lester Wunderman, widely known as “The Father of Direct Marketing,” as the venture called I-Behavior in which I participated as one of the founders actually originated from an idea on a napkin from Lester and his friends. Having previously worked in an agency that still bears his name, and having only seen him behind a podium until I was introduced to him on one cool autumn afternoon in 1999, meeting him at a small round table and exchanging ideas with the master was like an unknown guitar enthusiast having a jam session with Eric Clapton. What was most amazing was that, at the beginning of the dot.com boom, he was completely unfazed about all those new ideas that were flying around at that time, and he was precisely pointing out why most of them would not succeed at all. I do not need to quote the early 21st century history to point out that his prediction was indeed accurate. When everyone was chasing the latest bit of technology for quick bucks, he was at least a decade ahead of all of those young bucks, already thinking about the human side of the equation. Now, I would not reveal his age out of respect, but let’s just say that almost all of the people in his age group would describe occupations of their offspring as “Oh, she just works on a computer all the time …” I can only wish that I will remain that sharp when I am his age.

One day, Wunderman very casually shared a draft of the “Consumer Bill of Rights for Online Engagement” with a small group of people who happened to be in his office. I was one of the lucky souls who heard about his idea firsthand, and I remember feeling that he was spot-on with every point, as usual. I read it again recently just as this Big Data hype is reaching its peak, just like the dot.com boom was moving with a force that could change the world back then. In many ways, such tidal waves do end up changing the world. But lest we forget, such shifts inevitably affect living, breathing human beings along the way. And for any movement guided by technology to sustain its velocity, people who are at the helm of the enabling technology must stay sensitive toward the needs of the rest of the human collective. In short, there is not much to gain by annoying and frustrating the masses.

Allow me to share Lester Wunderman’s “Consumer Bill of Rights for Online Engagement” verbatim, as it appeared in the second edition of his book “Being Direct”:

  1. Tell me clearly who you are and why you are contacting me.
  2. Tell me clearly what you are—or are not—going to do with the information I give.
  3. Don’t pretend that you know me personally. You don’t know me; you know some things about me.
  4. Don’t assume that we have a relationship.
  5. Don’t assume that I want to have a relationship with you.
  6. Make it easy for me to say “yes” and “no.”
  7. When I say “no,” accept that I mean not this, not now.
  8. Help me budget not only my money, but also my TIME.
  9. My time is valuable, don’t waste it.
  10. Make my shopping experience easier.
  11. Don’t communicate with me just because you can.
  12. If you do all of that, maybe we will then have the basis for a relationship!

So, after more than 15 years of the so-called digital revolution, how many of these are we violating almost routinely? Based on the look of my inboxes and sites that I visit, quite a lot and all the time. As I mentioned in my earlier article “The Future of Online is Offline,” I really get offended when even seasoned marketers use terms like “online person.” I do not become an online person simply because I happen to stumble onto some stupid website and forget to uncheck some pre-checked boxes. I am not some casual object at which some email division of a company can shoot to meet their top-down sales projections.

Oh, and good luck with that kind of mindless mass emailing; your base will soon be saturated and you will learn that irrelevant messages are bad for the senders, too. Proof? How is it that the conversion rate of a typical campaign did not increase dramatically during the past 40 years or so? Forget about open or click-through rate, but pay attention to the good-old conversion rate. You know, the one that measures actual sales. Don’t we have superior databases and technologies now? Why is anyone still bragging about mailing “more” in this century? Have you heard about “targeted” or “personalized” messages? Aren’t there lots and lots of toolsets for that?

As the technology advances, it becomes that much easier and faster to offend people. If the majority of data handlers continue to abuse their power, stemming from the data in their custody, the communication channels will soon run dry. Or worse, if abusive practices continue, the whole channel could be shut down by some legislation, as we have witnessed in the downfall of the outbound telemarketing channel. Unfortunately, a few bad apples will make things a lot worse a lot faster, but I see that even reputable companies do things just because they can. All the time, repeatedly.

Furthermore, in this day and age of abundant data, not offending someone or not violating rules aren’t good enough. In fact, to paraphrase comedian Chris Rock, only losers brag about doing things that they are supposed to do in the first place. The direct marketing industry has long been bragging about the self-governing nature of its tightly knit (and often incestuous) network, but as tools get cheaper and sharper by the day, we all need to be even more careful wielding this data weaponry. Because someday soon, we as consumers will be seeing messages everywhere around us, maybe through our retina directly, not just in our inboxes. Personal touch? Yes, in the creepiest way, if done wrong.

Visionaries like Lester Wunderman were concerned about the abusive nature of online communication from the very beginning. We should all read his words again, and think twice about social and human consequences of our actions. Google from its inception encapsulated a similar idea by simply stating its organizational objective as “Don’t be evil.” That does not mean that it will stop pursuing profit or cease to collect data. I think it means that Google will always try to be mindful about the influences of its actions on real people, who may not be in positions to control the data, but instead are on the side of being the subject of data collection.

I am not saying all of this out of some romantic altruism; rather, I am emphasizing the human side of the data business to preserve the forward-momentum of the Big Data movement, while I do not even care for its name. Because I still believe, even from a consumer’s point of view, that a great amount of efficiency could be achieved by using data and technology properly. No one can deny that modern life in general is much more convenient thanks to them. We do not get lost on streets often, we can translate foreign languages on the fly, we can talk to people on the other side of the globe while looking at their faces. We are much better informed about products and services that we care about, we can look up and order anything we want while walking on the street. And heck, we get suggestions before we even think about what we need.

But we can think of many negative effects of data, as well. It goes without saying that the data handlers must protect the data from falling into the wrong hands, which may have criminal intentions. Absolutely. That is like banks having to protect their vaults. Going a few steps further, if marketers want to retain the privilege of having ample amounts of consumer information and use such knowledge for their benefit, do not ever cross that creepy line. If the Consumer’s Bill of Rights is too much for you to retain, just remember this one line: “Don’t be creepy.”

Why Contextual Advertising Is Still Hard

Contextualized advertising is serving the right message to the right person at the right time. Standing in the way of that goal are several hurdles. Among them: user personalization, segmentation and a deluge of data

Contextualized advertising is serving the right message to the right person at the right time. Standing in the way of that goal are several hurdles. Among them: user personalization, segmentation and a deluge of data.

Mobile personalization can create additional complexities that we don’t generally see on the PC side of the world. This can be both a challenge and an opportunity, but adds some new dimensions to how we work to connect with consumers.

This difficulty in leveraging user behaviors makes micro-segmentation more difficult. This is where real value from contextualized ads is found. As close to one-to-one as you can make an ad, the more value it has for the recipient. Having segments that are too large can decrease the overall impact of the ads for a given consumer.

Advertisers can improved their segmentation by sifting through omnichannel data sets. While there’s great progress in this area, attributing online, real world and mobile actions to an end result remains elusive to some of the industry.

New Tools Making Contextual Advertising Easier
New data tools, optimization techniques and leveraging exchanges are all emerging to make contextual advertising easier.

Algorithms that can recognize and contextualize mixed data sets are paving the way for more relevant contextual ads. You can target based on location (information that’s automatically provided by a mobile device), behavior and by predetermined personalization rules. So, someone is now seen as a specific category of user based on what they do, where they are and other relevant data. Messages can be personalized based on these characteristics to get the right ad in front of the right person.

Once a user can be tied to a mobile number, this opens up a world of contextual opportunities. These IDs can be closely tied to segment and location, and passed along to real-time-bidding (RTB) exchanges. Here, brands and advertisers can serve a contextualized ad to the correct mere moments after he/she makes a trigger action. Where as before, this data would be aggregated and analyzed monthly or weekly, we are getting close to the point of real time analysis and optimization.

The Impact of Future Technology and Contextual Ads
The “Holy Grail” of contextual advertising is connecting relevant ads that are optimized for a single individual. Technology is heading in that direction.

Wearables will feed advertisers never before accessible biometrics that could indicate when someone needs a sandwich or bottle of water before the person realizes it.

Interactive TVs and cross-screen attribution will pull together all parts of a person’s day. Ambient qualities, like time of day or weather, will become data points that advertisers can assess and use to target consumers.

As these technologies, combined with faster servers, make valuable contextual advertising an everyday occurrence, we will see a shift in the advertiser/consumer relationship. It will become more symbiotic. Users will be able to decide with what and whom they want to interact. Those advertisers who can use data to provide users the greatest value will prosper. Those who can’t make sense of data will suffer as consumers take their business elsewhere with a quick click on an iPhone.

Big Data Must Get Smaller

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Seriously, the word “Big” only emphasizes the size element, and that is a sure way to miss the essence of the data business. And many folks are missing even that little point by calling all decision-making activities that involve even small-sized data “Big Data.” It is entirely possible that this data stuff seems all new to someone, but the data-based decision-making process has been with us for a very long time. If you use that “B” word to differentiate old-fashioned data analytics of yesteryear and ridiculously large datasets of the present day, yes, that is a proper usage of it. But we all know most people do not mean it that way. One side benefit of this bloated and hyped up buzzword is data professionals like myself do not have to explain what we do for living for 20 minutes anymore by simply uttering the word “Big Data,” though that is a lot like a grandmother declaring all her grandchildren work on computers for living. Better yet, that magic “B” word sometimes opens doors to new business opportunities (or at least a chance to grab a microphone in non-data-related meetings and conferences) that data geeks of the past never dreamed of.

So, I guess it is not all that bad. But lest we forget, all hypes lead to overinvestments, and all overinvestments leads to disappointments, and all disappointments lead to purging of related personnel and vendors that bear that hyped-up dirty word in their titles or division names. If this Big Data stuff does not yield significant profit (or reduction in cost), I am certain that those investment bubbles will burst soon enough. Yes, some data folks may be lucky enough to milk it for another two or three years, but brace for impact if all those collected data do not lead to some serious dollar signs. I know how the storage and processing cost decreased significantly in recent years, but they ain’t totally free, and related man-hours aren’t exactly cheap, either. Also, if this whole data business is a new concept to an organization, any money spent on the promise of Big Data easily becomes a liability for the reluctant bunch.

This is why I open up my speeches and lectures with this question: “Have you made any money with this Big Data stuff yet?” Surely, you didn’t spend all that money to provide faster toys and nicer playgrounds to IT folks? Maybe the head of IT had some fun with it, but let’s ask that question to CFOs, not CTOs, CIOs or CDOs. I know some colleagues (i.e., fellow data geeks) who are already thinking about a new name for this—”decision-making activities, based on data and analytics”—because many of us will be still doing that “data stuff” even after Big Data cease to be cool after the judgment day. Yeah, that Gangnam Style dance was fun for a while, but who still jumps around like a horse?

Now, if you ask me (though nobody did yet), I’d say the Big Data should have been “Smart Data,” “Intelligent Data” or something to that extent. Because data must provide insights. Answers to questions. Guidance to decision-makers. To data professionals, piles of data—especially the ones that are fragmented, unstructured and unformatted, no matter what kind of fancy names the operating system and underlying database technology may bear—it is just a good start. For non-data-professionals, unrefined data—whether they are big or small—would remain distant and obscure. Offering mounds of raw data to end-users is like providing a painting kit when someone wants a picture on the wall. Bragging about the size of the data with impressive sounding new measurements that end with “bytes” is like counting grains of rice in California in front of a hungry man.

Big Data must get smaller. People want yes/no answers to their specific questions. If such clarity is not possible, probability figures to such questions should be provided; as in, “There’s an 80 percent chance of thunderstorms on the day of the company golf outing,” “An above-average chance to close a deal with a certain prospect” or “Potential value of a customer who is repeatedly complaining about something on the phone.” It is about easy-to-understand answers to business questions, not a quintillion bytes of data stored in some obscure cloud somewhere. As I stated at the end of my last column, the Big Data movement should be about (1) Getting rid of the noise, and (2) Providing simple answers to decision-makers. And getting to such answers is indeed the process of making data smaller and smaller.

In my past columns, I talked about the benefits of statistical models in the age of Big Data, as they are the best way to compact big and complex information in forms of simple answers (refer to “Why Model?”). Models built to predict (or point out) who is more likely to be into outdoor sports, to be a risk-averse investor, to go on a cruise vacation, to be a member of discount club, to buy children’s products, to be a bigtime donor or to be a NASCAR fan, are all providing specific answers to specific questions, while each model score is a result of serious reduction of information, often compressing thousands of variables into one answer. That simplification process in itself provides incredible value to decision-makers, as most wouldn’t know where to cut out unnecessary information to answer specific questions. Using mathematical techniques, we can cut down the noise with conviction.

In model development, “Variable Reduction” is the first major step after the target variable is determined (refer to “The Art of Targeting“). It is often the most rigorous and laborious exercise in the whole model development process, where the characteristics of models are often determined as each statistician has his or her unique approach to it. Now, I am not about to initiate a debate about the best statistical method for variable reduction (I haven’t met two statisticians who completely agree with each other in terms of methodologies), but I happened to know that many effective statistical analysts separate variables in terms of data types and treat them differently. In other words, not all data variables are created equal. So, what are the major types of data that database designers and decision-makers (i.e., non-mathematical types) should be aware of?

In the business of predictive analytics for marketing, the following three types of data make up three dimensions of a target individual’s portrait:

  1. Descriptive Data
  2. Transaction Data / Behavioral Data
  3. Attitudinal Data

In other words, if we get to know all three aspects of a person, it will be much easier to predict what the person is about and/or what the person will do. Why do we need these three dimensions? If an individual has a high income and is living in a highly valued home (demographic element, which is descriptive); and if he is an avid golfer (behavioral element often derived from his purchase history), can we just assume that he is politically conservative (attitudinal element)? Well, not really, and not all the time. Sometimes we have to stop and ask what the person’s attitude and outlook on life is all about. Now, because it is not practical to ask everyone in the country about every subject, we often build models to predict the attitudinal aspect with available data. If you got a phone call from a political party that “assumes” your political stance, that incident was probably not random or accidental. Like I emphasized many times, analytics is about making the best of what is available, as there is no such thing as a complete dataset, even in this age of ubiquitous data. Nonetheless, these three dimensions of the data spectrum occupy a unique and distinct place in the business of predictive analytics.

So, in the interest of obtaining, maintaining and utilizing all possible types of data—or, conversely, reducing the size of data with conviction by knowing what to ignore, let us dig a little deeper:

Descriptive Data
Generally, demographic data—such as people’s income, age, number of children, housing size, dwelling type, occupation, etc.—fall under this category. For B-to-B applications, “Firmographic” data—such as number of employees, sales volume, year started, industry type, etc.—would be considered as descriptive data. It is about what the targets “look like” and, generally, they are frozen in the present time. Many prominent data compilers (or data brokers, as the U.S. government calls them) collect, compile and refine the data and make hundreds of variables available to users in various industry sectors. They also fill in the blanks using predictive modeling techniques. In other words, the compilers may not know the income range of every household, but using statistical techniques and other available data—such as age, home ownership, housing value, and many other variables—they provide their best estimates in case of missing values. People often have some allergic reaction to such data compilation practices siting privacy concerns, but these types of data are not about looking up one person at a time, but about analyzing and targeting groups (or segments) of individuals and households. In terms of predictive power, they are quite effective and results are very consistent. The best part is that most of the variables are available for every household in the country, whether they are actual or inferred.

Other types of descriptive data include geo-demographic data, and the Census Data by the U.S. Census Bureau falls under this category. These datasets are organized by geographic denominations such as Census Block Group, Census Tract, Country or ZIP Code Tabulation Area (ZCTA, much like postal ZIP codes, but not exactly the same). Although they are not available on an individual or a household level, the Census data are very useful in predictive modeling, as every target record can be enhanced with it, even when name and address are not available, and data themselves are very stable. The downside is that while the datasets are free through Census Bureau, the raw datasets contain more than 40,000 variables. Plus, due to the budget cut and changes in survey methods during the past decade, the sample size (yes, they sample) decreased significantly, rendering some variables useless at lower geographic denominations, such as Census Block Group. There are professional data companies that narrowed down the list of variables to manageable sizes (300 to 400 variables) and filled in the missing values. Because they are geo-level data, variables are in the forms of percentages, averages or median values of elements, such as gender, race, age, language, occupation, education level, real estate value, etc. (as in, percent male, percent Asian, percent white-collar professionals, average income, median school years, median rent, etc.).

There are many instances where marketers cannot pinpoint the identity of a person due to privacy issues or challenges in data collection, and the Census Data play a role of effective substitute for individual- or household-level demographic data. In predictive analytics, duller variables that are available nearly all the time are often more valuable than precise information with limited availability.

Transaction Data/Behavioral Data
While descriptive data are about what the targets look like, behavioral data are about what they actually did. Often, behavioral data are in forms of transactions. So many just call it transaction data. What marketers commonly refer to as RFM (Recency, Frequency and Monetary) data fall under this category. In terms of predicting power, they are truly at the top of the food chain. Yes, we can build models to guess who potential golfers are with demographic data, such as age, gender, income, occupation, housing value and other neighborhood-level information, but if you get to “know” that someone is a buyer of a box of golf balls every six weeks or so, why guess? Further, models built with transaction data can even predict the nature of future purchases, in terms of monetary value and frequency intervals. Unfortunately, many who have access to RFM data are using them only in rudimentary filtering, as in “select everyone who spends more than $200 in a gift category during the past 12 months,” or something like that. But we can do so much more with rich transaction data in every stage of the marketing life cycle for prospecting, cultivating, retaining and winning back.

Other types of behavioral data include non-transaction data, such as click data, page views, abandoned shopping baskets or movement data. This type of behavioral data is getting a lot of attention as it is truly “big.” The data have been out of reach for many decision-makers before the emergence of new technology to capture and store them. In terms of predictability, nevertheless, they are not as powerful as real transaction data. These non-transaction data may provide directional guidance, as they are what some data geeks call “a-camera-on-everyone’s-shoulder” type of data. But we all know that there is a clear dividing line between people’s intentions and their commitments. And it can be very costly to follow every breath you take, every move you make, and every step you take. Due to their distinct characteristics, transaction data and non-transaction data must be managed separately. And if used together in models, they should be clearly labeled, so the analysts will never treat them the same way by accident. You really don’t want to mix intentions and commitments.

The trouble with the behavioral data are, (1) they are difficult to compile and manage, (2) they get big; sometimes really big, (3) they are generally confined within divisions or companies, and (4) they are not easy to analyze. In fact, most of the examples that I used in this series are about the transaction data. Now, No. 3 here could be really troublesome, as it equates to availability (or lack thereof). Yes, you may know everything that happened with your customers, but do you know where else they are shopping? Fortunately, there are co-op companies that can answer that question, as they are compilers of transaction data across multiple merchants and sources. And combined data can be exponentially more powerful than data in silos. Now, because transaction data are not always available for every person in databases, analysts often combine behavioral data and descriptive data in their models. Transaction data usually become the dominant predictors in such cases, while descriptive data play the supporting roles filling in the gaps and smoothing out the predictive curves.

As I stated repeatedly, predictive analytics in marketing is all about finding out (1) whom to engage, and (2) if you decided to engage someone, what to offer to that person. Using carefully collected transaction data for most of their customers, there are supermarket chains that achieved 100 percent customization rates for their coupon books. That means no two coupon books are exactly the same, which is a quite impressive accomplishment. And that is all transaction data in action, and it is a great example of “Big Data” (or rather, “Smart Data”).

Attitudinal Data
In the past, attitudinal data came from surveys, primary researches and focus groups. Now, basically all social media channels function as gigantic focus groups. Through virtual places, such as Facebook, Twitter or other social media networks, people are freely volunteering what they think and feel about certain products and services, and many marketers are learning how to “listen” to them. Sentiment analysis falls under that category of analytics, and many automatically think of this type of analytics when they hear “Big Data.”

The trouble with social data is:

  1. We often do not know who’s behind the statements in question, and
  2. They are in silos, and it is not easy to combine such data with transaction or demographic data, due to lack of identity of their sources.

Yes, we can see that a certain political candidate is trending high after an impressive speech, but how would we connect that piece of information to whom will actually donate money for the candidate’s causes? If we can find out “where” the target is via an IP address and related ZIP codes, we may be able to connect the voter to geo-demographic data, such as the Census. But, generally, personally identifiable information (PII) is only accessible by the data compilers, if they even bothered to collect them.

Therefore, most such studies are on a macro level, citing trends and directions, and types of analysts in that field are quite different from the micro-level analysts who deal with behavioral data and descriptive data. Now, the former provide important insights regarding the “why” part of the equation, which is often the hardest thing to predict; while the latter provide answers to “who, what, where and when.” (“Who” is the easiest to answer, and “when” is the hardest.) That “why” part may dictate a product development part of the decision-making process at the conceptual stage (as in, “Why would customers care for a new type of dishwasher?”), while “who, what, where and when” are more about selling the developed products (as in “Let’s sell those dishwashers in the most effective ways.”). So, it can be argued that these different types of data call for different types of analytics for different cycles in the decision-making processes.

Obviously, there are more types of data out there. But for marketing applications dealing with humans, these three types of data complete the buyers’ portraits. Now, depending on what marketers are trying to do with the data, they can prioritize where to invest first and what to ignore (for now). If they are early in the marketing cycle trying to develop a new product for the future, they need to understand why people want something and behave in certain ways. If signing up as many new customers as possible is the immediate goal, finding out who and where the ideal prospects are becomes the most imminent task. If maximizing the customer value is the ongoing objective, then you’d better start analyzing transaction data more seriously. If preventing attrition is the goal, then you will have to line up the transaction data in time series format for further analysis.

The business goals must dictate the analytics, and the analytics call for specific types of data to meet the goals, and the supporting datasets should be in “analytics-ready” formats. Not the other way around, where businesses are dictated by the limitations of analytics, and analytics are hampered by inadequate data clutters. That type of business-oriented hierarchy should be the main theme of effective data management, and with clear goals and proper data strategy, you will know where to invest first and what data to ignore as a decision-maker, not necessarily as a mathematical analyst. And that is the first step toward making the Big Data smaller. Don’t be impressed by the size of the data, as they often blur the big picture and not all data are created equal.

When a Customer Is Not Worthy

As business owners and employees of businesses, we all work diligently to acquire prospects, qualify leads and convert customers, but sometimes we need to stop and consider whether a particular person or company is worthy of our efforts. It makes our constituents feel appreciated and empowered when we treat them well and expend effort to develop the relationship, but

As business owners and employees of businesses, we all work diligently to acquire prospects, qualify leads and convert customers, but sometimes we need to stop and consider whether a particular person or company is worthy of our efforts.

It makes our constituents feel appreciated and empowered when we treat them well and expend effort to develop the relationship, but in some cases that empowerment can go to one of their heads and lead the person to behave in a manner not conducive to a healthy relationship.

There have been a number of instances over the years where I’ve needed to ask prospective or current customers to take their business elsewhere. While this is never a pleasant conversation, it can be critical in ensuring your company remains profitable, your employees remain appreciated and happy, and you remain sane. The best way to approach this conversation is with civility and a calm tone.

More often than not, an unhappy customer will vent their frustration on an underling with the assumption the person is unprepared to manage the onslaught. Annoyed customers will attack in a way they believes will result in a resolution favoring them—sometimes greatly and to the detriment of the employee’s wellbeing and the company’s profitability. We’re all able to take a loss every now and then to satisfy an unhappy customer, but when you have a repeat offender (customer), it’s time to step in.

Every employee and contractor in my organization knows they are never expected to submit to a venting, complaining or abusive customer—period. The employee’s response is mandatory and simple, “Please hold and I will have our manager help you.” From there, I am quick to set the ground rules as I take over the call. I will listen to the customer politely and allow that person to give voice to their entire complaint, but they may not scream, call names or be uncivil in any manner. If they are, I will hang up. I will continue to hang up each time the person calls back until they accept and adhere to the rules of this engagement (to date, it hasn’t gone beyond three hang ups).

Beyond this, I make it clear I am fully responsible for my team’s actions and responses, and we will not engage in a bashing of a personal nature. I will not side with the complainant against my team, but I will be empathetic to the customer’s plight and go to great lengths to find a resolution suitable to the situation—for as long as we can continue to have a professional, if not amicable, discussion.

For plaintiffs who cannot accept and follow the ground rules, it’s even simpler: “I’m sorry we did not meet your expectations, here is the phone number to another company providing this product/service. We’re confident you will be happier elsewhere.”

This type of response shifts the power from the complaining customer to the employee and fosters a better relationship between you and the person with whom you work every day instead of a customer whose value is far less. Yes, some customers have great monetary worth, and for those you will exert additional effort to resolve the situation before sending them on their way, but for most small businesses, individual customers have a smaller overall value than a dedicated employee.

With that said, there are ways for a customer to complain without aggressive discourse—those are the customers we want to please, keep, and reward—and for those, it’s best to keep the employee in the discussion. These are the customers whom I prefer to foster and benefit, even at a monetary loss to the company. They often turn out to be long-term, repeat customers because we have created an atmosphere of loyalty by tending to their concerns as a team. (Why would we allow an abusive customer to receive a more beneficial resolution than a kind, calm customer who truly wishes to resolve the condition?)

Sometimes customers are unworthy in other manners. We recently spent quite some time reviewing a lead’s current drip-marketing campaign, only to come to the conclusion we really couldn’t add enough value to their current process to make hiring our company beneficial to them. In this situation, we fired the customer before we were hired, and we were quite frank about why. I don’t know how this response was truly received by the customer; they did seem to be happy with our honesty. If I were on the receiving end of this conversation, I would rather have a company tell me genuinely they cannot help me than to have them take my money for months/years and be no wiser for the engagement—but not everyone thinks like I do. (Thankfully.)

In many ways, email marketing has cultivated an atmosphere allowing customers to be more unhappy and more quickly. The anonymity of email makes marketers seem less like a company of people here to serve their needs and more like a faceless organization poised to aggravate them. Gone are phone calls that allowed us to connect with at least a modicum human interaction, in their place we have electronic communications sent to thousands of people all at once. This is why personalization can be so important to you and to the recipient. Adding a bit or a lot of personalization warms the tone and the relationship. It reminds the receiver, you are a company of people who care about their success. It will also help lay a foundation of civility if a divorce is imminent.

If you must fire an email customer, don’t fire by email. Pick up the phone, set the ground rules, and be polite and professional. It’s the least you can do. You may not be able to salvage the relationship, but you’re less likely to leave them with a terrible last impression.

The Future of Online Is Offline

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

What if I receive an email offer from you, research the heck out of the product on the Internet, and then show up at a store to have instant gratification? Does that make me an offline person now? Sorry to break your channel-oriented marketing mind, but hey, I am just a guy. I am neither an online person nor an offline person; which, by the way, happens to be a dirty word in some pretentious marketing circles (as in “Eew, you’re in the offline space?!”).

Marketers often forget to recognize that all this “Big Data” stuff (or any size data, for that matter) and channel management tools are just tools to get to people. In the age of Big Data, it shouldn’t be so hard to know “a lot” about a person, and tailor messages and offers for that person. Then why is that I get confusing offers all the time? How is that I receive multiple types of credit card offers from the same bank within weeks? Don’t they know all about my banking details? Don’t they have some all-inclusive central data depository for all that kind of stuff?

The sad and short answer to all this is that it really doesn’t matter if the users of such databases still think only in terms of her division, his channel assignment, and only through to the very next campaign. And such mindsets may even alter the structure of the marketing database, where everything is organized by division, product or channel. That is how one becomes an online person, who might as well be invisible when it comes to his offline activities.

What is the right answer, then? Both database and users of such databases should be “buyer-centric” or “individual-centric” at the core. In a well-designed marketing database, every variable should be a descriptor for the individual, regardless of the data sources or channels through which she happens to have navigated to end up in the database. There, what she has been buying, her typical spending level, her pricing threshold, channels that she uses to listen, channels that she employs to make purchases or to express herself, stores she visited, lapsed time since her last activities by each channel, contact/response history, her demographic profile, etc. should all be nicely lined up as “her” personal record. That is how modern marketing databases should be structured. Just putting various legacy datasets in one place isn’t going to cut it, even if some individual ID is assigned to everyone in every table. Through some fancy Big Data tools, you may be able to store and retrieve records for every transaction for the past 20 years, but such records describe transactions, not people. Again, it’s all about people.

Why should marketing databases be “buyer-centric”? (1) Nobody is one-dimensional, locked into one channel or division of some marketer, and (2) Individualized targeting and messaging can only be actualized through buyer-centric data platforms. Want to use advanced statistical models? You would need individualized structure because the main goal of any model for marketing is to rank “people” in terms of your target’s susceptibility to certain offers or products. If an individual’s information is scattered all over the database, requiring lots of joins and manipulations, then that database simply isn’t model-ready.

Further, when I look into the future, I see the world where one-click checkout is the norm, even in the offline world. The technology to identify ourselves and to make payment will be smaller and more ubiquitous. Today, when we go to a drug store, we need to bring out the membership card, coupons and our credit card to finish the transaction. Why couldn’t that be just one step? If I identify myself with an ID card or with some futuristic device that I would wear such as a phone, glasses or a wristwatch, shouldn’t that be enough to finish the deal and let me out of the store? When that kind of future becomes a reality (in the not-too distant future), will marketers still think and behave within that channel-centric box? Will we even attempt to link what just happened at the store to other activities the person engaged in online or offline? Not if some guy is in charge of that “one” new channel, no matter how fancy that department title would be.

I have been saying this all along, but let me say it again. The future of online is offline. The distinction of such things would be as meaningless as debating if interactive TV of the future should be called a TV or a computer. Is an iPhone a phone or mobile computer? My answer? Who cares? We should be concentrating our efforts on talking to the person who is looking at the device, whether it is through a computer screen, mobile screen or TV screen. That is the first step toward the buyer-centric mindset; that it is and always has been about people, not channel or devices that would come and go. And it is certainly not about some marketing department that may handle just one channel or one product at a time.

The Big Data movement should about the people. The only difference this new wave brings is the amount of data that we need to deal with and the speed in which we need to operate. Soon, marketers should be able to do things in less than a second that used to take three months. Displaying an individually customized real-time offer built with past and present data through fancy statistical model via hologram won’t be just a scene in a science fiction movie (remember the department store scene in “Minority Report”?). And if marketing databases are not built in a buyer-centric structure, someone along the line will waste a lot of time just to understand what the target individual is all about. That could have been OK in the last century, but not in the age of abundant and ubiquitous data.

The Best Brand Gift Ever!

I know you are a YES person. A DIY person. A BRING IT person. A CAN DO person … excellent at all you do—conscientious, responsible, dependable, overachieving. No doubt, it’s how you got where you are. All wonderful qualities. So this Christmas, perhaps the last thing you need or want is something from “The 12 Days of Christmas.” What you just might need this month is 12 days and ways to say NO.

I know you are a YES person. A DIY person. A BRING IT person. A CAN DO person … excellent at all you do—conscientious, responsible, dependable, overachieving. No doubt, it’s how you got where you are. All wonderful qualities. So this Christmas, perhaps the last thing you need or want is, as the song says, some version of “12 drummers drumming, 11 pipers piping, 10 lords-a-leaping, nine ladies dancing, eight maids-a-milking, seven swans-a-swimming, six geese-a-laying, five golden rings, four calling birds, three French hens, two turtle doves or even a partridge in a pear tree.” You don’t need or want more stuff. You want a meaningful, long-lasting, brand-enhancing and life-affirming gift. Something useful and practical.

What you just might need this month is 12 days and ways to say NO.

The deal is that no one can give this gift to you. It’s a selfie. There’s no outsourcing this skill to a personal shopper, no concierge service that can do this for you. It’s a true DIYer.

As YES people, the word NO is an infrequent part of our vocabulary—in our brand lives and in our personal lives. But I have found that the happiest and most productive people have given themselves the gift of NO. They have learned to make NO a natural part of their DNA … both in and out of the office.

So, before you head out of the office to start holiday celebrations, why not raise a toast to that little two-letter word NO and see if these bits of inspiration may encourage you to treat yourself (and the brand you lead) to this very important present:

1. The gift of a new discipline … making no an art form. Missy Park, founder of Title Nine, echoes the power of no. “In my book, saying yes is over-rated. Fact is, it’s easy to say yes. No difficult choices, no disappointments. Ahh, but saying no. That is the real art form. There’s choosing to say no which can be wrenching. There is choosing when to say no, which is often. And then there’s saying it graciously, which is very hard indeed.”

2. The gift of throwing in the towel … the towel that really doesn’t matter. I greatly admire Bob Goff. He’s an author, an attorney and founder of Restore International, a nonprofit human rights organization. He wisely shares: “I used to be afraid of failing at something that really mattered to me, but now I’m more afraid of succeeding at things that don’t matter.” With that in mind, Goff makes it a habit to quit something every Thursday. It liberates him for new things. What can you be simply done with?

3. The gift of margin … build in white space … everywhere! Dr. Richard Swensen, a physician-futurist, educator and author, advocates for purposefully creating mental, emotional, physical and spiritual breathing room in our full-to-brimming professional and personal lives. He calls it margin—like the white space around pages of books. He counsels that we need it more than ever. Appropriately saying NO gives us more white space.

4. The gift of focus … just say no … perhaps three times or more! Steve Jobs, Apple’s brilliant and passionate founder, shared this: “Focusing is about saying no. You’ve got say no, no, no. The result of that focus is going to be some really great products where the total is much greater than the sum of the parts.”

5. The gift of eliminating even more non-urgent and unimportant time fritters. Stephen Covey, author of “Seven Habits of Highly Effective People” cautions us to be careful of defaulting too often into what he calls Quadrant 4 of his time management matrix … the place we naturally drift after spending lots of time in urgent and crisis modes: trivia, busywork, mindless surfing. Just say goodbye to all the nonessentials.

6. The gift of stopping … count the ways. Jim Collins, author of “Good to Great,” encourages us to create STOP DOING LISTS. That’s right … enumerate all things you are no longer going to do. Start by simply saying no to his Venn diagram of three crucial things-activities that are you are not deeply passionate about, that you feel you are not genetically encoded for and things that don’t make much economic sense.

7. The gift of holding back … a permission slip for more B+s. Must everything be done to an A+ perfection level? Pick and choose those activities that really warrant this kind of energy. Challenge yourself to not be an honors student in all you do. Award-winning author Anne Lamott had to remind herself in midlife that “a B+ is just fine.”

8. The gift of less … hit that delete key more often. Do we really need (or have time to read) all those subscriptions? Must we? Find satisfaction in architect Ludwig Mies van der Rohe “less is more” philosophy. Go ahead—delete, unsubscribe, edit, curate. Whatever you have to call this process, just do it.

9. The gift of simplicity … now. Years ago naturalist and poet, Henry David Thoreau warned us: “Our life is frittered away by detail … Simplify, simplify, simplify!” Alan Seigel updates that sentiment for brand leaders in his book: Simple: Conquering the Crisis of Complexity. Perhaps it’s time to give yourself and your brand the gift of a serious simplification process.

10. The gift of benign neglect … just ignore it! Do we really have to have a multiplatform constantly clean inbox? Who cares? What’s the point? Mani S. Sivasubramanian, author of “How To Focus – Stop Procrastinating, Improve Your Concentration & Get Things Done – Easily!” writes: “Information overload (on all levels) is exactly WHY you need an “ignore list.” It has never been more important to be able to say “No.”

11. The gift of checking back in with yourself … so, what matters now? In her book “Fierce Conversations,” leadership development architect Susan Scott suggests people change and forget to tell one another. That is true. Sometimes we even forget to tell ourselves. What has changed for you or your brand? Your energy level? Your tolerance? Your interests? Your competition? Your customers? What needs revisiting so that your yeses are truly yeses and your nos are truly nos?

12. The gift of a do-over … recycle your mistakes. We’ve all made the mistake of saying yes when we should have said no. Jot down a few of those do-overs on a post it note. What were the learning lessons? Keep that note to yourself handy.

‘Tis the season for gift-giving. Be kind to yourself and to your brand and make the practice of gracious NO saying not only a year end gift, but a long lasting part of your DNA.

The B-to-B Buying Revolution, and Five Ways Marketers Need to Change Their Game

The Internet has driven dramatic changes in business buying behavior. Just as no one buys a car anymore without first checking prices and features online, business buyers now research and educate themselves online, months—even years—before ever seeing a salesperson. This has big implications for B-to-B marketers.

The Internet has driven dramatic changes in business buying behavior. Just as no one buys a car anymore without first checking prices and features online, business buyers now research and educate themselves online, months—even years—before ever seeing a salesperson. This has big implications for B-to-B marketers.

In the old days-just a few years ago-when business buyers had a problem, they’d call in their vendors for advice on how to solve it. So a sales person was in a nifty position to educate—and influence—the buyer from the earliest stages of the process.

But these days, the sales person has lost control. Buyers don’t really want to talk to vendors until somewhere akin to 70 percent of the way down the road, at the stage of writing RFPs and getting quotes. By then, the possible solutions and the specifications are already set.

But there’s more. Business buying processes are getting longer, and-most important-involving more parties than ever before. The so-called Buying Circle in large enterprise B-to-B-the influencers, specifiers, users, decision-makers-comprises as many as 21 people, according to Marketing Sherpa.

So marketers have to think differently today. First, you need to take an active role in the early stages of the buying process, to ensure that your solutions are front and center, and that you are in the game of influencing buyers as they educate themselves online. Second, you must gain access to each member of the Buying Circle, so you can understand their needs and interests, and deliver relevant messaging to them as they move from stage to stage in their buying journey.

These developments bring front and center five important areas requiring renewed focus from marketers:

  1. Complete and accurate data on customers and prospects. To influence the multiple Buying Circle members, and get to them early, you need to know who they are. Not an easy task, but more essential than ever. Here are some resources for gaining access to prospect data, and keeping your database clean.
  2. A deliberate contact strategy. Beyond blasting out prospecting campaigns, marketers must move toward a series of ongoing outbound messages, via multiple communications channels, to connect with multiple parties, over time. Here’s where marketing automation becomes an important resource for B-to-B marketers.
  3. Active social media outreach. No longer an experiment, social media has become a must-have element of the B-to-B marketing toolkit. A well-written blog, promoted by Twitter and LinkedIn groups, is a good way to start.
  4. A superb website, the core resource for engagement with buyers at all stages of the process. Enhance its interactivity by adding downloadable content in exchange for registration.
  5. A library of content assets. Populate your website with white papers, research reports, videos, how-to guides, technical documents, archived webinars, all written in objective, non-salesy language, to help educate buyers and help influence them toward your solution. Be sure to title the documents with plenty of keywords.

It’s a different marketing world today. But an exciting one, as long as marketers evolve along with buyers as they change the way they work.

A version of this post appeared in Biznology, the digital marketing blog.

Don’t Be Creepy: Using Robust User Data for Ad Targeting While Respecting Privacy

When your brand possesses or has access to data that provides deep visibility into user interests, you should use that visibility to create more relevant ads, thus increasing performance while limiting costs. But with deep visibility comes deep responsibility to respect privacy. The fastest way to hurt performance is to cross into the creepy zone. Don’t be a creeper.

Google announced that on March 1 it will be updating its privacy policy to enable the integration of user data across Google properties. As it integrates more cross-property data, including information on user interests and demographics, Google can provide advertisers with more robust and precise targeting capabilities in search and display.

For instance, Google may uncover a certain person’s interests through their interactions on Gmail and Google+. This capability may someday enable an advertiser to direct search or display ads to that person based on those interests.

Although users can opt out of the experience and adjust and delete information that Google has collected, the privacy policy update has been met with some backlash. Lawmakers in the U.S. and European Union have asked Google to explain why the changes are necessary and how privacy will be protected. A privacy advocacy group sought a court order directing the FTC to sue Google, and sites like Gizmodo have urged people to take control of their privacy by switching to various non-Google-owned properties for search, email, social, photos, docs and video.

Assuming Google goes forward with the update and activates the potential for more robust targeting options, advertisers should keep a few things in mind to increase performance while respecting privacy:

1. There’s a fine line between appropriate and creepy. Ads that are more tailored to a person’s interests are more likely to satisfy that person’s needs. However, many users would prefer that advertisers don’t know everything about them. If a user sees an ad that’s eerily related to a Google+ or Gmail conversation they’ve just had with a friend, a line may have been crossed.

2. Users will blame you. Creepy ads harm people by making them feel as if they’ve been unwillingly observed. But who’s responsible for this unwanted observation? Technically Google observed the user, but the average web surfer doesn’t think about what’s happening behind the scenes. Users ask, “How did this brand know that about me? Have they been watching me?” Creepy isn’t always a label that users attach to Google (or Facebook or any other advertising platform). It’s a label attached to brands that push the platform’s capabilities too far.

3. Just because you can doesn’t mean you should. Users must agree to the new privacy policy in order to sign in to Google. Google provides an easy means within its Ads Preference Manager to opt out of customized or personalized search and display ads. Thus Google’s informing its users, obtaining their consent and providing them with privacy controls. But just because a person consents or fails to adjust their ad preferences doesn’t mean that it’s open season for creepiness. People shouldn’t have to choose between using Google and avoiding the creep factor.

As a marketer, don’t think that people agreed to diminish their right to privacy in order to use Google or another service. Respect privacy as a right that can’t be diminished, no matter whether a person opts in to a privacy policy.

When your brand possesses or has access to data that provides deep visibility into user interests, you should use that visibility to create more relevant ads, thus increasing performance while limiting costs. But with deep visibility comes deep responsibility to respect privacy. The fastest way to hurt performance is to cross into the creepy zone. Don’t be a creeper.

An Ill-Timed Folly for Facebook

Facebook has caused quite a stir lately. About two weeks ago, it revised its terms of use, but the change caused such a turbulence in the blogosphere that the social media pioneer backed off and reverted to its old terms — at least for now.

Facebook has caused quite a stir lately. About two weeks ago, it revised its terms of use, but the change caused such a turbulence in the blogosphere that the social media pioneer backed off and reverted to its old terms — at least for now.

What temporary revision caused the uproar? Basically, the terms said members own their information on the site and control who sees it. But when they’d go to delete their accounts, Facebook would retain the right to the information, so friends still would be able to access the shared information. Facebook sated that it would have an “irrevocable, perpetual, non-exclusive, transferable, fully paid worldwide license” to material on the site, per the short-lived terms.

But after the new rules were posted, many people contacted Facebook with questions and comments about the changes and what they meant for people and their information. Many expressed distrust and aired suspicions that the site would sell or share their information with third parties. Users protested on the site, while external groups also took action. The Electronic Privacy Information Center threatened legal action.

Data-sharing issues have been dicey stuff among American consumers since well before the economy tanked. Facebook has become such an American icon that this revision was ill-timed. Facebook made a mistake and had best rectify it quickly before the site becomes just another fad.

CEO Mark Zuckerberg explained in a Feb. 16 blog post that the revised terms were intended to make the site’s policies clearer to users. “One of the questions about our new terms of use is whether Facebook can use this information forever,” Zuckerberg wrote.

“When a person shares something like a message with a friend, two copies of that information are created — one in the person’s sent messages box and the other in their friend’s inbox. Even if the person deactivates their account, their friend still has a copy of that message. We think this is the right way for Facebook to work, and it is consistent with how other services like e-mail work. One of the reasons we updated our terms was to make this more clear.”

“In reality,” Zuckerberg continued, “we wouldn’t share your information in a way you wouldn’t want … Our goal is to build great products and to communicate clearly to help people share more information in this trusted environment.”

Nevertheless, based on the feedback on his blog on Feb. 18, Zuckerberg said Facebook had decided to return to the previous terms of use while it resolves the issues people have raised.
But the matter isn’t resolved. The Harvard-schooled boy wonder of social media said Facebook is working on a new version of terms. The next version, he said, will be a substantial revision from where Facebook is now. It will reflect the principles of how people share and control their information, and it will be clearly written in language everyone can understand.

He also said Facebook has created a “Bill of Rights and Responsibilities” and a forum where users can discuss the issues.

The incident marks the third time that Facebook has backed off changes after users voiced privacy concerns. The site’s news feed and its Beacon advertising program drew criticism, which prompted the social networking site to increase privacy protections.

So, what do you think? Is Facebook doing the right thing? Is the flip-flopping affecting your opinion of the site? Let us know.