What We Know About the FBI’s Alleged Use of Best Buy Employees as Informants

The relationship between the FBI and Best Buy’s Geek Squad was placed under the microscope in court last week.

[Editor’s note from Target Marketing: The second a brand harms its reputation, it’s putting the core of its marketing at risk. Here, we take a look at privacy and retailing — specifically, the impact invading privacy has on brand reputation.]

The relationship between the FBI and Best Buy’s Geek Squad was placed under the microscope in court last week.

Here’s a little background information on the case before we dive into this thing:

It all boils down to evidence being used in a child-pornography-possession case in Orange County, Calif. Mark A. Rettenmaier, a prominent local physician and surgeon, was having trouble with his HP Pavilion desktop computer and sought the assistance of his local Best Buy Geek Squad team. The computer ended up making its way to a Brooks, Ky., Geek Squad field location, where employees  uncovered the material in question and handed it over to the FBI — something that, according to Best Buy policy, they’re required to do. However, what the public has learned in the months since the case was brought to trial is that these Best Buy employees have routinely searched computers that have come across their stations for files that could earn them “$500 windfalls as FBI informants.”

Further complicating matters in the case, the child pornography files were found in an area of the computer where unallocated “trash” was stored, meaning “it could only be retrieved by ‘carving’ with costly, highly sophisticated forensics tools. In other words, it’s arguable a computer’s owner wouldn’t know of its existence.”

There are other issues surrounding the case: including shoddy investigative tactics, missing warrants and more. But we’ll look past all of that for the purposes of this story.

What’s most troubling in all of this is that Best Buy is (or at least several employees at this particular office are) working as an extension of the government. And, if these employees are getting paid for the information they come across, the searches could prove to be direct violations of a person’s Fourth Amendment rights against unreasonable searches and seizures.

In a statement, Best Buy denied the accusations, saying it has no relationship with the FBI.

“From time to time, our repair agents discover material that may be child pornography, and we have a legal and moral obligation to turn that material over to law enforcement. We are proud of our policy and share it with our customers before we begin any repair,” the company said. “Any circumstances in which an employee received payment from the FBI is the result of extremely poor individual judgment, is not something we tolerate and is certainly not a part of our normal business behavior. … Our policies prohibit agents from doing anything other than what is necessary to solve the customer’s problem so that we can maintain their privacy and keep up with the volume of repairs.”

To be clear, I don’t find anything wrong with the details around this case. If a Best Buy employee were to “accidentally” come across illegal materials on a user’s computer, I’d hope and pray that they do the right thing and hand it over to authorities. That’s not what I’m arguing here.

What I am arguing is that these employees shouldn’t be actively seeking this material knowing that they have the opportunity to make a few extra bucks off of the federal government. That essentially turns them into a branch of the FBI or CIA or NSA or whomever they happen to be informing.

Privacy is the one topic that will continue to rear its ugly head as tech companies ask consumers to connect to more and more devices. Though we’re allowing companies greater access to our information and habits, there still needs to be a certain reasonable expectation of privacy. Customer support centers like Geek Squad should understand that and operate in such a way that consumers don’t have to fear that their privacy is being violated. When I send something away for repair, I’d like to think that the person whose hands the device ends up in won’t go rummaging through every folder or area of the device just because they can, no matter what some waiver I sign has to say. That’s not why I’m paying them.

And even if this were just a handful of employees at one offshoot Geek Squad facility, all of this begs the question: How many more Best Buy employees in locations around the country have been contacted by the FBI and offered similar incentives? There are some 20,000 Geek Squad employees alone. It’s a scary thought and one that could seriously hurt Best Buy’s credibility, depending on how this case plays out.

Missing Data Can Be Meaningful

No matter how big the Big Data gets, we will never know everything about everything. Well, according to the super-duper computer called “Deep Thought” in the movie “The Hitchhiker’s Guide to the Galaxy” (don’t bother to watch it if you don’t care for the British sense of humour), the answer to “The Ultimate Question of Life, the Universe, and Everything” is “42.” Coincidentally, that is also my favorite number to bet on (I have my reasons), but I highly doubt that even that huge fictitious computer with unlimited access to “everything” provided that numeric answer with conviction after 7½ million years of computing and checking. At best, that “42” is an estimated figure of a sort, based on some fancy algorithm. And in the movie, even Deep Thought pointed out that “the answer is meaningless, because the beings who instructed it never actually knew what the Question was.” Ha! Isn’t that what I have been saying all along? For any type of analytics to be meaningful, one must properly define the question first. And what to do with the answer that comes out of an algorithm is entirely up to us humans, or in the business world, the decision-makers. (Who are probably human.)

No matter how big the Big Data gets, we will never know everything about everything. Well, according to the super-duper computer called “Deep Thought” in the movie “The Hitchhiker’s Guide to the Galaxy” (don’t bother to watch it if you don’t care for the British sense of humour), the answer to “The Ultimate Question of Life, the Universe, and Everything” is “42.” Coincidentally, that is also my favorite number to bet on (I have my reasons), but I highly doubt that even that huge fictitious computer with unlimited access to “everything” provided that numeric answer with conviction after 7½ million years of computing and checking. At best, that “42” is an estimated figure of a sort, based on some fancy algorithm. And in the movie, even Deep Thought pointed out that “the answer is meaningless, because the beings who instructed it never actually knew what the Question was.” Ha! Isn’t that what I have been saying all along? For any type of analytics to be meaningful, one must properly define the question first. And what to do with the answer that comes out of an algorithm is entirely up to us humans, or in the business world, the decision-makers. (Who are probably human.)

Analytics is about making the best of what we know. Good analysts do not wait for a perfect dataset (it will never come by, anyway). And businesspeople have no patience to wait for anything. Big Data is big because we digitize everything, and everything that is digitized is stored somewhere in forms of data. For example, even if we collect mobile device usage data from just pockets of the population with certain brands of mobile services in a particular area, the sheer size of the resultant dataset becomes really big, really fast. And most unstructured databases are designed to collect and store what is known. If you flip that around to see if you know every little behavior through mobile devices for “everyone,” you will be shocked to see how small the size of the population associated with meaningful data really is. Let’s imagine that we can describe human beings with 1,000 variables coming from all sorts of sources, out of 200 million people. How many would have even 10 percent of the 1,000 variables filled with some useful information? Not many, and definitely not 100 percent. Well, we have more data than ever in the history of mankind, but still not for every case for everyone.

In my previous columns, I pointed out that decision-making is about ranking different options, and to rank anything properly. We must employee predictive analytics (refer to “It’s All About Ranking“). And for ranking based on the scores resulting from predictive models to be effective, the datasets must be summarized to the level that is to be ranked (e.g., individuals, households, companies, emails, etc.). That is why transaction or event-level datasets must be transformed to “buyer-centric” portraits before any modeling activity begins. Again, it is not about the transaction or the products, but it is about the buyers, if you are doing all this to do business with people.

Trouble with buyer- or individual-centric databases is that such transformation of data structure creates lots of holes. Even if you have meticulously collected every transaction record that matters (and that will be the day), if someone did not buy a certain item, any variable that is created based on the purchase record of that particular item will have nothing to report for that person. Likewise, if you have a whole series of variables to differentiate online and offline channel behaviors, what would the online portion contain if the consumer in question never bought anything through the Web? Absolutely nothing. But in the business of predictive analytics, what did not happen is as important as what happened. Even a simple concept of “response” is only meaningful when compared to “non-response,” and the difference between the two groups becomes the basis for the “response” model algorithm.

Capturing the Meanings Behind Missing Data
Missing data are all around us. And there are many reasons why they are missing, too. It could be that there is nothing to report, as in aforementioned examples. Or, there could be errors in data collection—and there are lots of those, too. Maybe you don’t have access to certain pockets of data due to corporate, legal, confidentiality or privacy reasons. Or, maybe records did not match properly when you tried to merge disparate datasets or append external data. These things happen all the time. And, in fact, I have never seen any dataset without a missing value since I left school (and that was a long time ago). In school, the professors just made up fictitious datasets to emphasize certain phenomena as examples. In real life, databases have more holes than Swiss cheese. In marketing databases? Forget about it. We all make do with what we know, even in this day and age.

Then, let’s ask a philosophical question here:

  • If missing data are inevitable, what do we do about it?
  • How would we record them in databases?
  • Should we just leave them alone?
  • Or should we try to fill in the gaps?
  • If so, how?

The answer to all this is definitely not 42, but I’ll tell you this: Even missing data have meanings, and not all missing data are created equal, either.

Furthermore, missing data often contain interesting stories behind them. For example, certain demographic variables may be missing only for extremely wealthy people and very poor people, as their residency data are generally not exposed (for different reasons, of course). And that, in itself, is a story. Likewise, some data may be missing in certain geographic areas or for certain age groups. Collection of certain types of data may be illegal in some states. “Not” having any data on online shopping behavior or mobile activity may mean something interesting for your business, if we dig deeper into it without falling into the trap of predicting legal or corporate boundaries, instead of predicting consumer behaviors.

In terms of how to deal with missing data, let’s start with numeric data, such as dollars, days, counters, etc. Some numeric data simply may not be there, if there is no associated transaction to report. Now, if they are about “total dollar spending” and “number of transactions” in a certain category, for example, they can be initiated as zero and remain as zero in cases like this. The counter simply did not start clicking, and it can be reported as zero if nothing happened.

Some numbers are incalculable, though. If you are calculating “Average Amount per Online Transaction,” and if there is no online transaction for a particular customer, that is a situation for mathematical singularity—as we can’t divide anything by zero. In such cases, the average amount should be recorded as: “.”, blank, or any value that represents a pure missing value. But it should never be recorded as zero. And that is the key in dealing with missing numeric information; that zero should be reserved for real zeros, and nothing else.

I have seen too many cases where missing numeric values are filled with zeros, and I must say that such a practice is definitely frowned-upon. If you have to pick just one takeaway from this article, that’s it. Like I emphasized, not all missing values are the same, and zero is not the way you record them. Zeros should never represent lack of information.

Take the example of a popular demographic variable, “Number of Children in the Household.” This is a very predictable variable—not just for purchase behavior of children’s products, but for many other things. Now, it is a simple number, but it should never be treated as a simple variable—as, in this case, lack of information is not the evidence of non-existence. Let’s say that you are purchasing this data from a third-party data compiler (or a data broker). If you don’t see a positive number in that field, it could be because:

  1. The household in question really does not have a child;
  2. Even the data-collector doesn’t have the information; or
  3. The data collector has the information, but the household record did not match to the vendor’s record, for some reason.

If that field contains a number like 1, 2 or 3, that’s easy, as they will represent the number of children in that household. But the zero should be reserved for cases where the data collector has a positive confirmation that the household in question indeed does not have any children. If it is unknown, it should be marked as blank, “.” (Many statistical softwares, such as SAS, record missing values this way.) Or use “U” (though an alpha character should not be in a numeric field).

If it is a case of non-match to the external data source, then there should be a separate indicator for it. The fact that the record did not match to a professional data compiler’s list may mean something. And I’ve seen cases where such non-matching indicators are made to model algorithms along with other valid data, as in the case where missing indicators of income display the same directional tendency as high-income households.

Now, if the data compiler in question boldly inputs zeros for the cases of unknowns? Take a deep breath, fire the vendor, and don’t deal with the company again, as it is a sign that its representatives do not know what they are doing in the data business. I have done so in the past, and you can do it, too. (More on how to shop for external data in future articles.)

For non-numeric categorical data, similar rules apply. Some values could be truly “blank,” and those should be treated separately from “Unknown,” or “Not Available.” As a practice, let’s list all kinds of possible missing values in codes, texts or other character fields:

  • ” “—blank or “null”
  • “N/A,” “Not Available,” or “Not Applicable”
  • “Unknown”
  • “Other”—If it is originating from some type of multiple choice survey or pull-down menu
  • “Not Answered” or “Not Provided”—This indicates that the subjects were asked, but they refused to answer. Very different from “Unknown.”
  • “0”—In this case, the answer can be expressed in numbers. Again, only for known zeros.
  • “Non-match”—Not matched to other internal or external data sources
  • Etc.

It is entirely possible that all these values may be highly correlated to each other and move along the same predictive direction. However, there are many cases where they do not. And if they are combined into just one value, such as zero or blank, we will never be able to detect such nuances. In fact, I’ve seen many cases where one or more of these missing indicators move together with other “known” values in models. Again, missing data have meanings, too.

Filling in the Gaps
Nonetheless, missing data do not have to left as missing, blank or unknown all the time. With statistical modeling techniques, we can fill in the gaps with projected values. You didn’t think that all those data compilers really knew the income level of every household in the country, did you? It is not a big secret that much of those figures are modeled with other available data.

Such inferred statistics are everywhere. Popular variables, such as householder age, home owner/renter indicator, housing value, household income or—in the case of business data—the number of employees and sales volume contain modeled values. And there is nothing wrong with that, in the world where no one really knows everything about everything. If you understand the limitations of modeling techniques, it is quite alright to employ modeled values—which are much better alternatives to highly educated guesses—in decision-making processes. We just need to be a little careful, as models often fail to predict extreme values, such as household incomes over $500,000/year, or specific figures, such as incomes of $87,500. But “ranges” of household income, for example, can be predicted at a high confidence level, though it technically requires many separate algorithms and carefully constructed input variables in various phases. But such technicality is an issue that professional number crunchers should deal with, like in any other predictive businesses. Decision-makers should just be aware of the reality of real and inferred data.

Such imputation practices can be applied to any data source, not just compiled databases by professional data brokers. Statisticians often impute values when they encounter missing values, and there are many different methods of imputation. I haven’t met two statisticians who completely agree with each other when it comes to imputation methodologies, though. That is why it is important for an organization to have a unified rule for each variable regarding its imputation method (or lack thereof). When multiple analysts employ different methods, it often becomes the very source of inconsistent or erroneous results at the application stage. It is always more prudent to have the calculation done upfront, and store the inferred values in a consistent manner in the main database.

In terms of how that is done, there could be a long debate among the mathematical geeks. Will it be a simple average of non-missing values? If such a method is to be employed, what is the minimum required fill-rate of the variable in question? Surely, you do not want to project 95 percent of the population with 5 percent known values? Or will the missing values be replaced with modeled values, as in previous examples? If so, what would be the source of target data? What about potential biases that may exist because of data collection practices and their limitations? What should be the target definition? In what kind of ranges? Or should the target definition remain as a continuous figure? How would you differentiate modeled and real values in the database? Would you embed indicators for inferred values? Or would you forego such flags in the name of speed and convenience for users?

The important matter is not the rules or methodologies, but the consistency of them throughout the organization and the databases. That way, all users and analysts will have the same starting point, no matter what the analytical purposes are. There could be a long debate in terms of what methodology should be employed and deployed. But once the dust settles, all data fields should be treated by pre-determined rules during the database update processes, avoiding costly errors in the downstream. All too often, inconsistent imputation methods lead to inconsistent results.

If, by some chance, individual statisticians end up with freedom to come up with their own ways to fill in the blanks, then the model-scoring code in question must include missing value imputation algorithms without an exception, granted that such practice will elongate the model application processes and significantly increase chances for errors. It is also important that non-statistical users should be educated about the basics of missing data and associated imputation methods, so that everyone who has access to the database shares a common understanding of what they are dealing with. That list includes external data providers and partners, and it is strongly recommended that data dictionaries must include employed imputation rules wherever applicable.

Keep an Eye on the Missing Rate
Often, we get to find out that the missing rate of certain variables is going out of control because models become ineffective and campaigns start to yield disappointing results. Conversely, it can be stated that fluctuations in missing data ratios greatly affect the predictive power of models or any related statistical works. It goes without saying that a consistent influx of fresh data matters more than the construction and the quality of models and algorithms. It is a classic case of a garbage-in-garbage-out scenario, and that is why good data governance practices must include a time-series comparison of the missing rate of every critical variable in the database. If, all of a sudden, an important predictor’s fill-rate drops below a certain point, no analyst in this world can sustain the predictive power of the model algorithm, unless it is rebuilt with a whole new set of variables. The shelf life of models is definitely finite, but nothing deteriorates effectiveness of models faster than inconsistent data. And a fluctuating missing rate is a good indicator of such an inconsistency.

Likewise, if the model score distribution starts to deviate from the original model curve from the development and validation samples, it is prudent to check the missing rate of every variable used in the model. Any sudden changes in model score distribution are a good indicator that something undesirable is going on in the database (more on model quality control in future columns).

These few guidelines regarding the treatment of missing data will add more flavors to statistical models and analytics in general. In turn, proper handling of missing data will prolong the predictive power of models, as well. Missing data have hidden meanings, but they are revealed only when they are treated properly. And we need to do that until the day we get to know everything about everything. Unless you are just happy with that answer of “42.”

Data Deep Dive: The Art of Targeting

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

In my previous columns, I talked about the importance of predictive analytics in modern marketing (refer to “Why Model?”) for various reasons, such as targeting accuracy, consistency, deeper use of data, and most importantly in the age of Big Data, concise nature of model scores where tons of data are packed into ready-for-use formats. Now, even the marketers who bought into these ideas often make mistakes by relinquishing the important duty of target definition solely to analysts and statisticians, who do not necessarily possess the power to read the marketers’ minds. Targeting is often called “half-art and half-science.” And it should be looked at from multiple angles, starting with the marketer’s point of view. Therefore, even marketers who are slightly (or, in many cases, severely) allergic to mathematics should come one step closer to the world of analytics and modeling. Don’t be too scared, as I am not asking you to be a rifle designer or sniper here; I am only talking about hanging the target in the right place so that others can shoot at it.

Let us start by reviewing what statistical models are: A model is a mathematical expression of “differences” between dichotomous groups; which, in marketing, are often referred to as “targets” and “non-targets.” Let’s say a marketer wants to target “high-value customers.” To build a model to describe such targets, we also need to define “non-high-value customers,” as well. In marketing, popular targets are often expressed as “repeat buyers,” “responders to certain campaigns,” “big-time spenders,” “long-term, high-value customers,” “troubled customers,” etc. for specific products and channels. Now, for all those targets, we also need to define “bizarro” or “anti-” versions of them. One may think that they are just the “remainders” of the target. But, unfortunately, it is not that simple; the definition of the whole universe should be set first to even bring up the concept of the remainders. In many cases, defining “non-buyers” is much more difficult than defining “buyers,” because lack of purchase information does not guarantee that the individual in question is indeed a non-buyer. Maybe the data collection was never complete. Maybe he used a different channel to respond. Maybe his wife bought the item for him. Maybe you don’t have access to the entire pool of names that represent the “universe.”

Remember T, C, & M
That is why we need to examine the following three elements carefully when discussing statistical models with marketers who are not necessarily statisticians:

  1. Target,
  2. Comparison Universe, and
  3. Methodology.

I call them “TCM” in short, so that I don’t leave out any element in exploratory conversations. Defining proper target is the obvious first step. Defining and obtaining data for the comparison universe is equally important, but it could be challenging. But without it, you’d have nothing against which you compare the target. Again, a model is an algorithm that expresses differences between two non-overlapping groups. So, yes, you need both Superman and Bizarro-Superman (who always seems more elusive than his counterpart). And that one important variable that differentiates the target and non-target is called “Dependent Variable” in modeling.

The third element in our discussion is the methodology. I am sure you may have heard of terms like logistic regression, stepwise regression, neural net, decision trees, CHAID analysis, genetic algorithm, etc., etc. Here is my advice to marketers and end-users:

  • State your goals and usages cases clearly, and let the analyst pick proper methodology that suites your goals.
  • Don’t be a bad patient who walks into a doctor’s office demanding a specific prescription before the doctor even examines you.

Besides, for all intents and purposes, the methodology itself matters the least in comparison with an erroneously defined target and the comparison universes. Differences in methodologies are often measured in fractions. A combination of a wrong target and wrong universe definition ends up as a shotgun, if not an artillery barrage. That doesn’t sound so precise, does it? We should be talking about a sniper rifle here.

Clear Goals Leading to Definitions of Target and Comparison
So, let’s roll up our sleeves and dig deeper into defining targets. Allow me to use an example, as you will be able to picture the process better that way. Let’s just say that, for general marketing purposes, you want to build a model targeting “frequent flyers.” One may ask for business or for pleasure, but let’s just say that such data are hard to obtain at this moment. (Finding the “reasons” is always much more difficult than counting the number of transactions.) And it was collectively decided that it would be just beneficial to know who is more likely to be a frequent flyer, in general. Such knowledge could be very useful for many applications, not just for the travel industry, but for other affiliated services, such as credit cards or publications. Plus, analytics is about making the best of what you’ve got, not waiting for some perfect datasets.

Now, here is the first challenge:

  • When it comes to flying, how frequent is frequent enough for you? Five times a year, 10 times, 20 times or even more?
  • Over how many years?
  • Would you consider actual miles traveled, or just number of issued tickets?
  • How large are the audiences in those brackets?

If you decided that five times a year is a not-so-big or not-so-small target (yes, sizes do matter) that also fits the goal of the model (you don’t want to target only super-elites, as they could be too rare or too distinct, almost like outliers), to whom are they going to be compared? Everyone who flew less than five times last year? How about people who didn’t fly at all last year?

Actually, one option is to compare people who flew more than five times against people who didn’t fly at all last year, but wouldn’t that model be too much like a plain “flyer” model? Or, will that option provide more vivid distinction among the general population? Or, one analyst may raise her hand and say “to hell with all these breaks and let’s just build a model using the number of times flown last year as the continuous target.” The crazy part is this: None of these options are right or wrong, but each combination of target and comparison will certainly yield very different-looking models.

Then what should a marketer do in a situation like this? Again, clearly state the goal and what is more important to you. If this is for general travel-related merchandizing, then the goal should be more about distinguishing more likely frequent flyers out of the general population; therefore, comparing five-plus flyers against non-flyers—ignoring the one-to-four-time flyers—makes sense. If this project is for an airline to target potential gold or platinum members, using people who don’t even fly as comparison makes little or no sense. Of course, in a situation like this, the analyst in charge (or data scientist, the way we refer to them these days), must come halfway and prescribe exactly what target and comparison definitions would be most effective for that particular user. That requires lots of preliminary data exploration, and it is not all science, but half art.

Now, if I may provide a shortcut in defining the comparison universe, just draw the representable sample from “the pool of names that are eligible for your marketing efforts.” The key word is “eligible” here. For example, many businesses operate within certain areas with certain restrictions or predetermined targeting criteria. It would make no sense to use the U.S. population sample for models for supermarket chains, telecommunications, or utility companies with designated footprints. If the business in question is selling female apparel items, first eliminate the male population from the comparison universe (but I’d leave “unknown” genders in the mix, so that the model can work its magic in that shady ground). You must remember, however, that all this means you need different models when you change the prospecting universe, even if the target definition remains unchanged. Because the model algorithm is the expression of the difference between T and C, you need a new model if you swap out the C part, even if you left the T alone.

Multiple Targets
Sometimes it gets twisted the other way around, where the comparison universe is relatively stable (i.e., your prospecting universe is stable) but there could be multiple targets (i.e., multiple Ts, like T1, T2, etc.) in your customer base.

Let me elaborate with a real-life example. A while back, we were helping a company that sells expensive auto accessories for luxury cars. The client, following his intuition, casually told us that he only cares for big spenders whose average order sizes are more than $300. Now, the trouble with this statement is that:

  1. Such a universe could be too small to be used effectively as a target for models, and
  2. High spenders do not tend to purchase often, so we may end up leaving out the majority of the potential target buyers in the whole process.

This is exactly why some type of customer profiling must precede the actual target definition. A series of simple distribution reports clearly revealed that this particular client was dealing with a dual-universe situation, where the first group (or segment) is made of infrequent, but high-dollar spenders whose average orders were even greater than $300, and the second group is made of very frequent buyers whose average order sizes are well below the $100 mark. If we had ignored this finding, or worse, neglected to run preliminary reports and just relying on our client’s wishful thinking, we would have created a “phantom” target, which is just an average of these dual universes. A model designed for such a phantom target will yield phantom results. The solution? If you find two distinct targets (as in T1 and T2), just bite the bullet and develop two separate models (T1 vs. C and T2 vs. C).

Multi-step Approach
There are still other reasons why you may need multiple models. Let’s talk about the case of “target within a target.” Some may relate this idea to a “drill-down” concept, and it can be very useful when the prospecting universe is very large, and the marketer is trying to reach only the top 1 percent (which can be still very large, if the pool contains hundreds of millions of people). Correctly finding the top 5 percent in any universe is difficult enough. So what I suggest in this case is to build two models in sequence to get to the “Best of the Best” in a stepwise fashion.

  • The first model would be more like an “elimination” model, where obviously not-so-desirable prospects would be removed from the process, and
  • The second-step model would be designed to go after the best prospects among survivors of the first step.

Again, models are expressions of differences between targets and non-targets, so if the first model eliminated the bottom 80 percent to 90 percent of the universe and leaves the rest as the new comparison universe, you need a separate model—for sure. And lots of interesting things happen at the later stage, where new variables start to show up in algorithms or important variables in the first step lose steam in later steps. While a bit cumbersome during deployment, the multi-step approach ensures precision targeting, much like a sniper rifle at close range.

I also suggest this type of multi-step process when clients are attempting to use the result of segmentation analysis as a selection tool. Segmentation techniques are useful as descriptive analytics. But as a targeting tool, they are just too much like a shotgun approach. It is one thing to describe groups of people such as “young working mothers,” “up-and-coming,” and “empty-nesters with big savings” and use them as references when carving out messages tailored toward them. But it is quite another to target such large groups as if the population within a particular segment is completely homogeneous in terms of susceptibility to specific offers or products. Surely, the difference between a Mercedes buyer and a Lexus buyer ain’t income and age, which may have been the main differentiator for segmentation. So, in the interest of maintaining a common theme throughout the marketing campaigns, I’d say such segments are good first steps. But for further precision targeting, you may need a model or two within each segment, depending on the size, channel to be employed and nature of offers.

Another case where the multi-step approach is useful is when the marketing and sales processes are naturally broken down into multiple steps. For typical B-to-B marketing, one may start the campaign by mass mailing or email (I’d say that step also requires modeling). And when responses start coming in, the sales team can take over and start contacting responders through more personal channels to close the deal. Such sales efforts are obviously very time-consuming, so we may build a “value” model measuring the potential value of the mail or email responders and start contacting them in a hierarchical order. Again, as the available pool of prospects gets smaller and smaller, the nature of targeting changes as well, requiring different types of models.

This type of funnel approach is also very useful in online marketing, as the natural steps involved in email or banner marketing go through lifecycles, such as blasting, delivery, impression, clickthrough, browsing, shopping, investigation, shopping basket, checkout (Yeah! Conversion!) and repeat purchases. Obviously, not all steps require aggressive or precision targeting. But I’d say, at the minimum, initial blast, clickthrough and conversion should be looked at separately. For any lifetime value analysis, yes, the repeat purchase is a key step; which, unfortunately, is often neglected by many marketers and data collectors.

Inversely Related Targets
More complex cases are when some of these multiple response and conversion steps are “inversely” related. For example, many responders to invitation-to-apply type credit card offers are often people with not-so-great credit. Well, if one has a good credit score, would all these credit card companies have left them alone? So, in a case like that, it becomes very tricky to find good responders who are also credit-worthy in the vast pool of a prospect universe.

I wouldn’t go as far as saying that it is like finding a needle in a haystack, but it is certainly not easy. Now, I’ve met folks who go after the likely responders with potential to be approved as a single target. It really is a philosophical difference, but I much prefer building two separate models in a situation like this:

  • One model designed to measure responsiveness, and
  • Another to measure likelihood to be approved.

The major benefit for having separate models is that each model will be able employ different types and sources of data variables. A more practical benefit for the users is that the marketers will be able to pick and choose what is more important to them at the time of campaign execution. They will obviously go to the top corner bracket, where both scores are high (i.e., potential responders who are likely to be approved). But as they dial the selection down, they will be able to test responsiveness and credit-worthiness separately.

Mixing Multiple Model Scores
Even when multiple models are developed with completely different intentions, mixing them up will produce very interesting results. Imagine you have access to scores for “High-Value Customer Model” and “Attrition Model.” If you cross these scores in a simple 2×2 matrix, you can easily create a useful segment in one corner called “Valuable Vulnerable” (a term that my mentor created a long time ago). Yes, one score is predicting who is likely to drop your service, but who cares if that customer shows little or no value to your business? Take care of the valuable customers first.

This type of mixing and matching becomes really interesting if you have lots of pre-developed models. During my tenure at a large data compiling company, we built more than 120 models for all kinds of consumer characteristics for general use. I remember the real fun began when we started mixing multiple models, like combining a “NASCAR Fan” model with a “College Football Fan” model; a “Leaning Conservative” model with an “NRA Donor” model; an “Organic Food” one with a “Cook for Fun” model or a “Wine Enthusiast” model; a “Foreign Vacation” model with a “Luxury Hotel” model or a “Cruise” model; a “Safety and Security Conscious” model or a “Home Improvement” model with a “Homeowner” model, etc., etc.

You see, no one is one dimensional, and we proved it with mathematics.

No One is One-dimensional
Obviously, these examples are just excerpts from a long playbook for the art of targeting. My intention is to emphasize that marketers must consider target, comparison and methodologies separately; and a combination of these three elements yields the most fitting solutions for each challenge, way beyond what some popular toolsets or new statistical methodologies presented in some technical conferences can acomplish. In fact, when the marketers are able to define the target in a logical fashion with help from trained analysts and data scientists, the effectiveness of modeling and subsequent marketing campaigns increase dramatically. Creating and maintaining an analytics department or hiring an outsourcing analytics vendor aren’t enough.

One may be concerned about the idea of building multiple models so casually, but let me remind you that it is the reality in which we already reside, anyway. I am saying this, as I’ve seen too many marketers who try to fix everything with just one hammer, and the results weren’t ideal—to say the least.

It is a shame that we still treat people with one-dimensional tools, such segmentations and clusters, in this age of ubiquitous and abundant data. Nobody is one-dimensional, and we must embrace that reality sooner than later. That calls for rapid model development and deployment, using everything that we’ve got.

Arguing about how difficult it is to build one or two more models here and there is so last century.

Privacy – More or Less

As marketers, we should be gravely concerned about the questions of privacy and the ethics surrounding collection and use of what many email recipients consider private information. Please bear with me as I continue my commentary on the topic

As marketers, we should be gravely concerned about the questions of privacy and the ethics surrounding collection and use of what many email recipients consider private information. Please bear with me as I continue my commentary on the topics.

The line between business and marketing email is often blurred, and what affects one nearly always affects the other. Not surprisingly, privacy—and the lack thereof—is of heightened concern to businesses and individuals alike these days. With new and frequent discoveries concerning alleged abuse by both government and private agencies, this shows no signs of diminishing.

Google Is on the Hot Seat
It’s easy to despise Google. The company is a ridiculously successful behemoth that collects an immeasurable amount of data they then choose to use, sell, share and—seemingly arbitrarily—withhold in their quest to profit from what many recipients of email believe to be private thoughts, browsing experiences, correspondences, search phrases and more.

In two separate cases, Google’s collection and use of email data is being challenged.

In the first, a group of private email users have claimed Google illegally intercepted, read, and mined information from their private email correspondence in order to better understand the recipient’s profile and deliver targeted advertisements. (Wait. That sounds a bit like what I do as a marketer …)

In September, California Judge Judy Koh rejected Google’s bid to dismiss the case based upon their argument Gmail users had agreed to allow interception by accepting the company’s terms and privacy policies.

As the legal wrangling ensued, the lawsuit lost a bit of steam when the judge ruled these plaintiffs could not band together in a class-action suit because the proposed classes of people in the case aren’t sufficiently cohesive. Her ruling may well impact a number of other email-privacy cases in which she will be asked to rule, including lawsuits against Yahoo and LinkedIn. (In other cases, Facebook and Hulu are defending their right to monetize their members’ data.)

In a submission to the court, Google has said users of Google’s email service Gmail should have no “legitimate expectation” that their emails will remain private. A “stunning admission” of the extent to which internet users’ privacy is compromised, proclaims Consumer Watchdog (CW), a US pressure group.

This causes me to ponder: Yes, of course, Google collects more information than we do—but is it simply because they can? If we, as marketers, had the ability to collect to the same degree, would we? Is the difference between Google and my company the temperance with which the small business (compared to the conglomerate) would collect? As I said in my last blog, Spider Trainers—and other marketers—should proceed carefully, respectfully, and exercise care in not just what to collect, but how to use it. But is that a distinction without a difference to the average recipient?

Students’ Consent
In a similar lawsuit, students in California have come toe to toe with Google claiming the company’s monitoring of their Gmail violates federal and state privacy laws. This case, being heard by the U.S. District Court for the Northern District of California, was brought by nine students whose emails were subject to Google surveillance because their accounts were provided in part by Google in their Apps for Education suite; a suite touting more than 30 million users worldwide, most of whom are students under 18.

Google admits to scanning student emails to serve students targeted advertisements even though display ads are not shown in Apps for Education. Contained in a sworn statement, Google “does scan [student] email” to “compile keywords for advertising” on Google sites.

What’s different about this case is the age of the typical recipient. FERPA (Family Educational Rights and Privacy Act) issued in 1974, ensures the privacy of records of students under the age of 18 and, as big as Google is, they should not be immune to legal constraints of this act. Like the previous case, the students are seeking class-action certification for the case.

This begs the question: Are marketers immune? Perhaps in our B-to-C events, we too must be mindful of the age of our audience. Certainly we know that we are collecting more than most of our recipients imagine. What preteen suspects that emails from her favorite store are actually vehicles for accumulating information about her buying behaviors in order to send her more relevant email offers?

Extending Acceptance
The pivotal topic in many of these Google and Gmail users discussions should be this: Even if the sender understood and agreed to the terms and conditions, that consent could not and should extend to the recipient who has not consented and who is probably unaware their data and profile is being assimilated from these communications. The Electronic Privacy Information Center (EPIC) is also concerned about Google’s ability to build detailed profiles of Gmail users by augmenting email-collection data with information collected by Google’s search-engine cookies, though Google denies such cross referencing occurs.

The Government
For most adults, searches are easily defined. If law enforcement suspects of us wrongdoing, they get a warrant, search our house, our car, our locker, and then seize the evidence of a crime. With an email account—be that Gmail or any other—it’s different. Emails are seized first and then searched for evidence. It’s similar in approach to the argument of the Obama administration for collecting every American’s phone records—law enforcement doesn’t know what is relevant until they have reviewed it all. In other words, it’s a fishing expedition on a grand scale.

So, it’s not just the private sector misbehaving if a federal judge has found it necessary to admonish our own justice department for requesting overly broad searches of people’s email accounts—nearly all of whom have never been accused of a crime. It’s widespread, but worse; all data collectors are at risk of being painted with the same brush. It’s coming to this: If you’re collecting data, you must be committing some sort of offense in the form of invasion of privacy, and perhaps even acting illegally. (Wow! All I wanted was to send you a personalized dog food coupon because you have three mastiffs and a poodle named Fred, Pete, George, and Ginger.)

In this case against the justice department, Judge Facciola concluded prosecutors must show probable cause for everything they seize – especially since it is possible for companies to easily search for specific emails, names, and dates of content relevant to an investigation. It’s therefore not necessary to ask for all electronically stored information in email accounts, irrespective of the relevance to the investigation.

Education
It’s going to become necessary to become educators—we marketers must educate our clients on appropriate collection and use in order to delineate what we do from what is happening with Google, NSA, Yahoo, and others. Without our input, and our self-regulation, it is quite possible that we will be spoon-fed rules of engagement—and likely that those rules will reduce future access to less than what we have now.

With the caching of images and relegation of email to specific tabs, Google is already getting in between us and our recipients by intercepting data to which we’ve already become accustomed. It’s a slippery slope to be sure, but we do have an opportunity to steer this downhill roll in a direction that will protect our ability to be good marketers in a healthy balance with the privacy of our recipients.

In Other News…
In an ongoing case, a U.S. appeals court has again rejected Google’s argument that it did not break federal wiretap laws when it collected user data from unencrypted wireless networks for its Street View program.

In the U.K., the High Court ruled Google can be sued by a group of Britons angered when using Apple’s Safari browser by the way their online habits were apparently tracked against their wishes in order to provide targeted advertising. Google asserted the case is not serious enough to fall under British jurisdiction.

Microsoft is feeling the heat after acknowledging it read an anonymous blogger’s emails in order to identify one of their employees suspected of leaking information. The FBI was involved only after the emails had been read.

Maybe I need a new blog: Privacy Erosion.