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.

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.

Can Software Really Predict Our Emotions?

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to take action.

While I’m skeptical how sentiment analysis can be used without provoking consumer backlash, maybe we should reflect on this claim that software can predict people’s feelings.

In my last blog, I shared this thought-provoking quote from contemporary literature author Maya Angelou:

“I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Let’s take a deeper dive to see if this claim of inferring feelings from social media posts is not only possible, but if it’s smart. Or shameful.

A recent Wall Street Journal article on the topic of big data (“Marketers Want to Know What You Really Mean Online: Sentiment Analysis Aims to Decipher the Nuances of Social-Media Posts“) cites several examples of how it works. The article goes into more detail, but in summary, the process works like this:

  1. Software now can break down tweets and status updates to extract the literal meaning of what’s being said. This step is called natural-language processing.
  2. The software determines the emotion behind the statement. Was it written in earnest, or was it snarky? Was the emotion strong? That is: enthusiastic, angry or sad?

This technology has been used by pharmaceutical companies, hair product companies, food companies, political organizations, and even for the State of the Union address.

What the article doesn’t tell us is if the technology actually worked to increase engagement and ultimately sales.

The resulting analyses of sentiment analysis can be far from 100 percent accurate, but could be one of many resources used in your messaging strategy. Context, cultural and colloquial nuances, and length of message can lead software algorithms astray. The shorter the message, the more difficult it becomes for algorithms to correctly interpret intent. As we all know, people often misinterpret sentiment when reading each other’s messages (consider how many times you’ve read an email that was intended to be cute or poke fun, but backfired).

The CEO of a sentiment analysis software company is quoted in the WSJ article as saying that, “right when a person is first diagnosed with cancer, they are the most optimistic. So he advises pharmaceutical clients to target ads based on the emotion the person is experiencing in the moment.”

Is this smart, opportunistic, creepy or offensive? My mother is currently dealing with cancer and this feels to me like an example of cold-hearted marketers tapping into raw emotions and feelings of a vulnerable person’s emotional state-of-mind. I’m more personally involved, obviously, but using big data on someone just diagnosed with cancer feels shameful (and notice I’ve used the word feel or feelings three times in this paragraph).

On a different and more appropriately used level, sentiment analysis can be effective when monitoring social media for complaints. It enables marketers to more quickly address a complaint and correct a problem for the customer. This feels like a powerful and appropriate use of sentiment analysis.

If we take to heart Maya Angelou’s quote that people will always remember how you made them feel, taken across an emotional line in the sand, marketers would be well served to remember that the good feeling of the moment could quickly turn into a negative your customers and prospects will never forget.

Too Big to Fail – But Not Too Big to Suck

On a recent “Real Time With Bill Maher” show, Maher responded to the announcement that Time Warner Cable would merge with Comcast Corp. in a $45 billion purchase. He noted that, combined, the two cable systems represent 19 of the 20 largest U.S. markets; and, apart from suppliers like Dish and DirecTV, they have no competitors in these metros. Further, Maher said, the two companies have the lowest customer satisfaction ratings of any cable system. So, as he asked his panelists, where is the value for customers in this merger if both companies are known to have questionable service performance?

On a recent “Real Time With Bill Maher” show, Maher responded to the announcement that Time Warner Cable would merge with Comcast Corp. in a $45 billion purchase. He noted that, combined, the two cable systems represent 19 of the 20 largest U.S. markets; and, apart from suppliers like Dish and DirecTV, they have no competitors in these metros. Further, Maher said, the two companies have the lowest customer satisfaction ratings of any cable system. So, as he asked his panelists, where is the value for customers in this merger if both companies are known to have questionable service performance?

The Federal Communications Commission (FCC) will, of course, have a great deal to say about whether this merger goes through or not. During the past couple of decades, we’ve seen a steady decline in the number of cable companies, from 53 at one point to only six now. Addressing some of the early negative reaction to its planned purchase of TWC-which would increase Comcast’s cable base to 30 million subscribers from the 22 million it currently has (a bit less than 30 percent of the overall market)-Comcast has already stated that it will make some concessions to have the merger approved. But, that said, according to company executives, the proposed cost savings and efficiencies that will “ultimately benefit customers” are not likely to either reduce monthly subscription prices or even cause them to rise less rapidly.

Comcast executives have stated that the value to consumers will come via “quality of service, by quality of offerings and by technological innovations.” David Cohen, their Executive VP, said: “Putting these two companies together will not deprive a single customer in America of a choice he or she will have today.” (Opens as a PDF) He also said, “I don’t believe there’s any way to argue that consumers are going to be hurt from a price perspective as a result of this transaction.” But, that said, he also admitted, “Frankly, most of the factors that go into customer bills are beyond our control.” Not very encouraging.

As anyone remotely familiar with Comcast’s history will understand, this is not the first time the company has navigated the river of communications company consolidation: 1995, Scripps, 800,000 subscribers, 1998, Jones Intercable, 1.1 million subscribers; 2000, Lenfest Communications, 1.3 million subscribers.

In 2002, Comcast completed acquisition of AT&T Broadband, in a deal worth $72 billion. This increased the company’s base to its current level of 22 million subscribers, and gave it major presence in markets like Atlanta, Boston, Chicago, Dallas-Ft. Worth, Denver, Detroit, Miami, Philadelphia and San Francisco-Oakland. In a statement issued by Comcast at the time the purchase was announced, again there was a claim that the merger with AT&T would benefit all stakeholders: “Combining Comcast with AT&T Broadband is a once in a lifetime opportunity that creates immediate value and positions the company for additional growth in the future. Shareholders, employees, and customers alike are poised to reap considerable benefits from this remarkable union.”

There have been technological advances, additional content, and enhanced service, during the ensuing 13 years. But “immediate value” and “considerable benefits”? Having been professionally involved with customer research conducted at the time of this merger, there was genuine question regarding the value perceived by the newly acquired AT&T customers. In a study among customers who discontinued with Comcast post-merger, and also among customers who had been Comcast customers or AT&T customers prior to the merger, poor picture quality (remember, these were the days well before HD), service disruption and high/continually rising prices were the key reasons given for defection to a competitor.

Conversely, when asked to rate their current suppliers on both key attribute importance (a surrogate measure of performance expectation) and performance itself, the highest priorities were all service-related:

  • Reliability of cable service
  • Availability of customer service when needed
  • Speed of service problem resolution
  • Responsiveness of customer service staff

On all principal service attributes except “speed of service problem resolution,” the new supplier was given higher ratings than either Comcast or AT&T. And there were major gaps in all of the above areas. Overall, close to 90 percent of these defected customers said they would be highly likely to continue the relationship with their new supplier. When correlation analysis was performed, pricing and service performance were the key driving factors. In addition, even if Comcast were now able to offer services that overcame their reasons for defection, very few (only about 10 percent) said they would be willing to become Comcast customers again.

Finally, we’ve often focused on unexpressed and unresolved complaints as leading barometers, or indicators, of possible defection. Few of the customers interviewed indicated problems with their current suppliers; however, as in other studies, problem and complaint issues were frequently surfaced for both Comcast and AT&T.

It should be noted that having lost a significant number of customers to Verizon’s FiOS, Comcast has a winback program under way, leveraging quotes from subscribers who have returned to the Xfinity fold. In the usual Macy’s/Gimbel’s customer acquisition and capture theater of war, this marks a marketing change for Comcast. As often observed (and even covered in an entire book, with my co-author, consultant Jill Griffin), winback marketing strategies are rather rarely applied, but can be very successful.

One of the key consumer concerns, especially as it may impact monthly bills, is the cost and control of content. For example, Netflix has agreed to pay Comcast for an exclusive direct connection into its network. As one media analyst noted, “The largest cable company in the nation, on the verge of improving its power to influence broadband policy, is nurturing a class system by capitalizing on its reach as a consumer Internet service provider (ISP).” This could, John C. Abell further stated, be a “game-changer.” Media management and control such as this has echoes of Big Brother for customers, and it is all the more reason Comcast should be paying greater attention to the evolving needs, as well as the squeeze on wallets, of its customers.

Perhaps the principal lesson here, assuming that the FCC allows this merger to proceed and ultimately consummate, will be for Comcast to be proactive in building relationships and service delivery. There’s very little that will increase consumer trust more than “walking the talk,” delivering against the claims of what benefits customers will stand to receive. Conversely, there’s little that will undermine trust and loyalty faster, and more thoroughly, than underdelivery on promises.

Cheat Sheet: Is Your Database Marketing Ready?

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Many data-related projects end up as big disappointments. And, in many cases, it is because they did not have any design philosophy behind them. Because many folks are more familiar with buildings and cars than geeky databases, allow me to use them as examples here.

Imagine someone started constructing a building without a clear purpose. What is it going to be? An office building or a residence? If residential, for how many people? For a family, or for 200 college kids? Are they going to just eat and sleep in there, or are they going to engage in other activities in it? What is the budget for development and ongoing maintenance?

If someone starts building a house without answering these basic questions, well, it is safe to say that the guy who commissioned such a project is not in the right state of mind. Then again, he may be a filthy rich rock star with some crazy ideas. But let us just say that is an exceptional case. Nonetheless, surprisingly, a great many database projects start out exactly this way.

Just like a house is not just a sum of bricks, mortar and metal, a database is not just a sum of data, and there has to be design philosophy behind it. And yet, many companies think that putting all available data in one place is just good enough. Call it a movie without a director or a building without an architect; you know and I know that such a project cannot end well.

Even when a professional database designer gets involved, too often the project goes out of control—as the business requirement document ends up being a summary of
everyone’s wish lists, without any prioritization or filtering. It is a case of a movie without a director. The goal becomes something like “a database that stores all conceivable marketing, accounting and payment activities, handling both prospecting and customer relationship management through all conceivable channels, including face-to-face sales and lead management for big accounts. And it should include both domestic and international activities, and the update has to be done in real time.”

Really. Someone in that organization must have attended a database marketing conference recently to get all that listed. It might be simpler and cheaper building a 2-ton truck that flies. But before we commission something like this from the get-go, shall we discuss why the truck has to fly, too? For one, if you want real-time updates, do you have a business case for it? (As in, someone in the field must make real-time decisions with real-time data.) Or do you just fancy a large object, moving really fast?

Companies that primarily sell database tools often do not help the matter, either. Some promise that the tool sets will categorize all kinds of input data, based on some auto-generated meta-tables. (Really?) The tool will clean the data automatically. (Is it a self-cleaning oven?) The tool will establish key links (by what?), build models on its own (with what target data?), deploy campaigns (every Monday?), and conduct result analysis (with responses from all channels?).

All these capabilities sound really wonderful, but does that system set long- and short-term marketing goals for you, too? Does it understand the subtle nuances in human behaviors and intentions?

Sorry for being a skeptic here. But in such cases, I think someone watched “Star Trek” too much. I have never seen a company that does not regret spending seven figures on a tool set that was supposed to do everything. Do you wonder why? It is not because such activities cannot be automated, but because:

  1. Machines do not think for us (not quite yet); and
  2. Such a system is often very expensive, as it needs to cover all contingencies (the opposite of “goal-oriented” cheaper options).

So it becomes nearly impossible to justify the cost with incremental improvements in marketing efficiency. Even if the response rates double, all related marketing costs go down by a quarter, and revenue jumps up by 200 percent, there are not many companies that can easily justify that kind of spending.

Worse yet, imagine that you just paid 10 times more for some factory-made suit than you would have paid for a custom-made Italian suit. Since when is an automated, cookie-cutter answer more desirable than custom-tailored ones? Ever since computing and storage costs started to go down significantly, and more so in this age of Big Data that has an “everything, all the time” mentality.

But let me ask you again: Do you really have a marketing database?

Let us just say that I am a car designer. A potential customer who has been doing a lot of research on the technology front presents me with a spec for a vehicle that is as big as a tractor-trailer and as quick as a passenger car. I guess that someone really needs to move lots of stuff, really fast. Now, let us assume that it will cost about $8 million or more to build a car like that, and that estimate is without the rocket booster (ah, my heart breaks). If my business model is to take a percentage out of that budget, I would say, “Yeah sure, we can build a car like that for you. When can we start?”

But let us stop for a moment and ask why the client would “need” (not “want”) a car like that in the first place. After some user interviews and prioritization, we may collectively conclude that a fleet of full-size vans can satisfy 98 percent of the business needs, saving about $7 million. If that client absolutely and positively has to get to that extra 2 percent to satisfy every possible contingency in his business and spend that money, well, that is his prerogative, is it not? But I have to ask the business questions first before initiating that inevitable long and winding journey without a roadmap.

Knowing exactly what the database is supposed to be doing must be the starting point. Not “let’s just gather everything in one place and hope to God that some user will figure something out eventually.” Also, let’s not forget that constantly adding new goals in any phase of the project will inevitably complicate the matter and increase the cost.

Conversely, repurposing a database designed for some other goal will cause lots of troubles down the line. Yeah, sure. Is it not possible to move 100 people from A to B with a 2-seater sports car, if you are willing to make lots of quick trips and get some speeding tickets along the way? Yes, but that would not be my first recommendation. Instead, here are some real possibilities.

Databases support many different types of activities. So let us name a few:

  • Order fulfillment
  • Inventory management and accounting
  • Contact management for sales
  • Dashboard and report generation
  • Queries and selections
  • Campaign management
  • Response analysis
  • Trend analysis
  • Predictive modeling and scoring
  • Etc., etc.

The list goes on, and some of the databases may be doing fine jobs in many areas already. But can we safely call them “marketing” databases? Or are marketers simply tapping into the central data depository somehow, just making do with lots of blood, sweat and tears?

As an exercise, let me ask a few questions to see if your organization has a functioning marketing database for CRM purposes:

  • What is the average order size per year for customers with tenure of more than one year? —You may have all the transaction data, but maybe not on an individual level in order to know the average.
  • What is the number of active and dormant customers based on the last transaction date? —You will be surprised to find out that many companies do not know exactly how many customers they really have. Beep! 1 million-“ish” is not a good answer.
  • What is the average number of days between activities for each channel for each customer? —With basic transaction data summarized “properly,” this is not a difficult question to answer. But it’s very difficult if there are divisional “channel-centric” databases scattered all over.
  • What is the average number of touches through all channels that you employ before your customer reaches the projected value potential? —This is a hard one. Without all the transaction and contact history by all channels in a “closed-loop” structure, one cannot even begin to formulate an answer for this one. And the “value potential” is a result of statistical modeling, is it not?
  • What are typical gateway products, and how are they correlated to other product purchases? —This may sound like a product question, but without knowing each customer’s purchase history lined up properly with fully standardized product categories, it may take a while to figure this one out.
  • Are basic RFM data—such as dollars, transactions, dates and intervals—routinely being used in predictive models? —The answer is a firm “no,” if the statisticians are spending the majority of their time fixing the data; and “not even close,” if you are still just using RFM data for rudimentary filtering.

Now, if your answer is “Well, with some data summarization and inner/outer joins here and there—though we don’t have all transaction records from last year, and if we can get all the campaign histories from all seven vendors who managed our marketing campaigns, except for emails—maybe?”, then I am sorry to inform you that you do not have a marketing database. Even if you can eventually get to the answer if some programmer takes two weeks to draw a 7-page flow chart.

Often, I get extra comments like “But we have a relational database!” Or, “We stored every transaction for the past 10 years in Hadoop and we can retrieve any one of them in less than a second!” To these comments, I would say “Congratulations, your car has four wheels, right?”

To answer the important marketing questions, the database should be organized in a “buyer-centric” format. Going back to the database philosophy question, the fundamental design of the database changes based on its main purpose, much like the way a sports sedan and an SUV that share the same wheel base and engine end up shaped differently.

Marketing is about people. And, at the center of the marketing database, there have to be people. Every data element in the base should be “describing” those people.

Unfortunately, most relational databases are transaction-, channel- or product-centric, describing events and transactions—but not the people. Unstructured databases that are tuned primarily for massive storage and rapid retrieval may just have pieces of data all over the place, necessitating serious rearrangement to answer some of the most basic business questions.

So, the question still stands. Is your database marketing ready? Because if it is, you would have taken no time to answer my questions listed above and say: “Yeah, I got this. Anything else?”

Now, imagine the difference between marketers who get to the answers with a few clicks vs. the ones who have no clue where to begin, even when sitting on mounds of data. The difference between the two is not the size of the investment, but the design philosophy.

I just hope that you did not buy a sports car when you needed a truck.

5 Tips for Faster, More Confident, Direct Marketing Budget Decisions

As we enter the critical make-or-break fourth quarter, and you begin your 2014 direct marketing budget plans, you will likely be faced with many marketing decisions. Those decisions are usually needed quickly. But often they’re not made quickly. Whether it’s information overload from so many options, analysis paralysis or managers who are afraid to make a decision, today we explore five ways to

As we enter the critical make-or-break fourth quarter, and you begin your 2014 direct marketing budget plans, you will likely be faced with many marketing decisions. Those decisions are usually needed quickly. But often they’re not made quickly. Whether it’s information overload from so many options, analysis paralysis or managers who are afraid to make a decision, today we explore five ways to make marketing decisions quicker and more confidently.

A mere generation ago, direct marketing decisions were limited to direct mail customer file or rented lists, space ads in magazines, package inserts, direct response broadcast, and a few other media options.

Fast forward to now, and the direct marketing decision landscape has grown exponentially with online and cross-promotional media options. Every season reveals new, unexplored online opportunities. Some are fads. Some turn out to have real value.

So for your direct marketing budget planning, here are five recommendations of how to evaluate opportunities and make decisions more quickly and confidently.

1. Cost per Response
An important metric for most direct marketers is the marketing cost per response (per lead, inquiry, sale—whatever your situation). This core metric may be your most significant contributor to your decisions.

2. Allocation of Unknown Response Sources
If you’re in a situation where you have a significant number of responses for which you can’t pinpoint a specific marketing source, consider a weighted-average allocation of those responses across marketing activities. With some imagination, you should be able to calculate this on your own. (Let me know if you’d like an expansion of this concept, and perhaps we’ll do so in a future blog post.)

3. Summarize Results in a Matrix
Placing your data in a spreadsheet will put the numbers in front of you so you can see all your activity in one place. You may want the data by media type on separate spreadsheet tabs so you can see more granular data.

For example, on one tab you summarize results from direct mail (by list, or summed up by customer vs. rented lists) with cost per response. If you allocated unknown orders, be sure to include those. Another tab might concern email results that summarize opens, clicks, conversions and cost per response. Other tabs could summarize pay-per-click, social media, retargeting or whatever media you are using. Then roll up and summarize all of the media on a tab of its own. If cost per response is most important to you, then sort the data from the lowest cost per response to highest. Perhaps you have “soft data” that will be a factor in your decisions. If so, add columns to enable a written evaluation of each. Maybe your evaluation is as simple as “pluses” and “minuses” for each opportunity.

4. Parameters for Decisions
It happens all the time. With so many choices and options, and potentially several staff members wanting their piece of the budget, decisions can be contentious and slow. When that happens, everyone loses. When you establish the parameters for decision making upfront, it’s easier to slice the pie into the right proportions. More importantly, if the head of the organization or department has established those parameters in writing (avoid verbal direction to avoid future misunderstanding), staff is empowered to make more confident decisions without delay.

5. Don’t Forget Test Budgets
Know, ahead of time, how much money you can gamble in a test. You should view the money spent as having zero return so that when if it works you’re pleasantly surprised. A rule of thumb you might use is to allocate 10 percent of a total marketing budget to tests. Whether it’s a direct mail list test, or new online media, the only way you can learn if those options work for you is to test it. Remember, too, that marketing fads can fizzle quickly. The hot new opportunity of 2012—not even a full year ago—may already be a distant memory.

If you have processes, or recommendations, about how you make faster, more confident marketing decisions, please share them in the comments area below.

When Mistakes Happen

Mistakes are a part of the learning process. Every company will experience them at one time or another. Ideally, with good planning, they will be minor and won’t happen often. With better planning, there is an action plan in place to quickly right the wrong. Knowing what to do before it needs to be done simplifies fixing the problem.

My Coke Rewards Apology Email
This My Coke Rewards apology email was delivered quickly and followed the four best practices of making amends for a marketing mistake.

Mistakes are a part of the learning process. Every company will experience them at one time or another. Ideally, with good planning, they will be minor and won’t happen often. With better planning, there is an action plan in place to quickly right the wrong. Knowing what to do before it needs to be done simplifies fixing the problem.

Handling mistakes well is a great loyalty builder. You can measure the effect by conducting a comparative analysis. Pull two segments to compare from customers who made their first purchase five years ago. Choose customers who are very similar in order source, size and selection. Select people who had seemingly perfect orders for the first segment. “Perfect orders” describe orders that are processed quickly and delivered without issues. Place people who had problems quickly resolved for the second segment.

Detail sales history, average order and returns for each segment. Use the information to compare the value of the customers who had problems with the ones who didn’t. This analysis almost always finds that the people who had problems quickly resolved are much more valuable than those who had a perfect order. I believe there is a simple explanation for this: People who have problems resolved to their satisfaction trust the company more. Trust and loyalty go hand in hand.

Planning for failure seems counterintuitive, but it is the best way to be prepared. The first part of the action plan is determining the extent of the problem. Will an apology suffice, or does something need correcting? Apologies are sufficient when the mistake is simple and doesn’t overly inconvenience the person or create an expense.

My Coke Rewards provides us with a good example of a mistake where an apology is enough. Last month, the automated points’ expiration notice malfunctioned. Members received a notification that they needed to add or use points or they would expire. The deadline for keeping the account active was two weeks before the email was sent. The apology came quickly and followed best practices (refer to the image in the media player):

  • Be direct with the apology and explanation.
  • Tell people what they need to do (if anything).
  • Thank them for their business.
  • If necessary, offer a reward for the inconvenience. (If you offer a reward in the form of a discount, make it dollars off with no minimum. This is a payment for a mistake, not a marketing promotion.)

The email from My Coke Rewards was simple, to the point and didn’t offer compensation. The mistake was minor, so an apology after the correction was enough. Bigger mistakes require more. There isn’t a magic formula that determines the ideal response for every problem. Customers are individuals with unique expectations.

The second part of the action plan is determining the specific resolution for each problem. Creating a general list of potential problems and resolutions provides a guide for the customer service team. Anything that satisfies the customer and falls within the guidelines should be resolved immediately.

The best way to determine what needs to be done is to ask the customer with the problem. Lead with an apology and follow with the inquiry. For example: “I’m sorry this happened. What can we do to make it right?” There will occasionally be an outlandish demand, but usually the requested solution is less than you were prepared to do. Asking customers how to right a wrong simultaneously gives them respect and shows that you care. Here are some other best practices when a mistake happens:

  • Minimize customers’ investment in resolving issues. Strive to resolve issues on the first contact without involving other people whenever possible.
  • If you discover the mistake before the customer, reach out immediately. This shows your customers that you are watching their backs.
  • Use the appropriate communication tool. Email works well for most correspondence as long as the messages are not from “do not reply” boxes.
  • When the resolution process is complete, ask customers if they are satisfied with the solution. Every customer cannot be saved, but letting them go without trying is unacceptable.
  • Avoid fake apologies. Apologizing works so well in relationship building that people are making up reasons to do it. Don’t.

Reinventing Direct Marketers

Staying relevant requires reinventing your skills and marketing approaches. That’s why today we’re launching Reinventing Direct, a new blog where we share what we’re learning about new direct marketing approaches in practical, easy-to-understand recommendations, all geared toward direct marketers, so you can reinvent yourself and become a catalyst for change in your organization

Staying relevant requires reinventing your skills and marketing approaches. Just over a decade ago, many direct marketers moved beyond direct mail and reinvented their approach by creating basic websites, using email marketing and more. But now a decade later, reinventing direct marketing core competencies requires understanding and using even more tools.

As we have evolved and reinvented our traditional direct marketing skills over the years, the editors of Target Marketing have invited us to evolve from our online video marketing blog to broader topics.

Today we launch Reinventing Direct, a new blog where we share what we’re learning about new direct marketing approaches in practical, easy-to-understand recommendations all geared toward direct marketers, so you can reinvent yourself and become a catalyst for change in your organization.

(If the video isn’t just above this line, click here to view it.)

We’ve chosen online competitive analysis as our first blog topic. Why? Because every thoughtful new business plan and marketing plan includes an analysis of the competition. In addition, at least once every year you should investigate what your competitors are doing online. It will make you sharper and more competitive.

Today you’ll learn about 10 tools you can use to compare how you stack up with your digital direct marketing efforts compared to your competitors. The tools we share in today’s video will give you data points on several areas of online marketing including:

  • Where to get a grade for your website’s overall effectiveness
  • Where you stand with SEO
  • Inbound link comparisons (with domain authority)
  • How your website performs on mobile devices
  • Traffic to your website compared to competitors
  • Engagement and reputation metrics
  • Demographic data comparisons of the age, presence of children, income, education and ethnicity of those going to your site versus your competitors
  • Social media comparisons
  • How your site ranks for keywords compared to competitors
  • Competitor’s daily pay-per-click budgets, average paid position, and the estimated value of daily organic traffic
  • How to know when your competitor has new information posted on the Web
  • The source where you can go back in time to check what was on a competitor’s website in the past

There are many online analysis tools available, and we encourage you to search for them and check them out. We also invite you to share your recommendations of other services that you have successfully used. Please post your recommendations in the comments section below.

Judging the 2013 ECHOs: A View of Data-Driven Marketing’s Best

Two weeks back, I had the opportunity to judge Rounds 1 and 2 of the ECHOs this year—and while sworn confidentiality requires me to remain mum on actual campaigns I encountered there, I want to comment on the value of judging itself, from my perspective as a public relations practitioner in our field. The ECHOs have been around a long time—since 1929 to be exact. But what really makes me excited to see the campaigns as a judge each year, is that they represent agencies’ and brands’ self-selected choices on what they consider to be award-winning and innovative work

This past year, I had the honor of joining the Direct Marketing Association’s Board of Governors for the International ECHO Awards. That’s my disclaimer.

Two weeks back, I had the opportunity to judge Rounds 1 and 2 of the ECHOs this year—and while sworn confidentiality requires me to remain mum on actual campaigns I encountered there, I want to comment on the value of judging itself, from my perspective as a public relations practitioner in our field.

The ECHOs have been around a long time—since 1929 to be exact. But what really makes me excited to see the campaigns as a judge each year, is that they represent agencies’ and brands’ self-selected choices on what they consider to be award-winning and innovative work based on the three criteria: marketing strategy, creative and results in equal parts. 2013 is no exception. The honors—which will be announced on October 15 in Chicago—will be the world’s best in data-driven marketing. (Breaking News—comedian Jake Johanssen will be this year’s host.)

There are no longer media categories among the entrants—a reflection of how marketing has converged. Instead, channels serve as brand engagement vehicles, and what matters most is their effectiveness in design, dialogue and generating responses to calls for action—from leads, to sales, to audience engagement on a measured scale. So a direct mail piece that is entered may exist (and be judged) alongside entries that represent Web sites, search campaigns, mobile apps, call center efforts, or—most often—integrated marketing campaigns. Again what matters—and only matters—are the strategy, creative and engagement metrics that define marketing effectiveness. Both consumer and business-to-business markets are incorporated.

The categories where entrants are recognized are by industry—15 altogether. You can review the list here.

This is what being an ECHO judge tells me every year:

  1. How are brands and their agencies measuring effectiveness in data-driven marketing? What metrics have they chosen to index or communicate? How is marketing return on investment conveyed? Increasingly, marketing dashboards appear to be in use—with relevant components part of the external results story.
  2. What creative trends are in play? What constitutes break-through creative? What is the unusual and innovative? Where has risk been met with reward? And who (clients and agencies) are being the most courageous worldwide—while also being effective?
  3. How are data being collected, analyzed and—in some cases—visualized? While the entry forms this year were streamlined and don’t have as much budget information in the past—this really has served to heighten visibility on the data, analysis and segmentation techniques being deployed in the strategy.
  4. What is state-of-the-art in data-driven marketing on a global scale? This year, as always, entries were submitted through various partners and submitted to early judging in Denmark, Australia and the United States, comprising dozens of countries in nearly all continents. It is great to see how globally data-driven marketing is practiced—and the creative genius and extraordinary results achieved in both mature and less mature markets.
  5. Finally, judging happens on an individual basis—as a judge you evaluate a campaign, providing your own perspective. But the judging is a collective one—bringing together experienced peers from all over the nation and world. Once the entries and judging scores are in, we do tend to share with each other our impressions of the experience in the aggregate—and meet great people in the process.

In brief, the ECHOs are an idea store for marketing strategists, creative professionals—and the PR folks like me who support my clients in entering awards. I’ve learned not just about how to create great marketing—but how to tell the story behind great marketing. Both count when it comes to crafting an award entry that wins.

You can find out who the winners are firsthand by attending DMA2013 in Chicago, USA, this year (October 12-17, 2013). Make sure to indicate in your registration for a ticket to the ECHO Awards Gala where a separate registration is required: http://dma13.org/registration/

Come October, I’ll definitely be sharing in this blog snippets from some of my favorite campaigns this year!