The Purpose-Driven Brand

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Since the beginning of time to this very moment, we humans have been driven by purpose. Consciously and unconsciously, we seek meaning in our lives and the need to actively make a difference and leave a personal legacy of good when we move on from this existence. Jung addresses this in his Individuation process and so, too, do modern and past psychologists and researchers of human behavior drivers.

Rick Warren, founder of The Saddleback Ministries, and best-selling author, discovered just how powerful our need and drive for purpose is when he wrote, “The Purpose-Driven Life: What on Earth Am I Here For?” Written in 2003, this book became the bestselling hardback non-fiction book in history, and is the second most-translated book in the world, after the Bible.

Today’s consumer seeks purpose outside of the traditional methods of religion, volunteerism, and random acts of kindness toward friends and strangers. Many of us, in fact most of us, seek to further our sense of purpose with our choices at the grocery store, online shopping carts and more. According to research by Cone Communications and Edelman, consumers in the U.S. are more likely to trust a brand that shows its direct impact on society (opens as a PDF). Others, upwards of 80 percent, are more likely to purchase from a company that can quantifiably show how it makes a difference in people’s lives—beyond just adding to the investment portfolio of a very select few.

According to the Merriam Webster dictionary, purpose is defined as:

: the reason why something is done or used
: the aim or intention of something
: the feeling of being determined to do or achieve something

Consumers are not just expecting big business to define a social purpose for the brand, they are demanding it by how they are making purchasing and loyalty choices. Edelman’s “Good Purpose Study,” released in 2012 and covering a five-year study of consumers worldwide shows:

  • 47 percent of global consumers buy brands that support a good cause atleast monthly, a 47 percent increase in just two years.
  • 72 percent of consumers wouldrecommend a brand that supports a good cause over one that doesn’t, a 39 percent increase since 2008
  • 71 percent of consumers would help a brand promote its products or services if there is a good cause behind them, representing a growth of 34 percent since 2008
  • 73 percent ofconsumers would switch brands ifa different brand of similar quality supported a good cause, which is a 9 percent increase since 2009

Another research group, Cone Communications, showed that 89 percent of consumers are likely to switch brands to one that is associated with a good cause if price and quality are similar; and 88 percent want to hear what brands are doing to have a real impact, not just that they are spending resources toward a cause.

This new state of consumerism doesn’t just show people still have a heart and soul, it is a big flag to brands in all industries to integrate CSR or Corporate Social Responsibility into their brand fiber, customer experience and marketing programs.

I interviewed William L. “Toby” Usnik, Chief CSR Officer for Christie’s in New York City, who maintains that CSR has moved far beyond writing a check and then emotionally moving on from a cause or community in need. It is about a brand’s purpose being bigger than developing its return to shareholders. Validating Usnik is a recent article published in the March 21, 2015, edition of The Economist, quoting Jack Welch of GE fame as saying “pursuing shareholder value as a strategy was ‘the dumbest idea ever.’ ” While that might be debatable, it is becoming less and less debatable, per the statistics above that show how defining a brand’s purpose in terms of the social good it delivers to communities related to its business is anything but “dumbest”—and rather, is getting smarter and smarter by the day.

Charting new territory in his role as Chief CSR Officer for Christie’s, Usnik’s first step was to define CSR as it relates to human psychology and the values of the Christie’s brand. For Usnik, it starts with building a brand’s purpose around Maslow’s hierarchy of needs and helping your constituents get closer to self-actualization, or that state of reaching a higher purpose for a greater good.

“Moving customers upwards through Maslow’s hierarchy of needs is critical to address,” says Usnik. “Customers of all ages, and especially Millennials, are moving toward a state of self-actualization and looking to define their purpose and place in communities and the world. They seek relationships with brands that are doing the same within their own value set. As a result, any business today needs to ask itself, ‘What is the impact of our activities on each other, the community, the workplace, customers and the planet?’ “

Defining your brand’s purpose and corresponding CSR efforts is the first step to developing emotional and psychological bonds with internal and external customers. When you make your CSR actionable by engaging others in your cause, you can build passion and loyalty that not only define your brand, but also your profitability. Coke defines its brand through its happiness campaign that involves delivering free Coke and other items, like sports equipment and toys, to villages around the world, and through water sanitization programs.

Tom’s Shoes, an example that is known to most as one of the pioneers in philanthropic branding, went from $9 million to $21 million in revenue in just three years by being a “purpose-driven brand” that enables people to give back to others simply by making a purchase. With a cost of goods sold of $9 and a sale price of more than $60, that is not hard to do.

At Christie’s CSR, is a big part of CRM. According to Usnik, Christie’s helps many of its customers sell high-value works of art. Many customers then donate the proceeds to social causes that align with their personal values or passions. By helping customers turn wealth into support for charitable causes, they actually create strong emotional bonds with customers, rooted in empathy and understanding—which is far more critical for securing lifetime value than points and reward programs.

In just 2014, $300 million in sales were facilitated through Christie’s that benefited non-profit organizations. Additionally, Christie’s regularly volunteers its charity auctioneers to nonprofit events. And in 2014, he estimates they’ve raised $58 million for 300 organizations.

The key to successful branding via CSR programs and purpose-driven strategies that transcend all levels of an organization and penetrate the psyche of we humans striving to define our role in this world is sincerity. Anything less simply backfires. Brands must be sincere about caring to support worthwhile causes related to their field, and they must be sincere when involving customers in charitable giving.

Concludes Usnik, “You can’t fake caring. If you pretend to care about a cause you align with, or a cause that is important to your customer, [you] won’t succeed. Caring to make a difference must be part of your culture, your drive and your passion at all levels. If you and your employees spend time and personal energy to work closely with your customers to make a difference for your selected causes and those of your customers, you are far more likely to secure long-term business and loyalty and overall profitable client relationships.”

Takeaway: The five primary drivers of human behavior, according to psychologist Jon Haidt of the University of Virginia and author of “The Happiness Hypothesis,” are centered around our innate need to nurture others, further worthy causes, make a difference in the world, align with good and help others. When brands can define themselves around these needs, we not only influence human behavior for the greater good, we can influence purchasing behavior for the long-term good of our individual brands. And per the Edelman research, 76 percent of customers around the world say its okay for brands to support good causes and make money at the same time. So define your purpose, build your plan, engage your customers and shine on!

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.

Why Model?

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around? The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Why model? Uh, because someone is ridiculously good looking, like Derek Zoolander? No, seriously, why model when we have so much data around?

The short answer is because we will never know the whole truth. That would be the philosophical answer. Physicists construct models to make new quantum field theories more attractive theoretically and more testable physically. If a scientist already knows the secrets of the universe, well, then that person is on a first-name basis with God Almighty, and he or she doesn’t need any models to describe things like particles or strings. And the rest of us should just hope the scientist isn’t one of those evil beings in “Star Trek.”

Another answer to “why model?” is because we don’t really know the future, not even the immediate future. If some object is moving toward a certain direction at a certain velocity, we can safely guess where it will end up in one hour. Then again, nothing in this universe is just one-dimensional like that, and there could be a snowstorm brewing up on its path, messing up the whole trajectory. And that weather “forecast” that predicted the snowstorm is a result of some serious modeling, isn’t it?

What does all this mean for the marketers who are not necessarily masters of mathematics, statistics or theoretical physics? Plenty, actually. And the use of models in marketing goes way back to the days of punch cards and mainframes. If you are too young to know what those things are, well, congratulations on your youth, and let’s just say that it was around the time when humans first stepped on the moon using a crude rocket ship equipped with less computing power than an inexpensive passenger car of the modern days.

Anyhow, in that ancient time, some smart folks in the publishing industry figured that they would save tons of money if they could correctly “guess” who the potential buyers were “before” they dropped any expensive mail pieces. Even with basic regression models—and they only had one or two chances to get it right with glacially slow tools before the all-too-important Christmas season came around every year—they could safely cut the mail quantity by 80 percent to 90 percent. The savings added up really fast by not talking to everyone.

Fast-forward to the 21st Century. There is still a beauty of knowing who the potential buyers are before we start engaging anyone. As I wrote in my previous columns, analytics should answer:

1. To whom you should be talking; and
2. What you should offer once you’ve decided to engage someone.

At least the first part will be taken care of by knowing who is more likely to respond to you.

But in the days when the cost of contacting a person through various channels is dropping rapidly, deciding to whom to talk can’t be the only reason for all this statistical work. Of course not. There are plenty more reasons why being a statistician (or a data scientist, nowadays) is one of the best career choices in this century.

Here is a quick list of benefits of employing statistical models in marketing. Basically, models are constructed to:

  • Reduce cost by contacting prospects more wisely
  • Increase targeting accuracy
  • Maintain consistent results
  • Reveal hidden patterns in data
  • Automate marketing procedures by being more repeatable
  • Expand the prospect universe while minimizing the risk
  • Fill in the gaps and summarize complex data into an easy-to-use format—A must in the age of Big Data
  • Stay relevant to your customers and prospects

We talked enough about the first point, so let’s jump to the second one. It is hard to argue about the “targeting accuracy” part, though there still are plenty of non-believers in this day and age. Why are statistical models more accurate than someone’s gut feeling or sheer guesswork? Let’s just say that in my years of dealing with lots of smart people, I have not met anyone who can think about more than two to three variables at the same time, not to mention potential interactions among them. Maybe some are very experienced in using RFM and demographic data. Maybe they have been reasonably successful with choices of variables handed down to them by their predecessors. But can they really go head-to-head against carefully constructed statistical models?

What is a statistical model, and how is it built? In short, a model is a mathematical expression of “differences” between dichotomous groups. Too much of a mouthful? Just imagine two groups of people who do not overlap. They may be buyers vs. non-buyers; responders vs. non-responders; credit-worthy vs. not-credit-worthy; loyal customers vs. attrition-bound, etc. The first step in modeling is to define the target, and that is the most important step of all. If the target is hanging in the wrong place, you will be shooting at the wrong place, no matter how good your rifle is.

And the target should be expressed in mathematical terms, as computers can’t read our minds, not just yet. Defining the target is a job in itself:

  • If you’re going after frequent flyers, how frequent is frequent enough for you? Five times a year or 10 times a year? Or somewhere in between? Or should it remain continuous?
  • What if the target is too small or too large? What then?
  • If you are looking for more valuable prospects, how would you express that? In terms of average spending, lifetime spending or sheer number of transactions?
  • What if there is an inverse relationship between frequency and dollar spending (i.e., high spenders shopping infrequently)?
  • And what would be the borderline number to be “valuable” in all this?

Once the target is set, after much pondering, then the job is to select the variables that describe the “differences” between the two groups. For example, I know how much marketers love to use income variables in various situations. But if that popular variable does not explain the differences between the two groups (target and non-target), the mathematics will mercilessly throw it out. This rigorous exercise of examining hundreds or even thousands of variables is one of the most critical steps, during which many variables go through various types of transformations. Statisticians have different preferences in terms of ideal numbers of variables in a model, while non-statisticians like us don’t need to be too concerned, as long as the resultant model works. Who cares if a cat is white or black, as long as it catches mice?

Not all selected variables are equally important in model algorithms, either. More powerful variables will be assigned with higher weight, and the sum of these weighted values is what we call model score. Now, non-statisticians who have been slightly allergic to math since the third grade only need to know that the higher the score, the more likely the record in question is to be like the target. To make the matter even simpler, let’s just say that you want higher scores over lower scores. If you are a salesperson, just call the high-score prospects first. And would you care how many variables are packed into that score, for as long as you get the good “Glengarry Glen Ross” leads on top?

So, let me ask again. Does this sound like something a rudimentary selection rule with two to three variables can beat when it comes to identifying the right target? Maybe someone can get lucky once or twice, but not consistently.

That leads to the next point, “consistency.” Because models do not rely on a few popular variables, they are far less volatile than simple selection rules or queries. In this age of Big Data, there are more transaction and behavioral data in the mix than ever, and they are far more volatile than demographic and geo-demographic data. Put simply, people’s purchasing behavior and preferences change much faster than family composition or their income, and that volatility factor calls for more statistical work. Plus, all facets of marketing are now more about measurable results (ah, that dreaded ROI, or “Roy,” the way I call it), and the businesses call for consistent hitters over one-hit wonders.

“Revealing hidden patterns in data” is my favorite. When marketers are presented with thousands of variables, I see a majority of them just sticking to a few popular ones all the time. Some basic recency and frequency data are there, and among hundreds of demographic variables, the list often stops after income, age, gender, presence of children, and some regional variables. But seriously, do you think that the difference between a luxury car buyer and an SUV buyer is just income and age? You see, these variables are just the ones that human minds are accustomed to. Mathematics do not have such preconceived notions. Sticking to a few popular variables is like children repeatedly using three favorite colors out of a whole box of crayons.

I once saw a neighborhood-level U.S. Census variable called “% Households with Septic Tanks” in a model built for a high-end furniture catalog. Really, the variable was “percentage of houses with septic tanks in the neighborhood.” Then I realized it made a lot of sense. That variable was revealing how far away that neighborhood was located in comparison to populous city centers. As the percentage of septic tanks increased, the further away the residents were from the city center. And maybe those folks who live in scarcely populated areas were more likely to shop for furniture through catalogs than the folks who live closer to commercial areas.

This is where we all have that “aha” moment. But you and I will never pick that variable in anything that we do, not in million years, no matter how effective it may be in finding the target prospects. The word “septic” may scare some people off at “hello.” In any case, modeling procedures reveal hidden connections like that all of the time, and that is a very important function in data-rich environments. Otherwise, we will not know what to throw out without fear, and the databases will continuously become larger and more unusable.

Moving on to the next points, “Repeatable” and “Expandable” are somewhat related. Let’s say a marketer has been using a very innovative selection logic that she came across almost by accident. In pursuing special types of wealthy people, she stumbled upon a piece of data called “owner of swimming pool.” Now, she may have even had a few good runs with it, too. But eventually, that success will lead to the question of:

1. Having to repeat that success again and again; and
2. Having to expand that universe, when the “known” universe of swimming pool owners become depleted or saturated.

Ah, the chagrin of a one-hit-wonder begins.

Use of statistical models, with help of multiple variables and scalable scoring, would avoid all of those issues. You want to expand the prospect universe? No trouble. Just dial down the scores on the scale a little further. We can even measure the risk of reaching into the lower-scoring groups. And you don’t have to worry about coverage issues related to a few variables, as those won’t be the only ones in the model. Want to automate the selection process? No problem there, as using a score, which is a summary of key predictors, is far simpler than having to carry a long list of data variables into any automated system.

Now, that leads to the next point, “Filling in the gaps and summarizing the complex data into an easy-to-use format.” In the age of ubiquitous and “Big” data, this is the single-most important point, way beyond the previous examples for traditional 1-to-1 marketing applications. We are definitely going through massive data overloads everywhere, and someone better refine the data and provide some usable answers.

As I mentioned earlier, we build models because we will never know the whole truth. I believe that the Big Data movement should be all about:

1. Filtering the noise from valuable information; and
2. Filling the gaps.

“Gaps,” you say? Believe me, there are plenty of gaps in any dataset, big or small.

When information continues to get piled on, the resultant database may look big. And they are physically large. But in marketing, as I repeatedly emphasized in my previous columns, the data must be realigned to “buyer-centric” formats, with every data point describing each individual, as marketing is all about people.

Sure, you may have tons of mobile phone-related data. In fact, it could be quite huge in size. But let me turn that upside down for you (more like sideways-up, in practice). Now, try to describe everyone in your footprint in terms of certain activities. Say, “every smart phone owner who used more than 80 percent of his or her monthly data allowance on the average for the past 12 months, regardless of the carrier.” Hey, don’t blame me for asking these questions just because it’s inconvenient for data handlers to answer them. Some marketers would certainly benefit from information like that, and no one cares about just bits and pieces of data, other than for some interesting tidbits at a party.

Here’s the main trouble when you start asking buyer-related questions like that. Once we try to look at the world from the “buyer-centric” point of view, we will realize there are tons of missing data (i.e., a whole bunch of people with not much information). It may be that you will never get this kind of data from all carriers. Maybe not everyone is tracked this way. In terms of individuals, you may end up with less than 10 percent in the database with mobile information attached to them. In fact, many interesting variables may have less than 1 percent coverage. Holes are everywhere in so-called Big Data.

Models can fill in those blanks for you. For all those data compilers who sell age and income data for every household in the country, do you believe that they really “know” everyone’s age and income? A good majority of the information is based on carefully constructed models. And there is nothing wrong with that.

If you don’t get to “know” something, we can get to a “likelihood” score—of “being like” that something. And in that world, every measurement is on a scale, with no missing values. For example, the higher the score of a model built for a telecommunication company, the more likely that the prospect is going to use a high-speed data plan, or the international long distance services, depending on the purpose of the model. Or the more likely the person will buy sports packages via cable or satellite. Or the person is more likely to subscribe to premium movie channels. Etc., etc. With scores like these, a marketer can initiate the conversation with—not just talking to—a particular prospect with customized product packages in his hand.

And that leads us to the final point in all this, “Staying relevant to your customers and prospects.” That is what Big Data should be all about—at least for us marketers. We know plenty about a lot of people. And they are asking us why we are still so random about marketing messages. With all these data that are literally floating around, marketers can do so much better. But not without statistical models that fill in the gaps and turn pieces of data into marketing-ready answers.

So, why model? Because a big pile of information doesn’t provide answers on its own, and that pile has more holes than Swiss cheese if you look closely. That’s my final answer.

Boost Your Website Sales: 8 Simple E-commerce Tips That Really Work

OK, so you’ve won half the battle. You’re driving traffic to your site. Now what? How can you get your visitors to convert? This is a challenge that most every website that sells a product faces. The following are some tried and true tactics that, over the years, I’ve seen make a difference. Some may seem simplistic, but they DO most definitely impact your online conversion rate.

OK, so you’ve won half the battle. You’re driving traffic to your site. Now what? How can you get your visitors to convert?

This is a challenge that most every website that sells a product faces. The following are some tried and true tactics that, over the years, I’ve seen make a difference. Some may seem simplistic, but they DO most definitely impact your online conversion rate.

Here are a few things you could do to boost online sales and gain loyal customers. These can be applied and refined for most any business, industry or niche:

1. Make Sure Your SSL Seal And Other Consumer-Trust Logos Are Prominent. SSL or secure socket layer is a sign that the site is encrypted … that the information consumers enter, such as personal and credit card information, is protected. Most e-commerce sites must file for an SSL certificate from vendors such as VeriSign, GoDaddy, eTrust, TRUSTe and others. It’s a good practice to display the vendor’s logo on your order page, as well as make sure in the browser window the “https” or image of a lock is present. This is a clear and comforting sign to consumers that they can order online with confidence. Other logos that are in plain view and are anchors on each page of your website can also instill confidence with potential buyers. Some may require membership or purchase, when applicable, and may include Better Business Bureau (“BBB”), PayPal Verified, Authorize.net Verified Merchant and virus protection software (i.e. “McAfee Secure”). Also, if you accept credit cards and have a money back guarantee, there’s nothing more powerful than strong, eye-catching graphic image icons, such as “100% Money Back Guarantee” or “We Accept All Major Credit Cards” (than have images of Visa, Mastercard, Amex and Discover).

2. Encourage Online Sales vs. Other Response Mechanisms. Offer special “Internet Only Pricing” to customers. It could be a discount of 5 percent to 10 percent if they order online versus by phone, fax or mail. This reduces any potential overhead costs for staffing fees, such as telesales or order entry personnel. These Web-only specials can be highlighted on your homepage via a banner ad, as well as on your product pages near qualified items.

3. Offer Free Shipping. Many e-tailers already factor all or a portion of shipping into the retail price of an item as part of their COGS (cost of goods sold). If you are truly offering free shipping, already factored shipping into the product’s cost, or are simply having a limited time free shipping special—if you’re offering it, mention it—big and bold on your home page. Free shipping offers have a huge psychological affect on consumers when they’re comparing competitor’s products and websites. In addition to product quality and value, offering free shipping can make all the difference regarding the final purchase decision.

4. Use Buyer Feedback To Your Advantage. Have an area on your website or indicate next to select items “Customer Favorite” or “Hot Item.” Also, have some glowing customer testimonials or reviews next to the product itself for potential prospects to see. Sites like Amazon, Babies”R”Us and others are pros at this strategy as well as using ratings and ‘Likes’ to convey a product’s popularity. Consumers like to feel good about the item they are about to purchase. as well as see that it’s popular with the masses. Seeing a great testimonial and knowing that others purchased the product provides validation and a feeling of comfort to a consumer. In addition to helping the conversion rate, this tactic also helps reduce buyer’s remorse and product returns.

5. Advertise Products in Google Shopping (formerly Google Product Search, and before that, Froogle). http://www.google.com/shopping is a free product information platform from Google where you can post a single item or submit a data feed. Your products will appear in Google Product search and may also appear in Google.com search results, depending on keywords used. This is simple and easy way to increase your product’s visibility and market share.

6. Make Sure Your Product Pages are Optimized for Search Engines. Sounds obvious, but many folks overlook their catalog and product pages. After doing some keyword research on actual search behavior for your product, refine your meta description, meta keywords and title tag of your product pages. This will help consumers find your product in the organic listing of search engine results.

7. Have a Special Coupon Code “Call Out” On Your Home Page. This is a best practice with online fashion retailers who typically have a banner ad or interstitial ad on their homepage stating something like, “Summer Blow Out Sale, Use Coupon Code 1234.” But this concept can be applied to virtually any industry. This is another great way to offer a special discount for your online customers that makes them feel good about the purchase. You can also encourage viral activity by having “forward to friend” or “share” create viral marketing. Make sure to have some great intro copy mentioning how customers should “pass on the great savings to friends, family and colleagues.”

8. Consider Payment Plans. For higher-ticket items, consider setting up extended payment plans that allow customers to pay for an item over a few payments. HSN.com and QVC.com have mastered this. If an item is, let’s say, $200, you might want to offer a flex pay option of “6 easy payments of $33.33” that is conveniently auto-billed to their credit card. Just be diligent when calculating your payment prices, as well as creating your return/refund policy for these items. The general rule is that your actual production costs/hard costs should be covered in the first one to three payments.

It’s all about being strategically creative and taking the consumer’s point of view into account regarding e-comm strategies. Remember to keep testing methods that help improve sales and drive prospects to your storefront.

Make note of when you implement new tactics and then after a month of being live. Compare sales results year-over-year to see if your efforts had made an improvement. I’m confident that you will see a positive difference in your online conversion rates.

There’s an Ad for That

As the expression “there’s an app for that” reaches its cultural saturation point, advertisers need to gain a clear understanding of the differences between mobile web and in-app advertising, as well as the importance of context when setting performance expectations.

As the expression “there’s an app for that” reaches its cultural saturation point, advertisers need to gain a clear understanding of the differences between mobile web and in-app advertising, as well as the importance of context when setting performance expectations.

According to eMarketer, mobile ad spending in messaging, display, video and search is expected for the first time to top $1 billion in the U.S. this year, showing the highly fractured nature of the mobile ad market. Research from several mobile ad network providers shows the difference in performance between approaches and resulting user behaviors, with expanding ads performing extremely poorly in terms of clickthroughs versus simple animated banner or video ads. Adding to the challenge of choosing the right approach and setting expectations of performance is the sheer number of ad formats and networks available.

Consider Context
Don’t just think about how and when users are exposed to ads on their phones, but also where they are and what they’re doing at the time. This establishes a complete picture of the context for the ad. Some formats don’t make sense in a broad variety of contexts, therefore a critical consideration would be to ensure that whatever network you’re using offers this type of contextual placement in addition to other targeting options.

There are real differences when considering advertising in apps vs. mobile websites. While casual web surfing on a mobile or tablet device would support the use of display ads to reach an audience, in-app behavior is distinctly different from surfing. This means that even if in-app advertising is available, you need to carefully consider its effectiveness during real-world app usage and the overall impression it would give users encountering it in a particular context.

Consider the following: Do mobile users really need or want a banner ad consuming valuable screen space in the apps they frequent most? It’s this total picture of context that should be the driving consideration for design, placement and expectations of performance. Even if ads aren’t currently available in that location, the ability to leverage background application processing or emerging geo-fencing options allows marketers to take advantage of what would normally be a missed messaging opportunity.

Let’s consider in-ad gaming for mobile, specifically ads during active gameplay. Even at a load screen, would you really expect an ad to drive a clickthrough? Would it do anything but generate an ad impression? As a gamer, I’m not likely to click if I’m stealing a few minutes during the day for a casual gaming session to relax before resuming my day. However, seeing that ad still works for branding purposes as past data suggests.

Mobile is Actually Local
The reality is that the mobile device is inherently local, which needs to factor prominently into planning a mobile campaign. While mobile users are unlikely to be surfing and clicking on banners while walking within the proximity of a nearby coffee shop, you can use technologies such as geo-fencing and background application processing on mobile devices to offer them $1 off an oh-so-satisfying latte. This example makes a strong case for carefully considering branding versus direct response versus promotional programs. It definitely reinforces the importance of context.

Where this gets even more interesting for advertisers is in the ability to exchange data and share interaction points for local, geo-targeted ad or promotional models. If a loyalty or transaction app is already installed on a consumer’s phone, and it enables proximity notifications through access to the device’s location, a retailer can let five other retailers within walking distance leverage this trusted channel to provide truly localized messaging opportunities at a premium.

They can even support a performance-based model, which could accurately determine if the consumer subsequently walked into the establishment. This is all no more complex than any self-service ad model in place today, with legal and privacy concerns addressed via proper disclosures and notifications during installation and/or activation of the app.

Display advertising on mobile obviously isn’t going away. The sooner you realize that it’s not the web as you know it today, stop trying to force current ad models into current mobile platforms, and that context is key, the sooner you’ll be able to generate not only results you can brag about, but returns clients can truly appreciate.

Random Acts of Appreciation

So, will performing random acts of appreciation for your customers make a difference? Absolutely.

Rather than focusing on “the next big thing,” I decided to keep in the spirit of the season and celebrate the little things.

A few weeks ago, I found an unexpected package on my doorstep. It was from an online retailer I shop frequently with. Inside was a lovely, living holiday centerpiece and a note of thanks. While uncommon, gestures like this aren’t unheard of. For example, Starbucks is well known for surprising its Gold Card members with coupons for free beverages. One Starbucks fan blogged, “I have never figured out a rhyme or reason to how Starbucks distributes coupons.”

The difference between knowing that every 15 purchases gets you a free latte versus getting a freebie you didn’t expect is the difference between a transactional and an emotional relationship. A points program is purely a business exchange. The fact that you “earn” rewards clearly indicates this process is a task.

That’s not to say that traditional frequency reward programs aren’t effective. But these programs have turned into a cost of entry for marketers in countless industries. In fact, the average U.S. household belongs to 14 loyalty programs. While they may be popular, points programs are hardly differentiators.

So, will performing random acts of appreciation for your customers make a difference? Absolutely.

Robert W. Palmatier, associate professor of marketing at the University of Washington, studies this effect in his article, The Role of Customer Gratitude in Relationship Marketing. He found that these incremental and unexpected efforts result in feelings of gratitude which, in turn, positively impact purchase intentions. The word gratitude sums it up beautifully.

Ready to give it a go? Here are three rules to keep in mind:

1. It comes out of the blue. The element of surprise creates impact. That’s why they call it “surprise and delight.”

2. It’s about them, not you. A discount or free item is always appreciated, but it should be something your customers really want, not something you need to promote or unload. If you want to send a “gift,” avoid anything emblazoned with your company’s logo.

3. Focus on your best customers. Sounds like common sense, right? Maybe not. For example, if the surprise is a product discount and there’s little to no cost for you to distribute it, you may be inclined to make it available to every customer. In this case, resist the urge. If everyone is special, then no one is.

I never did make use of my centerpiece. Unfortunately, it arrived just as we were going out of town for the week. But the gesture will be remembered and, as they say, it’s the thought that counts. May your new year be brightened by random acts of appreciation.