Omnichannel Customers Are 2X as Valuable – How to Make Them Yours

With so many trying to sort out an “omnichannel” marketing strategy, I thought it would make the most sense this month to provide some structure around what it is, the best way to take the “buzz” out of the term, and provide a framework for thinking strategically about this new mandate in marketing and strategy. For starters, here’s a simple idea, or “true north,” you can use to drive your own marketing strategy as you embrace the omnichannel consumer. “Put the Customer First” and build your “omnichannel strategy” around them.

With so many trying to sort out an “omnichannel” marketing strategy, I thought it would make the most sense this month to provide some structure around what it is, the best way to take the “buzz” out of the term, and provide a framework for thinking strategically about this new mandate in marketing and strategy.

For starters, here’s a simple idea, or “true north,” you can use to drive your own marketing strategy as you embrace the omnichannel consumer. “Put the Customer First” and build your “omnichannel strategy” around them.

Let’s remember, connecting with, engaging and finding the right new customers are where customer value is created and realized in omnichannel marketing. Optimizing that value comes through studying and tuning communications, improving your relevance and becoming more creatively authentic, not in the boardroom, but in the eyes of your customer.

Today, marketers appreciate that consumers engage on multiple platforms, devices and channels—the ones they want, when they want. With mobile devices being a spontaneous window into their thoughts and an outlet for their wants and needs as they arise. What’s a bit more subtle and more often missed is the objective and capability to respect the way your customers choose to engage and buy across them in a scalable manner—as it will either fragment their relationship with your brand or galvanize it.

Consider Kohls. Not exactly a high tech player in most folks’ minds. However they now deliver an omnichannel experience that deepens relationships with them. Recently, my wife received a promotion by direct mail (I doubt if she remembers when they asked for her phone number the first time, making the connection between the POS and her online purchases), she had it in hand as she went to the website to browse. Later, she used another promotion from her email right at the POS with her iPhone.

In a single engagement with the brand, she hopped across three channels, not including a customer service call by phone. As a consumer, she didn’t even notice—she just expected it to work.

Similarly, OpenTable will consistently get you to a good restaurant based on where you’ve dined before, and what your current online browsing and mobile location is. You probably do it all the time. Your relationship with that brand hops between mobile, desktop and point of sale effortlessly—but as a consumer, you’re not exactly impressed: You expect it to work.

As a result, effective omnichannel organizations have become “stitched into” the lifestyles of their customers. Moreover, this supports the creation of competitive advantage in the measurable, trackable, digital age.

Omnichannel Means Understanding the Customer
Putting the customer first obviates really knowing and understanding your customer in more meaningful and actionable ways. Not just with an anecdote of the “average customer,” but with legitimate, fact-based methods that are built on a statistical and logical foundation. This is the basis for the “absolute truth” that your omnichannel source is dependent on.

This, too, is no small task for many organizations, but it’s becoming more “doable.” And it has to be—because your competition is thinking and investing in this path, and it’s not a long-term, viable position to not have an actionable strategy to miss the boat on knowing your customer in a way that is valuable, actionable and profitable.

But first, let’s clear up some of the confusion that we’ve been hearing for at least a year now: Is omnichannel more than the buzzword of 2015, or is it something much more important?

Multichannel
At the most basic level, “multi” means many. As soon as you adopted your second or third channel, be it a catalog or an e-commerce website, your organization became a multichannel organization. Multichannel came quickly—as it’s not uncommon that the majority of a customer base has made a purchase across more than one channel—whether you have that resolution or not is another matter, and often requires a smarter approach to collection.

Digital growth is accelerating channel expansion. With the explosion of online and digital channels and the rapid adoption of mobile smartphones, tablets and now wearables, digital can no longer be viewed as a single channel. We now have the merging and proliferation of digital, physical and traditional channels.

Many marketers have experienced as much challenge in juggling an increasing number of channels as there is opportunity. But digital channels, of course, are more measurable and challenge the traditional approaches by bringing a greater resolution and visibility for some, and confusion for others.

Key factors in leveraging, managing, and maximizing those channels include:

  • Competencies developed in the organization
  • Identifying third-party competencies, especially in digital partnerships
  • The culture of the organization
  • Support for change and innovation in marketing
  • The depth of technical capability in an organization

As channel usage expands, data assets “pile up,” though most of the data in its raw format is of limited practical use and less actionable as one would hope. From the inside of dozens of IT organizations, the refrain is common; “We’re just capturing everything right now.” Creating marketing value would require strategists and the business units.

Omnichannel Is the Way Forward
While most organizations are still working through mastering their channels and the data they perpetually generate, the next wave of both competitive advantage and threats have come with them. The customer learns what works for them relatively quickly and easily, adopting new channels and buying where they want, how they want. Those touches are often lower touch, and introduce intermediaries, and are surrounded by contextual advertising, often from competitors.

Omnichannel buyers aren’t just more complex, they are substantially more valuable. We’ve seen them be as much as twice as valuable as those whose relationship is on a single channel. Perhaps this a reflection of the greater engagement with the brand.

Delivering that omnichannel experience will require more thought, focus and expertise than before. It requires the integration of systems, apps and experiences in a way that’s meaningful—to the customer—and that of course requires an integration of the data about those purchases and experiences.

To serve the business, the Omnichannel Readiness Process has six components, each of which require thoughtful consideration:

1. Capture—many organizations are aware that they need to capture “the data.” The challenge here is shifting to what to capture, and what they may be missing. The key challenge is: It’s impossible to capture “everything” without understanding how it can and should be used and leveraged. How that data is captured in terms of format and organization is of great importance.

2. Consolidate—In order to act on the omnichannel reality, we must have all our data in one place. In the ongoing effort to find the balance between cost, speed and value, “silos” have been built to house various data components. Those data sources must be consolidated through a process that is not quite trivial if those data sources are to create value in the customer experience and over the customer lifetime.

3. Enhance—Even after we’ve pulled our data together into an intelligent framework and model, built to support the business needs, virtually every marketer is missing data that consumers generally don’t provide, or don’t provide reliably on a self-reported basis. “Completing the customer record” requires planning and investing in appropriate third-party data. This will be a requirement if we’re to utilize tools and technology to mine for opportunity in our customer base.

4. Transform—much of the data we need to perform the kinds of analysis and create the kinds of communication that maximize response now, and the customer value over time, utilizes the derivation of new data points from the data you already have. Here is one example: Inter-order purchase time. Calculating the number of days between purchases for every customer in your base allows you to see whose purchase cadences are similar, faster, slower or in decline. On average, we’ll derive hundreds of such fields. This is one example of how a marketer can “mine” data for evidence of opportunity worth acting on and investing in.

5. Summarize—The richest view of a customer with the best data in its most complete state is a lot to digest. So to help make it actionable, we must roll it up into logical and valuable cohorts and components. Call them what you will—segments, personas or models—they are derivative groups that have value and potential that you can act on and learn from.

Many marketers traditionally spend 80 percent to 90 percent of their time and effort on getting their data to a point where it serves both the omnichannel customer and their brand. However, marketers can do better with emerging tools and technologies.There is no replacement for solid data strategy that is built around the customer, but efficiencies can be gained that speed time-to-value in an omnichannel environment.

6. Communicate—The prep work has been done, you’ve found the pockets of opportunity, now it’s time to deliver on the expectations the omnichannel customer holds for marketers. At this juncture, we need to quickly craft and deploy messages that resonate in ways consumers will think about their situation and your brand. They must address the concerns they have and the desires and opportunities they tend to perceive.

Omnichannel customers expect you will recognize them for their loyalty and their engagement with your brand at multiple levels, and that those experiences will be tailored in small ways that can make a bigger difference.

They expect your story to better-fit with their own, if not complete it. That sounds like a dramatic promise, but the ability to know your customers and engage them in the way they prefer, and at scale, is upon us.

Keep It Relevant to Your Business
This entire process must include of course, the answers to key business questions about the types of discoveries we’d make and questions we’d answer with it—for example, does the Web cannibalize our traditional channels? (Hint: It surely doesn’t have to).

That said, we’ve learned to start with the most basic questions—and are not surprised when there are no robust answers:

1. How many customers do you have today?

2. Do you have a working definition of a High Value or Most Valuable Customer?

3. If so, how many of those customers do you have?

4. How many customers did you gain this past quarter? How many did you lose?

a. Assuming you know how many you lost, what was the working definition of a lost customer?

5. How many customers have bought more than once?

6. What’s the value of your “average” customer, understanding that averages are misleading and synthetic numbers are not to be trusted? But we can measure where other customers are in terms of their distance from the mean.

7. Who paid full price? Who bought at discount? Who did both? How many of all the above?

8. For those who bought “down-market,” did they trade up?

9. How many times does a customer or logical customer group (let’s call them “segments,” for now) buy? How long, on average, is it between their purchases? And the order sizes, all channels included?

10. All this, of course, gets back to understanding more deeply, “Who is your customer?” While all this information about how they engage and buy from us is powerful, how old are they? Where are they from? What is relevant to them?

Now, even if a marketer could get the answers to all of these questions, how does this relate to this “Omnichannel” Evolution?

Simple. It only relates to your customer. Of course, they are the most important actors in this business of marketing—in fact in the business of business. What this really means is deceptively simple, often overlooked, and awesomely powerful:

Omnichannel Is Singularly Focused on Customers, Not Channels
It’s about the customer, and having the resources, data and insights at your disposal to serve that customer better. Virtually all of your customers are “multichannel” already. Granted, some are more dominantly influenced by a single channel. For example, online through the voice of the “crowd.” But even then, the point of omnichannel only means one thing: Know your customers across all the channels on which they engage with you. Note the chasm between having the dexterity to examine and serve customers across all the channels, and just knowing their transactions, behaviors or directional, qualitative descriptors.

So “knowing the customer” really means having ready access to actionable customer data. Think about it. If your understanding of your customer data isn’t actionable, how well do you really know your customer in the first place?

Considering the 10 questions above, and evaluating the answers in terms of the most important questions about your customers, is a solid starting point.

When you’ve worked through all of these, you’re now ready to create experiences and communications for customers that are not only relevant, but valuable—to your customer and to the business.

When you’re adding value and are channel-agnostic, as you must become, you’ve achieved the coveted omnichannel distinction that market leaders are bringing to bear already.

Not only is this an impressive accomplishment professionally, it surely is—but remember—it’s the customer we have to impress.

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.