The Keyword in ‘Customer Journey’ Is ‘Customer’

The keyword in “Customer Journey” is “customer,” not “journey.” In fact, in this Omni-channel world, the word “journey” doesn’t even do much justice to what that journey study should be all about; there is no simple linear timeline about any of it anymore.

The keyword in “Customer Journey” is “customer,” not “journey.” In fact, in this omnichannel world, the word “journey” doesn’t even do much justice to what that journey study should be all about; there is no simple linear timeline about any of it anymore.

We often think about the customer journey in this fashion: awareness, research, engagement, transaction, feedback and, ever-important, repeat-purchase. This list is indeed a good start.

However, if you look at this list as a consumer, not as a marketer, do you personally go through all of these steps in this particular order? On a conceptual level, yes, but in the world where everyone is exposed to over five types of screens and interactive devices every day, old-fashioned frameworks based on linear timelines don’t always hold water.

I, as a consumer, often do research using my phone at the place of purchase. I may feel rewarded even before any actual purchase. I may provide feedback about my “experience” before, during or after a transaction. And being a human being with emotions, my negative feedback may not be directly correlated to my loyalty to the brand. (Actually, I am writing this piece while flying on an airline with which I have a premiere status, and to which I often provide extremely negative reviews.)

People are neither linear nor consistent. Especially when we are connected to devices with which we research, interact, transact and complain anytime, anywhere. The only part that is somewhat linear is when we put something in the shopping basket, make a purchase, and keep or return the item. So, this timeline view, in my opinion, is just a good guideline. We need to look at the customer journey from the customer’s angle, as well.

Understanding customer behavior is indeed a tricky business, as it requires multiple types of data. If we simplify it, we may put the key variables into three major categories. For a 3-dimensioal view (as I often do in a discussion), put your left hand out and assign each of the following dimensions to your thumb, index finger and the middle finger:

  • Behavioral Data: What they showed interest in, browsed, researched, purchased, returned, subscribed to, etc. In short, what they actually did.
  • Demographic Data: What they look like, in terms of demographic and geo-demographic data, such as their age, gender, marital status, income, family composition, type of residence, lifestyle, etc.
  • Attitudinal Data: Their general outlook on life, religious or political beliefs, priorities in life, reasons why they like certain things, purchase habits, etc.

One may say these data types are highly correlated to each other, and more often than not, they are indeed highly correlated. But not exactly so, and not all the time. Just because one keeps purchasing luxury items or spending time and money on expensive activities, and he is enjoying a middle-age life style living in posh neighborhood, we can’t definitely claim that he is politically conservative. Sometimes we just have to stop and ask the person.

On top of that, what people say they do and what they actually do are often not the same. Hence, these three independent axis of data types to describe a person.

If we have all three types of data about a person, prediction of that person’s intention — or his journey for commercial purposes or otherwise — will become incredibly accurate. But, unfortunately for marketers, asking “everyone about everything” simply isn’t feasible.

Even the most thorough survey is based on a relatively small sample response. One great thing about traditional primary research is that we often get to know who the respondents are. On the other hand, if we rely on social media to “listen,” we get to have opinions from far more people. But the tricky part there is that we don’t get to know who is speaking, as PII (personally identifiable information) is heavily guarded by the social media handlers. Basically, it isn’t easy to connect the dots completely when it comes to attitudinal data. (Conversely, connecting the dots between the behavioral data and demographic data is much simpler, provided with a decent data collection mechanism.)

Now let’s go back to the timeline view of the customer journey for an initial framework. Let’s list the key items in a general order for a simpler breakdown (though things may not be totally linear nowadays), and examine types of data available in each stage. The goal here is to find the point of entry for this difficult task of understanding the “end-to-end” customer journey in the most comprehensive way.

Listing typical data types associated with these entries:

  1. Awareness: Source (where from), likes/followings, clicks other digital trails, survey results, social media data, etc.
  2. Research: Browsing data, search words/search results, browsing length, page/item views, chats, etc.
  3. Engagement: Shopping basket data, clicks, chats, sales engagements, other physical trails at stores, etc.
  4. Transaction: Product/service (items purchased), transaction date, transaction amount, delivery date, transaction channel, payment method, region/store, discounts, renewals, cancelations, etc.
  5. Feedback: Returns, complaints and resolutions, surveys, social media data, net promotor score, etc.
  6. Repeat-purchase: Transaction data summarized on a customer level. The best indicator of loyalty.

Now, looking back at the three major types of data, let’s examine these data related to journey stages in terms of the following criteria:

  • Quality: Are data useful for explaining customer behaviors and predicting their next moves and future values? To explain their motives?
  • Availability: Do you have access to the data? Are they readily available in usable forms?
  • Coverage: Do you have the data just for some customers, or for the most of them?
  • Consistency: Do you get to access the data at all times, or just once in a while? Are they in consist forms? Are they consistently accurate?
  • Connectivity: Can you connect available data on a customer level? Or are they locked in silos? Do you have the match-key that connects customer data regardless of the data sources?

With these criteria, the Ground Zero of the most useful source in terms of understanding customers is transaction data. They are usually in the most usable formats, as they are mostly numbers and figures around the product data of your business. Sometimes, you may not get to know “who” made the purchase, but in comparison to other data types, hands-down, transaction data will tell you the most compelling stories about your customers. You’ll have to tweak and twist them to derive useful insights, but the field of analytics has been evolving around such data all along.

If you want to dig deeper into the “why” part of the equation (or other qualitative elements), you would need to venture into non-transactional, more attitudinal data. For the study of online journey toward conversion, digital analytics is undoubtedly in its mature stage, though it only covers online behaviors. Nonetheless, if you really want to understand customers, start with what they actually purchased, and then expand the study from there.

We rarely get to have access to all of the behavioral, demographic, and attitudinal data. And under those categories, we can think of a long list of subcategories, as well. Cross all of that with the timeline of the journey — even a rudimentary one — and having readily usable data from all three angles at all stages is indeed a rare event.

But that has been true for all ages of database marketing. Yes, those three key elements may move independently, but what if we only get to have one or two out of the three elements? Even if we do not have attitudinal data for a customer’s true motivation of engagement, the other two types of data — behavioral, which is mostly transaction and digital data, and demographic data, which can be purchased in large markets like the U.S. — can provide at least directional guidance.

How do you think the political parties target donors during election cycles? They at times have empirical data about someone’s political allegiance, but many times they “guess” using behavioral and demographic data along with modeling techniques, without really “asking” everyone.

Conversely, if you get to have access to attitudinal data of “some” people with known identities, we can build models to project such valuable information to the general population, only using a “common” set of variables (mostly demographic data). For instance, we may only get a few thousand respondents revealing their sentiment toward a brand or specific stances (for example, being a “green” conscience customer). We can use common demographic variables to project such a tendency to everyone. Would such a “bridging technique” be perfect? Like I mentioned in the beginning, no, not always. Will having such inferred information be much better than not knowing anything at all? Absolutely.

Without a doubt, understanding the customer journey is an important part of marketing. How else would you keep them engaged at all stages of purchases, leading them to loyalty?

The key is not to lose focus on the customer-centric side of analytics. Customer journey isn’t even perfectly sequential anymore. It should be more about “customer experience” regardless of the timeline. And to get to that level of constant relevancy, start with the known customer behaviors, and explain away “what works” in all channel engagements for each stage.

Channel or stage-oriented studies have their merits, but they won’t lead marketers to a more holistic view of customers. After all, high levels of awareness and ample clicks are just good indicators of future conversions; they do not instantly guarantee loyalty or profitability. Transaction data tend to reveal more stable paths to longevity of customer relationship.

You may never get to have explicit measurements of loyalty consistently; but luckily for us, customers vote with their money. Unlock the transaction data first, and then steadily peel away to the “why” part.

I am not claiming that you will obtain the answer to the “causality” question with just behavioral data; but for marketing purposes, I’d settle for “highly correlated” elements anytime. Because marketing activities can happen successfully without pondering upon the “why” question, if actionable shortcuts to loyalty are revealed through sold transaction data.

‘Killing Marketing’ to Save It

The book “Killing Marketing,” the latest from Joe Pulizzi and Robert Rose, says this: “We must kill marketing that makes a living from accessing audiences for short bursts of time so they might buy our product.”

Millennial marketing
“BMXr’s,” Creative Commons license. | Credit: Flickr by micadew

The book “Killing Marketing,” the latest from Joe Pulizzi and Robert Rose, says this: “We must kill marketing that makes a living from accessing audiences for short bursts of time so they might buy our product.”

It continues: “We must rebirth a new marketing that makes its living from building audiences for long periods of time, so that we might hold their attention through experiences that place us squarely in the initial consideration set when they are looking for a solution.

“This is the marketing of the future. It is achieving a long-term return on the one asset that will save our business: an audience.”

The book is wonderful — I highly recommend it. It’s chock-full of ideas about how to transform the marketing department from a cost center to a profit center. It details multiple ways to pull direct and indirect revenue from marketing, once true engagement with an audience has been established. In their words, it will transform your marketing into something more powerful than “the art of finding clever ways to dispose of what you make.”

But specific to the selection quoted above, for me it’s another spark of thought about the downside of personas based on demographics.

If you’re personas are demographic- lead rather than interest-led, then you’re setting yourself up for selling in short bursts of time. You’re not going to be able to establish a long-term relationship with an audience based on who they are and what they truly care about — because you simply won’t know what those things are. And you won’t create experiences that hold an audience’s attention for future consideration.

To truly build audiences for long periods of time, we need to start with interests and preferences rather than demographics.

To employ a far overused example …

Red Bull doesn’t define its audience as “Millennial males who want an energy drink.” The brand understands its audience by defining all of the facets of interests in a lifestyle of adventure — from edge (extreme) sports to music to fashion to travel and so on. And then Red Bull provides that audience with access to that lifestyle, through publications, events, social media content and more … and it sells some energy drinks, as well.

If Red Bull did the former (define a demographic), it would’ve been able to effectively place an ad for an energy drink on channels where Millennial males might be. And the brand would’ve sold some drinks, and perhaps captured some people who would continue to buy Red Bull through the years. But the brand affinity it would’ve created would’ve be thin, at best. And it’d be in a constant cycle of reloading short-term audiences. That’s a losing game.

Instead, Red Bull tilted toward the latter — personas based on interests. But … how did that happen?

Maybe the brand started with an idea like: “We see opportunity to engage the ‘extreme sports lifestyle audience regardless of age, location, etc.’ in a whole new, deeper way.” Or, perhaps Red Bull carefully observed its initial audience — the short-term customer audience it had when it first went to market with the drink — and asked questions like:

  • We see Millennial males are a big part of our initial audience, but what’s behind the demographic?
  • What commonalities does that portion of the audience share with the rest of the audience?
  • What is it that our audience — in aggregate — is telling us they care about most?
  • What information are they craving most?
  • And is anyone else providing that information? Access?

I wasn’t there, so I don’t know. And most of the stories we hear about Red Bull’s content marketing successes don’t focus on the starting point of audience understanding. But I imagine it was more along the lines of not resting on an initial, demographic-lead audience understanding. I imagine the brand had a short-term audience, but decided it didn’t want to have to constantly reload. Good for Red Bull!

Smart marketers will take note and do the same. They’ll dig deep. They won’t rest on the easy, starting answer. They’ll get past the simple, demographic personas, and they’ll start thinking about interests that transcend demographic as the path to building a long-term, engaged audience.

In short: Demographic-led personas lead to decent targeting and short-term sales. Simple ROI. Interest-led personas lead to engagement and brand affinity for the long-term: Simple ROI plus customer lifetime value.

Data Atrophy

Not all data are created equal. There are one-dimensional demographic and firmographic data, then there are more colorful behavioral data. The former is about how the targets look, and the latter is more about what they do, like what they click, browse, purchase and say.

Not all data are created equal. There are one-dimensional demographic and firmographic data, then there are more colorful behavioral data. The former is about how the targets look, and the latter is more about what they do, like what they click, browse, purchase and say. On top of these, if we are lucky, we may have access to attitudinal data, which are about what the target is thinking about. If we get to have all three types of data about the customers and prospects, prediction business will definitely get to the next level (refer to “Big Data Must Get Smaller”). But the reality is that it is very difficult to know everything about anyone, and that is why analytics is really about making the best of what we know. Predictive modeling is useful not only because it predicts the future, but also fills gaps in data. And even in the age of abundant data, there are many holes, as we will never have a complete set of information (refer to “Why Model?”).

Among these data types, some are more useful for prediction than others. Behavioral data definitely possess more predictive power than simple demographic data for sure. But alas, they are harder to come by. It could be that the target is new to the environment, so she may not have left much data behind at all. May be she just looked around and didn’t buy anything yet. Or she is very privacy-conscious and diligent about erasing her behavioral trails on the net or otherwise. Maybe she explicitly opted out of being traced at all, giving up much of the convenience factors of being known by the merchants. Then the data coverage comes into the equation, and that is why analysts rely on demographic and geo-demographic data for their readily available nature. Much of such data can easily be purchased and appended on a household or individual level, at least in the U.S. If we get to have some hint of identity of the target, there are ways to merge disparate data sets together.

What if we don’t get to know who are leaving data trails? Again, it could be about the privacy concerns of the target, or the manner by which the data are collected. Some data collectors avoid personally identifiable information, such as name, address or email, as they do not want to be seen as the Big Brother. Even if collectors get to have access to such PII, they do not share it with outsiders, to maintain dominance and to avoid the data privacy issue altogether. And there are many instances where that “who” part is completely out of reach. Movement data would be an example of that.

Weaving multiple types of data together is often the main source of trouble when it comes to predictive analytics. I have been talking about the importance of a 360-degree view of a customer for proper personalization and attribution, but the main show-stopper there is often the inability to merge data sources with confidence, not the lack of technology or statistical skills. That would be the horizontal challenge when dealing with multiple types of data.

Then there is the time factor. Like living organisms, data get old and wither away, too. Let’s call it the “data atrophy” challenge. Data players must be mindful about it, as outdated information is often worse than not having any at all for the decision-making or prediction business.

Now, not all data types deteriorate at the same rate. The shelf-life of demographic data are far longer than that of behavioral data. For example, people’s income levels or housing size do not change overnight, while usefulness of what we call “hotline” data evaporates much faster. If you get to know that someone is searching for a new car, how long will he be in the market? What if it is about a ticket or pay-per-view purchase for tonight’s ball game? Data that is extremely valuable this minute could be totally irrelevant within the next hour.

No One Is One-Dimensional

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.”

If anyone says to your face “You’re one-dimensional,” you would be rightfully offended by such statement. It would almost sound like “You are so simple that I just figured you out.” Along with that line of thinking, you should be mad at most marketers, as they treat consumers as one-dimensional subjects. Even advanced marketers who claim that they pursue personalized marketing routinely treat customers as if they belong to “1” segment along with millions of other people. Sort of like drones with similar characteristics. Some may title such segments with other names, like “clusters” or “cohorts.” But no matter. That is how personalization works most times, and that is why most consumers are not impressed with so-called personalized messages.

Here is how segments are built through cluster analysis. Unlike regression models, clusters are built without clear “target” (or dependent) variables (refer to “Data Deep Dive: The Art of Targeting”). Considering all available variables, statisticians group the universe with commonly shared characteristics. A common analogy is that they throw spaghetti noodles on the wall, and see which ones stick together. Analysts can control the number of segments and closeness (or “stickiness”) of resultant groups. I have seen major banks grouping their customers into six to seven major segments. Most commercial clustering products by data compilers maintain 50 to 60 segments or cohorts (I am not going to name names here, but I am sure you have heard of most of them). I was personally involved in a project where we divided every town in the U.S. into 108 distinctive clusters using consumer, business and geo-demographic variables. The number of segments may vary greatly, depending on the purpose.

Once distinctive segments are created through a mathematical process, then the real fun begins. The creators get to describe characteristics of each segment in plain English, and group smaller segments into higher-level “super” clusters. Some creative companies name each cluster with whimsical titles or dominant first names of each cluster (for copyright reasons, I wouldn’t use actual names, but again, I’m sure marketers have heard about them). To identify dominating characteristics of people within each cluster, analysts use various measurements to compare them against the whole universe. For instance, if a cluster shows an above-average index of post-college graduates, then they may call it “highly educated.” If analysts see a high index-value of luxury car owners, then they may label the whole cluster with some luxurious-sounding name.

Segmentation is an age-old technique and, of course, it still has its place in marketing. Let me make it clear that using segments for target marketing is much better than not using anything at all. It also provides a common language among various players in marketing, binding clients and vendors together. Marketing agencies, who cannot realistically create an unlimited number of copies, may prepare a set number of creatives for major segments that their clients are targeting. With descriptions of segments in front of them, copywriters may write as if they are talking to the target directly. Surely, writing copy for a “Family-oriented young couple with dual income” would be easier than doing so for some anonymous target.

However, the trouble begins when marketers start using such a “descriptive” tool for targeting purposes. Just because there is a higher-than-average index value of a certain characteristic in a segment, is it justified to treat thousands, or sometimes millions, of people in the target group the same way? Surely, not everyone in the “luxury” segment is about luxury automobiles or vacations. It is just that the cluster that someone happened to have belonged through some statistical process has a higher-than-average concentration of such folks.

Then how do we overcome such shortcomings of a popular method? I suggest we reverse the way we look at the behavioral indices completely. The traditional method defines the clusters first, and then the analysts put descriptions looking at various behavioral and demographic indices. For promotions for specific products or services, they may examine more than 50, sometimes more than a few hundred index values. Only to label everyone in a segment the same way.

Instead, for targeting and personalization, marketers should commission independent models for every type of behavioral or demographic characteristic that may matter for their campaigns. So, instead of using one “luxury segment,” we should build multiple models. For example, for a travel industry like airlines or cruise lines, we may consider the following series of model-based “personas”:

  • Foreign vacationers
  • Luxury vacationers
  • Frequent business travelers
  • Frequent flyers
  • Budget-conscious travelers
  • Family vacationers
  • Travelers with young children
  • Frequent theme park visitors
  • Bargain-seekers
  • Adventure-seekers
  • Wine enthusiasts
  • Gourmets
  • Brand-loyal travelers
  • Point collectors
  • etc.

This way, we can describe “everyone” in the target universe in a multi-dimensional way. Surely, not everyone is about everything. That is why we need a system under which one person may score high in multiple categories at the same time. We all have tendencies to be bargain seekers, but everyone has a different threshold for it (i.e., what length of trouble would you go through for a 10 percent discount?). If you have multiple descriptors for everybody, you can find the most dominant characteristics for one person at a time. Yes, one may have high scores in “luxury vacationers,” “frequent flyers” and “frequent business travelers” models, but which characteristic has the highest score for “him”?

Imagine having assigned scores for these “personas” for everyone. I may score nine out of nine in “frequent flyer” (and that is for certain, as I am writing this on a plane again), score six out of nine in “luxury vacation,” and score two out of nine in “family vacationers” (as my kids are not young anymore). If you have one chance to show me something that resonates with me this second, what would be the offer? Even a machine can decide the outcome with a scoring system like this. Now imagine doing it for millions of people, all customized.

Last month, I wrote that personalization is not an option anymore, and further, marketers should aspire to personalize their messages for most people, most times, through all channels, instead of personalizing only for some people sometimes through some channels (refer to “Road to Personalization”). Because “personas” based on statistical models will not have any missing values, we can achieve that ambitious goal with this technique.

With new modeling techniques and software, this is just a matter of commitment now. We are not operating in the 80s anymore, and it is time to move ahead from simple segmentation methods. Yes, using segments would be much better than no targeting at all. But with a few more tweaks, we can build more than 20 personas in the same time that we would spend for developing segments using a clustering technique, which isn’t exactly cheap even nowadays.

Another downside of a clustering technique is that, once the statistical work is done, it is very difficult to update the formula without changing existing marketing schemas. By nature, segments are very static. It is no secret that even some data compilers chose to stay with old models, as they are afraid of creating inconsistencies with newly updated ones. Some are more than a decade old.

Conversely, it is very easy to update personas, as it is not much different from refitting the models one at a time. And we don’t have to update the whole series every time, either. Just watch out for the ones that do not validate very well over time. With real machine learning techniques around the corner, we can even consider automating the whole process, from model update to deployment of messages through every channel.

The hard part would be imagining the categories of personas, but I suggest starting small with essential categories, and then keep building upon them. Surely, teenage apparel companies would have a very different list than business service companies that sell their services to other businesses. Start with obvious ones, like bargain seekers, high-value customers and specific key product targets.

Connecting personas to actual creatives will require some work in the beginning, too. However, if you plan the categories with set creatives in mind from the get-go, it won’t be so difficult. Again, start small and see how it goes, along with some A/B testing. Ten categories will be plenty for many businesses. But having more than 100 personas won’t take up much space in supporting databases, either. Once the system gets stable, marketers can automate much of the process, as most commercial software can take these personas like any other raw variable.

So, if your marketing team is committed enough to have purchased personalization engines for various channels, get out of the old segmentation method and consider building model-based personas. After all, no one is one-dimensional, and everyone deserves personalized offers and messages in this day of abundant data and machine power. This is not 1984 anymore.

Is There a Generation Gap Among Direct Mail Responders?

I was listening to a Direct Marketing Club of New York presentation recently by Covenant House, a nonprofit organization dedicated to helping homeless kids in various cities. One of the challenges that the organization is facing is that its donor base is aging. The need to attract a new demographic among donors is apparent. However, its direct mail efforts haven’t been performing as well among younger prospects as it continues to do with its best donors—so the organization has turned to online channels in a bid to find these new, younger donors

I was listening to a Direct Marketing Club of New York presentation recently by Covenant House, a nonprofit organization dedicated to helping homeless kids in various cities. One of the challenges that the organization is facing is that its donor base is aging. The need to attract a new demographic among donors is apparent.

However, its direct mail efforts haven’t been performing as well among younger prospects as it continues to do with its best donors—so the organization has turned to online channels in a bid to find these new, younger donors.

In 2012, Covenant House set up a series of petitions through Care2, an online community for social action. Taking on four subjects—child trafficking, emergency healthcare, aging out of foster care, and domestic violence—Covenant House asked consumers to sign petitions related to these various topics, some focused on Congressional action, for example.

With the names and online contact information of tens of thousands of signatories, Covenant House this year is taking on a three-part email series, each with specific creative relevant to the petition subject matter, to “nurture” the consumer toward becoming a donor—asking them to social share their petition support, watch and share a video related to the topic, and then, by the third email, respond to a call to action to become a donor. For those who take no further action by way of the three emails, telemarketing is used to reach and attempt to convert them to donors.

With positive early results, it looks as though Covenant House may find its way to a younger donor base successfully.

Covenant House has no plans to ditch its direct mail—even as it acquires new digital donors. That’s because its “omnichannel” donors (donors who give in more than one channel) are its most generous, giving significantly more than single-channel donors in either direct mail or digital alone. In addition, direct mail continues to be the “workhorse” for donor acquisition overall, and each channel has its own strategic use in such activities as reactivating former contributors, the organization reported.

But the younger=digital donor acquisition strategy identified here makes me wonder about direct mail’s future. Is Covenant House’s turn to digital because young adults don’t read their direct mail as closely as older Americans do—is there a “mail generation gap”? Does traditional fundraising creative in direct mail fail to resonate with younger people? Are digital natives simply online more often—and analog communication doesn’t register as forcefully?

Or, from the marketer’s perspective, is digital an easy, more affordable and more timely go-to for testing acquisition more efficiently?

Twenty years ago, before the commercial Internet, if a non-profit organization needed to attract a younger demographic, it simply tested a direct mail piece (or a TV ad, etc.) against the control within a targeted demographic segment—and adopted the new creative within the channel only when results proved themselves. That very same testing within mail could be just as effective today.

But why wait six weeks or more for a direct mail cycle to prove itself (or not)—when the availability of digital allows new formats, multivariate testing, and creative refinement and segmentation so readily and cheaply? Perhaps a generation gap does exist with direct mail—but also marketers are increasingly impatient: do not discount digital’s speed in testing, revising and engaging donors in real time, and how attractive these speedy attributes are to marketers and fundraisers looking to meet aggressive goals.

The only way to really know what works—and what doesn’t—is to test. Covenant House already knows its multichannel donors are worth more, so you can probably bet its digital donors will be getting a direct mail piece of one sort or another very soon.