The Keyword in ‘Customer Journey’ Is ‘Customer’
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:
- Awareness: Source (where from), likes/followings, clicks other digital trails, survey results, social media data, etc.
- Research: Browsing data, search words/search results, browsing length, page/item views, chats, etc.
- Engagement: Shopping basket data, clicks, chats, sales engagements, other physical trails at stores, etc.
- Transaction: Product/service (items purchased), transaction date, transaction amount, delivery date, transaction channel, payment method, region/store, discounts, renewals, cancelations, etc.
- Feedback: Returns, complaints and resolutions, surveys, social media data, net promotor score, etc.
- 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.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.