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.
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 principal and chief product officer at BuyerGenomics. Previously, Yu was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, he 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 email@example.com.