The Keyword in ‘Customer Journey’ Is ‘Customer’
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.
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.