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