All too often, we hear clients refer to their customer data according to how it is going to be applied – “Analytics Data” or “Marketing Data” or “CRM Data” and so on. Their definition presupposes certain end uses, and end users, when in fact, a company’s customer data (like its brand) is one of its most core assets, with many different potential applications.
While it’s important to distinguish between raw material and final product, referring to customer data by the tactic it enables is a bit like separating “shirt cotton” versus “pants cotton”, “gas oil” versus “plastics oil”, and “algebra numbers” versus “calculus numbers”.
Data is a raw material that can be transformed into many different things and needs to be thought of independent of any one particular use case.
For example, the same data that’s used to analyze product usage and group cohorts can be used to inform key marketing activities. Say you have an eCommerce app and you want to group cohorts who made a purchase within a given timeframe. In addition to capturing purchase events and all of the corresponding attributes of each purchase, you will most certainly want to capture all behaviors leading up to the purchase event. This will allow you to map the user journey from start to finish and understand what distinguishes users who purchased something versus those who didn’t. This is a fairly common analytics use case. The same events that are captured to measure the funnel should be used to drive engagement.
The problem that most organizations face is that stakeholders in certain functions think about data only as it relates to their job. And you can’t really blame them, they do have a job to do. The problem with this thinking is that when enough people start acting and thinking this way, value is lost on multiple levels.
First is the fact that data, assuming it’s properly captured, is what economists call a “non-rival” good. Whereas a given bale of cotton can be used to create only one thing (either a shirt or a pair of pants, let’s say), the same data can be used again and again for multiple purposes, at close to zero marginal costs. The problem is, when this property is misunderstood, and data is captured incorrectly, you will have multiple stakeholders spending time and effort collecting the same data point, which is a waste.
Secondly, even if companies do not see an immediate value in a data set beyond its initial use, it helps to leave your options open. If you are in the “plastics oil” business your options are a lot more limited than if you are simply in the “oil” business. The demand for plastic is much more limited and hence more volatile than the demand for oil as a whole. In much the same way, companies need to define their data applications broadly enough to take advantage of growth opportunities and not close them off.
There is a popular saying that “Data isn’t what matters, only what you do with it.” As much as this may be true, you need to conceive of and manage your data properly from the start in order to get the most from it.