Historically, transactional data was always considered the ceiling of your data-centric marketing strategy: If you knew what, when, and how much your customers were purchasing, you could begin to market to them on a one-to-one basis. Today, we're awash in so much more data - mobile data, web data, search data, loyalty program data, social data, and many more data besides - that transaction data now represents the floor of your customer strategy, rather than the ceiling.
By Rick Ferguson
By Rick Ferguson
And despite being awash in many companies still struggle to gain a complete picture of their best customers. In a new article series beginning on Customer Think, Bob Hayes pinpoints the problem that we all know exists as the primary culprit, but which remains as the one problem with which we all still struggle: data siloes. Money quote from Hayes:
"Siloed data sets prevent business leaders from gaining a complete understanding of their customers. In this scenario, analytics can only be conducted within one data silo at a time, restricting the set of information (i.e., variables) that can be used to describe a given phenomenon; your analytic models are likely underspecified (not using the complete set of useful predictors), decreasing your model's predictive power/increasing your model's error. The bottom line is that you are not able to make the best prediction about your customers because you don't have all the necessary information about them."
Hayes is right on. The problem with data siloes is often one of scale; the problem isn't necessarily that we don't have access to enough data, but rather that we have so much data that the data we really need gets left in the silo. At the Wise Marketer, we stress the importance not of Big Data, necessarily, but rather Small Data: That data most predictive of the future value of your customer relationships.
Start always with the transaction - knowing what, how much, and how frequently your customers buy at the level of the individual is the single most valuable data set you own. From there, you can begin to connect the "data dots" from interactions throughout the customer lifecycle, from the research/awareness stage, through in-store, online, and mobile interactions, and all the way through to post-purchase interactions. As you data analytics become more sophisticated, you'll learn to identify behavior early in the customer lifecycle predictive of future value - and you can focus your marketing dollars on those customers who demonstrate the most potential.
It may not be rocket science, but it does take determination and the willingness to fail. Authors like Bob Hayes provide some useful signposts to point us in the right direction - and we'll look forward to reading more in this article series.
Rick Ferguson is CEO and Editor in Chief of the Wise Marketer Group.