If the headline to this post is a surprise, hear me out. Consider that this idea is coming from someone who regularly invites colleagues to her desk for “math parties” and thinks of Excel hack blogs as light reading.
But I am a firm believer that analysts, especially at marketing agencies, should read poetry. There is something so poetic (forgive me) about being able to infer meaning from a close examination of sparse information.
So much of what I read these days about data and analytics is all about big data and how brands know consumers better than they know themselves. That’s all well and good if your organization is advanced and savvy and you have unfettered access to lakes of data. But what if your organization isn’t quite as sophisticated? What if you have limited budget, limited access or limited time?
Not every brand or agency can operate at the level of the NSA, Google or Facebook. Most of us have to make the most of the data and resources we do have. Surprisingly, that’s why a little poetic understanding helps.
Reading intent into data
At its most basic level, the kind of data I’m concerned with is a reflection of the people it describes. It shows human behavior, intent, need, journey, experience, abandonment and loyalty. Data is how analysts understand people, both individually and collectively, at the micro and macro levels. Data builds the picture. If we are able to step back and view it as a whole, we can turn a few colored dots on a confetti map into Georges Seurat’s “Sunday on La Grande Jatte,” and maybe, for those of us who love that sort of thing, even “Sunday in the Park With George.”
Poets use words the way analysts use data: to describe the world around them. But, of course, the poetic version of description is not always literal or simple, as it might be in prose. We don’t always get the full idea on a first reading of a poem. We read, we re-read, we analyze. Every word in a poem can have a multitude of meanings and layers of intent. Each word or data point can help us derive a distinct insight from the object we are examining.
In our work, we use these overlapping layers to form a deeper understanding of our audiences. For example, we ask ourselves why certain interests interact and overlap within our content. We look for loyal users and those who are disconnected. And then we build a universe out from those single grains: anything from personas to targeting to lead scoring models for the world of B2B.