Are You Asking Your Data the Right Questions?
"Inspiration is always a surprising visitor," late Irish poet John O'Donohue once wrote. That explains what happened in my bathroom recently.
Outside of making ridiculous videos that get viewed 18 million times and equally awesome new commercials that just happen to have been shot in the New Jersey town where I live, the Dollar Shave Club sends members like me a fun little bit of content marketing with our blades every month. And in the issue of The Bathroom Minutes that came in March, company CEO Michael Dubin wrote about his New Year's resolution to not check his email first thing in the morning. "The answers I'm looking for are rarely there," he wrote. "Instead I choose to fill my mind with positive thoughts."
I don't exactly expect Dollar Shave Club to contribute to my personal improvement beyond stubble removal, but Dubin is right about the better way to start the day – and about the nature of email. If the answers were abundantly there, would my inbox have more than 3,500 unread emails as I write this?
That admittedly ridiculous number gets to a larger point, though. If marketers like me struggle to stay on top of information served up directly to them the way email does, it's no wonder data remains such an opaque challenge for so many marketing teams. Unlike email, most of us would agree that many of the answers we seek actually are in our data. But do we have the time and focus to figure out where?
Given that 82% of CMOs feel unprepared to deal with the data explosion, I'm going to assume the answer to that is often no, and that assumption is at the heart of a new series of posts on the Quest for Data Clarity that we are launching here. What is the truth in our marketing data, how do I find it, and what do I do with it once I get it? Will I even recognize it as truth?
As a "content guy," I hope to approach this series like a reporter would – by talking to the right people first and foremost. People who understand the data connections that help marketers with the four aspects of building customer and prospect relationships that we will explore in this series:
- Better targeting
- Smarter optimization
- Accelerated sales
- More insightful performance evaluation
To kick this off, I didn't have to go far.
When he's not telling stories to a conference or a customer, you can find Anthony Scriffignano in a sunny third-floor corner of Dun & Bradstreet's New Jersey offices. Scriffignano is the chief data scientist here, and the man practically bathes in those data connections. If data were looking to hire a spokesperson, it better hire Scriffignano. While videos of him might not put the Dollar Shave Club to viral-success shame, he is a scientist in that finest sense – obviously smarter than anyone else in the room and just as obviously more passionate about the topic at hand than anyone else, too.
As you ask Scriffignano about finding truth and meaning in data, one of his best answers lies, in his words, in the "journey from the 'articulated want' to the 'discoverable need.'" Translated for distracted marketers with too much going on to even read their emails (ahem): Asking your data the right questions.
"How do you understand what you want to do next?" Scriffignano says. "Why are you doing this campaign? A common reply is something like, 'Because we need to.' And we haven't actually thought that through. Stop. Back up. What's the problem you're trying to solve? That's a critical new skill."
New from the standpoint of being a core need of marketing in the data age, yes. But hat-tip to my first boss, who would tell customers something similar: What you want and what you need are often two different things. Here's an example of how Scriffignano helps customers cross that bridge.
He describes an RFP process where Dun & Bradstreet couldn't submit without providing a detailed breakdown of our expected file growth for certain European countries. "To answer such a question," Scriffignano says, "I would need to make assumptions such as our investment and focus in those countries, organic growth, macroeconomic and other factors, emerging partner capabilities, etc." Not easy, even for a Ph.D.
Scriffignano began seeking additional context. How could he get from the "articulated want" of the customer – how many records will you have in countries X, Y and Z in two years? – to the "discoverable need?" He first discovered that the customer was doing sales and operations planning. After further discussion, it also became clear that their real focus was on growing their penetration of a particular manufacturing vertical. And beyond even that, they wanted to understand the potential growth in the target footprint in terms of preexisting customers vs. potential new customer acquisition.
By excluding certain types of businesses that were not in manufacturing, factoring in corporate linkage and some modeling of best potential new customers’ growth, Scriffignano was able to provide a much better and more actionable answer. A series of questions, one building upon the other, led from the ambiguity of the articulated want to a discoverable need that data science could address.
"It's been done in science for years," Scriffignano says. "The scientific method is all about asking the research question first, understanding what's been done before, figuring out what you're going to do, deciding that your method is the best thing to do... Then go collect your data and let the truth emerge."
I'm not a scientist. I'm probably much more like you – a marketer trying to separate the signals from the noise, looking for answers in those signals, and then acting on those answers with conviction to build the relationships that can drive our companies' growth. We can do this. About that, there is no question. With this series, we look forward to exploring how. Hopefully with some more surprise visits from inspiration along the way.View other posts in the "Quest for Clarity" series: