Chief Data Scientist Anthony Scriffignano Explains How Marketers Can Find Meaning in a Sea of Customer Data
Most marketers today aren't crying over a lack of data. Our pain comes from having so much that we don't want to do with it all.
"Our problem isn't that we don't have enough information," says Anthony Scriffignano, the chief data scientist here at Dun & Bradstreet. "Our problem is we're swimming in it."
Helping marketing teams stay above the surface of the modern data deluge is the goal of our new "Quest for Clarity" series. And it's not just the volume of data. To avoid drowning, you also must manage the churning of the seas – the constant changes of your customer data and, more importantly, the seismic shifts in customer behavior and activity that spin off all that data. "If you want to market in a space that's that chaotic and that dynamic, you can't use the same techniques that you've been using all along," Scriffignano says. "You've got to get a lot smarter about how you use that information, a lot faster, a lot more connected."
I had the pleasure recently of sitting down with Scriffignano to talk about how marketers can do more than stay afloat. How can smart data management propel them forward? "The opportunity space is enormous compared to the problem space," he says.
Nobody is more passionate or more eloquent than Scriffignano about that opportunity. Semantic disambiguation, anyone (2:32 mark)?
Other highlights from the conversation include:
- "When you think about data-driven marketing, nobody wants more contact in their life right now. They want more relevant contact." (0:35)
- "What more and more we need to realize, it's like playing chess. You don't think about every conceivable move and every countermove to every move and every countermove to those countermoves, because the size of that problem overwhelms the space very quickly." (1:43)
- "You don't have to outrun the bear. You have to outrun the other campers. We've got to figure out what to do to be relevant to our customers, to help them solve real problems that they have." (5:33)
- "Imagine you had a recording of the sound of all the people in a convention. 'There's nothing I could do with that.' Slow down. Could I build some kind of an algorithm to estimate the level of excitement in the crowd as things are going on? Yeah, probably could do it. Could I ..." (8:08)