Wunderman’s new global chief data officer, Gary Laben, saw his remit expand significantly last week, when he was appointed to the position. While Laben was and will remain CEO of KBM Group, Wunderman’s data-focused subsidiary, Laben will now oversee the deployment of Wunderman’s data capabilities globally.
“Some of what we have is mature in markets like the US. In other markets, it’s burgeoning,” he said. “We have to make sure there’s a process and infrastructure in place to ensure our capabilities are viable in any part of the world.”
Global chief data officer isn’t a new position within Wunderman – Andrew Rutberg previously held the role and now serves as president of Wunderman’s marketing solutions center, overseeing offshore production capabilities.
Laben, who reports to Wunderman CEO Mark Read, spoke with AdExchanger.
AdExchanger: Which countries has Wunderman targeted for expansion?
GARY LABEN: We’ve got very good capabilities in the APAC region, but we need to expand those in certain markets. We’re growing our data content, with emphasis on countries like Japan and Australia for example. Clearly we have tons of opportunity in China, but there’s more we need to do. Indonesia is a very fast market from a data-management capability. In Latin America, we continue to focus on Brazil as a fast-growing marketplace.
What does expansion entail? Is it stuff like understanding data laws or making sure you can integrate with local vendors?
It’s both. In the case of a mature and robust market like Germany, the privacy laws are quite strict and much different from what we have here. Matching the right set of capabilities and understanding those laws is very important. We also have to make sure where there’s demand, we build and deploy [marketing platforms] properly.
You’re also trying to bring together data and creative. What needs to happen there?
Whatever idea we deploy or create for our clients is founded and informed by data. At Wunderman, that’s about infusing from the outset all data we might have onto any creative or ideation process. Only some of it is data we own, other data might be part of WPP, or it might be client or other third-party data. We have a methodology to do that and my role is to continue building and augmenting it, making sure it’s a uniformly executed process.
What’s the methodology?
The best answer I can give is it’s a process by how we go about sourcing, integrating and infusing the data into a particular engagement, and how that data comes through on the other side to measure those results. It differs by engagement since not everyone is the same. It differs geographically because of what we have at our fingertips.
What is the one thing that could help improve the data-creativity partnership?
Decreasing the amount of time we need to do our data informed creative processes. It takes work and it’s bound by a host of factors, like all the data we have, our ability to crunch it, our ability to source the right stuff for the right job.
What are some common mistakes around merging data with creativity?
The most common mistake is to try to force a result. Sometimes, we come in with a preconceived notion of what the answer is and we try to force data into that notion. And then we end up with an unholy process. We have to be willing to accept that the answer we get is not what we might have thought or what we might have wanted to see.
What happens if a client wants to go one direction, but the data says go another?
We’ve had scenarios where we felt the data didn’t point to a particular position. More often than not, clients are very accepting of that. You’d much rather pivot in the marketplace than be wrong in the marketplace.
Sometimes, clients get steeped in a particular belief that might be informed by different data. In that case, we’ll work with them to ensure we come up with a mutually agreed execution.
How do you determine the validity of a certain data set?
Generally, everything we do has a line of sight through our client sales. It’s about making sure that what we do can be measured, and if everything we do can be measured, we can connect it back to the data inputs.
Given customers’ cross-channel movements, is that line of sight always clear?
It’s getting much clearer, but there are still improvements to be had. We’re getting much better at connecting data from different channels, measuring the combined impact and attributing value and success to these inputs. But today, as much as it is a science, there’s still a lot of art to it. And our goal, candidly, is to whittle away at the art part.
If you could improve one aspect around attribution, what would it be?
We’re still solving for missing variables because we don’t have the whole picture. Much of what we do in attribution is done based on the data we have and that we’re able to assemble at the time, which is not always 100% complete. If you think about attribution methods as basic as last-click, that is a function of the fact we don’t have all the data inputs. We’ve evolved far from last-click these days, but we’re still doing it absent of what I’d consider to be the entire picture.
