News & Insight
January 5th, 2017
Podcast: Nate Woodman on AdExchanger about Proprietary Machine-Learning Models
According to Nate Woodman, GM of demand solutions at IPONWEB, the deployment of brand data in the media-buying arena is at an early stage. His pet thesis: Now that CRM activation in programmatic is common, the next challenge will be the development of proprietary machine-learning models that are owned and controlled by brands.
“Most CRM data is activated through a DSP,” Woodman says in this latest episode of AdExchanger Talks. “That supports a segmentation strategy, but to drive real performance out of a system requires a machine-learning model, which can hit performance targets in a vastly superior way to segment-based buying.”
A tiny club of big marketers, such as Netflix, have initiatives in place today around proprietary algorithmic IP. And other performance-focused verticals like banks may be positioned to do so. But it’s a steep climb.
“The challenge to the industry, and it’s a daunting one, is to find a way to spread proprietary algorithms across programmatic platforms,” Woodman said. “Most brands aren’t even close to realizing this vision, but some are making overtures in the direction of proprietary machine-learning models.”
He added, “I don’t know that it’s going to go there, but it’s a vision.”
Also in this episode: Woodman talks about IPONWEB’s unique place in ad tech history, its current strategy and the evolution of the agency trading desk model.