DWA, a Merkle Company, a leading technology-enabled, data-driven performance marketing and media agency with a B2B focus, has appointed Michael Kostainsek to be VP, Global Client Partner. He will be based in DWA’s San Francisco office.
“The majority of conversations [in the wake of cookie restrictions] has been based around the shift of audience-based strategy to strategies that put contextual elements at the forefront,” said Emily Anthony, director of media services at data and analytics company Merkle.
In May 2018, the European Unions’ General Data Protection Regulation (GDPR) became a disturbance to the data marketplace. Data providers, both big and small, experienced a drastic decline in available cookies.
In early October 68 B2B marketers from across the US gathered in Austin, Texas for Merkle’s third annual B2B Exchange. Attendance included representatives from multinational companies such as Cisco, Dell, and Unilever. Our strategic partners Adobe and LinkedIn sponsored the event with discussions about the opportunity gap in thought leadership and delivering great customer experiences (CX).
All great brands have one thing in common: By reducing friction, they increase their customer’s velocity. And this is as true for B2B brands as it is for consumer brands. By now most B2B marketers recognize the importance of data. However, just collecting data is not enough. What’s important is using that data to understand and capitalize on users’ intent in a manner that galvanizes them to take action — that increases their velocity.
“Relative to the big players in the space, it’s not a core component of media plans or activation in 2020,” said Emily Anthony, senior director of media services at performance marketing agency Merkle. “It’s something to watch for advertisers who are setting aside test budgets, and it’s a really interesting opportunity for more mid-funnel and upper-funnel engagement. But we’re not going to see it as a core line item on media plans in FY20.”
Google recently announced two significant changes to its Google Ads policies. Both reflect Google’s increased trust in machine learning to optimize performance over time. Each promises better performance, but each requires practitioners to adjust their habits in order to make the most of them.