• by Laurie Sullivan  @lauriesullivan, May 28, 2019

Merkle is developing a Performance Marketing Lab to assist brands in building better marketing teams, technologies, and strategies within their own organizations.

“We realized that even within our company we need to stitch together a cross-functional team,” said Matthew Mierzejewski, SVP of search capability lead at digital advertising agency Merkle. “So we’re flipping the script by creating a smart performance marketing lab because we need to educate our clients on these emerging best practices.”

Brands still miss the connections between departments and technology, in a way that is similar to how data sits in silos of search, display, social and television.

While many of the kinks are still being worked out, the Lab brings together departments focused on data science, web development and IT, product development and design, market research, media analytics, and subject matter experts in media.

The strategy relies on concepts such as cross-channel marketing and uses it to integrate strategies such as predictive lifetime and CRM modeling into search, social and display campaigns.

Many of the Merkle employees participating in the project are located in the company’s Charlottesville, Virginia and New York offices. The plan is to launch a web page describing the best practices that will more clearly explain the strategy.

As part of the information, the group will provide insight into when marketers should license specific technology and when to build it.

Mierzejewski said one of the major challenges is the inability of developers and brands to communicate with each other. For example, many of the web developers working on a brand’s project will never directly talk with the marketer spearheading the project, but they write the code and build the project’s platform.

Sometimes the developer will even spearhead the integration of Merkle’s priority data with the brand’s CRM data into Google’s cloud platform -- all without having direct content with the brand’s marketer.

There are back-end requirements. Data scientists need that direct contact with the brand to know how to best integrate the data into machine-learning applications for text mining, for example, Mierzejewski said, adding that many of the interfaces are built through the media analytics and media agents in specific subjects without ever having direct contact.