How to Solve 4 Common Digital Advertising Challenges With Better Measurement

Marketers who think about campaigns holistically from start to finish, objective to outcome and across platforms, channels and publishers have a more complete picture of what is driving success. But all that information can be difficult to analyze, especially if it is spread across multiple campaigns, as was the case for social game developer Zynga.

Zynga was running 40 individual campaigns—each based on a distinct strategy. Through a partnership with Facebook’s Marketing Science team, the company deployed a strategy called “account simplification,” in which it reduced the number of distinct campaigns to fewer than 15. By streamlining its account structure, Zynga gave Facebook’s machine learning mechanisms the ability to more quickly optimize ads and allocate budget based on bid strategy. The result? Campaigns were 1.9 times as efficient.

Today’s consumers spend more time on their mobile devices than they do watching television¹, and advertisers are taking note. But to truly get the most from mobile, marketers must be aware of rapidly changing viewing habits and their implications on mobile ad performance. For appliance maker SharkNinja, it was clear that investing in mobile was key to staying ahead of competitors and consumer behavior, but it also required a significant shift in strategy.

SharkNinja opted to create a campaign on Facebook that complemented its existing television ads. But mobile presented unfamiliar challenges, as the company had traditionally allocated its marketing budget to a combination of in-store display ads, short-form TV, advertorials and direct mail. With the help of a Facebook Brand Lift test, the company was able to compare ads that utilized mobile best practices to ones that were simply edited television ads. It discovered that when ads were optimized for mobile—for example, when they were designed in the vertical format native to mobile—they drove a 12-point lift in ad recall and an 8-point lift in awareness.

By definition, experimentation involves failure. For that reason, many marketers who would take risks are met with resistance from their organizations, especially when there is no top-down mandate to embrace change. Among marketers who use marketing mix or multi-touch attribution models, only 60% also use experiments, according to BCG. Yet experiments often lead to unexpected opportunities, as was the case when Ribbow Media Group used learnings from Facebook Attribution to help a client rethink a long-running promotional strategy.

Ribbow Media Group develops campaigns for feature films. Using learnings from Facebook Attribution, which showed unique reach across certain channels, the agency tested new campaign strategies to demonstrate to a client that its typical marketing approach could be improved. The client usually held onto 2-5% of digital spend to promote films during their second week of release; Ribbow suggested it expand its second-week spending in several channels to promote the film I Can Only Imagine. The recommendation, which was based on experiments Ribbow ran in the film’s first week at the box office, resulted in precedent-setting sales—overall, the film took in $83 million on a $7 million production budget.