Where is Google focusing its AI efforts in advertising? What learnings have emerged from their work? Find out what Ben Royce, Head of Performance Data Science, Global Agency, at Google shared on these topics.
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John Wanamaker’s quip “Half the money I spend on advertising is wasted; the trouble is don’t know which half” takes on new meaning with the measurement options available today. But which work best in quantifying the impact of marketing activities? The ARF’s Scott McDonald tackled the topic by providing tips on the best use of A/B tests vs. Market-Mix Models vs. Multi-Touch Attribution to measure marketing ROI.
When you hear claims on generational differences in media and market behavior, your first reaction should be: “Show me the data.” Two such claims—that younger cohorts do not care about privacy, and that they are averse to advertising—might be misinforming strategic marketing and advertising decisions, raising the distinction between longitudinal vs. attitudinal research.
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Interested in AI best practices? Wish you had a succinct definition of what AI does? Curious about a simple visual that explains the ability of five different levels of AI to tackle tasks? Want to understand better how machine learning differs from deep AI-powered learning?
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Who doesn’t like less complex and less costly solutions, if similar results are achieved? This study claims to deliver on that goal by validating the single-item measurement approach (preferred by practitioners) vs. using multiple measures of attitudes (preferred by academics. It’s a welcome outcome in the current era of decreasing survey response rates and respondents’ attention spans.
Member Only AccessNew neuroscience and biometric research methods enable marketers to measure ad response in ways only dreamed of until recently. Understand what is behind the growth of neuroscience and biometric measures, the pros and cons of different measures, and what best practices can take creative messages to a new level in this ARF original report.
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Popularity can come with a price. Although mobile survey research is increasingly popular, it often suffers from high drop-off rates. What can marketers and researchers do to reduce drop-off? This ARF-original Knowledge At Hand article identifies best practices to overcome the problem and improve mobile survey effectiveness.
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The ARF has announced two new efforts to tackle data quality questions:
Data Labeling Initiative – with the ANA’s Data Marketing & Analytics division, the IAB Tech Lab, and the Coalition for Innovative Media Measurement (CIMM).
Data Validation Initiative – independently conducting research to determine whether surveys can be used to measure the accuracy of those targets.
