Addressable advertising on television enables better targeting and measurement of TV ad campaigns, but gaining access to consumer data is essential for its effectiveness and development. So, what can advertisers do to make people more willing to share their data? New research offers insights into developing personalization initiatives aimed at alleviating privacy concerns.
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Data clean room technology has had a place in the advertising ecosystem for years but has become increasingly prominent in today’s landscape where major disruptions in data governance and privacy are emerging. Data cleanroom companies provide environments for two or more companies to share first-party data in a neutral, secure, privacy-compliant manner. They are used for activation, media measurement, and insights. At this event, Working Group Chair Sable Mi (VP of Analytics, Epsilon) moderated a powerful discussion with guests Alya Adelman (Director of Product, Blockgraph), Devon DeBlasio (VP of Product Marketing, InfoSum), Matt Karasick (Chief Product Officer, Habu), and Alysia Melisaratos (Head of Solutions Engineering, LiveRamp) to unpack the value that data clean rooms can provide.
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On April 26, 2022, the ARF Offline-Online Metric's Working Group brought together three marketers to discuss their needs and priorities on online-offline metrics, exploring current topics such as reconciling digital and traditional metrics, emergence of new currencies, first-party data/identity resolution, and the advent of streaming. Working Group Chair Charles Buchwalter moderated a dynamic discussion with guests Christine Beaufait (Cross-Brand Program Manager, GM), Karen Chisholm (Transformation Analytics Director, Pernod Ricard), and Jeremy Wasson (Global Media and Data - ROI Engine Sr. Director, PepsiCo).
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While Randomized Controlled Trials (RCTs) are ideal for advertising measurement, it is not always feasible to conduct one. Until advertising platforms are willing to provide more information about how they deliver targeted ads or implement auctions, alternative observational methods are unlikely to reliably estimate causal effects. That includes utilizing newer machine learning techniques.
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