Addressable targeting in paid social and search has played an increasing role in most major advertisers’ strategies. Advertisers can also now target a traditional TV spot to an individual household drawing on addressable inventory, or they can slot a targeted video into an individual OTT user’s experience.
But – and there is always a but – as inventory and capabilities in addressability scale, balanced media planning is critical. I have never seen a 100% addressable plan perform.
In a rare bipartisan effort, Sens. Chuck Schumer (D-N.Y.) and Tom Cotton (R-Ark.) are leading a new charge against ByteDance Technology — the Beijing-based parent company of video app TikTok.
After noting that TikTok has been downloaded by more than 110 million U.S. consumers, Senator Schumer tweeted on Thursday: “It’s required to adhere to Chinese law. That means it can be compelled to cooperate with intelligence work controlled by China’s Communist Party.”
Although contextual is one of the most traditional forms of targeting, it doesn’t mean it’s a step back for advertisers. On the contrary, by combining this method of targeting with today’s technology and smart use of first party data, contextual presents brands with an even greater opportunity to get closer to consumers with the content that is most likely to engage and inspire them.
After a slow start, U.S. programmatic ad spending is expected to reach $29.2 billion this year, according to eMarketer, rising to $34 billion by 2020. The data firm also estimates that U.S. digital video views will reach 240 million.
Marketers appear to have gained more confidence in programmatic video. Research from Chocolate shows confidence is led by an 11% decline in ad fraud in 2019, compared with 2017, after the company partnered with White Ops, a third-party fraud detection company. Since the third quarter of 2019, it measured 97.74% of its programmatic traffic as valid. Chocolate also points to an increase in adoption rates for ads.txt and app-ads.txt, along with TAG certification against fraud.
Google is applying its BERT models to search to help the engine better understand language. BERT stands for Bidirectional Encoder Representations from Transformers, a neural network-based technique for natural language processing (NLP). It was introduced and open-sourced last year
As the global head of customer analytics at Google, Neil Hoyne has spent years studying the technical aspects of measurement and how to strengthen the analytical cultures at world-leading organizations. Here he shares what the top companies get right about measurement.
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