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When I discuss "signal engineering," I'm often asked if I've written any articles on the topic. As a brief primer: signal engineering is the process of designing and capturing high-quality outcome proxies (eg., pLTV) so they can be reported back to ad platforms in real time such that those platforms optimize campaign targeting for them. I have a few resources to share (links below), but some caveats and background, first: - I didn't coin the phrase "signal engineering." As far as I know, Eran Friendinger did. I interviewed him on the MDM podcast on the topic last year, which I'll link to below. - The topic of signal engineering is newly relevant (and organizationally critical) as a function of platform automation tools like PMax and Advantage+ taking a greater share of budget. This trend will not abate: advertisers should expect that every channel they deploy budget to will apply some end-to-end automation framework at some point. In that environment, surfacing "signal" (predicted user value) becomes the primary, if not the only, influence that an advertiser has on performance. - My belief is that most advertisers focus too intently on outputs when they consider the value of AI enablement. I think the product economy / journey personalization opportunities with AI tools are far more exciting and substantial than the ad creative opportunities, which I'd mostly expect the platforms to absorb. More resources on signal engineering below: