Clicks Are a Vanity Metric: Ranking Ads for What Actually Converts
A couple of years ago I shipped an ads ranking model that looked like a clean win. Click-through rate went up. The offline metrics held, the A/B test was solid, and the launch note practically wrote itself.
Then I looked at what those extra clicks were doing for the advertisers paying for them. Too often, the answer was nothing. People clicked, glanced, and left. The merchant paid for the click. Nobody bought.
That gap is one of the easiest traps in ads ranking. Click data is abundant, easy to model, and gives you fast feedback, so teams naturally optimize for it. But a model trained to maximize clicks will happily surface ads that attract attention without creating value. The click is cheap. The purchase is what pays.
I've worked on ads ranking at DoorDash, and previously on YouTube Ads and Assistant NLP at Google. Across those systems, I learned that...
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