Google Ads learns from past data to predict what’s likely to convert in the future. But what happens when the future no longer follows the same rules?
In data science, this is called Concept Drift—when the relationship between signals (like location, keyword, or audience) and outcomes (like conversions) starts to shift.
Often, this happens because of unobserved confounding—real-world factors influencing performance that Google simply can’t see. So the algorithm keeps bidding based on outdated assumptions, and results suffer.
Here are a few examples of when this happens in practice:
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Staff availability in certain regions changes, making it harder to fulfill demand
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Product availability fluctuates due to supply chain delays, but campaigns still push those SKUs
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Weather changes impact interest in categories like travel or outdoor gear, but the algorithm doesn’t know it rained all week
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Target audience behavior shifts after a campaign (for example, after a TV ad), but Smart Bidding continues as if nothing changed
Moral of the story: your bidding is only as smart as the data it sees. Feed it the context it’s missing, or risk letting automation chase shadows.
In my next post, I will share a concrete example of how you can help Google’s algorithm adapt better in these kinds of situations. Stay tuned.
