Forums Forums PPC Anyone use statistical methods to analyze ad campaign performance data to help with predictive modelling – what Google Ads metrics do you typically prioritize as predictor variables?

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    Anyone use statistical methods to analyze ad campaign performance data to help with predictive modelling – what Google Ads metrics do you typically prioritize as predictor variables?

    Posted by Inquiz_ on May 17, 2026 at 2:51 pm

    I've been reading up on multiple regression modelling and see it as something I could potentially use to analyze past campaign data to extract insights that inform the settings we tune for future campaigns – with the purpose of, of course, maximizing ROAS.

    Your model's only as good as the predictor variables you choose to include in it, and there's a LOT of Google Ads metrics that could go in there. I mean when you click on 'Add Column' in the reports there's like 50+.

    So I wanted to ask around a bit and see what the consensus is on this; aside from, ofc, conversions, ad spend, SIS etc, what all have you guys been incorporating into your predictive models?

    Inquiz_ replied 1 hour, 37 minutes ago 2 Members · 1 Reply
  • 1 Reply
  • ppcwithyrv

    Guest
    May 17, 2026 at 7:52 pm

    Does’t Google ads already have a predictive model?

  • QuantumWolf99

    Guest
    May 17, 2026 at 8:33 pm

    The metrics that actually predict future ROAS in regression models are impression share lost to budget, impression share lost to rank, and conversion lag by day… not CTR or CPC.

    The variables most people include are the ones Google reports loudly… the ones that matter are the ones revealing structural constraints and intent quality deterioration before ROAS moves.

  • CheetahsNeverProsper

    Guest
    May 17, 2026 at 8:40 pm

    I’ve been down this road; I focus on variables we directly control. CPC, CTR, and CVR are shiny metrics but aren’t *directly* controllable at scale. As another poster said, look at IS lost to budget, rank, as well as the usual suspects to create a better picture of where your opportunity lies. I find that companies that look at the big 3 only often miss that their market may not even be expandable in Search, for example, since their spend and quality are already at or past the point of diminishing returns.

    I used to run a multi-variant model that incorporated these metrics to arrive at “quick win” places to invest/pull back. It’s a bit different in today’s ecosystem but it’s mostly still relevant. How are you modeling things like budget changes right now?

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