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Understanding query volume as a confounder for search ads
Perhaps the biggest challenge in causal inference when applied to marketing is
that advertisers often spend more on marketing when there is stronger demand for
their product. Disentangling whether an increase in the KPI is due to an
increase in marketing spend or due to an increase in inherent demand is a
primary concern when one is analyzing causal effects of marketing spend.
The strong relationship between inherent demand and marketing spend is
particularly salient when it comes to search ads. This is because a search ad is
only shown on the page if a search query matches certain keywords targeted by a
set of advertisers. When inherent demand is high, organic query volume will also
be high, and so the total spending on search ads will be high. Therefore,
organic query volume is an important confounder for search ads. It is hard to
get good inference on search ads without it.
This is particularly an issue for advertisers with high search budgets because
their paid search ad volume tends to track more closely with organic query
volume. However, this also affects lower budget advertisers who increase their
budgets during periods of high demand, or who only run search campaigns during
these periods.
Meridian provides the option to include Google organic query volume
(GQV) in the model as a
confounder for Google Search ads. Organic query volume from non-Google search
engines is often unavailable. If you want to model non-Google paid search ads,
and organic query volume from the corresponding search engine is not available,
the following alternatives might work for you:
Bias can be mitigated if GQV is a good proxy for the non-Google query
volume. We recommend assessing this assumption. One way to help assess the
assumption is by creating a plot, for example:
The previous plot shows the correlation between media impressions and brand
GQV on the y-axis, and the correlation between media impressions and generic
query volume on the x-axis.
If you don't want to assume GQV is a good proxy for the non-Google query
volume, you might need to omit the non-Google search engine from the model.
For more information about the challenges of selection bias due to ad targeting,
see Bias Correction For Paid Search In Media Mix
Modeling.
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Last updated 2025-06-11 UTC.
[null,null,["Last updated 2025-06-11 UTC."],[[["\u003cp\u003eUnderstanding the impact of marketing spend on key performance indicators (KPIs) for search ads can be difficult because inherent demand and marketing spend are often correlated.\u003c/p\u003e\n"],["\u003cp\u003eOrganic query volume is a significant confounding factor for search ads as it influences both inherent demand and ad spending.\u003c/p\u003e\n"],["\u003cp\u003eMeridian offers the option to incorporate Google organic query volume (GQV) into the model to address this confounding factor.\u003c/p\u003e\n"],["\u003cp\u003eIf non-Google organic query volume is unavailable, using GQV as a proxy or excluding non-Google search engines from the model can be considered.\u003c/p\u003e\n"]]],["Advertisers face the challenge of distinguishing between increased marketing spend and inherent demand when analyzing marketing's causal effects. Search ad spending correlates with organic query volume, making it a key confounder. High query volume leads to higher ad spending, impacting both large and small advertisers. Meridian allows inclusion of Google organic query volume (GQV) to mitigate this. If non-Google query volume is unavailable, GQV can be used as a proxy or the non-Google search engine omitted.\n"],null,["# Paid search modeling\n\nUnderstanding query volume as a confounder for search ads\n---------------------------------------------------------\n\nPerhaps the biggest challenge in causal inference when applied to marketing is\nthat advertisers often spend more on marketing when there is stronger demand for\ntheir product. Disentangling whether an increase in the KPI is due to an\nincrease in marketing spend or due to an increase in inherent demand is a\nprimary concern when one is analyzing causal effects of marketing spend.\n\nThe strong relationship between inherent demand and marketing spend is\nparticularly salient when it comes to search ads. This is because a search ad is\nonly shown on the page if a search query matches certain keywords targeted by a\nset of advertisers. When inherent demand is high, organic query volume will also\nbe high, and so the total spending on search ads will be high. Therefore,\norganic query volume is an important confounder for search ads. It is hard to\nget good inference on search ads without it.\n\nThis is particularly an issue for advertisers with high search budgets because\ntheir paid search ad volume tends to track more closely with organic query\nvolume. However, this also affects lower budget advertisers who increase their\nbudgets during periods of high demand, or who only run search campaigns during\nthese periods.\n\nMeridian provides the option to include [Google organic query volume\n(GQV)](/meridian/docs/basics/using-mmm-data-platform) in the model as a\nconfounder for Google Search ads. Organic query volume from non-Google search\nengines is often unavailable. If you want to model non-Google paid search ads,\nand organic query volume from the corresponding search engine is not available,\nthe following alternatives might work for you:\n\n- Bias can be mitigated if GQV is a good proxy for the non-Google query\n volume. We recommend assessing this assumption. One way to help assess the\n assumption is by creating a plot, for example:\n\n The previous plot shows the correlation between media impressions and brand\n GQV on the y-axis, and the correlation between media impressions and generic\n query volume on the x-axis.\n- If you don't want to assume GQV is a good proxy for the non-Google query\n volume, you might need to omit the non-Google search engine from the model.\n\nFor more information about the challenges of selection bias due to ad targeting,\nsee [Bias Correction For Paid Search In Media Mix\nModeling](https://research.google/pubs/bias-correction-for-paid-search-in-media-mix-modeling/)."]]
