As much as we spend measuring and reporting results at aggregate levels, when it comes to optimizing an account, we need to be precise. After all, when you Industry Email List find a campaign that's underperforming, fixing it requires digging deeper into settings, messaging (ads), or targeting (keywords, audiences, placements). But since the introduction of close variants has made exact match keywords no longer exact, you should go further and analyze the Industry Email List queries. For example, when you see that campaign performance deteriorates with RSAs, is it because RSAs are worse than ETAs? Or did adding RSA change the query mix and that's the real reason for the performance change?
Here's what makes PPC so challenging (but also fun). When you edit anything , you're likely changing the auctions (and queries) your ads participate in. A Industry Email List change in the auctions your ad participates in is also called a change in query mix . When you analyze performance at an aggregate level, you may be missing the mix of queries and not Industry Email List necessarily making an apples-to-apples comparison. The query mix changes in three ways: Old queries that are still triggering your ads currently New queries that previously weren't triggering your ads Old queries that stopped triggering your ads Only the first bucket is close to an apples to apples comparison. With the second compartment, you introduced the oranges into the analysis.
And the third bucket represents the apples Industry Email List (good, bad, or both) you threw away. Query mix analysis explains why results changed Query-level analysis is useful because it can more clearly explain the “why” rather than the “what.” Why has performance changed? Not just what changed? Once you understand “why”, you can take corrective Industry Email List action, such as adding negative keywords if new queries are giving poor performance. For RSA query analysis, you want to see a query-level report with performance metrics for RSAs and ETAs. Then you can see if a