Thanks to my work with Thesis, I have the opportunity to regularly audit paid search accounts from various ecommerce/D2C, lead gen brands, and more. Over the years, I've developed a few quick reports that I use to gauge account health & expansion opportunities. In our first blog post in this series, I shared our approach to measuring search term efficiency. In this second post, I share our approach to...
I recognize that Google is slowly deprecating the role that match types have traditionally played in paid search advertising (see: the planned July 2021 depreciation of broad match modified keywords). Despite that, I still generally prefer for a search term to match to a phrase or exact match keyword as those match types give me more control as a search advertiser to specify ad copy and typically are more efficient in CPA terms (as a downside, they also are inherently limiting in that they might prevent my campaigns from competing for relevant terms in the auction).
When looking at an account’s health, I look to see what percentage of search keywords are matching on a broad basis versus on a phrase or exact match basis. If broad is capturing a vast majority of searches, it usually means there is an opportunity in the account to boost efficiency by introducing more specificity/control by adding in more phrase and exact keywords with more specific ad copy.
To establish baseline metrics, I’ve pulled search data from 49 accounts. I analyzed a 12 month period starting with March 2020. In each case, I’ve summed total spend & calculated the percentage of spend for each match type. Notably, this analysis excludes "Smart" Shopping campaigns, which, by definition, do not have search term data associated with them (presumably because Google doesn’t want you to see what percentage of conversions are coming from branded terms, but that’s a topic for another time). Here are the averages & medians for each match type:
The average account spent a little over $900k over the 12 month period and matched 46% of spend to keywords on a broad match basis, 13% on a phase match basis, and 41% on an exact match basis. The median values are close to the averages, though we have a few large spenders in the data set that skew the spend numbers significantly.
Below is the raw data for the full set of accounts in case you are curious! (apologies for the tiny font)
Obviously, every situation is different! One major variable that skews this sort of analysis is whether or not your brand invests heavily in your own branded terms (which, as an aside, would make your brand an EXCELLENT candidate for incrementality testing). In that case, a lot of your brand spend is likely to match on a phrase or exact basis.
Personally, going forward I’m going to use a 50/50 rule… if broad is more than 50% of spend than it probably merits deeper investigation.
A) Create a Custom Report for any duration (usually we look at either the last 90 or 365 days, depending on the size of the account).
B) In your report, specify Search Keyword Match Type term as the Row. We typically add columns for Cost, Cost / conv., and conversions.
PS: If you have any feedback (especially critical feedback) on this approach I'd love to hear it... Adam @ thesistesting.com!
With the release of TikTok’s Creative Center, we’ve been taking a deep dive into the Top Ads section to find some trends in what is working on the platform. It’s a great resource for brainstorming new ad ideas for your ads.
At the suggestion of our Snapchat rep, we tested Snapchat's Lifestyle Categories that an account indexed strongly with to see how they performed relative to our standard broad targeting.