Mike Ruins Standard Search Query Reports

When it comes to search query reports, most people manually download their reports from the AdWords interface and it comes out looking something like this…

 

One query per line that you can filter and sort at will.  Then you sort by largest-to-smallest Impressions or Cost or whatever and work your way down the list analyzing one query at a time.  If you’re like me, you get to about 50 to 100 queries before you start zoning out and want to look for something a little more entertaining to do.

You’re ignoring the long tail

So that report you downloaded has thousands of queries…and you only review 50 to 100 of them.  That seems sub-optimal, right?!?!  After all, the savvy search marketer that you are knows he long tail is YUUUGE.

So you’re looking at the overall most popular search terms, but you’re getting a blurry picture of what’s really going on collectively.  If there’s a specific irrelevant word in 1,000 terms that get 1 impression each (exaggeration of course), you never see it because you don’t ever get to terms with 1 impression.  So you end up missing the opportunity to exclude it and save some cash.

If only there was a way to weight words by how many impressions they occurred in or how much cost they accrued across all search terms…

Use N-Grams instead

N-grams are a way to organize text to see how many occurrences of each word or phrase is contained within a set.  For your SQRs, an n-gram can answer the question, “How many impressions contained the word widgets?” or “What words did I spend the most advertising dollars on?”  Basically, this consolidates your normal search query report into something much more usable and effective.

Let’s take a look at the same report from above in an n-gram format…

 

 

Upon review, you’ll notice some things.  For example, the word “oklahoma” only appears in 2 queries in the first report.  But it’s the 6th-most popular word across all queries in terms of Impressions.  By consolidating the data this way, you can then do things like filter your original SQR for all queries with this word in it…

 

 

Now you can quickly and easily add the phrases that contain this all-important word all at once instead of skipping over those that were past the top 50 and you never got to.

Or how about this…what if we filter for the highest Cost words that have driven 0 conversions?

 

 

The 2nd and 3rd-highest Cost keywords (“chemistry” and “algebra”) don’t show up in the top keywords of your first report.  But they do here.  These may not even be relevant to the advertiser.  But now you know they’re spending money without driving Conversions.

Analyze multi-word phrases

Not only can you access 1-word reports, but 2 and 3-word reports as well.  Think about how helpful this is when prioritizing the granular ad groups to create to write more targeted ads for searchers.  Take a look at this 3-word n-gram for an ad group targeting excel training…

 

 

Now you know you can prioritize granular ad groups for online, advanced, beginner, etc.

Resources

So how do you set this up?  There are some paid tools that make this easier by including this as a feature – like AdAlysis.  But there are also free ways to implement this like this AdWords script.  Whichever way you choose, you’ll say goodbye to standard SQRs and speed up your keyword addition and exclusion process.

 

 

What's Next?

Make sure to check out more posts in the Mike Ruins Digital Marketing series, where I challenge the status quo by tackling digital marketing topics that most practitioners have all wrong.

Mike Fleming

Mike Fleming is a Senior Client Manager for Point It, and has been managing PPC accounts of all kinds for over 6 years; with a strong emphasis in Analytics and Conversion Optimization. He’s a respected digital marketing blogger and speaker whose articles can be found on industry blogs like SEMRush.com and SearchEngineGuide.com. He also contributed to a published book called The Best Damn Web Marketing Checklist, Period!. Mike enjoys playing, writing and recording music, playing basketball and investing. He resides in Canton, Ohio with a girl who threw a snowball at him one day…then married him.

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