Once you’ve spent well over a decade mastering the art of the ideal pay-per-click campaign structure, you tend to start taking certain so-called “best practices” on faith. Concepts like device-segmenting and geo-segmenting becoming no-brainers, not even worth the time it takes to question whether or not they are a good idea. However, a recent issue with a new client has made me rethink and question everything I thought I knew about PPC.
This problem started after I sold a new client on my services, which revolved around creating the ideal campaign structure.
“It is important to hyper-segment your campaigns so that you get the most out of your advertising spend,” I said proudly. “Customers in New Orleans will respond differently to your product than customers in Des Moines, and therefore, your campaigns should be segmented so you can bid differently in one region than you would in another.”
I really sold it. The client was impressed. I then went on to talk about the trivial nature of keyword match types.
“Exact match keywords are exactly what the user typed. If the keyword is extremely relevant to the ad it serves and the landing page is extremely relevant, also, then Google will reward you by raising your quality score for that keyword, which will lower your cost.”
I was on a roll…
“Exact match keywords are going to be much more relevant to your ad and landing page than broad match, because you’re targeting exactly what that user is looking for with your keyword, ad, and landing page.”
Damn, I am good…
So the client was sold, and I began the process of building out their campaign based on what we had discussed. That is where we ran into some issues that I’m still trying to sort out.
I created, as I always do, segmented campaigns. Each campaign targeted only the U.S. I like to start there and segment by state, then city once I get enough data to start selecting the highest performing areas for the particular product or service. I created separate campaigns based on device type: a computer campaign, a mobile campaign, and a tablet campaign since people behave differently on each device.
I then segmented by keyword match type. Each campaign was exactly the same with the same ad groups and ads. The only difference was device targeting and keyword match type. The types I selected were modified broad match, phrase, and exact. I knew there would be some natural crossover, but I just wanted to get the campaigns live, and I would sort out any duplicate targeting issues later.
Here is a breakdown of the campaigns I created:
- U.S. Non-Brand – Computers (Modified Broad)
- U.S. Non-Brand – Mobile (Modified Broad)
- U.S. Non-Brand – Tablet (Modified Broad)
- U.S. Non-Brand – Computers (Phrase)
- U.S. Non-Brand – Mobile (Phrase)
- U.S. Non-Brand – Tablet (Phrase)
- U.S. Non-Brand – Computers (Exact)
- U.S. Non-Brand – Mobile (Exact)
- U.S. Non-Brand – Tablet (Exact)
I thought about adding a negative exact match keyword to the phrase and broad matches but decided against it since I just wanted to get some traffic going first before refining the campaign. What happened over the next several weeks startled me.
I will say again that every campaign was an exact duplicate of every other campaign. The only difference was the device targeting and keyword match type. The first thing I noticed was that my cost per click on the exact match campaigns was almost double the duplicate modified broad match campaigns. This really bothered me because exact match is supposed to perform better since you’re targeting the exact search term. At least, that’s what I thought.
I dove in further and discovered that Google was apparently penalizing my campaign structure. Specifically, it was penalizing my exact match campaigns and rewarding my modified broad match campaigns.
Here is a breakdown of what my average quality score looked like:
Average Quality Score
|U.S. Non-Brand – Computers (Modified Broad)|
|U.S. Non-Brand – Mobile (Modified Broad)|
|U.S. Non-Brand – Tablet (Modified Broad)|
|U.S. Non-Brand – Computers (Phrase)|
|U.S. Non-Brand – Mobile (Phrase)|
|U.S. Non-Brand – Tablet (Phrase)|
|U.S. Non-Brand – Computers (Exact)|
|U.S. Non-Brand – Mobile (Exact)|
|U.S. Non-Brand – Tablet (Exact)|
Obviously, Google favored my modified broad match over my phrase match and favored my phrase match over my exact match. This is exactly the opposite of everything I ever thought I knew about PPC marketing. This prompted me to satisfy my curiosity by testing out a duplicate campaign that targeted just broad match (without the modifier). The results after just a week were stunning:
Average Quality Score
|U.S. Non-Brand – Computers (Non-Modified Broad)|
|U.S. Non-Brand – Mobile (Non-Modified Broad)|
|U.S. Non-Brand – Tablet (Non-Modified Broad)|
Quality score is directly tied to what you end up paying per click, so it’s no surprise that my average cost per click for the new non-modified broad match campaign dropped considerably. In some cases, I was now paying only $1 per click, whereas I had been paying upwards of $3 for the same modified keyword. Another interesting note is that my average ad position jumped up several positions. I now had the #1 ad, instead of the #2 ad or the #3 ad, for most keywords, and I was paying a fraction of what I had been paying for more specific, modified, broad match keywords.
So why would Google penalize you for targeting exact match, phrase match, and even modified broad match, while favoring non-modified broad match instead?
Perhaps their recent default “feature” that targets synonyms and misspellings on phrase and exact match keywords is a hint. It’s apparent that Google doesn’t want you to hyper-target. They don’t seem to care that exact match, targeted, long-tail keywords will obviously perform better for most businesses that are focused on leads or sales than broad match, one-word or two-word keywords. The fact that you typically can’t target a keyword made up of four or five words because they will immediately deactivate it based on “low search volume” is a perfect example of that. If I have a “George Foreman Super Champ Power Press Grill” for sale, then why should I be prevented from targeting that exact term?
It appears instead that Google just wants you to build some poorly structured, broad match campaigns, trust their better judgement, hand over your credit card, and shut the hell up. That’s the impression I got from their customer support, which kept reciting the same tired script over and over to me whenever I called.
Unfortunately, this article ends on less of a solution than I’d like to offer. Basically, you have two choices: advertise on Google and do whatever they want you to do or skip search engine pay-per-click advertising entirely. Bing’s market share is still only a meager 15% in the U.S. with only a 4.4% global market share, and I don’t see any other competitors, other than Baidu, who are useful if you’re targeting China.
So we are stuck with Google favoring their profits over the success of our campaigns. The solution? Stop segmenting traffic. Build a non-segmented campaign. Group all the device types, all the keyword match types, all the regions you sell in, and don’t even think about day-time parting! Let that campaign build traffic and quality score, and then, maybe, just maybe, you can “test” segmenting it out after maybe six, nine, or even twelve months. Perhaps by then Google will have figured out that it can’t screw its customers for long before they cancel their credit cards.