Want to more reliably test key variables in your Google AdWords accounts? Try these two tests.
Issues of test design, keyword auction dynamics, and statistical significance are complex. To help marketers more reliably test key variables in their AdWords accounts, Google has created a killer little feature called AdWords Campaign Experiments.
You've no doubt heard of the concept of A/B testing landing pages by evenly rotating them to website visitors. The same concept, applied to ad copy, is behind the ad rotation feature in major paid search platforms, introduced by Google in February 2002. (Note documentation in today's version of the feature. Ever sticklers for accuracy, Google now promises to rotate ads "more evenly," not entirely "evenly.") The reason these tests must be done with strict form is, of course, the scientific notion of control and an experimental group that lend statistical validity to any experiment. As much as possible, all other factors must be held constant to gain insight into the independent impact of changes in a single variable. Since those changes in themselves can touch off a dynamic response involving other variables, the two traffic streams being compared must be run concurrently with no bias in conditions.
The same principle can now be applied to various elements of your account, like keyword bids, matching options, ad group structure, and the presence or absence of certain keywords. That, in a nutshell, is what the AdWords Experiment feature does.
Although it's bound to be developed further in the future, the Experiments setup already has some flexibility. You can decide whether to split the traffic flowing to the experiment and control groups 50-50, for example, or some other proportion.
Make no mistake: a lot of complexity lurks behind a simple test. So it doesn't pay to get too fancy at first, either in testing or in explanation.
So here, I'll start the conversation with two simple uses of the Experiments feature: (1) testing the (ROI and volume) impact of significant increases in bids on a few high-value keywords; (2) adding a new match type for a keyword to an ad group, overlapping with the same keyword that uses a different match type.
This change creates a potential cascade of responses. Just by moving ad position and possibly showing up in the top ("premium") positions more of the time, you speculate that you're likely to see drastically different conversion behavior, to say nothing of increases in CTR, impressions, clicks, sales, etc. The impact on sales volume and ROI is unknown. And as we saw from a previous bid test that did not use the Experiments setting, it's more than a simple matter of being "slotted" in a higher ad position due to your higher bid. Due to the complex way that Google Quality Score works, you may be "eligible" to show up more often. So many moving parts! Gaaah!
Experiments, then, are going to come in very handy. You don't really want to run a serial test as we did previously, as helpful as it was. You'll be left with experiment results that feel helpful, but are statistically invalid. Experiments will allow the dynamics of either bid amount to play out on their own, splitting the two bid settings evenly and concurrently.
And what statistics will you be interested in? With a 60 percent higher bid running against your original bid, and assuming you have AdWords conversion tracking set up to your liking, you will quickly see:
How does the interface handle the issue of statistical significance in results? There is a notation system that shows one arrow for the resulting outcome being 95 percent likely to be accurate (not random), two arrows for 99 percent, and three arrows for 99.9 percent. This way, you can decide for yourself if the change was worth it, even if the differentials in outcome are small.
Prima facie. I tend to believe that the new modified broad match option is more cost-effective than the ordinary broad match setting that allows for "expanded match." But wouldn't it be nice to see valid statistics for an ad group showing – not before and after – but concurrent A/B, what the ad group does with and without the addition of, say, the keyword: +wedding +dresses. So, I'd like to come in with the modified broad match on that keyword, and bid it higher, and then see how the two versions of the ad group do on all the core metrics, especially transactions and cost per order.
Now, with Experiments, we can do just this.
To me, the above tests are in themselves quite complex. Before rushing into esoteric experiments, try simple tests, building on the solid fundamentals of what you know about the workings of your campaigns. This setting isn't for amateurs. Even pros should take care not to overdo it. Remember, you're a marketer first, and a mad scientist in your spare time.
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Goodman is founder and President of Toronto-based Page Zero Media, a full-service marketing agency founded in 2000. Page Zero focuses on paid search campaigns as well as a variety of custom digital marketing programs. Clients include Direct Energy, Canon, MIT, BLR, and a host of others. He is also co-founder of Traffick.com, an award-winning industry commentary site; author of Winning Results with Google AdWords (McGraw-Hill, 2nd ed., 2008); and frequently quoted in the business press. In recent years he has acted as program chair for the SES Toronto conference and all told, has spoken or moderated at countless SES events since 2002. His spare time eccentricities include rollerblading without kneepads and naming his Japanese maples. Also in his spare time, he co-founded HomeStars, a consumer review site with aspirations to become "the TripAdvisor for home improvement." He lives in Toronto with his wife Carolyn.
This Magic Quadrant examines leading digital commerce platforms that enable organizations to build digital commerce sites. These commerce platforms facilitate purchasing transactions over the Web, and support the creation and continuing development of an online relationship with a consumer.
Google reports that paid search ads are currently driving 40+ million calls per month. Cost per click is increasing, paid search budgets are growing, and mobile continues to dominate. It's time to revamp old search strategies, reimagine stale best practices, and add new layers data to your analytics.
June 10, 2015
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