Many companies pay large amounts of money through corporate research to gain a small amount of information into their customer base. However, you are already spending money with paid search and you can use the insights from that spend to learn a lot about your customers through just good ad testing.
The first thing you want to do is list out the main questions you want to know, such as:
Then you need to determine how to test the question. For instance, if you want to know if consumers are price sensitive (and how), you can test:
If you want to know if location matters, then you test:
None of these tests are that difficult to determine – they just take a bit of thought. However, the setup and measurement can be complex.
Since you are looking for an insight across your customer base with these questions, you can’t just run a test in a single ad group. You need to run these tests across many ad groups and then look at the overall data changes for each test. Therefore, you need to be consistent in how you are creating these tests.
For instance, if you want to test a call to action versus free shipping versus a price, then you’ll create 3 ads in every ad group that leverages a similar headline and description line 1 and then the description line 2 will change for each ad, such as:
If you are testing geography versus non-geography in ads, then you might have one headline that contains the relevant geography and another one that does not for each ad group.
The ad setup process can take a little while depending on how many ads you need to create; but the insights are worth the setup time. The next step is the measurement of the ads.
The next step is determining how you want to measure the test. If your ad lines are very consistent, you can use pivot tables or software to help you measure the tests. If your lines vary a lot, or if you are testing multiple lines then you usually want to apply labels to the ads so you can combine the data at the ad label level.
For instance, in Adalysis, this is how we would setup the test:
Finally, we’d want to take a look at each line to see how it performed. You can run this data with pivot tables or have it automatically calculated for you with software.
Here’s the results for a similar test:
What we see is that the fourth line is the highest CTR, CPI, and Conversion rate. In fact, if the losing CTR ad didn’t run last month, these ads would have produced another 1,058 clicks and 58 more conversions.
This data would then tell us what mattered most to our customers (the winning ad line) and what mattered the least (the losing ad line).
We can take this information and then decide on the next steps. In this case, since the ‘best line’ only ran in 7 ad groups, we’d want to expand the test to include many more and make sure that this theory was correct across the entire account. If this test had been done across the entire account, then we could take those insights and use it to include the proper benefits on our website and then move on to the next test to gather even more insights from our customers.
Having customer insights is invaluable. Getting customer insights as part of your regular PPC optimization process means that you don’t need to spend excessive amounts of time and resources treating them as different projects. There is value in 3rd party research and deep customer insights. However, there are a lot of easy wins to be had in learning about your customers just by testing ads in a strategic manner.
If you need any help testing ads or want to make the process easier, take a look at Adalysis – powerful testing made easy.
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