Testing Pay Per Call Ad Variations
The primary objective of testing is to continually refine pay-per-call landing pages in order to maximize pay-per-call earnings. Pay-per-call advertising can only be considered effective when there’s a constant testing of different variables such as headlines, calls to action, and value propositions. The first set of tests should address broad concepts, examining emotional versus rational appeals for pay-per-call plumbing leads in terms of fear-based messaging (“Stop water damage now!”) as opposed to solution-based techniques (“Expert leak repair in 60 minutes”). Using your pay-per-call tracking software, assess whether the call volume, call quality, and required call duration are all measuring up to your expectations. A number of pay-per-call networks propose that one test entirely different campaign structures rather than only adjusting ads for existing ones. For an example, in pay-per-call HVAC leads, separate campaigns should be set up for emergency calls and scheduled maintenance to find out which positioning leads to a higher number of qualified calls.
Driven Optimization Methods
To up your pay-per-call earnings, refine your testing with these advanced strategies. For example, multivariate testing can be done once more call traffic allows for a grading of multiple facets together; one might find that certain images only work with certain headlines. Test various call extensions and click-to-call placements to reduce any friction in the call process. Many successful pay-per-call affiliate programs deploy heat mapping tools to collaborate with pay-per-call service data to know exactly how ad variations influence user behavior before the call. For long-term services like pay-per-call legal services, try testing long-form against short-form to see which drives more qualified inquiries. And always remember that it takes traffic to test pay-per-call ads! Usually around 50 calls minimum per variation should be enough to reach statistical significance considering natural call quality variability.
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