Picture this scenario — Your team created a great marketing campaign with a mixture of landing pages, google ads, Facebook ads, and radio ads. For landing pages, google ads, and Facebook ads, you can A/B test different formats.
You know that with radio advertising, you will reach the largest audience for the lowest price. Unfortunately, you have no way of knowing which radio ad format will drive listeners to your website. That is the information we cannot know today, and if we were to wait until those people visited the website, it would be too late to optimize their experience.
This seems to a classic traditional media Catch-22 situation!
TuneURL technology gives you conversion data for each radio ad (date and time) to analyze which format worked best, which show worked best, and which time worked best. You can learn which time works best for exposure, interest, and acting on a call-to-action.
Table of contents:
- What is A/B testing?
- Mistakes we must avoid while conducting the A/B test
- When to use A/B test
What is A/B testing?
A/B testing (also known as bucket testing or split-run testing) is a user experience research methodology. A/B tests consist of an experiment with two variants, A and B.
Experimentation with advertising campaigns, which has been compared to modern A/B testing, began in the early twentieth century. The advertising pioneer Claude Hopkins used promotional coupons to test his campaigns' effectiveness, which Hopkins described in his book, Scientific Advertising.
An example, you own a company and want to increase the foot traffic to your store. There are different advertising methods you can do on the radio. For instance, you can give away discounts, use personality sponsorships, or just share your website. Here, either you can use random experiments, or you can apply scientific and statistical methods. A/B testing is one of the most prominent and widely used statistical tools.
In the above scenario, you pick two methods — A and B. Based on the audience's response, who respond better to method A and B respectively, you try to decide which is performing better.
When Should You Use A/B Testing for Radio?
A/B testing works best when testing incremental changes, such as time, tone, voice actor, method, ad length. Here you may compare to decide which works best for the exact audience you hope to reach.
A/B testing doesn’t work well when testing major differences, like rock vs. talk, regional differences, or completely new user experiences. There may be effects that drive higher than normal engagement or emotional responses that may cause listeners to behave differently in these cases.
To summarize, A/B testing is at least a 100-year-old statistical methodology, but in its current form, it comes in the 1990s. Now it has become more eminent with the online environment and availability for big data. It is easier for companies to conduct the test and utilize the results for better user experience and performance.
There are many tools available for conducting A/B testing, but you must understand the factors working behind it. You must also be aware of the statistics to validate the test and prove its statistical significance.
To know more about hypothesis testing, I will suggest you read the following article:
If you would like to learn more about our product, please come to our website: www.tuneurl.com.