In the fast-paced world of digital marketing, it is all about staying one step ahead of the competition and driving results based on informed decisions, especially when it comes to your PPC campaign.
Successful PPC efforts can have many benefits, including increased brand awareness and reach, specific audience targeting, and increased visibility on search engine results pages.
However, for your campaign to be successful, you need to consider user preferences and behaviours, the effectiveness of your content, and more importantly, what is and isn’t working.
That is where A/B testing can become an inherent part of your PPC campaign management strategy.
Continue reading to learn about how you can maximise your PPC campaign’s potential using A/B testing, the benefits of conducting one, and how to run an A/B test.
What is A/B testing?
A/B testing, otherwise known as ‘split testing,’ is a form of market experimentation whereby you create two variations of a campaign, web page, or ad to compare their performance.
You can also run your test on landing pages, apps, and emails, as well as specific elements of your campaign, including headlines, call to action, and the keywords your ad appears for.
To perform an A/B test, you will need to split your audience randomly into two groups, ensuring each group only sees one version of your campaign at the same time.
Conducting your tests simultaneously ensures that you have a fair comparison between the two versions and reduces the chance of factors such as differences in user behaviour skewing your results.
What are the benefits of using A/B testing for your PPC campaign?
Learn about your audience
This is perhaps one of the most important benefits of A/B testing. You can gain a comprehensive understanding of your audience’s behaviour, that will in turn inform how you adapt and enhance the user experience.
You can pick up valuable insights such as:
- the colours your audience prefers
- how they respond to various multimedia
- the buttons they are most likely to click on
- which call to action they respond to more readily
Moving forward, you can then use this data to inform and structure future campaigns.
Adobe states that split testing can help you gain a greater understanding of what motivates your target audience – and how you can use this knowledge to ensure they choose your brand over others.
Reduce bounce rate
Are you finding that users are not engaging with your content or that they are leaving your website just moments after landing on the page?
A/B testing can provide invaluable insights into what is causing this issue, so you can rectify it as soon as possible.
Looking into why users are not interacting with your campaign can only be seen as a positive, as this allows you to get creative and assess aspects of your site you may not have considered before.
For example, you can check:
- if your content is optimised correctly for both desktop and mobile
- if your message is effective by testing different variations of headlines, call to actions or message length
- what language your users best respond to by experimenting with different tones of voice or comparing descriptive and direct language
- if your content’s structure and layout could be improved by testing different paragraph lengths, trialling variations of headings and subheadings, and including imagery
Cost-effective
A/B testing can be seen as a cost-effective research method, as it provides detailed insights into what is working and what is not, allowing you to re-target your efforts and avoid dipping into your budget unnecessarily.
In addition, A/B testing allows you to focus and only invest in the changes that are going to matter most to your target audience, avoiding guesswork and saving you time and money that you could be spending on needless resources.
Improve conversion rate and click through rates
Using A/B testing to inform your PPC campaign optimisation is a great way to ensure that your target audience responds in a desired way; after all, you have designed it to suit their needs, behaviours, and responses.
For example, if you have effectively tailored your ad design and copy using the findings from A/B testing, this could result in users wanting to interact with other aspects of your brand, such as signing up to your mailing list or responding to the call to action to invest in your product or services.
Less risk, more reward
By utilising A/B testing throughout your PPC campaign, you can test a range of ideas without it having any negative impact on your brand.
AB Tasty states that if you did not reap the benefits of A/B testing, you would never be able to know if your audience would respond positively to your ideas or if they were worth the investment.
Conducting tests allows you to present ideas and innovations to a select audience before issuing them out to the wider world, so you can learn what is really driving and influencing your users.
As with any experiment, do not worry if you thought you were onto a great idea, but the results say otherwise. Use it as a learning opportunity to inform your campaign going forward.
How can I carry out A/B testing on my PPC campaign?
Define your goals
The first and most important step before carrying out A/B testing is to assess your current performance using tools like Google Analytics, SEMrush, and Google Search Console, to easily identify any areas of concern.
By doing this you can then decide what needs improving and what you hope to achieve.
To achieve your goals, it is essential that they are clear and measurable. A useful way to do this is by using the S.M.A.R.T. acronym, so your goals are, Specific, Measurable, Attainable, Relevant, and Time-bound.
Don’t worry if you find more than one element you would like to test; this is natural when looking to make improvements to your PPC campaign.
SEMrush advises listing the potential tests you would like to run in priority order – i.e. which is going to have the most impact.
Choose a variable to test
After conducting your research, you will have a better idea of which variables you would like to test.
For example, you can look at your:
- ad headline
- ad copy
- call to action
- landing page content
- ad position
- keyword performance
- ad design and format
Once you have chosen the variable you would like to test first, this can then help you form the hypothesis for your A/B test.
Decide on your test hypothesis
Choosing a hypothesis for your test is essentially making a prediction of what you expect to happen to your chosen variable at the end of the test.
For example, your hypothesis could be as simple as ‘shorter call to actions will increase click- through rates more than longer call to actions.’
Having a hypothesis gives your A/B test a structure and a framework to follow so that you can draw meaningful conclusions that will have an impact on your business’s results.
Create two variations based on your hypothesis
When creating the variations, it is important to remember that you will need to test two different versions of the same content.
HubSpot states that you should be testing the original version of your content, known as the ‘control variable,’ and a new version, otherwise known as the ‘treatment variable.’
For example, if you are finding that users are not responding to your current ad, you could create a new version that includes different colours or a tweaked call to action.
Testing two versions allows you to draw an informed comparison between the variations, so you can react accordingly.
Run your test
You have done all the research, you have formed your hypothesis, and you have created your variations, so now it is time to run your test.
Use the below as a handy checklist to ensure your A/B testing runs smoothly:
- Split your sample groups randomly and equally
- Ensure you are only testing one variable at a time, so you can accurately assess what is having the most impact on your content’s performance
- Test your variations at the same time to avoid market changes impacting results
- Make sure the treatment variable is not indexed by search engines, until you are sure which has been most successful
- Once you have identified the winning variation, you can then reindex
- Give your test enough time to gather meaningful data
Analyse the results and implement your findings
Once you have gathered all the information you need, you can start looking at what the data tells you.
It is important to remember that A/B testing can be used to prove and disprove hypotheses, so do not be disheartened if the results are not what you were hoping for, as this is still valuable data.
You can use this as an opportunity to learn what has not worked and formulate innovative ideas for your next test.
Alternatively, if your hypothesis has been proven, you can start applying the required changes to your content based on your findings.
You can then continue to monitor the results and return on investment over time while planning your next A/B test to further optimise your PPC campaign.
A/B testing: A win for PPC optimisation
A/B testing is an invaluable tool for getting the most out of your PPC campaign, improving your ad performance, and staying ahead of your competitors.
However, it is essential that any A/B tests carried out as part of your PPC strategy have a detail-oriented approach, plenty of research, and that changes are not made for the sake of it.
Need help with A/B testing?
Now that you are aware of the impact A/B testing can have, if you would like some help applying it to your PPC campaign, get in touch today to see how we can help.
We also offer Free Acquisitions Workshops, where you can gain expert advice from our experienced marketers, a tailored action plan to keep you on the right track, and free resources to kick-start your strategy.
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