Yes, you can do A/B testing on LinkedIn ads. A/B testing, also known as split testing, is an important tool for optimizing LinkedIn ad campaigns. It allows you to test different versions of your ads to determine which one performs better. By A/B testing elements like ad copy, images, headlines and call-to-action, you can improve your ad click-through rates, conversion rates and return on investment.
What is A/B testing?
A/B testing, or split testing, is a method of comparing two versions of something to find out which performs better. In digital marketing and PPC advertising, it involves:
- Creating two versions of an ad, landing page or email
- Showing one version to some site visitors/email recipients and the other version to others
- Measuring the response to both versions
- Determining which version drives more conversions
The version that drives more conversions is the “winner” that you rollout going forward.
Some key things to know about A/B testing:
- It can test any element including copy, headlines, images, calls-to-action, layouts, offers, subject lines and more.
- Each version should be shown to a statistically significant number of people for valid results.
- A/B testing is also called split testing or bucket testing.
- Free and paid tools are available to help run and analyze A/B tests.
Overall, A/B testing is essential for optimizing marketing campaigns and improving conversion rates over time.
Why A/B test LinkedIn ads?
Here are some of the key reasons to A/B test your LinkedIn ads:
- Improve ad relevance – Test different ad copy, headlines and images to create more relevance for your target audience.
- Increase engagement – Test calls-to-action and formatting to improve click-through rates.
- Boost conversions – Test landing page design and offers to increase LinkedIn ad conversions.
- Lower cost per conversion – Optimize ads to get more conversions for your budget.
- Outperform competitors – Continuously test and optimize to stay ahead of competitor ads.
In short, taking the time to repeatedly A/B test your LinkedIn ads will ensure you get the highest performance and returns from your ad spend on the platform.
What to A/B test for LinkedIn ads
Here are some of the key LinkedIn ad elements that you can A/B test:
Ad copy
The ad copy includes the headline, body text and description. Try different copy variations to see what resonates most. Test different headlines, call-outs, body length and value propositions.
Images
The visuals used in an ad influence performance. Test different types of images, as well as image text, sizes, shapes and more.
Calls-to-action
The CTA is critical for driving clicks. Test CTAs like “Register Now”, “Start Free Trial” and “Learn More” to see which get more responses.
Targeting
Test narrowing down or expanding your target audience, such as by seniority, job role, company size and interests.
Landing pages
Send traffic from ads to different landing pages, or test variations of the same landing page.
Offers
See if different offers or discounts affect your conversion rates.
How to set up A/B testing for LinkedIn ads
Here is a step-by-step guide to setting up your first LinkedIn ad A/B test:
- Create two ad variations – Make copies of an existing ad and edit elements like the headlines, text, images or CTAs.
- Set up conversion tracking – Make sure you have conversion tracking implemented on your website so you can measure performance.
- Create an A/B ad test campaign – In LinkedIn Campaign Manager, create a new campaign and select “A/B Test” as the objective.
- Add the ad variations – Add the two ad variations you created to the campaign as the split test.
- Set significant budgets – Allocate significant daily budgets to each ad variation to get enough data.
- Launch campaign – Review targeting and start running the campaign.
- Let the test run – Let the test run for at least one week before evaluating results.
- Review results – After sufficient time, review performance data and declare a winner.
- Implement winning variation – Pause the losing variation and implement the winning ad variation.
Be sure to continue iterating and testing new variations over time. Ongoing testing is key to continuous optimization.
Tools for A/B testing LinkedIn ads
Here are some tools you can use for A/B testing LinkedIn ads:
- LinkedIn Campaign Manager – Create split tests directly within LinkedIn’s ad platform.
- Google Optimize – Use this free A/B testing tool for landing pages.
- Optimizely – Leading A/B testing tool for websites and landing pages.
- Unbounce – Create and optimize high-converting landing pages.
- VWO – Visual Website Optimizer is an easy-to-use testing tool.
Many analytic tools like Google Analytics also include built-in A/B testing capabilities you can leverage.
Optimizing your LinkedIn ad A/B tests
Here are some tips to optimize your LinkedIn ad A/B tests for better results:
- Test one element at a time – Only test one variable, like the headline or image. Don’t change everything at once.
- Try big changes – Make bold changes between variations to see noticeable differences in performance.
- Use relevant creatives – Ensure your ad copy and visuals closely relate to your offer for higher relevance.
- Target properly – Make sure both ad variations have the exact same targeting parameters.
- Check daily – Monitor daily performance trends to identify the winning variation sooner.
- Run tests long enough – Let tests run for at least 7 days to account for fluctuations.
- Analyze results – Review statistical significance and make data-driven decisions.
Following best practices for your test setup and execution will help you get actionable results from your LinkedIn ad experiments.
Benefits of A/B testing LinkedIn ads
Here are some of the benefits that proper LinkedIn ad A/B testing can bring:
- Higher click-through rates – Find headlines, images and CTAs that drive more clicks.
- Improved conversion rates – Increase signup rates, leads and sales from LinkedIn ads.
- Better ROI – Lower your cost per conversion and improve return on ad spend.
- Greater relevance – Create more relevant ads through testing ad copy and targeting.
- Increased engagement – Drive higher post comments, likes, shares and overall engagement.
- Faster learning – Quickly test what resonates with your audience.
- Lower risk decisions – Take the guesswork out by leveraging hard data.
- Continuous optimization – Ongoing testing means your ads are always improving.
In short, A/B testing allows you to make data-driven decisions that increase your LinkedIn ad results over time.
Common LinkedIn ad A/B testing mistakes
Here are some common mistakes to avoid when A/B testing LinkedIn ads:
- Changing too many variables – Only test one element at a time between variations.
- No tracking – Make sure conversion tracking is implemented so you can measure performance accurately.
- Small budgets – Allocate significant budget to each variation to collect enough data.
- Unequal targeting – Ensure targeting settings are exactly the same between variations.
- Stopping too early – Run tests for at least one week before evaluating results.
- Creative fatigue – Don’t reuse the same creatives repeatedly in different tests.
- No statistical checks – Confirm results are statistically significant, not just random chance.
- No clear winner – Don’t end a test without a definitive winning variation.
- Not iterating – Conduct regular tests building on what you learned previously.
Avoiding these common pitfalls will ensure you get statistically valid, actionable results from your LinkedIn ad experiments.
Frequently Asked Questions
How much budget do I need for LinkedIn A/B testing?
You should allocate at least $5/day to each ad variation you are testing. The minimum daily budget is $10 total across both ads to run an A/B test campaign on LinkedIn.
How long should I run my LinkedIn ad A/B tests?
Run your tests for at least one full week before evaluating results. This gives enough time to account for daily fluctuations in performance.
How much should ad variations differ in an A/B test?
This depends on what you are testing, but in general you want bold differences between the ad variations to get noticeable results. Change images, headlines, copy, CTAs etc.
Can I automate LinkedIn ad A/B testing?
Yes, tools like AdEspresso, Adstage, and others allow you to automate the process of creating variations, testing, and analyzing results. But you still need to manually implement the winning variation.
Should I pause the losing variation when the test ends?
Yes, when you conclude the test and have a definitive winner, you should pause the losing variation and reallocate its budget to the winning ad.
Conclusion
A/B testing is a highly effective tactic to improve the performance of your LinkedIn ad campaigns. By testing elements like ad copy, visuals, calls-to-action and offers, you can optimize your ads to maximize click-through rate, conversion rate and ROI.
Take the time to properly set up your tests, run them for an adequate duration, and thoroughly analyze the results. Ongoing iteration and testing will ensure your LinkedIn ads stay highly tuned for relevance and engagement. Following best practices will help you turn LinkedIn advertising into a data-driven optimization process over time.