A/B Test Linkedin Ads With Artificial Intelligence (AI)

Creating good Ad copy can be a hassle. Regardless if you’re a seasoned copywriter or not a copywriter at all; producing good, sticky, punchy copy from a variety of angles and en masse, can get pretty exhausting.

Especially if it’s not just the one Ad campaign you’re running.

There’s gotta be a better way to come up with ideas, or to create variations based on what you’ve already identified as a (potential) winner

Why not let AI help you?

How AI helps you create great LinkedIn Ads A/B Tests

Let’s assume you’ve already been running LinkedIn ads for some time.

You’ve gathered your data and know which ads have been performing best.

Now. It’s time to test new variations. Here’s where the problem comes in. How do you step outside of ‘the known’ and create new variations?

Let’s see how an AI-Powered Paraphrase Generator can help.

For this exercise, we want to A/B test the Intro Text.

To add some spice to this exercise, let’s take an existing LinkedIn ad HubSpot created.

The text we’re going to be creating an A/B test for is as follows:

Spend less time wrangling clunky software and more time generating leads, closing deals, and helping customers.

Here are variations AI has provided us by simply entering that ad copy in the Paraphrase Generator:

• Cut the amount of time you spend using complex software and use that time to increase leads, close deals, and provide assistance to clients.
• Use up less of your valuable time wrangling obtrusive software, and more of it making connections, sealing the deal and aiding customers.
• Instead of wasting time trying to navigate complicated software, you can invest more energy generating leads, negotiating contracts and aiding customers.
• Free up your time from inefficient software and use it to generate leads, seal deals, and assist customers.

Create bolder LinkedIn ad A/B tests

The examples above were slight variations of the original LinkedIn ad. The more you change the input or the larger the copy, the more variation you’ll get.

But let’s say you want to create bolder A/B tests. Let’s say you want to go with the same angle ‘generating leads‘ + ‘not wasting time’ but want to explore the fields a bit.

Enter our Social Media Ad Copy Generators.

This time, I’ve used one of the paraphrased examples:

Free up your time from inefficient software and use it to generate leads, seal deals, and assist customers

Here are the outputs I’ve received:

• Are you spending countless hours on tedious software? Free up time in your day, and increase efficiency by generating leads quickly with our easy-to-use
• Put in the hard work without wasting time on inefficient software. Take advantage of our lead generation features, which will help you get the most out of your
• Streamline your bussiness operations and free up valuable time to generate leads, seal deals, and build relationships with customers. Automate tedious tasks with
• Unlock the power of efficient lead generation software and free up your time for what’s important. Find more leads, close bigger deals and serve customer needs
• Are you struggling to find the time to take on new leads and close deals? Our lead generation software can do that for you. Let us free up

As you can see, the AI is now free to find new angles like ‘Streamline your business operations’, ‘Unlock the power of efficient lead generation’, etc.

Perfect your A/B test by adding your human touch

We don’t advise using outputs generated by AI without adding your unique human touch. As you can see, AI helped us find new variations within two clicks of a button which sparks creativity. It’s up to you to perfect your ad copy and run awesome LinkedIn ads.

FAQ

What is A/B testing for LinkedIn ads, and why is it important?

A/B testing for LinkedIn ads involves creating multiple versions of an ad and testing them against each other to determine which performs better in terms of engagement and conversions. It’s important because it helps advertisers optimize their ad campaigns for maximum effectiveness and ROI.

How do you set up an A/B test for LinkedIn ads?

To set up an A/B test for LinkedIn ads, start by defining your objectives and selecting the elements you want to test, such as ad copy, imagery, targeting criteria, or call-to-action buttons. Then, create multiple variations of the ad, assign them to different audience segments, and run the campaign simultaneously to collect data.

What are some common elements to A/B test in LinkedIn ads?

Common elements to A/B test in LinkedIn ads include headline variations, ad copy length and messaging, imagery or video content, ad formats (e.g., single image vs. carousel), targeting criteria (e.g., job title, industry), and call-to-action buttons.

How do you measure the results of an A/B test for LinkedIn ads?

To measure the results of an A/B test for LinkedIn ads, track key performance indicators (KPIs) such as click-through rate (CTR), conversion rate, cost per click (CPC), and return on ad spend (ROAS). Compare the performance metrics of each ad variation to determine the winning version.

What statistical significance level should you aim for in A/B testing LinkedIn ads?

In A/B testing for LinkedIn ads, it’s recommended to aim for a statistical significance level of at least 95%. This ensures that the differences observed between ad variations are unlikely to be due to random chance and are statistically significant.

How long should you run an A/B test for LinkedIn ads?

The duration of an A/B test for LinkedIn ads depends on factors such as the size of the audience, the frequency of ad delivery, and the desired level of statistical confidence. Generally, it’s recommended to run the test for at least one to two weeks to capture sufficient data.

What are some best practices for A/B testing LinkedIn ad creatives?

Best practices for A/B testing LinkedIn ad creatives include testing one variable at a time, ensuring each variation has a clear objective, using a large enough sample size, and analyzing results based on meaningful performance metrics to inform future optimizations.

How can you use audience segmentation in A/B testing LinkedIn ads?

Audience segmentation in A/B testing LinkedIn ads involves dividing your target audience into distinct segments based on demographics, job titles, industries, or interests. By testing different ad variations on specific audience segments, you can tailor your messaging to better resonate with each group.

What tools or platforms can you use to A/B test LinkedIn ads?

There are several tools and platforms available to A/B test LinkedIn ads, including LinkedIn Campaign Manager, third-party advertising platforms such as Google Optimize or Optimizely, and marketing automation software that integrates with LinkedIn’s ad API.

How often should you A/B test LinkedIn ads?

It’s recommended to A/B test LinkedIn ads regularly, especially when launching new campaigns, introducing significant changes to ad creative or targeting, or aiming to optimize performance. Testing on an ongoing basis allows advertisers to stay agile and continuously improve their ad campaigns.

What are the benefits of A/B testing LinkedIn ads?

A/B testing LinkedIn ads offers several benefits, including improved ad performance, better understanding of audience preferences, increased return on investment (ROI), and data-driven insights for future campaign optimization.

Can A/B testing LinkedIn ads help improve ad relevancy?

Yes, A/B testing LinkedIn ads can help improve ad relevancy by testing different messaging, visuals, and targeting options to ensure alignment with audience interests and needs, ultimately leading to higher engagement and conversions.

How can you ensure accurate results in A/B testing LinkedIn ads?

To ensure accurate results in A/B testing LinkedIn ads, it’s essential to maintain consistent testing conditions, use proper tracking and attribution methods, avoid biases in sample selection, and adhere to statistical principles when analyzing data.

What types of ad formats can be A/B tested on LinkedIn?

Various ad formats on LinkedIn can be A/B tested, including sponsored content (single image, carousel, video), text ads, message ads, and dynamic ads. Each format offers opportunities for testing different creative elements and targeting options.

Is it necessary to A/B test LinkedIn ads with a large budget?

A/B testing LinkedIn ads can be beneficial regardless of budget size. Even with limited resources, advertisers can test small variations in ad creative or targeting to identify effective strategies and optimize performance within their budget constraints.

How can you determine the winning variation in an A/B test for LinkedIn ads?

The winning variation in an A/B test for LinkedIn ads is determined by comparing performance metrics such as click-through rate (CTR), conversion rate, cost per acquisition (CPA), or return on ad spend (ROAS) between the different ad variations.

Are there any common mistakes to avoid when A/B testing LinkedIn ads?

Common mistakes to avoid when A/B testing LinkedIn ads include testing too many variables simultaneously, misinterpreting results without statistical significance, neglecting to monitor campaign performance during the test, and not implementing learnings into future campaigns.

How can you leverage A/B testing to improve ad targeting on LinkedIn?

A/B testing can help improve ad targeting on LinkedIn by testing different audience segments, demographics, job titles, or interests to identify which segments respond best to specific ad messaging, allowing for more precise targeting in future campaigns.

Can A/B testing LinkedIn ads help optimize bidding strategies?

Yes, A/B testing LinkedIn ads can help optimize bidding strategies by testing different bid amounts, bidding strategies (e.g., automated bidding vs. manual bidding), or bidding optimization settings to maximize ad performance and budget efficiency.

What role does iterative testing play in A/B testing LinkedIn ads?

Iterative testing in A/B testing LinkedIn ads involves continuously refining and iterating on ad variations based on previous test results and performance insights. This iterative approach allows advertisers to incrementally improve ad effectiveness over time.

Author bio:

Raul Tiru: Raul loves to build companies and help startups and scale-ups grow. Raul started his first website when he was 17 years old, has held several growth marketing positions in fast-growing companies, and has helped companies via his Freelance Marketing services. You can find Raul on his community GlobalOwls where he helps Nonprofits and Startups to do better marketing.