November 26, 2024
Avoiding the Top Pitfalls of Founder Led Sales for Sustainable Growth
Discover essential strategies to avoid common pitfalls in founder-led sales and achieve sustainable growth with best practices for startups and B2B founders.
When running LinkedIn ads, it can be tough to know which version of your ad will truly connect with your audience. That’s where A/B testing comes in, giving you the power to test and learn what works best, all based on real data. It’s like allowing your campaign to fine-tune its messaging and maximize results before committing fully.
A/B testing is a method used to compare two different versions of an ad or webpage to determine which one performs better. In the context of A/B testing LinkedIn ads, it means running two variations of the same ad—changing elements like headlines, images, or calls-to-action—and showing each version to different segments of your audience. The goal is to see which version delivers the best results, whether it’s generating more clicks or increasing conversions.
A/B testing is essential for lead generation campaigns. It enables campaign managers to experiment with different strategies, test key variables, and make real-time data-based decisions to optimize LinkedIn ads for better results and improved performance.
Transform Your LinkedIn Ads Performance
Before diving into A/B testing, it’s essential to understand the types of ads available on LinkedIn and the key metrics to track for success. With the right ad format and ad placement, you can craft a powerful marketing strategy that drives results.
Sponsored Content: These ads appear directly in users' feeds as native content, making them feel more like organic posts while boosting visibility.
Text Ads: Small, straightforward ads that appear on the sidebar of LinkedIn, perfect for driving traffic on a budget.
Sponsored InMail: Personalized messages delivered directly to users’ inboxes, ideal for targeted communication and higher engagement rates.
Video Ads: Engaging video content that captures attention in the feed, often leading to higher interaction rates and deeper audience engagement.
Click-Through Rate (CTR): Measures the percentage of users who clicked on your ad after seeing it. A high CTR indicates that your ad placement and message are resonating with your audience.a
Conversion Rate: The percentage of users who completed a desired action, such as filling out a form or downloading a resource, showing the effectiveness of your marketing strategy.
Cost Per Click (CPC): Tracks how much you pay each time someone clicks on your ad, helping you optimize your budget in the LinkedIn campaign manager.
Return on Investment (ROI): Measures the overall value generated from your ad spend, essential for understanding the long-term success of your campaigns.
Before running A/B tests on LinkedIn ads, starting with a strong foundation is important. Set clear objectives, define success metrics, and identify the right target audience. Clear objectives will help you figure out what you want to achieve, like generating more leads or increasing engagement. Success metrics, such as CTR or conversions, will help you track progress while identifying the target audience and ensuring your tests are relevant to those most likely to engage with your ads.
When designing A/B tests, it’s essential to choose variables that could significantly impact your ad performance.
Headlines: Your headline is the first thing users see, so testing different messaging can dramatically impact engagement.
Ad Copy: The body text of your ad can influence whether someone takes action. Experimenting with tone, length, and wording is key.
Images and Videos: Visual elements often capture attention more than text. Testing different visuals helps identify which media format performs best.
Call-to-Action (CTA): The right CTA can guide users toward the desired action. Try variations of language and placement to find the most effective approach.
Ad Formats: Different ad formats, like Sponsored Content or Video Ads, may perform differently depending on your audience. Testing formats help you discover the most effective type for your campaign.
When designing an A/B test, the first step is to create a clear hypothesis that defines what you are testing and why. Your hypothesis should focus on the expected impact of a change, such as whether using single image ads will result in a higher click-through rate than a dynamic ad. The key is to make the hypothesis specific and testable, so the results can directly inform decisions about your campaign.
A proper structure begins by setting up your two campaigns (one for the control group and one for the variations) using LinkedIn's campaign manager. For example, you can split your audience between those seeing the original ad and those seeing different versions of the ad, such as alternative headlines or split testing various ad formats.
Ensure you select a sufficiently large sample size to provide statistically reliable data, and run the test for an appropriate length of time to capture meaningful trends without overextending. Timing your test right, especially with the LinkedIn audience network, is also a key factor in structuring a successful test.
To implement A/B tests on LinkedIn, use LinkedIn’s campaign manager, which supports setting up tests within a campaign group. Start by defining your test campaign, selecting the audience, and setting up the variations to test, such as different headlines or ad formats. Once your campaign is structured, strategically allocate your budget and bids to ensure each variation gets enough exposure for reliable results.
After launching the A/B test, monitor real-time performance through the dashboard to see which version performs better. Adjust variables like audience or budget to optimize results and ensure your tests lead to actionable insights if necessary.
Boost Campaign Performance with Testing
Whether you’re focusing on driving more clicks or lowering your cost per lead, understanding the specifics of your test results will guide better decision-making for future campaigns.
Start by examining key metrics like CTR, CPC, and conversion rates. For example, if your single image ad generated a higher CTR but at a much higher CPC, you’ll need to weigh the trade-off between engagement and cost. In addition to these metrics, check for statistical significance—if your test only ran for a short period or had a small audience, the results might not be reliable. For instance, if one variation had ten clicks and the other had 8, the difference isn’t enough to draw a conclusive winner.
Be mindful of false positives/negatives in your results. For example, if one ad variation received fewer clicks due to timing (perhaps it was shown during off-peak hours), this could lead to inaccurate conclusions. Similarly, sample bias can skew results if your audience isn’t properly segmented. If a test is only shown to a subset of users in a specific region or industry, the insights might not apply to the rest of your target audience.
Once the data is interpreted, determining the winning variation involves more than just picking the version with the highest CTR or lowest CPC. You’ll want to consider the overall campaign objectives—did the version that performed better align with your long-term goals, such as lead quality or revenue? You may prioritize the other variation if the dynamic ad showed better engagement but resulted in low-quality leads. Use these insights not only to declare a winner but also to apply learnings to future campaigns.
Once you're comfortable with basic A/B testing, you can explore more advanced methods to get better insights and improve your campaigns:
Multivariate Testing vs. A/B Testing: Multivariate testing involves testing multiple variables at once (e.g., headlines, images, and CTAs), while A/B testing focuses on one element at a time, providing more precise results for each change.
Segmented A/B Testing: This method involves testing variations across different audience segments, allowing you to see how each group, such as specific industries or demographics, responds differently to your ad.
Sequential Testing: Instead of running tests all at once, sequential testing implements tests over a period of time to ensure results remain consistent and unaffected by short-term trends or anomalies.
To increase returns, allocate more budget to the best-performing ads. At the same time, continuous optimization is key. Running iterative A/B tests on smaller elements like headlines or visuals keeps your ads fresh and aligned with changing market trends. As the market evolves, adapting your campaigns ensures they stay relevant and effective in driving results.
A/B testing LinkedIn ads isn’t just about finding what works—it's about continually learning and improving your strategy. By testing different ad elements, you gain valuable insights that can lead to better performance and more effective campaigns. The key takeaway is to never stop optimizing. As digital marketing evolves, so do the opportunities for A/B testing. Staying adaptable and committed to testing new ideas will help you stay ahead in a competitive landscape, ensuring your ads continue to resonate with your audience and drive results.
Start A/B Testing Today with DemandOS
Powerful, self-serve product and growth analytics to help you convert, engage.
Dive into a world of knowledge, trends, and industry updates on the Variable blog. Our curated content covers a spectrum of topics,
