LinkedIn A/B testing essentials - LinkedIn Tutorial

- [Instructor] Testing is the key to improvement. On LinkedIn, A/B testing allows you to compare two variations of your content and ads to see what works best. In this video, we'll walk through how to design an A/B testing plan and how to use LinkedIn's tools so you can improve your performance. First, start with a clear plan. Focus on testing just one variable at a time. This could be the copy, the image, or the audience. For example, you might test two different images to see which gets a higher engagement rate. Before running your test, create a hypothesis. For example, I believe that using technical images will increase click-through rates. A hypothesis gives your test a purpose and a benchmark for success. Be sure to run your test long enough to gather meaningful data. Too few impressions or clicks can lead to unreliable conclusions. Aim for statistically significant results before declaring a winner. LinkedIn's Campaign Manager has built-in tools to support your A/B testing. When starting your test, set up two variations with your chosen variable, which can be your ad, audience, or placement. Outside of the built-in A/B testing tool, you can also use LinkedIn's rotate ads evenly feature. This prevents the platform from favoring one ad early on and serves your ads more equally for testing. In Sales Navigator, you can split test your targeting by building lists using different criteria. You can segment your list by industry or job title to figure out if a certain persona responds at a higher rate than another. You can also split test the messages you send to those lists to figure out what catches the attention of your prospects. Are they more interested in feature A or feature B? Do they want to read a report or receive a video? Test that in your messaging. On the social side, you can split test your time of posting your post contents and your post formats. Does your audience prefer to read your content in a text or document format, or do they prefer to watch a video? Test if they're interested in tutorials and tips and tricks, or if they want to hear stories from other customers. Once your test is complete, analyze the results. If one version significantly outperforms the other, apply those learnings to your next campaign. Remember, testing doesn't stop after just one iteration. A/B testing is an ongoing process of learning and improving. Those iterations can help you create a truly impactful campaign.