Increase your Linkedin campaign performance with this simple optimization hack
Today, I’m sharing a small but impactful Linkedin best practice with you B2B marketers out there. Linkedin is a powerhouse when it comes to targeting audiences based on their business profiles. If you don’t run any campaigns on the network, you are missing out big time. Linkedin amasses 310 million monthly active users globally, and according to their stats, Linkedin makes up more than 60% of all social traffic to B2B websites and blogs. https://sumo.com/stories/linkedin-traffic
No matter if you want to fuel your sales funnel, pick up leads, or hire great talent: Linkedin is a must-have in your B2B plan. Setting up a campaign in its campaign manager is relatively straight forward. If you have done it before on any of the other channels such as Facebook, the process feels familiar, so I won’t deep dive in here.
Fast forward to the first results of your Linkedin campaign rolling in, and that’s not after spending thousands of bucks. You can start using demographic data optimization already after a couple of hundreds spent. For me, this already worked with ads that didn’t have a budget bigger than 300 EUR per flight. I am always keen to get the initial targeting right, which is why I’m excited about the options Linkedin provides, especially the straight Job Title and company targeting, as well as seniority level targeting, are fantastic. But even if you did everything right at setup, set and forget will leave you with this missed opportunity: check out the demographics charts after you have delivered a decent amount of impressions. You have these options to display:
- Job function
- Job title
- Company
- Company industry
- Seniority
- Company size
- Location
- Country
So, in one of my cases, I’ve been tasked with boosting a Project Manager job opening’s visibility. Even with a proper initial targeting in place, there have been still people from legal roles or research slipping through. The simple thing to do was to go back to my targeting setup and manually exclude these job functions. Step 1 completed. 48 hours later, I was curious and rechecked the results. None of the unwanted functions showed up this time, but I could see enough Clicks and CTR data to optimize further. It turned out that the most interested job titles by click rate were producers while product managers underperformed.
Right when the CTR started a slow downwards trajectory, I excluded them, and the campaign kept giving until its final day. Linkedin predicted a CTR of 0.63% to 0.94% for my target audience, and these small tweaks had me end up at an overall average CTR of 1.6% — for around 1 hour of optimization effort. Test it yourself! If you create results based on this best practice, please let me know in the comments.
