Tips for Optimizing LinkedIn Ad Audience Size

"What’s the ideal audience size for LinkedIn ads?"   ↑ An important question I get every few weeks, and it recently surfaced in the Fibbler community. Without the right audience, advertising is pointless. So what is the answer? For years, I defaulted to the classic rule of thumb: ~50,000 per audience segment, but 3 years ago, I stopped as it's misleading. I've been in and around over 1,000+ accounts now and have seen audiences from 1,000 people to 12m (y𝘦𝘴, 12 𝘮𝘪𝘭𝘭𝘪𝘰𝘯) achieve top 1% results. 𝐓𝐡𝐞 𝐨𝐧𝐥𝐲 𝐫𝐢𝐠𝐡𝐭 𝐚𝐧𝐬𝐰𝐞𝐫 - your audience size is your audience size, it's just tactic dependent. The question people 𝘴𝘩𝘰𝘶𝘭𝘥 be asking is "how do I know I've targeted the right audience?" The variables in targeting the right audience are: → Strategy (why this audience) → Segmentation (can you split it up) → Penetration (do you want new reach or to be frequent) → Tactics (brand tactics require looser audiences than activation) When thinking about 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲, the questions you need to ask are: → Who is this offer for? → Who is actually going to care? As LinkedIn is mainly B2B, I match the answer to these questions to targeting and I ALWAYS first start with the company. The 3 options: ↳ Company list (most accurate) ↳ Company size + industry (next best) ↳ Company size (for those with industry-agnostic solutions) Then I work on defining who the people we need to target are. Some variations we often use (there is no right answer here): Functions + Seniority + Skills + JT Exclusions Supertitles + JT Exclusions Functions + JT Exclusions Supertitles + Skills + Excl Function + Groups + Excl 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 is the next thing to consider. The reasons you should segment: → Geography i.e. do you advertise to different time zones? → Internal structure i.e. having priority companies based on size? → Buying committee i.e. is MQL:SQL rate higher for certain functions? If the answer to any of these is yes, you should consider segmenting your audience pool by that variable. If you have a mass market product, then I'd suggest staying with as large an audience as possible. 𝐏𝐞𝐧𝐞𝐭𝐫𝐚𝐭𝐢𝐨𝐧 is achieved only by a very simple ratio Budget:Audience The higher the budget and narrower the audience, the higher the frequency. The lower the budget and wider the audience, the lower the frequency. You can control this by ↳ How many targeting variables you add ↳ How many AND layers you apply ↳ How many exclusions you appy ↳ How much budget you spend Finally, you need to consider 𝐭𝐡𝐞 𝐭𝐚𝐜𝐭𝐢𝐜 - in short, the most important point is how tight or loose you WANT to be with this targeting. Be looser with roles for brand awareness and tighter if you have say an incentivised offer. — Bottom line: Only segment for logic and understanding, not to satisfy a rule-of-thumb number. Your audience should be exactly as big (or small) for your company/goal - nothing more, nothing less.

What you exclude is just as important as what you include. You set up your LinkedIn campaigns, define your ICP, and layer in job function targeting. Everything looks solid. But when you check the demographic report, the reality doesn’t match your intent. We’ve audited millions in LinkedIn ad spend, and one of the biggest inefficiencies we see is companies relying too much on job function without checking who they’re actually reaching. 🔹 Example 1: A cybersecurity SaaS company targeted "IT" job functions to reach CISOs and security leaders. 🔹 Reality: 30%+ of their budget was spent on IT support specialists - roles that don’t buy their product. 🔹 Example 2: A B2B finance platform set up campaigns for CFOs and Controllers, using "Finance" job functions. 🔹 Reality: They were hitting entry-level accountants at large enterprises, burning budget on the wrong audience. The problem? Job functions cast too wide of a net, and LinkedIn’s default settings don’t help. The fix? Treat exclusions like negative keywords in Google Ads. ✅ Check the demographic report. See which job titles, industries, and company sizes are actually consuming budget. ✅ Exclude job titles that don’t align with your buyers. If you’re selling to VPs and Directors, remove Analysts and Associates. ✅ Filter out irrelevant industries. If higher education or nonprofits are taking up spend and aren’t a fit, exclude them. ✅ Refine company size targeting. If you only sell to mid-market, remove small businesses and Fortune 500s. Optimizing exclusions is incremental, but every small adjustment compounds over time. LinkedIn will spend your budget and the question is whether it’s on the right people.

One of the biggest mistakes I see in LinkedIn retargeting? Over-restriction. Marketers start by slicing their audience too thin, adding filters, exclusions, exclusions on top of exclusions… and then wonder why the campaign doesn’t perform. My advice? Start broad. See how big your audience really is. Watch your frequency—the average number of ads a person sees in a set period. If frequency is high, your audience is too small. If frequency is low, your audience is too big (or your budget’s too low). Only once you have that baseline should you layer in qualifiers. Oh, and chill with the exclusions. I’ve seen even the most advanced marketers cut out ideal prospects because LinkedIn’s title buckets aren’t literal. Exclude “Marketing Manager” and you might also be cutting “Head of Marketing,” “VP,” and more. Don’t outsmart your own campaigns. Target smart. Watch frequency. Then get surgical. #B2Bmarketing #LinkedInAds #RetargetingTips #MarketingMistakes

My first LinkedIn Ads Flopped (Until I cracked the code): I went in confident, thinking I’d nailed my targeting and messaging. But within weeks, my budget was running low, and I had nothing to show for it. I didn’t know how to: - Optimize my campaigns - Target the right people - Retarget warm leads It forced me to get serious. I learned everything I could about ads. It was a major turning point in my career. Here are 5 things I wish I knew before I started: 1) Define your audience with laser focus Don’t go too broad. Use LinkedIn’s data to target specific job titles, industries, and company sizes (just to name a few). Example: Instead of targeting all marketers, focus on "CMOs in SaaS companies with 50-200 employees." 2) Start with a multi-touch strategy LinkedIn Ads are NOT about quick wins—your prospects need nurturing. A typical LinkedIn Ads funnel might look like this: Awareness → Engagement (optional) → Lead Generation → Retargeting → Conversion. Think of it as building a relationship, not just closing a deal. It’s a marathon, not a sprint. 3) Invest enough to actually test Running campaigns without a proper budget is like trying to drive without gas. LinkedIn’s platform needs a minimum ad spend of $1,500/month to gather enough data and optimize effectively. LinkedIn Ads aren’t cheap, but when done right, the ROI is incredible. 4) A/B test everything Test different versions of your headlines, visuals, and CTAs to see what resonates. Even small tweaks—like adjusting a headline—can lead to major improvements. Track performance metrics like CTR (click-through rate) and engagement to let the data guide your decisions. 5) Retarget warm audiences People don’t usually convert the first time they see your ad. Use retargeting to reach people who’ve engaged with your ads or visited your website. It’s a huge indicator that they’re interested in your offering. Plus, warm audiences convert better—and cost less over time. Thanks for reading. Enjoyed this post? Follow Connor Bell for more LinkedIn ads strategy like this. And share this post with your network. P.S. - Struggling with LinkedIn Ads? Send me a DM with the word “LINKEDIN.” Let’s have a chat and see if we can turn things around.

People always ask me “Hey Terry, how can we use AI SDRs to close more deals?” And I always say the same thing: My names not Terry, I don’t know sheet about AI SDRs but let me tell you about Linkedin ads because it’s amazing but wow it’s easy to lose all your money. So here’s some rules (35) to live by: 1/ Don’t target a huge audience size 2/ Don’t target too narrow of an audience 3/ Your audience size is your audience size. Calculate it and then run ads 4/ Track EVERYTHING --> demos, pricing page visits, closed won 5/ Don’t make decisions solely on what’s trackable or attributable, you’ll lose 6/ Job title targeting isn’t perfect. Exclusions matter 7/ Don’t skimp on exclusions --> competitors, interns, students, etc. 8/ Don’t over-exclude --> Some specialist titles will remove real buyers 9/ Do NOT use audience expansion 10/ Do NOT use LinkedIn Audience Network (If you do, separate to its own campaign) 11/ Manual bidding over maximum delivery (in most cases) 12/ Use company upload lists when possible 13/ Industry targeting is flawed, not useless, just unreliable 14/ Use company lists AND titles over contact list uploads 15/ If you can use Thought Leader Ads, DO IT 16/ Running TLAs? Edit the post and add a CTA link. Seriously do it 17/ Outside of LGFs and TLAs, use the Website Visits objective 18/ Don’t use the Campaign Group objective. Zero value, pure annoyance 19/ Only running lead gen forms is silly 20/ Not testing lead gen forms is just as silly 21/ Remarketing is absolute gold, use it 22/ Don’t ungate everything just because your LinkedIn feed says not to 23/ OR audiences vs AND audiences are two very different things 24/ Text and Spotlight ads have super low CPMs if you use manual CPC bidding 25/ Start by bidding under the "What others are bidding recommendation" 26/ Job title targeting works 27/ Seniority/Function targeting also works, test both 28/ Test 4+ ad variations per campaign (At minimum) 29/ Image/video for Sponsored Content first, then everything else 30 / When in doubt, square it out. Square images are the best overall ad size 31/ People see your ad first, then description, then headline. Adjust accordingly 32/ Landing page clicks, clicks and engagements are different things 33/ Don't listen to the auto-recommendations Linkedin gives you. You'll regret it 34/ Frequency matters --> 12 to 15 at the campaign level, 2 to 3 at the ad level 35/ Check the demographics report, and use the companies feature. Crucial for optimization As always, it depends.