Six LinkedIn ads hacks that most B2B marketers learn the expensive way

TL;DR

• LinkedIn’s default campaign settings (geography, audience expansion, audience network, and bidding) can sometimes lead to higher costs if they aren’t adjusted intentionally.
• If you're running ABM campaigns, roughly 25% of your target accounts are consuming 95% of your impressions. Companies like Microsoft, Google, and Salesforce eat your entire budget before smaller accounts ever see an ad.
• Uploading company lists with LinkedIn company page URLs instead of relying on native industry filters dramatically improves match rates and targeting accuracy.
• LinkedIn's Conversions API (CAPI) is becoming critical for campaign optimization. Capture the LinkedIn fat ID parameter on every click to guarantee a 100% match rate on conversion data sent back to the platform.
• The website visits objective is often a more flexible choice than the brand awareness objective for many B2B campaigns. Even for awareness plays, the website visits objective with manual CPC bidding will give you better data, cheaper clicks, and more meaningful engagement signals.

I’ve spent an unreasonable number of hours inside LinkedIn Campaign Manager, thinking everything was set up correctly, only to realize that I wasn’t using the platform’s settings to my advantage. It was a bit like driving with the parking brake slightly on. Technically, everything still works… just not quite as smoothly as you’d expect.

This became especially clear during a conversation between AJ Wilcox, founder of B2Linked and a person who has spent over $200 million on LinkedIn ads across 14 years, and Praveen from Factors.ai. What emerged wasn't a generic "optimize your campaigns" talk. It was a specific, data-backed breakdown of exactly how LinkedIn’s default settings can shape campaign performance and spend efficiency, why most B2B marketers don't catch it, and what to do instead.

The six hacks they covered are the kind of operational fixes that, once implemented, make you wonder how you ever ran campaigns without them. If you're spending any meaningful budget on LinkedIn, even a few thousand dollars a month, at least one of these is likely affecting your campaign efficiency right now.

Let's walk through each one.

The geography setting that's targeting the wrong continent

Before I tell you more, just know that this mistake is very easy to make. You set your campaign to target the United States, you see leads coming in, and everything looks normal. Then, sometime around September, your sales team starts flagging leads from the Philippines, Europe, and Africa. You double-check your targeting. It still says United States. So what happened?

LinkedIn's default geography setting is "Recent or Permanent." That sounds reasonable until you learn what ‘recent’ really means: six months. If someone from India traveled to the US in April for a conference and updated their location or simply connected to a US network, LinkedIn will happily serve your ads to them through October. They're back home, scrolling LinkedIn from Mumbai, and your budget is paying for those impressions.

The fix is almost insultingly simple. When you're setting up your campaign geography, there's a dropdown that most people never click. Change it from "Recent or Permanent" to "Permanent" only. With this setting, the only way someone enters your geographic audience is if their LinkedIn profile explicitly states they live in that location.

This isn't about lead quality in the traditional sense. The people you're reaching aren't "bad" leads. They're just not in the geography you're targeting for a reason, whether that's sales territory alignment, regional product availability, or compliance requirements. You chose that geography deliberately, and LinkedIn’s default setting may not always align with how advertisers intend to target geography.

AJ mentioned this issue surfaces predictably every year after summer, when international travel peaks. If you've ever had a mysteriously international batch of leads from a US-only campaign, now you know why.

Why should you always uncheck audience expansion?

Here's the philosophical question at the heart of LinkedIn advertising: if you're paying a premium for precise professional targeting, why would you broaden that precision?

That's exactly what LinkedIn's Audience Expansion checkbox does. It's enabled by default, tucked into your campaign settings, and it allows LinkedIn to show your ads to people outside your defined target audience who the algorithm thinks might be similar. The algorithm making this decision, by the way, is the same one that powered LinkedIn's lookalike audiences. LinkedIn shut down lookalikes last year because they weren't performing well. But the same logic still runs quietly through this checkbox.

AJ’s take was pretty direct: audience expansion can make targeting less controlled than many advertisers expect. I know that sounds dramatic, but the point stands. You can't even see what percentage of your engagement came from expanded audiences versus your actual target. So you're flying blind on a feature that's actively spending your budget on people you didn't choose to target.

The percentage of budget that goes to expanded audiences seems to sit between 5% and 15%. That might sound small, but consider this: if your budget is sized to reach your target audience, or if it's even slightly under what you need, yo're now diverting a chunk of that budget to people who weren't qualified enough to be in your original targeting. There's no scenario where that math works in your favor.

The one edge case where expansion might theoretically make sense is if your audience is extremely small and you're struggling to spend your budget at all. But even then, LinkedIn offers predictive audiences and other options that give you more control. Audience expansion as a default is a legacy setting that may not fit every advertiser’s targeting strategy today.

Uncheck it (every time, on every campaign).

What to know before enabling LinkedIn Audience Network

The LinkedIn Audience Network, or LAN, is LinkedIn's version of showing your ads to your target audience while they browse other websites and apps outside of LinkedIn. On paper, this sounds fantastic. LinkedIn users don't spend much time on the platform compared to other social networks, so reaching them across the broader internet should extend your reach efficiently.

However, AJ's experience across hundreds of accounts tells a consistent story: you might pay one-tenth the cost per click on LAN traffic compared to on-platform LinkedIn traffic. But your conversion rates drop by roughly 90%. The math cancels itself out, and you're left with a bunch of cheap clicks that never turn into pipeline.

The quality issues go deeper than just low conversion rates. Some advertisers report inconsistent traffic quality within parts of the LAN ecosystem. AJ described situations where advertisers accidentally left LAN enabled and watched their entire daily budget disappear in 20 minutes, consumed by two Android apps with suspiciously high 3% click-through rates and $1 CPCs. The numbers looked great in the dashboard, but the results weren’t as good because some of the traffic was from bots.

If you still want to use LAN, and there are some legitimate use cases for retargeting, the approach is to use a block list. AJ released a free block list on one of his LinkedIn Ad Show podcast episodes that you can upload to LinkedIn. It essentially tells the platform to only show your ads on pre-approved, high-quality publications like the New York Times and Business Insider, while blocking the low-quality inventory that generates bot traffic.

One interesting nuance came up during the discussion with AJ. Many B2B marketers are comfortable running display advertising through programmatic exchanges via ABM platforms or DSPs, which often serve ads on very similar inventory to what LAN uses. The argument that "people are spending on display elsewhere, so LAN should be equivalent" has some logic to it. But the difference is that those other platforms often layer on retargeting or intent signals that LAN doesn't provide. On LinkedIn, you're paying a premium for professional targeting precision. Letting that precision leak into unverified display inventory defeats the purpose.

The default for LAN is on. It's buried under the "Placements" section of your campaign setup. Go find it and turn it off, or at minimum, upload a block list before you let it run.

How do ABM campaigns spend budget on the wrong accounts?

If you're running account-based marketing campaigns on LinkedIn, this section might be the most expensive lesson in this entire article. Not because the fix is costly, but because the problem has likely been draining your budget for months without you noticing.

Here's the pattern. You build a target account list of, say, 1,000 companies. You've aligned with sales. You've carefully curated the list. You upload it to LinkedIn, launch your campaigns, and start spending. Everything looks fine in the dashboard. Budget is being consumed, impressions are rolling in, and you feel good about the reach you're building across your target accounts.

Then you pull the demographic reports.

AJ shared his own experience with this. When B2Linked was running ABM campaigns targeting enterprise ad spenders, their list included around 400 companies. After analyzing LinkedIn's demographics data, they found that three companies, Google, Facebook, and Twitter, were consuming 96% of all impressions. The other 397 companies on the list were essentially invisible. The campaign budget was being heavily concentrated by massive organizations with thousands of employees who matched the targeting criteria, leaving nothing for the smaller companies that were actually better prospects.

This isn't an edge case. Factors.ai shared data from their customer base that paints a similar picture across the board. Before implementing controls, the top 25% of accounts on a target list typically consume around 95% of impressions. For the remaining 75% of accounts, the ad exposure is so minimal it might as well not exist.

Think about what that means for your ABM strategy. Your sales team is reaching out to 1,000 accounts, expecting LinkedIn advertising to have warmed them up. In reality, 750 of those accounts haven’t really registered your brand. Your SDR may be reaching accounts that received far less ad exposure than expected, but they have no idea who you are.

The usual suspects are predictable. Microsoft regularly consumes 5-10% of a campaign's budget on its own. Salesforce takes another significant chunk. Google, Meta, Amazon, and other tech giants with enormous LinkedIn employee bases round out the top of the list. These companies have so many employees matching common B2B targeting criteria that LinkedIn's auction naturally gravitates toward them.

What can you actually do about this?

Manually managing this is technically possible but, practically, a little insane. You could go into Campaign Manager every day, check which accounts are over-indexing, temporarily exclude them, and add them back later. But if you're running 50 campaigns across multiple audiences, that's a full-time job (that nobody wants).

Factors.ai built a feature called Smart Reach that automates this process. It monitors impression distribution across your target accounts in near real-time and caps how many impressions any single account can consume. When a heavy hitter like Microsoft hits its daily threshold, it gets temporarily removed from the audience, and the budget flows to accounts that haven't been reached yet.

The results from customers using Factors.ai’s Smart Reach tell a clear story:

The CPM decrease is a nice bonus, but it's not the main point. The main point is that your ABM campaign is actually doing what you designed it to do: building awareness across your entire target list, not just the three biggest tech companies on it.

Why do native LinkedIn filters need help, and what to use instead?

There's a meaningful difference between telling LinkedIn "show my ads to companies in the software industry" and uploading a curated list of specific companies you want to reach. The difference mostly comes down to how LinkedIn categorizes companies.

LinkedIn's industry targeting relies on how each company categorizes itself on its own company page. This sounds reasonable until you realize that the classifications are often set by whoever created the company page years ago and might not reflect reality. AJ and Praveen shared several examples that illustrate the problem.

Spotify is categorized under something related to "Musicians." Airbnb shows up as "Software Development" rather than a marketplace. ADP, clearly a technology company, is classified under "Human Resource Services." If you're targeting the technology industry on LinkedIn, you'll miss ADP entirely. If you're targeting software companies, you might accidentally include Airbnb while missing companies that should obviously be in your audience.

The better approach is building your company list outside of LinkedIn using data sources you trust, whether that's your CRM, a data provider like ZoomInfo, or a custom research process. Once you have a clean list, upload it directly to LinkedIn as a matched audience.

The match rate problem and how to solve it

Uploading a company list sounds straightforward, but there's a catch. LinkedIn's match rates on company names can be frustratingly low. If your list has "I.B.M." and LinkedIn's database has "IBM," that might not match. Abbreviations, alternate spellings, and DBA names all create gaps.

The solution is to include LinkedIn company page URLs in your upload. When LinkedIn sees its own URL format, the match is guaranteed. It's their data, and they recognize it immediately. Match rates jump to near 100% when you include this field.

Getting those URLs is the annoying part. AJ mentioned a resource called Free People Labs that publishes a massive company data set (well over a million rows) that includes LinkedIn URLs. It requires some technical work to filter and match against your list, but it's free. Some people in the discussion also mentioned using Fiverr freelancers for smaller lists, which is a pragmatic option if your target list is a few hundred companies.

Factors.ai handles this automatically for customers using their audience sync features, matching company domains to LinkedIn URLs and pushing updated lists into LinkedIn daily. But regardless of how you solve it, the principle is the same: bring your own list, include LinkedIn URLs, and don't trust native industry filters for precision targeting.

Layer intent signals on top of your company lists

A company list tells LinkedIn who to target. Intent data tells you when to target them. Most people on LinkedIn aren't actively buying software on any given day. They're scrolling through posts, reading articles, and occasionally updating their profiles. If you can identify which accounts on your list are showing buying signals right now, you can prioritize your budget toward the accounts most likely to convert.

Intent signals can come from multiple sources. Website visits are the most obvious: if someone from a target account just spent time on your pricing page, that's a strong signal. Third-party intent data from platforms like G2 or review sites adds another layer. Factors.ai customers who start using intent-based audiences typically see a 30-40% improvement in campaign performance, which makes intuitive sense. You're concentrating spend on accounts that are already in some stage of a buying journey rather than spray-and-praying across your entire list.

This approach also serves as a better alternative to LinkedIn's native website retargeting, which brings us to a problem that's only getting worse.

The limitations of cookie-based retargeting

LinkedIn's website visits retargeting is built on cookies. Someone visits your website, the LinkedIn Insight Tag drops a cookie, and when they return to LinkedIn, the platform checks for that cookie to decide if they belong in your retargeting audience. The system works well in some cases, but browser privacy changes have made it less reliable over time.

The problem is that cookies are increasingly unreliable. Apple devices and Safari browsers either block or delete third-party cookies almost immediately. Firefox does the same. Even on Chrome, cookie consent banners mean many visitors never get tagged in the first place because they decline or ignore the prompt.

The result is what AJ described as a leaky bucket. You invest in driving traffic to your website to build retargeting audiences, but those audiences drain faster than you can fill them. Someone visits your site on Monday, gets cookied, and by Thursday their browser has already tossed the cookie. When they're back on LinkedIn, the platform doesn't see a match, and they fall out of your retargeting pool. For many B2B companies, especially those with lower traffic volumes, the audience never gets large enough to run a campaign against.

The alternative approach is to shift from cookie-based retargeting to company-level identification. When someone visits your website, tools like Factors.ai identify what company they represent through IP intelligence and other signals, not cookies. That company gets added to a dynamic audience list that syncs with LinkedIn. Since the identification happens at the company level and lives in Factors' system rather than in a browser cookie, it can't be erased by privacy settings or browser updates.

You do lose individual-level precision with this approach, since you're pushing a company name rather than a specific person. But you can layer job function and seniority targeting on top of the company list in LinkedIn to narrow down to the right buying committee members within each account. It's not a perfect 1:1 replacement for cookie-based retargeting, but it's a retargeting mechanism that actually works reliably in a post-cookie world. And that's a trade-off worth making.

The conversions API is about to become non-negotiable

LinkedIn's Conversions API, or CAPI, has been available for a while now, but it's about to become significantly more important. LinkedIn is investing heavily in using CAPI signals for campaign optimization, which means the advertisers who send the richest conversion data back to LinkedIn will get the best algorithmic optimization in return.

The concept is straightforward. Instead of relying solely on the LinkedIn Insight Tag (a cookie-based pixel) to track conversions, CAPI lets you send conversion data directly from your server or CRM to LinkedIn. This fills in the gaps where cookie tracking fails, giving LinkedIn a more complete picture of which ad interactions actually led to conversions.

The email match rate problem

There's a catch, though, and it's a significant one. Most B2B form fills collect professional email addresses. That's what sales wants, and it's the right thing to collect. But when you pass those professional emails back to LinkedIn through CAPI, LinkedIn tries to match them against user profiles. The problem is that most people log into LinkedIn with personal email addresses, not work ones. The result is a match rate of around 30%.

So you're in this awkward situation where your pixel-based conversion tracking is missing maybe 20-30% of conversions due to cookie issues, and your CAPI implementation is only matching 30% of what you send back. There's overlap between what each system catches, and neither is complete on its own.

LinkedIn fat ID fix that gets you to 100% match rate

This is the single most actionable tip in this entire article, and it came directly from AJ.

Every time someone clicks a LinkedIn ad, the destination URL contains a parameter called `li_fat_id`. This is LinkedIn's own user identifier. It's a unique number that represents exactly who clicked that ad. If you can capture this parameter when someone lands on your website, store it, and then include it when you send conversion data back through CAPI, LinkedIn will match it with 100% accuracy.

It doesn't matter if the person's name is misspelled in your form data. It doesn't matter if you have their work email instead of their personal one. LinkedIn issued that ID themselves, and they'll always recognize it.

Here's the implementation path:

1. Capture the `li_fat_id` parameter when someone lands on your site from a LinkedIn ad. Store it in a hidden form field, a cookie (yes, ironically), or your analytics system.
2. Associate it with the form submission when the visitor converts. Your form handler needs to pass this ID along with the conversion data.
3. Send it back to LinkedIn via CAPI along with whatever other conversion data you have (email, name, conversion type, conversion value).
4. LinkedIn matches on the fat ID first, falling back to email and name matching only when the ID isn't available.

Send conversion values, not just conversion events

One additional recommendation that came up: don't just send binary "conversion happened" signals. Send conversion values. The way many teams do this is by assigning a value based on ICP tiering. If a converted user comes from a Tier 1 account, the conversion value is higher than one from a Tier 3 account. This gives LinkedIn's algorithm a signal about which conversions are more valuable, which in turn helps it optimize toward higher-quality outcomes.

LinkedIn automatically deduplicates conversions between pixel tracking and CAPI, so you don't need to worry about inflated numbers if both systems catch the same conversion. It'll count it once.

Whether you implement CAPI through Google Tag Manager, a direct integration, or a platform like Factors.ai that handles both website and CRM data piping, the important thing is to get it running now. LinkedIn's optimization algorithms are increasingly going to favor accounts that provide richer conversion signals. Early adopters will have a meaningful advantage.

LinkedIn’s bidding system is designed to balance delivery and competition across advertisers

Now we arrive at the hack that AJ literally said he'd shout from the rooftops until the day he dies. If you've ever set up a LinkedIn campaign and accepted the default bidding recommendation, there’s a good chance you may have paid more than necessary for some clicks.

Maximum Delivery for getting traffic on LinkedIn

LinkedIn's default bidding option is called "Maximum Delivery." It's a CPM-based bid where LinkedIn charges you for impressions, not clicks. You pay every time your ad is shown, regardless of whether anyone engages with it. For the average LinkedIn campaign with a typical click-through rate, this means your effective cost per click ends up being roughly double what you'd pay with manual CPC bidding.

The alternative, manual CPC bidding, is hidden. LinkedIn shows two bidding options by default and buries a third behind a "show me more options" link. That third option is manual CPC bidding, and it's where you should start 90% of the time.

LinkedIn's suggested bid ranges

When you select manual CPC bidding, LinkedIn auto-fills a suggested bid and shows a "competitive range." Something like: "Your competitors are bidding between $4.40 and $90 per click. We suggest $18." These suggested ranges can sometimes feel significantly higher than what many advertisers actually end up paying. AJ ran three separate tests totaling over $100,000 in spend, deliberately bidding high, low, and in the middle, tracking lead quality across all three.

The result: there was zero correlation between bid level and lead quality. Bidding higher did not get you access to better prospects. Bidding lower did not mean you were scraping the bottom of the barrel. The quality of leads was statistically identical across all bid levels.

This differs from some commonly shared bidding guidance. Some reps genuinely believe that higher bids unlock "premium inventory" or "higher quality members." AJ's advice: push back and ask for data. Because the data from $100K+ in testing doesn't support that claim.

The optimal bidding strategy, step by step:

Here's the approach that AJ uses, and it's the methodology that consistently drops costs by an average of 57% when B2Linked takes over existing accounts:

1. Start low. For North American audiences, begin with a $7 CPC bid. This feels uncomfortably low compared to LinkedIn's suggestions, and that's fine.
2. Wait 2-3 days. If your campaign barely spends and gets very few impressions, your bid was too low. That's useful information, not a failure.
3. If you're spending your full daily budget at $7, you've found a strong starting point. But you might be able to go even lower.
4. Set your daily budget about 30% higher than your actual target. This lets you distinguish between "I'm spending my budget because my bid is just right" and "I'm spending my budget because I hit the cap early in the day and could have bid less."
5. Decrease your bid in small increments ($0.50 or $1 at a time) if you're consistently hitting budget. Find the floor.
6. Increase your bid gradually if you're under-spending. But don't jump to LinkedIn's suggested ranges. Go up by $0.50-$1 and wait another 2-3 days.
7. Segment campaigns by seniority level. Run separate campaigns for C-level, VP, Director, and Manager audiences. This lets you see the minimum bid required for each tier and adjust independently.

The beauty of bid adjustments is that they take effect immediately. You can change your bid multiple times per day if you need to, though AJ recommends not making changes more than once every few days so you can actually learn what's working. Budget changes, by contrast, don't take effect until the end of the day (midnight UTC).

When does maximum delivery actually make sense?

There is one scenario where the math flips in favor of CPM-based maximum delivery bidding, and it's worth understanding why.

AJ shared a graph showing the relationship between click-through rate and effective cost per click under both bidding models. The crossover point is around a 0.8-1.2% link click-through rate. Below that threshold, which is where the vast majority of LinkedIn ads fall (the benchmark is around 0.4-0.46%), CPC bidding is significantly cheaper. Above that threshold, CPM bidding starts to win because you're paying a fixed price per impression while getting a disproportionate number of clicks.

The rule of thumb: start every campaign on manual CPC. If you discover that a particular ad is performing exceptionally well with a link click-through rate above 1%, consider switching that specific campaign to maximum delivery to capitalize on the high engagement. You can always switch back.

When is the ‘website visits’ objective a better fit than brand awareness?

This one came through with genuine passion from AJ, and it deserves its own section even though it's closely related to bidding strategy.

LinkedIn's brand awareness objective limits you to CPM-based bidding only, either maximum delivery or manual CPM. We've already established that CPM bidding can often be less cost-efficient for traffic-focused campaigns. But the problem goes beyond cost.

When your campaign objective is brand awareness, the only metric you can really optimize toward is impressions and CPM. That tells you almost nothing about whether your ads are actually resonating. You can get a million impressions with a terrible ad. Impressions don't measure engagement, recall, or intent. They measure that your ad appeared on someone's screen, potentially for a fraction of a second while they scrolled past.

Even if your actual marketing goal is brand awareness, which is a perfectly valid goal, you're better off running that campaign under the website visits objective with manual CPC bidding. Here's why:

You still get all the impressions. Your ads still appear in feeds and build familiarity. But now you're also measuring which ads people actually click on, giving you a real engagement signal. The clicks you pay for are landing page clicks only, meaning all the other interactions (hashtag clicks, "see more" expansions, profile clicks) are free. And your effective CPM will likely be lower because manual CPC bidding is more cost-efficient for campaigns with standard click-through rates.

The only exception AJ mentioned is Connected TV (CTV) ads, which require the brand awareness objective because LinkedIn doesn't offer other objectives for that format. For everything else, including thought leader ads and standard sponsored content, the website visits objective with manual CPC bidding is the better choice.

Someone in the audience asked what a good CPM to aim for is when running awareness campaigns. The answer isn't really a CPM target. It's to reframe the question entirely. Instead of asking "what CPM should I aim for," ask "what cost per engaged click am I paying, and is the engagement meaningful?" That's a much better measure of whether your awareness campaign is actually building awareness.

Building follower audiences without burning ad budget

One last topic that came up during the Q&A: how to grow LinkedIn company page followers efficiently. This isn't strictly an "ads hack," but it's relevant to anyone investing in LinkedIn as a channel.

LinkedIn offers a dynamic ad format called Follower Ads that appears in the right rail on desktop. It's purpose-built for growing followers, with a single call-to-action and very limited text (around 30-40 characters). It works, but it's not the most cost-effective approach.

The approach AJ recommends instead costs nothing. Every super admin on your company page gets approximately 250 follower invitations per month. These are direct invitations that appear in the recipient's network notifications tab. The acceptance rate is surprisingly high because it feels personal rather than promotional.

The tactic: temporarily grant admin access to two or three people in your company. Have each person send their 250 monthly invitations to people in your target industry or ICP. That's potentially 750 free follower invitations per month from three people. Once you've burned through the invitations, you can revoke the admin access if needed.

You can't customize the invitation message, which is a limitation. It's a standard LinkedIn notification that says the company page invited them to follow. But for a zero-cost tactic, the results are meaningful. Layer follower ads on top if you want to accelerate the growth, but start with the free invitations first.

In a nutshell

Six things. That's all it takes to meaningfully change how much value you're getting from LinkedIn ads. Change your geography setting to "Permanent" and stop paying for travelers who left the country six months ago. Uncheck audience expansion on every single campaign. Disable the LinkedIn Audience Network or use a block list to filter out bot traffic. Switch from maximum delivery to manual CPC bidding and ignore LinkedIn's inflated suggested ranges. If you're running ABM campaigns, audit your impression distribution because a handful of large companies are almost certainly eating your entire budget. And set up CAPI with the LinkedIn fat ID capture so your conversion data is actually complete.

AJ's methodology of taking over existing accounts and applying these changes produces an average cost reduction of 57%. That's not a rounding error. That's the difference between a LinkedIn channel that "kind of works but is expensive" and one that generates pipeline efficiently enough to justify scaling.

The recurring theme across all six hacks is the same: LinkedIn’s default settings are designed to work broadly across advertisers, but they may not always align with every campaign’s specific performance goals. Many default settings prioritize delivery and scale, which may not always match an advertiser’s efficiency goals. Your job is to methodically override each one with settings that align with your actual goals. None of these fixes require advanced technical skills. They require awareness, and now you have it.

Frequently asked questions about LinkedIn ads hacks

Q1. What is the single most impactful change I can make to reduce LinkedIn ad costs?

Switch from maximum delivery bidding to manual CPC bidding and start with a bid well below LinkedIn's suggested range. For North American audiences, try starting at $7 per click and adjust from there. This single change can cut your effective cost per click in half, and AJ's data across $100K+ in testing shows it doesn't affect lead quality.

Q2. Should I ever use the brand awareness objective on LinkedIn?

In almost all cases, no. The brand awareness objective restricts you to CPM-based bidding, which is the most expensive way to pay for traffic. Even if your goal is genuinely building awareness, use the website visits objective with manual CPC bidding instead. You'll still get impressions and visibility, but you'll also get engagement data and pay less per interaction. The only exception is CTV ads, which require the brand awareness objective.

Q3. How do I fix the ABM impression distribution problem without a tool like Factors?

The manual approach is to regularly check LinkedIn's demographics reports to see which companies are consuming the most impressions. When you spot heavy hitters like Microsoft or Google dominating your budget, temporarily exclude them from your campaign's company targeting. This is time-consuming and doesn't scale well across many campaigns, but it works as a stopgap until you implement an automated solution.

Q4. What is the LinkedIn fat ID and why does it matter for conversions API?

The `li_fat_id` is a unique user identifier that LinkedIn appends to the URL every time someone clicks on a LinkedIn ad. If you capture this parameter when the user lands on your website and send it back to LinkedIn through the Conversions API when that user converts, LinkedIn can match the conversion with 100% accuracy. Without it, CAPI relies on email matching, which typically achieves only about 30% match rates because people use personal emails for LinkedIn but submit professional emails on forms.

Q5. What's the minimum audience size for a LinkedIn campaign to perform well?

For standard top-of-funnel campaigns, aim for an audience between 20,000 and 100,000 members. Audiences under 20,000 can still work, but you'll likely need to bid higher to win auctions, and maximum delivery bidding may be necessary to ensure consistent impression delivery. Very small audiences of 1,000-5,000 members are common in ABM and retargeting scenarios. They're worth running, but expect higher CPMs and adjust your bidding strategy accordingly.

Q6. How often should I adjust my manual CPC bids on LinkedIn?

Check your campaigns every 2-3 days and make small adjustments of $0.50-$1 at a time. Changing bids more frequently than that makes it difficult to isolate what's actually affecting performance. Unlike budget changes, which don't take effect until midnight UTC, bid changes are immediate. This gives you flexibility but also means you need discipline to avoid over-optimizing based on insufficient data.

LinkedIn’s suggested bid ranges aren’t always the most cost-efficient benchmark to follow.