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Catch 22: Resolving LinkedIn’s Frequency Capping Paradox

Ranga Kaliyur
May 16, 2024

Coined by Joseph Heller in his 1961 satire of the same name, Catch-22 refers to a paradoxical absurdity in which there is no escape because of mutually conflicting conditions. In the novel, Doc Daneeka, a WWII army psychiatrist explains that a pilot requesting mental evaluation — hopeful of being diagnosed with insanity to avoid flying dangerous missions — is, ironically, demonstrating their sanity by making the request in the first place.

catch 22 flow chart

Albeit a tad less dramatic, B2B marketers face a similar dilemma when it comes to their LinkedIn ads. By design, LinkedIn offers advertisers limited control over the frequency distribution of their ads. That is to say, advertisers don’t really have a choice in how many times each target user/account is served an ad. This results in the majority (≈80%) of ad impressions being served to just about 10% of audiences. 

linkedin ad impression distribution pie chart

This lopsided ad distribution occurs primarily because bigger companies with larger social presence tend to inadvertently eat up the majority of impressions (and consequently, budgets). In turn, advertisers are impacted by a combination of overexposure or ad fatigue amongst a few accounts, underexposure with the majority of accounts, and worst of all: woeful, wasted marketing dollars.

🧢 Read about the impact of frequency capping (or the lack thereof) on your LinkedIn Ads. 🧢

To solve for this, B2B advertisers typically segment their campaigns based on firmographic properties: big accounts in one bucket, medium sized accounts in another bucket, and small accounts in a third bucket. Given that accounts within each bucket are of similar sizes, ads tend to be distributed more evenly across the board. While this is an effective stop gap, it’s far from perfect:

In other words, larger audiences result in lower CPMs but lopsided ad frequency distribution. Smaller, segmented audiences result in better frequency distribution but increased CPMs. And the end result in either case? Wasted ad spends. This edition of Factors Labs crunches the numbers to qualify this Catch-22 and highlights how Factors solves for it.

linkedin frequency capping flowchart

Crunching the numbers: Audience size vs. CPM

If you’ve run LinkedIn ad campaigns, you’re likely already familiar with the negative correlation between audience sizes and cost per mille (CPMs). As a quick refresher, here’s what our data look like:

LinkedIn CPM vs audience size scatter plot

While this slope is relatively shallow, the result is clear: the bigger the audience, the cheaper the ads. Especially at scale, the difference between a CPM of $42 and $25 can make considerable difference. However, as we qualified in the pie chart, consolidating all your target accounts into a single audience inevitably leads to asymmetrical, unbalanced ad distribution. Here’s a detailed breakdown of the same:

Most marketers would agree that this is a problem. A problem with no native solution in place as of yet. Accordingly, Factors has built out frequency capping functionality for LinkedIn that we think you might appreciate. Let’s explore how it works. 

LinkedIn frequency capping with Factors

Our team at Factors has been cooking up a few actionable features to help B2B marketers optimize their LinkedIn advertising efforts. Frequency capping is the first of these functions. While frequency capping isn’t itself a new concept to most marketers, introducing this ability within LinkedIn is a game changer. Here are a few use-cases empowered by LinkedIn frequency capping with Factors…

1. Budgeting use-case: Frequency-based exposure control

The most basic frequency capping rule is anchored around a simple frequency condition: if any target account views an ad X times, pause ad service to said accounts. As aforementioned, this prevents ad fatigue by ensuring no one account is served too many ads at once. In turn, this prevents overexposure while simultaneously freeing up budgets for otherwise underserved accounts. This acts as the foundation for the next two advance frequency capping conditions.

linkedin frequency capping UI on Factors

2. Retargeting Use-case: Fitment-based exposure control

Rather than retargeting every account that visits your website with equal vigor, Factors helps you control exposure based on your ICP or fitment criteria. In other words, automatically show more retargeting ads to accounts that your business would actually sell to, and fewer ads (if any) to irrelevant inbound accounts. 

Another use-case around this feature involves surfacing different ad creatives/campaigns to different audiences based on their firmographic features. SMEs may warrant messaging A while Enterprise target accounts seem to resonate better with messaging B. 

3. ABM Use-case: Intent-based exposure control

All buyers are equal — but some are more equal than others. As a marketer, you’ve invested A LOT into building out the perfect ABM audience lists. But even within these sets of accounts, propensity to purchase will always vary. In other words, some accounts are more sales-ready than others. 

What if you could identify these accounts based on their intent signals and fine-tune your frequency capping accordingly? Here are a few examples of intent-based exposure control with Factors:

  • “Increase frequency cap to accounts that visit the pricing page OR open a sales email?”
  • “Limit ad frequency to accounts that have already booked a demo call”
  • “Decrease ToFu campaign frequency, and increase BoFu campaign frequency to accounts that have visited a blog”

This provides far more granularity than a simple “cap impressions if any account sees an ad this many times”.

stylised product image of factors linkedin management

And there you have it! A solution to the paradox of high CPMs and limited reach on your LinkedIn ads. Curious to learn more about how Factors can help you achieve the best of both worlds?

Want to make the most of your LinkedIn ads?

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Ad Account No. of target accounts Total impressions Impr delivered to top 10% of target accounts % of Impr delivered to top 10% of target accounts
 A  187  4,280 2,346  54.8% 
 B 10,000  1,555,369  935,463  60.1% 
 C  5,801  668,462  478,176 71.5% 
 D  4,640  4,064,176 3,474,681  85.4% 
 E 6,113   171,126 97,009  56.6% 
 F  2,043 104,526   80,300 76.8% 
 G  2,628 557,381  516,090  92.5% 
 H  10,000  431,642  265,132 61.4% 
 I 4,687  3,516,461  2,754,925  78.3% 
 J 6,912   499,642 357,044  71.4% 
 K 1,532   299,426 235,817   78.7%
 L  815  13,904 9,138  65.7% 
 M 9,081