Account-based marketing (ABM) campaigns make it easy to precisely target the brands in your ideal customer profile (ICP) without a pricey martech platform.
LinkedIn’s native targeting capabilities let you avoid third-party fees and keep your budget focused on the accounts that matter most.
The tradeoff is cost: LinkedIn ABM campaigns can be expensive to run, which means they require careful planning and measurement.
Many advertisers default to lead gen ads because they’re straightforward to execute and track, while overlooking upper-funnel campaigns whose long-term impact is harder to measure.
But ABM works best when both upper- and lower-funnel tactics work together.
To test this, our team developed a new LinkedIn ABM framework designed to integrate upper-funnel campaigns with lead gen campaigns – and measure how awareness efforts influence downstream results.
This article breaks down that approach and the test design behind it.
Our ABM test design
In this approach, our client shared a list of >5,800 accounts (out of a total list of nearly 20,000) to target based on firmographics, intent data, and/or fit scoring.
From there, our team devised a method to split the account list roughly in half to create test and control segments.
In the test segment, we would:
- Deploy upper-funnel LinkedIn campaigns (sponsored image ads with an objective of website visits) to warm up those target accounts.
- Then run lower-funnel/lead gen LinkedIn campaigns (sponsored image ads with a lead gen objective) to capture the demand we were building in the upper funnel.
Accounts in the control segment were shown only the lower-funnel/lead gen ads.
At launch, we used LinkedIn’s targeting capabilities to focus ads toward a set of job titles, including:
- Data scientists.
- Data engineers.
- Data architects.
- Data platform leaders (the brand is a SaaS platform that helps customers build data pipelines).
After roughly six weeks, we iterated and split out data science/data engineer titles from the data architects/data platform leaders titles.
The data science/data engineer titles were dominating impression volume when all were combined.
Our test evaluation methodology
The idea was to create an online controlled experiment that would measure the incrementality of upper-funnel paid media in ABM campaigns, in addition to regular lead gen paid media.
Without this type of test, measuring the value of upper-funnel media is difficult. Multiple teams – marketing, sales, customer success, and executives – often target and nurture the same accounts, complicating attribution.
To divide the 5,800 accounts into test (2,949) and control (2,876) lists, we identified groups of accounts that had performed similarly in terms of daily lead volume over the past few months.
The test and control segments were large enough and had established similar enough results, over a long enough window, for us to…
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