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We're loving the heated debate about whether "influenced pipeline" is trash or not 😅
As a tool that offers this revenue attribution model, we obviously don't hate it. But we also don't think it's magic. It works... as long as you use it the right way.
So what does "the right way" actually mean? How many impressions does it take before you can honestly say a LinkedIn ad "influenced" a deal? 50 impressions in 90 days? 10 in 60? And should you trust the number at all?
Here's the formula we use to set influenced pipeline for LinkedIn Ads, why we still think it makes sense, and how to stress test it so nobody in the board meeting can poke holes in it.
Patrick Cumming, founder of Ad Juice, posted about this today, and honestly, he said what a lot of us have been thinking.
The anti influenced pipeline arguments usually sound like this:
Patrick's point: both arguments assume sales and marketing work in silos. They don't. Marketing warms the account up, sales has an easier conversation, and both deserve some credit.
My take (I left it in the comments too): VCs have bred a culture of trying to find deterministic answers to non deterministic questions. Marketing impact is never about a single touchpoint or a single channel. But marketers have to defend their spend, so we end up building imperfect proxies for "revenue attribution". Influenced pipeline is one of them.
The question isn't whether it's perfect (it isn't). The question is whether it's less wrong than the alternative. And the alternative is usually last touch.
Many people don't get how LinkedIn clicks and last touch attribution actually work 👀
For HubSpot to attribute a deal to your LinkedIn ad, the contact has to:
Miss any one of those and the deal goes to "direct" or "organic search". And LinkedIn's own attribution is contact based under the hood too, so it has similar blind spots (we broke down exactly where LinkedIn conversion tracking loses ABM deals in a separate post).
A LOT of clicks go unreported because of privacy rules, on both company and contact level. Patrick estimates click attribution only credits marketing for about half of the value it drives. In the ABM program I ran, it was worse: judging ABM on contact level (MQL) attribution, at least 60% of ABM revenue went unreported, and account level reporting showed 10x more revenue. If we'd trusted last touch, we would have killed the program.
That's the whole reason ZenABM matches deals at the company level instead of the contact level: it takes every deal in your CRM, finds the company behind it, and checks how many LinkedIn impressions, clicks and engagements that company had before the deal was created. No cookie, form fill or 30 minute window required.
Here's what that looks like on real deals:
So the real question for ABM isn't "which ad got the click?" It's "did cold accounts that saw and engaged with our ads open deals afterwards?" That's exactly what influenced pipeline answers. (If you want the longer comparison of methods, here are the 4 ways to attribute revenue from LinkedIn ABM and where each one breaks.)
Impression threshold = number of members you're targeting per account (company) x 7 to 15 impressions.
How many people are you targeting per account, on average? Multiply that by 7 to 15 impressions. If one person saw your ads 10 times, you can be fairly confident they were influenced. If the whole account racked up 10 impressions spread across 50 people, nobody was.
We target small accounts (10 to 200 employees) with about 3 ICP members each, so 3 x 10 = 30 impressions per account is our influence threshold.
Don't guess the "members per account" number. In ZenABM, open the Companies tab of a campaign: you see impressions, clicks and engagements per company, so you can check how many impressions a typical target account actually gets in a month and sanity check your threshold against it. (More on measuring LinkedIn ad engagement for target accounts here.)
If you target enterprise accounts with 50 people in the buying committee, 30 impressions means almost nothing. Go higher: 350 to 550 is more reasonable.
Who you target | ICP members per account | Impressions per member | Influence threshold |
|---|---|---|---|
Small accounts (10 to 200 employees) | 3 | 10 | 30 impressions |
Mid market | 10 | 10 | 100 impressions |
Enterprise buying committee | 50 | 7 to 11 | 350 to 550 impressions |
A few things to tune:
In ZenABM you set this once, under your CRM settings: Deal influence threshold (impressions on a company before a deal counts as LinkedIn influenced). It applies portal wide, and every influenced pipeline number in your deal attribution dashboard recalculates from it.
Lookback window = 2x your sales cycle.
If your sales cycle is 90 days, use a 180 day window.
Why double? Buyers see your ads well before they enter your CRM. A window that only matches the sales cycle cuts off the start of the journey, which is exactly where LinkedIn does its work.
Our trial lasts 37 days, so 2 x 37 = 74 days. Anything between 60 and 90 days makes sense in our case.
So for us: 30+ impressions in the last 60 days = influenced.
In ZenABM this is the Deal influence window: how far back before a deal was created we look for LinkedIn impressions. You can also pick which deal types count (new business, existing business, renewals) and which deal property is the deal value (Amount or your ARR property).
Pro tip: report new business separately. Tim Davidson builds his influenced pipeline view from new business only, with renewals and expansion as optional filters. Renewals will happily rack up impressions from your existing customers and inflate the number. In ZenABM that's one checkbox: untick "All deal types" and keep only New Business.
Let's be honest about what influenced pipeline is: a correlation. An account saw your ads enough times, and then it opened a deal. That doesn't prove the ads caused the deal.
So why bother with an imperfect metric?
Because the alternative signals all point the same way. Patrick says accounts that see LinkedIn ads almost always close at higher rates and higher ACVs than accounts that don't. Our customers see the same trends over and over (FlowFuse scaled LinkedIn to 80% of their marketing budget once they could see the account level pipeline it drove).
He also sees outbound reply rates and call show rates go up when prospects have seen your ads, even if nobody clicks. How come?
First of all: brand recognition. Familiarity and trust affect the propensity to buy. Your SDR's cold email isn't cold anymore if the prospect has scrolled past your face 40 times.
Second of all: all those clicks that never got reported 👩🏫
Marketing never wins a deal through one touchpoint. Influenced pipeline is a proxy we had to adopt to please the revenue obsessed, but "we can't track it perfectly" is no reason to discard the influence.
It's all directional, I'm afraid. BUT if someone (your CFO, your sales team) is being very dismissive, don't argue about the threshold. Overlay your other sources on top of LinkedIn and show the level of engagement before the deal opened.
Here you can see an uptick in website visits and that ONE Google Ads click right before the deal opened. Last touch would hand the whole deal to Google. But would anyone seriously say LinkedIn didn't play any role, given the level of engagement in the 365 days before? 😅
In ZenABM this is the company Performance view: LinkedIn impressions, clicks, engagements and cost on one timeline, with Google Ads, Reddit Ads, organic, website visits and AI clicks overlaid, plus contact events and the date the deal opened.
Do incrementality testing.
But don't compare the ACV "lift" of LinkedIn reached deals to inbound deals. That's not fair! Accounts on your ABM list are there precisely because whoever built the list thought they'd close at a higher rate and a higher ACV. Of course they look better than random inbound. That comparison is biased before you start.
Here's the fair version:
If the half that saw ads still closes more or bigger deals, that's real evidence LinkedIn Ads are doing the work.
But you will have missed out on many deals in the "other half" for the sake of "proving" attribution. That's the price. Most teams decide it isn't worth paying more than once, and that's a perfectly reasonable call.
If you want something cheaper, try Patrick's test: point a new campaign at all your outbound contact lists and watch your reply rates over the next 30 days.
Influenced pipeline is generous by design. Some of the deals it counts were also driven by other channels (cold search ads, events, a great SDR). At small scale you often can't separate them. These are the rules we use to keep it honest:
You don't have to build that report by hand either: Zena, the AI agent inside ZenABM, writes weekly, monthly and quarterly reports with insights and action items and sends them to your inbox.
For the journey check, connect Claude to the ZenABM MCP server (OAuth sign in, no API keys) and run the /revenue-attribution skill, or paste a prompt like this one (we use the same setup for ABM campaigns with Claude Code):
Pull every deal created in the last 90 days that ZenABM marks as LinkedIn influenced. For each one, show the company's LinkedIn impressions, engagements and clicks before the deal was created, the deal's original source in the CRM, and any other touchpoints (UTMs, paid search, events). Flag deals where LinkedIn is the only meaningful touchpoint and deals where another channel clearly did the heavy lifting.
We built ZenABM to report on LinkedIn at the account level, because that's where ABM actually happens:
If you want to go deeper on the reporting side, read our guide to building a LinkedIn revenue attribution report and our walkthrough of ABM analytics dashboards.
Yes. With guardrails.
Use it because last touch and click attribution will tell you LinkedIn drives almost nothing, and that's simply not true for a demand generation channel.
Set the threshold from your targeting (members per account x 7 to 15), set the window from your sales cycle (2x), write both down, and don't change them every month to make the number look nicer.
Then pair it with marketing channel attribution and, at least once, an incrementality test on the same list. That's when you get a picture of marketing's impact that's honest enough to make budget decisions with.
Makes sense?
Influenced pipeline is the value of deals opened by companies that saw your LinkedIn ads enough times before the deal was created. You set two rules: an impression threshold per account (for example 30 impressions) and a lookback window (for example 60 days). Any new deal from an account that crossed the threshold inside the window counts as LinkedIn influenced, whether or not anyone clicked.
Multiply the number of ICP members you target per account by 7 to 15 impressions. With 3 members per account, around 30 impressions is reasonable. With a 50 person enterprise buying committee, go to 350 to 550. Stricter teams add an engagement gate on top, such as 3 or more clicks or engagements.
Use roughly twice your sales cycle, counted back from the day the deal was created. A 90 day sales cycle means a 180 day window. Buyers usually see ads long before they show up in your CRM, so a window that only matches the sales cycle cuts off the start of the journey.
No. Sourced pipeline credits a deal to the channel that created it, usually through a click and a form fill. Influenced pipeline credits every channel that touched the account enough before the deal opened. Sourced undercounts LinkedIn heavily; influenced is generous by design. Report both, and use influenced to compare campaigns rather than as a single proof number.
Run an incrementality test: split one ABM target list randomly, show ads to half, exclude the other half from all LinkedIn campaigns, and compare deals created, win rate and ACV after a full lookback window. Don't compare ABM deals to inbound deals, because ABM accounts were picked for being likelier to close.
