Hey friends,
Everyone thinks they need better marketing attribution models.
Which is true. Attribution models like first-touch and last-touch that we’ve all used for years are garbage.
But the first step to fixing this is for marketers to collectively stop trusting the "original source" field in our CRM. It’s been lying to us for years. And that’s a huge issue because incorrect attribution can:
Make us double down on channels that steal the credit for other marketing efforts
Mess up quarterly + annual budget allocations for paid channels
Hurt marketing’s reputation in the revenue department (wtf are they even doing over there?!)
If you fix your attribution, the above gets a lot easier to deal with.
Here is my exact recipe for defending my LinkedIn Ads spend to the CEO: from attribution fixes to narrative building.
The CEO’s $25K LinkedIn Ads Question—Is It Even Working? 🤔
Picture this: You're spending $25K/month on LinkedIn ads, and at the end of the quarter, your CEO drops a Slack DM asking: Where’s the ROI? Has any of this spend actually influenced our pipeline?!
What’s your next move?
Marketers are in the habit of running to the CRM, pulling up a dashboard showing “original source” for a deal, and handing it over.
Original source data for opportunities from paid social looks consistently abysmal for us. But this HubSpot dashboard doesn’t tell the full attribution story.
But there’s a huge accuracy gap when we do this. Dreamdata’s Steffen Hedebrandt (who I recently had on the KlientBoost Kitchen pod to talk about this exact problem) described it perfectly:
"Direct is never direct. It always stems from some sort of activity. But your CRM doesn't care about the 18-month journey that actually happened.”
The 18-month journey Steffen mentioned is a multi-touchpoint deal of ours that ended up being attribution spaghetti—this thing went wayyyy beyond the last-touch attribution in our HubSpot dashboards.
When I dug into the deal, here’s what I found:
18 months ago: Company’s original VP of Marketing discovered KlientBoost on LinkedIn
12 months ago: VP gets fired, but CFO remembered us
6 months ago: New hire does research for a new agency (with ad blockers, so no tracking), but CFO recommended putting us on the shortlist
Last week: New hire finally visits klientboost.com "directly" and asks to jump on a call
Our CRM recorded "Direct Traffic" as the original source, but this whole journey was really an 18-month, multi-person attribution nightmare.
This is why it’s a mistake to defend your LinkedIn Ads spend just with CRM attribution data.
How to Overhaul Your LinkedIn Ads Attribution
You can’t “gut check” how well LinkedIn Ads are performing.
Your spend must tie to your pipeline and build a strong narrative around how it impacts your marketing team’s main KPI. MQLs, SQLs, Closed/Won. Whatever it is, attribution is your chance to prove LinkedIn Ads are key to hitting goals.
Here’s the 3-part recipe to overhaul your LinkedIn Ads attribution 👇
📈 Method #1: Influenced Deals Analysis
Do a retro of your closed deals in the previous quarter or two to see how many LinkedIn touchpoints the deal had:
Pull all deals from the previous quarter
Filter for accounts LinkedIn touched at ANY point (use Dreamdata, Fibbler, or LinkedIn’s Revenue Attribution report for this)
Tie those accounts to your North Star marketing KPI (e.g. MQLs or demo booked)
Show how many of your best deals had LinkedIn influence
This will give you a baseline narrative to build on, like "LinkedIn influenced 50% of our closed deals this quarter."
Now you can build on that narrative with more in-depth attribution analysis.
🍒 Method #2: Build a Cherry-Picked Narrative
Steffen actually recommended this strategy in our KlientBoost Kitchen pod, and it’s something I’ve been using to build an internal narrative for our LinkedIn Ad spend.
Here’s how it works:
Pull your 5-10 best deals from the quarter. This could be based on ACV, MRR/ARR, or even just a big brand name everyone was stoked about landing
Build their complete buyer journey. From the very first touch point, and plot every single touchpoint from that until the ink was wet on the contract. This is the only way to give a complete picture of every step of their journey
Map out the LinkedIn ads touches before "direct" conversion. Everything from impressions to video views, ad clicks, and engagements. You want the data to tell a story.
Look at this example I pulled from last quarter. This account was a whale, so I knew it would help build a positive narrative to defend our ad spend 👇
The first-touch was paid social, and in between LinkedIn Ad impressions and video views, a bunch of people from the company visited our website. Right before they asked for a free marketing plan (a main KPI for our marketing team), they checked us out on G2 and were hit with a bunch of retargeting ads.
Now, I can use this method to build on the narrative from Method #1:
They didn’t visit G2 and then immediately convert
They were influenced by LinkedIn Ads throughout their journey
These ads kept our brand on their radar during their decision-making process
Trust me on this one. A visual customer journey that shows multiple touchpoints for your LinkedIn Ads is worth more than a thousand spreadsheets.
More from Steffen on how to cherry-pick data for attribution reporting 👇
📶 Method #3: Unexpected High-Correlation Signals
This is a new method, but definitely one that will bring major value for marketers who need to defend their paid spend. I use AI signals to analyze ALL our go-to-market data to identify buying intent patterns unique to our customers. In fact, Dreamdata has a new feature that does this automatically now, which is really f*cking cool.
Pull all of the deals from the previous quarter, filter them by your North Star marketing KPI, and analyze signals like:
Influenced Value. This maps out the actual $$$ of influenced value your LinkedIn Ads have on prospects and closed/won deals.
Influenced Prospects. Dreamdata can pull the number of influenced deals over a month/quarter/year from your entire LinkedIn Ads spend and then break it down by campaign and KPI.
These are all of the Influenced Prospects tagged with Linked Ads in our pipeline from the last quarter. Source: Dreamdata
It will then break down how much each ad campaign has influenced prospects in your pipe, which is the detail you need to dial successful campaigns up and down depending on how positive their influence is.
This attribution strategy maps correlation strength ratings so you know which signals actually matter.
Build a Narrative and Defend Your Spend to the CEO
I know how hard it is to defend spend, especially when there are thousands of $$$ at play every month. My advice is to tie your overhauled attribution data to a strong narrative around overall company goals:
Start with the story. Lean on last quarter’s deals to build a baseline narrative for what your LinkedIn Ads spend achieved.
Follow with the numbers. Use data-driven attribution to add real ### to your narrative. I recently used influenced CPA data and stack ranked it vs our other channels. This particular LinkedIn Ads dataset hammered home why this channel made so much sense as an investment. It also helped our CFO give the green light to increase spend for these campaigns!
End with strategic recommendations. End with next steps and map out how to optimize for the future based on the data to build on your wins.
TLDR: Defend your spend. Consistently smash your KPIs.
Hope you’ve found this useful and I’ll catch ya in the next one!
Patrick 🤘
P.S. Steffen mentioned that B2B attribution has been the "curse of B2B marketing for the whole eternity." Finally, we have tools (like Dreamdata) that can break that curse. I recommend looking into them if you're tired of playing defense with your marketing budget.
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