🍝 Does Your Marketing Work?

Hey friend, 

I've had the same conversation over and over this year with other marketers: We know marketing is working, we just can't prove it.

It’s so common for brands to spend $25K/month on LinkedIn Ads and generate 15 SQLs. The problem is, HubSpot attribution says 12 came from "direct traffic," and the rest from Google Search and organic.

Zero credit to LinkedIn.

Marketers know LinkedIn is working and they can feel it in the conversations they have with prospects. But execs see this data and (quite rightfully) ask why the team is spending $25K on a channel that isn't driving any revenue.

This is the #1 problem I see in B2B paid advertising right now. 

We've lost the ability to measure our marketing efforts properly. And without measurement, we can't build a narrative to execs that what we are doing works.

Here's how to fix it 👇

For anyone who has been around the marketing game long enough, we all know it used to be very easy to track everything we did.

But the measurement infrastructure we relied on for the last decade is completely broken, and there are three major problems. 

Number one is 30-50% of conversions are now lost just due to cookie rejection. Add in the 75-95% tracking opt-out rate on iOS, and most B2B companies are operating in a data black hole. 

Here’s what happens: 

Pretty easy to see why marketing budget gets cut from LinkedIn Ads, even when its influencing prospects.

The second problem is most execs are still obsessed with outdated marketing funnels. 

The traditional model assumes MQLs convert to SQLs at predictable rates, which means 100 MQLs should equal 50 SQLs at a 50% conversion rate. Executives love this math because it gives them a sense of control, but buyers don’t move linearly through a funnel. 

This completely destroys pipeline forecasting accuracy and how much trust execs have in your marketing strategy. 

The final breakdown is how marketers build a narrative to prove impact.

We walk into exec meetings on the defensive and apologize for gaps in attribution. But what we should do is lead with a compelling narrative, backed by multiple data layers, to show the complete picture of marketing's impact.

Traditional attribution tells you where a deal came from, but influenced attribution tells you the real story of how a customer converted. 

For example, LinkedIn has self-contained data that doesn't rely on cookies or browser tracking. Tools like Fibbler connect that platform data to company accounts in your CRM to track influence.

This allows you to see that 10 people at a target account each received 20 LinkedIn ad impressions over 5 months before the deal was created in your CRM with "Google Ads" as the last-click source.

Let's look at a real example from KlientBoost’s pipeline. We had a deal close in January 2026. HubSpot showed the source as "direct traffic", but influenced attribution reveals:

If I just relied on HubSpot data, the deal apparently appeared out of nowhere. But influenced attribution data shows a 4-month nurture journey that started with LinkedIn ad campaigns.

This is how brand awareness or out-market campaigns work in practice, but it's harder to measure on some channels than others. 

If you run brand campaigns on Google, you might be able to connect it to a brand campaign and say: if the LinkedIn ads didn't work, how did they find out about the brand name?

It's not like it just randomly came to mind. There's work that goes into nurturing that person beforehand to generate recall.

My advice is to use impression and engagement data as a leading indicator of deals that will close 3-6 months later. For example, when running LinkedIn ABM campaigns, you can measure:

The higher the engagement threshold an account hits, the higher the likelihood they become pipeline. This is what the data shows consistently across Fibbler's customer base.

Influenced attribution is powerful, but don't run it in isolation.

Here is how I build a narrative to show how marketing impacts revenue.

Start with high-level correlation modeling or channel mix analysis—something that links up to goals they care about.

I did this for end of year reporting to my own board. I literally just plotted out how much we spent on ads in each month of 2025 and underneath that: pipeline, revenue, SQLs, MQLs, impressions, clicks. I then fed all the data into a custom prompt to analyze correlations between the data points and pipeline/revenue. 

As you can see, the more we put into ads, the more pipeline we had. When we decreased spend, pipeline dropped.

This isn't sophisticated MMM modeling but its simple correlation analysis (you can do for free!) to point out the clear relationship between ad spend and pipeline.

Next, bring in the platform data and influenced attribution to strengthen the case.

You can do this with offline conversion tracking and server-side tracking for Google Ads. For LinkedIn, we use Fibbler to prove metrics like impressions and engagement influence deals. This means deals previously getting zero attribution can show clear connections to LinkedIn Ads campaigns.

You can even break down performance by content type to show which formats or messages drive more efficient conversions.

Most execs just want to know: if I put $X into this channel, roughly how much revenue will I get out?

This is where I really love influenced attribution because you can give them a dollar-in, dollar-out figure based on previous ROI data. 

"Based on our influenced attribution data, every dollar we put into get us 1.13x ROAS and 2.15x influenced pipeline value.”

That's a number they can work with to understand how well this channel works. 

Finally, validate everything with controlled experiments.

We ran a test in November targeting a completely cold audience of B2B SaaS marketing leaders we had never targeted before. The hypothesis was if we hit them with high frequency, high penetration campaigns that had hyper-targeted messaging, we could influence more pipeline. 

It resulted in a 50% lift in B2B SaaS SQLs from that segment, with influenced attribution connecting 8 of those companies back to the experimental campaign. 

When you put these four layers together, you suddenly build a really compelling case around how your marketing efforts impact revenue. 

And critically, you show the real buyer journey. 

The cost of not fixing your marketing narrative is huge.

Expect budget cuts to channels that work and execs pushing more investment in channels that get false attribution credit.

There’s also a risk where marketers can't prove their value to the company. Some of this is about survival, too.

When you can't see the data, you start doubting yourself and its why so many marketers keep saying the same thing: We know marketing is working, we just can't prove it.

Fix your measurement infrastructure and it’ll be so much easier to build out a multi-layered narrative to tell your execs.

If you can do that, marketing's impact will be undeniable.

Hope you've found this useful and I’ll catch ya in the next one!

🤘

Patrick

P.S. If you are having a tough time proving LinkedIn Ads work to your exec team, hit me up. We're opening 10 spots for a B2B Pipeline Sprint where we take over your account for 90 days and build the influenced attribution reporting that finally proves marketing works. Drop me a message to get started!

📔 A note on Fibbler: Adam and the team are offering a 60-day free trial so you can start tracking influenced attribution for LinkedIn Ads. Drop Adam a DM on LinkedIn to try it out!