🍝 Stop Optimizing for Conversions

Hey friends,

I talk a lot about how marketers should stop using the traditional marketing funnel to plan campaigns. Now, I have some data to back up my argument. 

We ran an experiment this quarter that stopped optimizing LinkedIn Ads for conversion goals. It used the same budget and same team, just completely different optimization strategy.

The results are in: we saw a 61% increase in SQLs this quarter versus Q2. 

Marketers are obsessed with conversions because this metric has helped prove marketing’s value. But attribution tools have come a long way. We don’t have to rely on last-touch fields in our CRM to prove marketing is working, even if a certain ad or organic content piece doesn’t get the initial credit. 

It’s time to move away from the conversion mindset and build marketing efforts to match people’s real buying behavior. Here is the exact recipe I’m using at KlientBoost to do just that 👇

B2Bs often prioritize intent data in an attempt to only target in-market buyers. But existing demand is finite and this strategy impossible to scale.

Here’s a tactic you should try in Q4 instead: Build ICP targeting criteria based on your most valuable clients that includes both in-market and out-market prospects.

Your campaigns should target only this audience to help build mental availability across your entire addressable market:

The prospects not ready to buy today will remember you when they move in-market 6-18 months from now.

📈 How to measure this approach

Use influenced deals analysis to show how many "direct" conversions actually started with LinkedIn impressions months earlier. Tools like Dreamdata can track the complete journey and prove that broad targeting drives long-term pipeline instead of just immediate conversions.

I broke down how to actually set up these LinkedIn Ads campaigns in this newsletter 👇

Optimizing just for conversions skips over the brand building element of reaching your out-market audience.

I recommend optimizing for maximum audience penetration (50%+) and reasonably high frequency (10+) so your ads reach both in-market and out-market prospects.

This approach works because LinkedIn's algorithm doesn't know who's actually in-market.

When you optimize for conversions, you're asking it to predict complex B2B buying behavior it fundamentally can't understand. When you optimize for reach, you're building mental availability for when prospects actually need your solution.

To optimize for reach and frequency:

This approach means you’re not competing for a tiny slice of "ready buyers” in the market. Instead, you will dominate mindshare across your entire ICP and be on their shortlist when they move in-market.

📈 How to measure this approach

Use cherry-picked deal analysis to show how high-frequency exposure correlates with deal quality and map customer journeys showing multiple LinkedIn touchpoints before conversion. To do this:

Look at this example I pulled from last quarter using Dreamdata 👇

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. This analysis shows how LinkedIn Ads influenced their buying journey and 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.

Content based on category entry points helps prospects think of you when they move in-market. For example, as part of our ICP targeting, we mapped out our most common category entry points:

Instead of generic "solution selling," we're associating our brand with specific problem scenarios our ICP faces.

To copy this tactic, dig into why people are coming to you to solve problems and then map out what content will actually target these entry points. Category entry content should always focus on building mental availability around problem scenarios, not just pitching your brand.

When prospects experience these problems, you want to be the first company they think of.

Dwell time is a seriously underrated metric for measuring performance across paid and organic. It measures how long someone spent watching your content, even if they didn’t engage with it, which is super valuable.

Real audience behavior like this is the reason we experimented with optimising for recall using these in-platform signals:

If prospects engage with ads and spend more time viewing them, they're more likely to remember you when purchase intent strikes. To test this tactic out, I recommend you:

📈 How to measure this approach

Use influenced deals analysis to correlate engagement patterns with deal outcomes. High dwell time and engagement often predict deal quality better than immediate conversions. Advanced attribution can track which creative formats and messaging drive influenced pipeline.

More on how to do this here 👇

B2B sales cycles can extend 12+ months, you won't see conversions that respect your cost targets immediately. You need leading indicators that predict pipeline performance before the conversions show up.

The right leading indicators depend on whether you are targeting in-market or out-market audiences.

For your out-market audience, aim for:

These metrics matter because they are a strong indicator of what will happen if people move in-market. They predict how well you penetrate your target audience and build mental availability, the exact ingredients needed for conversions in your pipeline later in their buying journey.

Stop worrying about conversions and reconfigure your campaigns to meet people where they actually are in their buying journey.

Your pipeline will thank you 😉

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

Patrick 🤘

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