Marketing @ KlientBoost - The Performance Marketing Agency

Struggling to make your paid ads profitable? I can help.

I've generated $50M+ pipeline (& counting) for B2B Tech and SaaS companies through content marketing, paid search, and paid social demand generation and GTM strategies.

"We've never been able to make paid ads work for us." — common feedback from happy B2B clients.

Some of the leading minds in B2B follow my content for fresh LinkedIn and Google Ads growth recipes.

Right now, I'm leading marketing @ KlientBoost to add $250K MRR using the exact same strategies, principles, & processes we roll out for clients.

Here's how we can help you: 
- Done for you marketing—starts with a free marketing plan
- Fresh growth recipes to your inbox—klientboost.com/kitchen
- Follow me on LinkedIn to learn the latest winning B2B strategies

Got a burning question about B2B paid ads? My inbox is always open too.

LFGTM 🤘

Patrick Cumming

8 years ago, landing page projects took 8+ weeks and $20k+.

Yesterday, I did the same work in under 2 hours with AI and got a better output.

Here's what changed:

Back then, agencies would spend weeks learning your audience, translating insights into positioning, and building a page that would bomb on launch day.

3 months later, you'd roll back because it performed worse than the original.

Yesterday, I rebuilt our entire B2B SaaS positioning strategy and landing page from scratch.

And the output is better than I saw from traditional agency work when I first started out 8 years ago.

Here's the exact 7-step process:

1. Fed Claude 182 ICP interviews, closed/won call transcripts from 2025, and Wynter feedback on our existing site.

2. Asked Claude to build a positioning strategy using April Dunford's Obviously Awesome framework plus competitive differentiation gaps. 

3. Got a 21-page positioning doc that's genuinely excellent.

4. Applied that positioning to the 5-act landing page framework through Claude.

5. Received landing page copy that needed minimal tweaking.

6. Uploaded our homepage to Replit for style reference and had it build a new page matching our brand guidelines.

7. Made minor tweaks, pitched it to our VP Growth same day, got approved.

Total time: under 2 hours.

It'll take me another hour to build it in WordPress tomorrow. Live by end of week.

While competitors spend months in revision rounds, we'll have this launched and running split-tests with real market data.

And I'll have more time to create the content that actually drives traffic to the page.

That's the shift: from laboring over details the market won't care about to testing what actually works.

Speed to market beats perfect every time.


P.S. Swing me a DM if you wanna read the full positioning doc 😎

If I had $1 for every time a B2B SaaS exec didn't believe marketing was contributing to pipeline...

I wouldn't be that much richer.

It's happened maybe 20 times in my career. But it's still a huge problem for most B2B marketers despite having more tools to measure it than ever.

The problem is 3-dimensional:

1. Execs think every impression and click is trackable because it used to be. But those wild west days are over. Privacy changes killed that.

2. The funnel concept we pitched made execs believe we could nurture any lead into a customer with the right flow. That's not how buying journeys work.

3. We defend marketing instead of showcasing a data-driven narrative that highlights the multiple ways marketing contributes to revenue.

This leads to execs who don't understand marketing, real buyer journeys, or how to measure performance.

I'm teaming up with Fibbler's Adam Holmgren on a livestream to fix this... or at least make your life a little easier. 

We'll cover:
- Why it's so hard to prove marketing is working
- The pros and cons of influenced attribution
- Frameworks for building convincing exec-ready reports

I'm not promising this will solve all your attribution problems. But you'll walk away with actionable tools to strengthen your case and win over your exec team.

Hit the link to sign up: https://luma.com/mvbujb74

In 2025 we turned $190K of LinkedIn Ads spend into ~$2.5M influenced ARR. That's a 13X ROAS and we did it using campaign types most B2Bs are afraid to use. Read the document for a detailed breakdown of the playbooks:

KlientBoost - 13X ROAS From LinkedIn Ads

7 predictions for B2B demand generation in 2026:

1. More B2Bs will abandon LinkedIn Ads as CPMs rise, chasing "cheaper" B2C channels. Smart B2Bs will stay put and use the cheaper LinkedIn ad formats everyone's afraid to touch: CTV and ungated document ads. They'll get LinkedIn's unmatched targeting without paying the premium that companies running boring single image conversion ads are stuck with.

2. B2Bs will become obsessed with in-market intent signals to spam weak offers that don't convert to revenue. We'll see a rise in integrated high-intent campaigns pushed to Reddit, YouTube, and Meta. It'll be AI-slop at scale. Smart B2Bs will use intent signals tactfully, but the bulk of pipeline will come from campaigns following foundational principles: broad targeting with messaging pulled directly from what the market is asking for, refined every quarter.

3. We'll have more measurement tools than ever, but it'll get harder to prove marketing's influence. There's a 30-50% loss in click data from cookie rejection and ad blockers. Platforms are missing half the value they deliver. More B2Bs will cut spend based on incomplete data and watch pipeline collapse next quarter. Smart B2Bs will triangulate: use MTA for insights, then validate with incrementality testing and lift analysis.

4. There will be a bigger push to improve marketing efficiency. And it needs to happen. Many teams use "it takes time" to justify marketing that isn't working. The most successful teams will obsess over lead quality, pipeline, and contribution to revenue.

5. AI will finally create complex imagery. Our feeds will flood with AI-slop infographics on topics prospects never asked for. Smart B2Bs will use AI for wireframes but add an inimitable human touch to every piece of published content.

6. B2Bs will obsess over GEO and AI visibility rankings. Many will fail for the same reason past SEO and paid ads strategies failed: skipping foundational ICP research on pains, gains, and jobs to be done. They'll flock to tools promising the world instead of doing the deep strategy work.

7. Competition will get fiercer. Ad costs are rising, price sensitivity is rising, fear of risk is rising. More deals get lost to inaction. B2Bs without clear differentiation will push spend to retargeting competitor visitors and matching feature sets, instead of listening to customers to find out what they actually need.

I'm at the end of that and it sounds doom and gloom.

But I do think it's gonna be a ludicrously fun year for marketers who love chaos, uncertainty, and challenge.

But there's no two ways about it. It's going to be tough.

What would you add?

If I was planning a 2026 paid ads strategy for a B2B SaaS company, I'd skip the shiny tactics and focus on the boring stuff that actually drives pipeline.

Here's the 7 step process that works:

Step 1: Before you launch anything, fix your foundations

1. Targeting: Use Primer or Clay to build company lists. Push 80% of spend to proven segments, 20% to testing new ones.

2. Messaging: Pull first-call transcripts from closed deals. Build a POV on every pain point prospects actually mention.

3. Attribution: Set up multi-touch or self-reported attribution. Last-click misses 90% of what influenced the deal. B2B buyers now touch 76 interactions across 6.8 stakeholders before close.

4. Tracking: Send MQL, SQL, and Closed/Won data into your ad platforms. Set conversion windows to max (365 days for LinkedIn, 90 for Google).

Step 2: You need an optimal platform split

Check Google's search impression share. Under 10%? Scale budget. Over 90%? You've maxed out.

For LinkedIn, look at audience penetration and frequency over 90 days. Below 80% penetration and 10 frequency? Room to scale.

Start with 60:40 weighted to LinkedIn. Lower CAC, more scalable, compounds over time.

Step 3: Optimize for the 95:5 rule

80% of LinkedIn spend goes to saturating cold audiences with pain-focused content. Hit 80% penetration and 10+ frequency to reach in-market buyers now while building demand for next quarter.

20% goes to high-intent audiences: page visitors, existing pipeline, buying committees.

Use engagement, video views, and awareness objectives. Website visits and conversions have insane CPMs.

Google is for high-intent search only. Run keywords through Ahrefs to find transactional intent. Start with exact match, add phrase match after 90 days.

Step 4: Ditch generic branded ads

Branded single image ads had 4X higher CAC in 2025. Worst performers by miles.

For cold prospecting, run three campaign types: Ungated Document Ads, Thought Leader Ads, CTV Ads.

For retargeting, single images work better since you've built trust.

Step 5: Be careful with shiny AI tools

On LinkedIn, switch off audience expansion and LinkedIn ad network. They'll burn budget on low quality impressions that don't drive pipeline.

For Google Ads, you can test Performance Max, but most B2Bs should avoid it. Lower cost product-led SaaS products see better results since volume is higher and barrier to entry is lower.

Step 6: Cautious channel expansion

Most B2Bs don't need this yet. Max out LinkedIn and Google first.

But if you've hit 80%+ penetration on LinkedIn or 90%+ impression share on Google, try Reddit, YouTube, or Meta.

Use Primer or Vector to target matched ICP audiences.

Step 7: Measure what matters

After 90 days, cut anything with 2X higher CAC than average. Push 80% of spend to what's left. Use 20% to test new bets.

Paid Ads planning isn't rocket science. Master the fundamentals, trust the process, and watch pipeline follow.

KlientBoost B2B SaaS Paid Ads Strategy 2026

LinkedIn's campaign objectives don't work the way you think they do.

Most marketers see "conversions" and assume it'll drive more demos. See "website visits" and expect more traffic.

The theory: LinkedIn's algorithm knows who's likely to complete specific actions and serves ads accordingly.

The reality: After 3 years of testing, I've learned to ignore the labels entirely.

Here's what I found analysing a client's campaigns last week:

Single image ad (website visits objective): 1% CTR
Single image ad (brand awareness objective): 0.9% CTR
Video ad (website traffic objective): 0.8% CTR
Thought-leader ad (engagement objective): 1.7% CTR

The awareness campaign reached 6X more people with basically the same CTR as the website visits campaign.

And it outperformed the video campaign that was literally optimised for website traffic.

The engagement objective campaign crushed everything, despite being designed for post interactions and page follows.

We ran similar tests on our own accounts last year. The brand awareness objective drove 10X higher conversion rates than the conversion objective.

Here's why it happens:

LinkedIn's algorithm isn't sophisticated enough to understand B2B buying intent.

It can't distinguish between someone clicking out of curiosity and someone ready to book a demo.

So stop trusting the objective labels.

Test multiple objectives. Measure what actually moves you closer to your goals.

The objective name doesn't matter. The results do.

3 paid ads audits in 27 days.

All mid-market B2B SaaS.

Same problem: high-intent non-brand keywords aren't converting enough. CAC is unsustainable.

Here's the fix I recommended:

1. Stop chasing form fills. Focus on pipeline.

One account was burning 60% of spend on keywords that never converted to pipeline. I recommended an all-star campaign with only proven pipeline-generating keywords and to push 80% of budget there.

Most B2B SaaS need fewer high-intent keywords, not more.

2. Use conversion-focused bidding with caution if you're under 30 conversions/month.

The algorithm needs data. Without it, you're limiting reach.

Experiment with max clicks or target impression share to get in front of more high-intent searchers. You're trying to find the sweet spot of high search impression share with low CPCs.

3. Use phrase match for keyword mining.

Stop guessing which keywords work. Let the market tell you.

Add phrase match variations of your all-star keywords. Mine search terms monthly for new pipeline converters. Add them back to your all-star campaign.

This is how you develop a Google Ads account that actually improves over time.

CPCs are up 29% vs last year. Efficiency goals are getting harder. But Google Ads still works when you stop spreading budget thin and double down on what's proven.

After 2 weeks intensive filming in LA, 100+ takes of the same screen recording, and more caffeine than I thought it was humanly possible to drink, we're pumped to launch Boost U today.

It's a learning platform for marketers who want the stuff they don't teach you at marketing school.

How to actually use ad platforms. How to create better ads. And then translate it all into real marketing results that help you get promoted and grow.

Here's what makes it different:

Every course is built on validated playbooks from working with hundreds of clients. Not theory. Not outdated tactics from 2019. Real plays that work right now.

We're starting with our LinkedIn Ads course (the one I had 15 diva-level tantrums filming every time I messed up a click 😅). More courses coming soon.

If you've ever felt like traditional marketing education skips the actual useful bits, this is for you.

Check it out: https://lnkd.in/gq_H_Y6s


P.S. If I never have to "act" on camera again in my career, it'll be too soon. But massive credit to the Deadpan 💀 🎬 team for absolutely nailing the video work.

Last week I posted a 7 step AI-powered workflow B2B's could use to build a data-driven repositioning strategy AND turn it into a landing page in <2 hours.

Most people found it helpful, a few folks HATED IT (#sorrynotsorry), and a handful of people DM'd me asking for more details.

I promised them I'd record a full walkthrough of the process. Watch the full process in <3.5 mins below:

We reduced paid ads CAC 14% in 2025 using 3 processes that most B2B SaaS companies completely ignore.

Here's the exact playbook:

1. Efficiency Farming

Stop launching new campaigns every week.

Start reading your data over 90, 180, and 365-day windows to see what actually drives revenue.

Pause campaigns with high CAC. Double down on campaigns with low CAC.

Scale winners by testing variations of what already works.

Do this monthly.

2. Segment Optimization

Targeting is the most overlooked lever in paid ads.

Most B2Bs spread budget across their entire TAM because they're scared of missing prospects.

This kills performance.

Instead, analyze which segments close fastest with the lowest acquisition cost. Build audiences of only these segments using a tool like Primer.

Then exclusively target them.

Do this quarterly.

3. Reverse ICP Exclusions

If you know your ICP, you also know who isn't your ICP.

Build exclusion lists of companies outside your ideal size, industry, or profile.

Upload them to your ad platforms so they stop serving ads to bad-fit audiences.

Example: Only targeting $10-100M IT companies? Exclude everyone under $10M and over $100M.

Update this every time your ICP changes.

The biggest irony in B2B marketing is that most companies think they need to do more when the real gains come from doing less but better.

Paid ads costs aren't dropping. But with these processes, you can stay ahead.

We closed 4 deals from AI platforms in H2 2025.

44 qualified leads came through ChatGPT, Perplexity, and Gemini. Not massive numbers, but enough to pay attention.

Here's what surprised me: our AI visibility sits at 0.5% in our category. That ranks us #6 against competitors.

We're getting deals despite barely showing up in AI responses.

So we ran the math on what happens if we fix that gap.

Q1 target: increase AI citations by 50%. If the conversion rate holds, that's 8-10 additional qualified leads per quarter from a channel that's still growing.

The play is simple:

1. Track where competitors appear in AI responses
2. Identify which content AI platforms cite as sources
3. Build content designed to get picked up by these systems
4. Monitor citation frequency across ChatGPT, Perplexity, Google AI Mode, and Gemini

We're testing Search Intelligence AI to handle the tracking and citation analysis. It scrapes real responses from live AI interfaces and shows exactly which pages get cited.

Early mover advantage matters here. Most brands aren't tracking AI visibility yet. The ones who start now will own the category recommendations before this becomes table stakes.

If you're seeing inbound from AI platforms, you're probably underindexed too. Worth checking where you actually rank.

Get 50% off if you join in the first 7 days: https://lnkd.in/gfKcDJsS

B2Bs are obsessed with "B2C" channels like Meta, Reddit, and YouTube this year for the cheap CPMs promising better efficiency. 

But it's a bad strategic move for most of them. 

Here's how to know if you're ready to expand outside of LinkedIn Ads and Google Ads:

Paid ad costs are getting unsustainably high.

LinkedIn CPMs are up 28% and Google Ads CPCs are up 19% vs 2024. Not exactly in-line with inflation.

But many B2Bs aren't just dealing with rising costs. They're also wasting what budget they do have on bad targeting.

On LinkedIn Ads, it shows up as spreading budget too thin across large audiences. Reach and frequency stay too low to build trust and recall. No trust = no pipeline.

On Google Ads, it's spreading budget across too many keywords that don't convert at a sustainable CPA. More spend, same (or worse) results.

The good news is both problems are fixable with the right approach.

In the latest KlientBoost Kitchen podcast, I break down:

• How to calculate the budget you actually need to win
• Why audience FOMO is killing your pipeline efficiency
• How to target in-market buyers more effectively
• When B2C channel expansion makes sense

These are the same principles we used to reduce marketing CAC 14% in 2025 vs 2024. And the same playbooks our team uses to help clients do the same.

Hit the link in the comments to watch the full breakdown 👇

Revenue Attribution◾️ Dreamdata◾️ Sales leader with a crush on marketing

👩🏻‍💻 Professionally :
- Sales leader with a flavour for Marketing 🤝 
- Sales Pathfinder
- Doubling my LinkedIn followers base each year through content creation here.
I generate all my sales leads inbound from Linkedin Social Selling.
How do I know that Linkedin delivers Revenue?
Follow my Linkedin course on how to measure Linkedin Social Selling to Revenue: 

https://www.linkedin.com/learning/how-to-measure-linkedin-social-selling-to-pipeline/[…]g-and-adjusting-strategies-based-on-performance?


💃 Personally:
- Interested in B2B Influencer marketing.
- Morning 5K runner
- Appointed as an upcoming talent in Denmark u34
https://www.berlingske.dk/karriere/til-kamp-for-kvinder-i-it

Currently testing out the hypothesis: 
If you want to work in the past - join an enterprise.
If you want to work in the future - join a startup.

Fun fact: I was a weekend radio DJ for 2 years in my teen years 🎧

Laura Erdem

We are hiring! 
I am hiring! 

Account Executives in New York!

You will love to sell Dreamdata
- Amazing team
- Market winning product
- Inbounds that are not just qualified, but close within the quarter! 
- Competitive pay

Ping me!

B2B Attribution & Activation◾️ Dreamdata◾️ Sales director with a crush on marketing

🟠 - Marketing
⚪️ - Sales 

Organic Search and Direct are the least favourite sources for Inbounds. Both means "something is working" but we can't put the finger on it due to:
- No proper website tracking
- Deals not joined to Linkedin engagement
- Buyers just not clicking on the sponsored Google link you pay for.

As soon as you see an overview over a full customer journey, it gets so much easier to aggregate the data and understand which channels help educate the prospects and which of those are helping them to convert. 

This specific deal came through Organic - Straight to the pricing page! 
Seems like those LinkedIn targeted ads have worked out after all!

Please, you don’t need more intent signals. 
You need the right ones, the kind that actually lead to revenue.

That’s what we’ve been solving with Dreamdata Signals.

"Show me which Signals became Closed Won opps?"

Since launch, I’ve been watching how teams use it to sharpen their go-to-market. And the difference is wild.

Here’s how I’ve seen B2B teams use it best:
- Build precise audiences around signals and instantly sync them to ad platforms
- Create customized engagement scores to define what “intent” means for your business
- Trigger notifications to sales (on Slack/Teams) when an account shows intent 
Use AI to scan all your Go-To-Market data to uncover hidden intent signals that actually correlate with pipeline and revenue. 

Now you have a loop of success

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