Co-Founder @ Omni Lab | Paid Media for B2B SaaS brands

Over the last 15+ years, I've worked for 4 VC backed SaaS companies, consulted with more than 100+, helped 1 get acquired, 1 reach unicorn status, and many more generate millions in net new revenue.

I've also seen a handful of those startups go out of business...including my own SaaS business so I know many of the mistakes. 

Over that time I've noticed that the buyer's journey has evolved. 

Today buyers have the power.

Yet, many SaaS businesses still continue to run the same programs that worked a decade ago. 

They continue to over-rely on lead gen and create friction in the buying process. 

This is pushing customer acquisition costs sky high and causing many to miss their targets. 

The way forward is to take a buyer-centric approach to creating and capturing demand. 

Buyer-centric demand gen is when your funnel aligns with how the buyer wants to learn about your company. 

It means you write in the language your buyer understands, you consider how they consume information, and when and how they want to buy. 

When you take a buyer-centric approach you generate more pipeline and revenue and an enduring brand. 

This is why we started Omni Lab.

------------------------------------------------------------

What we do for Series A to Series C SaaS startups:

→ Manage and optimize paid media across any digital channel
→ Distribute content/messaging via paid to grow pipeline and revenue
→ Build out engaging ad creative
→ Focus on optimizing the funnel based on pipeline and revenue. 

Results from SaaS companies we have worked with: 

→ Unicorn Status in 10 months
→ Grew pipeline 372% and lead > opp rate 266%
→ 1,300% increase in revenue Q3 > Q4
→ 20% increase in revenue Q over Q

If you are looking to create and capture demand via paid media, let's chat :) 

email: jonathan@omnilabconsulting.com or DM me

Jonathan Bland

🦄 A customer of ours, Upwind Security, just raised $250M at a $1.5B valuation!! 

Can you say uunnniiiicccoooorrrnnnnn. :) 

Bessemer Venture Partners led the round, bringing total funding up to $430 million! 

Wild for a company that was founded in 2022. 

Its core focus is helping organizations prioritize real risks over irrelevant alerts and streamline cloud security operations in complex environments.

Rinki Sethi, the CSO at Upwind, argues that cloud security has fundamentally changed: modern cloud, data, and AI environments are too dynamic and complex for traditional, static security tools to work effectively.

I just had to give a huge shoutout to the whole team and especially Chas Larios and Jeanette Stepanchuk, whom I've now had the pleasure of knowing at two different companies. 

Anyone looking for a great marketing leader to work for, they are it. 

It's going to be a fun year, ya'll! :)

Co-Founder @ Omni Lab | Paid Media for B2B SaaS brands (HIRING)

The future of agencies will move away from pure execution.

And agencies built solely to produce deliverables will struggle as AI takes over their creation.

Everything from ad copy and creative to campaign planning, budget recommendations, and even audits can be done with AI. 

That doesn’t kill agencies. It changes where the value lives.

The future role of agencies is not just make things; it’s to decide what matters, interpret outputs, and give brands direction.

I recently read an article from Marketing Week called "Why AI-powered agencies will be at the forefront of marketing’s next digital revolution."

The biggest takeaway is that the value exchange will start to shift to buyers looking for things like: 

- Diagnose the market and the buyer
- Segment and choose a target properly
- Position a brand in a way that is actually distinctive
- Decide the right balance between brand, activation, and channels.

Funnel found that 72% of agency marketers believe generative AI will replace some of the services they provide to clients.

And In-house marketers agree even more strongly at 88%.

At Omni Lab, we have invested significant resources in AI-based workflows over the last two years to streamline our services, improve consistency, increase quality, and ultimately enable our team to focus more on strategy and interpretation.

And very soon, we will be releasing Omni OS, which will power Omni Lab with budget pacing, bid management, audience creation, campaign planning, and more. 

That means more time to focus on deciding what matters, interpreting outputs, and giving brands direction.

It's already starting to happen, but if you're a CMO or VP of marketing, you should be asking how your agency is leveraging AI.

You don't only want to pay for deliverables and repetitive work. 

Check out this guide from Funnel below. 

#funnelpartner

We just wrapped 2026 planning, and we’re hiring two new roles at Omni Lab!

And it’s not because we want to do more of the same.

It’s because the way B2B paid media has been run for the last decade is broken.

Categories have become significantly more crowded and complex. 

And many SaaS brands are competing using the same channels, the same intent data, and increasingly similar messaging and tactics, making meaningful differentiation harder to achieve. 

In less competitive markets, it was rational to rely heavily on short-term demand capture.

Buyers were more responsive as there was less available information on the web, media was cheaper, and intent signals were scarce enough to provide a real advantage.

The core problem is not a lack of tools, data, or optimization sophistication. 

It is a mismatch between how paid media programs are designed and how modern B2B buyers behave. Buyers spend long periods out of the market, are highly risk-averse, and actively avoid friction. 

By the time intent is expressed, preferences are often already formed, leaving programs focused solely on in-market demand competing too late and on increasingly unfavorable terms. 

You don't need an "execution factory."

You need someone who can: 
- Build category presence before demand exists
- Earn attention in zero-click environments
- Allocate budget to make capture channels more efficient

Behind this approach is an AI platform we internally call "Omni OS" to automate repetitive work, so we can focus more on strategy, interpretation, developing creative campaigns, and making better optimization decisions. 

 If any of that sounds interesting and up your alley, we are hiring for two new roles: 

- Performance Marketing Manager ($70-90K base + variable comp) ; U.S. Based 
- Sr. Demand Gen Manager ($90-$120k base + variable comp); U.S. Based 

Please tag someone who's looking! Thank you. :) 

Go to https://buff.ly/FGtIIFp to find the recs.

Short-termism is killing good marketing. 

But it doesn't change the reality of what execs want: 

- Fast results
- Predictable returns
- Easy to measure channels

It then forces all product category competitors to optimize for:

- The same in-market keywords
- The same intent/signal audiences
- The same platforms
- At the same moment

That means: 

- Higher CPCs & CPMs
- Rising CAC
- Declining marginal returns

Even with “best-in-class” execution, you'll see prices increase and volume decline as you narrow down audiences. 

Meanwhile, 71% of buyers reported purchasing their first-choice product according to TrustRadius. 

And 9 out of 10 consumers name trust as a primary purchasing criterion, driving buyers to rely on familiar, trusted brands.

Keyword = "famiilar"

If you aren't a familiar, and have established brand preference BEFORE someone comes in-market, it will make it even harder to compete. 

And let's not forget how competitive product categories have become. Here's what the call recording space looks like: 

1.) Fireflies.ai
2.) Otter.ai
3.) Grain
4.) Avoma
5.) MeetGeek
6.) Read.ai
7.) Fathom
8.) Krisp
9.) Sembly AI
10.) Jamie
11.) Tactiq
12.) Supernormal
13.) Notta
14.) Fellow
15.) Nyota
16.) Airgram
17.) Gong
18.) Chorus
19.) Clari Wingman
20.) ExecVision
21.) Jiminny
22.) Modjo
23.) Observe.AI
24.) Dialpad
25.) RingCentral
26.) Aircall

Now let's go after the most obvious target, high-intent keyword "call recording software" = 880 monthly searches at $51/click (top bid)

The top three vendors will likely capture most of the demand, leaving the rest to fight over the scraps. 

And that's just one category. 

- This isn’t a tooling problem.
- It’s not a tactic problem.

It’s a misunderstanding of how B2B buyers actually make decisions.

Short-term optimization is necessary, but insufficient on its own.

Real leverage comes from shaping preferences before buyers ever show in-market signals. 

That means adjusting channel mix, objectives, budget allocation, and CTAs to focus on your narrative, what you do, your POV, proof of work, etc. before they come in-market. 

That will make your capture channels more efficient.

If your AI output feels ‘generic,’ that’s not an AI problem. That’s a brand problem.

Models trained on undifferentiated inputs produce undifferentiated outputs.

Many don’t like the results they get because they go to an LLM and, in broken English, ask it to write a blog post about "the difference between demand gen and lead gen."

When it has: 
- No context
- No idea your core POV
- No idea about your messaging
- No idea about your brand
- No idea about your tone
- No idea about your audience
- No idea what format you want the output in

Funnel released a report that showed 39% of agency marketers and 23% of brand marketers
say AI tools create repetitive, generic campaigns.

I honestly think there are even more people who think this. 

The reason they sound repetitive, generic, or use words that you’d never say to a friend is that you haven’t built AI workflows that pull from a store of data to provide guardrails and direction on exactly what you want. 

That store of data can hold your:
- POV
- Tone
- Messaging pillars
- Positioning
- Audience
- And more

Just like talking with a human, AI needs context. 

And the more input you give it (e.g. draft, transcript, sample blog post, etc.) the better it's going to be.

Check out the report from Funnel below. 👇

P.S. You can even program in grammatical mistakes.

#FunnelPartner

It should honestly be criminal to spend paid dollars promoting a message/content that has never been tested organically.

Yet I see teams do this all the time.

They use paid media to test messaging pillars, even though they already have more signal than they realize.

Think about how much information already exists:
- Sales calls
- Customer interviews
- Organic posts
- Events and conversations
- Comments, DMs, and replies

And if you’ve been close to your market long enough, you often know what resonates before you ever open a dashboard.

Remember, organic content is why people come to platforms like LinkedIn, Instagram, Reddit, and X in the first place. They’re there to learn, observe, and form opinions, not to be offered $100 giftcards. 

When something lands organically, it’s a signal, especially when engagement consistently clears that 3–4% range.

And I'm not just talking about thought leader ads (promoting organic posts on LI) 

I'm talking about posting the blog, guide, or short-form video BEFORE you decide to put paid dollars behind it. 

And bonus points if you can retain the same look and feel once it gets created. 

Using Rand Fishkin as an example looks like: 

1.) He posts message/content organically
2.) Looks for a high engagement rate + reviews comments (3-5%)
3.) He takes the same theme/message, builds new content pieces, and creates new ad formats
4.) Then he amplifies the messages with paid dollars that already resonate

This is how you earn attention in an increasingly crowded space where more people are tuning you out. 

The mistake isn’t spending on paid.

The mistake is failing to use internal data to derisk that investment. 

That way, you are already scaling winners vs. trying to figure out what works. 

The teams that win are paid to reinforce familiarity, not manufacture it.

undefined undefined

Omni Lab 

Snapdata, in the business of waste and recycling analytics, was selected to participate in NUMA's Smart City Program in Paris to connect with corporate leaders like Cisco, Suez, and City of Paris Government to discuss innovative solutions for cities. Out of 200 startups, we were one of 4 selected to participate. 

Google Exchange Program Finalist - NUMA; Paris

Ogilvy partnered with IE Business School to invite IE based startups to pitch their ideas to Ogilvy to compete in getting the chance to have Ogilvy support their branding & marketing needs as well as partnering with some of their clients to bring about new innovation. 

Ogilvy Competition, Top 3 Startups

Achieving outstanding performance during the IE Business School Startup Lab. Voted best Startup to invest in. 

Investors' Award

Small team of marketing professionals at IE Business School were tasked to develop a new marketing strategy for a SMB company called DriveGum. DriveGum distributes and manufactures CPG goods like gum, granola bars, and others in Europe. We:

•	Repositioned the key value proposition.
•	Created three new strategies to grow brand recognition and improve sales.
•	Made improvements and updates to current marketing material. 




IE Business School - Marketing Case Competition, Finalists

James Madison University Austin Alumni Chapter

Managing Director

Snapdata designs and develops a range of sensors and data analytics platforms for the Internet of Things.

Primarily responsible for European operations developing a go to market strategy and doing business development in the waste and recycling sector.

Confirmed 3 letter of intents with governments and corporate size clients to test our product. 

Developed pricing matrixes for products and services, ROI for our clients, and detailed process for proof of concepts. 

Managed Google AdWords campaigns and analyzed Google Analytics to improve our messaging and positioning. 

Won four separate competitions for leading innovative technology and startup in Spain:

Venture Day Finalist, IE Business School
Investors Award, IE Business School
Google Exchange Program Finalist, NUMA 
Top 3 Startups, Ogilvy


Snapdata

Learn how Omni Lab helps 100+ B2B SaaS brands create and capture demand with paid ads.

Book a Call

I see a lot of teams frustrated with AI right now. 

They open an LLM, type something like “write a blog post on demand gen vs. lead gen,” and then feel disappointed when the result sounds… hollow. 

Not quite right. Not something they’d ever actually say.

But think about what you’ve given it.

- No real context.
- No point of view.
- No sense of your brand, your voice, or who you’re actually trying to reach.

So, of course, the output feels generic. That’s exactly what you asked for.

Not sure if you all saw this report, but Funnel released a report showing that 39% of agency marketers and 23% of brand marketers say AI tools produce repetitive, generic campaigns.

Sidebar: Honestly, surprised it's not more. 

That tracks with what I’m seeing. And it’s not because AI “doesn’t work.” It’s because most teams haven’t done the unsexy work of giving it something to work from.

AI needs a source of truth, just like a new hire would.

Your POV.
Your tone.
Your messaging pillars.
Your positioning.
Your audience.

When you build workflows that pull from those inputs and feed them real material like drafts, transcripts, and examples, the output changes completely.

Not because the AI got smarter.

But because you did a better job teaching who you are.

Context isn’t optional. It’s the whole game.

Worth reading the report from Funnel below, some interesting stuff there. 👇

#funnelpartner

For all marketers, what newsletters do you "actually" read?

And for those you read, why do you like them? 

Tag a company/person below that's creating awesome content. 

I want to subscribe.

I’ve been writing on LinkedIn for around 5 years now, which is wild to think about. 

Contrary to what all the LI creators say, I had no plan, no goal, and no topic. 

I posted random Einstein quotes, random personal photos, and observations with no clear theme. 

I suppose my goal at the time was simply to build the habit of sharing publicly and get comfortable with the possibility of judgment.

After months of random posts, I started focusing on writing about sales and marketing, since it seemed easier given my background, and I wanted to get better at both. 

After a while, I started meeting a few people, including some who were looking for help. 

So I started doing a little consulting, and my writing started to center more on demand gen and paid media. 

I shared what I was learning, patterns I was seeing, and opinions on where brands could improve, basically anything that seemed like it might be interesting to others. 

Four years later, I have nearly 30,000 followers (if followers matter now at LI). More importantly, I’ve met a lot of great people, which is what social media is all about. 

I'm no Ernest Hemingway, but writing has been one of the most valuable disciplines I’ve developed over the years. 

It really forces you to think through ambiguity, clarify your stance, and better communicate your ideas 

And this is part of the reason I feel it's so important to stay close to the creative process rather than outsourcing everything to an LLM.

The more I delegate to an AI assistant:
- the less I wrestle with ideas
- the less I refine my own communication
- the less I strengthen my writing muscle

I mean, there are actual cognitive benefits of writing/thinking/doing.

Not sure if ya'll listened to an episode with Steven Bartlett, Dr. Daniel Amen, and AI neuroscientist Dr. Terry Sejnowski, but they discussed the possibility that overreliance on AI tools like ChatGPT could contribute to cognitive decline over time. 

The argument is straightforward: the brain functions like a muscle. Without regular effort, it weakens.

That doesn't mean you shouldn't use AI to help think through things, find different angles, and help edit, but I still think taking a bit of extra time to really think through what you are writing is so important. 

To each is their own, but that's my story and perspective at this point.

Here's what I said to a Head of Demand Gen who wanted to 2x SQL volume on Google Ads. 

"Sure, but here's what we need to do first. 

- Analyze what keywords have the highest conversion rate + lowest cost per SQL
- Determine if there is enough in-market volume to capture
- Understand if we have enough budget based on established baselines (e.g., cost per SQL)."

After analyzing a mix of offline conversion data in Google, paired with Salesforce data, we identified ~10 keywords driving the majority of SQLs over the last 6 months. 

Then we reviewed the available impression share and whether we were losing impressions due to the budget. 

And then we reviewed baseline data from the last 6 months, which showed that: 

The ~600k in spend over the last 6 months resulted in a cost per SQL of around $3,500.

For context...SQL = demo request that meets ICP criteria

However, across all keywords, the baseline is closer to $4,700. 

The primary reason?

- Some keywords have a lower conversion rate
- Some have much higher costs due to competition

So then we had to make a decision...

1.) Stay live on all keywords, accepting a higher cost per SQL
2.) Narrow down on the 10 keywords with a more efficient cost per SQL

No surprises here... we are narrowing down for now, as we have more efficiency with these 10 keywords + available in-market impression share, since all keywords are high-intent. 

So to 2x the number of SQLs, here's the quick math: 

- $100k spend at $4,700 = 21 SQLs / month

Compared to the 10 keywords at a lower cost per SQL

- $100k spend at $3,500 = 28 SQLs /month

We're still a bit short on the same spend, so we'd have to increase the budget or find another lever to pull (e.g., improve the conversion rate or invest further in out-of-market buyers on social channels to increase awareness). 

I'll let you know in the next month or two once we get more data, but this is the high-level process to think through. 

A few caveats to be aware of:
- Avoid dramatic swings in budget
- As you scale budgets, be prepared for efficiency to fall
- We didn't analyze ACV/pipeline value for this (due to some data accuracy issues with sales), but is important as some keywords could be more valuable than others. 

P.S. While this is a performance marketing approach to scaling budgets, channels like Google Ads don't operate in isolation. 

Google Ads heavily favors last-touch models where CRMs single-source conversion points. 

It's important to understand the other trackable touchpoints across the buyer's journey to make capture channels like Google even more efficient. 

The efficiency of your capture channels is strongly correlated with your investments in out-of-market buyers, where brand preference is established.

We need to stop running marketing on sales math alone.

Marketing and sales serve the same outcome, revenue, but they operate differently.

Sales are primarily focused on converting in-market demand 

Marketing shapes preference before buyers come in-market and converts in-market demand. 

We know most buying decisions are influenced long before a form fill, a demo request, or a reply to an outbound email. By the time a prospect talks to sales, their shortlist is often already formed.

So when we reduce marketing to:
- MQL volume
- CPL targets
- “Marketing-sourced” pipeline
- Last-click attribution

We narrow its role to demand capture only, which changes the tactics and objectives marketing uses. 

Let me be clear, demand capture matters. It should be measured and optimized.

But it is not the full job of a marketing manager. 

Marketing’s broader responsibility is to:
- Position the company clearly within the category
- Establish a distinct narrative and point of view
- Build trust at scale before buyers are in-market
- Inform product and pricing with real market insight
- Create brand preference, so you enter deals already in the top three

If you’ve worked in sales at a strong brand, you feel the difference immediately.

Instead of:
“What do you guys do again?”

It’s:
“Our VP has been following you for a while.”

That familiarity lowers friction, shortens cycles, improves win rates, and protects pricing. It makes every outbound sequence, paid campaign, and sales conversation more efficient.

The tension between “sales-sourced” and “marketing-sourced” pipeline didn’t appear by accident. It’s a byproduct of how we chose to measure impact. Which means: 

- Over-reliance on last-click attribution
- Short-term pressure from leadership and boards
- Optimizing for immediate lead volume over long-term demand
- Treating marketing as a quota-carrying SDR team

None of this is about avoiding revenue accountability.

Marketing should be accountable for revenue.

But revenue accountability should include:
- Pipeline velocity
- Win rates
- Sales cycle length
- Brand search growth
- Direct traffic
- Share search
- Efficiencies gains in the bottom-of-the-funnel

Not just how many MQLs were sourced from marketing this quarter.

It’s solved by redesigning how we measure and compensate marketing teams. By establishing a shared number between sales and marketing and evaluating the impact not just on a single UTM-appended contact in the CRM, but on broader funnel efficiencies across all sources.

And we aren't going to solve anything by simply ungating content or testing out the next trendy tactic. 

It is solved by addressing the systematic issues that cause these symptoms and ensuring we get marketing back to doing what marketing should have been doing.

For those who haven't heard, LinkedIn launched a new feature last year called Partnerships. 👇  

What is it?

Any time someone posts and mentions your brand, you will see it in the partnership section, allowing you to get approval to promote that post via thought leader ads. 

Discoverable posts in Partnerships include: 

- Employee posts from your organization
- LinkedIn articles authored by members
- Newsletter articles published by members
- Posts from 1st, 2nd, and 3rd degree+ connections
- Video content from vetted publishers available for in‑stream ad placement for select accounts 

For those not already using a brand monitoring tool, this is pretty cool. 

It's pretty straightforward: 

- Go to campaign manager
- Go to the partnerships section
- Click on the post that mentioned your brand
- Request to promote
- Promote

The biggest use case we've seen for Omni and other SaaS brands is creating dedicated influencer campaigns featuring customers, prospects, or people who give positive brand mentions. 

Instead of relying on organic reach, you pay for more reach, plus control who actually sees the post. 

Trust and authentic real stories are a top priority for many brands and this is one way to bring those stories to light.

Seeing what your competitors are bidding on is mostly a waste of time.

Yes, it’s “directionally interesting.”

But, at the end of the day: 

1.) You have no clue how that keyword is performing. 
2.) They may not even be intentionally bidding on those keywords. 
3.) They likely have a different strategy and ICP definition than you

Instead, you should focus more on your own customers to understand:

- The words your customers use to describe your product on sales calls.
- The problems and jobs they repeat in demos. 
- Then see what keyword themes have keyword volume and which ones have historical performance.

TL;DR Sure, you might get some keyword ideas from Ahrefs, Spyfu, Semrush, and others, but take anything you see with a grain of salt. 

Focus more on your customers and your own performance. 

It was awesome to finally meet Valerie Riley! Appreciate you having me on the Unbounce show. 

Want to watch the rest, just go to YouTube and type in: Paid media strategy 2026: Surviving rising CPCs and budget shifts in SaaS

Please be careful when interpreting these emails from Google. 

While the intentions behind this email, I'm sure, mean well. 

These can have serious consequences without additional thought. 

For example: 

1.) Does lower CPA mean better quality down-funnel to SQL/pipeline?
2.) Does this mean I'll have better coverage on key high-intent terms?
3.) As I scale, what control do I have over keywords I'm spending money on?

No doubt, you, me, your mom, and your 2nd-cousin-removed all want greater efficiency. 

But not at the cost of quality. 

Stay safe out there.

Mark Huber

Bringing Advocates, Evidence, and References into one platform for B2B marketing teams

This isn't your typical LinkedIn bio because I’m not here to talk about myself in the third person.

You might wonder, “Why has this guy worked in so many different marketing roles? Can’t he make up his mind?”

Over the last 13 years, I’ve worked in many different marketing roles at bootstrapped and venture-backed B2B startups. It’s all been intentional too. I wanted to see firsthand what actually works.

I’ve seen a lot, learned a lot, and made my fair share of mistakes.

If you want help from someone who has actually done the work before, check out markhuber.co.

Head of Brand & Product Marketing

B2B marketers use Metadata’s Marketing OS to drive more revenue without all the manual and repetitive work.

From running paid campaigns to personalizing web experiences to optimizing everything to revenue – Metadata automates all of this.

This means less time spent on low-value tasks and more time spent on strategy, creativity, and driving revenue.

2026 is going to be a huge year for AI-generated marketing plans, excited to see everyone run the same strategy at the same time.

VP Marketing @ UserEvidence | Advisor to Early-Stage B2B Startups

We hosted a dim sum cooking class with a small group of B2B marketing leaders last Thursday night in SF.

Sure, the regular ol' dinner format works. But this year, we're doing something a little different as we double down on unique in-person events.

We’re intentionally building these around doing something together, not just sitting across a table with a drink in hand.

Cooking side by side changes the vibe. It lowers the guard. You get past the "how long have you been at your company?" boring surface questions pretty quickly.

It was great to see a mix of new faces and catch up with some familiar ones.

And the best part of bringing a photographer is that they make it look like you actually know how to make dim sum. Speaking from experience.

Huge thanks to RevAura for helping make this one happen.

I read something last night that made me realize how little I actually know about marketing. And I straight up loved it.

On my couch, half-watching my Chicago Blackhawks, half-doom-scrolling LinkedIn, I clicked into a new post from Jason Lemkin on how inference is the new sales and marketing spend.

My immediate reaction was very on brand for me:
- Uhhh… what are inference costs?
- Why haven’t I heard about this before?
- Is this one of those things I should pretend to understand?

I almost closed it since it made me feel dumb… But I had to keep reading.

The Cursors and Lovables of the world aren’t just “marketing better.” They’re spending a TON on inference costs and pouring money into making their product so good (and so fast) that people can’t help but talk about it.

A Twitter thread showing something that used to take an entire engineering team months to build, now built in a few hours.

A LinkedIn video that starts with, “I’m not even sure how to explain this, so just watch.”

A Loom for a teammate that wasn’t meant for anyone else and ends up doing more selling than any big marketing campaign.

That IS the marketing.

Not every company can be the next Cursor or Lovable. But there’s still something important to learn here.

The real work for marketing teams doesn’t end at explanation anymore. It’s distribution. That’s the part that stuck with me.

Because it makes all “best practices” feel… not so “best” after all. The marketing game is changing right in front of our eyes while we’re still arguing on LinkedIn over which tactic is dead.

It’s overwhelming and a little scary. But also, weirdly, fun as hell.

It made me realize I don’t understand the game as well as I thought. And instead of doubling down, I need to unlearn a LOT and get comfortable not having the answers yet.

Back to being a beginner.

We’re all guilty of this.

We hear a stat or a benchmark from someone that sounds real. We start using it.

And over time, it just becomes “the number". Even if none of us remember who said it, where it actually came from, or whether it was ever accurate to begin with.

Inbound conversion benchmarks are the perfect example of that for B2B marketing teams.

RevenueHero just published a new inbound conversion benchmark report based on 1M+ inbound form fills across their customer base, different industries, and company stages.

That’s a big enough data set to take seriously. Here are three things that stood out to me in their new report:

1. Short forms don’t automatically convert better

Teams converting well had both short forms and long ones. The difference wasn’t the number of fields.

It was whether the form actually helped route the lead or gave the rep something useful to work with.

2. Letting fewer leads through doesn’t hurt conversion

The teams booking the most meetings weren’t trying to pass everyone through. 

They were comfortable saying no earlier. Not because they wanted less demand, but because they didn’t want reps spending time on conversations that were never going anywhere.

3. “Book a demo” outperforms “Request a demo”

In the top-performing group, about 88% use “Book” or “Schedule.” Only ~12% still use “Request.” 

“Book” or “Schedule” usually means you’re about to see a calendar and get time on the books. “Request” usually means you’re about to wait.

What caught me off guard is how easy this problem is to miss once inbound feels “fine.”

Leads are coming in. Meetings are happening. Nothing appears to be broken.

So nobody really slows down to look at what happens in between.

Meanwhile, qualified buyers are just sitting there…waiting.

And that doesn’t always show up cleanly in a dashboard, which is probably why it sticks around.

Interactive product demos are showing up in WAY more places than they used to, with real numbers to back it up.

Navattic just released The State of the Interactive Product Demo 2026, based on an analysis of their top 1%, 10%, and 25% performing demos.

Across more than 40,000 demos built on Navattic in the past year, the patterns are consistent too:

- 18% of B2B SaaS websites now have an interactive demo CTA, up from 12% last year
- The top 1% of demos average 71% CTR, up from 54%
- 52% of top performers place demos in multiple locations across their site
- Product pages are now the most common placement. 62%, up from 19% last year
- 80% of top demos are promoted above the fold or in the navbar

The report also breaks down what’s working inside the demo (length, flows, gating, CTAs, step design).

Get the full ungated report here: https://lnkd.in/d9rnpzvM

taplio

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Jonathan Bland

Mark Huber

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Performance ScoreJonathan Bland's LinkedIn Performance

Performance ScoreMark Huber's LinkedIn Performance

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Jonathan Bland

Mark Huber

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Jonathan Bland's LinkedIn Performance

Performance ScoreMark Huber's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreJonathan Bland's LinkedIn Performance