Peep Laja

CEO @ Wynter. 3x Founder. Insights over assumptions. Host of How to Win podcast.

Most of your future revenues are gonna come from future customers. 

If you knew *exactly* what your target customers

think about your category (and your company in particular),
what their top priorities and very big problems are,
what unsaturated channels they use to learn about solutions, 

this is what'd happen to your business:

- you'd nail your messaging, speaking exactly to their pain points
- your conversion rates would go up because your pitch resonates better,
- your CAC would go down as you'd use the right channels with the right message,
- you'd create more meaningful content that moves the dial,
- you'd be more deliberate and accurate in providing more value at every touch point,
- you'd know how to price your products to maximize willingness to pay,
- you'd know how to differentiate,
- you'd know where your go-to-market strategy is off, and could fix it. 
 
The beautiful thing?

You can know all of this with Wynter in under 48 hours.

Agencies are getting fired.

We surveyed B2B SaaS mid-market marketing leaders about where they're cutting budgets.

The #1 answer wasn't paid ads. Wasn't tools. Wasn't events.

It was agencies.

Here's what they told us:

"Agencies have no concept of what it is to actually run a business."
"They assume we have endless time to implement new ideas."
"I need execution, not strategy. I have plenty of strategy."

58% of these leaders are under extreme or high pressure to show short-term pipeline.

They don't have 90 days for a "discovery phase." They don't want another brand positioning deck. They don't need a 50-slide audit.

They need someone to do the work.

Yet most agencies still sell like it's 2015:

❌ Weeks 1-4: Discovery 
❌ Weeks 5-8: Strategy development
❌ Weeks 9-12: The "Playbook" presentation 
❌ Week 13+: "Now you execute it"

Meanwhile, the CMO needed results in Week 2.

Here's the truth agencies refuse to hear:

Mid-market companies don't hire you because you're smart. They hire you because they're underwater.

When you show up with strategy theater instead of execution, you're solving a problem they don't have.

The agencies that survive won't be the ones with the best decks.

They'll be the ones who show up Day 1 and ask: "What can I take off your plate today?"

If you want to have access to the same set of data, you can subscribe to Wynter's ICP Pulse: https://lnkd.in/ggh7yHPf

(Data from Wynter's Dec 2025 survey of mid-market B2B SaaS marketing leaders)

Everyone is doubling down on AI. And most everyone hates the results.

We used Wynter to survey 50 B2B mid-market marketing leaders. 56% are increasing their AI budget in Q1.

But when you read the fine print, the mood isn't excitement. It’s frustration.

Actual quotes from the survey:

"Often AI turns out slop."
"My team is just bumbling through it."
"It's all garbage."

The disconnect is massive.

We have AI SDRs sending robot emails. AI content that sophisticated buyers smell instantly. AI ad variations that are uninspiring.

The problem isn't the technology. The problem is that we are trying to automate the thinking instead of the grunt work.

B2B buyers have excellent BS detectors. Your AI-generated email might save you 20 minutes. But if it costs you the deal, you optimized for the wrong thing.

Efficiency at the cost of effectiveness is just a faster way to fail.

Use AI to execute. Do not use it to think for you.

The math isn't mathing.

1 in 3 mid-market marketing leaders are under extreme pressure to show pipeline results immediately. Not "high" pressure. Extreme.

(58% report extreme or high pressure combined).

The problem? They are being asked to do it with less. Budgets are flat or decreasing across the board.

This is the Impossible Equation of 2026: 

→ Budgets: 📉 Down 
→ Goals: 📈 Up 
→ Attribution: 🤷‍♂️ Broken

One marketing leader told us: "We experience limited faith in performance and slower execution."

Another said: "We don't have a big enough pipeline... but I have to show results now."

When leadership confuses urgency with strategy, you get a disaster. You cut brand. You pump everything into short-term lead gen.

You aren't building a go-to-market engine. You are building a bonfire.

It burns bright for 90 days. You hit your Q1 number. But in 18 months, when the pipeline dries up because nobody knows who you are, the Board will ask: "Why didn't anyone think long-term?"

Because you didn't give them permission to.

The fix isn't more "hacks." 

It's understanding what your ICP is going through, then building brand around those insights.

When you know exactly which problems your ICP cares about, you don't need to burn cash on awareness. You invest in resonance.

But if your Board is demanding 40% growth on a flat budget with zero insight?

They aren't asking for execution. They are asking for magic.

And magic isn't a strategy.

Stop letting the board treat brand as a discretionary expense. Prove your mental availability with hard data. Run a brand tracking study to see if your ICP actually knows you exist: https://lnkd.in/gmKPZB8P

(Data from Wynter's survey of 50 mid-market B2B SaaS marketing leaders)

The "Attribution Death Spiral" is claiming victims.

We used Wynter to survey 50 B2B mid-market marketing leaders. 58% are under extreme or high pressure to show pipeline results. Most can't prove what's working.

Actual quotes:

"Bad attribution data makes it difficult to tie efforts back to ROI." 
"So many visits are categorized as 'Direct'." 
"Not enough confidence to defend marketing spend."

Here's the Death Spiral:

1→ You can't perfectly track Brand or Dark Social 
2→ So you cut those budgets and move to "trackable" channels
3→ Those channels only capture existing demand, not create it 
4→ CAC spikes, pipeline dries up, you blame "lead quality"

The truth: B2B deals involve 5-12 people over months. The LinkedIn post from July. The conference conversation in September. The analyst report in November.

You can't pinpoint which one "caused" the deal. That's fantasy.

But leadership demands certainty. So you build dashboards that give them lies dressed up as data.

First-touch. Last-touch. Multi-touch. All just educated guesses with impressive visualizations.

If you only fund what your CFO can understand, you'll starve what builds your business.

The most profitable marketing often shows up as "Direct" in your analytics.

Stop letting attribution models design your strategy.

You can start measuring what truly drives deals; top-of-mind awareness. Use Wynter to tun a brand tracking study with your ICP: https://lnkd.in/gmKPZB8P

Everyone hates AI SDRs.

We mock the robot emails. We laugh at the hallucinations. We call it spam at scale.

But here is the irony revealed in our latest data: Marketing leaders are buying them anyway.

We used Wynter to survey 50 mid-market SaaS marketing leaders. They know AI isn't perfect and often churns out slop.

So why are they doubling down? Why did one CMO explicitly say they want to "stop relying on human SDRs"?

Because the math is broken.

Why pay a human $65K plus benefits to send a template 36 hours late?

We looked at the complaints in the data:

Speed: Humans are taking 1.5 days to respond to high-intent leads.
Effort: They send generic messages that are just as bad as AI.
Reliability: Leaders have "lack of faith" in the ROI.

If you are just copy-pasting templates from a playbook, let's be honest: you're not an SDR, you're an expensive API call.

The bar for human performance has dropped so low that a $200/month chatbot is stepping over it.

The entry-level SDR isn't being replaced by superintelligence. They are being replaced because they stopped doing anything a script couldn't do.

If you want to survive 2026, you have to be better than a database query. Right now, the data says most aren't.

(Data from Wynter's survey of 50 mid-market B2B SaaS marketing leaders)

I've attended 89 marketing conferences over the years. 

Almost all of them had the same problem.

The best conversations happened in the hallway. At the bar. Never during the actual sessions.

So I built Spryng differently.

We hand-match attendees before the event. Similar company stage, similar challenges, similar goals.

Then we give you space to actually talk. Hallway conversations are the main event.

No panels. Minimal presentations. Just structured conversations with people who get your exact problems.

Next one is coming up March 24-25 in Austin. Built specifically for B2B SaaS marketers who are tired of generic conference advice.

But here's the thing.

If you grab a ticket before January 31st, you get access to something special: 

A VIP boat experience the day before the conference.

30 people total. Two boats. Split by company size so the conversations actually matter. The boat capacity is fixed. Once it's full, this offer disappears.

You'll spend an afternoon on Lady Bird Lake with people who understand 
your world. Facilitated discussions. Real problems. Real solutions.

Then we head straight to the pre-party.

The conference doesn't start until the next day, but the connections start on the water.

Go check out the Spryng website and grab your spot.

Most B2B vendors treat mid-market like "Enterprise Lite."

That's why you're losing deals.

We surveyed 50 mid-market marketing leaders. Here's what they told us:

"Tool vendors think they can solve our problems. But we still need someone on staff to set up, use, and learn from the tool. Sometimes tools create more work, not less."

This is the Mid-Market Reality Gap:

What vendors pitch: 
❌ "Infinite flexibility" 
❌ "Robust dashboards"
❌ "Dedicated onboarding manager" 
❌ "Enterprise-grade maturity models"

What buyers actually have: 

✅ Flat or decreasing budgets 
✅ Extreme pressure for short-term pipeline 
✅ Teams so lean they're bumbling through execution

When you sell "robust capabilities" to a team with zero bandwidth, you're not selling a solution.

You're selling homework.

Mid-market CMOs don't want to "unlock value." They want the value already unlocked.

They don't want to configure a dashboard. They want a decision handed to them.

If your product requires a dedicated Ops hire, a 6-week implementation, or a certification to use it properly, you're not mid-market friendly.

Here's what mid-market actually needs:

Can start using right away, no setup period. Delivers value in Week 1. Requires zero ongoing maintenance.

If using your tool feels like work, you've already lost.

"We can't afford to be right"

A startup founder told me over the holiday break: "My competitors spend $50k a year on market research. I can't afford a single study."

He's pitching the same buyers. Fighting for the same budget.

But his go-to-market strategy is based on limited customer conversations and gut feel sense of the market. 

His well-funded competitor is citing fresh data: "Our research from last month shows 67% of buyers rank [X] as their top concern." 

He's guessing the message. They're citing the pain.

Not because they're smarter. Because "being right" costs somewhere between ~$20,000 and $150,000 per study when you go to a market research firm.

If you're bootstrapped or early-stage, that's not a line item. That's runway. That's a hire.

So you're flying blind on positioning. Guessing at messaging. Making up what buyers will pay for.

Here's what kills you: Startups need precision more than the rich incumbents.

Big players can survive a bad campaign. Wasting $2M is cost of doing business.

You? One bad positioning decision costs you 6 months. 

Meanwhile, what buyers care about changes every month. Their concerns shift. Budget priorities flip. New competitors change the conversation.

The answers you needed in March are wrong by August. But you can't drop $20k every quarter to stay current.

We got tired of seeing startups priced out. So we built ICP Pulse at Wynter, essentially research-as-a-subscription.

Monthly surveys of 50 target buyers. Fresh answers every month. Summary and slide deck with what it all means. 

The incumbent pays $15,000 per study. You pay a ridiculously low price of $208/month. The playing field has been leveled. 

Now piloting with mid-market B2B SaaS marketing leadership. Google 'ICP pulse Wynter' or look for the link in the comments.

We tested Goldcast's homepage with their ICP.

In this episode of "Do you even resonate?" we learn what B2B demand gen and ABM directors actually think of them. (Volunteered by Anand "AP" Patel)

Clarity score: 3.9/5
Interest to learn more: 3.9/5

Both solid scores.

So what did people think?

The good:
• 86% understood what Goldcast does. That's excellent for B2B.
• "Create video content at scale" really resonates. People want to believe this promise.
• Big logos work as social proof. (They drew attention, but people still wanted quantified impact).

What they can improve:

• Positioning confusion. Is it a webinar platform? Virtual event platform? Video editing tool? 
• The word "agentic" is killing triggering people in a negative way. Direct quote: "the agentic promises make me skeptical of whether this is overhyped AI or it actually gets the job done." AI buzzwords trigger skepticism.
• "AI at scale" makes people think: this is slop. Need to address that objection immediately after making this statement.
• The page feels text‑heavy, busy, and repetitive 
• The “why us” and differentiated benefits are underplayed 

Want to featured next and get a message test on your homepage? Drop your URL and ICP below.

Ben Labay

CEO at Speero | Experimentation & Research Program Leader

CEO at Speero. Recovering Research Scientist. Experimentation Obsessive.

I help SaaS and Ecommerce brands turn data into a competitive advantage. Over the last 8 years, I’ve led research and testing programs for Miro, P&G, ADP, and MongoDB—helping them bridge the gap between complex statistics and better customer experiences.

My mission? To help organizations use experimentation as a superior way to grow.

Areas of Expertise: ◽️ Experimentation Strategy & Scaling ◽️ UX & Usability Research ◽️ Agency & Brand Growth Marketing

Looking for help with your testing program? Drop me a line at ben@speero.com or follow my journey here on LinkedIn.

CXL, and by extension Speero, is an inspired workplace, constantly pushing and questioning services and products and what works and what doesn't. The agency pushes on experimentation and customer experience research for SaaS, Ecommerce, and Lead Gen clients around the world. 

These organizations and teams have business and customer strategies, we test them. Ben works within Speero to scope research and test program strategy for companies like Proctor & Gamble, ADP, Codecademy, MongoDB, and Toast. https://speero.com

I run experimentation programs for companies.  I also… run one for myself. 😄
 
Today’s datapoint:  According to Function Health, my 𝘣𝘪𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘢𝘨𝘦 is 𝟯𝟴.𝟭 — about 𝟲.𝟳 𝘆𝗲𝗮𝗿𝘀 𝘆𝗼𝘂𝗻𝗴𝗲𝗿 than my calendar age of....as of today, 𝟰𝟱. (happy birthday me!)
 
Apparently, my cells didn’t get the memo about birthdays.
 
It got me thinking about how similar my day job and my personal life have become.
 
At work, experimentation looks like:
 
• Forming hypotheses
• Running controlled tests
• Measuring outcomes
• Iterating based on evidence
 
At home, it’s surprisingly similar:
 
• 📊 Fitness tracker to monitor sleep, HRV, and recovery (who's using PAI points?)
• 🩸 Playing with blood glucose monitoring to see how food actually affects 𝘮𝘦
• 🧪 Blood work to track what’s improving, stagnating, or regressing
• 🔁 Small behavior changes → measure → adjust → repeat (𝘵𝘪𝘯𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵𝘴 anyone?)
 
Same mindset. Different system.
 
Whether it’s an enterprise experimentation program or my own health, the lesson is the same:  𝗗𝗮𝘁𝗮 𝗯𝗲𝗮𝘁𝘀 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀. 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻 𝗯𝗲𝗮𝘁𝘀 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. 𝗔𝗻𝗱 𝘀𝗺𝗮𝗹𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱.
 
Curious, where else are people running “experiments” in their lives outside of work?

CEO @ Speero | Experimentation for growing SaaS, Ecommerce, Lead Gen

I’ve been thinking a lot about where the experimentation world is headed, and I’ve had this "aha" moment that’s honestly feeling a little bit urgent.

For years, we’ve been comfortable in the world of A/B testing. We know how to test a headline, a button color, or a checkout flow. Even a pricing model or positioning. It’s predictable. But as we start shoving LLMs into every corner of our digital products, from chatbots to dynamic ad copy, we’re realizing that traditional A/B testing just isn't built for this.

The real game-changer isn't just "testing a prompt." It’s this concept of Evals, and if you’re in CRO or product innovation, I feel like you (we) need to be thinking about this 𝘯𝘰𝘸.

The Problem: We’re Testing "Clouds," Not "Bricks"

In the old world, a variant was a static thing—like a brick. You put it there, it stays there. But an LLM is non-deterministic; it’s more like a cloud. It changes every time a user touches it.

You can’t just look at a conversion rate and assume your AI is working. 

Your conversion rate might be up, but what if your chatbot is subtly insulting 5% of your customers or hallucinating fake discounts? (we don't have a lot of this happening yet, but its coming and via chatbots first, have you seen Podium . ai yet? How they pivoted to AI is fascinating.

A standard A/B test won't catch these subtle shifts of the AI until the brand damage is already done.

The Realization: We Need "Agentic" Quality Control. Even synthetic versions.

This is where it gets interesting. To actually control this process, we have to move beyond just "human-in-the-loop" testing (which is way too slow). We need to start thinking about groups of self-evaluating agents.

AI debate clubs on steroids. 

Imagine this: instead of you manually checking if an AI-generated article is good, you set up a team of agents to do it for you. You have one LLM generating the content, another "adversarial" agent trying to find factual errors, and a third "Judge" agent scoring the whole thing against your brand guidelines.
It’s a self-correcting cycle that happens 𝘣𝘦𝘧𝘰𝘳𝘦 the customer ever sees a single word.

Why This is Our Job as experimenters

I truly believe experimentation teams are the only ones equipped to lead this. We’re the ones who already understand hypothesis testing and statistical rigor. 

But we have to shift our mindset from "winning vs. losing" to "quality vs. risk."

We’re essentially becoming the architects of these "eval loops." It’s about setting up the lab (the offline eval) to make sure the AI is safe and accurate, and 𝘵𝘩𝘦𝘯 hitting the real world (the online A/B test) to see if it actually makes money.

If we don't grab the reigns on this process of testing and controlling LLM groups, we’re just going to be cleaning up the mess when these models inevitably go off the rails. 

The workflows and org context are where to start. 

We'll be talking a lot about this at #CircusLondon, May 11-12th. Join us. speero .com / circus

"Product Ops" perspectives are all the rage atm. It's the missing link. It's what most orgs are lacking right now. (We call them XOS here at Speero btw ;)

There are new tools to try to solve this. Tools like Jira's 'Product Discovery' and Airtable's 'Product Central' and even dedicated solutions like Dotwork and Doubleloop. 

We have task management (Jira/Monday), we have wiki's and docs... but what is the tool that allows a team or program to do resource and portfolio management connected to a set of cascaded goals and org-level strategic initiatives? 

At it's simplest, it can look like the pic below here. 

"Testing Learnings," here is a program dash. Do you have a picture like this for your program/team? A perspective across the portfolio of bets that the team is doing? 

This shows a dashboard with several metrics and charts.

> Includes a summary of test results: 9 "Win," 11 "Flat," 5 "Loss," and 0 "Error". 
> displays the monthly relative potential in dollars for discovered wins, discovered saves, and the total. > > Charts showing the breakdown of test results, recommended actions (like abandon, implement, and iterate), and optimization pillars (conversion, frequency, merchandising, and spend)
> Can have charts that link to yearly/quarterly strategic goals as well.... not only the always on stuff but the bigger investments and bets. 

This isn't about a sheet of AB tests that are being worked on. It's a tactical, tangible mechanism that represents and aligns strategy (activities) and outcomes(!) for a team. Boom. It's what helps make a team accountable but also not stuck in an optimization rut.

Recently, a CMO from the supplement industry told us he was working with a performance-based agency. He liked them, and they were making him money. However, the site began to resemble an arcade game. 

He felt that all of the flashing bells and whistles were resulting in cheap conversions, eroding the reputation of the brand as a whole. 

The agency’s singular metric focus backfired when it came to brand loyalty. He needed a more long-term approach to optimization. 

Your customer service can act as a key support optimization factor. You can pull customer data from them, analyze it, fix FUDs, and use it to improve the product, offering, or even the customer service itself.

They provide specific insights into customer struggles, act as a vital feedback loop for website changes (alerting you to increased support tickets), and should even integrate experimentation into their daily work— something that is still surprisingly rare.

Start by integrating support data into your experimentation backlog, automate where it adds value (not noise), and continuously test improvements. 

Efficiency isn't doing less, but learning faster and acting smarter.

#CircusLondon 🎪 AI Workshops, Keynotes, Lightning Talks, Tickets...all coming to the site this week. Stay tuned for the launch and in the meantime register your interest on the LP https://speero.com/circus to get notified when it all drops

𝗪𝗲'𝗿𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗮𝗴𝗲𝗻𝗱𝗮 𝘁𝗼 𝗲𝗻𝗴𝗮𝗴𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘆𝗼𝘂 𝘄𝗶𝘁𝗵 𝗽𝗲𝗲𝗿𝘀 𝘄𝗵𝗼 𝗮𝗿𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 + 𝗔𝗜: 
• 80% in-house/brand-side attendance 
• Workshops, Dinners, and Masterclass events based on core roles
• Thoughtful parties and activation events
• Keynotes by leading in-house practitioners on AI+Experimentation
• 20+ Posters on AI+Experimentation + lightning talks on 10 of them
• 100+ round tables, matching you with people you want to know

Join the show, Circus London May 11-12, an AI conference for experimentation leaders. Participate with a Poster+Lightning Talk on it, covering topics on AI+Experimentation. 

See the doc here, context on page 1, and the poster 'template' on page 2.

The "poster slam" works like this:

1. you have topic and submit an abstract 
2. we get 20 or so from submissions
3. during conference, we print your poster and display it
4. you present a 1 slide, 2-3min talk at the conference on the topic

𝗪𝗛𝗢 𝗪𝗘’𝗥𝗘 𝗟𝗢𝗢𝗞𝗜𝗡𝗚 𝗙𝗢𝗥?

We’re inviting practitioners to submit posters on how AI is being applied inside modern experimentation programs, including:

• AI embedded in experimentation workflows (research, hypothesis generation, QA, analysis, interpretation)
• AI-assisted experiment interpretation & decision systems (judgment models, debate setups, copilots for test outcomes)
• Experimentation operating models augmented by AI (how teams plan, prioritize, run, and learn at scale)
• Research → testing → shipping pipelines where AI reduces friction or increases learning velocity
• AI-driven QA, guardrails, and reliability systems for running trustworthy experiments
• Using AI to scale experimentation (velocity, complexity, orchestration, multi-surface testing)
• Experimentation stacks with AI in the loop (how tools, agents, and automation actually connect)
• Real-world case studies showing how AI supports experimentation in practice - not theory.

If it helps experimentation teams run better tests, learn faster, or make stronger decisions, it belongs here.

𝗪𝗛𝗔𝗧 𝗪𝗘’𝗥𝗘 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚

• 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗽𝗶𝘁𝗰𝗵 𝘀𝗲𝘀𝘀𝗶𝗼𝗻. 
• 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘃𝗲𝗻𝗱𝗼𝗿 𝗲𝘅𝗽𝗼.
• 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘁𝗮𝗹𝗲𝗻𝘁 𝘀𝗵𝗼𝘄.

This is a 𝗰𝘂𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 — where experimentation practitioners teach each other how the work is actually done.

Link to submit a poster abstract in comments below!

Circus London Lighting Talk Poster Template (2)

Experimentation COEs have been a big interest of mine for some time, this POV here is something I've been wanting to write down and share for a while now

A year ago, I spent a few months interviewing 19 experimentation leaders across tech companies (even published a paper on it). I asked a bunch of them

Open article: Most Centers of Excellence Become Bureaucratic Graveyards. Here's What the Successful Ones Do Differently. by Ben Labay • 7 min read

Most Centers of Excellence Become Bureaucratic Graveyards. Here's What the Successful Ones Do Differently.

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