Bunes & Ferenczi

At Bunes & Ferenczi we believe in today’s uncertain and ever-changing environment there is a smarter and faster way for B2B Tech companies to acquire new customers than only by increasing sales capacity​.

We work with early-stage companies, companies looking to geographically expand, or someone looking to expand their solution portfolio to:
* Co-create their Ideal Customer Profiles (ICPs) in which their value creation is the highest
* Build a Serviceable Addressable Market (SAM) which is prioritized order of all the ICPs
* Audit and recommend data/analytics tools for sales and marketing to support the acquisition of customers
* Provide interim CMO/CRO support for expansion

| Co-founder |  B2B Tech Growth Strategist | 

GrowthMentor

I am grateful for the opportunity to support NEXT GEN entrepreneurs with some of my experience through specific conversations about their challenges. The best part of this? I learn so much myself every time,

Growth Mentor

GlobalGrowth Consulting (GG Consulting AB)

Experienced hands-on business executive with strong focus on teamwork and managing multi-cultural teams thrives at driving global or local growth or profitability-focused change initiatives

AREAS OF EXPERTISE
* Development and advice on Sales and Marking programmes, Business Strategy and Planning
* Design and implementation of organisational change including providing interim management  
* Deep industrial knowledge of IT and telecoms, both B2B and B2C
* Set up and management of new international businesses including creation of company structures

| Principal Consultant | Business Owner |

Bisnode

Advising Bisnode B2B Marketing Steering council with GTM plans for new offers.

B2B Sales and Marketing GTM Advisor

Vendemore

Enabling global companies to implement Account Based Marketing as a part of their marketing mix.

Developing internal sales and marketing strategies at Vendemore.

Global Account Director

GlobalGrowth Consulting & Investment LLC

Vision: 
Grow local small businesses to International medium-sized businesses

We are: 
Senior practicioners with experience from both small businesses to  Fortune 20, including experience in sales, marketing, business development, business management, and Management Consulting.  Proven track record of developing international businesses

Our approach: 
Focus on technology firms (Internet, SW, and HW) with base in Scandinavia looking for international expansion or businesses looking to expand to Scandinavia. We look for 1-4 year exclusive partnerships focusing on profitable new business. Our model can be tailored to each engagement. In some cases, we may make a significant investment in the business. However, we exclusively engage in businesses with a buy-out plan

Founder, Majority Partner, and Chairman

Libendo Education

Member of Board for holding company in Luxemburg (Avaros SA)

Member of Board

Managing the Libendo Group business across all markets

Managing Director

Member of Board Libendo Norway AS

Member of Board Libendo UK&I Ltd (Dormant)

Member of Board Libendo Sweden AB

Driving the expansion of Libendo from Sweden to UK and Norway.  In charge of all expansion work in addition to hosting the management structure, business planning, and overall sales results for the business

Global Sales Director

SPConsulting

Consultant in Change Management Practise at SPConsulting: Boutique global consulting firm based in Stockholm, Sweden

Consultant

Build HP's Direct Online sales in EMEA; employee number 1

Our team took an existing business from $35m to $105m over two years.

Consumer Online Business Development Manager EMEA

Managed a product line E2E, turning around $5m loss to $3m in profit in two years and order book grew by 30%.

Member of management board, and numerous project steering committee chairman roles as well as sponsor.

Business Line Manager EMEA

Provided country representation to EMEA based organisation for support experience and delivery models. Included business development of support services (fee based). Also included deployment of retail channel support programmes.

Consumer Support Country Manager Nordics

European Product Manager for the HP ScanJet line of products. Core focus on all 4 Ps

Product Manager EMEA

Provided final level support for WW contact centres for HP ScanJet products. Would drive current engineering for updates of current products and ensure fixes were transferred to the development of next generation products.

Escalation Manager

I help B2B Tech companies to grow by focusing on the right customers.

I am passionate about B2B tech, but more passionate about the customer value deriving from deploying the right technology. 

It’s always about the customer, and it’s always about the value they get from technology.

The rest are just means to an end.

I am your international businessman of mystery! 

I have lived and worked in Norway, US, Germany, Switzerland, and Sweden.

I am like the business equivalent of the Swiss Army knife, a utility you can use to solve many a challenge. 

I have done sales, marketing, financial management, management consulting, P&L ownership, product management, customer support/operations, escalation management, Account Based Marketing, EdTech, AdTech, MarTech, B2B, B2C, B2B2C. You name it, I have done it!

I have worked for myself, in an organization with four people, in an organization with +300,000 employees and anything in between.

Hans Bunes

Gil Allouche

CEO at metadata.io | Helping B2B marketers automate paid campaigns 🦄 | We're hiring

A software engineer turned data-driven growth marketer, I founded metadata.io to make demand generation easy for non-technical marketers.

Metadata is a patented technology that combines predictive scoring and programmatic advertising to deliver a predictable pipeline of opt-in lead qualified leads for B2B enteprises. Among our customers: Cisco, UserVoice, Mulesoft, Salesforce, Infoblox, Aerospike and many others

Chief Executive Officer

AI Autonomous Marketing. 4 issued patents.
Inc #233 Fastest Growing Company 2020, 

Companies like Drift, Zoom, UIPath, Udacity and 100+ others rely on metadata.io to generate pipeline.

We just put our logo on an F1 car. And it has nothing to do with brand awareness.

Nicole Havrda became the first female Formula Pro USA Champion by being faster, sharper, and more relentless than everyone else on the track. No shortcuts. No waiting for permission. Just thousands of hours of optimization, iteration, and refusing to settle.

When we first talked to her about a partnership, she said something that stuck with me. Racing and marketing share the same foundation. Data. Precision. Continuous optimization. Split-second decisions that compound over time. She wanted to work with a company whose technology captured that same energy.

I've spent a decade building Metadata with that exact belief. The teams that win aren't the ones with the biggest budgets. They're the ones that iterate faster and learn quicker than their competition. And this conviction started long before we had a product. 

Back in 2016, I was invited to share some marketing frameworks with a room full of CMOs. After the presentation, they asked where they could get this kind of solution. I told them I had started a company that did exactly that. It didn't exist yet. I walked outside during lunch, changed my LinkedIn profile, and made it real. 

Today we have five granted patents, hundreds of customers to the likes of Zoom, Okta, and ThoughtSpot, and an agentic platform that takes campaigns from prompt to pipeline.

Nicole proved what relentless optimization looks like on the track. We're trying to prove the same thing in B2B marketing.

That's why this partnership made sense. Not for the impressions. Not for the logo placement. Because we believe the same things about how you win.

Not going to lie, seeing our logo on her car feels pretty incredible. But what gets me more excited is what this partnership represents. And where we're both headed next.

CEO at metadata.io | Making B2B Marketing Teams Predictable & Profitable 🦄

When we started Metadata in 2016, we filed patents for technology that was ahead of its time.

The ideas were there. Six patents covering AI-driven campaign optimization, ICP-based targeting, closed-loop audience feedback, autonomous bid management. The vision was complete automation of B2B demand generation.

But execution? That was the problem. Because the patents were too early and difficult to execute without spending a lot of overhead capital.

So we built what we could. Launched our first version in 2019. Focused on removing manual work from paid campaigns. Watched the technology landscape slowly catch up to what we'd imagined.

Then LLMs changed everything. 

What required massive engineering overhead in 2016 became achievable through natural language in 2024. The gap between our vision and what we could offer marketing teams quite literally collapsed over the last few months. 

The result of which is a massive 112.5% year-over-year growth in customer adoption we’ve been experiencing. 

Nearly half a million campaigns executed through our platform. 
24% average reduction in cost per lead. 
3X faster campaign deployment.

Last month we launched what we believe is going to be the future of B2B marketing: MetadataONE. You describe your campaign in plain language. AI agents handle audience building, creative generation, bid optimization, budget allocation, performance analysis. 

We call it Agentic GTM. 

Lisa Sharapata🌟, from our team, walks through what that actually looks like in the video below.

A decade of building for a market that didn't exist taught me this: vision without survival is just a good idea that ran out of money. You can see exactly where things are headed and still not make it there. The companies that win aren't always the ones who see the future first. They're the ones stubborn enough to stay alive until it arrives.

That's what we did. And what we stayed alive to build is a future where AI handles the execution so marketers can get back to what actually requires a human. The thinking. The creativity. The judgment calls no model can make for you.

"Using Metadata MCP server tools, perform the following actions:
1. Fetch the brand kit for MetadataONE.com.
2. Generate 3 resource sets, each including:
* 1 ad creative
* 1 firmographic audience (vary industry, title, or company size)
* 1 landing page offer (demo request, whitepaper, or case study)
Ensure that creative and offer includes a direct URL.
3. Schedule the campaign for January 2026 with a $20,000 total budget.
4. Enable autonomous optimization and launch.” 

One prompt. That's it. 

Here’s what that single prompt to MetadataONE’s MCP server just executed inside Claude (check video below):

→ Retrieved the MetadataONE brand kit
→ Generated brand creative assets
→ Built firmographic audiences based on industry, title, and company size
→ Assembled multiple resource sets combining creative, audience, and offer with direct destination URLs
→ Created a campaign scheduled for January 2026 with a $20,000 budget
→ Prepared ad sets for LinkedIn and Facebook
→ Requested execution authorization
→ Launched the campaign once authorized
→ Enabled tracking and autonomous optimization

If you’ve ever launched a multi-channel paid campaign, you know what this normally looks like.

You log into LinkedIn Campaign Manager. Set up the campaign objective. Build the audience manually, selecting job titles, industries, company sizes. Save it.

Then you do the same thing in Meta Business Suite with different targeting logic. 

A straightforward two-channel campaign like this typically takes a demand gen manager 2–3 days when you factor in the back-and-forth with creative, the platform hopping, and the review queues.

Larger campaigns with more channels or variations can stretch into a full week.

Now it happens in a conversation.

Execution still takes time. Campaigns don’t appear instantly. But the bigger shift is what happens after launch.

Every campaign is continuously optimized by an engine in the background. Budgets are reallocated. Bids are adjusted. Audiences are refreshed. Winning experiments are cloned into lookalikes. Incoming leads are enriched. New audiences are created automatically.

The system can surface account engagement for ABM teams or report all the way down to ROAs, pipeline, and revenue at any point in time.

Most teams either don’t do this work consistently or leave it to the channels themselves. And channels optimize for one thing: maximizing ad spend.

MetadataONE optimizes for outcomes.

And you can keep iterating in the same window:

“Shift 30% of budget to LinkedIn.”
“Create three headline variations for the Facebook ad.”
“Show me cost-per-lead by channel so far.”
“Pause the underperforming ad set.”

The MCP server connects MetadataONE directly to Claude. You stay in one interface. AI agents handle execution, optimization, and reporting in the background. You focus on strategy.

B2B paid marketing has been unnecessarily complicated for too long. The technology exists to make it intuitive. We’re just trying to make it happen.

What's the right size for a B2B marketing team generating $50M in revenue?

Most CEOs would guess somewhere between 15 and 30 people. Alina Vandenberghe 🌶️ did it with two.

At our Agentic GTM event last week, Alina shared how Chili Piper cut their marketing team from 22 to 2 while generating 1,400 sales-qualified opportunities in 2024. I want to break down what actually made that possible because it wasn't just "using AI."

The first unlock was data clarity. She consolidated everything into Snowflake and discovered something uncomfortable: their high-LTV customers looked nothing like the ICP they'd been targeting. The real opportunity wasn't thousands of accounts. It was 872.

The second unlock was redefining what marketers actually do. Her philosophy is simple: marketers get attention and orchestrate everything else. Agents handle the campaign logistics, the funnel tracking, the partner coordination. Humans focus on creativity and trust.

The third unlock surprised me most. After all that automation, Chili Piper's biggest conversion lift came from a three-day training on trust. Close rates jumped from 22% to 40%. Not from better targeting. From teaching reps to actually connect with buyers.

That's the part most AI conversations miss. Agents scale efficiency. Humans close belief gaps. You need both.

Full session link below. Worth the watch.

Check out: https://lnkd.in/g8-pkQuG

Most marketing teams review their ad budgets weekly. The good ones, anyway. 

But a lot happens between Monday meetings. The campaign that starts outperforming overnight. The one that quietly bleeds budget through the weekend. By the time you're back in the spreadsheet, those windows have closed.

This is the hidden tax on manual budget management. Not the big mistakes. The small ones that compound invisibly.

The pattern I see is almost universal. 

Marketers export data into spreadsheets. They build pivot tables, schedule weekly syncs to discuss what happened last week, and then make adjustments. 

But between those weekly syncs, several opportunities may have emerged and passed away too. And in this case, the problem isn’t with marketers not putting in enough effort to leverage those opportunities either. It's that markets move faster than humans can react.

When we built MetadataONE, we designed it to run continuously. The system scores every active experiment based on what's driving pipeline and revenue, then reallocates budget accordingly. Underperforming campaigns get paused automatically. The budget flows to what's working before the window closes.

Having said that, reallocation alone isn't enough if you're only testing two variables at a time. 

That's why we run multivariate experiments at scale, testing every combination of audience, channel, offer, and creative simultaneously, then doubling down on what wins.

David Zeledon during his time at WRITER ran their entire paid program as a team of one. He set hard targets for CPL and cost per MQL. Our system handled the rest. 86% reduction in cost per MQL. 3 to 5 hours saved daily. Time that then went straight to strategy instead of handling to deal with repetitive tasks.

Every satisfying prediction I've made about AI timelines has been wrong.

I kept underestimating how fast things would move.

So this time, I'm not hedging. Here are my 2026 predictions for AI in B2B marketing.

I've spent a decade in this space. Four patents. Nearly half a million campaigns running through our platform. And the lesson that keeps repeating: whatever timeline you have in your head, cut it in half.

I first shared these predictions at our Agentic GTM event earlier this month. Here's the version I'd commit to in writing:

1. We're living in a technological singularity. Most marketers haven't noticed.

The pace of improvement is accelerating faster than even people working with AI daily can track.

2. AI today is where the internet was in 1993.

Clunky. Slow. Unreliable. And obviously going to change everything. The best time to build familiarity is while the tools are still rough.

3. "AI-powered" stops meaning anything. "Agentic" becomes the filter.

Every martech tool will slap AI on the label. The question that matters: does it give you recommendations, or does it actually execute the work?

4. Marketing teams of 10 output what teams of 50 did in 2023.

The leverage comes from AI agents handling execution. The new bottleneck becomes strategic clarity, not headcount.

5. The "wait until AI is ready" crowd falls behind permanently.

The adoption window is closing. Marketers building fluency now are in the top 1%. In 18 months, that fluency becomes table stakes. If you feel overwhelmed, that's a sign you're early.

6. Even 10 minutes a day compounds.

Consistent, small usage of AI tools creates massive long-term returns. The skill gap between marketers who use AI daily and those who dabble occasionally will be enormous by end of 2026.

7. First-attempt AI output stays mediocre. Iteration becomes the skill.

First outputs get you 15-20% of the way there. Feedback loops get you to 80-90%. Tasks that fail today may work perfectly in a few months as models improve. Bet on the trajectory.

8. When choosing tools, think 6 months ahead.

Don't evaluate AI tools based on what they do today. Evaluate based on where they're going. Bet early and grow alongside the technology instead of catching up later.

9. New marketing roles emerge that don't exist today.

Prompt strategists. Agent operators. AI workflow architects. This is a once-in-a-generation chance to redefine what leadership looks like in marketing.

10. The marketers building in public will become the guides for everyone else.

No playbook exists for AI-native marketing. The ones experimenting, sharing, and iterating in the open will define the category.

You can watch the full session of mine where I dive deeper into my AI predictions for the near future, here: https://lnkd.in/g3ppMQfE

What if more than 30% of your "intent signals" are coming from bots?

According to Cloudflare, that’s how much non-human traffic it has received in this year. If you take a look at the image I’ve added with this post, you can notice how AI crawlers, bots, and automated agents generate a constant, non-trivial share of global web requests.
 
This is the data layer most ABM strategies are built on. And it explains a pattern that's been frustrating B2B teams for years.

Your system flags an account as "high intent." SDRs reach out. They get ghosted. Turns out the VP who searched for "sales enablement" fixed the problem three weeks ago. Or it was an intern downloading a white paper. 

Now the system can't tell the difference. So your reps chase ghosts, get ghosted, and stop trusting marketing's account lists. 

The deeper issue is that intent data assumes buyers follow a predictable path.

Third-party signals at the top, second-party in the middle, first-party at the bottom. 

Neat. Linear. And completely disconnected from how people actually buy.

Today's buyers research in ChatGPT. They ask peers in Slack communities and private WhatsApp groups. They listen to podcasts on their commute. None of this shows up in your dashboard. Your intent data is blind to it.

And even when intent data was accurate, it always felt a little off. The moment someone engaged with content, the chase began. Retargeting ads, "warm" calls, "just checking in" emails. But turns out buyers never wanted to be hunted in the first place. They just needed help with specific bottlenecks.

B2C figured this out years ago. They shifted toward brand building, communities, subscriptions, and owned experiences. B2B went the opposite direction. Bigger intent budgets, more complex platforms, strategies built entirely on chasing accounts based on intent signals. 

This is why we built MetadataONE around a different model entirely. We took what was already working for our B2B customers, and implemented the tech developments in the B2C marketing ecosystem to give you the best of both worlds. 

Instead of waiting for intent signals that may or may not be real, autonomous AI agents proactively discover, engage, and qualify accounts based on real-time first-party behavior. 

Accounts that actually engage with your ads, consume your content, convert on your site. Real humans on properties you control.

Attention helps you understand awareness. But attention isn't intent. And it's definitely not buying readiness. The opportunity now is to build systems that respond to real buying behavior, not signals that might be real.

Every marketing tool claims to be AI-powered now. Most of them aren't.

They're rule engines with better interfaces. If the lead score hits 80, send the email. 

Take any average rule-based AI marketing tool. If CTR drops below 2%, pause the ad. If the budget hits 90%, ping Slack. Someone still has to define every trigger, every threshold, every exception. The software follows instructions. It doesn't think.

I've watched this play out for years. A team buys an "AI" tool expecting leverage. Six months later, they've hired two more people just to manage the rules they built inside it. The tool scaled. The team scaled with it. That's automation with extra steps if you really think about it. 

Agentic systems work differently. You give them an outcome: pipeline from this ICP at this cost. They figure out the path. Which channels. Which creative. Which bids. When something stops working, they adapt. You're not managing rules anymore, just stating your goal and watching the system self-optimize to reach the target(s).

Rule-based systems scale linearly. More campaigns, more rules, more people.

Agentic systems scale exponentially. More data makes them smarter, not more dependent on you.

That's what we built MetadataONE to do. Not faster execution of your playbook. Autonomous execution toward your pipeline goals.

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The Battle

Hans Bunes

Gil Allouche

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The Battle

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Gil Allouche

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Engagements

Likes

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);}Gil Allouche's LinkedIn Performance

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