Founder, P33 | Executive Thought Leadership Agency

Helped 50+ B2B tech companies build their Founder/CEO Brand and implement Executive Thought Leadership Programs for LinkedIn. Customers, results & process: project33.io

About me:

I'm Finn, 29, grew up in Bremen, Germany, studied Physics & Philosophy in Heidelberg for 3 semesters, dropped out of college in 2017 because I was doing it for other people, flew to New York with no plans, documented the journey on Linkedin, Instagram and Youtube, and started my first company (Project 33) on the back of having grown a small audience. We're a small remote agency that helps B2B founders & execs create thought leadership content for Linkedin.

In my free time, which I try to have as much as possible of, I read (fav author: Nassim N. Taleb), write, run, lift, code (currently building an app), cook, make Youtube videos, turn books into Instagram accounts, grapple (BJJ), and travel (lived in NYC, Cape Town, Bali, Minsk, CMDX, Lima & Antalya so far). In December 2020 I married the most beautiful woman in the world. In January 2023, I became dad to the most beautiful baby girl. We currently live in Berlin, Germany.

Finn Thormeier

Growth Dynamics

Is your Go-to-Market strategy falling short of delivering the returns you need? 
Are misaligned teams causing delays and missed revenue opportunities? 
Is marketing seen as a cost center instead of a growth engine?

And what about your attribution models? Are they too opaque to trust, or are you relying on outdated multi-touch attribution for demand generation, leaving you with incomplete insights that lead to poor investment decisions?

I Solve These Growth Barriers:

- Misaligned Teams Slowing You Down? I bring marketing, sales, and finance into alignment, ensuring all teams work together towards shared goals that drive revenue impact.

- Opaque Attribution Hurting Your Investment Decisions? I implement clear, data-driven attribution models that give you full transparency on what’s driving real results across all your marketing channels.

- Struggling to Scale Predictably? I optimize your Go-to-Market strategy to unlock new growth opportunities and ensure your resources are allocated where they generate the highest ROI.

How I Help You Succeed:

1. Align your revenue teams to drive maximum performance.

2. Set up transparent attribution tools that show exactly which channels drive demand, whether you’re using multi-touch or something else.

3.Scale strategically with custom performance insights that ensure your investments fuel measurable growth.

Ready to Turn Your Go-to-Market Strategy into a Revenue Powerhouse?

Let’s talk. I specialize in solving opaque attribution problems, aligning teams, and creating demand generation strategies that produce predictable, scalable growth.

Founder

Oracle

Principal Data Strategist

•	Committed to hands-on, inspiring leadership - promoting growth, teamwork, and innovation within teams.
•	Collaboratively established the roadmap with data producers and users to align objectives with senior leadership vision.
•	Fostered robust relationships with Oracle's senior stakeholders for enhanced collaboration and addressing needs.
•	Initiated and managed the execution of high-impact internal data initiatives and products, ensuring compliance with Oracle Security and Compliance Standards, and navigated the approval process with key stakeholders including executives, legal, compliance, and IT teams.
Team key projects:
-	Implemented the new Media Data Model following the Company strategy reorganisation: implementing new data onboarding using APIs, metric definitions, and robust data governance across Search, Social, Digital, Online comparison, Content Syndication and Emails.
-	Developed an automated data quality and control tool, significantly reducing error rates from 10% to 0.1% for both qualitative and quantitative metrics.
-	Leveraged automated behaviour analysis to uncover key insights into user journeys, and optimisation opportunities using Multi-Touch Attribution.
-	Developed a model to estimate the impact of marketing initiatives on organic traffic
-	Devised a Mixed Media Modelling optimisation tool, employing non-linear regression analysis to maximise ROI
-	Pioneered a Pipeline/ARR forecasting tool utilising Boosting Trees (Cat Boost) for accurate long-term predictions, integrating GTM initiatives and external factors impacting demand with a 20% accuracy on year-long funnels.
-	Developed measurement frameworks to define the contribution of each channel on a single strategy
-	Designed a Demand Generation and Capture model to drive revenue growth and business success.
-    Lead and contact scoring model
-      API connections to Google Ads, Campaign Manager, Google Search Console, Linkedin

Manager of Media Analytics

• Successfully built and led a diverse, remote team focused on Data Science, Data Engineering, and Analytics projects, driving revenue growth. 
• The objective of my team is to support Senior Management, Strategy and Channels in the optimisation of marketing strategy towards:
 - creation of tools (Forecasting , Marginal ROI )
 - data access and reporting improvements ( data and taxonomy control tools, additional data requests)
 - Performance investigation and creation of insights on live campaigns

Manager of Data Science - Paid Media

Skai

• Investigating opportunities for insight creation and marketing optimisation using a wide variety of data
• Leading key multi-person project agenda on “digital user intent” using NLP and clustering
• Leading the knowledge agenda of the team, mentoring to close knowledge gaps
• Ensuring team alignment with market through various meetings with Sales, Client Services and Strategists
• Evaluation of data quality and behaviour to ensure smooth integration into models using Machine Learning
• Investigation of performance anomalies to provide solutions or model adaptations for clients and stakeholders
• Meeting clients to assess business needs, and to recommend ideas and testing frameworks
• Participation in client pitches and new client on-boarding.
• Creation and maintenance of Python tools and libraries. Key contributor to transfer of R to Python tools packages
• Designing dashboards to automate and speed up performance investigations
• Introducing new team process to improve synergies and actively contributed to tools standardisation

Product Data Scientist

Societe Generale Corporate and Investment Banking - SGCIB

•	Managed product life-cycle events and the correct integration and pricing of initiated deals into the systems
•	Controlled and monitored trade amendments and cancellation of pending trades; troubleshooted P&L issues
•	Supervised compressions and initial margin optimisation on Rates and FX 
•	Actively engaged with the development team to increase the automation of life cycle events
•	Improved KPI and workflow process controls with scripts created in Python:
    o	A text-mining model to cross-check bookings with client contracts.
    o	Daily automated reports to ensure workflow quality control and chase interested parties required actions

Exotic Credits Trade Support

Pordum Investments

•	Designed and implemented statistics-based strategies for day trading and swing trading
•	Undertook quantitative market analysis to provide forecasting and market trend analysis 
•	Supervised and managed large market research projects to successfully find new tradable edges
•	Undertook many pieces of statistical research to improve quantitative understanding of market behaviour 


Main research areas:
- Movement-based charts trading strategies
- Multi-timeframe trading strategies 
- Profitability of trading market sentiment (retail positioning contrarian)
- Absolute currencies’ strength trading strategies

FX Trader

BNY Mellon

•	Responsible for the management of high risk Corporate Actions (up to 800 clients) and the follow up of VIP accounts (with assets under custody up to 5 billion USD)

•	Managed cash accounts and FX deals to ensure liquidity requirements for Corporate Events processing

•	Ensured accounts reconciliation by investigating Asset, Cash and FX related issues 

•	Engaged in process optimisation and risk management improvement by developing macros,  working closely with IT for the creation of new systems, and updating procedures

•	Participated in the impact analysis of changes in market specificities and regulations and submitted changes to processing

•	Avoided losses for the Bank and clients thanks to excellent problem solving aptitudes within extreme time limits and very good negotiation skills with brokers and sub-custodians

•	Successfully managed the contingency tests with the Corporate Actions Teams around the world

•	Conducted in-depth analysis of the implementation of the TARGET systems in the European custodial market and defined business opportunities (in Product Management team)

Operations Associate - Market Expert

Corporate Actions - Summer Trainee

•	Generated a deep analysis of the implementation of the TARGET systems in the European custodial market and researched new opportunities for the Bank

Assistant Product Manager (Internship)

Fortis Insurance Belgium

•	Designed the product brochure of 2008, presented it to the Board and enabled its acceptance

•	Proposed product improvements to increase their competitiveness and instilled pressure on the different sales departments

Marketing Intern

AXA Investment Managers

•	Followed trainings on asset valuation and asset allocation, and answered to client queries

•	Analysed customers needs to increase sales productivity

Asset Management Intern

Data Strategy and Optimisation expert - More Revenue, Less Cost

𝐆𝐫𝐨𝐰𝐭𝐡 𝐢𝐬 𝐒𝐭𝐚𝐥𝐥𝐢𝐧𝐠—𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐲 (𝐀𝐧𝐝 𝐇𝐨𝐰 𝐭𝐨 𝐅𝐢𝐱 𝐈𝐭)

Most companies think they have a marketing problem. In reality, they have a misalignment problem—between sales, marketing, and finance.

🔴 Sales says marketing leads don’t convert.
🔴 Marketing says sales doesn’t follow up properly.
🔴 Finance is questioning why CAC keeps rising.

𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫?

📉 Companies over-focus on short-term lead generation and under-invest in brand; shrinking their pipeline before sales even starts.

📉 They track what’s easiest to measure (MQLs, last-click attribution) instead of what actually drives revenue (brand, demand, buyer trust).

📉 They optimise for conversion, not consideration; forgetting that only 5% of buyers are in-market at any given time, and 80% will pick a brand they already know before they even start their buying journey.

𝐖𝐡𝐚𝐭 𝐈 𝐃𝐨

I help B2B companies with $12M+ sales & marketing budgets turn misaligned teams into predictable revenue machines by fixing their growth strategy, marketing measurement, and go-to-market execution.

✔ Marketing shouldn’t just generate leads—it should shorten sales cycles, improve conversion rates, and increase retention.
✔ Attribution should guide better decisions—not justify what already happened.
✔ Sales & marketing should work together—not fight over credit.

𝐖𝐡𝐲 𝐖𝐨𝐫𝐤 𝐖𝐢𝐭𝐡 𝐌𝐞?

📊 Built and scaled GTM strategies for companies managing $100M+ in marketing budgets across Tech, Finance, Automotive, and more.

📊 Ex-Oracle Principal Data Strategist. Designed advanced measurement systems that aligned marketing investment with revenue impact.

📊 Background in capital markets & quantitative finance. I don’t just look at marketing & sales in isolation; I help companies make financially sound go-to-market decisions that improve margins, efficiency, and long-term value.

𝐈𝐟 𝐘𝐨𝐮’𝐫𝐞 𝐑𝐞𝐚𝐝𝐲 𝐓𝐨:

✔ Stop chasing cheap leads that don’t convert
✔ Build a marketing function that improves outbound success
✔ Reduce CAC and increase pipeline velocity
✔ Align sales & marketing so your sales team actually hits quota

📩 𝐋𝐞𝐭’𝐬 𝐭𝐚𝐥𝐤.

👉 https://calendly.com/charlie-7y0/30min

Charles-Antoine D.

𝐒𝐚𝐦𝐞 𝐬𝐩𝐞𝐧𝐝. +𝟏𝟓% 𝐒𝐡𝐚𝐫𝐞 𝐨𝐟 𝐒𝐞𝐚𝐫𝐜𝐡. +𝟲𝟎% 𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝗶𝗻 𝘁𝘄𝗼 𝘆𝗲𝗮𝗿𝘀

This client achieved it by reallocating budget toward discoverability. Reducing bottom-of-funnel spend and increasing top-of-funnel investment.

𝐇𝐨𝐰 𝐬𝐭𝐫𝐨𝐧𝐠 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐒𝐨𝐒 𝐚𝐧𝐝 𝐫𝐞𝐯𝐞𝐧𝐮𝐞?

There is clear time dependency, with both metrics fluctuating in tandem. Looking at lag correlations, we see positive relationships in both directions, but the correlation is much stronger when Share of Search leads Sales, suggesting that changes in search interest are more likely to influence future sales than the other way around.

Spend was used as a control. Identifying which months sat above or below the trendline, it is highly likely that adjusting for yearly seasonality would  strengthen the relationship further.

It doesn’t prove causation, but if it walks like a duck, quacks like a duck, and flies like a duck… it’s probably a duck. 🦆

𝐖𝐡𝐚𝐭 𝐰𝐞 𝐚𝐫𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬

𝟏. 𝐘𝐨𝐮 𝐜𝐚𝐧 𝐠𝐫𝐨𝐰 𝐦𝐞𝐧𝐭𝐚𝐥 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐞𝐚𝐫𝐜𝐡 𝐬𝐡𝐚𝐫𝐞 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐦𝐚𝐬𝐬𝐢𝐯𝐞 𝐟𝐮𝐧𝐝𝐢𝐧𝐠.

Shifting focus from efficiency to discoverability can expand reach and preference without outspending competitors.
📘 See Udi Ledergor’s Courageous Marketing for more on this mindset.

𝟐. 𝐌𝐚𝐫𝐤𝐞𝐭𝐬 𝐚𝐫𝐞 𝐥𝐞𝐬𝐬 𝐬𝐭𝐚𝐛𝐥𝐞 𝐭𝐡𝐚𝐧 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐦𝐚𝐫𝐤𝐞𝐭𝐞𝐫𝐬 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐛𝐞𝐥𝐢𝐞𝐯𝐞

EBI reminds us that gravity exists but gravity never stopped anyone from building planes.

Yes, it depends on your category and how strong your competition is. Mats Georgson, Ph.D., found 150 companies over the last 10 years achieving drastic revenue growth. Mats wrote an incredible book called "Epic Growth".

New challengers keep emerging every day, some unfunded, some not. 

Those examples show it’s possible; not common, but achievable.

Of course, brand is not the only factor for success but you can't do it without brand.

𝟑. 𝐇𝐨𝐰 𝐲𝐨𝐮 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐜𝐚𝐧 𝐬𝐡𝐚𝐩𝐞 𝐲𝐨𝐮𝐫 𝐦𝐚𝐫𝐤𝐞𝐭 𝐬𝐡𝐚𝐫𝐞.

Last-touch and MTA models focus on the bottom of the funnel, often at the expense of brand and preference building.

If you rely solely on those models while your competitors invest in top-of-funnel growth, you’ll likely lose share.

But if you don’t, and your competitors do,  that’s your opportunity to capture it.

------
Hey, I’m Charlie, and my team at Growth Dynamics helps enterprises optimise $150 million + in marketing spend every year. We specialise in long consideration journeys and brands with offline sales.
If that’s you,reach out.

Charlie de Thibault

Turn Wasted Budget Into Profitable Growth.

MMM Isn’t Causation
MMM shows patterns between ads and sales, but it can’t prove ads caused sales. It’s useful, but you should test in the real world before betting big money.

In Ehrenberg-Bass “laws”, awareness is treated as an outcome. Something that follows sales. In the attention economy, it’s the opposite. 👇 

🚀 Attention drives awareness.
🚀 Awareness drives preference.
🚀 Preference drives sales.

PR, influencer moments, create attention before distribution catches up.

🍏 Apple builds cultural moments before products even ship.
🫧 Liquid Death turned water into entertainment before it was everywhere.
🤸 Gymshark built a movement before it had retail presence.

That’s how new brands grow.
That’s how categories shift.

👉 Awareness is the spark that builds penetration.

If you’re in FMCG, banking or telco, Sharp’s Double Jeopardy makes sense.
But not necessarily outside those industries.

EDIT: I realise the wording of this post might not have been chosen wisely. The goal wasn’t to question whether the laws exist, it was to highlight that there’s a lot we can do to defy their constraints. If you’re curious about the reasoning, I shared more context in my next post.

------
Hey, I’m Charlie, and my team at Growth Dynamics helps enterprises optimise $150 million + in marketing spend every year. We specialise in long consideration journeys and brands with offline sales.
If that’s you,reach out.

“Why did revenue drop last month?”The question every analyst dreads because there’s never just one reason. The question sounds much simpler than the answer.

It can be:
- local elections
- consumer confidence dropped
- layoffs in key regions
- a store or distributor closed
- a campaign holdout or pause
- inventory or supply chain issues
- a targeting change
- creative fatigue 
- competitor price cuts or promotions
- organic traffic dip after an algo update
- a brand initiative that ended months ago
- bad Reddit or Trustpilot reviews
- a hurricane or wildfires

And most of the time it’s five of them, all tangled together.

So this one’s for everyone trying to explain that complexity every day.
People don’t realise how hard it is  but you do.

If you can find the culprit in this level of noise, you’re doing great.

Anything you’d add to the list?

The Granularity Mirage in MMM
More detail doesn’t always mean more truth. Too much detail in MMM just creates noise. Vendors won’t say this, but “granular insights” can be misleading.

Let's not forget that science is a robust interpretation of what we know today, not the truth 🙃 .

It’s a moving frontier.

It gets better every time someone challenges the model we rely on.

Bloodletting was once considered scientific.

Astronomy keeps evolving. What we thought we knew about matter twenty years ago is very different from today 
 
Nutrition is another case. For years we were told low-fat was healthy and saturated fat caused heart disease. But large trials later showed that cutting fat didn’t reduce risk and that food quality mattered far more.

Some still see MTA as rigorous. But incrementality shows it for what it is: the bloodletting of measurement.

The science didn’t fail; our model of it evolved.

Always push back when someone presents science as truth. 

Science is a process, not a belief.

The lesson?
Question the model, not the method.
That’s how science, and progress, actually work.

The Incrementality Problem
MTA shows clicks. MMM shows patterns. Neither proves what actually changed because of your ads. Real experiments are the only way to know if your spend works.

The trap of audience targeting is ignoring placement...

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I love surveys but sometimes it is really not necessary to know the answer...

If you run TikTok, influencers, podcasts, PR, or any awareness channel… your dashboard is undercounting your results.
Episode will breaks down why attribution fails and the 6 methods that reveal the impact happening off-platform.

Watch the full episode: https://lnkd.in/en-vRirh

MMM’s Noise Problem
MMM works okay for short buying cycles. But with long, complex sales, the data gets messy. It often gives too much credit to retargeting and misses the impact of brand building.

Marketing measurement is not the same as looking at spend and channels. That is distribution measurement.

If the only things you analyse are where you spend and how much, you are not measuring marketing. You are only doing distribution measurement.

A lot of the performance does not come from shifting budget between platforms. 

It comes from what happens inside the platforms: the creative signals, the targeting logic, the bidding setup, the exclusions, the frequency structure and the overall input quality the algorithm receives.

This is why when a « measurement  » vendor claims “we increased TikTok performance by 30%”, in most cases the spend was simply reduced, which makes efficiency numbers look better without improving the marketing.

ROAS isn’t the point… marginal returns are.
 Your first $10K may bring 3X return, but the next $10K could drop to 1.5X. Every channel has a curve, and scaling blindly kills profit.
 Map the curve, find your ceiling, and scale with precision.

𝐋𝐨𝐧𝐠 𝐭𝐞𝐬𝐭 𝐰𝐢𝐧𝐝𝐨𝐰𝐬 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞 𝐭𝐡𝐫𝐞𝐞 𝐫𝐢𝐬𝐤𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐢𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥𝐢𝐭𝐲 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐡𝐚𝐫𝐝𝐞𝐫 𝐭𝐨 𝐭𝐫𝐮𝐬𝐭.

𝟏. 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐠𝐫𝐨𝐮𝐩 𝐜𝐨𝐧𝐭𝐚𝐦𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐬 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞
Even when the control never receives test ads, they get exposed indirectly:
- Organic content, PR, influencers
- Word of mouth from the treatment group
- Brand searches (especially in long consideration cycles)
- Stores, retail, other channels

The longer the test runs, the more leakage erodes the difference between groups.

𝟐. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 𝐝𝐞𝐭𝐞𝐫𝐢𝐨𝐫𝐚𝐭𝐞𝐬 𝐨𝐯𝐞𝐫 𝐭𝐢𝐦𝐞
Controlled experiments rely on random assignment to ensure the treatment and control groups are balanced at the start of the test. But as time passes, the underlying audience shifts:
- new users enter
- some users churn
- seasonality changes behaviour
- external events alter demand
- algorithms (or market dynamics) reallocate exposure

The initial balance weakens, increasing noise and reducing the reliability of the lift estimate.

𝟑. 𝐋𝐨𝐧𝐠 𝐭𝐞𝐬𝐭𝐬 𝐚𝐬𝐬𝐮𝐦𝐞 𝐬𝐭𝐚𝐛𝐥𝐞 𝐬𝐩𝐞𝐧𝐝 𝐚𝐧𝐝 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐰𝐡𝐢𝐜𝐡 𝐢𝐬 𝐮𝐧𝐫𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜
Lift tests work best when delivery conditions are relatively stable.
Over long windows, that becomes impossible:
- CPMs fluctuate with auction pressure
- creative fatigue forces rotation
- competition changes
- baseline demand moves
- audience mix drifts

It’s impossible to keep spend, CPMs, placements, or delivery conditions stable for more than a short window. Market drift overwhelms the ability to isolate the incremental effect.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐥𝐨𝐧𝐠 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐜𝐲𝐜𝐥𝐞𝐬
These issues compound in high-AOV, long-cycle categories:
- longer cycles mean more contamination
- offline influence increases leakage
- brand effects accumulate slowly and are harder to isolate
- audience drift weakens group balance
- noise grows faster than signal

𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧𝐬 𝐢𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞
If your lift test shows a large, obvious effect, these issues matter less as the signal cuts through the noise.

But if you’re measuring small changes (3–5%), be careful:
noise can look like signal, and real signal can get buried in noise.

A null result doesn’t automatically mean the initiative isn’t working. Look for earlier funnel signals (brand search, pricing-page visits, mid-funnel activity,...). If those are moving, the channel may be driving impact even if a long-window lift test on revenue fails to detect it.

Display ads can be 80% bots.
 Check GA4: if add-to-cart ratios exceed 1, something’s off.
 Linux devices, odd Android builds, or strange mobile brands = red flags.

GA4’s UI hides the truth, BigQuery doesn’t.
 Export your data to uncover long journeys, detect bots, and link upper-funnel touches to conversions.
 If you haven’t set it up yet, you’re missing real insight.

I believe AI will not replace user generated content. It will just kill the perceived value of generated content.

The reason why consumers like user generated content is because it feels more original and more genuine. Even if it is not, it feels like it.

Now if the word that almost every generated content is AI goes to the street, people will not perceive value from it anymore.

For example, I deleted the TikTok app recently because I was fed up with seeing AI videos.

I have to say, not all was bad. I probably watched the cat playing music on the porch at midnight at least five times. But this is an exception to the rule.

Brands are running towards AI because it is cheaper. It is true. But to me, it is forgetting why user generated content was working in the first place.

A failed incrementality test doesn’t mean the channel’s bad.
 Small channels get buried under noise, their lift is invisible until they grow.
 Watch early signs: traffic, mentions, mid-funnel activity. That’s where promise starts.

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The Battle

Finn Thormeier

Charles-Antoine D.

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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);}Charles-Antoine D.'s LinkedIn Performance

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