Charlie de Thibault's Linkedin Analytics

Your team is working harder. The dashboards are full. But growth is flat. Why? Because most marketing budgets are optimized for what’s easy to track Not what actually drives revenue. We’ve all seen it:  ✅ More MQLs ✅ More paid retargeting ✅ More reps hired to chase pipeline But somehow… 🚫 Sales keeps missing quota 🚫 CAC keeps climbing 🚫 The funnel keeps overflowing with noise This is what happens when you fund volume instead of trust. If your pipeline is stuck, here’s what might be happening: 🔸 You’re measuring form fills, not buyer intent 🔸 You’re attributing success to brand search, not brand belief 🔸 You’re investing in short-term wins that don’t compound So what’s the fix? It’s not more budget. It’s not more headcount. It’s a complete shift in how you build demand: ✅ Start by rebuilding your narrative, focus on long-term cash flow, not just lead volume ✅ Create assets that educate, differentiate, and build belief ✅ Right-size your sales org so reps chase fewer, better-fit buyers ✅ Track signals of trust, not just transactions, like direct traffic, referrals, and inbound This isn’t brand vs. performance. It’s about building a go-to-market engine that doesn’t need to be reset every quarter. And when you get it right? ✅ Better pipeline ✅ Lower CAC ✅ Higher LTV ✅ Happier reps ✅ Leadership alignment (without a fight) This carousel breaks it all down: 🧠 The problem 🔁 The flip 📏 What to measure 🛠 How to fix it 🚀 What happens when you do

𝐇𝐨𝐰 𝐈 𝐔𝐬𝐞 𝐐𝐮𝐚𝐥𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐖𝐡𝐞𝐧 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐈𝐬𝐧’𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 Everyone talks about testing new channels with incrementality. But: - What if the lift is too small to show up? - What if the budget’s too tight to get signal? - What if the timeline doesn’t allow for statistical purity? That’s the reality for most teams. A lot of tests end up being inconclusive but that doesn’t mean there was no lift. The problem is that incrementality is a statistical tool. It tries to separate signal from noise. So if the new channel isn’t performing strongly enough, or takes time to show impact, you won’t see anything. And noise increases over time. If your ad effect has a long delay (adstock), you’ll miss the signal entirely. I have a client spending close to 8 figures a month. We wanted to test a new channel with $100K which is a decent budget. The signal was diluted. No way to isolate lift. No way to move confidently. No way to justify reallocation. Unless… You look into qualitative data. Then you don’t need a strong deviation from the mean to detect lift and you can run tests with much smaller budgets. It’s not perfect, but it works in scenarios where MMM and incrementality fail. Most tests fail because the signal’s too small. Incrementality has a blind spot. Qualitative feedback helps cover it. It also enables you to test fast and small, which can give you a major advantage. Some would say we should just increase the size of the test but experienced leaders know that’s rarely practical. Budgets are allocated months in advance. It’s a hard sell to ask your CFO to cut spend from well-performing campaigns to try something new.

You can’t brute-force your way to growth. When your GTM system lacks brand trust, more headcount just adds noise ,not clarity. Ever been in a revenue review where marketing celebrates lead volume while sales says, “These are junk”? That’s not a team issue , it’s a system design failure. I’ve seen dashboards claim top ROI from paid search... until we compare them with sales notes. The truth always shows up in the disconnect. If you're still rewarding vanity metrics, you’re just fueling the misalignment. 👉 Want better growth? Build with trust, not tricks.

There’s a major flaw in how most marketing teams “measure success.” We build dashboards to track distribution. But we rarely stop to ask: Did this even work? Measurement frameworks like MTA promise precision, but only tell part of the story. • They can’t explain why a buyer converted • They often contradict what sales is hearing • They fuel finger-pointing between teams That’s because most attribution tools optimize for what’s trackable, not what’s true. If you're serious about building a marketing engine that drives growth, you need to go deeper: 🧠 Run customer surveys. 🧠 Conduct interviews. 🧠 Get inside the buyer’s head. Because the buyer journey isn’t linear. It’s emotional. It’s influenced by context, trust, timing, and conversations you’ll never see in a UTM string. Multi-touch attribution can’t capture that. But real understanding can. The path to predictable growth? → Ditch the illusion of precision → Invest in clarity around customer preference

If everyone’s busy, but revenue’s flat , your KPIs might be killing your growth. This is the GTM blueprint you didn’t know you needed. It’s not about chasing every lead. It’s about earning customer preference, reducing churn, and aligning your teams on truthful metrics. We’re exposing: ●    Metrics that feel safe but sabotage performance ●    Attribution models that mislead your strategy ●    Systems that reward noise over results This is the B2B GTM reset,  designed for impact, not just activity. Share this with your team if you’re ready for real alignment.

If your CEO says “We don’t have budget for brand.” They’re telling you they don’t understand how buying works. Because brand = preference. Preference = conversion, velocity, margin, retention, LTV. No brand, no preference. No preference, no pricing power. No pricing power, no profitable growth. This isn’t a “performance vs brand” debate. It’s a misunderstanding of how demand is actually created. To be fair, it’s not just on the CEO. Sometimes marketing’s to blame too: Bloated dashboards, fluffy awareness metrics, and attribution models that reward the visible, not the valuable. When leadership hears noise, they ask for sourced pipeline. That’s fair. But when they stay in that mindset, that’s when the GTM breaks down into performance theatre. 🚫 Chasing trackable over impactful 🚫 Optimizing what’s visible, not what matters 🚫 Mistaking efficiency for effectiveness If your GTM feels broken, it might not be your team. It might be the system you're forced to work in.

You’re not underperforming because your team isn’t trying. You're underperforming because your system is overbuilt at the bottom of the funnel. It’s easier to track lead gen than to measure brand preference. So, we reward what we can count , not what moves revenue. But here's the truth: ●    Only 13% of MQLs become SQLs. ●    Only 5% of your ICP is ever in market. ●    80% of buyers pick brands they already know and trust. If you’re only measuring clicks and demo requests, you’re flying blind. 📉 Your data problem is costing you pipeline.  Time to fix what you're tracking , and why.

Marketing Mix Modeling is having its comeback moment, this time with experiments layered in. On the surface, it’s exciting. Experiments should make the models smarter, right? But here’s the honest breakdown: MMM is still fundamentally a correlation model. And correlation has a shelf life. It looks for signals. But the longer the conversion time frame, the weaker those signals get. Like fog rolling in, at two meters, you can still see. At ten? You’re guessing. That’s the core flaw: MMM tends to overweight short-term data and underrepresent long-term impact which means that your channel creating demand will be penalised. The solution? 📊 Combine models with market knowledge. 🔍 Cross-check patterns with buyer insights. 🧩 Use MMM as one lens, not the whole map. Because when you're managing millions in budget, you can't afford to fly blind. Strategy isn’t about chasing perfect models. It's about building conviction in imperfect conditions.

Running multiple CTV vendors? They’ll both claim credit for the same conversion. When running campaigns with two CTV vendors, be aware that they may bid on the same auctions, therefore same audience. As a result, you should expect duplicated conversions when using view-through attribution. This is why a geo lift test is the only reliable way to measure performance accurately. You should not rely solely on the results reported within the platform. Don't bleed margin, Better Call Charlie!

Here’s what I fix for my clients: ✅ Break the silos between brand, performance marketing, sales and finance ✅ Replace attribution theatre with decisions that move the needle ✅ Build measurement systems that reflect reality, not just track what’s easy ✅ Drive efficient growth, lower CAC

Google’s so good at Cloud, it even takes a cut of AWS and Azure’s revenue through Paid Search. Let’s talk about Paid Brand Search The easiest way to burn margin and justify it. It’s almost always unprofitable. But hey, it gets defended nine different ways: → “We’re in a competitive industry.” → “It protects our brand.” → “It’s part of the journey.” Cool. So why not test its actual incrementality? Here’s the real reason: 👉 Paid Brand is the easiest way to slap a “marketing-sourced” sticker on every deal. If you’re tracking last-touch, multi-touch, or “marketing-sourced pipeline”... Paid Brand is a cheat code to make the team look good even when it’s not driving growth. How much you wanna bet AWS and Azure’s marketing teams use that kind of attribution? And for SaaS companies? It’s also a great way to burn money on your own customers. The ones Googling your company name just to log into the site. (Special big up to Oracle — who finally got it and turned it off.) I’ve seen it too many times: End of quarter’s coming. MQL target’s missed. → Suddenly Paid Brand spend spikes. → Just in time to “save” the QBR. 🎯 Congrats — you hit the number. 💸 But at the cost of your margin and long-term growth. This isn’t performance marketing. It’s performance theatre. And it’s costing Microsoft and AWS a fortune every single month.

🚨 New Episode: Why Your GTM Machine Looks Busy But Isn’t Driving Revenue Most GTM teams aren’t broken because they’re lazy or unfocused. They’re broken by design. Marketing’s dashboards are green. Sales is grinding on demos. But revenue? Flat or falling. In Episode 1 of The Broken GTM Playbook, I unpack the root cause: We’ve built our systems around what’s easy to track, not what actually drives growth. You’ll learn: ✅ Why 80% of buyers choose a brand they already trust ✅ The real cost of chasing MQLs that don’t convert ✅ How flawed attribution models quietly misguide your budget ✅ And why adding more sales reps can actually hurt performance If you're a founder, CMO, CRO, or RevOps leader staring at a forecast that doesn’t add up—this episode is for you. 🎥 Watch Episode 1 here: https://lnkd.in/esyiJmbG Let’s build GTM systems that align truth, trust, and revenue, not just activity. 👇 Drop your thoughts in the comments. I read everything.

If you're still chasing every lead and optimizing for clicks, you're building on noise,not signal. To fix your GTM engine, you need to go beyond dashboards and uncover the real drivers of buyer decisions. Ask: – How did you hear about us? – Why now? Why us? – What almost stopped you from buying? These aren't marketing fluff. They're your competitive edge. Start by analyzing your sales transcripts. They hold the unfiltered truth that no spreadsheet will tell you. Your baseline percentage, the revenue not tied to paid search or retargeting, tells you how strong your real demand is. This is the foundation of a GTM strategy that doesn't just look good, it performs.

Why Multi-Touch Attribution is Burning Your Budget and Breaking Your Strategy 𝐌𝐓𝐀 𝐭𝐨𝐨𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐢𝐦𝐩𝐚𝐜𝐭, 𝐭𝐡𝐞𝐲 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐯𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲. And that’s the problem. 🧨 They reward noise over value. Here’s what that leads to: 👉 Teams optimising for more touchpoints, not better messaging. 👉 Spend shifting to channels that “show up” in reports, not the ones that actually drive conversions. 👉 An incentive structure that makes it look like retargeting is doing a lot of the heavy lifting… when it’s really it is not Let’s break this with facts: ✅ 80% of buying journeys are influenced before attribution even begins. ✅ Most MTA tools fail to track dark social, peer DMs, offline referrals, or the 3-month-old podcast that built the trust. ✅ In tests across $100M+ budgets, we found Meta’s real contribution was 20x higher than what MTA tools reported. ✅ In many businesses, 30% of revenue is word-of-mouth, MTA will allocate everything to marketing. So what actually happens? 👉You over-invest in what’s easy to track, not what drives demand. 👉You inflate CAC while missing what’s compounding quietly in the background: preference, brand, trust. 💡Here’s the kicker: 𝐓𝐡𝐞 𝐦𝐨𝐫𝐞 𝐭𝐨𝐮𝐜𝐡𝐞𝐬 𝐲𝐨𝐮 𝐫𝐞𝐜𝐨𝐫𝐝, 𝐭𝐡𝐞 𝐛𝐞𝐭𝐭𝐞𝐫 𝐌𝐓𝐀 𝐥𝐨𝐨𝐤𝐬 𝐰𝐡𝐢𝐜𝐡 𝐦𝐞𝐚𝐧𝐬 𝐲𝐨𝐮’𝐫𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐢𝐧𝐜𝐞𝐧𝐭𝐢𝐯𝐢𝐬𝐞𝐝 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲. 👉 More performance marketing touches = more credit = more spend = worse margins. We don’t need more touchpoints. It’s noise disguised as data. We need more impact. ✳️ Where MTA can help: MTA is useful for account scoring: helping sales teams prioritize in-market buyers based on recent engagement. That’s it. But if you’re using it to decide what to fund and scale in your marketing strategy? You’re not optimizing. You’re inflating. ------------ I’m Charlie. I come from investment banking. Now I help companies with GTM budgets from $10M to $100M+ clean up their go-to-market, cut CAC, and scale with confidence. No fluff. No attribution theatre. Just data that drives real revenue. 📩 DM open if you’re ready to stop chasing clicks and start building compounding growth.

Most marketers think narrowing targeting is a smart move. But what looks like optimisation on a dashboard is often the first step toward starving your future pipeline.

Marketing effectiveness ≠ just MMM and incrementality. That’s distribution measurement. It tells you where to spend, not what actually works. Your CAC problem probably isn’t media. It’s a boring-ass marketing problem. Safe message. Invisible brand. Creative nobody remembers. Then you wonder why distribution looks inefficient? You’re scaling nothing. Dull marketing is expensive. Interesting marketing compounds. So before you optimise media, fix the creative. I highly recommend you to read the paper "The extraordinary Cost of Dull"

Some MMM vendors don’t measure performance. They bake it. Pick your lift: 3%, 5%, or 7%. Keep the client happy. Ship the ROI they expected. No questions asked. 📉 That’s not measurement. That’s dessert theatre. Here’s the smell test I use: Would I put my own money behind this insight? Too often, I’ve seen models where coefficients are shaped by belief, not data. It’s scalable. It’s comfortable. But it’s not real. And when the CFO finds out the “model” was just an opinion, it backfires. Every time. There’s no excuse to bake a MMM coefficient. If you can’t measure it, don’t invest in it. In trading, belief is a capital killer. Data is how you grow capital. Same goes for your marketing budget. Use baked coefficients and you're just burning company cash. There are ways to validate assumptions: - Triangulate with external data - Test surprising outputs - Run incrementality experiments If you skip that? You’re not modelling. You’re storytelling with spreadsheets. 👉 Real models don’t ask for outcomes. They reveal them.

When I was trading, my nickname was Le Chiffre. Yeah, the Bond villain. No emotion. Just math. No choices where made based on convictions but only probabilities. This is what makes the difference between a trader and a gambler. Because in trading, it's simple: You make money, or you're out. 👉 There’s no “good-looking dashboard” to save you. 👉There’s no “nice narrative” to soften the blow. 👉The P&L is the truth. And when I moved into tech? I was shocked how much decision-making was everything but that: ❌ Highest Paid Person’s Opinion wins ❌ Dashboards built to confirm biases ❌ Data manipulated to make teams look good ❌ No feedback loop between what we measure and what moves revenue That’s why growth is stalling. That’s why CAC keeps creeping up. That’s why your pipeline’s full of noise. 𝐌𝐨𝐬𝐭 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐯𝐞𝐧𝐝𝐨𝐫𝐬 𝐝𝐨𝐧’𝐭 𝐬𝐞𝐥𝐥 𝐫𝐞𝐯𝐞𝐧𝐮𝐞. 𝐓𝐡𝐞𝐲 𝐬𝐞𝐥𝐥 𝐫𝐞𝐩𝐨𝐫𝐭𝐬. Nice graphs. Glossy dashboards. Just enough for a little back-patting in the meeting. But if your CAC is creeping up, your margin’s leaking, and your GTM’s stalling… ❌ You don’t need another dashboard. 👉 You need decisions that actually move the number. 🌟 You need to bring a trader’s mindset into GTM. No ego. No fluff. No theatre.Just results. Because if the data doesn’t make you money, it’s just a vanity metric with a prettier font. 💬 DM open if you’re ready to fix this.

Why I Moved Away From Multi-Touch Attribution…and What I Use Instead

This is exactly what we do for clients: we measure how brand impacts performance marketing and revenue.