Attribution

Cyberweek is coming soon — and here’s the uncomfortable truth: ❌ Last-click is lying to you. ❌ Google Analytics can’t see your real ROI. ❌ AI won’t save you if your attribution is broken. This year, customers will cross 9+ touchpoints before buying — TikTok ➝ Meta ➝ Shopify ➝ in-store ➝ email ➝ AI recs. If your attribution doesn’t match your bank account, you’re not running campaigns — you’re gambling. On Tuesday, August 26 (next week), our Founder & CEO Ryan Koonce joins Twilio Segment for Dollars and Sense to show how brands like Rocket Lawyer, Livestorm, and eForms are cutting through the chaos: -- Real CAC and payback windows (not modeled guesswork) -- Attribution that’s auditable by finance, trusted by marketing, and actionable by product -- AI that drives conversions instead of adding more “organized noise” Cyberweek is the Super Bowl. Don’t go in blind. (Link to sign up in first comment)

Every Cyberweek feels like déjà vu: Brands overspend on ads. Teams can’t prove auditable “true” CAC. Execs ask what the spend actually drove end-to-end. Here’s the problem: customers don’t buy in funnels — they shop in chaos. This year it’s 9+ touchpoints: TikTok ➝ Meta ➝ Google ➝ Shopify ➝ email ➝ AI recs ➝ in-store. If you’re still leaning on last-click or Google Analytics/GA4, you’re flying blind. I’ve spent three decades building companies that help brands turn data into growth — from startups like Dutch Pet, Inc. to enterprises like Dropbox and Siemens. At Attribution, we work with marketers at MyComputerCareer, Livestorm, and eForms to connect every dollar to every conversion with surgical clarity. On Tuesday, August 26 (next week), I’ll be joining Twilio Segment’s Darcelle Pluviose for Dollars and Sense where we’ll unpack: -- The Rocket Lawyer playbook: how they cut wasted spend in weeks, not quarters, with Twilio Segment + Attribution -- Why companies ditch “black-box” analytics for attribution that matches the bank account -- The AI strategies that actually lift ROI during Cyberweek — and the ones that are just noise If you want Cyberweek to be your best growth story (instead of your biggest budget regret), join us. (Link to sign up in first comment)

As Head of Marketing and later VP of Operations from $4M to $150M+ ARR at ClickUp, multi-touch attribution didn’t just help us grow the business—it helped us run the business at scale during a pivotal growth inflection. Early on at ClickUp, before we had multi-touch attribution and data infrastructure fully stood up, I remember spending half my week in spreadsheets trying to piece together what was working across a complex user journey—between our PLG funnel, paid programs, and our exploding organic footprint, I was retroactively aligning data, channels, and decisions. Eventually, we invested the time and team to build a deep, warehouse-first multi-touch attribution model (shoutout to Marc Stone), and that changed everything. It cut down our time-to-decision across both paid and organic by more than half and gave us the clarity to double down on what was actually scaling ARR based on our "true" CAC Payback for all channels. Now, I’ve seen companies hit $100M ARR without proper attribution, and I’ve also seen it transform how marketing, sales, and finance teams make decisions—if implemented right. It’s safe to say attribution is one of the most overhyped, underutilized and hardest to implement parts of a growth engine. I joined Bill Macaitis, Kyle Poyar, and Ryan Koonce for a live, no-BS debate on what actually works in multi-touch attribution—and what’s just noise. I enjoyed sharing frameworks from my time at ClickUp, which we also use now as playbooks at Craft Ventures to support our entire portfolio.

At Mammoth Growth and GrowthBench, working with companies like Dropbox, DoorDash, and Calendly, attribution was a nightmare—and the off-the-shelf tools weren’t cutting it. So I built one. Since then, I’ve worked with 1000's of companies at Attribution to help companies actually connect marketing to revenue. Now, I’m thrilled to host Aaron Cort (AC), Bill Macaitis, and Kyle Poyar—three people who’ve shaped how modern SaaS grows—for a candid, live panel:  Attribution Smackdown: Myth vs. Reality It’s going to be honest, tactical, and packed with real-world lessons. The event is currently at capacity but if you mention you signed up / were referred by myself, the team will make sure you have a spot reserved / included to attend! (Link in comments)

Attribution Smackdown 🤜 🤛. To my fellow attribution nerds, join me as I go to battle in this epic attribution debate with Aaron Cort (AC), Kyle Poyar and Ryan Koonce. I am a staunch believer in the reality that B2B software buying is a long process and 1st and Last touch just don't cut it anymore. I'll be arguing for more cutting edge AI regression models and even stuff outside of traditional MTA. Sign up link is in the comments. We are officially at full capacity but if you mention my name when signing up they will let you in :)

Marketers love asking, “How did you hear about us?” It’s a nice touch. It can give you directional insights. But here’s the problem: It’s not attribution. Why? Because people don’t remember their full buying journey. 👉 They saw a Facebook ad last month but don’t mention it. 👉 They read three blog posts and opened five emails—but only recall the last one. 👉 They say “word of mouth” when, in reality, your retargeting ads kept them coming back. Self-reported attribution is full of blind spots. And if you’re using it as your only source of truth, you’re making multi-million dollar marketing decisions on faulty human memory. What self-reported attribution CAN’T do: 🚨 Track cross-channel influence. 🚨 Show you how multiple touchpoints worked together. 🚨 Connect marketing spend to revenue. What real attribution CAN do: ✅ Tie customer journeys to actual data. ✅ Show the touchpoints that drove revenue—not just what people remember. ✅ Give you a full-funnel view of what’s actually working. Now, don’t get me wrong—self-reported data has its place. It can highlight qualitative insights that data alone might miss. But if you’re relying on it to decide where to put your budget? You’re gambling, not marketing. Attribution isn’t perfect—but it’s a hell of a lot better than relying on vibes.

Let’s be real: attribution models aren’t perfect. But I’ve seen firsthand that well-built attribution can tell you a lot—like which channels are driving revenue, how touchpoints work together, and where your budget is best spent. That said, there are still things it can’t tell you. 🚫 It won’t explain exactly why a prospect converted. 🚫 It won’t capture offline conversations, event chatter, or what someone saw on their friend’s screen. 🚫 It can’t predict the future—it’s built on what already happened. The mistake many marketers make is treating attribution as an answer key—when it’s really a tool. A good model is deterministic. It tells you what happened and lets you verify it with real data. It shows you what’s known, and just as importantly, where the gaps are. But using that information still takes human judgment. Here’s what that looks like in practice: 📍 Your attribution platform shows that a paid campaign contributed to a conversion—alongside email, organic, and direct. 🧠 But your team knows that this campaign was primarily focused on brand repositioning, and success wasn’t just about conversions—it was about shifting perception ahead of a major launch. 📍 Your reports show that certain content pieces are lightly touched across multiple journeys. 🧠 But your sales team keeps hearing the same article referenced on intro calls—so you assign it more strategic weight, even if it doesn’t look “high-performing” in the model. 📍 A new channel shows minimal impact in the platform. 🧠 But your pipeline velocity data shows that deals influenced by that channel close 30% faster—so you give it more credit internally, even before the attribution model catches up. Even the best attribution models can’t read intent, context, or strategy. That’s why attribution should enhance your judgment—not replace it. ✅ Use it to surface what’s measurable. ✅ Use it to inform your decisions—not replace them. ✅ Use it as a tool—not a crutch. If you expect an attribution platform to think for you, you’ll get misled. Ignore it entirely, and you’ll miss what’s working. The best approach is somewhere in between: trust the data you can verify, and pair it with the judgment only you can bring.

Hot take: Attribution without cost data is just expensive guesswork Let’s play a game. Imagine you're running ads on two platforms: 📍 Google Ads – 500 conversions with a $30 CAC 📍 Facebook Ads – 300 conversions with a $50 CAC Your attribution tool says Google Ads is crushing it over Facebook Ads. More conversions with a lower CAC? Easy decision. Double down on Google! 🚀 But then you look at what you *actually spent* and *what you actually made*: 📉 Google Ads: • 500 conversions • $30 CAC • $100,000 in ad spend • $120,000 in revenue • ROI: 20% 📈 Facebook Ads: • 300 conversions • $50 CAC • $10,000 in ad spend • $90,000 in revenue • ROI: 800% Now ask yourself: Which channel is actually more valuable? This is what happens when you don’t include cost and revenue in your attribution model. You end up optimizing for volume, not value. This is the Problem With Most Attribution Platforms ❌ Google Analytics? No cost data. ❌ Most native ad platforms? They only show you what *they* want to take credit for. ❌ Most attribution tools? They attribute user conversions, but never tie them back to what you spent or what you made. If your attribution model only tells you *where* conversions happened—but not what it cost you to acquire them or how much you earned from them—you’re making million-dollar decisions on incomplete data. Real Attribution = Conversion Data + Cost Data + Revenue ✅ If a customer clicked 5 ads before converting, you should know **how much was spent on each one**. ✅ If a platform says it drove 40% of your conversions, you should see **how much revenue that actually created—and at what cost**. ✅ If a campaign looks like it’s performing, you should know whether it’s actually bringing in **high-margin, high-LTV customers**—not just any customers. Attribution without cost and revenue data is like running a business with just your top-line metrics. * might look good in a dashboard—until you check your margins.** Attribution isn’t just about credit. It’s about **profit.** And if your attribution tool can’t show you that, it’s just guessing.