Attribution Model Comparison

I spent over $3M on Meta ads in January. And I use 3 attribution models: Ad platforms are notorious for taking credit for view-through conversions they didn't drive. They do it to bait you into spending more. The issue is that your top 1-2% of ads should drive ~50% of your spend and revenue. If you're relying on bad attribution, you won’t be able to find them. This is why 8-9 figure brands (that NEED their tracking to be faultless), use 3 attribution models: 1. Multi-touch attribution (MTA) - for ad and campaign level optimization. This is your Triple Whale. Great for knowing which ads are performing best, which ones to scale, which to cut. Not as good for comparing channel to channel. It also will overcount total revenue, which you need to be careful about. To make sure your account is well optimized, plot CPA vs Spend on a scatter plot. The top ads should be in the low CPA, high spend zone. 2. Post-purchase survey - for channel level allocation. Get a 35%+ response rate, extrapolate to all new customers, and calculate your cost per new customer response per channel. This tells you which channel to push into. Click-based attribution overvalues lower-funnel performance by up to 250%. Post-purchase surveys catch what click attribution misses - top-of-funnel creative can drive 13X more incremental acquisitions than bottom-of-funnel. 3. Marketing Mix Model (MMM) - for validating direction. You can't use this daily, but it confirms your post-purchase survey is sending you the right way. Then you use post-purchase on a daily basis to optimize channel allocation. Some channels drive low-quality customers that look good on ROAS but don't stick around. MMM helps you optimize for 12-month profit as opposed to just immediate return. The other thing to know is that view-through attribution is poor signal. Make sure your attribution is set up for 7 or 14 day click, depending on your purchase funnel. One day view will overcount. Here's what this gives you: When performance drops, you know exactly where to pull budget to create the smallest impact on revenue while keeping the company profitable. When things are going well, you know exactly where to push budget to scale effectively. Bottom line: -> Use MTA for ads and campaigns.  -> Use post-purchase surveys for channel allocation.  -> Use MMM to validate you're heading the right direction. This is how 8-9 figure brands figure out where every dollar should go.

💡 Everyone's measuring #marketing #performance. But I find that a lot of great marketers are not combining the right #measurement protocols to tell the real story behind the numbers. The pressure to prove #ROI is intense. And yet most teams are either drowning in #data they can't action, or relying on metrics that only tell part of the story. The problem isn't effort, but it may be a lack of experience in using the right framework. There are two models that are essential in a marketer's toolbelt - Marketing Mix Modeling (#MMM) and Multi-Touch Attribution (#MTA). They're not competitors. Frankly they solve different problems and together, they give you a more comprehensive understanding of #marketing performance than either can alone. 🧠 Marketing Mix Modeling (MMM) :: Your top-down view. ✨ It uses aggregated data such as spend, revenue, pricing, seasonality, even external economic factors to model how your entire marketing mix drives business outcomes over time. → Mechanics: Statistical regression across channel-level data, typically requiring 2+ years of historical to be reliable. → Use Case: Annual budget planning, scenario modeling, and measuring channels that are hard to track individually. → Primary Limitation: It won't tell you what's happening in your campaigns right now. It's a strategic lens, not a real-time one. 🧠 Multi-Touch Attribution (MTA) :: A bottom-up analysis. ✨ It tracks individual user journeys across digital touchpoints such as impression, clicks, search, conversions and distributes credit across each interaction. → Mechanics: User-level data stitched together across sessions and platforms to map the path to purchase. → Use Case: Real-time digital campaign optimization, creative testing, and understanding which touchpoints are actually moving people through the funnel. → Primary Limitation: It's increasingly fragile in a privacy-first world, and it systematically undervalues anything offline or upper-funnel. As with any valuable framework, there is great benefit in pairing these two models together in partnership as they truly fact check one another. This is what's called a Unified Marketing Measurement, using MMM to set your strategy and allocate budgets at a macro level, while MTA helps you optimize the execution of your digital campaigns week to week. MMM tells you where to invest. MTA tells you how it's performing. One gives you the long-term baseline. The other gives you real-time signal against it. It may sound like a lot, but it doesn't have to be. Start with the #analysis that fits your immediate need and build the other alongside it. Let them inform each other over time. Marketing measurement doesn't need to be perfect from day one. It just needs to be pointed in the right direction. Are you using one, both, or something else entirely? I'd love to hear how your team is approaching measurement right now. #MarketingMeasurement #MMM #MTA #MediaMix #MarketingAnalytics #DataDrivenMarketing

Last year, I asked 200+ marketers one question: "How many of you believe there's a single source of truth that tells you everything about your marketing performance across all channels?" Almost no hands went up. They're right to be skeptical. If there is one thing that I've taken away from managing $2B+ in total marketing budgets, it’s that every measurement method tells a different story. - Google Analytics says one thing.  - Ad platforms say another.  - MMM disagrees with both. And the problem gets worse. - Third-party cookies might disappear. - iOS tracking is limited. - AI bidding systems are black boxes. We're facing unknown returns on ~$700 billion in annual digital ad spend. The result: stakeholders get puzzled and frustrated because they don't know what the real ROI of your marketing campaigns is. The answer I found most effective is not to rely on the 1 “perfect" methodology. It's triangulation - getting 3 methodologies working together in orchestration: Lift Testing - the gold standard for establishing causality. Run geo-experiments on the biggest channels to understand what conversions you wouldn't get without advertising. Statistically accurate and privacy-proof, but difficult to scale with opportunity costs. Marketing Mix Modeling - holistic regression linking all inputs to outputs. Captures seasonality, promotions, pricing, offline media, competitor activity. Gives you baseline conversions if you stopped all marketing. Privacy-proof but limited granularity. Multi-Touch Attribution - user-level methodology tracking touchpoints in conversion paths. Real-time and granular for daily optimization. But myopic - only sees UTM-tracked visits, misses offline marketing, inflates attribution on bottom-of-funnel channels. This is what combining them looks like: - Start with a Bayesian MMM as your absolute framework. - Inject your lift test results as prior knowledge to calibrate the model - this ensures your MMM output stays grounded in reality instead of producing unrealistic attribution from finding a local optimum. - Then layer MTA underneath on a relative basis. Take your realistic channel-level results from the calibrated MMM and use MTA to break them down to campaign-level granularity or below within each channel. This gives you a complete picture: holistic insights for strategic budget allocation and granular data for daily optimization decisions. Most companies pick one methodology and force it to answer every question. That's like using a hammer for surgery. Each method has blind spots. Together, triangulation fulfills most of your marketer needs. I've seen this approach work across industries through my work at Rocket Internet and with @Growth Vision Partners clients. The single source of truth is still a myth, and there is no silver bullet. But triangulation gets you as close as you’ll ever get to knowing your marketing’s real ROI.

Here’s what actually works, Integration. Run experiments to get ground truth on what’s driving incremental sales. Use MMM to understand the macro picture across all channels. Use attribution to find relative performance within channels, across campaigns. Each method covers the gaps in the others. Experimentation gives you causality but limited coverage. MMM gives you a comprehensive channel view but works on correlation. Attribution gives you real time granularity but can’t tell you what’s incremental. Use one in isolation and you’ll get precise numbers that are very wrong, or fuzzy numbers that miss the future. The companies getting this right aren’t picking one method and hoping it works. They’re combining all three and validating them against each other. Measurement is either done right or it's done easily. #MarketingMeasurement #MMM #Incrementality #MarketingScience #PerformanceMarketing

Triangulation. Why do we need 3 methods to measure the impact of media? We use measurement to... - Identify what worked in the past - Optimize the present - Forecast/Plan the future Unfortunately, there is no single tool that can do everything. But you can use the following methods together: 1.) Media Mix Modeling (MMM) 2.) Experiments (Geo Tests, Lift Studies) 3.) Multi-touch Attribution (MTA) Let's break down what each is good for. 1.) Media Mix Modeling (MMM) This considers your media (impressions, spend) and models it to your outcome (revenue, leads, profit). It answers which factors, channels, and tactics impact that outcome. Pros - Holistic, can measure all channels - Calculates incrementality - Can give you a baseline - Can measure lag (ad-stock effect) - Privacy-proof - Incorporated factors beyond media Cons - Not granular - Can be technically challenging to run We use MMM for... ✅ Measuring the past ❌ Optimizing the present ✅ Plan the future 2.) Experiments Geo-tests are the most popular. This method finds similar geographies (city, state, DMA). Which allows you to measure the impact of pulsing media up/down/off. Pros - Statistically accurate - Calculates incrementality - Privacy-proof Cons - Time-intensive for many channels - Challenges in smaller countries - Lost revenue from holdouts We use experiments for... ❌ Measuring the past ✅ Optimizing the present ✅ Plan the future 3.) Attribution (MTA) This stitches journeys together at the user-level, and assigned credit to the channels/campaigns that the user engaged (click, view). Tools like Google Analytics or even Meta/Google's internal platforms use attribution. Pros - Data is realtime - Easy to get the data - Visitor/User-level data Cons - Blind to offline/non-click channels - Relies on cookies, not privacy-proof - Does not measure incrementality We use MTA for... ✅ Measuring the past ✅ Optimizing the present ❌ Plan the future So, how do mature brands put this all together (triangulation)? 1.) Measure the past using MMM and MTA - What worked? - Which channels were incremental? - What is our baseline? 2.) Use MTA and Experiments to optimize the present - MTA for campaign-level data in a single platform - Experiments to validate the MMM 3.) Forecast and Plan the future - MMM to model and scenario plan What would you change/add about this approach? #triangulation #measurement #methods