Attribution Analysis Framework

I've spent $100M+ on Meta in DTC space 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 or Northbeam. 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.

4 attribution sources. 4 different answers. Your MTA tool, GA4, Meta, and Shopify can't agree on attributed $$$. And you can't audit why. Standalone MTA tools add proprietary pixels and enforce vendor-locked query params. Meanwhile, your GA4 + CAPI + Shopify + survey data already contains SUPERIOR insights. Your existing 1st party data captures DEEP attribution insights: → Server-side CAPI (Elevar, Blotout, Littledata) + GA4: event-level data of purchase journeys with identity resolution → Shopify: Order-level attribution (1st & last touch) → Zero-party surveys (Fairing, KnoCommerce): Awareness channel credit The goal: Join these sources to create verifiable attribution, complete funnel visibility, and methodology you control. 1. Start Simple 🚀 Sync GA4 to BigQuery (free). This unlocks event-level data and real-time reporting GA4's UI can't provide. Elevar users: their Pub/Sub feature gives you a real-time firehose of server-side events. 2. Unify Sources 🔗 Join touch points from GA4, CAPI, Shopify, survey responses by order IDs. Now you can do journey analysis. 4. Build Logic 🧮 Start with rule-based models (first/last/linear) to establish baselines. Then test more advanced models customized to your typical purchase journeys. 5. Visualize 📊 GSheets or LookerStudio will often suffice (free). Experiment with different views to fit your decision-making process. 6. AI Validation 🤖 Export sample data with human-verified calculations. Feed to Claude/ChatGPT to validate logic, catch edge cases, and generate SQL for advanced models like Markov chains or Shapley values. 7. Scale Up 🐍 Move complex analysis to Python notebooks. Libraries like pandas for data manipulation, lifetimes for CLV modeling, and scikit-learn for ML-based attribution are battle-tested and FREE. Reality check: More work than SaaS, but you get complete confidence, custom logic, and zero vendor lock-in. This makes sense if you're: • Running $1M+ monthly digital spend • Already investing in data infrastructure • Competing on marketing efficiency • Fed up with conflicting attribution sources Choose your data battles wisely. If attribution accuracy drives your growth, owning this capability changes everything. Wanna nerd out? Comment or DM. 😎

Groundbreaking Research Alert: Correctness ≠ Faithfulness in RAG Systems Fascinating new research from L3S Research Center, University of Amsterdam, and TU Delft | AI reveals a critical insight into Retrieval Augmented Generation (RAG) systems. The study exposes that up to 57% of citations in RAG systems could be unfaithful, despite being technically correct. >> Key Technical Insights: Post-rationalization Problem The researchers discovered that RAG systems often engage in "post-rationalization" - where models first generate answers from their parametric memory and then search for supporting evidence afterward. This means that while citations may be correct, they don't reflect the actual reasoning process. Experimental Design The team used Command-R+ (104B parameters) with 4-bit quantization on NVIDIA A100 GPU, testing on the NaturalQuestions dataset. They employed BM25 for initial retrieval and ColBERT v2 for reranking. Attribution Framework The research introduces a comprehensive framework for evaluating RAG systems across multiple dimensions: - Citation Correctness: Whether cited documents support the claims - Citation Faithfulness: Whether citations reflect actual model reasoning - Citation Appropriateness: Relevance and meaningfulness of citations - Citation Comprehensiveness: Coverage of key points Under the Hood The system processes involve: 1. Document relevance prediction 2. Citation prediction 3. Answer generation without citations 4. Answer generation with citations This work fundamentally challenges our understanding of RAG systems and highlights the need for more robust evaluation metrics in AI systems that claim to provide verifiable information.

💡 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

Programmatic buyers who recognize the flaws in user-based attribution will appreciate this approach. In platform we measure the impact of each channel/tactic on conversion pixel fires over time, which has proven more effective than user-based attribution in numerous cases. Instead of the traditional user-based attribution method (“I showed this user an ad, I cookie the user, and if within 30 days they fire the pixel, the last ad shown gets credit”), we measure how spend in each channel/tactic impacts pixel fires. Our methodology: “I’m allocating spend to this channel/tactic; over time, we observe its impact on total conversion pixel fires and adjust budgets based on each channel/tactic’s effectiveness in driving those fires.” This enables more accurate measurement of hard-to-track channels in-platform, such as CTV, or environments where user ID is blocked or absent (e.g., iOS). It also eliminates lower-funnel bias and budget waste on organic conversions, a frequent user-based attribution pitfall. Prospecting spend has a longer attribution window but still demonstrates impact. Over-prioritizing retargeting and lower-funnel tactics reduces overall impact by depleting budget from channels/tactics that feed the lower funnel. We can still measure and report user-based attribution to clients. However, we present this impact analysis and allocate budgets based on measured impact.

𝗧𝗵𝗲 "𝗕𝗹𝗮𝗰𝗸 𝗕𝗼𝘅" 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗟𝗠𝘀 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗲𝗻𝗱! Especially in high-stakes industries like 𝗙𝗶𝗻𝗮𝗻𝗰𝗲, this is one step in the right direction. Anthropic just open-sourced their powerful circuit-tracing tools. This explainability framework doesn't just provide post-hoc explanations, it reveals the actual c𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝘁𝗵𝘄𝗮𝘆𝘀 𝗺𝗼𝗱𝗲𝗹𝘀 𝘂𝘀𝗲 𝗱𝘂𝗿𝗶𝗻𝗴 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲. This is also accessible through an interactive interface at Neuronpedia. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: ▪️𝗔𝘂𝗱𝗶𝘁 𝗧𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆: For the first time, we can generate attribution graphs that reveal the step-by-step reasoning process inside AI models. Imagine showing regulators exactly how your credit scoring model arrived at a decision, or why your fraud detection system flagged a transaction. ▪️𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗠𝗮𝗱𝗲 𝗘𝗮𝘀𝗶𝗲𝗿: The struggle with AI governance due to model opacity is real. These tools offer a pathway to meet "right to explanation" requirements with actual technical substance, not just documentation. ▪️𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: Understanding 𝘄𝗵𝘆 an AI system made a prediction is as important as the prediction itself. Circuit tracing lets us identify potential model weaknesses, biases, and failure modes before they impact real financial decisions. ▪️𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗧𝗿𝘂𝘀𝘁: When you can show clients, auditors, and board members the actual reasoning pathways of your AI systems, you transform mysterious algorithms into understandable tools. 𝗥𝗲𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗜 𝘁𝗲𝘀𝘁𝗲𝗱: ⭐ 𝗜𝗻𝗽𝘂𝘁 𝗣𝗿𝗼𝗺𝗽𝘁 𝟭: "Recent inflation data shows consumer prices rising 4.2% annually, while wages grow only 2.8%, indicating purchasing power is" Target: "declining" Attribution reveals: → Economic data parsing features (4.2%, 2.8%) → Mathematical comparison circuits (gap calculation) → Economic concept retrieval (purchasing power definition) → Causal reasoning pathways (inflation > wages = decline) → Final prediction: "declining" ⭐ 𝗜𝗻𝗽𝘂𝘁 𝗣𝗿𝗼𝗺𝗽𝘁 𝟮: "A company's debt-to-equity ratio of 2.5 compared to the industry average of 1.2 suggests the firm is" Target: "overleveraged" Circuit shows: → Financial ratio recognition → Comparative analysis features → Risk assessment pathways → Classification logic As Dario Amodei recently emphasized, our understanding of AI's inner workings has lagged far behind capability advances. In an industry where trust, transparency, and accountability aren't just nice-to-haves but regulatory requirements, this breakthrough couldn't come at a better time. The future of financial AI isn't just about better predictions, 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝘄𝗲 𝗰𝗮𝗻 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱, 𝗮𝘂𝗱𝗶𝘁, 𝗮𝗻𝗱 𝘁𝗿𝘂𝘀𝘁. #FinTech #AITransparency #ExplainableAI #RegTech #FinancialServices #CircuitTracing #AIGovernance #Anthropic

I just reviewed a $50M company's marketing stack and found the same red flag I see everywhere. When I see a B2B company relying almost entirely on Google Analytics for attribution, I know their growth strategy is headed for trouble. Don't get me wrong - Google Analytics is brilliant. It's comprehensive, feature-rich, and lethal for B2C companies tracking quick purchase decisions. But here's where it falls apart for B2B In B2C, a customer sees your ad, clicks, and buys your sneakers in 20 minutes. Google Analytics captures that journey perfectly - from first touch to purchase. In B2B? Your prospect downloads a whitepaper in January, attends your webinar in March, gets nurtured through 47 emails, has 12 sales calls, involves 6 decision-makers, and finally signs in September. Google Analytics sees the whitepaper download and... that's pretty much it. It's like trying to track a 9-month relationship through text messages alone. What B2B attribution needs: → Multi-touch tracking: Every interaction across the entire buyer journey → Account-level insights: Understanding how multiple stakeholders engage → Intent signal detection: Knowing when accounts are in-market → Cross-platform visibility: Connecting LinkedIn, email, events, and sales calls → Long sales cycle mapping: Attribution windows that match your actual sales process Using Google Analytics for B2B attribution is like using a tennis racket in a cricket match - wrong tool, wrong game. Smart B2B companies layer their attribution stack: > Foundation: Dedicated B2B attribution platform (like Factors) > Enhancement: CRM integration for sales activity tracking > Intelligence: Intent data for account prioritization > Validation: Regular attribution model testing and refinement This isn't just about better reporting - it's about understanding which marketing activities drive pipeline and revenue. With economic headwinds, every marketing dollar needs to prove its worth. Companies using tennis-racket attribution are losing deals to competitors with cricket-bat precision. Your sales team deserves to know which leads are ready to buy. Your marketing team deserves credit for the pipeline they're driving. Your board deserves accurate ROI data for marketing investments. Curious about what red flags might be lurking in your attribution setup? I'd love to hear what challenges you're facing with tracking your B2B marketing performance. #GTM #B2B

Despite what you may think about me, I actually believe touch-based attribution has a clear place in a good measurement system. I like to follow a five-tier hierarchy measurement framework to assess marketing effectiveness and business health (BEATS): • [B]usiness metrics • [E]xperiments • [A]nalyses • [T]racking • [S]urveys They all do different things well, and struggle at others. Ultimately all of marketing measurement is to try to decompose the impact of marketing on the overall business. Like I always say, "the single source of truth is the P&L." If your business is struggling getting cute with sophisticated marketing measurement is a waste of energy and resources. You need to focus on the foundational elements and shift aggressively. Experiments are great for proving overall incrementality in a constructed and "safe" scientific manner. But you can't run experiments on everything, and it's very hard to run highly-granular experiments. Analyses -- such as MMM -- are top-down data aggregators that infer causality. If you lack data saturation, variability, and granularity then it's pretty much little better than a regression you can run in Excel. Because of that MMM is weak at understanding individual ad groups, ads, or keywords. It might be able to split out and estimate impact based on trends or be informed by tests, but, in general the statistical methods just aren't able to detect such minute changes' impact on a big thing like revenue. Which brings us to tracking -- or traditional digital, deterministic attribution. What is attribution really good at? Granularity. And what do performance marketers crave? Granularity. Things that I'm happy to use attribution tracking for: • Comparing ad A vs ad B • Judging creative engagement metrics • Finding new keywords that got clicks • Serving as a "baseline" to apply incrementality coefficients to • Any highly granular, tight, micro optimization Some things to watch out for: • You shouldn't compare ad A in prospecting vs ad B in retargeting • You shouldn't compare prospecting and retargeting at all • You shouldn't compare between funnel stages • You shouldn't compare between channels And most importantly, never ever EVER use attribution to determine budget allocation overall for the business. That's a fool's errand. A good operator knows when to use what tool. It's easy to vilify attribution because it is the source of so much evil in marketing land today, but it still has a solid place in the hierarchy. Just remember to keep it in its place in the pecking order. #mta #mmm #incrementality #attribution

I’m surprised we are still here. So many marketing measurement conversations still collapse into: “What channel did this deal come from?” “Did sales source it or did marketing?” That is a one-dimensional lens on a multi-dimensional system. Enterprise buying journeys are long, non-linear, and 75% complete before a buyer ever speaks to sales. Dark social is real. Off-platform influence is real. Multiple stakeholders shape the outcome long before an opportunity is created. Yet, I'm learning there are still so many organizations are still trying to force that complexity into a single attribution answer. Here is how I approach it. 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐞𝐱𝐢𝐬𝐭𝐬 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐰𝐨 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: 1. What is working and why? 2. What is not working, and what are we doing about it? That's it. 𝐈 𝐠𝐞𝐭 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐚𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐨𝐧 𝐚 𝐟𝐞𝐰 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐮𝐩𝐟𝐫𝐨𝐧𝐭: 1. All channels matter. 2. No single model tells the full story. 3. First-touch, last-touch, and multi-touch each have specific use cases. 4. Attribution in enterprise GTM can suggest influence. It cannot prove causality. Without that alignment, every deal becomes a courtroom. Marketing vs sales. Brand vs demand. Channel vs channel. Having a shared understanding of this helps us make decisions based on directional truth, not perfect certainty. 𝐓𝐡𝐞𝐧 𝐜𝐨𝐦𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐚𝐦 𝐥𝐚𝐲𝐞𝐫. I share a clear playbook: 1. What signals we care about and why. 2. When to trust system-level reporting. 3. When to zoom into individual accounts and buyer behavior. 4. Why attribution responsibility is shared across everyone carrying a quota, not just marketing. The goal is more confident judgement, smarter risks and quicker growth. Advanced cross-platform measurement absolutely helps. The ability to understand exposure across environments, link perception to behavior, and see influence before form fill changes the strategic conversation. If you have a good system in place for this, you probably feel like you've found the holy grail 😅 But even without sophisticated tooling, the mindset is available to any team. Assume the journey is complex. Treat attribution as a decision-support system. Optimize for probability, not credit. 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐰𝐡𝐨 𝐬𝐨𝐮𝐫𝐜𝐞𝐝 𝐭𝐡𝐞 𝐝𝐞𝐚𝐥. 𝐈𝐭 𝐢𝐬 𝐰𝐡𝐞𝐭𝐡𝐞𝐫 𝐲𝐨𝐮𝐫 𝐠𝐨-𝐭𝐨-𝐦𝐚𝐫𝐤𝐞𝐭 𝐬𝐲𝐬𝐭𝐞𝐦 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭𝐥𝐲 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐬 𝐭𝐡𝐞 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐝𝐞𝐚𝐥𝐬 𝐜𝐥𝐨𝐬𝐢𝐧𝐠. If we keep asking the wrong measurement question, we will keep optimizing the wrong parts of the system.