Attribution Reporting and Visualization

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. 😎

𝐇𝐨𝐰 𝐈 𝐁𝐮𝐢𝐥𝐭 𝐚 𝐌𝐮𝐥𝐭𝐢-𝐂𝐡𝐚𝐧𝐧𝐞𝐥 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐔𝐬𝐢𝐧𝐠 𝐎𝐧𝐥𝐲 𝐒𝐐𝐋 That’s how I built a Multi-Channel Attribution Dashboard that tracks revenue back to the real drivers, across Google Ads, Facebook Ads, email, and organic traffic — without a single BI tool. Here’s how it happened - The Problem: The client was spending across multiple channels, but GA4’s last-touch model wasn’t giving the full picture. Marketing teams were flying blind, and every channel wanted credit. So, I set out to answer: "Which channels actually drive conversions, and how do they work together?" The Solution: SQL-Only Attribution in BigQuery Using only SQL in BigQuery, I built a dashboard that: ✅ Tracks first-touch, last-touch, and linear attribution ✅ Allocates revenue proportionally to every user journey ✅ Connects ad spend to conversions across sessions and sources ✅ Supports flexible filters by date, campaign, device, and region How I Did It (Simplified) 1. Unified All Touchpoints • Pulled raw GA4 events data, including session_source, user_pseudo_id, and event_name. • Mapped all key user interactions — from ad clicks to checkout completions. 2. Created a Conversion Timeline • Used LAG() and ARRAY_AGG() to reconstruct user journeys. • Tracked each session leading up to a conversion event. 3. Applied Attribution Logic - Wrote modular SQL views for: • First-touch • Last-touch • Linear Each logic had its own SQL CTE, allowing a quick switch for comparison. 4. Joined Spend Data • Brought in Google Ads + Facebook Ads costs from external tables. • Linked spend to sessions via gclid and UTMs. 5. Final Output - A single BigQuery table showing attributed revenue by: • Channel • Campaign • Source/Medium • Attribution model Bonus: I connected it to Looker Studio later for visualization, but the real power? It’s all SQL. Why This Matters • Marketing teams don’t just need numbers; they need trust in their data. • When you eliminate the black-box tools and own your logic in SQL, you unlock freedom and transparency. Curious to see the SQL behind it? Drop a “SQL” in the comments and I’ll share a simplified version. #SQL #BigQuery #Attribution #MarketingAnalytics #DigitalAnalytics #GA4 #DataEngineering #MarketingOps #LookerStudio

Is your multi-touch attribution data lying to you? Your MT reporting is probably making everything look good. Here's why: Most companies attribute pipeline/revenue to ALL touchpoints from ALL contacts under an account. Then look at the total # and $ value of opportunities influenced. The result? • High-volume channels look amazing (even when they're not) = volume bias • Every marketing activity appears to influence deals =  if everything is working, is anything 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 working? There's a better way to analyze MT data (see image): Look at win rates relative to channel/campaign touchpoints. This strips out volume bias and shows you what's moving deals forward vs generating noise. Example: Paid Search: • Influenced ~1400 deals BUT the average win rate of those deals is 20% C-suite dinner: • Influenced 300 deals BUT the average win rate is 40% If you just looked at total influence, you'd think that the dinners are underperforming paid search. But when you look at influence conversion, it tells you the opposite. Linkedin influencers will tell you MT sucks. But it's more nuanced than that. It's actually the way most companies set up their reports misleads them. We need to be smarter about how we leverage the data. ______________ p.s. also worth saying no attribution model, report, or dashboard will be perfect. Each version has pros/cons and tells a different story. The goal is to leverage multiple methods to help triangulate what is working to help make better decisions going forward.

#Attribution doesn’t get a lot of love these days, but it creeps into our lives almost every time we want to see the volume or proportion of conversions by channel—which is, let’s face it, a very frequent question asked by marketing/sales teams. The simplest form of attribution (data structure-wise) is populating acquisition columns (source/medium, channel grouping etc) for individual event records, typically (though not exclusively) conversions. This then allows us to count events by acquisition channel. It’s different to session attribution (where we can even split conversions up and distribute them across multiple sessions); in this case we put only one channel against each event record. #GA4 does this automatically in various ways, i.e. traffic_source, collected_traffic_source, session_traffic_source_last_click fields. But the problem is that they all come with certain baked-in rules in the background. They’re great when we want to match numbers to the GA4 frontend, but often businesses want to “tweak” these rules and have their own definition. This is where #BigQuery #SQL comes to the rescue: we can build our own rules using the raw event level data. The most basic way to do this is to counteract a “bug” (or “feature”) whereby acquisition channel information is only recorded to page_view and session_start and not on other (eg conversion) events. With window functions, we can propagate these source, medium etc values from the start of the session (or from any mid-session event where it’s populated) down to all subsequent events in that session. Then there are more refined ways, e.g. different flavours of last non-direct click attribution. In a similar manner to the above (see also the picture), it’s possible to go further back in the user’s journey. And in this logic you can make a lot of decisions: ✅ considering attribution values only from session start VS also from mid-session ✅ Adjusting the lookback window (7 days? 58 days? Etc) ✅ Baking channel preferences into the mix (e.g. paid channels only) ✅ etc! I’ve always loved the power of window functions (in this case, LAST_VALUE() IGNORE NULLS); now coupled with a CASE WHEN, we can implement pretty much any rule we need. Of course, we need to build some interim helper columns to do that (e.g. days since the previous event), but we can do all that using SQL too. We can achieve a notable “uplift” in attributed conversions using this method, even on the demo GA4 dataset, if one wishes to try this out there. Read the full article on GA4BigQuery. (It is a premium article, but in line with our usual “policy”, the approach concept is visible for everyone. Plus one of the “main ingredients” is right here in the post image.) -- Follow me Balazs Vajna for updates on #digitalanalytics, BigQuery and #digitalmarketing #data solutions, and hit the bell on my profile to receive updates. Also, visit GA4BigQuery and browse among numerous tips on analyzing GA4 and other digital platform data.

Most attribution models are “directionally correct”… right? We decided to test that. Attribution is messy in real life: multiple buyers, long cycles, patchy CRM data, and lots of unstructured activity across email, meetings, website, and events. So most teams (my old self at Branch included 🙋♀️) reach for something simple: - Look at all touchpoints before an opp is created - Apply a rules-based model ( account-level multi-touch when contact roles are not well updated) A customer recently asked us to put that assumption under a microscope. They were using a pretty advanced, account-level multi-touch model for one of their key campaigns. Because opportunity contact roles were spotty, their model spread influence across any engaged contact at the account and created touchpoints only for the last activity before key milestones. We ran the same campaign through Upside’s AI Deep Research Agents and compared results. What we found: 1️⃣ Over-attribution on New Business On New Business deals, the rules-based model over-credited influenced opportunities by ~42% (by deal count). Why? Because it attributes to all engaged contacts at the account. It can’t tell the difference between real buying group members and random people who happened to engage with the campaign but were never part of the deal. Our AI reconstructs the actual buying group (from meetings, email threads, engagement patterns, etc.) and only counts campaign touches that involved those people. ⸻ 2️⃣ Massive under-reporting of overall influence At the same time, the rules-based model dramatically under-reported campaign impact. • Upside detected campaign influence on 763 opportunities • Only 134 of those showed up in the rules-based attribution That’s a 5.7x gap in influence. The reason: their custom rules setup only created a touchpoint for the last activity before a milestone, which is pretty standard for rules-based systems. If the meaningful campaign engagement didn’t happen to be that last activity, it simply vanished from the model. Timing quirks in the CRM became the difference between “this campaign influenced the deal” or “it didn’t exist.” How did we look at it? Our Deep Research Agents: • Reconstruct the true buying group for each opportunity • Scan all structured + unstructured signals (meetings, emails, web, events) • Identify when and how that campaign touched the real decision-makers Once you have accurate influence detection, you can layer any credit model you want on top - zero-sum, weighted, position-based, whatever fits your philosophy, and we can help. But if the underlying detection is broken, no amount of clever rule-writing will save the model. Here are a few screenshots from the analysis in the carousel 👇 Curious: if you’re using a rules-based attribution system today, how confident are you that it’s actually directionally correct?