Multi-Touch Attribution

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

"If I only looked at last-touch attribution, I would have killed everything driving our growth." Kacie Jenkins 🎁 uncovered a scary truth about B2B marketing metrics: Sendoso's best-performing channel is direct website traffic. But traditional attribution missed that those "direct" visitors had already: + Interacted with partners + Opened nurture emails + Seen organic content + Taken a product tour + Engaged at events + Received a gift The pipeline was there. The attribution wasn't. If you saw their multi-touch data, you'd see something fascinating about these "direct" visitors... Most of them had interacted with the exact channels that looked like they were failing. The same channels a finance team would have flagged for cuts. This pattern kept showing up: High-intent buyers were consuming 7-8 different marketing touches. None of them showed up in pipeline reports. Then they'd visit the website directly and convert. Without multi-touch analytics, every investment driving those conversions looked worthless. That's when they made a radical change to their attribution model. The results transformed not just their pipeline reporting, but their entire relationship with finance. Your "worst performing" marketing channels might actually be your best. Most CMOs get forced cut them before they ever find out. If you're looking to transition away from being a lead-gen machine, this is the way.

💡 Why is everyone so hooked on First-Touch Attribution (aka “Lead Source”)? Sure, it’s tempting (and easy) to give 100% credit to that first touch that brought in a lead. But if you're only looking at the "Lead Source," you’re probably doing your marketing team a disservice—and missing out on true funnel optimization insights. First-Touch is like a snapshot of your funnel’s entryway. But what about all the steps that happen _after_ that? If you’re not tracking the full journey, you’re missing what actually drives conversions in the middle and bottom of the funnel. ⚠️ What you’re missing with just First-Touch Attribution: - Key touchpoints that nurture leads into actual buyers - A balanced look at which channels deliver conversions at different stages, not just leads - Strategic insights to optimize your channel mix, budget, and targeting 🔍 Enter Multi-Touch Attribution: Instead of a single snapshot, you’re capturing the entire movie. Multi-touch attribution gives credit to all touchpoints, showing how your channels and interactions work together to drive conversions. With a multi-touch approach, you can: - Identify _exactly_ which channels drive meaningful engagement at each funnel stage - Invest where it truly matters by optimizing spend across the journey, not just at the top - Refine targeting to better resonate with prospects from awareness to decision - First-Touch is only part of the story. Get the full picture with Multi-Touch, and watch your marketing (and budget!) become smarter and more impactful. #MarketingOps #RevOps #MultiTouchAttribution #ChannelOptimization

Your donor just gave $100 after clicking your #GoogleAd. So Google Ads deserve all the credit? (Maybe you stop here and report the good news if you're the person running the ads ;) Let's talk about the reality of digital impact measurement. That $100 donation? It's actually the result of: • 3 Instagram post views • 2 email newsletter opens • 1 word-of-mouth conversation • 4 website visits • And finally, that Google Ad click But most #ga4 analytics only show you the last touch out-of-box. At Whole Whale, we've analyzed millions in nonprofit donations across 100+ organizations, and here's what we know: The average donor needs 7-13 meaningful touchpoints before converting over a fix period. This is why we help nonprofits build holistic digital strategies that: ✓ Track the full donor journey ✓ Value each marketing channel appropriately ✓ Optimize based on real impact (not just last-click data) ✓ Build meaningful, long-term engagement Because when you understand true attribution, you make better decisions. And better decisions = bigger impact. Don't let the last-click fallacy sink your marketing strategy. Want to dive deeper into multi-touch attribution? Let's talk. #analytics #attribution

𝗪𝗲 𝗷𝘂𝘀𝘁 𝗰𝗹𝗼𝘀𝗲𝗱 𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗶𝗻𝗯𝗼𝘂𝗻𝗱 𝗱𝗲𝗮𝗹 𝗲𝘃𝗲𝗿—$3B+ ARR, 20,000+ employees. 𝗕𝗿𝗮𝗻𝗱 𝗸𝗲𝘆𝘄𝗼𝗿𝗱 𝗴𝗼𝘁 𝘁𝗵𝗲 𝗰𝗿𝗲𝗱𝗶𝘁, 𝗯𝘂𝘁 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵. When I saw this deal come through on Slack, I was pumped. The last touch attribution said: Brand Keyword. Most B2B companies would stop there, assume the deal came from a Google search, and pour more budget into branded keywords. But here’s the thing: that’s NOT what actually happened. 𝗪𝗵𝗲𝗻 𝗜 𝗱𝘂𝗴 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱: → 21 unidentified visitors from the account → 4 identified visitors with 10+ web visits → 5 visits to our case study page → 1,000+ LinkedIn impressions with 100+ engagements over the past year This deal wasn’t the result of one touchpoint. It was the culmination of countless interactions across multiple channels over time. 𝗬𝗲𝘁, 90% 𝗼𝗳 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝘁𝗶𝗹𝗹 𝗿𝗲𝗹𝘆 𝗼𝗻 𝗳𝗶𝗿𝘀𝘁 𝗼𝗿 𝗹𝗮𝘀𝘁 𝘁𝗼𝘂𝗰𝗵 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻. In 2025, with tighter budgets and growing pressure to deliver more with less, that’s a dangerous game. Because if you don’t see the full buyer journey, you’ll end up misallocating resources—like pumping 90% of your budget into branded keywords while ignoring the touchpoints that actually influenced the deal. Here’s the takeaway: People don’t make decisions because of one touchpoint. They make decisions because of many. The question is: do you have visibility into those touchpoints? What’s your approach to mapping the full buyer journey?

New Metric on LinkedIn Ads: “Conversions (Data-Driven Attribution)” If you’re running LinkedIn campaigns, you may have seen this option show up in the performance view: - Conversions (Data-Driven Attribution) (and similarly for Leads). This is still new, and I had to look up some info on it, but here’s what to know: What it is: This metric uses a machine-learning model to allocate credit across multiple ad touchpoints in the buyer’s journey - not just the last click or last view. What’s different / what to watch: It only appears in the performance chart view, so you may not (yet) see full campaign- or ad-level breakdowns. Because it’s giving “true contribution” rather than full touch counts, you might see lower numbers than you’re used to with “Each” or “Last-Touch” models. For longer B2B journeys (multiple ads, impressions, clicks) this gives a more realistic assessment of which ads actually drove conversion. Why you should care: You can now lean in on the metric that better reflects impact, not just activity. It lets your audit/strategy discussions shift from “how many touches” to “which touches were meaningful”. Suggested action: Continue tracking your standard conversion metrics (Each, Last Touch) for context. Add the DDA metric in your reporting and call out the difference: more realistic contribution vs broad exposure. Use this as part of the conversation: “Here’s what the model shows as true performance, here’s what our touch-path exposure looked like, so here’s where optimizations go.” Note the limitation: if you can’t break the DDA metric down by campaign or ad yet, flag that in your audit and plan for when deeper granularity becomes available. - - This is one of several new features I’ve seen rolling out lately, but they also seem to be immature or not ready for full rollout yet. I want to share this in case anyone else has any additional insights they can contribute here, or so you can check it out for yourself if it’s available! #linkedinads #newfeature #b2bmarketing

Marketers, are you still measuring email the old way? We get told email is dead, but everyone reading this has most likely read an email, logged in using it & made a purchase with it. So it's not dead, but how we judge its effectiveness hasn’t evolved fast. We’ve relied on open rates & click-through rates (CTR) — metrics that, frankly, are no longer fit for purpose. Why open rates are no longer reliable Open tracking depends on image loading, which Outlook often blocks, & Apple & Gmail preload by default. As a result, you might see machines open, not human ones. And proper visibility is vanishing with more “text-only” creatives or image-blocked environments. And CTR? It’s got its own problems Think about user intent. If a customer reads “50% off this weekend” in your subject line, they may just go straight to your site—no click needed. Even Gmail’s AI summarising content & extracting voucher codes means users engage without clicks. Email is quickly becoming a powerhouse for brand awareness, but it doesn't have the metrics to prove this. So, what should we look at? As the rest of adtech races toward incrementality, attention, and post-impression attribution, email needs to catch up. Here’s how: 1. Conversion Attribution (Beyond Last Click) Don't stop at click-based conversions. Track who received the email, & assign influence weightings to openers, clickers, & even non-clickers who later convert. This mirrors how display and social now assess "view-through" impact. 2. Frequency & Multi-Touch Engagement Did the recipient open on mobile in the morning, revisit via desktop, & convert on payday? That’s a multi-touch journey. Look at repeat site visits, device switching, & re-engagement post-send. 3. Pay Day or Trigger-Based Lift Create holdout groups and measure uplift around high-conversion moments (e.g., end-of-month). This mirrors the incrementality testing often used in paid social or programmatic, proving that email drives behaviour, not just volume. 4. Attention Metrics Use tools to estimate dwell time on emails or the time between opening& clicking. These are soft proxies for intent, similar to how platforms measure scroll depth, hover rate, and ad exposure time in other channels. 5. Site Quality Metrics Did email recipients spend longer on site, view more pages, or have higher AOVs? Your session quality tells you if email delivers high-intent traffic, something brands already monitor from Google Ads or affiliates. 6. Ask them! Simple, but powerful: survey your audience. What emails did they find valuable? Did it change their behaviour? Self-reported attribution, done well, can give you what click-tracking can’t. Email deserves more credit than. If adtech is shifting toward attention, incrementality, & deeper behaviour analysis, email should, too. Let's measure actual impact, not just opens & clicks. I bet you will discover that email isn't just for conversion but also a branding-building superpower.

Why Attribution Is Broken in Digital Marketing and What to Do Instead https://lnkd.in/gtQD2J7Z Most businesses think they understand where their sales come from. In reality, they are looking at partial data and drawing full conclusions. Attribution models were built for simpler buying journeys. Today, buyers touch multiple channels before making a decision. They read content. They search. They scroll. They compare. They leave and come back. By the time they convert, the final click gets the credit, while everything else disappears from the report. This creates bad decisions. Channels get cut that are actually doing the heavy lifting. Budgets move toward what looks good, not what works. Long term strategies are abandoned too early. The solution is not better tools. It is better thinking. Instead of asking what caused the sale, the better question is what supported the decision. At Preo Communications, attribution is viewed as a directional signal, not a scoreboard. SEO, content, email, and brand presence are treated as cumulative forces. Each touchpoint increases confidence. Each interaction reduces friction. When marketing is evaluated holistically, growth becomes clearer and decisions become smarter. If your data keeps telling conflicting stories, the problem is not confusion. It is an outdated way of measuring modern behavior.

I’m changing my mind on a pretty hard stance I’ve had for years. 😱 Influenced Pipeline. For years, I’ve run revenue performance analyses for multiple companies -- digging into their CRMs, building semi-standardized approaches, and finding performance trends at scale. And one of my hard stances was that you can’t use multi-touch or influenced models to understand what’s truly working. Because they can create the illusion something is working when it’s not. Instead, I’ve always believed you should look at: → What created the interest → What captured the interest (at the point of conversion and deal creation) But there’s always been one part that didn’t sit right with me: ✨ the messy middle ✨ Buying isn’t linear. It’s messy. And when you rely only on first/last touch, you risk cutting something that’s actually helping move the deal along. It’s the same reason I’m still such an advocate for self-reported attribution. Here's a nuance that I've always been bullish on: → You need different attribution models for different There will never be a one-size-fits-all approach here. Where I’m now interested in influence is to answer a different question: → Is this assisting in their buying journey? Example: Marketing Partners Let’s take our marketing partners for example (we ❤️ 30MPC & SellBetter!). When I look at influence now, I might use filters like: → If they were part of the registration list (we sponsor quite a few webinars/podcasts and do outreach to those registrants) → If they viewed the partner content deal room (we host templates and examples there — like our 30MPC Mutual Action Plan template) → If they had a last touch that included that partner → If they mentioned the partner in self-reported attribution Then I layer that with metrics like sign-ups, deal creation, and self-serve or sales closed-won. Am I saying a registration list or content view is equal weight to a conversion attribution? Of course not. But marketing is an ecosystem. Everything works together to create performance. So I don’t look at it to claim credit. I look at it to understand influence. To see if something is assisting the journey — and use my best judgment to spot bias. In the same way, I’ve softened my stance on “marketing influenced pipeline/revenue.” I’m not so against it anymore. It’s another data point — one that helps paint a fuller picture. Because at the end of the day, attribution and data are just tools to help us steer growth. They’ll never be 100% accurate. But they’ll always give us insight. You just have to use your best judgment and treat it like a compass, not a courtroom.

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.