Data-Driven Attribution Systems

Look at these 5 leads. If you ask HubSpot, they came from Direct, Paid Search or Organic Search. If you ask the lead, all 5 said "ChatGPT". This is the new attribution reality, and here's how to get ready for it in less than 24 hours. The user journey has changed. Someone asks ChatGPT for a recommendation, gets your brand mentioned, then opens Google and types your name. Or goes directly to your site. Click-based attribution (HubSpot, GA4, whatever you use) gives AI search zero credit. It shows up as Direct traffic. Or Paid. Or Organic. Never as "ChatGPT recommended us." We see this across a lot of our B2B SaaS clients. But most marketing execs still make budget decisions based on incomplete click data. ____________ Here's the attribution setup that we set up for our clients. It's based on 3 layers, and yes, none of them alone gives you the full picture. You have to triangulate to make sense of them. # Layer 1: Click-based attribution (what you probably already have) This is your HubSpot, GA4, or CRM data based on cookies, UTMs, and referrer information. It tells you what the last (or first) touchpoint was. It's useful but increasingly unreliable for discovery channels. Keep it, but stop treating it as the only truth. # Layer 2: Self-reported attribution (the missing piece for most companies) Add a field to your signup, demo request, or contact form that asks "How did you hear about us?" Best option: mandatory free-text field. People write things like "ChatGPT", "podcast", "colleague mentioned you." This is the highest quality data. The only reason why we didn't use it a couple of years back was how hard it is to analyze. But now we have LLMs. If your team still pushes back on that, here's the priority order: → Mandatory free-text field (best data quality) → Mandatory dropdown (limits what people can tell you) → Optional free-text field (decent, lower response rates) → Optional dropdown (better than nothing) This takes 30 minutes max to implement on any form tool. # Layer 3: Verbal attribution from your sales team Your SDRs and AEs just have to ask people where they found you. "I asked ChatGPT and your name came up." "I saw you recommended on Perplexity." Most of this intel dies in the call and never makes it into your CRM. Fix this by having a custom field (either free-text or dropdown) in your CRM that sales sets. If that's not possible, have sales share what they hear in a Slack channel with marketing. You might wonder why you need verbal attribution on top of self-reported. Because sometimes people just write ":)" in the form field. Trust me, I've seen it. ____________ These 3 layers give you an attribution picture that's 10x more accurate than what 90% of B2B companies are currently working with. Companies that understand where leads are actually coming from invest with confidence. The rest will keep optimizing for channels that get credit but didn't do the work. What's your current setup?

Marketers are selling themselves short if they rely on pixel attribution alone for CTV. For one recent CTV campaign, we worked with our client’s CRM analytics partner, Fueled, to match users who were served ad impressions against those that had converted on the website. The point was to see how many purchases could be tied back to CTV impressions, so as to not solely rely on pixel based DSP reporting as the source of truth. Over the course of 30 days, the campaign recorded 2,482 attributed unique hompepage visitors via pixel tracking, but 8,777 verified visitors through CRM analysis...nearly a 4x difference! At checkout completion, pixels logged 109 conversions, while CRM-verified data identified 1,252 actual purchasers. That means over 90% of real sales were never credited in pixel-based attribution! Why the gap? Because CTV introduces a fundamental shift in how attribution works. People see an ad on a connected TV but complete their purchase later on a different device, their phone, tablet, or laptop. Pixels were originally designed to measure direct, same-device activity against which both the impression and conversion occurred. While most platforms now use cross-device graphs to bridge that gap, those graphs rely on probabilistic modeling and partial identifiers. Their accuracy is often overstated, and they can’t compensate for the scale of signal loss we’re seeing today. Compounding this are modern privacy dynamics: browsers like Brave and Firefox block tracking scripts, iOS strips campaign parameters off URLs, and many users exit before a “thank you” page fires a conversion event. Each of these weakens the connection between ad exposure and the eventual sale. As James Borow recently said "pixels are for targeting, not measurement". That’s why Conversion APIs (CAPIs) have become critical. Instead of depending on browser-side events, CAPIs send verified conversion data directly from the advertiser’s server to the media platform’s server, bypassing browsers entirely. Each transaction is transmitted with hashed identifiers, email, phone, or customer ID, enabling privacy-safe reconciliation between ad impressions and downstream purchases. Platforms like Meta, Shopify, Google, and The Trade Desk now treat CAPIs as the backbone of modern attribution. For CTV in particular, where conversions don’t happen on the same device, server-to-server data exchange restores visibility and gives marketers a true view of how their media performs across screens. Big thanks to Fueled and founder Sean Larkin for partnering with us on this initiative, and exciting to see Fueled’s new CAPI integration with The Trade Desk rolling out this week.

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.

Friday morning client call. Their head of growth asked why I wanted to cut their "best performing" ASC campaigns. The Facebook dashboard showed ASC driving 70% better efficiency than manual campaigns. Every metric screamed "winner." But here's what happened when we ran marketing mix modeling on their data. Those same ASC campaigns? Actually less efficient at driving incremental sales. The manual campaigns were quietly outperforming by 20-30% in true business impact. The problem isn't ASC itself. It's running multiple ASC campaigns across different ad accounts for competing products. Each campaign claims credit for the same purchase. Platform attribution becomes a hall of mirrors. Health brands face this constantly. Someone searches for "gut health" but buys a "weight management" product. Six different ASC campaigns take credit. Dashboard metrics look fantastic. Real incrementality gets cannibalized. Most media buyers optimize for what platforms show them. Smart ones optimize for what actually moves business metrics. The difference? We've seen 40% gaps between attributed efficiency and incremental efficiency. That's not measurement error. That's real money flowing to the wrong campaigns. Before your next budget allocation meeting, ask this: → Are you scaling campaigns that look good or campaigns that actually work? Platform dashboards tell you what happened. MMM tells you what you caused. Have you noticed similar gaps between your attribution and incrementality data?

I’ve mapped out the framework I use to build a custom multi-touch attribution system in HubSpot. 1. The Data Layer (Custom Objects) HubSpot's native Campaigns tool isn't robust enough to support the level of granularity most companies need from multi-touch. By using 3 Custom Objects for Marketing Campaigns, Channel Campaigns, and individual Touchpoints, you create a structured hierarchy. This allows you to track spend and ROI at both the macro and granular levels. 2. The Operational Layer (Workflows) Automation handles the heavy lifting so you can focus on launching campaigns. Campaign Initialization: Select your channels and have a workflow generate Channel Campaign records and unique UTM tracking links automatically. Touchpoint Capture: Real-time triggers (like form fills) that create Touchpoint records and pull in UTM data (or anything else you might want to capture). Association: Automate the essential link between those Touchpoints and the Deals they influenced. 3. The Results Layer (Reporting) By associating touchpoints with deals, you can calculate: Influenced Revenue: Total deal value touched by a campaign. Attributed Revenue: A slice of the pie based on the number of touchpoints associated. ROI: How campaign spend compares to attributed revenue Stop guessing which channels are driving growth and start measuring the actual influence of every touchpoint. Want to master this framework? I cover it and several others in Attribution Academy's Mastery Certification Course. Sign up here: https://lnkd.in/erY2QhA3

Dropping part 2 of our internal training series on Marketing Attribution Models. 25+ pages. High-level and conceptual. I created this for our data and engineering teams who need to understand the measurement concepts behind the data they're modeling. Download it. Save it. Share it w/ your team. Google Link is in the comments. ----- Why this matters: Attribution tells you who touched a customer before they converted. It does NOT tell you what caused the sale. That's a different question entirely. Most people treat attribution reports like truth. They're not. They're model outputs based on rules someone picked. ----- The core point: Attribution is just math that assigns credit to channels. Last-click, first-click, linear, time-decay, position-based, data-driven. Same customer journey run through six models = six different top performers. None of them are wrong. None of them are right. They're just different views of the same data. ----- What's covered: Foundations - What attribution actually is (and isn't) - Where attribution models live: GA4, ad platforms, MTA vendors Model types - Last-click, first-click, linear, time-decay, position-based, data-driven - Same journey showing 6 different credit assignments Click vs view - Why this matters more than people think - View-through inflation (platforms love it) Where it breaks - Non-digital touchpoints - Cross-device journeys - Logged-out users - Attribution windows Platform attribution - Why every platform grades its own homework - Adding up platform revenue = 3x your actual revenue When to use which model - Different models answer different questions - Common mistakes that lead to bad budget decisions Engineer context - GA4 clickstream data structure - Key attribution fields in BigQuery ----- We don't talk about attribution much at Power Digital. We lean heavily on incrementality-based measurement for strategic decisions. But attribution still matters for day-to-day optimization and real-time use cases. And if you're building data models that touch marketing performance, you need to understand how credit gets assigned. This pairs w/ the incrementality guide we dropped last week. Together they cover: who touched the customer (attribution) vs. what actually caused the sale (incrementality). What's your team's biggest attribution headache? #attribution #marketinganalytics #measure

If your Amazon agency isn't leveraging Amazon Marketing Cloud (AMC), they are managing your brand using only 10% of the available map. Standard Amazon Advertising reports show you the last click. AMC shows you the entire battlefield. At the enterprise level, relying on standard reports to attribute sales is like a lawyer trying to win a case while ignoring half the evidence. It’s incomplete, and it’s costing you money. To scale an 8- or 9-figure brand, you have to move beyond Last-Touch Attribution and adopt a surgical view of the customer journey. We use AMC to solve for three critical blind spots: 1. The Multi-Touch Reality AMC allows us to see exactly how your DSP top-of-funnel awareness is actually fueling your bottom-of-funnel PPC conversions. Without this, you’re likely cutting underperforming ads that are actually driving your most profitable sales. 2. Gateway ASIN Identification We use data to find the specific products that serve as the entry point for new-to-brand customers. Once identified, we shift budget aggressively to these "Gateway" items to maximize Lifetime Value (LTV). 3. Frequency Capping Logic Most brands over-saturate their existing customers, wasting ad spend on people who would have bought anyway. AMC lets us identify the point of diminishing returns so we can cap impressions and pivot that spend toward acquisition. The logic is simple: If your data is siloed, your strategy is fragmented. If X (Cross-channel data) is missing from your decision-making, then Y (Maximum Efficiency) is impossible to achieve. We aren't interested in platform averages We are interested in unit economics and the total path to purchase Is your agency still bragging about ROAS based on last-click attribution, or are they showing you the macro-level view of your entire customer journey? Stop guessing. Start using the full data set.

𝗧𝗵𝗲 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝗻 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 In B2B SaaS, where the buyer's journey is long and complex, understanding what truly drives revenue and pipeline can feel like solving a Rubik's Cube. For CXOs and finance leaders, one of the biggest headaches is attributing revenue directly to specific deals. The stakes are high—misattributed data leads to misguided decisions and inefficient spending. Here are some emerging trends reshaping the attribution landscape: 👉 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀: Tools like Factors.ai use advanced algorithms to analyze customer journeys, ensuring no touchpoint is overlooked. These tools provide actionable insights into what’s moving the needle in your pipeline. 👉 𝗠𝘂𝗹𝘁𝗶-𝗧𝗼𝘂𝗰𝗵 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 (𝗠𝗧𝗔): Gone are the days of “last-click wins.” Multi-touch attribution models distribute credit across all touchpoints, offering a holistic view of your marketing efforts. 👉 𝗦𝗲𝗹𝗳-𝗥𝗲𝗽𝗼𝗿𝘁𝗲𝗱 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻: Let’s face it—digital tracking can’t catch everything. Asking customers directly, “How did you hear about us?” is becoming a crucial layer to complement digital data. 👉 𝗙𝗶𝗿𝘀𝘁-𝗣𝗮𝗿𝘁𝘆 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗞𝗶𝗻𝗴: Privacy concerns and tightening regulations are driving a shift to first-party data. This enhances both compliance and the accuracy of your attribution efforts. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗖𝗫𝗢𝘀 𝗮𝗻𝗱 𝗙𝗶𝗻𝗮𝗻𝗰𝗲: Revenue leaders want to know which campaigns are driving high-value deals. Finance teams need precise attribution for budgeting and ROI analysis. Without clarity, decisions are based on gut feelings rather than data, leading to inefficiencies and missed opportunities. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹: While there’s no one-size-fits-all approach, consider: Linear Attribution: Distributes equal credit to all touchpoints—a balanced starting point. Time-Decay Attribution: Gives more weight to recent interactions—ideal for long sales cycles. Custom Models: Tailored to your unique customer journey using insights from tools like FactorsAI. 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲—𝗶𝘁’𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝘀𝘀𝘂𝗲. 𝗧𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗱𝗼𝘁𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝘀𝗽𝗲𝗻𝗱 𝗮𝗻𝗱 𝗰𝗹𝗼𝘀𝗲𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲, 𝗲𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗮𝘁 𝗲𝘃𝗲𝗿𝘆 𝗹𝗲𝘃𝗲𝗹. #B2BSaaS #MarketingAttribution #RevenueGrowth #CXOInsights #DigitalMarketing

We use marketing mix models (MMM) or multi-touch attribution (MTA) to get valuable guidance for our media planning and budgeting. But when are we really 'data-driven' versus just creating the results we want to see? Many market solutions, particularly for MMM, only 'work' if we fix the model from the beginning. This happens through the use of strong priors (in so-called Bayesian models) or other constrained optimisation. In other words, our data-driven solution is not really data-driven anymore. Sometimes, small fixes can be useful and sensible. We can learn from existing theory and past studies, particularly when data is limited. But everything needs to be in balance. In other words: 1️⃣ How useful is a model that may 'go off track' unless there is a lot of manual adjustment? 2️⃣ If the model can only move in one direction, is the outcome already predetermined? Always ask your MMM provider, whether a vendor or in-house team, how much they intervened and set certain values to arrive at the results. Request more than one model (varying the number of 'fixed' parameters) to see whether the results hold. And if you're completely uncertain about a finding, test and confirm it with some experiment.

Most teams treat attribution like it’s the ultimate answer. It’s not. It’s a lens - one of several you should be looking through. The goal isn’t to debate models.  It’s to make better decisions, faster. In fact, the smartest teams use multiple models, Each with a different edge - to triangulate the truth. Here’s how we think about them: 1️⃣ Need one clear, quick source of truth? → Last‑click or first‑click (UTM‑based) Instant, dead‑simple, good for daily pulse checks. Just remember: it’s a keyhole view, not the whole picture. Far from it. 2️⃣ Need fast feedback on hard‑to‑track channels to expand your investment? → MTA / DDA / model‑based (Shapley value) Uses impression or click‑level data to credit every touch. Select the right approach to minimize collection lift while maximizing insight value. Perfect for tuning display, native, paid social, sometimes YouTube. 3️⃣ Juggling a complex media mix or big budget allocation calls? → Marketing Mix Modeling (MMM) Econometric model across TV, offline, brand, and digital. Takes time to build, but reveals marginal ROI and new pockets of efficiency. Attribution isn’t the only answer. It’s a set of tools. Use them in combination with incrementality tests, To validate insights and calibrate the models further. Choose based on the decision you need to make - not the theory you like best. At Violet Growth, we don’t just build attribution models. We design decision systems that show what’s moving, why, and what to do next. Which attribution tools are in your mix? Let’s compare ⬇️ * * * DM me if you want to explore how you can spend your next growth dollar for the highest return.