Attribution In Marketing

Attribution is overrated. Incrementality is what actually matters Every new-age brand wants to know what’s working. Meta ROAS is looking good. CAC is steady. Revenue is growing But here’s the truth: Your Meta ad might get the conversion. But did it cause the conversion? That’s the difference between attribution and incrementality. Most dashboards, attribution tools, and agency reports stop at attribution. But if you’re a brand selling across Amazon, Flipkart, GT, MT, Q-com, and D2C—pure attribution will always lie to you Because the sale might happen on Amazon. But it might have been nudged by a Meta video or a YouTube bumper ad 4 days ago. You don’t need a full-blown Marketing Mix Model to get started. There are simpler, street-smart ways to directionally understand what’s working—and what’s not. Here are 4 that have worked for us at Atomberg: 1. Geo Split Testing Pick two similar markets. Run campaigns in one. Don’t run in the other. Then track: • Branded search volume • Sell-through on marketplaces • Secondary sales from GT counters If the test market moves faster than the control, you’re seeing true lift. That’s incrementality. 2. First-Time Buyer Growth vs Returning Buyer Growth Track whether your growth is coming from first-time buyers or repeats. If your campaigns are just bringing back old customers—you’re not creating net new demand. But if there’s a spike in new buyers across Amazon, Flipkart, D2C—your campaigns are likely working at an incremental level 3. Paid Traffic vs Organic Trend Lines If paid traffic, clicks and spends are going up—but your organic sales or branded search isn’t moving—you’re likely just harvesting demand that already existed. But if organic lifts alongside paid—your ads are creating interest. Not just closing it. Directionally, this is one of the simplest sanity checks most teams ignore. 4. Channel Crossover + Offline Signal Mapping Your Meta ad may not show up in last-click attribution. But it might have nudged the consumer to visit your store or buy on Amazon. You can detect this through: • Post-purchase surveys (Where did you first hear about us?) • Branded search + store footfall spikes in campaign-active cities • And most powerfully—offline signals passed back to Meta At Atomberg, we pass back data from installations and warranty registrations—including pincode and purchase timelines Sometimes, we’re even able to identify this at a unique customer level through their cookies for warranty registration This has helped us understand true incrementality of perf marketing campaigns even for offline sales If you’re only measuring ROAS, you might scale what’s only taking credit for sale about to happen anyway If you chase incrementality, you’ll scale what’s working. For more details, read the full post- link in first comment.

What’s the ROI of LinkedIn? For Refine Labs, it’s $50MM in HIRO pipeline and $14MM ARR in net new closed won revenue over the past 2 years since we implemented self-reported attribution in July 2021. I think most people would agree - pretty damn good ROI. But if we measured the ROI of LinkedIn using multi-touch attribution like most B2B SaaS companies do, it only shows $977k in closed won revenue (93% lower measured ROI). And that’s why most B2B companies don’t take LinkedIn or other forms of dark social seriously, while we’ve generated tremendous ROI for 5 years straight. And that's because most B2B companies still use the same underlying principles to measure the success of Marketing & content that they did in 2013 when B2B professionals went into the office, booted up their desktop computer, and consumed blogs & PDFs - based on tracked digital touches and form fills that were easy to track on a desktop computer from a company IP address. Basically everything has changed about the internet since then - including content formats, distribution, tracking & privacy policies, rapid evolution of social media, etc. Yet the way we measure success basically hasn’t changed. There's more tech and jargon around it, but the underlying tech & principles haven't changed. In today’s World, it’s time to focus on the bigger picture. STOP trying to prove the “ROI” of each individual piece of content using touchpoint-based digital attribution. Instead, understand that the results are built through the accumulation of tons of content & touch points over a sustained period of time - most of which never get tracked by digital attribution tools. START measuring the “ROI” of each channel overall by getting direct insights from customers about what they say is working in your Marketing. -Self Reported Attribution / How did you hear about us? Automate in SF / MAP -Sales rep asks on first call, use tags in conversation intelligence tools to automate  -Execute market research surveys to ICP buyers at target accounts that are not in-market  -Conduct win/loss analysis using primary market research interviews In a World where the most impactful programs & activities don’t get tracked by digital attribution, it’s time to be customer-centric and get insights directly from the market. #demand #marketing #b2b #sales p.s. To be clear, Attribution software, Salesforce campaigns, and UTM tracking are great ways to measure Demand Capture. But are definitely not appropriate to measure the entire marketing mix across demand creation, demand capture, and demand conversion. Step 1 in unlocking the next level of growth is changing the Marketing KPIs and Attribution models that keep Marketing teams stuck in the past.

Attribution is BS. There, I said it. Despite being a past proponent of attribution, I've come to believe that it’s a lie, that we're using flawed math to make critical business decisions… and it’s hurting us. THE ASSUMPTION PROBLEM Every attribution model — first-touch, last-touch, multi-touch, AI-powered — makes fundamental assumptions about buyer behavior: ⊙ What interactions to count (and which not to) ⊙ How far back in time to look ⊙ How to weight different touchpoints But as you may have heard: when you assume, you ‘make an ass out of u and me’. B2B buying is a complex, nonlinear system. Like weather or stock markets, it has sensitive dependence on initial conditions, emergent behaviors, and feedback loops that make precise prediction impossible. Example: A junior analyst downloads your whitepaper but takes no action. Two years later, she’s a Director at a new company and her team faces the exact problem you solve. Your attribution model will never connect that original download to the eventual seven-figure contract. There are millions of examples like this. Attribution pretends buying is a tidy cause-and-effect machine. It's not. Buyers are two-thirds through their process before they engage with vendors. By then, they've often defined needs, shortlisted options, and chosen favorites. The touches that actually influence the deal — thought leadership consumed anonymously, word-of-mouth recommendations, prior experience with your brand — happen long before we can track anything. Yet we give credit to whatever campaign happens to be running when they finally fill out a form, or whichever SDR happens to call at the right moment. We're high-fiving the wrong tactics and teams entirely. THE REAL DAMAGE When teams focus on attribution credit, four things break: 1️⃣ Over-attributing success to demand starves brand and early-stage programs 2️⃣ Short-termism replaces strategic thinking 3️⃣ Marketers optimize for measurable touches instead of buyer experience 4️⃣ The sales-marketing teamwork required for complex deals breaks down I've watched companies gut brand investments because they "couldn't prove ROI" while doubling down on lead magnets that generate terrible experiences but great attribution scores. THE BETTER WAY 𝐔𝐬𝐞 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞, 𝐧𝐨𝐭 𝐩𝐫𝐨𝐯𝐞, 𝐲𝐨𝐮𝐫 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠. Don't claim your webinar ROI is exactly 114%. But you can use attribution to guide decisions, e.g. perhaps webinars seem to perform better than content syndication (at least given a set of assumptions). Focus on directional insights, not false precision. The most successful teams use shared metrics: ✅ Everyone-sourced pipeline ✅ Account progression ✅ Net revenue retention across the full customer journey Stop grading your marketing with broken math. Start guiding it with better questions. What's your take? Is attribution valuable or BS? #B2BMarketing #Attribution #MarketingOps #GoToMarket #MarketingStrategy

Head of Marketing: We're turning off all attribution tracking. CEO: Now I know you've lost it! Explain.. Head of Marketing: Hear me out. Attribution is why we're losing. CEO: We need to know what's working. Head of Marketing: We know exactly what's working. We just refuse to believe it. 73% of our closed deals touched 8+ marketing assets. Our attribution gives 100% credit to the last click - usually a brand search. CEO: So fix the attribution model. Head of Marketing: I did. Six times. Multi-touch, linear, time-decay, custom ML model. You know what happened? We spent more time debating the model than doing actual marketing. CEO: But how do we optimize spend? Head of Marketing: By talking to customers. Novel concept, right? Last week I called 20 closed deals. Not one mentioned the channel we credit. They all mentioned that one LinkedIn post from 6 months ago that made them rethink their entire workflow. CEO: The board wants numbers. Head of Marketing: The board wants revenue. Our competitor grew 300% YoY. Their attribution? "Marketing works when you do good marketing." They invest in what customers actually talk about, not what pixels claim. CEO: This is insane. Head of Marketing: You know what's insane? We killed our podcast because attribution said zero ROI. Three months later, our biggest enterprise deal told us they binged all 40 episodes before reaching out. CEO: Sales will revolt. Head of Marketing: Sales already agrees. They're tired of leads who hit 47 touchpoints but have zero intent. They want the one person who read our deep dive and is ready to buy. CEO: One quarter. That's it. Head of Marketing: Deal. But when revenue jumps 50%, I want that podcast back. CEO: We'll see. PS - The best marketers measure what matters, not what's measurable. Sometimes the most important touch is the one you'll never track. I'm Chris Cunningham - I run social media at ClickUp. Follow me for more actionable marketing tips & tricks.

Attribution dashboards don’t tell you why customers buy. They only tell you what your pixels managed to notice. Which is *not* the same thing. You’ve probably sat through this kind of presentation. Polished slides. Immaculate journey maps. Attribution percentages that miraculously total 100. MQLs cascading seamlessly into SQLs. Then the CMO lands on the final slide: “According to our multi-touch attribution model, email drives 23% of conversions, social contributes 18%, and content delivers 31% of qualified pipeline.” Everyone nods. It looks airtight. Credible. And then someone asks: “When’s the last time you talked to a customer about why they actually bought?” Crickets. I was in a marketing leadership roundtable recently, and a SaaS team was celebrating their new attribution dashboard. They’d mapped 47 touchpoints across a 180-day buyer journey. Their model showed a webinar series as the highest-converting channel. So we called five recent customers. - Customer 1: “I signed up after your CEO posted about API security on LinkedIn.” - Customer 2: “My developer recommended you.” - Customer 3: “I asked ChatGPT for solutions and you came up.” - Customer 4: “The sales rep just got our problem.” - Customer 5: “Someone in my mastermind group told me about you.” Not one mentioned the webinar. Not one followed the journey the dashboard had mapped. That’s because attribution models don’t measure customer behavior. They measure the data exhaust left behind by customer behavior. And those are very much not the same thing. Customers think in moments, not touch points. - The late-night frustration. - The colleague’s offhand comment. - The demo that finally made sense. - The competitor that dropped the ball. - The rep who actually listened. Most of those moments happen in places your tracking pixels could never reach. You can build the most sophisticated attribution model on the planet. You can layer in AI, predictive scoring, fancy dashboards. But if the inputs are incomplete, all you’ve done is build a faster, smarter way to be wrong. It’s expensive theater. Start with customer listening. I dedicated an entire chapter to it in my book ‘High-Impact Content Marketing’ because it’s *that* important. Your customers will tell you exactly why they bought. They’ll name the moments that mattered. But only if you ask them directly. Not if you expect your dashboard to tell their story. #AttributionModel #CustomerListening #ContentMarketing #CustomerInsights #hicm

Perhaps the key to fixing our perennial attribution problem in B2B isn't to focus on "marketing sourced pipeline" but rather to eliminate that metric altogether. In complex selling situations, first-touch is a joke. A mere starting point, a blip on the journey. Last-touch is icing. It's the culmination of countless other touches, activities and influences. There is no marketing-sourced or sales-sourced, there's just we-sourced. That's an inconvenient truth for those that want a cleaner dashboard. The reality is that buying journeys are extremely messy, and a body of work mentality is required by integrated go-to-market teams to lasso that behavior into any sort of predictable, repeatable pipeline. For organizations that still worship at the altar of the almighty MQL, establishing a marketing-sourced pipeline goal may feel like a step in the right direction. And absolutely the farther you take accountability deeper into the pipeline the better. Sourced doesn't matter nearly as much as velocity. Consensus-building. Commitment to change. Measured, predictable and repeatable sequencing of cross-channel and cross-team motions that drive engagement and conversion. It's messier. And it's far more effective.

Meta, Google, TikTok, and other ad channels are misleading you. Third-party attribution tools like Triple Whale and North Beam aren't better—they’re flawed too. Tracking has always relied on estimated models, not hard numbers. After iOS 14, tracking became harder, leading to a surge in third-party solutions. But these also provide conflicting data, making it tough to find the truth. So, what is the truth? The only reliable way to measure your marketing efforts is through incrementality tests. These tests answer the question, "What if this channel or ad never existed?" By showing ads to one group and withholding from another, you can measure the true impact on revenue and profit. For example, if you're running Facebook ads and selling on Shopify and Amazon, incrementality tests reveal how Facebook ads impact Amazon sales. Without the initial Facebook touchpoint, an Amazon purchase might not have happened, even though traditional attribution wouldn’t show this. This is why ROAS and third-party attribution aren’t accurate. They use models that can be thwarted by privacy settings and cross-channel purchases. By running incrementality tests, you discover the true impact of your marketing efforts. We ran a 14-day Meta holdout test and found that zip codes shown ads generated 50% more Amazon revenue than those not shown ads, despite sending traffic to Shopify. Now is the perfect time to run these tests. Q3 is calm, free from major holidays that skew results. This is your chance to optimize before Q4. If your brand generates seven figures annually, this should be a top priority to grow profits in Q4.

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?

Measurement obsession is creating significant gaps in our marketing understanding. I recently observed a company significantly reduce their paid social budget after their last-click attribution model suggested Google search was driving all meaningful conversions. The pipeline showed a marked decline as a result. The reason was straightforward: social had been priming their audience before they searched, but this connection wasn't visible in their outdated attribution model. This pattern is more common than typically acknowledged: • 64% of marketing leaders express scepticism about their tracking data reliability • 42% of the buying decision process occurs before tracking systems detect intent • The average B2B buying committee consists of 5.4 stakeholders • Only 5% of your addressable market is actively purchasing at any given time Most prospect journeys happen in unmeasurable channels: private WhatsApp conversations, Slack communities, LinkedIn DMs, and professional networks where buyers exchange perspectives. This measurement gap is particularly evident in mature B2B categories with higher annual contract values. As sale complexity increases, attribution systems capture proportionally less of the complete journey. Practical approaches to address this: • Develop content that stimulates genuine conversation, reaching your ideal customer profile before active purchase intent • Implement intent-based and behavioural signals to help your sales team prioritise meaningfully engaged prospects • Utilise brand tracking metrics such as share of voice to better understand your brand's presence prior to measurable touchpoints • Account for what you can measure while acknowledging the limitations Marketing strategies that focus exclusively on immediate, attributable ROI often miss critical engagement points. Your brand exists in unmeasured spaces—in professional conversations and prospect consideration—long before formal engagement. Balance short-term metrics with long-term brand development. This isn't about abandoning measurement but rather complementing it with a more complete view of market engagement.

Incrementality testing is crucial for evaluating the effectiveness of marketing campaigns because it helps marketers determine the true impact of their efforts. Without this testing, it's difficult to know whether observed changes in user behavior or sales were actually caused by the marketing campaign or if they would have occurred naturally. By measuring incrementality, marketers can attribute changes in key metrics directly to their campaign actions and optimize future strategies based on concrete data. In this blog written by the data scientist team from Expedia Group, a detailed guide is shared on how to measure marketing campaign incrementality through geo-testing. Geo-testing allows marketers to split regions into control and treatment groups to observe the true impact of a campaign. The guide breaks the process down into three main stages: - The first stage is pre-testing, where the team determines the appropriate geographical granularity—whether to use states, Designated Market Areas (DMAs), or zip codes. They then strategically select a subset of available regions and assign them to control and treatment groups. It's crucial to validate these selections using statistical tests to ensure that the regions are comparable and the split is sound. - The second stage is the test itself, where the marketing intervention is applied to the treatment group. During this phase, the team must closely monitor business performance, collect data, and address any issues that may arise.  - The third stage is post-test analysis. Rather than immediately measuring the campaign's lift, the team recommends waiting for a "cooldown" period to capture any delayed effects. This waiting period also allows for control and treatment groups to converge again, confirming that the campaign's impact has ended and ensuring the model hasn’t decayed. This structure helps calculate Incremental Return on Advertising spending, answering questions like “How do we measure the sales directly driven by our marketing efforts?” and “Where should we allocate future marketing spend?” The blog serves as a valuable reference for those looking for more technical insights, including software tools used in this process. #datascience #marketing #measurement #incrementality #analysis #experimentation – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWKzX8X2