Attribution In Marketing

Paid media is often the largest expense on your P&L. It’s easy to lose sight of this and chalk up the "attribution wars" (as they are known on Twitter) to a silly debate or a waste of time, but a few % improvement on your paid media efficiency might be worth millions (or tens of millions depending on the size of your business). It’s a worthwhile investment to try and cut through all the noise and to find “true north”. But we see so much confusion around the best tools and approaches. For me, it boils down to a few core principles: 1. Be skeptical of anyone who has a financial incentive to deliver good news or grow your ad spend. When I first joined Netflix and ran controlled experiments, it felt like the red pill moment in The Matrix. So much of the reporting you see out there from vendors and (some) agencies is more of an illusion than reality, with all of them taking credit when your business is doing well. 2. Search for the true *causal* effects. Incrementality is such a big buzzword now and it is often so misused that it doesn’t mean anything. Attribution is built on correlation, and has become even less reliable since ios14. Experiments establish causation by introducing a control group so you can see what would have happened anyway (think of RCTs in healthcare). Since you still need a more real time view of performance, use experiments to calibrate your day to day attribution. 3. Prioritize scientific rigor. I can’t tell you how important it is to sweat the details - there is so much you can miss if you don’t have a background in data science and statistics. Re: vendors who offer all in one solutions or claim they have solved this problem with a “magic” pixel or something, dig in. Achieving both accuracy and precision in marketing measurement is very, very hard. We work with PHD economists and world renowned professors on this stuff and even they will say that marketing measurement is extremely difficult for a whole host of reasons (economists reading this, please chime in here!). I’ve linked some more objective 3rd party resources in the comments for those just starting on the journey.

On the 10 year journey to Chubbies’ IPO, the realization that changed how we invest marketing resources was this --> Increasing ROAS * decreased * our growth. btw, I was the world’s largest ROAS (AKA Return on Ad Spend) fanboy for embarrassingly too long, but hey, my loss is your gain, so here's: 1. Three counterintuitive things I learned about ROAS 2. Two new ways to think about it 3. Three things you can do about this right now let's do it. ** Three counterintuitive things I learned about ROAS ** 1. “ROAS has been presented as a growth metric, when it’s actually anything but. In fact, ROAS is precision-engineered to keep brands small,” says Tom Roach. Chasing ROAS chases easy sales, not growth. Brand growth comes from light buyers, but focusing on high ROAS can lead to you targeting heavy buyers, therefore limiting growth. 2. ROAS is not actually a measure of *effectiveness* but how *efficiently* you achieved it. As Les Binet says: “Effectiveness first, efficiency second.” 3. Simply put, ROAS is the opposite of incrementality. ** Two new ways to think about it ** 1. It's like hiring an employee to stand just inside the entrance of your shop and tap shoppers on the back as they enter. A week later, the employee demand a raise, claiming credit for all the customers they’ve “enticed” to come in. 2. Imagine a soccer coach believing their forward is entirely responsible for every goal. As a result, in their infinite wisdom, they ditch their defense and midfield, only keeping their center forward. They end up losing every future game, but their “Goals Per Player” (the ROAS of this example) is higher than ever! ** Three things you can do about it right now ** 1. Vanity VS Value: Understand the negative externalities of the metrics we goal our teams on. For example, because many of us are seeing headwinds, brands either cut marketing spend or increase the ‘accountability’ of the dollars spent. The negative externality is that we're over-harvesting our existing customers in order to hit our numbers. ROAS and revenue from returning customers may be up (vanity metrics), but contribution dollars, share of search, and new customer revenue from unpaid sources (real business metrics) are likely down. 2. Party & Ponder: Spend half a day with your team and deeply consider the metrics you want to optimize your team’s efforts around in 2024. The whole team needs to take ownership of the metrics that matter AND have a deep understanding of the negative externalities of vanity metrics like ROAS. This is a super high-leverage use of time 3. Cultivate Creativity Completely (the 3C's of winning): Since marketing works by influencing future buyers, think about developing creative that gets noticed and gets remembered. Give your team permission to be bold, put on a show and have a little fun. As John Dawes of the Ehrenberg-Bass Institute says, “The brand that gets remembered is the brand that gets bought." Enjoy

There’s a commonly held belief among Meta advertisers that view-through conversions aren’t truly incremental to your business. However, you can’t click on a TV ad, and those conversions have proven impactful for businesses for over 70 years! By ignoring advertising viewers and focusing only on clickers, you might be missing out on a key demographic: younger users. These younger age groups convert more without clicking, leading to inaccurate attribution. It’s not a small difference. Click-based measurement undervalues younger age groups twice as much as other age groups. This is part of why Meta introduced a new engaged-view through attribution lookback. The other reason is the different user behavior with Reels ads compared to Feed ads. A recent Meta study of 15 A/B tests showed that advertisers who included Engaged-View in their attribution settings saw an average 3% lower cost-per-result compared to using Click-Through only or Click-Through + View-Through, with 95% confidence. Instead of assuming which efforts are incremental to your business, it's better to dispel long-held convictions through testing. Advertisers that ran 15 experiments in a year saw 30% higher ad performance.

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 

Most ROAS reports are just good storytelling. They look great on slides. But here’s what they rarely tell you: 1. Attribution is broken. That last click didn’t do all the work. Search brand terms? Often piggybacking on organic demand. The real drivers — brand, content, referrals — get buried. 2. ROAS hides the real costs. Returns, discounts, agency retainers, platform fees — conveniently left out of the equation. You’re not tracking profitability. You’re tracking presentation. 3. High ROAS ≠ scalable growth. A 10x ROAS on a tiny remarketing list feels great — Until you try to scale and it crumbles. Low CAC doesn’t mean you have a growth engine. Good marketing isn’t just about showing numbers. It’s about knowing what they really mean.

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 𝗮𝘁���𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻. 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?

You walked out of your house, took the bus, walked some more, took the subway, walked some more, and got to the lift. After the lift button is pressed, you took the lift to your floor and then walked to your final destination. But because the lift button was pressed, the person who pressed it for you gets the credit for getting you to your destination. This is exactly how last click attribution works. Similarly, someone sees and likes your LinkedIn post. A week later, they get re-targeted and they also see a friend liking your post. Then he Googles you and clicks on a search ad that leads them to contacting you. Click. Conversion. Was it your Google ad that did it? Perhaps it helped. Somewhat. But the truth is that the person searched for you because your brand was credible and familiar to them. Assuming that was your only conversion, a week later, you look at your search performance and lament that conversions are down. So because it was the one that brought the numbers the last time, you put more money into search in order to drive more conversions. But it’s just like the next time you take the lift, you keep telling people to press the button for you, because it got you to your destination before. You cannot measure conversions this way. You cannot give all the glory to last click attribution like this. Your branding did a lot of the work, your walking and commute, your deliberate actions, got you to your destination, but they don’t get the glory. Last click is still useful - it is simple, auditable and close to revenue - but it doesn’t tell you the full story. If you’re optimizing your budget & business on part of the story, don’t be surprised if performance and/or growth disappoints.

Click-based attribution still shows up everywhere- dashboards, quarterly reviews, campaign scorecards. This is counter-intuitive as we move towards a zero-click search behavior amongst prospective buyers. Click models like first-click, last-click, linear, and time-decay make sense at a glance because they are simple and easy to report. They map a conversion back to a tracked interaction, and that feels concrete. When the conversion path is multi-device, multi-channel, and non-linear, click models end up only showing what happened last, not what really influenced the journey. Relying too heavily on clicks can skew strategy and budgets toward lower-funnel tactics that generate measurable clicks often at the expense of brand, content, and touchpoints that shape intent long before that click ever happens. Clicks still matter but if executive dashboards lean on clicks as the foundation, they risk overlooking the parts of the journey that actually build demand, trust, and consideration. Measurement frameworks tied to real outcomes and not just clicks will provide a more useful view of how marketing drives business value. How are you balancing click metrics with broader outcome-oriented measurement in your reporting?

Your ad platform is telling you one number. The truth is somewhere else entirely. I ran the same period of data through Google Ads, Meta Ads and independent measurement tools simultaneously. The gaps were not small. Google Ads claimed 9.3x ROAS. Independent measurement showed 3.8x to 6.2x. Meta claimed 11x. Independent measurement showed 2.6x to 6.5x. And GA4 - which most brands are using as their revenue source of truth - undercounted total revenue by 27% versus Shopify. This is not a coincidence. Ad platforms are incentivised to show high ROAS so you keep spending. Their attribution windows are set to maximise credit for their own channel. The practical implication is straightforward: do not make budget allocation decisions based on in-platform ROAS. In this sample, true performance was overstated by between 50% and 330% versus independent measurement. There is a free setup that fixes this. No expensive tools required. Full breakdown in the comments.

Attribution has never been perfect, but for DTC brands, it has become significantly harder in the past few years. Apple’s iOS14 updates, third-party cookie deprecation, and increased privacy regulations have disrupted traditional attribution models. Brands that once relied on last-click attribution, ad platform reporting, or rule-based LTV calculations now face major blind spots in understanding which marketing efforts drive long-term value. Even those investing in first-party data strategies, post-purchase surveys, and media mix modeling (MMM) struggle to fully connect the dots. The reality is that data is still fragmented across multiple platforms such as Shopify, Klaviyo, Google Analytics, ad networks, and third-party analytics tools. Most solutions focus on aggregating data, but aggregation alone doesn’t tell the full story of how customers move through the funnel and what actually drives retention. Rob Markey - In his article, "Are You Undervaluing Your Customers?" published in the Harvard Business Review, Markey emphasizes the significance of measuring and managing the value of a company's customer base. He advocates for creating systems that prioritize customer relationships to drive sustainable growth. Chip Bell - Recognized as a pioneer in customer journey mapping, Bell has contributed significantly to the field of customer experience. In an interview titled "The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership," he discusses how organizations can co-create with customers to drive innovation and enhance the customer journey. So how do brands solve this? 1. Shift from static LTV models to predictive insights - Traditional LTV calculations are backward-looking, often based on averages that don’t account for future behavior. Predictive analytics, using real-time behavioral and transactional data, can provide a more accurate forecast of customer lifetime value at an individual level. 2. Invest in first-party data strategies that go beyond acquisition - Many brands have adapted to privacy changes by collecting more first-party data, but few are fully leveraging it. Loyalty programs, surveys, and on-site behavioral tracking can provide valuable insights into retention and repeat purchase drivers, helping brands reallocate spend more effectively. 3. Adopt AI-driven segmentation and customer equity scoring - RFM segmentation and standard cohort analysis have limitations. AI-powered models can help identify high-value customers earlier in their lifecycle, predict churn risk, and optimize acquisition based on true long-term value, not just early spend. Markey and Bell have long emphasized that customer loyalty isn’t built on transactions alone, it’s about the entire journey. Brands that can better understand and predict customer value will be the ones that thrive in a world where third-party tracking is no longer a reliable option. #CustomerJourney #Attribution #CustomerEquity