Marketing Attribution Models

Just because "google" shows up in attribution doesn't mean it's what driving buyers to buy. Ask "how did you hear about us" in a free-text required field upon conversion and you'll get the real stuff: -Social media (LinkedIn, Tik Tok, Reddit, Instagram, etc.) -Podcasts (owned, earned, paid) -Communities (Slack, discord, private groups, etc.) -Referrals / Word of Mouth (colleagues, friends, investors, etc.) -3rd party events (e.g. I saw your CEO speak in Belgium last summer) ^^These insights will RARELY or NEVER show up in attribution software. Most B2B companies never ask this question. And most B2B companies don't actually know what's creating their demand. #attribution #revenue #sales #marketing #b2b p.s. This is not meant to be a replacement to digital touchpoint based attribution. It's a different measurement strategy used for a different purpose - to know what buyers report as the most *impactful* touches. p.p.s. Self reported attribution is a *directional* insight that you get directly from customers. Many marketing activities will not get measured by touchpoint based digital attribution and we need another strategy to measure these - podcast, social media, connected TV, Out-of-Home (OOH), referrals, influencer marketing, word of mouth, etc. p.p.p.s. Most companies don't get value from self-reported attribution because they don't use it properly. Require it for all declared intent submissions. Copy it from the lead/contact to opportunity object. Track the results against qualified pipeline and revenue, not just "leads". p.p.p.p.s. Self-reported attribution is 1 of 6 different measurement strategies we use at Passetto to analyze the impact of all GTM Investments. A one-size fits all approach of using touchpoint based digital attribution to measure all Marketing, Sales, and SDR investments is a losing strategy.

"Google says 10 orders. Meta says 10 orders. My store only received 15 orders total." Just heard this from a frustrated D2C founder, perfectly capturing why attribution is broken. The dirty secret of performance marketing: •Most brands are unwittingly optimizing for attribution theft, not business growth. •When you scale Google while cutting Meta budgets (or vice versa), you're not seeing the complete ecosystem: •Google brand search often harvests demand created by Meta awareness •Meta remarketing often closes sales initiated by organic discovery •Last-click attribution rewards bottom-funnel tactics at the expense of growth One ecommerce brand I analyzed doubled their Google spend in a week while halving their Meta budget. ROAS looked amazing...for exactly 9 days. Then it crashed. Hard. They had cut off the very source that was creating their downstream conversions. Modern attribution requires understanding: → Channel interdependencies → Incrementality testing → True blended CAC (not platform-reported CPA) → Full-funnel visibility The brands winning today ignore platform-reported ROAS entirely. They track real business metrics: -> Actual customer acquisition cost -> New vs. returning customer ratio -> Profit per order -> True MER (marketing efficiency ratio) If you're still measuring channels in isolation, you're optimizing for platform metrics, not business outcomes. What metrics are you using to evaluate real marketing impact beyond platform-reported ROAS?

Your 50% email open rate is likely closer to 18%. 💩 We analyzed various Klaviyo campaigns and found that roughly two-thirds of the reported opens were generated by Apple Mail Privacy Protection, proxies, or security tools rather than people actually opening the email. In one campaign, Klaviyo reported a 34.36% open rate. But after filtering likely machine activity, the estimated human open rate was only 11.68%. In another campaign, Klaviyo reported a 48.44% open rate. After filtering for robotic opens, the estimated human open rate was 17.62%. In both cases, the reported open rate was inflated by nearly 3x. One frustrating detail is that Klaviyo provides a filter to exclude bot activity from reporting, which is what we used to calculate the real versus robot open rates above. However, bot activity is still included in Klaviyo’s default open-rate reporting. Maybe inflated numbers make marketers feel better. But they can also hide a real deliverability problem. A fake open does not necessarily mean a fake person. The subscriber may be completely real. They simply did not open the email. Apple, a proxy server, or a security tool opened it on their behalf. It is a fake metric, not necessarily a fake contact. So if your open rates look incredible but purchase rate, revenue per recipient, and total email revenue remain weak, do not assume your deliverability is healthy. An inflated email open rate may be masking the exact problem you need to fix.

“Blogging is dead.” // “AI killed the blog” // “No one reads blog posts” // “Google is dead” — These are some of the wild (misguided) takes flooding the feed and inboxes right now… Here’s the harsh truth though: That’s all false. The real issue is that most marketers are creating reports that aren’t connected to what matters. They’re not talking about RESULTS.. Most marketers track page views and social shares, but real ROI is about revenue impact. Here’s how to show the ROI of blogging: 1. Define What “Return” Means for You Not all blogs are designed for direct revenue. Some drive leads, some build brand authority, and others improve retention. Choose the right KPI: ✅ Lead Generation – Track blog-assisted form fills, newsletter signups, and gated content downloads. ✅ Sales Impact – Analyze closed-won deals where a blog was a touchpoint. ✅ SEO Value – Measure the cost savings from organic search traffic vs. paid traffic (organic traffic value). ✅ Customer Retention – Track whether blog readers have a higher LTV (lifetime value). 2. Content ROI Modeling: Connect Content to Business Outcomes The biggest mistake? Giving blog posts content zero credit: ➡ First-touch attribution: When a blog is the first interaction before a lead enters your CRM. ➡ Last-touch attribution: When a blog post is the final touchpoint before conversion. ➡ Multi-touch attribution: Assigns weighted value across all touchpoints, showing how blogs contribute throughout the journey. Use tools like: • Google Analytics: Event-based tracking + attribution modeling. • CRM Reports (HubSpot, Salesforce): Tie blog traffic to closed deals. • UTM Parameters: Track conversions from blog-specific campaigns. And ask: “How’d you hear about us?” 3. Lead Quality: Not Just Quantity Traffic means nothing if it doesn’t convert. • Measure Traffic-to-Lead Ratio: (Total Leads from Blog / Total Blog Traffic) x 100 • Analyze MQL to SQL Progression: Are blog leads actually converting into sales-qualified leads (SQLs)? • Check Lead Source Data: Identify high-intent pages driving conversions. 4. Revenue Per Asset: The best way to quantify blog impact? Directly assign revenue. Use CRM + analytics tools to calculate: (Total Revenue from Blog-Assisted Deals / Number of Blog Posts Published) = Revenue Per Blog Post. Example: If 10 deals closed in a quarter where a blog was a touchpoint, and those deals totaled $100K, that blog is worth $10K. 5. Is Your Blog Profitable? Calculate true content ROI using: Blog ROI = (Revenue Attributed to Blog – Blog Production Costs) / Blog Production Costs x 100 • Include writer salaries, SEO, distribution, and promotion in costs. • If a blog generates $50K in sales and costs $10K to create, ROI = 400%. The Bottom Line: Blogging isn’t just about traffic. It’s about leads, opportunities, conversion rates, and revenue impact. If you’re not optimizing for this — you’re leaving money on the table. #ContentMarketing #SEO

We just published new research on the TikTok Halo Effect and the results are hard to ignore. Most brands still measure TikTok Shop in isolation. Platform-level profitability. Did it 'work' on TikTok or not. That approach is fundamentally broken. We analysed aggregated data across TikTok Shop brands to understand what actually happens after someone discovers a product on TikTok. What we found: • TikTok Shop activity and Amazon sales show a strong correlation of ~0.86–0.87 once customer decision timing is accounted for • Amazon sales consistently rise 2–3 days after TikTok activity increases • On average, every £1 of TikTok Shop GMV is associated with ~£0.50–£0.60 of incremental Amazon revenue • TikTok is acting as a demand creation engine, not a standalone checkout channel In short: People discover on TikTok. They often convert on Amazon. And most attribution models miss this entirely. If you are judging TikTok Shop purely on same-day profitability, you are almost certainly underestimating its true impact. We published the full research here 👇 https://lnkd.in/ezWP3j6y This is exactly why cross-channel measurement matters in discovery-led commerce. Would be curious to hear how others are currently measuring TikTok’s downstream impact.

𝑯𝒐𝒘 𝑻𝒐𝒅𝒂𝒚’𝒔 𝑻𝒐𝒑 𝑪𝒐𝒏𝒔𝒖𝒎𝒆𝒓 𝑩𝒓𝒂𝒏𝒅𝒔 𝑺𝒖𝒄𝒉 𝒂𝒔 𝑵𝒆𝒕𝒇𝒍𝒊𝒙, 𝑨𝒎𝒂𝒛𝒐𝒏, 𝒂𝒏𝒅 𝑨𝒊𝒓𝒃𝒏𝒃 𝑴𝒆𝒂𝒔𝒖𝒓𝒆 𝑴𝒂𝒓𝒌𝒆𝒕𝒊𝒏𝒈’𝒔 𝑰𝒎𝒑𝒂𝒄𝒕 (65+ 𝑪𝒂𝒔𝒆 𝑺𝒕𝒖𝒅𝒊𝒆𝒔) I had a client for whom we simultaneously ran an offline PR campaign and a Facebook marketing campaign. They spent £50k on the former and afterwards, we ran some experiments to test effectiveness. Here are the results: → PR campaign CAC: ~£450 → Facebook CAC: £18 It shows the importance of measuring impact in both traditional marketing and growth hacking. Michael Kaminsky (Co-founder of marketing analytics firm, Recast) and Mike Taylor (founder of training platform, Vexpower) recently guested on Lenny’s Newsletter, explaining how today’s top brands measure marketing attribution and incrementality through 65+ case studies. Links to Lenny’s Newsletter and the database are in the comments. Understanding these 3 techniques will help you avoid the costly mistake made by my client: 🔸 Digital Tracking/Multi-touch Attribution (MTA) Benefits: > Leverage the data you’re accumulating daily > Easy to implement Drawbacks: Attribution can lead to a bun fight over resources when one channel appears better than another. Airbnb reminds us that MTA shows what your customer journey looks like today – indicating the touchpoints for improvement, so you can adjust your marketing mix. 🔸 Marketing Mix Modelling (MMM) Benefits: > Uses aggregated data, so no privacy concerns > Facilitates measurement of offline/traditional marketing campaigns Drawbacks: Historically, MMM is expensive to implement and slow to produce results, but Uber is just one of the companies returning to MMM in light of the impending cookie switch-off and Apple IDFA changes. 🔸 Testing/Conversion Lift Studies (CLS) Benefits: > More accurate measurement than click-through rates > Can be tailored to specific campaigns, making it ideal to measure growth hacking experiments Drawbacks: CLS can be impractical or cost-prohibitive to set up. Geography is quite often the go-to, allowing brands like Netflix to test the impact of their billboard campaigns, for example. I’ve said it before, “no amount of data is enough data”, which is why these measurement techniques should be used in combination. I’m pleased to see that Kaminsky and Taylor and 40% of their sample population are in agreement, including Mcdonald's, who used CLS to validate their MMM model. Measurement is only part of the equation. You need to interpret the results for them to have any utility. I recommend a weekly sprint approach for monitoring experimentation results, which can be adapted to impact measurement: → Review performance against your benchmark/hypothesis → Are the results and benchmarks aligned? If not, why not? → Action any lessons learned and repeat How do you measure impact? 👇 #marketing #growthhacking #impact 

Many traditional methods for measuring the effectiveness of advertising, such as multi-touch attribution (MTA), are becoming less effective due to stricter privacy regulations and the phasing out of third-party cookies. 👉 To fill this gap, geo-tests are reliable tools that can be used to measure "incrementality," which is the core concept in advertising impact assessment. Geo-tests can help answer a fundamental question: How much of your key performance indicators (KPIs) can be attributed to your advertising efforts, and how much would have been achieved without them? With geo-tests, you can differentiate between the audience that was exposed to your ads and the audience that would have acted the same way without them. The best part is that geo-tests can be run on both digital and offline mediums, such as social media, paid ads, TV, radio, OOH, mail, etc. For instance, a women's lifestyle and personal care brand based in Texas was experiencing stagnant growth and struggling to quantify the real impact of its diverse media channels and tactics on the business's bottom line. The brand opted to conduct geo-tests and understand the true revenue drivers for their brand. The result was a 3.1x uplift in their marketing efficiency with Lifesight | Unified Marketing Measurement Platform. #measurement #geoexperiments #marketinganalytics

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.

I've spent $100M+ on Meta in DTC space And I use 3 attribution models: Ad platforms are notorious for taking credit for view-through conversions they didn't drive. They do it to bait you into spending more. The issue is that your top 1-2% of ads should drive ~50% of your spend and revenue. If you're relying on bad attribution, you won’t be able to find them. This is why 8-9 figure brands (that NEED their tracking to be faultless), use 3 attribution models: 1. Multi-touch attribution (MTA) - for ad and campaign level optimization. This is your Triple Whale or Northbeam. Great for knowing which ads are performing best, which ones to scale, which to cut. Not as good for comparing channel to channel. It also will overcount total revenue, which you need to be careful about. To make sure your account is well optimized, plot CPA vs Spend on a scatter plot. The top ads should be in the low CPA, high spend zone. 2. Post-purchase survey - for channel level allocation. Get a 35%+ response rate, extrapolate to all new customers, and calculate your cost per new customer response per channel. This tells you which channel to push into. Click-based attribution overvalues lower-funnel performance by up to 250%. Post-purchase surveys catch what click attribution misses - top-of-funnel creative can drive 13X more incremental acquisitions than bottom-of-funnel. 3. Marketing Mix Model (MMM) - for validating direction. You can't use this daily, but it confirms your post-purchase survey is sending you the right way. Then you use post-purchase on a daily basis to optimize channel allocation. Some channels drive low-quality customers that look good on ROAS but don't stick around. MMM helps you optimize for 12-month profit as opposed to just immediate return. The other thing to know is that view-through attribution is poor signal. Make sure your attribution is set up for 7 or 14 day click, depending on your purchase funnel. One day view will overcount. Here's what this gives you: When performance drops, you know exactly where to pull budget to create the smallest impact on revenue while keeping the company profitable. When things are going well, you know exactly where to push budget to scale effectively. Bottom line: -> Use MTA for ads and campaigns. -> Use post-purchase surveys for channel allocation. -> Use MMM to validate you're heading the right direction. This is how 8-9 figure brands figure out where every dollar should go.

Reminder: Attribution is tempting and easy to calculate. The numbers look impressive. But noticing a touchpoint does not mean it influenced a consumer. This issue becomes even more problematic when each platform conducts its own attribution, counting only its own or some selected touchpoints to claim credit. However, the biggest problem is actually that there is no true counterfactual. For attribution models, we don't know who would have converted anyway or was influenced by other channels. In case of last-touch or click-based attribution, we don't even know what happened to those who saw ads but did not buy (or click on) our product. A little fun test to illustrate these points - check what your attribution solution would tell you if you ran only blank ads across all channels (still creating viewable impressions). If you just count arbitrary touchpoints, you will always get a positive result - and that's the problem. Attribution models may create an illusion that our ads 'work', when in reality our brand is not growing at all and we are just wasting ad dollars. What we need is insights into incremental conversions, both long and short term. And that's why marketers should embrace a combination of experimentation (geo-experiments and randomised controlled trials=RCT) and Marketing Mix Models (MMM). Image credit (an all time classic): Tom Fishburne https://lnkd.in/ggmEN9gp