fundamentals
What Is Multi-Touch Attribution? (And the 8 Models That Distribute Credit)
Multi-touch attribution credits multiple touchpoints for one conversion. Compare 8 models — first-touch through data-driven — and when to use each.
Why Did GA4 Remove 4 Attribution Models? (And What DDA Needs to Replace Them)
GA4 removed 4 attribution models in 2023, leaving only Data-Driven Attribution (which needs 400+ conversions to run). Here's what changed and what to do.
What's the Difference Between MTA and MMM?
Multi-touch attribution (MTA) and media mix modeling (MMM) are complementary measurement approaches. Learn when to use each, how they differ, and how to combine them for complete marketing measurement.
Measurement Maturity Map: Most Teams Report Like Level 3 & Operate at Level 1
Four-level maturity framework: Ad Hoc, Operational, Analytical, Leader. Most teams sit at Level 1 while reporting numbers as if they were at Level 3.
Why Does Server-Side Tracking Capture More Data?
Server-side tracking captures 25-40% more events than client-side JavaScript by bypassing ad blockers, browser restrictions, and network issues. Learn how it works and when to implement it.
Why Your GA4, Meta and Google Numbers Don't Match
Your Meta, Google and GA4 conversion counts never agree. Here's why they conflict, which one to trust, and how to get one deduplicated number.
Agree on the Number of Sales Before You Argue About Credit
Every platform claims credit for the same conversions, so your channel debate is built on a number nobody agreed on. Fix the count first: dedupe and identity resolution before attribution models.
What Attribution Can't Track (The Dark Funnel)
30-50% of purchase influences happen in channels attribution can't see: word of mouth, private communities, podcasts, dark social. Learn what the dark funnel is, why it matters, and how to measure the unmeasurable.
Meta Removed Your Attribution Windows. Here's What's Broken Three Months Later.
Meta killed 7-day and 28-day view-through windows on January 12, 2026. Some advertisers lost 30-40% of reported conversions overnight. Here's what to do.
Attribution for B2B: Long Sales Cycles and Multiple Decision-Makers
B2B attribution breaks when sales cycles stretch 60-180 days and 6-10 stakeholders influence the deal. Learn how to adapt attribution for enterprise sales: account-based models, marketing-sourced vs influenced, and connecting marketing to pipeline.
iPhone & iOS Tracking: What's Lost, What Still Works
Apple's privacy changes broke mobile attribution. Learn what ATT, ITP, and Private Click Measurement mean for your tracking, what data you've lost, and how to adapt your attribution strategy.
Is Your ROAS Real? How Ad Platforms Inflate Attribution by 134%
Meta over-reports conversions by 134%. Google by 18%. We compiled 792 models and peer-reviewed research into a 5-minute check you can run right now.
MTA, MMM & Lift Studies: The Triangulation Approach
No single measurement method tells the whole truth. Learn how to combine multi-touch attribution, marketing mix modeling, and incrementality testing into a unified measurement framework that's greater than the sum of its parts.
GA4 Removed Your Attribution Models. Here's What to Do Now.
Google removed first-click, linear, time-decay, and position-based from GA4. Only DDA and last-click remain—and DDA silently fails below 400 conversions. Here's what actually works instead.
Why Google Ads and GA4 Show Different Conversion Numbers
Google Ads says 85 conversions. GA4 says 62. Your CRM shows 100 actual sales. Here's why the numbers never match—and which one to trust for which decisions.
Is That Channel Incremental, or Would It Have Converted Anyway?
The channel with the best ROAS is usually the one sitting closest to the purchase, not the one causing it. Here's why proximity bias makes your winner un-scalable, and the one test that tells you the truth.
How Marketing Mix Modeling Actually Works: Adstock, Saturation, Priors
Marketing mix modeling is a regression on sales, plus four corrections that teach it how advertising behaves: adstock, saturation, a baseline, and experimental priors. Here's the machinery.
Why Your Attribution Numbers Don't Match: Fix Inputs, Not Models
Which attribution model is most accurate? None of them, in the abstract. Accuracy lives in your data, not your model. Fix capture, dedupe, and identity first.
How Much of Your Attribution Data Is Modeled vs Observed?
Your conversion column blends orders the system watched happen with orders it estimated. Coverage is the fraction that was actually observed. Below about 70%, model choice is noise.
Do Data Clean Rooms Actually Solve the Attribution Problem?
A data clean room matches your first-party data against a platform's ad events and returns only aggregated output. Here's what that fixes, what it can't, and how to use it.
How to Measure the Halo Effect Between Marketing Channels
TV and brand campaigns make your other channels convert better. That amplification lives between channels, not inside any touchpoint, which is why MTA can't see it and MMM can.
Why Your Attribution Data Credits the Wrong Channels
The 30-40% of touchpoints client-side tracking loses is not a random sample. It skews toward Safari, ad-block, and consent-refusing users, and that changes which channel wins your budget review.
Will AI Make Bad Attribution Data Worse? Yes, at Scale
AI agents are already reallocating ad budget in production. The risk isn't the agent, it's the number underneath it. Here's the bar your attribution has to clear before a machine acts on it.