How to Use Clickstream Data for Easy Attribution

For all the #DTC #marketers out there—if you’ve already built: Conversion_LC → super easy with a solid clickstream product Conversion_FC → also super easy with a solid clickstream product And platforms like Meta, Google, TikTok, etc., are already providing click + view attribution data, isn’t that good enough for most merchants to understand overlaps? For example: I create Purchase_Paid_Facebook I attribute it to Google—Google claims 12% of those AND I create Purchase_Paid_Google I also create Purchase_Organic_Google I attribute it against Facebook’s reports and get a 33% overlap These results are much cleaner than any MTA (multi-touch attribution) model, because most MTAs are just modeling (i.e., guessing) clickstream behavior. But you can already get cleaner, deterministic data directly in-platform—no modeling required. As #MCP servers enable campaign data and clickstream systems like Blotout generate ID-stitched, UTM-stitched datasets, #AI + #MCP systems will soon make legacy MTA tools obsolete. Here’s what’s coming: Simple overlap creation tools—measured directly in-platform Data pulled directly from each ad campaign platform #AI-powered visualizations of attribution and overlap Next week, we’re launching #overlap tools—i.e., how many Applovin users drove the first touch vs. the first purchase. Who brought the user to your site initially, and who actually closed the sale? You may find Applovin is a better post-click performer than expected. Holdout testing and MTA don’t help with these questions in real time. Your own 1st-party data can—and it’s unclear why #merchants and #leadgen advertisers are still relying so heavily on outdated tools. #SaaSisDead #LongLiveSaaS #EdgeComputing