
| Channel | Native caption |
|---|---|
| Tiktok | They say AI will finally tell you which channel drove the sale. It can, but only after the boring part: clean UTM tags and server-side events flowing into GA4. Then the model compares paths. Standardize your UTMs this week. (AI-assisted) |
| "AI does attribution" It can, but first: Clean UTM tags on every link. Server-side events into GA4. Then let the model compare paths. The cost is tagging discipline. #attribution #ga4 #marketinganalytics #utm #marketingops | |
| The promise: AI will finally tell you which channel drove the sale. The reality: a model can only compare paths once the data is clean. What works is getting standardized UTM tags and server-side events flowing into GA4 first, then letting the model do the comparison. The cost is the unglamorous part: tagging discipline on every link, every time. Start by standardizing your UTMs this week. | |
| X | "AI does attribution." It can, but only after clean UTM tags and server-side events flow into GA4. Then the model compares paths. The real work is tagging discipline. temerarii.xyz |
| You will hear that AI can finally tell you which channel drove the sale. It can, but only after the boring part is done: clean UTM tags and server-side events flowing into GA4. Then the model compares the paths. The cost is tagging discipline on every link. Standardize your UTMs this week. | |
| Threads | "AI does attribution" they promise. It can, but only after the unsexy part: clean UTM tags and server-side events flowing into GA4 first. Then let the model compare paths. The cost is tagging discipline, every link, every time. Standardize your UTMs this week. |
| Marketing attribution with AI, explained plainly. A model can only tell you which channel drove a sale after you set up clean UTM tags and server-side events flowing into GA4. Then it compares the paths. The real work is tagging discipline on every link. A simple first step: standardize your UTMs. | |
| Bluesky | "AI does attribution." It can, but only after clean UTM tags and server-side events flow into GA4. Then the model compares paths. The real cost is tagging discipline. |
| Youtube | Title: AI Attribution, Minus the Hype: Why Clean UTMs Come First The promise is that AI will tell you which channel drove the sale. It can, but only after you get clean UTM tags and server-side events flowing into GA4. Then the model compares the paths. We walk through the tagging discipline that makes it work, so you can standardize your UTMs and trust the result. |
| # | Layer (comp_id · still · tier) | Beat | Timecode | Motion | Logo | Audio | VO / on-screen / caption |
|---|---|---|---|---|---|---|---|
| 1 | ![]() matches intent shared field signature-3d | hook | 0–5.0s | kinetic-build | icon·wireframe | ♪ node_lock | AI will finally tell you which channel drove the sale, they promise. on-screen: "AI does attribution" |
expected on screen: white ground · dodeca hero in the shared Signal Field · Mensor leads · mark · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual markshape dodecaground whitetreatment wireframemotion kinetic-buildpower laser-lockinstrument laser-trace→"AI does attribution" | |||||||
| 2 | ![]() matches intent LogStream template | diff | 5.0–12.4s | crossfade-8f | icon·wireframe | ♪ node_lock | What works: get clean UTM tags and server-side events flowing into GA4 first, then let the model compare paths. on-screen: Tag, then model |
expected on screen: white ground · a LogStream panel over a dimmed Signal Field · Mensor leads · code · crossfade-8f · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id LogStreamvisual codeshape dodecaground whitetreatment wireframemotion crossfade-8fpower morphinstrument morph→Tag, then modelcurate codeLines, rows | |||||||
| 3 | ![]() matches intent ChecklistCard template | teach | 12.4–19.1s | kinetic-build | icon·wireframe | ♪ node_lock | The cost is the unsexy tagging discipline every link, every time. Standardize your UTMs this week. on-screen: The cost: tagging discipline |
expected on screen: white ground · a ChecklistCard panel over a dimmed Signal Field · Mensor leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape dodecaground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→The cost: tagging disciplinecurate items, nodes | |||||||
| 4 | ![]() first render · fix pending RankList templatedead_airgeneric_scene | proof | 19.1–24.1s | receipts-count | icon·wireframe | ♪ node_lock | Minus the Hype |
expected on screen: white ground · a RankList panel over a dimmed Signal Field · Mensor leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id RankListvisual receiptsshape dodecaground whitetreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Minus the Hypecurate rows, statsLabels | |||||||
| 5 | ![]() first render · fix pending shared field signature-3ddead_airgeneric_scene | resolve | 24.1–29.1s | coalescence | icon·3d-extrude | ♪ bed_out | Minus the Hype |
expected on screen: white ground · dodeca hero in the shared Signal Field · Mensor leads · coalescence · coalescence · icon·3d-extrude logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape dodecaground whitetreatment 3d-extrudemotion coalescencepower coalescenceinstrument coalescence→Minus the Hype | |||||||