Board-ready attribution surface for journey stitching, model comparison, and reviewable revenue-credit logic.
- Live target: http://attribution.kineticgain.com/
- Repo: mizcausevic-dev/dbt-multi-touch-attr
Attribution usually fails in the same way:
- teams see credit outputs but not the touch path that produced them
- finance sees influenced revenue without enough model explainability
- RevOps inherits weighting logic that cannot be challenged lane by lane
- budget conversations happen after credit has already hardened into dashboard mythology
dbt-multi-touch-attr keeps the transformation flow, journey rows, and revenue-credit allocations visible in one place so channel influence remains inspectable.
- TypeScript executive intelligence surface for attribution posture and model governance
- journey-level touch inspection next to final credit allocations
- dbt-style staging, intermediate, and mart SQL assets
- prerendered static site, JSON payloads, screenshots, and docs
dbt-multi-touch-attr turns marketing touch data, journey windows, and revenue-credit outputs into an attribution control plane. The point is not to publish another attribution chart. The point is to keep the SQL path, touch sequence, model choice, credit allocation, and commercial recommendation visible enough for Growth, RevOps, finance, and leadership to challenge the logic before budget moves.
From a SaaS go-to-market analyst lens, this is useful when channel performance is disputed, paid spend is shifting, partner influence is overstated, lifecycle touches need credit, or pipeline forecasts depend on attribution math that should be explainable. It gives teams a shared evidence surface instead of forcing every budget conversation through disconnected dashboards and spreadsheet exports.
From a SaaS value architect lens, the margin leak is budget being allocated from unreviewable credit logic. Bad attribution turns into inflated CAC narratives, weak partner payouts, overfunded channels, underfunded lifecycle programs, and board decks that imply precision the system cannot defend.
The technical proof is intentionally inspectable: dbt-style staging, intermediate, and mart SQL assets, TypeScript scoring logic, JSON outputs, prerendered pages, tests, smoke checks, and screenshot evidence. Like the broader Kinetic Gain suite, the repo converts messy operational evidence into board-readable decisions: owner, signal, model, risk, value, route, and verification stay visible together.
//model-lane/journey-credit/verification/docs
/api/dashboard/summary/api/model-lane/api/journey-credit/api/journeys/api/verification/api/sample
cd dbt-multi-touch-attr
npm install
npm run verify
npm run prerender
npm run render:assets
