Revenue Attribution Model
| Aspect | Traditional Attribution | Revenue Attribution Model |
|---|---|---|
| Focus | Clicks and touches | Incremental revenue |
| Control | Rarely used | Built-in holdout or geo split |
| Data granularity | Channel-level | Play-level with UUID |
| Proof | Correlation | Causal lift with confidence |
💡TL;DR
Revenue Attribution Model replaces vanity-channel credit with causal proof. Each play logs treatment/control, revenue impact, and confidence. For SMBs, it answers “Which automation actually made money?” enabling budget reallocation every month. It nests under the ROI Ledger but can be run lightweight with geo or time splits.
Definition
A causal-first framework that attributes incremental revenue to specific plays and signals using holdouts or geo splits, not just multi-touch click weights.
🏢What This Means for SMB Teams
Boards ask which programs to cut. A causal model shows which plays create revenue lift so SMBs can defend or double-down budgets.
📋Practical Example
B2B SaaSがメールシーケンスA/Bと地域ホールドアウトを組み合わせ。シーケンスBの増分ARR +$64k/四半期（p=0.06）。モデルが証明したため、Bに予算を2倍割当、Aを停止。CAC/ARR比が1.4→1.1に改善。
🔧Implementation Steps
- 1
Select 2-3 plays to measure; define control via holdout or geo split.
- 2
Assign UUIDs to every action and log revenue outcomes.
- 3
Compute incremental lift with confidence intervals; tag as measured or directional.
- 4
Reallocate spend monthly toward plays with proven lift; sunset non-performers.
❓Frequently Asked Questions
Do we still need MTA weights?
Use MTA for directional insights; use causal attribution for decisions. MTA can suggest candidates; holdouts confirm.
How big should holdouts be?
10-20% is typical. Smaller samples need longer durations; very small teams can rotate weekly geo splits to accumulate evidence.
⚡How Optifai Uses This
ROI Ledger ingests this model to surface play-level causal lift each week.
Self-Improving ROI Ledger📚References
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Related Terms
ROI Ledger
A ledger system that tracks every AI action with a UUID and attributes actual revenue contribution using holdout testing.
Revenue Lift
The incremental revenue increase attributed to AI actions, measured against a holdout control group that received no AI intervention.
Holdout Test for RevOps
A testing methodology where a percentage of accounts receive no AI actions (control group) to measure the true incremental revenue impact of automation.
Revenue Attribution
Methods for assigning revenue credit to marketing and sales interactions. Modern practice blends multi-touch models with causal tests to validate lift.
Experience Revenue Action in Practice
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