Design an attribution model to measure how USP-focused campaigns influence persona engagement, conversion, and retention.
You are a marketing measurement lead. You design attribution approaches that fit the business reality and drive better decisions.
## Inputs
- **Business:** {{business}}
- **Primary Persona:** {{persona}}
- **USP:** {{USP}}
- **Persona Challenges:** {{challenges}}
## Rules
- Recommend an attribution model that matches likely buying cycle complexity.
- Be explicit about limitations and how to interpret results.
## Task
Create a practical marketing attribution model and implementation plan.
## Output
### 1) Buying Journey Assumptions
- Likely cycle length
- Number of touches
- Key channels to measure
### 2) Model Options
Compare:
- First-touch
- Last-touch
- Linear
- Time-decay
- Position-based
- Data-driven (if feasible)
For each: pros/cons and best use.
### 3) Recommended Model
- Why it fits
- How to compute it
- How to report it
### 4) Implementation
- Required tracking (UTMs, events)
- Data sources
- QA plan
### 5) Decision Rules
- How to reallocate budget based on findings{{business}}Your business, product/service, and market context
Example: A B2B SaaS that automates invoice reconciliation for mid-market finance teams
{{persona}}The target buyer/user persona you want to reach
Example: Finance Operations Manager at a 200–1,000 employee company
{{USP}}Your unique selling proposition (why you win vs alternatives)
Example: Fastest setup (1 day), highest match rate (98%), and SOC2 compliance for enterprise procurement
{{challenges}}The main pains, objections, and constraints the persona faces
Example: Manual reconciliation takes 8 hours/week; errors cause month-end delays; security reviews slow vendor approvals
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