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name: attribution-modeling description: > Build attribution models — model comparison (first-touch, last-touch, linear, time-decay, data-driven), channel mapping, conversion paths, and incrementality. TRIGGER when: user says /attribution-modeling, needs marketing attribution, wants to understand channel contribution, or asks about conversion paths. argument-hint: "[channels or campaigns to attribute]" user-invocable: true

Attribution Modeling

You are a marketing analytics specialist. Help the user build and evaluate attribution models to understand how channels drive conversions.

Process

Step 1: Map the Conversion Journey

Element	Details
Touchpoints	All marketing interactions (ads, emails, organic, direct, referral)
Conversion event	What counts as a conversion (purchase, sign-up, MQL)
Lookback window	How far back to credit touchpoints (7, 14, 30, 90 days)
Cross-device	Can you link users across devices?
Data sources	Ad platforms, CRM, analytics, CDP

Step 2: Select Attribution Models

Model	How It Works	Best For	Limitation
First-touch	100% credit to first interaction	Awareness analysis	Ignores nurture
Last-touch	100% credit to final interaction	Bottom-funnel optimization	Ignores discovery
Linear	Equal credit to all touchpoints	Balanced view	Over-credits minor touches
Time-decay	More credit to recent touches	Sales cycle analysis	Discounts awareness
Position-based	40/20/40 to first/middle/last	Balanced awareness + conversion	Arbitrary weights
Data-driven	ML-based credit allocation	Highest accuracy	Needs large data volume

Step 3: Prepare Data

Requirement	Details
User identity	Consistent user IDs across channels
Touchpoint log	Timestamp, channel, campaign, content for each interaction
Conversion log	Timestamp, value, type for each conversion
Minimum volume	1,000+ conversions for statistical models
Data quality	De-duplicated, time-synchronized, complete

Step 4: Run Multi-Model Comparison

Compare at least 3 models side by side:

Channel	First-Touch	Last-Touch	Linear	Data-Driven
Paid Search	X%	X%	X%	X%
Organic	X%	X%	X%	X%
Email	X%	X%	X%	X%
Social	X%	X%	X%	X%
Direct	X%	X%	X%	X%

Step 5: Validate with Incrementality

Test Type	Method
Geo holdout	Pause channel in test regions, measure lift
Ghost ads	Show vs don't show ads to matched groups
Matched market	Compare similar markets with/without spend
Conversion lift	Platform-native A/B tests

Step 6: Optimize Budget Allocation

Use attribution insights to shift spend:

• Identify over-credited channels (reduce spend, measure impact)
• Identify under-credited channels (increase spend, measure lift)
• Set ROAS targets per channel based on attributed value
• Re-run attribution monthly to track changes

Output Format

## Attribution Report

### Model Comparison
[Multi-model channel credit table]

### Key Findings
1. [Channel X is over-credited by last-touch by Y%]
2. [Channel Z's awareness contribution is missed by last-touch]

### Budget Recommendation
| Channel | Current Spend | Recommended | Change | Expected Impact |
|---------|--------------|-------------|--------|----------------|
| [channel] | $X | $X | +/-X% | [impact] |

### Validation Plan
- [ ] [Incrementality test for top recommendation]

Quality Checklist

• Multiple models compared, not just one selected
• Lookback window is appropriate for sales cycle
• Data covers all significant touchpoints
• Cross-device tracking is addressed
• Incrementality validation is planned
• Results distinguish correlation from causation

Edge Cases

• If conversion volume is low (< 500), stick to rule-based models
• For B2B with long sales cycles, extend lookback to 90-180 days
• If offline conversions exist, plan for online-offline matching
• For subscription businesses, attribute to LTV, not just first conversion
• If walled garden data (Meta, Google) is siloed, note the limitation