# Calculate Influencer Campaign ROI

Campaign teams often lack a consistent way to connect influencer spend with revenue, media value, and customer outcomes. This skill applies transparent formulas and records assumptions, evidence gaps, comparisons, and practical recommendations.

## Install

```bash
npx skillstore add aaron-he-zhu/roi-calculator
```

## Metadata

- Status: approved
- Slug: aaron-he-zhu-roi-calculator
- Version: 17.0.0
- Author version: 17.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: 67e28a0c0c96693d949d5a303478be81b3982ad0310a7437e3be54c8bc34e30c
- Author: aaron-he-zhu
- GitHub username: aaron-he-zhu
- License: Apache-2.0
- Repository: https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/influencer/measure/roi-calculator
- Ref: d71c7417a35d5c2624161bd2fe8de8a41a362128
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, filesystem, network
- Quality score: 77
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/aaron-he-zhu-roi-calculator
- Manifest: https://skillstore.pages.dev/api/skills/aaron-he-zhu-roi-calculator/manifest

## Capabilities

- Calculates profit, direct ROI, and ROAS from supplied investment and revenue.
- Estimates impression-based and engagement-based earned media value with declared rates.
- Calculates CPM, CPR, CPE, CPV, CPC, CPA, and CAC when required inputs exist.
- Compares first-touch, last-touch, linear, time-decay, and position-based attribution scenarios.
- Projects lifetime value impact and ranks returns by influencer or influencer tier.
- Produces a concise summary with formulas, assumptions, evidence gaps, and recommendations.

## Use Cases

- Review Campaign Profitability: Calculate direct ROI, ROAS, profit, and cost efficiency after an influencer campaign.
- Defend Marketing Investment: Present return methods, assumptions, evidence gaps, and recommendations for a budget discussion.
- Compare Creator Value: Rank creators and tiers by revenue return, budget share, and investment efficiency.

## Prompt Templates

### Calculate Direct Return

```
Calculate direct ROI and ROAS for [campaign]. Spend was [amount], attributed revenue was [amount], and the reporting period was [dates]. Show every formula.
```

### Measure Media Efficiency

```
Calculate CPM, CPE, CPC, CPA, and EMV for [campaign]. Use these results: [metrics]. Use only these declared comparison rates: [rates and sources].
```

### Compare Attribution Models

```
Model campaign revenue using first-touch, last-touch, linear, time-decay, and position-based attribution. Use this journey data: [touchpoints]. Explain the recommended model.
```

### Build a Complete ROI Assessment

```
Analyze [campaign] using direct ROI, EMV, attribution, LTV, and creator-level returns. Mark missing evidence and incompatible benchmarks. Provide three decision-ready recommendations.
```

## Limitations

- Results depend on the accuracy and attribution quality of supplied campaign data.
- Earned media value and lifetime value outputs are estimates, not guaranteed financial returns.
- External benchmarks require compatible sources, dates, markets, windows, currencies, and attribution definitions.
- Typed C3 scoring requires trusted repository scripts and related skill outputs that may be unavailable.

## Best Practices

- Include every campaign cost and state the currency, reporting window, and attribution basis.
- Provide source-dated comparison targets with compatible markets, cohorts, and cost definitions.
- Separate direct revenue, estimated media value, and projected lifetime value in every conclusion.

## Anti Patterns

- Do not combine earned media value with direct revenue as if both were collected cash.
- Do not claim benchmark outperformance when the source or comparison basis is missing.
- Do not project lifetime value without documented customer counts, value assumptions, and time horizons.

## Security Audit

- Audited at: 2026-07-12T13:51:34.4\+00:00
- Summary: Most static findings are false positives caused by Markdown links, code fences, placeholders, and ordinary marketing terms. Three findings confirm one underlying behavior: the skill uses shell substitution and invokes a repository-local Python scoring script.

## Stats

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- Downloads: 6
- Favorites: 0
- Popularity score: 0