Roi Calculator

ROI Calculator

This skill helps you calculate and communicate the return on investment for influencer marketing campaigns using various methodologies appropriate for your goals and available data.

Quick Start

Shortest invocation:

Calculate ROI for our influencer campaign: $25K spend, $72K revenue, 2.1M reach

Common scenario — compare methods before reporting:

What's the ROI of our campaign using direct revenue, EMV, and LTV-based methods?

Skill Contract

• Reads: campaign spend breakdown, results data (reach, impressions, engagements, clicks, conversions, revenue, new customers), AOV and repeat-rate data if LTV is in scope, any prior performance output from performance-analyzer.
• Writes: ROI calculation file at memory/influencer/roi-calculator/YYYY-MM-DD-.md containing direct ROI/ROAS, EMV, cost-efficiency metrics, attribution-modeled revenue, LTV-based ROI, by-influencer ROI, and a summary report block.
• Promotes: durable headline numbers (final ROI %, ROAS, total investment, net profit, recommended attribution model) to memory/hot-cache.md.
• Done when:

1. At least one ROI methodology is computed with the inputs and formula shown.
2. Each headline metric is stated against a benchmark with a pass/fail status.
3. A bottom-line assessment (profitable / break-even / loss) and 1-3 recommendations are written.

• Primary next skill: [report-generator](../../track/report-generator/SKILL.md)

Handoff Summary

> Emit the standard shape from [skill-contract.md §Handoff Summary Format](../../references/skill-contract.md).

Data Sources

This family is Tier 1 — it works with no live integrations. Ask the user for spend and results data and compute everything from those inputs. Connectors below can pull the numbers automatically when available:

• ~~social platform analytics — reach, impressions, engagements, video views per platform for EMV and cost-per-metric math.
• ~~ecommerce / analytics — revenue, conversions, link clicks, and AOV for direct ROI and attribution.
• ~~CRM — new-customer counts, repeat-purchase rate, and lifetime value for LTV-based ROI.
• ~~influencer database — per-influencer fees and tier data for by-influencer ROI.

With zero integrations, supply the investment and results tables by hand and the skill still produces every calculation. See [CONNECTORS.md](../../CONNECTORS.md) for the free/keyless recipe per category.

Instructions

When a user requests ROI calculation:

1. Gather ROI Inputs

```markdown ### ROI Calculation Inputs

Campaign Details:

• Campaign: [name]
• Duration: [dates]
• Objective: [awareness/consideration/conversion]

Investment (Total Spend): | Category | Amount | |----------|--------| | Influencer fees | $[X] | | Product/Gifting | $[X] | | Production costs | $[X] | | Paid amplification | $[X] | | Agency/Tools | $[X] | | Total Investment | $[X] |

Results Data: | Metric | Value | |--------|-------| | Total Reach | [X] | | Total Impressions | [X] | | Total Engagements | [X] | | Video Views | [X] | | Link Clicks | [X] | | Conversions/Sales | [X] | | Revenue | $[X] | | New Customers | [X] | ```

1. Calculate Direct ROI

```markdown ## Direct ROI Calculation

### Simple ROI

Formula: (Revenue - Investment) / Investment × 100

``` Revenue: $[X] Investment: $[X] Profit: $[X]

ROI = ($[Revenue] - $[Investment]) / $[Investment] × 100 ROI = [X]% ```

### Return on Ad Spend (ROAS)

Formula: Revenue / Investment

``` ROAS = $[Revenue] / $[Investment] ROAS = [X]:1

Interpretation: For every $1 spent, generated $[X] in revenue ```

### Direct ROI Summary

| Metric | Value | Benchmark | Status | |--------|-------|-----------|--------| | ROI % | [X]% | [X]% | ✅/❌ | | ROAS | [X]:1 | [X]:1 | ✅/❌ | | Profit | $[X] | - | |

Assessment: [Profitable/Break-even/Loss] ```

1. Calculate Earned Media Value (EMV)

```markdown ## Earned Media Value Calculation

### EMV Methodology

EMV estimates the equivalent paid media cost to achieve the same results.

### Impression-Based EMV

Formula: Impressions × Industry CPM / 1000

| Platform | Impressions | CPM | EMV | |----------|-------------|-----|-----| | Instagram | [X] | $[X] | $[X] | | TikTok | [X] | $[X] | $[X] | | YouTube | [X] | $[X] | $[X] | | Total | [X] | - | $[X] |

### Engagement-Based EMV

Formula: Engagements × Cost per Engagement

| Engagement Type | Volume | CPE | EMV | |-----------------|--------|-----|-----| | Likes | [X] | $[X] | $[X] | | Comments | [X] | $[X] | $[X] | | Shares | [X] | $[X] | $[X] | | Saves | [X] | $[X] | $[X] | | Video Views | [X] | $[X] | $[X] | | Total | - | - | $[X] |

### Combined EMV

| Method | Value | |--------|-------| | Impression EMV | $[X] | | Engagement EMV | $[X] | | Average EMV | $[X] |

### EMV ROI

``` EMV Generated: $[X] Investment: $[X] EMV Multiple: [X]x

For every $1 spent, earned $[X] in equivalent media value ```

### EMV Caveats

⚠️ Note: EMV is an estimate and varies by methodology. Use for directional comparison, not absolute measurement. ```

1. Calculate Cost Efficiency Metrics

```markdown ## Cost Efficiency Analysis

### Cost Per Metrics

| Metric | Formula | Result | Benchmark | Status | |--------|---------|--------|-----------|--------| | CPM | Spend ÷ (Impressions/1000) | $[X] | $[X] | ✅/❌ | | CPR (Reach) | Spend ÷ (Reach/1000) | $[X] | $[X] | ✅/❌ | | CPE | Spend ÷ Engagements | $[X] | $[X] | ✅/❌ | | CPV (Video) | Spend ÷ Views | $[X] | $[X] | ✅/❌ | | CPC | Spend ÷ Clicks | $[X] | $[X] | ✅/❌ | | CPA | Spend ÷ Acquisitions | $[X] | $[X] | ✅/❌ | | CAC | Total Spend ÷ New Customers | $[X] | $[X] | ✅/❌ |

### Efficiency Score

| Rating | CPM Range | CPC Range | CPA Range | |--------|-----------|-----------|-----------| | Excellent | $[X] | >$[X] | >$[X] |

Your Campaign: [Rating]

### vs. Other Channels

| Channel | CPA | vs. Influencer | |---------|-----|----------------| | Influencer Marketing | $[X] | - | | Paid Social | $[X] | [+/-X%] | | Paid Search | $[X] | [+/-X%] | | Display Ads | $[X] | [+/-X%] | | Email Marketing | $[X] | [+/-X%] | ```

1. Apply Attribution Modeling

```markdown ## Attribution Analysis

### Attribution Methods

| Method | Description | Result | Notes | |--------|-------------|--------|-------| | First Touch | All credit to first interaction | $[X] | Awareness focus | | Last Touch | All credit to last interaction | $[X] | Conversion focus | | Linear | Equal credit across touchpoints | $[X] | Balanced view | | Time Decay | More credit to recent touches | $[X] | Recency bias | | Position Based | 40/20/40 first/middle/last | $[X] | Common B2C model |

### Attributed Revenue by Model

| Model | Attributed Revenue | ROI | |-------|-------------------|-----| | First Touch | $[X] | [X]% | | Last Touch | $[X] | [X]% | | Linear | $[X] | [X]% | | Time Decay | $[X] | [X]% | | Position Based | $[X] | [X]% |

### Recommended Model for Your Business

Recommended: [Model] Rationale: [Why this model fits your customer journey]

### Multi-Touch Journey Example

``` Customer Journey:

Day 1: Sees @creator1 TikTok (Awareness) ─────┐ Day 3: Sees @creator2 Instagram Reel ─────────┤ Day 5: Clicks @creator1's link (Consideration)┼── Purchase Day 7 Day 7: Uses @creator2's code (Conversion) ────┘

Attribution: Last Touch: 100% to @creator2 First Touch: 100% to @creator1 Linear: 50% each Position Based: 40% @creator1, 40% @creator2, 20% repeat exposure `` ``

1. Calculate Customer Lifetime Value Impact

```markdown ## Lifetime Value Analysis

### New Customer Metrics

| Metric | Influencer Acquired | Overall Average | |--------|--------------------|--------------------| | New customers | [X] | - | | First order AOV | $[X] | $[X] | | Repeat purchase rate | [%] | [%] | | Customer lifetime value | $[X] | $[X] |

### LTV-Based ROI

Formula: (New Customers × Avg LTV) - Investment / Investment

``` New Customers: [X] Average LTV: $[X] Total LTV: $[X] Investment: $[X]

LTV-Based ROI = ($[X] - $[X]) / $[X] × 100 LTV-Based ROI = [X]% ```

### Short-term vs. Long-term View

| Timeframe | Revenue | ROI | |-----------|---------|-----| | Immediate (this campaign) | $[X] | [X]% | | 6-month projected | $[X] | [X]% | | 12-month projected | $[X] | [X]% | | Lifetime projected | $[X] | [X]% |

### Customer Quality Indicators

| Indicator | Influencer-Acquired | Organic | Paid Ads | |-----------|--------------------|---------| ---------| | AOV | $[X] | $[X] | $[X] | | Return rate | [%] | [%] | [%] | | Repeat rate | [%] | [%] | [%] | | NPS/Satisfaction | [X] | [X] | [X] | ```

1. Calculate By-Influencer ROI

```markdown ## Influencer-Level ROI

### Individual Influencer Performance

| Influencer | Investment | Revenue | ROI | ROAS | Rank | |------------|------------|---------|-----|------|------| | @[handle1] | $[X] | $[X] | [X]% | [X]:1 | 1 | | @[handle2] | $[X] | $[X] | [X]% | [X]:1 | 2 | | @[handle3] | $[X] | $[X] | [X]% | [X]:1 | 3 | | @[handle4] | $[X] | $[X] | [X]% | [X]:1 | 4 | | @[handle5] | $[X] | $[X] | [X]% | [X]:1 | 5 |

### ROI Distribution

``` Influencer ROI Distribution:

@handle1 |████████████████████| 320% @handle2 |██████████████ | 180% @handle3 |████████████ | 150% @handle4 |██████ | 75% @handle5 |████ | 45%

Campaign Average: 180% ```

### Investment Efficiency

| Influencer | % of Budget | % of Revenue | Efficiency | |------------|-------------|--------------|------------| | @[handle1] | [%] | [%] | [X]x | | @[handle2] | [%] | [%] | [X]x |

### ROI by Tier

| Tier | Investment | Revenue | ROI | Avg ROAS | |------|------------|---------|-----|----------| | Macro | $[X] | $[X] | [%] | [X]:1 | | Micro | $[X] | $[X] | [%] | [X]:1 | | Nano | $[X] | $[X] | [%] | [X]:1 | ```

1. Generate ROI Report Summary

```markdown # ROI Summary Report

## Campaign: [Name] ## Period: [Dates]

---

## Investment Summary

| Category | Amount | % of Total | |----------|--------|------------| | Influencer Fees | $[X] | [%] | | Product/Gifts | $[X] | [%] | | Amplification | $[X] | [%] | | Other | $[X] | [%] | | Total Investment | $[X] | 100% |

## Returns Summary

| Return Type | Value | |-------------|-------| | Direct Revenue | $[X] | | Earned Media Value | $[X] | | New Customers | [X] | | Projected LTV | $[X] |

## ROI by Methodology

| Methodology | ROI | Notes | |-------------|-----|-------| | Direct Revenue ROI | [X]% | Hard returns | | ROAS | [X]:1 | Revenue per dollar | | EMV Multiple | [X]x | Media value generated | | LTV-Based ROI | [X]% | Long-term value |

## Key Metrics

| Metric | Result | Benchmark | Status | |--------|--------|-----------|--------| | CPM | $[X] | $[X] | ✅/❌ | | CPA | $[X] | $[X] | ✅/❌ | | ROAS | [X]:1 | [X]:1 | ✅/❌ |

## Bottom Line

Investment: $[X] Return: $[X] Net Profit: $[X] ROI: [X]%

Assessment: [Campaign was profitable/broke even/lost money]

## Recommendations

1. [Key recommendation 1]
2. [Key recommendation 2]
3. [Key recommendation 3]

---

Report Generated: [Date] ```

1. Roll up into the C³ Campaign Value Index (CVI)

This skill emits the ROI scope score of [C³](../../references/c3-benchmark.md) and the CVI rollup. Score ROI on the 0–100 rubric in [c3/roi-campaign-benchmark.md](../../references/c3/roi-campaign-benchmark.md) (Return · Orchestration · Impact, each on Pass/Partial/Fail → scaled to 0–100). This 0–100 ROI score is not the financial ROI % from steps 1–8 — feed the rubric score into the formula, never the percentage (R1 simply consumes your ROI%/ROAS as one of its inputs). Then combine it with the Creator and Content scope scores — from [fit-scorer](../../map/fit-scorer/SKILL.md) (ACE) and [content-reviewer](../../activate/content-reviewer/SKILL.md) (ART) — as a geometric mean:

`` CVI = ( ACE_avg × ART_avg × ROI )^(1/3) ``

ACE_avg is the budget-weighted mean of the campaign's creator ACE scores; ART_avg is the simple mean of its content ART scores (per scoring-architecture §8). Keep the three scope scores beside the CVI — the index ranks and alerts, the three scores diagnose. If ACE or ART is unavailable, emit the ROI score and mark CVI pending (needs ACE/ART) rather than guessing. A blocked scope (e.g. an ART T1/T2 veto on the content, or an ACE A2/C1/E2 veto on the creator) caps the rollup — surface it, don't average it away.

Example

User: "Calculate ROI for our influencer campaign: $25K spend, $72K revenue, 2.1M reach"

Output:

# ROI Calculation Summary

## Investment & Returns

| Item | Value |
|------|-------|
| Total Investment | $25,000 |
| Direct Revenue | $72,000 |
| Total Reach | 2,100,000 |

## ROI Results

### Direct ROI
- **Profit**: $47,000
- **ROI**: 188%
- **ROAS**: 2.88:1

For every $1 spent, you generated $2.88 in revenue.

### Earned Media Value
- **EMV** (at $8 CPM): $16,800
- **EMV Multiple**: 0.67x

### Cost Efficiency
- **CPM**: $11.90 (Good)
- **Est. CPA**: ~$54 (if 460 conversions)

## Assessment: ✅ Strong Performance

This campaign outperformed the typical 2:1 ROAS benchmark for influencer marketing. Recommend increasing investment in similar campaigns.

Industry ROI Benchmarks

| Industry | Avg ROAS | Good ROAS | Excellent ROAS | |----------|----------|-----------|----------------| | Beauty/Skincare | 3:1 | 5:1 | 8:1 | | Fashion | 2.5:1 | 4:1 | 6:1 | | Food & Beverage | 2:1 | 3.5:1 | 5:1 | | Tech/Electronics | 2:1 | 3:1 | 4:1 | | Health/Fitness | 2.5:1 | 4:1 | 6:1 |

Reference Materials

• [skill-contract.md](../../references/skill-contract.md) — shared contract and Handoff Summary format.
• [state-model.md](../../references/state-model.md) — memory tiers and save-path conventions.
• [CONNECTORS.md](../../CONNECTORS.md) — free/keyless data recipe per connector category.
• C³ scoring: [c3-benchmark.md](../../references/c3-benchmark.md) (CVI rollup formula) and [c3/roi-campaign-benchmark.md](../../references/c3/roi-campaign-benchmark.md) — the ROI Campaign rubric this skill emits into the CVI.
• [performance-analyzer](../performance-analyzer/SKILL.md) — supplies the results data this skill consumes.
• [report-generator](../../track/report-generator/SKILL.md) — wraps these numbers into a full report.
• [budget-optimizer](../../plan/budget-optimizer/SKILL.md) — uses ROI output to reallocate spend.
• [campaign-planner](../../plan/campaign-planner/SKILL.md) — sets the ROI targets these results are checked against.

Next Best Skill

Primary: [report-generator](../../track/report-generator/SKILL.md) — turn the ROI numbers into a stakeholder-ready report.

Alternates (same Track family):

• [performance-analyzer](../performance-analyzer/SKILL.md) — go back for deeper performance breakdowns if the ROI math exposed gaps.
• [budget-optimizer](../../plan/budget-optimizer/SKILL.md)

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: aaron-he-zhu
• Source: aaron-he-zhu/aaron-marketing-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.