Influencer ROI

v1.0.0

Calculate and compare return on investment across influencer partnerships by tracking attribution from creator content to actual sales, factoring in product...

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Purpose & Capability
Name, description, and SKILL.md all describe computing ROI, CPA, ROAS and producing a ranked report from user-provided campaign and cost data. Nothing requested (no env vars, no binaries, no installs) is out of scope for that purpose.
Instruction Scope
SKILL.md confines itself to processing user-supplied data and producing structured reports; it explicitly states it does not pull live platform data. It does not instruct the agent to read system files, search environment variables, or contact unexpected external endpoints.
Install Mechanism
No install spec or code files are present (instruction-only). This minimizes disk/write risk and matches the described functionality.
Credentials
The skill requires no credentials, config paths, or environment variables. That is proportionate for a tool that operates on user-provided campaign exports.
Persistence & Privilege
The skill does not request always-on presence and uses default invocation settings. It does not indicate any modification of system or other-skill configuration.
Assessment
This skill appears internally consistent, but keep these practical points in mind before using it: (1) You must supply campaign data yourself — do not paste PII (customer names, full emails, or raw order payment details) unless you are sure you want that data processed by the agent. (2) The skill's attribution estimates (assisted conversions) are based on benchmarks, so validate conclusions against your platform reports before making contractual decisions. (3) If you later add connectors or an implementation that pulls live platform data, require only the minimum credentials needed and review that integration separately. (4) Review the generated recommendations manually before acting on them (budget reallocation and renewal decisions should be validated with business context).

Like a lobster shell, security has layers — review code before you run it.

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Updated 2d ago
v1.0.0
MIT-0

Influencer ROI

Influencer marketing spend is notoriously hard to measure because attribution is fragmented across platforms, discount codes get shared beyond the intended creator, and brands rarely account for hidden costs like product seeding, shipping samples, and content licensing fees. This skill solves that by building a comprehensive ROI model for each creator partnership, giving ecommerce operators a clear picture of which influencers actually drive profitable sales versus those who merely generate vanity metrics.

Use when

  • You want to compare the true cost-per-acquisition across five or more influencer partnerships on TikTok Shop or Instagram
  • A brand manager asks "which creators should we renew contracts with based on actual sales performance, not just views?"
  • You need to build a post-campaign report showing influencer ROI including product seeding costs, commissions paid, and content licensing fees
  • Your team is planning next quarter's creator budget and needs data-driven allocation recommendations across micro, mid-tier, and macro influencers

What this skill does

This skill takes your influencer campaign data — including creator names, content metrics, attributed sales, product costs, commission rates, and any additional fees — and calculates a detailed ROI breakdown for each partnership. It computes cost-per-acquisition (CPA), return on ad spend (ROAS), and net profit contribution per creator. The analysis accounts for direct attribution via discount codes and affiliate links, estimates assisted conversions from brand awareness lift, and flags partnerships where the all-in cost (seeding, shipping, commissions, licensing) exceeds the revenue generated. The output ranks creators by profitability so you can make confident renewal and budget allocation decisions.

Inputs required

  • Creator list with campaign data (required): A list of influencer names or handles along with their content performance metrics (views, clicks, engagements) and attributed sales figures. Example: "Creator A — 500K views, 2,300 clicks, 187 orders, $14,200 revenue"
  • Cost breakdown per creator (required): All costs associated with each partnership including product seeding value, shipping costs for samples, commission rate or flat fee, and any content licensing or usage rights fees. Example: "Creator A — $200 product cost, $15 shipping, 15% commission, $500 licensing fee"
  • Campaign time period (required): The date range for the campaign to normalize performance comparisons. Example: "March 1–31, 2026"
  • Attribution method (optional): Whether you track via discount codes, affiliate links, UTM parameters, or a combination. Specifying this helps the skill assess attribution confidence and flag potential leakage.
  • Target CPA or ROAS threshold (optional): Your internal benchmarks for what constitutes a profitable partnership. If provided, creators will be flagged as above or below target.

Output format

The output is structured as a multi-section report. The first section is an Executive Summary with total spend, total attributed revenue, blended ROAS, and blended CPA across all creators. The second section is a Creator Ranking Table sorted by net ROI, with columns for creator name, total cost (broken into sub-components), attributed revenue, net profit, ROAS, CPA, and a performance tier label (high-performer, break-even, underperformer). The third section provides Individual Creator Cards with detailed breakdowns showing cost composition pie chart descriptions, attribution confidence notes, and specific renewal recommendations. The fourth section is a Budget Reallocation Recommendation suggesting how to redistribute spend from underperformers to high-performers with projected impact estimates. All monetary values use the currency specified in inputs or default to USD.

Scope

  • Designed for: ecommerce operators, brand managers, DTC marketing teams, and influencer marketing agencies
  • Platform context: TikTok Shop, Instagram Shopping, YouTube Shopping, platform-agnostic creator partnerships
  • Language: English

Limitations

  • Does not pull live data from influencer platforms or affiliate networks — you must provide the campaign performance data manually or from exported reports
  • Attribution modeling for assisted conversions is estimated based on industry benchmarks rather than your specific multi-touch attribution setup
  • Does not account for long-term brand equity effects of influencer partnerships, focusing instead on direct and near-term measurable ROI

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