Influencer ROI Calculator
Calculate defensible influencer-campaign returns across ROI, ROAS, EMV, attribution, and lifetime value.
The skill asks users to supply data, defaults to no live integrations, and separates persistence and hot-cache promotion behind authorization. It also requires attribution windows, sources, uncertainty, and low-confidence handling for unverified conversions. Deductions apply because sensitive marketing-data handling, rollback, write-failure recovery, and dependency boundaries are not fully specified within this skill; much of the security posture depends on shared protocols not provided as target evidence.
Inputs, outputs, numbered procedures, formulas, and several abnormal cases such as missing conversions, missing benchmarks, and unverified results are described coherently. Deductions apply because attribution models, LTV projections, cross-channel comparisons, and per-influencer calculations lack fully reproducible algorithms, validation rules, and diagnostic failure messages; nothing was executed, so the static cap of 10 applies.
Triggers, expected inputs, outputs, and the non-fit case of a full written report are explicit. The Tier 1 manual-input route is also clear and does not depend on overseas services. Deductions apply because Chinese support is mostly limited to metadata and headings, while core templates and outputs are English, and semantic false-trigger boundaries are not specified in depth.
The skill includes quick starts, a contract, data-source guidance, numbered instructions, templates, an example, references, version metadata, and an Apache-2.0 license. Deductions apply because it lacks a skill-specific changelog, named maintenance responsibility, update path, installation troubleshooting, and dependency-version policy; several important references are only linked and were not included in the supplied evidence.
The documented workflow can produce direct ROI/ROAS, EMV, cost-efficiency metrics, attribution views, LTV analysis, per-influencer results, and an executive summary from pasted data, with a readable worked example. Deductions apply because advanced outputs require substantial data and judgment, attribution and LTV results need human review, and the files contain no real execution evidence proving stable end-to-end usability; the static cap of 7 applies.
Explicit formulas, benchmark-compatibility fields, source dates, attribution labels, and Measured/User-provided/Calculated/Estimated distinctions improve auditability, and the repository context includes general CI tests. Deductions apply because the supplied CI tests do not cover this skill's key calculation paths and no third-party corroboration or independently reproducible run is provided; static evidence supports at most 5.
- All calculations depend on user-supplied data; without verified conversions, incremental baselines, compatible benchmarks, or LTV parameters, results should not support causal claims or scaling decisions.
- Attribution, EMV, and LTV projections are methodological estimates; verify cost inclusion, currency, observation window, repeat-purchase assumptions, and denominator validity.
- The skill writes to memory paths, but actual authorization, ignore rules, recovery, and privacy controls depend on shared host runtime behavior; confirm those controls before use.
- Core templates are primarily in English, so Chinese users may need manual translation or output adaptation.
What does this skill do, and when should you use it?
This skill measures or projects the return on influencer marketing campaigns and helps teams defend budget decisions. It uses supplied spend, reach, engagement, click, conversion, revenue, customer, and optional LTV data to calculate direct ROI/ROAS, earned media value, efficiency metrics, modeled attribution revenue, and LTV-based ROI. Its output includes formulas, declared comparison targets, a profitable/break-even/loss assessment, and recommendations. It also prepares Return (R) evidence for the STAR gate, but does not run the scorer or produce the composite SQS.
Reads campaign investment tables, results data, optional AOV, repeat-rate and LTV inputs, and prior performance output; calculates direct ROI and ROAS, impression- and engagement-based EMV, CPM, CPR, CPE, CPV, CPC, CPA, CAC, attribution-modeled revenue, LTV-based ROI, and per-influencer or tier-level returns; compares efficiency metrics only with declared, source-dated targets; labels figures as Measured, User-provided, Calculated, or Estimated; produces a summary with inputs, formulas, methodology results, comparison targets, bottom-line assessment, and recommendations. With separate authorization, it can write the calculation file to the designated memory path.
- A marketing lead has campaign spend and revenue data and needs a profitability assessment.
- A creator-marketing team needs to rank individual influencers or compare macro, micro, and nano tiers.
- Finance or executives need direct-revenue, EMV, and LTV views of campaign value.
- A growth team needs to compare first-touch, last-touch, linear, time-decay, and position-based attribution.
- A paid-ads team wants to reuse the repository’s shared ROAS, CPA, and return-math engine.
What are this skill's strengths and limitations?
- Covers ROI, ROAS, EMV, attribution revenue, and LTV-based ROI in one workflow.
- Shows inputs and formulas and requires declared, source-dated comparison targets.
- Supports per-influencer and tier-level analysis plus an executive-ready summary.
- Works with manually supplied data and no live integrations.
- It does not build the full slide deck or written report; report-generator is the stated next skill for that task.
- Without a compatible, source-dated benchmark, it reports metrics descriptively rather than claiming outperformance.
- EMV is explicitly directional rather than absolute.
- Unverified conversions reduce confidence in R1, R2, and R5 and do not support attributable-return claims.
- Memory persistence and hot-cache promotion require separate authorization.
How do you install this skill?
To install the repository collection in Claude Code, run /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, then /plugin install aaron-marketing@aaron. On an Agent Skills-compatible host, run npx skills add aaron-he-zhu/aaron-marketing-skills; alternatively, run git clone https://github.com/aaron-he-zhu/aaron-marketing-skills. The supplied source does not give a concrete single-skill installation command for this skill.
How do you use this skill?
Place or enable the skill at influencer/report/roi-calculator/SKILL.md, then provide campaign spend and results. Example: Calculate ROI for our influencer campaign: $25K spend, $72K revenue, 2.1M reach. You can also ask it to compare direct revenue, EMV, and LTV-based methods. Manual input is sufficient; live connectors are optional.
How does this skill compare with similar options?
performance-analyzer supplies the results data this skill consumes; report-generator wraps its numbers into a full stakeholder report; budget-optimizer uses influencer and tier ROI for subsequent allocation. The skill is also the shared return-math engine delegated to paid-measurement-loop, attribution-reconciler, and budget-optimizer.