Overview
What it does
Maps creative themes to audience segments, surfaces mismatches and under-served audiences, and flags untested high-potential combinations.
When to use it
You're noticing that the same creatives perform wildly differently across audiences. Or you want to know which of your audiences is being under-served by your current creative mix.
Pairs with MCP
The MCP cross-references creative tags with audience-level performance breakdowns. Without that join, "audience–creative fit" is a manual spreadsheet exercise that takes a day.
Best for
Performance marketers, media buyers, growth teams.
Install
Drop this entire block into a SKILL.md file inside your Claude project’s .claude/skills/audience-creative-fit/ folder. Claude auto-invokes it when you ask which creatives work for which audiences or where your creative mix has gaps.
---
name: audience-creative-fit
description: Use this skill when the user wants to know which creative themes work with which audiences, which audiences are under-served by current creative, and where the matrix gaps create growth opportunities.
---
# Audience–Creative Fit Diagnostic
You map creative themes to audience segments and surface mismatches.
## Data you need
- Per-creative per-audience performance breakdown from Uplifted + Meta/TikTok MCP
- Required: ad_id, theme_tag (pillar or theme), audience_segment, spend, conversions, CPA, ROAS
- Audience segments can be: age bracket, gender, geo, device, lookalike vs. interest vs. retargeting, or whatever segmentation the user uses
## How to analyze
1. Build a theme × audience matrix. Each cell shows ROAS and statistical significance (sample size).
2. For each audience segment, identify:
- Best-fit themes (top 2 ROAS, min 50 conversions)
- Worst-fit themes (lowest ROAS, min 50 conversions)
3. For each creative theme, identify:
- Audiences it wins with
- Audiences it bombs with (so you know not to expand spend there)
4. Find the GAP QUADRANT: audience–theme combinations with zero spend that look high-potential based on adjacent cells.
## Output format
AUDIENCE × CREATIVE FIT MATRIX
[Render the ROAS matrix as a markdown table with audience rows and theme columns. Highlight cells: green = winning, yellow = neutral, red = losing, grey = no data.]
PER-AUDIENCE SUMMARY
- [Audience]: best fit themes / worst fit themes / current spend allocation
PER-THEME SUMMARY
- [Theme]: audiences it wins with / audiences it bombs in / current spend allocation
GAP OPPORTUNITIES (untested but high-potential)
1. [Audience] × [Theme] — why it should work + how to test
REALLOCATION RECOMMENDATIONS
- Move [$ amount] from [low-ROAS combo] to [high-ROAS combo]
## Guidelines
- Never call a cell a "winner" with fewer than 50 conversions. Statistical confidence matters more than gut feel.
- The "gap quadrant" recommendations are hypotheses, not certainties — frame them as test proposals.
- Always show current spend distribution next to recommendations so the user sees the magnitude of the shift you're proposing.Prompt
Maps your creative themes against audience segments to expose mismatches, under-served audiences, and untested combinations worth a shot. Run it when you have more creative than you have a plan for who should see what.
Map my creative themes to audience segments and surface mismatches.
Data: per-creative per-audience performance from Uplifted + {{Meta/TikTok}}. Columns: ad_id, theme_tag, audience_segment, spend, conversions, CPA, ROAS. Audience segmentation: {{describe how you segment}}. {{paste or confirm MCP}}
Build a theme × audience matrix (markdown table, ROAS per cell, highlight green = win, yellow = neutral, red = lose, grey = no data, min 50 conv to color anything).
Then produce:
- Per-audience summary (best/worst themes, current spend)
- Per-theme summary (winning/bombing audiences, current spend)
- Gap opportunities — untested cells that look high-potential based on adjacent winning cells
- Spend reallocation recommendations with $ amounts
Rules: nothing called a winner under 50 conversions. Gap recommendations framed as test proposals, not certainties. Always show current spend next to recommended spend.
EXPECTED OUTPUT:
- A visual ROAS matrix of theme × audience
- Per-audience and per-theme strengths/weaknesses
- A short list of untested high-potential combos and the spend shifts to fund them