Performance Analysis

Creative Performance Analysis

Analyze creative performance using the creative-strategist skill methodology — multi-metric landscape, demographic overlay, and creative taxonomy. Produce an actionable report of what's working, what's scaling, and what's dying.

Phase 0: Orient

Before pulling data:

1. Acknowledge: "I'll analyze your creative performance to find what's working, scaling, and dying."
2. Detect complexity: Is this a quick question ("how's ROAS?") or a full deep dive ("what's working?")? Quick questions get 1-2 tool calls and a direct answer. Full analysis gets the multi-metric sweep.
3. Ask if ambiguous: "Quick top-line or full deep dive? Any specific metric or time period you care about?"
4. Connected workflow: After analysis, you can create concepts (/create-concepts), find iterations (/find-iterations), or dive into a specific ad (/analyze-ad).

If the user provides clear, specific intent (e.g., "full performance analysis for last 30 days"), skip questions and deliver.

Phase 1: Setup

1a. Parse Arguments

• --datePreset: Time window for analysis. Default: LAST_30_DAYS. Options: TODAY, YESTERDAY, THIS_MONTH, LAST_MONTH, LAST_7_DAYS, LAST_14_DAYS, LAST_30_DAYS, LAST_90_DAYS.
• --limit: Max creatives per metric query. Default: 10.
• --metric: Optional focus metric (SPEND, SCALING, HOOK, CPC, CTR_ALL, PURCHASES, PURCHASE_VALUE, or the workspace's goalMetric). If provided, lead the analysis with this metric. If not, use the standard multi-metric approach.

1b. Load Settings & Auth

1. Read ${CLAUDE_PLUGIN_ROOT}/motion-creative.config.md for org-specific configuration. If the file does not exist, use these defaults and suggest the user run /customize:
   • primary_kpi: use goalMetric from first get_creative_insights response
   • default_date_preset: LAST_30_DAYS
   • default_creative_limit: 10
   • demographic_focus: both
   • primary_metrics / secondary_metrics / exclude_metrics: auto-detect
   • priority_glossary_categories: use all
   • Brand guidelines: pull from get_workspace_brand
2. Call get_auth_context() to resolve workspaceId (use settings workspace_id as fallback context).
3. If settings contain a primary_kpi and no --metric was specified, use the primary KPI to lead the analysis.
4. If settings contain target_demographics, weight demographic analysis toward those segments.
5. If settings contain primary_metrics, ensure those metrics lead the analysis. If secondary_metrics, include after primary. If exclude_metrics, omit those from all queries and output.
6. Use default_date_preset from settings as the datePreset for all calls unless the user provided a --datePreset argument. Use default_creative_limit from settings as the limit unless the user provided a --limit argument.

If auth fails, stop and tell the user to check their Motion workspace connection.

1c. Load Methodology

Read the creative-strategist skill and references/performance-metrics.md for metric definitions and interpretation patterns.

Phase 2: Data Collection

The SPEND call must come first (it returns goalMetric and spendThreshold). Then dispatch remaining calls in parallel.

1. get_creative_insights(workspaceId, insightType="SPEND", datePreset, limit, withAggregatedInsights=true) — spend leaders + account-level aggregates

→ Extract goalMetric and spendThreshold from the response. If goalMetric.isCustomConversion is true, find the matching conversion in the customConversions array and include ["{id}_cost", "{id}_count"] in tableKPIs on all subsequent calls. Use goalMetric for all efficiency-sorted calls.

2. get_creative_insights(workspaceId, insightType="SCALING", datePreset, limit) — what's gaining/losing allocation
3. get_creative_insights(workspaceId, insightType=goalMetric, datePreset, limit) — efficiency leaders by workspace goal metric
4. For hook rate leaders: filter SPEND results (call #1) to video creatives and sort by thumbstop_ratio descending. Do NOT use insightType="HOOK" — it returns the same ranking as SCALING and misses high-spend proven performers.
5. get_demographic_breakdown(workspaceId, datePreset) — age/gender performance
6. get_glossary_values(workspaceId) — creative taxonomy categories

If --metric is specified, ensure that metric is included and prioritized in the analysis.

Phase 3: Analysis

3a. Build Creative-Level View

For each creative appearing across the results, collect:

• Its rank/position in each metric query it appeared in
• Key performance values (spend, goalMetric, hook rate via thumbstop_ratio, hold rate via video_thruplay_ratio, and other relevant metrics)
• Whether it's scaling, stable, or declining
• Its campaign and ad set context — campaignName, campaignIds, adsetName, adsetIds

Cross-reference to identify:

• All-rounders: Creatives that appear in top results across multiple metrics
• Specialists: High on one metric but absent from others (e.g., high hook rate but not in goalMetric top — hooks but doesn't convert)
• Hidden gems: Strong goalMetric but low spend — underexploited

3a-ii. Map Campaign & Ad Set Structure

This step is critical — campaign and ad set context changes the meaning of every metric.

1. Group creatives by campaign, then by ad set within each campaign
2. Apply campaign tier classification and structural issue detection from references/campaign-context.md

Consult references/campaign-context.md for campaign tier naming heuristics, ad set strategy signals, and structural issue patterns.

3b. Pattern Extraction

Use glossary values to tag each creative with its taxonomy categories. Then analyze:

• Which categories (format, hook type, asset type, messaging angle) are over/under-represented in top performers?
• Which categories are scaling vs. declining?
• What combinations appear in winners but not losers?
• What hasn't been tried? (categories with zero or minimal coverage)
• Which campaign tiers favor which creative patterns? (e.g., UGC dominates testing but studio images dominate scaling — or vice versa)

Consult references/performance-metrics.md for metric combination stories.

3c. Demographic Analysis

From the demographic breakdown:

• Which age/gender segments drive the best performance?
• Are there underserved segments where creative investment could expand?
• Do certain creatives skew strongly toward one demographic?

3d. Apply Brand Context from Settings

If config contains brand guidelines (Brand Voice, Creative Do's/Don'ts, Target Audience):

• Frame recommendations through brand constraints — don't suggest approaches that violate creative don'ts
• Weight audience insights toward the brand's stated target audience
• Use brand voice to inform how insights are framed

If settings contain priority_glossary_categories, prioritize those categories in pattern extraction (3b).

3e. Apply Insight Quality Bar

Before writing findings, check each insight against references/insight-quality.md. Every finding must explain the "why" — what's happening in the viewer's mind — not just state a metric.

Phase 4: Output

Open by reflecting what data you pulled: time window, number of creatives analyzed, any filters applied. Keep it conversational — one sentence, not a formatted log.

Report Structure

If You Read Nothing Else 3-5 bullets. The most actionable findings. Lead with what changed or what's surprising. Every bullet should make the reader want to do something.

Top Performers Creatives with the best combination of goal metric efficiency + meaningful spend. Not just highest efficiency (which could be low-spend noise). Show the creative, its key metrics, which campaign/ad set it runs in, and WHY it works — the behavioral insight. Contextualize efficiency against the campaign tier (e.g., below-breakeven on cold prospecting may be acceptable if LTV justifies it; below-breakeven on scaling is a problem).

Scaling Winners Creatives gaining spend allocation with stable or improving efficiency. These are the ones the algorithm is betting on. Note what they have in common. Flag whether they're scaling in an uncapped environment (strong signal) or a cost-cap environment (inflated signal).

Efficiency Standouts Strong goalMetric but not yet scaling — hidden gems that deserve more budget. Explain why they might be underexploited. Note their campaign context — a gem in a cost-cap ad set needs uncapped validation before you declare it a winner. Recommend the graduation path: which campaign/ad set to move it to next.

Campaign Structure Issues Structural problems in how campaigns and ad sets are organized that work against performance. This section surfaces:

• Testing creatives competing with proven winners inside the same CBO (budget starvation)
• "Top performers" ad sets containing fatigued creatives (budget traps)
• Creatives ready to graduate from Testing → Scaling
• CBO allocation conflicts where budget flows to high-spend losers over low-spend winners
• Cost-cap artifacts inflating efficiency metrics

Declining Creatives Losing share or performance dropping. Don't just flag them — recommend what to do: refresh, pause, test new hooks on the same body, etc. If a declining creative sits in a "Top performers" ad set, explicitly recommend removal — it's dragging down the ad set.

Demographic Sweet Spots Where performance concentrates by age/gender. Which segments are strongest, which are underserved, and what that means for creative strategy.

Recommendations 3-5 specific next actions. Each recommendation must be:

• Specific enough to act on today
• Grounded in a finding from the analysis
• Framed as "do this because the data shows X"
• Campaign-aware — recommendations should specify where creatives should move (e.g., "graduate BRoll TO from Testing 1 to Top performers ad set"), not just "increase budget"

Error Handling

• Auth fails: Stop and tell user to check Motion workspace connection
• One metric query fails: Note the gap, continue with remaining data
• All queries fail: Report the failure, suggest checking the Motion MCP connection
• Empty results: Note that no creatives match the criteria — suggest broadening the datePreset or checking spend thresholds
• Limited data (few creatives): Adjust analysis depth — don't over-interpret small samples