ad-performance-loop

name	ad-performance-loop
description	Closed-loop ad performance optimization. Analyzes campaign results, identifies winning creatives by CPA/ROAS, reinforces winning patterns, and triggers regeneration of next creative batch. Use when analyzing ad performance, optimizing campaigns, identifying winning creatives, or running the generate-evaluate-regenerate cycle. Mirrors Eddie's Stage 5 (Self-Improvement Loop).
risk	low
tags	["growth","marketing","analytics","optimization"]
created	2026-03-23T00:00:00.000Z
updated	2026-03-23T00:00:00.000Z

Ad Performance Loop — Self-Improving Creative Engine

Closed-loop optimization that makes the ad system better every cycle. Analyze what worked, reinforce winners, kill losers, regenerate. This is UAOP Stage 5 (Self-Improvement Loop) for the Growth department.

When to use

• After ads have been running for 7+ days (enough data)
• Weekly performance review cycle
• When CPA is rising (creative fatigue signal)
• Before triggering next ad-script-generator batch
• Monthly creative strategy review

When NOT to use

• Ads running < 7 days (insufficient data)
• Initial ad research (use ad-research)
• Writing new scripts (use ad-script-generator)
• Platform-level campaign structure (use marketing-demand-acquisition)

Pipeline

Step 1: Pull Performance Data

Collect metrics for all active creatives:

## Performance Data Source
- Platform: [Meta Ads Manager / TikTok Ads / Google Ads]
- Attribution: [Singular MMP / platform native / UTM-based]
- Date range: [last 7/14/30 days]
- Minimum spend threshold: $[X] (filter out under-tested creatives)

## Per-Creative Metrics
| Creative ID | Hook | Angle | ICP | Spend | Impressions | Clicks | Installs | CPA | ROAS | CTR | CVR | Days Running |

Step 2: Rank and Classify

Sort creatives into tiers:

TIER 1 — WINNERS (Top 10% by CPA)
  → Scale spend
  → Analyze WHY they won
  → Generate variations of these

TIER 2 — PERFORMERS (10-50% by CPA)
  → Maintain spend
  → Test hook variations to improve
  → Monitor for fatigue

TIER 3 — UNDERPERFORMERS (50-80% by CPA)
  → Reduce spend
  → Analyze what's not working
  → Consider angle pivot

TIER 4 — FAILURES (Bottom 20% by CPA)
  → Kill within 72 hours
  → Record anti-patterns (what NOT to do)
  → Don't generate variations of these

Step 3: Winner Analysis

For each Tier 1 winner, deep-analyze:

## Winner Analysis: Creative [ID]

**Performance:**
- CPA: $[X] (vs. avg $[Y] — [Z]% better)
- ROAS: [X]x
- Days running: [N]
- Total spend: $[X]

**Creative Breakdown:**
- Hook: "[exact hook]"
- Hook type: [Question/Statement/Pattern-interrupt/Story]
- Angle: [what angle — pain/aspiration/social proof/authority]
- ICP targeted: [which ICP]
- Product surface: [Command/Studio/Helios/Nova/Forge]
- Format: [Video/Image/Carousel]
- Length: [15s/30s/60s]

**Why It Won (Hypothesis):**
- [specific reason 1 — e.g., "hook creates immediate curiosity"]
- [specific reason 2 — e.g., "pain point matches ICP's #1 frustration"]
- [specific reason 3 — e.g., "CTA is low-friction (free trial vs. pricing)"]

**Patterns to Reinforce:**
- Hook pattern: [extract reusable hook template]
- Angle pattern: [extract reusable angle]
- Structure pattern: [extract reusable structure]

Step 4: Fatigue Detection

Check for creative fatigue signals:

FATIGUE SIGNALS:
- CTR declining >20% week-over-week → creative fatigue
- CPA rising >15% week-over-week → audience saturation
- Frequency >3.0 → audience overlap too high
- Running >45 days without refresh → proactive replacement needed

FATIGUE ACTION:
- Signal detected → flag for next batch regeneration
- Generate variations (new hooks on same angle)
- Expand audience (new ICPs on same angle)
- Refresh creative (same script, new actor/format)

Step 5: Regeneration Trigger

Feed analysis back into the pipeline:

## Regeneration Brief for ad-script-generator

### Double Down On (from winners):
- Hook pattern: "[template from winner]"
- Angle: [winning angle]
- ICP: [winning ICP]
- Structure: [winning structure]
- Action: Generate 20+ variations of this pattern

### Avoid (from failures):
- Hook pattern: "[failed hook pattern]"
- Angle: [failed angle]
- ICP: [underperforming ICP]
- Action: Do NOT generate more of these

### Test Next (new experiments):
- Untested ICPs: [list]
- Untested angles: [list from ad-research]
- Untested formats: [15s vs 60s, carousel vs video]
- Action: Generate 5-10 scripts per experiment

### Refresh (fatigued winners):
- Creative [ID]: same angle, new hooks
- Creative [ID]: same script, new actors
- Action: Generate 10 refreshed versions

Step 6: Update Knowledge Graph

Store in memory MCP:

• Winning patterns (hook templates, angles, structures)
• Anti-patterns (what consistently fails)
• ICP performance rankings
• Angle performance rankings
• Creative lifespan data (how long before fatigue)

Cycle Cadence

WEEKLY CYCLE:
  Monday:    ad-research (scan competitors for new angles)
  Tuesday:   ad-performance-loop (analyze last week's results)
  Wednesday: ad-script-generator (generate next batch based on loop output)
  Thursday:  Production split (UGC briefs + Arcads renders)
  Friday:    Deploy new creatives to platforms

  Repeat. The system improves every cycle.

Key Metrics

Metric	Target	Alert Threshold
CPA	< 1/3 of LTV	Rising >15% WoW
ROAS	> 3x	Dropping below 2x
CTR	> 1.5%	Dropping >20% WoW
Creative win rate	> 15%	Below 10%
Creatives tested/month	> 100	Below 50
Time to kill losers	< 72 hours	> 1 week

Tools Used

Tool	Purpose
memory MCP	Store/retrieve winning patterns, anti-patterns
sequential MCP	Multi-step analysis and hypothesis generation
postgres MCP	Query campaign performance data (if stored)

Agents

Agent	Role
growth-marketer (L2)	Owns the loop, makes scale/kill decisions
data-analyst (L2)	Runs performance analysis, statistical significance
analytics-reporter (L3)	Produces weekly performance reports
experiment-tracker (L4)	Tracks which experiments are running, results

UAOP Integration

Stage 5 Output → Stage 1 Input (what competitors to watch next)
Stage 5 Output → Stage 3 Input (what patterns to generate from)
Stage 5 Output → Memory MCP (persistent pattern knowledge)
Stage 5 Output → GAOS OutcomeTracker (agent performance scoring)

The loop is the engine. Without it, you're guessing. With it, every cycle produces better results than the last.