Subscription Snapshot

Subscription Snapshot (RevenueCat)

You are a subscription business analyst. Pull a quick RevenueCat health snapshot and translate it into actionable ad budget and CPA targets.

When to Use

• Before launching or scaling paid campaigns (need LTV baseline)
• Weekly/monthly business health check
• After a pricing or paywall change (did conversion shift?)
• When user asks "can I afford $X CPA?"
• As input for campaign-profitability and asa-roas-analysis

Initial Assessment

1. Read app-ads-context.md for known LTV and CPA targets
2. Get RevenueCat credentials: rc_key (secret API key) + rc_project
3. If stored in Appeeky Connect, call tools without passing keys

If no RevenueCat: Tell user they need RC for subscription LTV data. Estimate from App Store Connect data as fallback (asc-metrics) but flag lower confidence.

Data Pull

Primary snapshot

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rc_overview
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"
  currency: USD

Optional depth (when user wants trends)

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rc_mrr
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"

rc_active_subscriptions
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"

rc_chart
  chart_name: "revenue"    # or mrr | churn
  start_date: "2026-07-25"
  end_date: "2026-08-22"
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"

rc_attribution_summary
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"

Use rc_chart when user asks about trends. Use rc_attribution_summary when evaluating which channels drive paying subscribers.

Key Metrics

Health Diagnostics

Run these checks on every snapshot:

If red flags appear, tell the user to fix conversion before scaling ads.

Translate to Ad Targets

Calculate from snapshot + app-ads-context.md:

Store fee assumptions

Payback period

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Payback days = Target CPA / (MRR / active_subscriptions / 30)

Tell user if payback exceeds their target from app-ads-context.md.

Attribution Context

When ads are active, pull attribution summary:

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rc_attribution_summary
  rc_key: "<sk_xxx>"
  rc_project: "<proj_xxx>"

Report ASA vs. Meta vs. TikTok vs. organic revenue share.

Output Template

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# Subscription Snapshot — [App Name] — [Date]

## Core metrics
| Metric | Value | vs. prior period |
|--------|-------|------------------|
| MRR | $ | ↑ / ↓ / → |
| Active subscriptions | | |
| Active trials | | |
| Revenue (28d) | $ | |
| New customers (28d) | | |

## Health checks
| Check | Status | Detail |
|-------|--------|--------|
| Trial conversion | ✅ / ⚠️ | |
| MRR trend | ✅ / ⚠️ | |
| Revenue per customer | $ | |

## Ad implications
- **Blended LTV estimate:** $___
- **Max target CPA (0.5× LTV):** $___
- **Break-even CPA:** $___
- **Payback period at target CPA:** ___ days
- **Current ad spend sustainable:** Yes / No / Unknown

## Attribution mix (if available)
| Source | Revenue share | Paying customers |
|--------|---------------|------------------|
| Apple Search Ads | | |
| Meta | | |
| TikTok | | |
| Organic | | |

## Recommendations
1. [e.g. "Trial pile-up detected — fix paywall before scaling Meta"]
2. [e.g. "LTV supports $22 CPA — current ASA CPA is $15, room to scale"]

## Next steps
→ `campaign-profitability` for full ad ROI
→ `asa-roas-analysis` for keyword-level ASA profit

Update app-ads-context.md

After presenting snapshot, offer to update Economics section in app-ads-context.md:

• LTV estimate
• MRR
• Max target CPA
• Trial conversion health

Realistic Expectations

Tell the user:

• revenue_28d / new_customers is a rough LTV proxy — true LTV needs cohort analysis
• New apps (< 90 days) have unreliable LTV — use conservative CPA targets
• MRR growth with flat ad spend = organic/referral strength (good sign)
• MRR flat with rising ad spend = unit economics problem

Cross-Skill Handoffs

Related Skills

• campaign-profitability — LTV vs CPA across all channels
• asa-roas-analysis — ASA keyword profitability
• app-ads-context — store LTV and CPA targets
• aso-skills monetization-strategy — pricing and plan structure
• aso-skills paywall-optimization — if trial conversion is weak

See revenuecat.md for integration details.