PodAudit - Real-Time Podcast Audience Health Dashboard (Retention, Churn, Growth Signals)

Podcasters get RSS numbers (downloads) but no insights. Why did 2,000 listeners drop this episode? Which episode made people unsubscribe? PodAudit aggregates data from RSS feeds, Spotify, Apple, YouTube, and analyzes listener retention, churn triggers, and growth levers — dashboards for the podcast business.

Difficulty

intermediate

What is it?

Podcasters are flying blind. They see total downloads but can't answer: Are listeners dropping off mid-episode? Which guests drive retention? Are new listeners staying? Did that joke cost me 500 subs? Spotify and Apple hide listener data behind paywalls. RSS gives raw numbers but no insight. PodAudit solves this by: (1) connecting to podcast RSS feed, Spotify for Business, and Apple Podcasts Connect (if available via API), (2) parsing listener data to infer episode-level retention (comparing unique listeners across sequential episodes), (3) flagging 'churn episodes' (where retention dropped >30% week-over-week), (4) analyzing guest correlations (does this guest drive better retention?), (5) predicting growth levers via Claude (e.g., 'Episodes with guest interviews have 40% higher retention. Your solo episodes averaged 60% retention but interviews averaged 85%'). All in a Slack-friendly dashboard. Why 100% buildable right now: RSS is public and free. Spotify Web API is mature. Apple Podcasts Connect API is limited but documented. Claude can infer retention patterns from data. Supabase stores historical data. Zero regulated territory (analytics only, no medical claims). The hardest part is getting access to granular listener data (Spotify rate-limits insights), solved via caching and batch analysis.

Why now?

Podcast listenership stabilized post-pandemic boom (2024-2026); now creators focus on quality and retention, not just reach. Spotify and Apple restricted analytics access in 2024 (privacy), creating gap for indie tools. Claude's ability to analyze timeseries data improved significantly in late 2025.

▸RSS feed sync (automatic weekly crawl)
▸Episode-level listener data (inferred from RSS unique listener counts)
▸Retention tracking (% of listeners who stayed for next episode)
▸Churn detection (episodes where 30%+ of listeners didn't return)
▸Guest impact analysis (does this guest improve retention?)
▸Growth lever recommendations (Claude-powered insights on what works)
▸Slack dashboard with weekly summary

Target Audience

Podcast creators with 5k-100k listeners per episode. ~45,000 podcasts in US above 5k weekly listeners. Early target: independent creators (no network) and small networks (2-5 shows) who monetize via sponsorships and need to prove ROI.

Example Use Case

Indie creator Sarah runs a true crime podcast with 30k weekly listeners. PodAudit shows her that episodes with guest investigators average 85% retention while her solo research episodes average 50%. She pivots to guest-heavy format, retention climbs to 78%, and sponsor rates justify higher CPM. Revenue increases $2k/month.

User Stories

▸As a podcast creator, I want to know which episodes caused listeners to leave, so that I stop doing the things that don't work. As an indie creator, I want to understand if my guest strategy actually works, so that I spend my time on guests who drive retention.
▸As a podcast network owner, I want to measure creator performance, so that I can make data-driven decisions about show renewals.

Acceptance Criteria

RSS Sync: done when episodes from Spotify/Apple podcast feed are fetched and stored daily. Retention Metric: done when system calculates % of listeners from episode N who returned for episode N+1 (within 30% margin). Churn Detection: done when episodes with 30%+ listener drop are flagged in dashboard. Slack Summary: done when weekly Slack message includes retention trends and 1 growth recommendation.

Is it worth building?

$29/month × 60 creators = $1,740 MRR by month 3. $79/month × 100 creators = $7,900 MRR by month 8.

Unit Economics

CAC: $20 via Twitter organic (assume 2% conversion from 1k impressions). LTV: $348 (12 months at $29/month with 60% retention). Payback: 1 month. Gross margin: 80% (after Claude, hosting, payment).

Business Model

SaaS: $29/month (1 podcast), $79/month (3 podcasts + Slack integration).

Monetization Path

Free tier: 1 podcast, 30-day history. Upgrade at day 31 or when adding 2nd podcast.

Revenue Timeline

First dollar: week 3 (beta). $1k MRR: month 4. $5k MRR: month 11. $10k MRR: month 18.

Estimated Monthly Cost

Claude API: $20, Vercel: $20, Supabase: $25, Spotify API: $0, Slack API: $0, Stripe fees: ~$10. Total: ~$75/month at launch.

Profit Potential

Full-time viable at $3k–$8k MRR. Networks (10+ shows) could generate $30k+ MRR.

Scalability

High — can expand to influencer tier metrics (downloads by region, listener growth trends), sponsorship ROI calculator, guest database/network, multi-creator networks, video podcast support (YouTube).

Success Metrics

Week 1: 80 signups. Week 2: 20 paid creators. Month 2: 60% weekly dashboard check-in rate. Month 3: 50% of users export growth recommendations and act on them.

Launch & Validation Plan

Survey 30 podcast creators on Twitter/X about their biggest questions. Build landing page with sample analytics. Get 5 beta creators to sync RSS feeds and manually upload Spotify CSV. Validate that retention metrics are directionally useful.

Customer Acquisition Strategy

First customer: DM 20 indie podcast creators on Twitter/X with 10k+ listeners; offer 2 months free in exchange for weekly feedback. Ongoing: ProductHunt, Twitter dev/creator communities, Indie Hackers, Spotify for Podcasters community, podcast subreddits (r/podcasting, r/podcasts), LinkedIn podcast creator groups.

What's the competition?

Competition Level

Low

What's the roadmap?

Feature Roadmap

V1 (launch): RSS sync, retention tracking, churn detection, Claude insights, Slack summary. V2 (month 2-3): Manual Spotify/Apple CSV upload, guest impact correlations, growth trend graphs. V3 (month 4+): Multi-podcast dashboards for networks, sponsorship ROI calculator, listener feedback sentiment analysis.

Milestone Plan

Phase 1 (Week 1-2): RSS parsing, episode storage, retention calculation. Done when test podcast episodes are fetched and retention metrics calculated. Phase 2 (Week 3): Churn detection, Claude insights, Slack integration. Done when Slack receives weekly summary with flagged churn episodes and 1 recommendation. Phase 3 (Month 2): Stripe, dashboard UI, beta testing with 5 creators. Done when 5 beta creators report using insights and 2+ act on recommendations.

How do you build it?

Tech Stack

Next.js, RSS parser (feedparser), Spotify Web API, Apple Podcasts API (limited), Claude API for insights, Supabase for analytics storage, Stripe for subscriptions, Slack API for dashboard — build with Cursor for backend, Lovable for dashboard UI, v0 for Slack components.

Time to Ship

3 weeks

Required Skills

RSS parsing, API integration (Spotify, Apple), Claude API, basic analytics logic.

Resources

Feedparser docs, Spotify Web API docs, Apple Podcasts Connect docs, Anthropic Claude docs, Supabase timeseries querying.

MVP Scope

1) Next.js landing page + auth (Supabase). 2) RSS feed URL input and sync (weekly cron). 3) CSV/JSON data ingestion from Spotify/Apple (manual upload). 4) Analytics database schema (episodes, listeners, retention metrics). 5) Retention calculation logic (infer from listener counts). 6) Churn detection (flag episodes with 30%+ drop). 7) Guest impact analysis (correlate guest appearances with retention). 8) Claude insights endpoint (analyze trends, recommend growth levers). 9) Slack integration for weekly summary. 10) Stripe subscription. No real-time updates, no YouTube/video support, no listener demographic data.

Core User Journey

Sign up -> paste RSS feed URL -> wait 2 minutes for first sync -> see retention graph + this week's insights in Slack -> upgrade to paid.

Architecture Pattern

RSS feed URL -> scheduled cron job -> feedparser extracts episode metadata and listener counts -> store in Supabase -> Claude API analyzes trends + generates insights -> Slack message posted with summary, dashboard updated.

Data Model

Creator has one Podcast. Podcast has many Episodes. Episode has one Analytics record (listener_count, unique_listeners_estimate, churn_rate). Creator has one SlackWorkspace (optional). Insights are generated from Episodes data.

Integration Points

Spotify Web API (optional, manual data upload), Apple Podcasts Connect (optional, manual data upload), Supabase for analytics data, Claude API for insights, Slack API for dashboard, Stripe for billing.

V1 Scope Boundaries

V1 excludes: real-time listener updates, YouTube/video podcast support, demographic breakdowns, listener location data, sponsorship integration, multi-host analytics.

Success Definition

A podcast creator syncs their RSS feed, sees retention metrics and a churn episode flagged, reads Claude's recommendation (e.g., 'Try guest interviews'), and reports back that they acted on it and saw improvement.

Challenges

RSS provides limited data; inferring true 'retention' from aggregate numbers is approximation (you don't know if same listener returned). Spotify/Apple won't provide detailed listener data to indie creators (privacy + product strategy). Requires creative data modeling to estimate meaningful metrics. Churn may be false signal (summer break, not quality issue).

Avoid These Pitfalls

Do not promise precise listener data; RSS is approximate. Do not assume Spotify/Apple data is available (they restrict access). Start with RSS-only and make manual CSV upload optional. Do not over-claim causation (a bad episode might cause churn, but so might external factors like holidays). Always include caveats in insights.

Security Requirements

Auth: Supabase Auth with email + Google OAuth. RLS on creators and podcasts tables: users see only own data. Rate limiting: 10 RSS syncs per hour per creator (prevent spam). Input validation: RSS feed URL validated. Data retention: keep 52 weeks of analytics, auto-delete older records. GDPR: offer data export and deletion endpoints.

Infrastructure Plan

Hosting: Vercel for Next.js. Database: Supabase (Postgres, good for timeseries data). Scheduled tasks: node-cron for weekly RSS syncs (or Vercel Cron). CI/CD: GitHub Actions. Environments: dev (localhost), staging (preview), prod (main). Monitoring: Sentry for errors, Vercel Analytics. Cost: Vercel $20, Supabase $25, Claude API $20, Stripe ~$10. Total: ~$75/month.

Performance Targets

Launch load: 40 DAU, 50 weekly syncs/day. RSS parsing: under 10s per feed (feedparser optimized). Analytics query (retention calc): under 500ms. Dashboard load: under 2s. Caching: retention metrics cached for 24 hours.

Go-Live Checklist

☐Data accuracy: validate retention calculations on 3 test podcasts against known listener counts
☐RSS parsing: test with feeds from Spotify, Apple, and direct RSS
☐Claude insights: verify recommendations are actionable and not generic
☐Slack integration: test message format and link generation
☐Stripe: test subscription signup and cancellation
☐Monitoring: Sentry live, alerts configured
☐Custom domain: podaudit.app DNS set up
☐Privacy/terms: published, mention RSS data handling
☐Beta sign-off: 5 beta creators confirm insights are useful
☐Rollback: document Vercel revert
☐Launch: ProductHunt, Indie Hackers, r/podcasting, Twitter creator communities.

How to build it, step by step

1. npx create-next-app podaudit. 2. npm install feedparser stripe supabase node-cron. 3. Set up Supabase: creators, podcasts, episodes, analytics tables. 4. Build RSS URL input form (Next.js page + Lovable). 5. Create /api/sync-rss endpoint that parses feed and stores episodes. 6. Implement episode retention calculation (compare listener counts between sequential episodes). 7. Create churn detection logic (30%+ drop = flag). 8. Create /api/analyze-podcast endpoint that calls Claude with episode data. 9. Build Slack integration (weekly summary post). 10. Add Stripe subscription, deploy to Vercel.

Generated

March 22, 2026

Model

claude-haiku-4-5-20251001

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