Google Analytics for Podcasts (Open Source)

Hacker News

We are building the open source tool Open Podcast, which provides deep listener insights to podcast hosts. Demo dashboard here -> https://openpodcast.dev/demo Get insights about your audience: - Episode performance: When do people drop out? - What are interesting topics? How long do people listen to it? - Age/Gender groups and their favorite episodes. - Supports custom graphs and SQL queries (everything is transparent and open). Tech details: - Open Source! We love to get feedback or contributions at https://github.com/openpodcast - We extract data from Spotify and Apple and store all raw- and historical data in a MySQL database - More data sources (e.g. hosting platforms, Google, etc.) are planned - The raw data can be crunched, correlated, compared, and analyzed with any SQL-compatible tool (we recommend and use Metabase for our Demo) You can run the full stack yourself using Docker images or reach out to us and we'll set up a hosted demo for you. We believe that Open Podcast will be a valuable tool for podcasters and help them grow their audience and improve their podcast's performance. Give it a try and let us know what you think! We're curious about your feedback.

Visit product View on Hacker NewsFeb 12, 2023

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11points

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Made the leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, dock · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, compatible · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, ide · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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