I remember staring at my Transistor.fm dashboard after a big episode launch. Downloads spiked, but I needed those numbers outside the platform for my team reports. You know the drill: sponsors want proof, marketers crave trends.
Transistor makes it simple to grab that data. No fancy tools required. I pull CSVs or tap the API, then analyze in Sheets. This guide walks you through my exact steps.
What Podcast Analytics Data Transistor Exports
Transistor tracks key metrics like episode downloads, player breakdowns, and trends. I export these to spot patterns fast.
Downloads show totals per episode. You see averages over 7, 30, or 90 days post-publish. Player data reveals if listeners use Spotify or Apple Podcasts most.
Subscribers give a rough count since RSS feeds limit precision. Spikes highlight sudden jumps, often from shares or features.
I start here to understand my audience. For example, one episode’s Apple spike meant I pushed harder there next time.
This dashboard view feels like a treasure map. Charts rise and fall with listener habits. Exports turn it into actionable files.
High-level stats stay on the page. But CSVs let me filter and chart deeper. I combine them for sponsor decks.
Step-by-Step Guide to Export CSV Files
Log into Transistor.fm. Pick your podcast. Head to the Analytics tab. That’s your starting point.
Scroll to Episode Breakdown. This section lists every episode with download counts. Choose a timeframe from the dropdown: today, past week, or custom dates.
Click the Export button. Select your range if needed. Hit Download Episodes CSV. The file lands in seconds.
Open it in Google Sheets. Columns include episode names, daily downloads, and publish dates. Sort by spikes to find winners.
Next, check Podcast Players. Scroll there. Click Export. Pick dates, then download. This CSV breaks down apps and devices.
I do this weekly. One file per section keeps things clean. Stack them later for full reports.
That export click pulls gold. My laptop hums as numbers flow to a spreadsheet.
These CSVs work in Excel or Numbers too. No special software. I add charts for visuals.
For deeper dives on spikes, see Transistor’s spike troubleshooting guide. It matches CSV steps.
Tap the API for Automated Analytics Exports
CSVs suit quick pulls. But I automate with Transistor’s API for history or scripts.
Grab your API key from account settings. Docs live at developers.transistor.fm.
Use GET /analytics/:showid for podcast-wide data. Replace :showid with your ID. It defaults to 14 days of daily downloads.
Add ?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD for ranges. JSON returns days and counts. Single episodes use /analytics/episodes/:episodeid.
I wrote a Python script once. It fetches months of data, saves as CSV. No daily logins.
Check the API changelog for endpoints. Simple calls yield raw power.
This beats manual exports for teams. Pull data into tools like Google Data Studio.
If you repurpose episodes into clips, pair this with Transistor.fm analytics for viral segments.
Handle Limitations and Smart Workarounds
Transistor shines, but limits exist. No one-click full history export. You pick ranges per CSV.
Old data from prior hosts stays behind. I screenshot those or grab old CSVs.
Subscriber counts approximate. RSS nature causes gaps. Focus on downloads instead.
API skips player details. Use CSVs there.
Workarounds fix most issues. Stack multiple CSVs in Sheets. Copy columns, paste, sort.
Add prefixes like Podcorn in Settings > Analytics Prefixes. It layers extra stats.
Script the API for automation. Python or Zapier pulls data on schedule.
Check Apple or Spotify dashboards separately. They fill unique gaps.
Screenshots work for quick shares. Advertisers love 30-day averages.
These steps keep my reports tight. No data left behind.
Key Takeaways for Your Exports
Exporting podcast analytics from Transistor.fm saves hours. CSVs handle basics; API scales up.
You now pull downloads, players, and trends on demand. Use them for pitches or planning.
My workflow starts simple. One dashboard, clicks, done. Data fuels growth.
Stick to ranges and stack files. Your reports impress. What’s your next export?
