Podcaster monthly revenue.

Podcast revenue scales with download count, ad CPM, and sponsorship frequency. Host-read ads (pre-roll, mid-roll) command 20-50 CPM because listener engagement is high. Programmatic ads (auto-inserted) run 5-15 CPM. Two ads per episode (one mid-roll, one pre-roll) is standard. Patreon-style subscriptions supplement ad revenue and often exceed it at smaller scale.

10,000 downloads/episode × 4 episodes/month × 2 ads × 25 CPM ÷ 1000 = 2,000/month ad revenue. Add 500/month Patreon: 2,500 total monthly, 30,000 annual. At this scale a podcast is still a side project for most; crossing 100k/year typically needs 50k+ downloads/episode or a premium sponsor ladder.

Dynamic ad insertion (DAI) is changing the math. Modern podcast hosts insert new ads into old episodes, so a back catalogue of 200 episodes with 1k downloads each per month = 200k extra impressions/month beyond new releases. Well-produced shows with 2-3 year back catalogues often earn 30-50% of revenue from old episodes, compounding over time.

A worked example

Try the defaults: avg downloads per episode of 10,000, episodes per month of 4, ad cpm of 25, ads per episode of 2. The tool returns 2,500.00. You can adjust any input and the result updates as you type — no submit button, no reload. That's the real power here: seeing how sensitive the output is to one or two assumptions.

What moves the number most

The result responds to Avg Downloads per Episode, Episodes per Month, Ad CPM (£), Ads per Episode, and Patron Revenue Monthly. Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

The formula behind this

Impressions per episode = downloads × ads. Ad revenue = impressions × CPM ÷ 1,000. Monthly = per-episode × episodes. Total = ads + patrons. Everything the calculator does is shown in the formula box below, so you can check the math against your own spreadsheet if you want.

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.