Track every episode.
Make them earn forever.
I log every podcast episode in one structured file with date, title, length, thumbnail, excerpt, product link, and stats. Once a week a best-of bot reads that file, picks a top dormant episode, and re-airs it to my audience. New listeners discover it, click the product link, and the old episode earns again. The catalog stops being dormant and starts being a quiet sales engine.
Old podcast episodes that quietly keep earning.
Most podcasters ship an episode, ride the first-week bump, and watch the play count flatten. The episode then sits in the feed forever, earning nothing. There is a different way. Track every episode in one structured file, enrich each row with stats and an excerpt, then let a small weekly bot pick a top dormant one and re-air it to your audience. New listeners discover it, click the embedded product link, and the back catalog starts earning again. Here is who it is for, what goes wrong without it, how it works, and what you get back.
Who it’s for
Podcasters, interview-show hosts, course creators, and anyone who has shipped more than 20 episodes that quietly disappeared into the feed. Especially useful if every episode links to a product, service, or lead magnet you actually want to sell. If you have a back catalog and no system to mine it, this is for you.
What goes wrong
Without a tracker, episodes are scattered across a podcast host, a Drive folder, and your memory. You cannot find which episode covered which topic. You cannot rank by performance because no stats are written down. The catalog is invisible to you and to new listeners. A back catalog you cannot search is a back catalog you cannot monetize.
How the machine works
Every new episode auto-writes a row in a tracker (Google Sheet, Airtable, or Notion) with eight fixed columns. A best-of bot reads the stats column once a week, ranks dormant episodes, picks the top one, and posts a re-air to your audience. The post links to the original episode, which carries an embedded product link. You do nothing weekly. The bot mines the catalog for you.
What you get back
A searchable catalog of every episode you ever shipped, with stats and excerpt for every row. A weekly re-air that quietly drives new listeners through the embedded product link. Roughly $10 to $15 per re-air on autopilot, around $500 to $750 a year, compounding into thousands as the catalog grows. Old episodes that keep paying, on a schedule.
I was sitting on a back catalog I could not even search.
After the first 30 podcast episodes, the problem hit. I knew I had covered a topic six months earlier, but I could not find which episode it was. I scrolled through my podcast host, opened five tabs, and gave up. The information was in there somewhere, but it was useless because nothing was indexed.
Worse, the stats lived in a place I never looked. I did not know which old episodes had quietly outperformed the rest. New listeners who joined six months later had no way to discover the strong episodes from before they subscribed. The catalog was technically there, practically invisible.
I built a small tracker. Every podcast episode now lives as one row in a single file, with eight columns: date, title, length, thumbnail, embed link, excerpt, product link, and a stats snapshot. New episodes auto-write their own row from the RSS feed. The excerpt column makes the whole catalog searchable, so I can ask in plain English which episode covered a topic and get the timestamp in one second.
Then the second piece of the engine clicked into place. A small weekly bot reads the stats column, ranks dormant episodes, and picks the top one. It posts a re-air to my audience as a listener favourite of the week. The post links to the original episode page, which has the embedded product link. New listeners who joined in the last six months suddenly discover an episode they never heard. A small share click the product link.
The numbers are modest by design and real on the page. Each re-air earns me around $10 to $15 in product clicks from listeners who did not know the episode existed. Once a week for 50 weeks a year is roughly $500 to $750 from re-airs alone, on top of regular podcast revenue. After two years the catalog brings ~$1,500 to ~$2,000 a year on autopilot. Over five years that compounds into ~$8,000+ from episodes that would have been forgotten.
Proof point: the catalog this engine runs against is my own podcast Freedom by Choice, the full playlist is here on YouTube, so the modest weekly re-air numbers on this page are checkable against a real back catalog.
Three moves that turn a dormant catalog into a weekly sales channel
What made this work was treating the back catalog as an asset that earns instead of a feed that scrolls. Every old episode is a small machine waiting to be re-aired. The framework hangs on three moves: track every episode in a fixed structure, enrich the row with the data that ranks it, then let a small bot mine the catalog on a schedule.
Track every episode in one structured row
The tracker only works if it has the same eight columns for every episode. Date, title, length, thumbnail, embed link, excerpt, product link, and stats. Skip a column and you break the engine. The excerpt is the column most people skip and it is the most important one, because it is what makes the catalog searchable months later. Once the row is written, the episode stops being lost in the feed and becomes a piece of inventory the rest of the system can read.
Enrich the row with stats the bot can rank
A row without stats is a row the bot cannot pick. Every episode needs a stats snapshot updated on a schedule: downloads, listen-through rate, product-link clicks, or whatever single number tracks performance on your host. The bot reads this column to rank dormant episodes. A consistent stats column is the difference between a bot that picks the strong episodes and a bot that picks at random. This is the column that decides whether the rebirth is worth anything.
Let the best-of bot mine the catalog weekly
Once a week the bot does three things. It ranks dormant episodes by the stats column. It picks the top one that has not been re-aired in the last six months. It posts a listener-favourite-of-the-week post to your audience, linking to the original episode page with the embedded product link. The cadence is what makes the small per-re-air number compound. Modest weekly, real yearly. The catalog earns while you ship the next episode.
Once those three moves are in place, the back catalog stops being dead weight and starts being a quiet weekly sales channel. The tracker is the seed, the stats are the rank, the bot is the rebirth.
Before the tracker
- Episodes scattered across host, Drive folder, and memory
- Could not find which episode covered which topic without scrolling
- Strong old episodes invisible to listeners who joined later
- Product links in old episodes never clicked because nobody re-listened
- Back catalog earning nothing past the first-week launch bump
After the tracker
- Every episode logged in one searchable row with eight fixed columns
- Ask the catalog in plain English, get the right timestamp in one second
- Best-of bot re-airs a top dormant episode every single week
- Embedded product link in old episodes starts earning ~$10 to ~$15 each re-air
- Back catalog brings ~$500 to ~$750 a year from re-airs, compounding over time
Prompt 1: design the tracker schema for your podcast catalog
Before you log a single episode, you need the schema locked. Eight columns, same for every row, no exceptions. Use this prompt to adapt the schema to your specific show so the rest of the machine works without rework later.
Tracker schema designer
Act as a podcast operations strategist. I want to build a tracker file that logs every episode of my podcast in one structured row, so I can search the catalog later and a weekly best-of bot can rank dormant episodes. My podcast topic is: [describe in one sentence]. My podcast host is: [Buzzsprout, Spotify for Podcasters, Captivate, etc]. Every episode links to: [the product, service, or lead magnet I want to sell]. Propose a tracker schema with exactly eight columns, including date, title, length, thumbnail URL, embed link, full excerpt, product link, and a stats snapshot. For each column, explain in one line what data type goes in it and where I source it. Then list the three columns that should be filled automatically by a workflow versus the ones I fill manually.
The output of this prompt is the schema for the whole tracker. Lock it before you log episode one. Changing the schema after fifty rows is painful and breaks the bot.
Prompt 2: write the excerpts that make the catalog searchable
The excerpt column is what makes the catalog searchable months later. Every episode needs one. Use this prompt to turn an episode transcript or audio file into a clean excerpt that the search layer can index.
Episode excerpt writer
Act as a podcast content editor. I run a tracker where every episode has an excerpt column that makes the back catalog searchable later. I need to turn an episode transcript into a clean, searchable excerpt that captures every topic covered with a timestamp. Episode title: [paste] Episode length: [paste] Transcript or summary: [paste] Produce an excerpt with two parts. Part one: a 120-word summary of the episode in plain English written in the first person. Part two: a topic index with one line per topic, each line in the format `MM:SS - topic in 6 to 10 words`. Cover every distinct topic discussed in the episode so that a future search like 'which episode covered lead magnets' finds the right line.
The excerpt is the column most people skip and the column that decides whether the catalog is searchable. Run this prompt for every episode you have ever shipped.
The 3-minute overview of how this works
Before the build steps, watch this short overview. It’s the exact video from the Automations Made Easy page, and it walks through the mechanics behind machines like this one. 1,000+ students have used these mechanics to save two hours a day, with zero coding.
Want to learn the mechanics behind back-catalog automations like this?
I teach the same mechanics that make this rebirth engine work inside Automations Made Easy. 1,000+ students have used these mechanics to save two hours a day and pull money out of assets they had already built. No coding required.
Week one: one re-air, one quiet sale you forgot you owned.
Most people quit a system like this in the first week because the first re-air feels too quiet. There is no big spike, no viral moment, no inbox flood. The first week of the rebirth engine is meant to feel small. The point is to confirm the row is written correctly, the stats column ranks sensibly, and the re-air post lands cleanly in front of the audience.
Week one looks like this. The tracker has the first batch of episodes logged with proper excerpts. The bot ranks them, picks one dormant episode that scored well in the past, and posts the re-air. A small share of listeners who joined after the original drop tap through. One or two click the embedded product link. That is roughly $10 to $15 of revenue from an episode you had written off as done.
The point of the first week is not the dollar amount. The point is to prove the loop closes: tracker writes the row, bot ranks correctly, post lands, product link gets clicked, money lands in the account from an asset you already owned.
From there the maths is simple. ~$10 to ~$15 a week across 50 weeks is ~$500 to ~$750 a year. After two years the catalog brings ~$1,500 to ~$2,000 annually because there are more dormant episodes to mine and more new listeners to expose them to. Over five years that compounds into ~$8,000+ from a back catalog you had already paid to produce.
All of this runs while you record the next episode, take a holiday, or sleep. The bot does not care what day it is. The tracker holds the inventory, the stats column does the ranking, the re-air does the selling.
Prompt 3: rank dormant episodes for the weekly re-air
The weekly best-of bot needs a ranking rule. Use this prompt to define the exact rule that decides which dormant episode gets re-aired next, so the bot picks strong ones, not random ones.
Best-of ranking rule
Act as a back-catalog strategist. I want to pick one episode each week from my podcast tracker to re-air to my audience as a listener favourite. The bot needs a ranking rule that picks strong dormant episodes, not random ones. My tracker has these data points per episode: [list the stats columns you actually track, for example downloads, listen-through rate, product-link clicks, share count]. My audience size is: [number]. A re-air cooldown should be at least: [6 months, 9 months, etc]. Write a clear ranking rule in plain English that the bot can follow each week. The rule must combine the stats columns into a single score, exclude any episode re-aired inside the cooldown, and break ties in a sensible way. Show the rule as a numbered list of steps the bot follows from top to bottom.
This rule is the brain of the rebirth bot. Get it right once and the bot picks well every week for the rest of the catalog’s life.
Prompt 4: write the listener-favourite post the bot publishes weekly
The bot picks the episode. The bot also needs the words. Use this prompt to write the template the bot fills in with the chosen episode each week, so the re-air post sounds like you, not a script.
Re-air post template writer
Act as a podcast marketing copywriter. I run a weekly best-of bot that picks one dormant episode from my podcast catalog and posts it to my audience as a listener favourite of the week. The bot fills in a template each week with the chosen episode. My podcast tone is: [casual / business / educational / your three adjectives]. The post is published on: [newsletter / Substack / Twitter / LinkedIn]. Every episode has a product link the post should drive clicks to. Write three template versions the bot can rotate through. Each template uses placeholders for episode number, episode title, one-line hook from the excerpt, the original publish date, and the product-link CTA. Each template must end with a clear call to listen and a soft second call to the product link. Mark which template performs best for a re-air audience that has never heard the episode.
These three templates are what the bot pastes every week. Spend an hour on them. The compounding payoff lasts the life of the catalog.
The exact build, step by step
Pick the tracker file (Sheet, Airtable, or Notion)
Open a clean Google Sheet, Airtable base, or Notion database. Pick one and stick with it. The choice matters less than the commitment. The tracker becomes the single source of truth for every episode you have ever shipped and ever will. Put it somewhere you can read from a script later (Airtable has the cleanest API, Sheets is the simplest, Notion is the prettiest). Name the file plainly so the rest of the system can reference it without confusion six months from now.
Lock the eight columns that make every episode searchable
Add the eight columns in this exact order: episode number, date published, title and length, thumbnail URL, embed link, full excerpt, product link, and stats snapshot. The excerpt column is the one most people skip and the one that decides whether the catalog is actually searchable later. The product link column is the one that decides whether the re-air earns anything. Lock the schema before you log episode one. Changing it after fifty rows is painful and breaks the bot downstream.
Wire the auto-fetch from your podcast RSS feed
Set up a small workflow (n8n, Make, or Zapier all work) that watches your podcast RSS feed. Every time a new episode publishes, the workflow reads the feed, pulls date, title, length, thumbnail, and embed link, and writes a new row in the tracker automatically. You fill in the excerpt and product link yourself once a week, in fifteen minutes. The auto-fetch is what stops the tracker from becoming yet another file you stop maintaining after a month.
Add the AI search layer over the excerpt column
Point a small AI lookup tool at the excerpt column. Airtable AI, a custom GPT, or a tiny n8n flow over the rows all work. Once it is live, you can ask the catalog in plain English which episode covered a topic and get the right episode plus the exact timestamp in one second. This single move stops you from ever again scrolling your podcast host looking for an old episode. It also makes the catalog re-usable inside other content (Substack posts, social posts, sales pages).
Build the weekly best-of bot that re-airs the top dormant episode
Schedule a small workflow that runs once a week. It reads the stats column, ranks dormant episodes by a single performance number (downloads, completion rate, or product-link clicks), excludes anything re-aired inside the last six months, and picks the top one. It then publishes a listener-favourite-of-the-week post to your audience (newsletter, Substack, Twitter, LinkedIn) using a template that links to the original episode page. The bot runs for free, every week, for the rest of the catalog’s life.
Connect the re-air to a product link and track the clicks
Every episode page already has an embedded product link. The re-air post sends new listeners back to that page. Use a per-post UTM or a Bitly tag so you can see which week brought which click. After eight weeks you know which re-air format performs best, which episodes carry the catalog, and how much revenue the rebirth engine adds on top of regular podcast revenue. The catalog stops being dormant and becomes a measurable weekly channel.
Tracker locked
One file, eight columns, every episode logged with date, title, length, thumbnail, embed, excerpt, product link, stats.
Auto-fetch wired
RSS feed writes a new row every time an episode publishes, you only fill excerpt and product link.
Search layer on
Ask the catalog in plain English, get the right episode and timestamp back in one second.
Weekly re-air live
Best-of bot ranks, picks a top dormant episode, posts the re-air with the product link, every single week.
Build this catalog rebirth engine inside the same playbook 1,000+ students use
Automations Made Easy teaches the mechanics behind back-catalog bots like this one. Step by step, no code, plain English. Pull money out of content you have already paid to produce.
The six months after I switched it on
Here is the shape of the first six months after I turned the weekly re-air on for my podcast catalog. The line is intentionally modest in the early months and steady from there, because that is how a quiet rebirth engine actually behaves.
Revenue from weekly re-airs per month, after switching on
Three things matter on this chart. The line stabilises around $50 to $60 a month and stays there. The revenue comes from episodes you had already written off. And every single one of those months happens without you recording, editing, or promoting a single new piece of content.
What other students built
I teach the simple skills behind machines like this in Automations Made Easy. Students who built their own version sent back what changed in their first month.
“I had 84 episodes sitting in my feed and I could not tell you what any of them were about. After two weeks of building the tracker, I could search the whole catalog from a single prompt.”
“The first re-air week I almost turned it off because the post felt too quiet. Three clicks on the product link from that one post paid for the whole setup. Then it kept happening every week.”
“What surprised me is that my best-performing episodes were ones I would never have picked myself. The stats column showed me a clear winner I had forgotten existed.”
“I stopped re-recording old material. The bot just re-airs the original. Same revenue, none of the work, and the catalog gets richer every month instead of more cluttered.”
What’s inside Automations Made Easy
AME isn’t a library of pre-built automations. Every business is slightly different. What’s reusable across all of them is the underlying mechanics: how to set up little machines that listen, write, and follow up while you sleep, and how to wire the pieces together without writing code.
The program walks you through six modules: The Right Tools (the cost-effective, no-code stack I actually use), Task Selection Mastery (which automations are worth building first), Design Secrets (mapping an automation before you build it), Zero to Hero (complete beginner to confident automator), Real-World Application (we build a full automation together, end to end), and Monetization Mastery (turn the skill into a side-business).
It also includes done-for-you templates you import in two clicks, over-the-shoulder training videos, and the same playbook 1,000+ students have used to save two hours a day. No coding required. If you can copy and paste, you can build this.
The podcast tracker: common questions
Pulled from what readers and Automations Made Easy students ask most.
Do I need a big back catalog for this to work?
No. The tracker is worth setting up from episode one, because the cost of building it is the same whether you have 5 episodes or 500. The rebirth engine becomes meaningful once you have around 20 to 30 episodes in the tracker, because that is when there are enough dormant ones to rank and re-air without repeating yourself. If you already have 50 plus episodes, the engine starts paying back from week one.
Should I use Google Sheets, Airtable, or Notion?
Any of the three works. Pick the one you already pay for and stop second-guessing. Sheets is the simplest to set up and the easiest for the auto-fetch workflow. Airtable has the cleanest API and the best built-in AI search. Notion is the prettiest if you also publish the catalog as a public page. The choice matters less than locking the eight columns and committing to fill them every week.
What if my podcast host does not give me good stats?
Most hosts give you downloads at least. That is enough for the bot to rank. If your host gives you completion rate or chapter retention, even better. If you have nothing, fall back to a manual signal: which episodes get the most replies, the most shares, the most comments. Write that signal in the stats column as a simple number. The bot does not care where the number comes from, only that the column is consistent.
How much do the re-airs actually earn?
Conservatively, around $10 to $15 per re-air for a small to mid-sized audience. Some weeks will be $5, some will be $35, the average lands in that range once the system stabilises after the first month. Across 50 weeks a year that is around $500 to $750 from re-airs alone, on top of whatever your regular podcast revenue is. Bigger audiences scale this number up. The point is that the per-week amount is small, believable, and real.
Do I need to code to set this up?
No. The tracker is a Sheet or Airtable base. The auto-fetch is a no-code workflow in n8n, Make, or Zapier. The AI search layer is a built-in feature in Airtable or a tiny GPT. The weekly best-of bot is one more no-code workflow on a schedule. The skills you need are designing the schema and writing the post template, which is exactly what Automations Made Easy teaches.
More Money Makers like this one
Built from the same handful of mechanics. Each one captures, converts, or earns money that would have walked out the door.
Run this catalog rebirth engine yourself, or learn the mechanics inside Automations Made Easy.
If you want to learn the mechanics behind back-catalog automations like this and build your own at home, Automations Made Easy is the playbook. Step by step, no code, plain English. If you want to talk through which tracker shape and ranking rule would work for your specific show first, I take a small number of consulting clients each month.
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