Your support inbox is your product roadmap (here’s how to use it)

Your support messages are a goldmine.

They already contain the answers to what you should build next — the problem is, those answers are buried in noise.

Here’s how to extract them, with help from AI.

What tools to use for this

You’ll be using two types of tools in this workflow:

AI assistants like ChatGPT, Claude, or Gemini — to help you tag, cluster, score, and write tasks
Product research tools like Glean.io or Dovetail — to organize, search, and analyze large sets of messages

If you're working with a small number of messages, you can do everything in ChatGPT, Claude, or Gemini — just copy, paste, and prompt.

If you’re working with a bigger dataset or with a team, Glean or Dovetail can help organize everything, but you’ll still need ChatGPT (or a similar AI) to help you figure out what to fix and how to fix it.

Step 1 — Get your data ready for real analysis

AI can work with messy input, but the messier it is, the harder it is to get useful, actionable insights.

So before you do anything else, clean up the raw messages. A little structure goes a long way.

What to do:

Export the last month or two of support messages — wherever they’re stored: Intercom, HelpScout, Zendesk, Gmail, Slack, Discord, app store reviews, etc.
Clean them up:

Remove internal notes
Remove agent replies
Strip out greetings and sign-offs (“Hi,” “Thanks,” etc.)
Keep only the actual user message

Tag 20–30 messages manually

Drop the cleaned messages into a spreadsheet. You’ll tag them here so AI can learn from it and take over later.

For every message, figure out:

What kind of user sent this? (New, paid, trial, churned, etc.)
What part of the product it’s about
What type of message it is (Bug, question, feature request, cancellation, etc.)

Use AI to tag the rest

Once you've tagged your first 20–30 messages, you can use AI (like ChatGPT, Gemini, and so on) to speed up the rest.

Break the rest into small batches (about 20–30 at a time), and use a prompt like:

"Classify each message with:
Type of message (bug, feature request, question, etc.)
What part of the product it's about
What kind of user sent it (new, trial, paid, churned)
Whether the user sounds frustrated, confused, or just curious"

That’s all you need. Three labels per message. It doesn’t have to be perfect. It just needs to be clear enough for the next step: pattern detection.

Step 2 — Cluster messages by meaning, not keywords

People rarely say things the same way.

One says: “How do I cancel?”
Another: “Stop charging me.”
Another: “Close my account.”

They use different words, but they have the same problem.

You want to group these messages by what they mean, not the exact words — i.e., semantic clustering.

Here’s how:

Take your labeled messages from Step 1.
Use any tool that supports clustering by meaning — e.g., Glean.io, Dovetail, or ChatGPT Advanced Data Analysis (with a CSV).
Prompt: “Group these support messages by the problem they’re describing — even if the wording is different. Give each group a clear label and list which messages belong to it.”

You’ll end up with clear clusters like:

“Can’t cancel”.
“Pricing is unclear”.
“Users didn’t realize they had to verify their email”.
“Feature doesn’t work the way the users expected”.

Now, clear themes are starting to emerge — themes that you can actually build from.

Step 3 — Score and rank the problems

Once your messages are grouped, the next step is to determine which problems are worth fixing first.

Go back to your AI and ask it to go through each group and do three things:

Break the group into smaller issues if people are struggling for different reasons
Count how many users are affected
Describe what kind of fix each issue might need — copy, UX, feature, backend

Then, have it rank the list based on:

How common each issue is
How many paying users vs non-paying users are affected
How easy it might be to fix

Prompt example:

"For each group of user messages:
Break it into smaller problems if needed (e.g. people can't cancel for different reasons)
For each problem, tell me: 
    - How many messages mention it 
    - What % are from paying users
     - What kind of fix is likely: copy, UX, feature, or backend
Then, rank all the problems from highest to lowest priority based on how common they are, how many paying users are affected, and how easy they are to fix"

What you’ll get back is a sorted list of issues:

Quick wins
High-impact projects
Low-priority problems

This becomes the starting point for what to build next.

Step 4 — Write a task for each problem

Pick 3 to 5 of the biggest problems, then ask AI to answer the following questions:

“

What is the user trying to do?
What’s getting in their way?
What should we change?
Are there edge cases?
Write one sentence that describes the task.”

You’ll get back something like:

“Users can’t find how to cancel. The button is too hidden.
Task: Move the cancel button to the main menu. Add a confirmation.
Edge case: Don’t show it to people without the right access.”

Now you’ve got something you can build. Not an idea. A fix.

Step 5 — Make it repeatable (and maybe automated)

Once your system is in place, it only takes a few hours per month to repeat.

Here’s the monthly loop:

Export the last 30 days of support
Run through steps 3–4
Fix 1–2 things that show up often

You can automate some parts of this — like pulling the messages or tagging them — with tools like Zapier, internal scripts, or built-in exports. But even if you do it manually, it’s fast.

That's it.

This is how to build exactly what users need and want.