A good product idea usually starts with a problem someone wants solved. Reddit has thousands of discussions about frustrating workflows, expensive software, and tools people wish existed, but finding useful opportunities among those conversations takes more than a keyword search.
In this tutorial, you'll use Spidra to collect Reddit posts and comments, organize the data, and analyze it with AI to identify product ideas backed by real discussions.
We'll work with data collected from 18 Reddit threads across communities for agencies, freelancers, small businesses, and automation enthusiasts. The goal is to turn scattered conversations into a shortlist of problems worth investigating.
Find problems people are already discussing
A common approach to finding startup ideas on Reddit is searching phrases such as "I wish there was an app" or "What software should I use?"
These searches can be useful, but they also attract people pitching products, asking hypothetical questions, or sharing ideas they haven't experienced as problems themselves.
Other searches can be surprisingly noisy. For example, searching Reddit for "tired of manually" returned numerous discussions about driving manual-transmission cars. Those posts matched the keywords but had little to do with repetitive business workflows.
A more useful approach combines two sources: targeted searches that reveal potential problems and discussions from communities where your intended customers already spend time.
For this project, we focused on freelancers, agency owners, and small business operators. These groups deal with client communication, project delivery, payments, lead generation, and managing everyday operations.
We used four of Spidra's ready-made Reddit Templates to collect and investigate those conversations.
Collect Reddit discussions with Spidra Templates
Spidra Templates provide ready-made data collection workflows. Instead of building a scraper and defining selectors, you choose a task, provide its inputs, and get structured results.
Spidra has five Reddit Templates, including subreddit posts, post details, comments, user activity, and search. We'll use four of them.
Search Reddit for potential problems
Open the Reddit search template. Start with search phrases that suggest someone is looking for a solution:
I wish there wastired of manuallylooking for a toolalternative to
Run each query separately and review the results. The template returns information such as post titles, subreddit names, authors, scores, and other available metadata.
The initial search results are for discovery, not final analysis. A post asking people to suggest apps isn't necessarily evidence that a specific problem exists.
Look for descriptions of actual experiences. A discussion where someone explains that a task consumes several hours every week is more useful than a post asking strangers to invent startup ideas.
Collect posts from relevant subreddits
Next, open the Reddit subreddit posts template. For this project, we collected posts from:
r/agencyr/freelancer/smallbusiness
We also included a relevant discussion from r/automation. The subreddit posts template supports sorting by hot, new, top, or rising. Sorting by new helped us find current conversations, while searching for specific problems helped identify discussions that might not be highly upvoted.
At this stage, collect the posts that describe real situations. Don't limit the selection to the highest scores.
A discussion with five upvotes and a detailed description of a broken workflow may provide more useful research material than a popular question with hundreds of unrelated answers.
Retrieve the full posts and comments
The next two Templates give us the context needed for analysis.
- The Reddit post details template accepts a list of Reddit post URLs and returns the full available post information, including text, scores, and engagement data.
- The Reddit post comments template collects available comments from those posts, including comment text, score, reply depth, date, and URL.
We selected 18 discussions and ran both Templates against their URLs.
The exports contained 18 full post records and 306 comment records. After removing records without text and comments from deleted users or AutoModerator, 292 comment records remained for review.
These are the comments retrieved by the collection, not necessarily every comment visible on the original Reddit threads.
Understand the problem behind each discussion
A Reddit post can tell you what someone is frustrated about. Its comments can reveal what other people have experienced, which solutions already exist, and what makes the problem difficult.
One discussion from r/agency illustrates this. The author wanted recommendations for a social media management tool. Their agency was coordinating content through Google Sheets, with approvals spread across email, WhatsApp, Slack, and sometimes Telegram.
At first, this sounds like demand for another social media scheduling tool.
But the discussion revealed a more specific problem.
Several commenters focused on whether clients would actually use a new platform. They described situations where clients abandoned approval tools and returned to email or WhatsApp because those channels were easier.
Other comments recommended approval links that don't require accounts, automated reminders, mobile-friendly previews, and a timestamped record of which content version a client approved.
One comment summarized the distinction:
imo the build is the easy part, getting clients to actually adopt a custom tool is a whole different problem
The opportunity isn't necessarily to build another complete social media management platform.
A narrower hypothesis would be a client approval system that lets clients review content from a link, approve without creating an account, and gives the agency a reliable record of the decision.
That idea comes from understanding the workflow and the replies, not simply extracting keywords from the post title.
It is still a hypothesis. We would need to speak with agencies and investigate existing products before deciding whether it deserves to become a business.
Prepare the Reddit data for AI analysis
The exported JSON files contain the information we need, but the posts and comments are separate datasets.
We want to connect each comment to its original discussion so an AI assistant can analyze the conversation without losing the source.
You can do this with a small Node.js script. Install Node.js if you don't already have it, create a project folder, and place the two JSON exports inside it. Rename them posts.json and comments.json.
Create reddit-opportunity-research.mjs:
// reddit-opportunity-research.mjs
import { readFile, writeFile } from "node:fs/promises";
const load = async (path) =>
JSON.parse(await readFile(path, "utf8"));
const posts = await load("posts.json");
const comments = await load("comments.json");
const usable = comments.filter(
(c) => c.text?.trim() &&
!["AutoModerator", "[deleted]"].includes(c.author)
);The script first reads the exports and excludes comments without text, deleted-user records, and AutoModerator messages.
Continue in the same file:
const evidence = posts.map((post) => ({
title: post.title,
subreddit: post.subreddit,
url: post.url,
body: post.text ?? post.textMarkdown ?? "",
comments: usable
.filter((c) => c._sourceUrl === post.url)
.map(({ text, score, depth, url }) => ({
text, score, depth, url
}))
}));
await writeFile(
"reddit-evidence.json",
JSON.stringify(evidence, null, 2)
);
console.log(`${evidence.length} posts; ${usable.length} comments`);Run the script:
node reddit-opportunity-research.mjsUsing the collected data, the output is:
18 posts; 292 commentsThe resulting reddit-evidence.json groups each discussion with its retrieved comments.
Every record retains the original Reddit URL. This matters because an AI model should be able to point to the evidence behind a finding instead of generating an unsupported claim.
You can extend this preprocessing step to exclude repeated comments, promotional replies, or unrelated discussions. Don't automatically treat every remaining comment as useful evidence.
Use AI to identify recurring problems
Now we have the Reddit discussions in a format an AI assistant can work with.
Upload reddit-evidence.json to your preferred AI assistant and provide instructions that focus on analyzing problems rather than inventing businesses.
Here's a prompt you can adapt:
Analyze the Reddit discussions and comments in the attached JSON file to identify potential software or service business opportunities.
Group related discussions by the underlying problem, not just shared keywords.
For each problem, identify:
Include the original Reddit URLs supporting each finding. Distinguish firsthand experiences from speculation, jokes, and product promotions. Count independent discussions separately from replies within the same discussion.
Don't invent customer quotes, market sizes, willingness to pay, or evidence that isn't present in the dataset. Treat proposed products as hypotheses, not validated business opportunities.
This gives the assistant a specific research task.
Instead of asking it to produce 20 startup ideas, we're asking it to identify the problems the source material actually supports.
You should still review the results. AI can group related information, but it can also combine problems that sound similar even when they affect different customers.
What product ideas can we identify from the Reddit data?
The discussions contain several potential opportunities. Some have detailed firsthand accounts, while others are still early signals.
Here are four worth investigating further.
1. A simpler client content approval workflow
The agency management discussion described content approvals scattered across multiple apps.
Its comments repeatedly returned to client adoption, approval history, reminders, and managing content across different brands.
Potential product: A lightweight approval system that lets agencies send clients a preview link, collect feedback, and record approval of a specific version without requiring another account.
The next step would be interviewing agency account managers who already use tools such as Kontentino, rather than assuming these needs are entirely unaddressed.
We'd want to learn which workflows they cannot complete with their existing tools and whether a separate approval layer would be worth paying for.
2. A project scope and payment workflow for freelancers
One freelancer described a client who owed $250 after a website had already gone live.
The freelancer had a signed agreement but was facing a difficult decision about whether pursuing the remaining payment was worthwhile.
Another discussion about scope creep focused on handling additional requests without damaging client relationships.
A third described a client who had rescheduled seven times.
These aren't identical problems, but they involve a related challenge: managing expectations, approvals, changes, and payment milestones throughout a client project.
Potential product: A client project workflow that combines documented deliverables, change requests, approvals, payment milestones, and reminders.
However, this is already a crowded category. The research would need to identify a specific audience or workflow that existing tools don't serve well.
A developer building a product for freelance web designers, for example, might discover different needs from someone providing ongoing marketing services.
3. Quality control for creative deliverables
Another discussion in r/agency asked how editors verify large batches of video files before sending them to clients.
A single campaign can require different aspect ratios, durations, languages, subtitle versions, and client-specific exports.
The comments described using spreadsheets, naming conventions, manual spot-checking, and contact sheets.
One commenter described missing a language version that required captions and spending time correcting the delivery.
Potential product: A file validation tool that checks a delivery folder against a list of required files and specifications, flags missing versions, and produces a review report before handoff.
The evidence here is narrower than the approval discussion. We'd need additional conversations with production teams to understand how frequently these mistakes happen and which checks existing software already performs.
Still, it's a specific problem with a clear workflow and measurable outcome.
4. Managing shared AI workflows across clients
An agency owner described a problem with reusable AI skills.
A workflow written for one client often needs changes for another. But once every client has a customized copy, updates to the original become difficult to distribute.
The discussion described separating reusable instructions from client-specific knowledge, while commenters raised concerns about outdated documentation, changing file paths, and version management.
Potential product: A management layer for reusable AI workflows that separates shared instructions from client settings, tracks versions, and checks whether updates introduce unexpected changes.
The idea would need to compete with existing development workflows and tools. The first research question is whether agencies need a standalone product or simply better practices around version control and documentation.
The discussion gives us a useful starting point, but not proof of a market.
How to compare the opportunities
Finding an interesting complaint is the beginning of product research. To decide which opportunity deserves more attention, consider several questions.
Question | What to look for |
|---|---|
Is the problem repeated? | Independent people describing similar situations |
Does it have a clear audience? | A defined role or type of business affected |
What happens today? | Existing tools, spreadsheets, manual workarounds |
Is the problem expensive? | Evidence of lost time, money, mistakes, or missed opportunities |
Are current solutions insufficient? | Specific shortcomings rather than general dissatisfaction |
Can the idea be tested cheaply? | A small prototype or manual service that could validate the need |
Don't equate comment counts with demand. A popular discussion may attract people who enjoy debating a topic but would never buy software to address it.
Likewise, one detailed firsthand account can be worth investigating even when the discussion receives little attention.
The most useful output from this process is a prioritized research shortlist, not a declaration that you've discovered the next successful startup.
Turn the findings into a research dashboard
Once you've reviewed the opportunities, you can turn the analysis into a simple dashboard.
Each opportunity can have a title, target audience, problem description, supporting Reddit links, existing solutions, proposed MVP, and evidence rating.
For example, clicking the client approval opportunity could reveal the original agency discussion, selected comments, and the specific requirements mentioned by participants.
A second view could group discussions by problem category, making it easier to compare agency operations, client management, creative production, and automation.
This is also a useful project for an AI coding agent. Give it the cleaned JSON and a clear specification for the dashboard, keeping the original URLs attached to the corresponding evidence.
Rather than creating an interface around invented market statistics, let the dashboard present what was collected and what still needs validation.
Make the research repeatable
A one-time Reddit collection can produce useful insights, but ongoing discovery can help identify new discussions as they appear.
Spidra provides several ways to automate this workflow.
You can use the Spidra API or SDKs to run Templates programmatically, rather than collecting everything manually through the dashboard.
In Node.js, for example, getTemplate() retrieves a template's required inputs, and runTemplate() runs it and returns the results after handling the normal job-polling process.
You can also save a configured Template as a preset and use Spidra Monitors to run collection jobs on a schedule and watch for new or changed results.
That makes it possible to maintain an ongoing source of discussions for research.
However, you don't need to analyze every new Reddit post immediately. A weekly or monthly review may be more useful for product discovery than a constant stream of loosely related complaints.
Start with a few relevant communities and expand only when your findings suggest a reason to look elsewhere.
Validate the problem before building the product
Reddit can help reveal problems and the language people use to describe them. It cannot reliably tell you how large a market is or whether someone will pay for your solution.
The posts are self-selected, the comments may be incomplete, and some replies promote tools or businesses.
A useful next step is to investigate existing products and speak directly with people who experience the problem.
Ask how they handle it today, when it last happened, what it cost them, and what they've already tried.
For the client approval idea, that could mean interviewing agency account managers, watching their actual approval process, and testing whether clients will use a simpler alternative.
For the creative delivery idea, it could mean giving production teams a small file-checking prototype and seeing whether it catches mistakes they currently miss.
The Reddit evidence helps determine which questions to ask and whom to approach.
When collecting Reddit content, also review Reddit's terms and the rules that apply to your use. See our guide to web scraping and legal considerations for more information.
Start with the problem, then build
The strongest result from this project wasn't a list of startup ideas. It was seeing how much more specific an opportunity became when we examined the discussion behind it.
A request for a social media management tool turned into a question about client adoption and approval workflows. Complaints about freelance clients revealed problems around project boundaries and payment. Discussions about video delivery exposed practical quality-control needs.
Using Spidra, we collected the posts and comments as structured data, joined the sources, and prepared them for AI-assisted analysis.
You can follow the same approach for another audience or industry.
Open Spidra's Reddit Templates, collect a few discussions about a problem you understand, and use the evidence to develop a product hypothesis worth testing.