idea sourcing
How to find startup ideas: 38 methods that actually work
Startup ideas are not invented at a whiteboard. They are found, in public, in the places where people describe what is not working — and the difference between a founder who has ideas and one who does not is almost always a reading habit rather than a talent. Below are 38 sourcing methods, grouped by what kind of evidence they produce, each with the specific place to look and the specific result that counts as a signal instead of noise.
By The Marketinque TeamReviewed September 2026
First, the filter: what makes an idea worth anything
Every method on this page can produce output forever. Volume is not the constraint and never was — the constraint is judgement about which observation is a market and which is one person having a bad week. Before you run a single method, fix the four questions you will use to throw things away.
- FrequencyHow often does this problem occur?
- A problem someone hits weekly gets bought. A problem someone hits once a year gets bookmarked. Frequency is the single best predictor of whether a tool becomes a habit or a trial that lapses.
- BudgetWho already spends money here?
- Existing spend — on a competitor, a freelancer, a salaried person, or a stack of subscriptions — proves the job is worth paying for. If the current solution is “nobody does this,” you are selling a new behaviour, which is a much longer road.
- UrgencyWhat breaks if it is not solved?
- Painkillers sell, vitamins get evaluated forever. Look for problems attached to a deadline, a compliance requirement, a customer complaint, or lost revenue — something with a consequence and a clock on it.
- ReachabilityCan you find a hundred of these people this month?
- A perfect idea for a group you cannot reach is not an idea, it is a hobby. Before you build, name the subreddit, the association, the conference, the newsletter, or the search query where your buyer is already gathered.
An idea that passes all four is worth two weeks. An idea that fails reachability is worth nothing yet, however good it sounds — which is why the reachability answer is also the first draft of your demand generation plan.
Mine complaints about software people already pay for
The highest-quality signal in idea sourcing is a paying customer describing what their tool will not do. Budget is already proven, the category is already understood, and the buyer has already been through a purchase. You are not looking for a market — you are looking for the gap inside one.
Read the one- and two-star G2 and Capterra reviews
Sort a software category by lowest rating and read every review from the last eighteen months. Discard the price complaints — those are people who bought the wrong tier. What you want is the sentence pattern “we love it, but we still export to a spreadsheet to do X,” which is a paying customer specifying a product that does not exist yet.
Where to look: G2 category pages with the rating filter set to 1–2 stars; on Capterra the structured “Cons” field gives you the same thing pre-separated. Paste them into a sheet and tag each one with the job it failed at, not the feature it lacked.
What counts as a signal: The same failed job named by ten or more reviewers across two or more competing tools. One angry reviewer bought the wrong product; ten describing the same workaround is a market.
Scrape App Store and Play Store reviews for the same patterns
Mobile reviews are blunter than B2B reviews because nobody is writing them for a vendor relationship. Critical reviews cluster around three things: a workflow that takes too many taps, data that will not sync, and a redesign that broke a habit. The third one is a migration window — users actively looking to leave.
Where to look: The public review feeds on each store, sorted by Most Critical, read per country. On Google Play, filter by star rating and by app version so you can see exactly which release turned sentiment.
What counts as a signal: A rising share of critical reviews naming one workflow, on an app whose developer has stopped replying. Demand plus abandonment is the opening; demand alone just means the incumbent has work to do.
Read the public feature-request boards
Canny, Productboard, and UserVoice portals are ranked backlogs that vendors publish voluntarily. A request sitting at the top of the votes with a “planned” tag two years old is not planned — it is a decision not to build. That decision is the space you get to occupy.
Where to look: Search `site:canny.io <category>` or “<product> feature requests” and sort by votes. Read the comment threads under the top requests, not just the titles.
What counts as a signal: A high-vote request with no ship date whose comments describe manual workarounds. Those comments are your MVP specification, written by the people who will pay for it.
Read Product Hunt comments instead of counting upvotes
Upvotes measure how well a launch was promoted. Comments measure what people actually wanted when they arrived. The most useful artifact on any launch page is the repeated “does it do X?” question — a requirement the whole category has failed to serve, asked by people who showed up looking for it.
Where to look: Product Hunt product pages, comment tab. Pay particular attention to maker replies of the “not yet, it is on the roadmap” variety, which mark unbuilt surface area publicly.
What counts as a signal: The same question asked on three different products in one category within a year. That is a requirement the category treats as optional and buyers treat as table stakes.
Follow churn post-mortems in founder communities
Founders write up why customers left in far more detail than they write up why customers stayed. A churn reason is a demand statement in the negative: it names a job the product could not do for a segment it could not serve. Collect enough of them and the underserved segment names itself.
Where to look: Indie Hackers threads and MicroConf talks, searched for “churn,” “cancelled,” and “why customers left.” Founder podcast transcripts work too and are less picked over.
What counts as a signal: A churn reason that is structural — the tool architecturally cannot do X for this segment — rather than incidental, like weak onboarding. Structural churn is a product someone else has to build.
Mine communities for problems described in the buyer’s own words
Communities give you the vocabulary. Review sites tell you what is broken; forums tell you how the person suffering it talks about it, which is what your landing page, ads, and search targeting will eventually have to match. Read these for language as much as for ideas.
Run a systematic mining pass on the subreddits your buyer lives in
Pick the five to ten subreddits where your target buyer actually posts, then read the top posts of the last year sorted by upvotes, filtered to complaint and question threads. Doing it by hand for a week beats any tool, because you are building a mental model, not a keyword list.
Where to look: Subreddit search with the Top / Past year filter, plus the “new” feed for a fortnight to catch what has not accumulated votes yet. Read the comment sections — the correction to a wrong answer is often the real insight.
What counts as a signal: A complaint that recurs across months with different authors and no linked solution. Recurrence with no product link is the definition of an unserved need.
Search Reddit for the exact words frustration uses
People do not post “I have an unmet need.” They post “I am so frustrated,” “is there anything that,” “I need a way to,” and “am I the only one who.” Searching the emotional vocabulary instead of the topic vocabulary surfaces the posts where a problem is being felt rather than discussed.
Where to look: Reddit search restricted to a subreddit: `subreddit:<name> "I wish there was"`, and the same for “frustrated,” “I need a way to,” and “has anyone found a tool.” Google works too: `site:reddit.com/r/<name> "is there a tool"`.
What counts as a signal: A frustration post with more comments than upvotes. That ratio means people came to say “me too” rather than to press a button — the cheapest proof of shared pain you can get.
Harvest the ideas people abandoned for tooling reasons
r/startups and r/entrepreneur are full of post-mortems that end in “it was too complex to build” or “the API did not exist yet.” Those constraints expire. An idea that was correctly abandoned in 2021 for cost or capability reasons may be trivial now, and the abandonment thread already contains the demand research.
Where to look: Search those subreddits for “gave up on,” “too hard to build,” “shut it down,” and “post-mortem.” Read for the blocker, then ask whether the blocker still exists.
What counts as a signal: A well-argued abandonment where the stated blocker has since been removed by a new API, a price drop, or a model capability. You are inheriting validated demand with the obstacle deleted.
Find fast-growing subreddits with no tool built for them
A community whose membership is climbing is a market forming in public. If nobody has built for it yet, the first useful tool tends to become the default by acclamation, because the community itself does the distribution. Growth rate matters more than absolute size here.
Where to look: Subreddit growth trackers such as subredditstats-style services, sorted by growth rate rather than subscriber count. Then check the sidebar and the pinned resources: an empty tools list is the whole thesis.
What counts as a signal: Steep membership growth, an active weekly thread, and a sidebar with no software links in it. Size is not the point — an unserved 40,000-member community beats a saturated 4,000,000-member one.
Use X advanced search for the “I wish there was a tool” phrasing
X advanced search is an unusually direct demand instrument, because people announce product wishes there casually and publicly. The phrasings are formulaic, which means you can run the same handful of queries on a schedule and treat the results as an inbound feed.
Where to look: X advanced search with exact phrases: “I wish there was a tool,” “someone should build,” “why is there no,” “does anyone know a tool that.” Add `min_faves:20` to filter for wishes other people agreed with.
What counts as a signal: A wish tweet with meaningful engagement and a reply thread of people naming the same gap. Engagement is the crowd validating the problem before you have written a line of code.
Watch for price complaints about specific SaaS tools
Price complaints are underrated because they look like whining. They are not: a person complaining about a $400/month tool has told you the job is worth paying for, that they are currently paying, and that the incumbent has priced a segment out. That segment is a reachable market with a proven need.
Where to look: X and LinkedIn searches for “<tool> pricing,” “<tool> is too expensive,” and “<tool> alternative.” Cross-reference with the alternative-seeking searches on Reddit for the same product.
What counts as a signal: Complaints concentrated in one identifiable segment — solo operators, agencies under ten people, non-profits — rather than spread evenly. A priced-out segment is a product; general grumbling is not.
Sit in founder Slack and Discord groups and log repeated questions
The value of a private group is not the answers, it is the questions that get asked over and over. Every group has four or five perennials that no linked resource resolves. Keep a running tally for a month and the recurring technical or operational problem will be obvious.
Where to look: Any founder or operator community you have legitimate access to. Use search for the group’s history rather than only reading live, and note who answers — the repeat answerer is your first interview.
What counts as a signal: A question asked at least monthly by different members, answered each time with a bespoke explanation rather than a link. No canonical link means no product.
Track high-engagement asks in niche Facebook Groups
Facebook Groups remain where many non-technical trades, professions, and hobby industries actually organise — the exact markets with the least software built for them. A help request with a hundred comments in a group of contractors or clinic owners is a demand signal with almost no competition reading it.
Where to look: Groups for a trade or profession rather than for startups. Sort the feed by top posts, and search within the group for “recommend,” “anyone use,” and “how do you handle.”
What counts as a signal: A help post whose comments are a long list of incompatible manual workarounds. Fragmented workarounds mean no default tool exists, in a market that clearly wants one.
Filter Quora by topic for questions that never get a good answer
Quora’s useful property is that a question’s follower count keeps rising even when every answer is bad. That gap — sustained demand, poor supply — is both a product opportunity and a ready-made content plan for reaching the same people once you build something.
Where to look: Quora topic pages sorted by most-followed questions, then filter by hand for the ones whose top answer is thin, ancient, or a thinly-veiled ad.
What counts as a signal: High follower count, low answer quality, and recent activity. The follower count is people subscribing to a problem, which is about as close to a waiting list as free research gets.
Read industry newsletters for repeated complaints and tool shout-outs
Vertical newsletters are pre-filtered market intelligence: the writer already talks to the practitioners. Two things matter — the complaint they keep returning to, and the tools they recommend. A recommendation with a caveat attached is a category with room in it.
Where to look: Two or three newsletters per target vertical, read as an archive rather than as a subscription so you can see themes across a year in an afternoon.
What counts as a signal: The same operational complaint raised in three or more issues, or a repeatedly recommended tool that is always recommended with the same “shame it does not do X” qualifier.
Follow the money that is already moving
These methods skip the question of whether anyone will pay, because you start from evidence that somebody already does. What you are testing is not demand but whether the incumbent supply is weak, expensive, or aimed at the wrong buyer.
Browse Acquire.com for profitable micro-SaaS you could modernise
Marketplace listings publish revenue, churn, and traffic for small software businesses. Read them as a free market-research database rather than as a shopping list: every listing is a proven willingness to pay, annotated with what the seller thinks is wrong with the business.
Where to look: Acquire.com and similar marketplaces, filtered to SaaS with real MRR. Read the “reason for selling” and “growth opportunities” sections first — sellers are unusually candid there.
What counts as a signal: A listing with steady revenue, a dated product, and a founder leaving for reasons unrelated to demand. Revenue proves the job matters; a dated product proves the job is not being done well.
Use BuiltWith to find legacy software still installed at scale
Technology-detection data tells you which tools are actually deployed across thousands of live sites, including the ones nobody talks about any more. Widespread installation of an old product with a dead-looking roadmap means a large captive user base and an unmaintained incumbent.
Where to look: BuiltWith or Wappalyzer trend data by technology and category, cross-checked against the vendor’s changelog and support forum to confirm the product is genuinely stagnant.
What counts as a signal: Flat or slowly declining installs numbering in the thousands, plus a changelog with nothing meaningful in eighteen months. That combination is a migration market, and migration markets convert on a single feature.
Read job boards for the tools companies are hiring around
A job description is a budget document. When a listing asks for someone to “manually reconcile X weekly” or names five tools that must be kept in sync, it is describing a salaried workaround. If enough companies are hiring for the same workaround, the workaround is a product.
Where to look: Search a job board for a tool name plus a verb — “reconcile,” “export,” “compile,” “manually” — and read the responsibilities section rather than the requirements section.
What counts as a signal: The same manual responsibility appearing in twenty or more listings across different companies. You now know both the market size and the price ceiling, because you can see the salary.
Ask freelancers which task they bill for every single month
Freelancers are a live index of repetitive, valuable work. Any task billed repeatedly at a stable price, to different clients, with the same output shape each time, is a task that has already been productised in everything but name. Ask what they would automate if they were not paid by the hour.
Where to look: Freelancer communities, Upwork and Fiverr category listings sorted by number of orders, and direct conversations. The gig titles that repeat across hundreds of sellers are the durable ones.
What counts as a signal: The same deliverable sold by many freelancers at a similar price with a similar turnaround. High repetition and low variance is exactly the profile software eats.
Study what is selling on Gumroad and Etsy
Creator marketplaces show you what people buy when nobody is selling to them. A best-selling template, checklist, or spreadsheet is a workflow that people will pay for even in its most primitive delivery format — which is the strongest possible argument for building the software version.
Where to look: Gumroad and Etsy category bestsellers, plus the review counts, which stand in for sales volume. Read the reviews for what buyers wish the file also did.
What counts as a signal: A digital product with hundreds of sales whose reviews ask for automation, syncing, or collaboration. Those three asks are the file format hitting its ceiling.
Autopsy failed Kickstarter and Indiegogo campaigns
A failed campaign that still raised real money is a rare artifact: demand was proven and execution was not. Read the updates and the comments to find why it died — usually manufacturing, timing, or a founder running out of runway — and ask whether the failure was about the idea or about the delivery.
Where to look: Crowdfunding archives filtered to campaigns that hit meaningful funding but never shipped, or shipped badly. Backer comments after the collapse are the most honest post-mortem available.
What counts as a signal: Substantial backing plus a fixable, non-demand failure mode. If a thousand people paid up front and the reason for death was logistics, the demand test already passed.
Read search demand directly
Search data is the only source here that gives you volume as a number. It will not tell you whether a problem is painful, but it will tell you how many people go looking each month — and, crucially, whether anything decent is waiting for them when they arrive.
Find high-intent keywords with no real product behind them
Keyword research for idea sourcing is not about volume, it is about mismatch. You are hunting for queries that describe a job to be done — “how to X,” “X template,” “X calculator,” “best way to X” — where the results page is entirely blog posts and no software. A results page with no product on it is an unserved commercial intent.
Where to look: Any keyword tool, filtered to question and job-shaped modifiers within a vertical, then eyeball the actual SERP for each candidate. The SERP is the research; the tool only generates candidates.
What counts as a signal: Meaningful monthly volume where page one is thin content, forum threads, and no tool. A single-purpose free tool that answers the query outright can take that page, and the page then sells the product behind it.
Use Google Trends to catch a rising topic before tooling exists
Trends will not find you an idea on its own — it is a confirmation instrument. Its real use is timing: taking a candidate you found elsewhere and checking whether interest is climbing, flat, or a spike that already passed. Rising interest plus absent supply is the window.
Where to look: Google Trends, five-year view rather than twelve-month so seasonality does not read as growth, compared against two or three adjacent terms to check the rise is real and not just renaming.
What counts as a signal: A steady multi-year climb, not a spike, in a topic where a search for tools returns nothing purpose-built. Spikes are news cycles; slopes are markets.
Search “best [tool] for [niche]” and find the categories with no winner
This query is what a buyer types when they have budget and no shortlist. If the results are affiliate listicles recycling the same five general-purpose tools, none of which was built for the niche, then the niche has money and nobody has claimed it.
Where to look: Run the pattern across every niche you know: “best CRM for physiotherapists,” “best scheduling tool for tattoo studios.” Then check whether any result is actually built for that niche or is a generic tool with a landing page.
What counts as a signal: No purpose-built result on page one, plus forum threads where members of the niche ask each other the same question. Vertical software wins these on positioning alone.
Exploit weak execution in a category that is already proven
The safest ideas are not new categories, they are old categories done properly. Demand is established, the buyer already knows they need this kind of thing, and your entire job is to be visibly better along one dimension the incumbent has neglected.
Find Product Hunt launches with big upvotes and no traction since
A launch that got a thousand upvotes and then went quiet is a validated idea with a failed execution. Check the site: still up but unchanged, a dead changelog, a founder who moved on. The upvotes proved people wanted it; the silence proved the team could not deliver it.
Where to look: Product Hunt archives sorted by upvotes for the last three years, then check each product’s current site, changelog, and social accounts.
What counts as a signal: High launch-day interest, a live but frozen product, and users still asking about it. You are re-running a proven demand test with a team that will actually ship.
Unbundle an “all-in-one” category into one job done properly
When a category fills up with suites, every one of those suites does eight jobs at a six-out-of-ten standard. The unbundling play is to pick the single job people complain about most and do it at a ten, then integrate back into the suite everyone already pays for rather than trying to replace it.
Where to look: Look for categories where the top five products all describe themselves as platforms. Read their review pages for the module that gets criticised most consistently.
What counts as a signal: One module criticised across every competitor in the category. Universal weakness at one job is the cleanest unbundling target there is.
Rebuild the tool everyone uses and nobody likes looking at
Plenty of profitable software still looks like 2009 because it was never under design pressure. Interface quality is now a legitimate purchase criterion, and a modern rebuild aimed at the same buyer wins on the demo alone — provided you match the workflow exactly, because those users chose function over form and will not accept losing function.
Where to look: Vertical software in logistics, healthcare admin, legal, education, and local government. Trade-show exhibitor lists and vendor comparison threads are full of them.
What counts as a signal: A tool with a real installed base, an ugly interface, and reviews that praise the capability while apologising for the experience. Apologetic praise is a category waiting to be reskinned.
Host a popular open-source tool for people who cannot deploy it
A well-starred repository is a demand test that has already passed with technical users. The commercial opportunity is the much larger non-technical population who need the same outcome and will never run a container. You sell the hosting, the onboarding, the backups, and the support — the software is the least of it.
Where to look: GitHub search by stars within a category, filtered to projects with an active community, a permissive licence, and issues asking “is there a hosted version?”
What counts as a signal: Recurring hosted-version requests in the issue tracker, and a licence that permits commercial hosting. Always read the licence first — this method fails badly if you skip that step.
Turn the Notion template everyone copies into an actual app
A widely duplicated Notion template or Airtable base is a functional specification that thousands of people have voluntarily adopted. Templates hit a ceiling at automation, permissions, and multi-user state — precisely the point where a standalone product becomes worth paying for.
Where to look: Template marketplaces and creator template shops sorted by duplicates or sales, plus the community threads where people ask how to extend one.
What counts as a signal: Thousands of duplicates plus a stream of “how do I make this do X automatically” questions. The automation question is the template announcing its own replacement.
Productise the spreadsheet hack going around TikTok
Short-form video surfaces clever manual workflows before software catches up, and the comment section does your validation for free. When tens of thousands of people save a video explaining a fiddly process, the fiddliness is the product.
Where to look: TikTok, Reels, and YouTube Shorts within a professional or hobby niche, sorted by saves and shares rather than likes. Saves indicate intent to use.
What counts as a signal: A high-save video whose comments are dominated by “is there an app that just does this?” That comment is a product brief with a built-in audience.
Watch industry tutorials for the manual steps nobody questions
Long-form tutorials show real practitioners doing real work, including the parts they have stopped noticing are absurd. Watch for the moment the presenter says “now you just have to do this for each one” — that sentence is the automation boundary, and it recurs across an entire profession.
Where to look: YouTube tutorials for professional software in a vertical, watched at normal speed for the workflow rather than skimmed for the tips. Comments will confirm which step everyone hates.
What counts as a signal: The same manual step appearing in multiple tutorials from different creators. Independent repetition means it is inherent to the workflow, not one person’s bad habit.
Ask people directly
Everything above is a way of eavesdropping at scale. This section is slower and produces far fewer ideas, but the ideas are higher confidence, because you can ask follow-up questions and — the part that matters — ask what they currently spend to make the problem go away.
Walk into local businesses and ask what software they gave up on
Local operators are chronically underserved and unusually willing to talk, because nobody in software ever asks them anything. Do not ask what they want built — ask what they tried, what it cost, and why they stopped using it. Abandoned purchases are far more informative than wish lists.
Where to look: Trades, clinics, studios, restaurants, and independent retail in your own city. Fifteen minutes each, at a quiet hour, with no pitch attached.
What counts as a signal: Three unrelated businesses in the same trade describing the same abandoned tool or the same paper-and-phone workaround. Repetition across businesses that do not know each other is the whole point.
Interview niche professionals about the worst thirty minutes of their week
Specific questions beat general ones. “What annoys you?” yields nothing; “walk me through the last time you did your monthly reporting, screen by screen” yields a task inventory with timestamps. Ask them to actually share their screen — people cannot accurately describe their own workarounds.
Where to look: Short calls with practitioners found through LinkedIn, professional associations, or a niche community you already participate in. Ten interviews is enough to see the pattern.
What counts as a signal: The same thirty-minute ritual described by most of the people you speak to, with different improvised tooling each time. Different workarounds for one shared ritual means no standard exists.
Start a narrow newsletter and build the ask into the format
A newsletter turns idea sourcing into a standing instrument instead of a one-off project. Write for one narrow audience, end every issue with a single question about their work, and you accumulate a searchable archive of problems from people who have already opted in to hearing from you — the same list you will later launch to.
Where to look: Any lightweight email platform. Narrowness is the entire strategy: “operations for veterinary clinics” outperforms “startups” by an order of magnitude on reply rate.
What counts as a signal: The same problem raised unprompted by multiple subscribers across separate issues. Because they are named and reachable, each reply is also a design partner and a first customer.
Mine your own inbox, DMs, and comments with an agent
If you have any audience at all, you are already receiving unstructured demand data and losing it. Point an agent at your own message history with a strict brief — extract every request, complaint, or question about a task — then cluster the output and count. The clusters are your ideas, ranked by frequency.
Where to look: Your DMs, comment threads, and support inbox, exported and processed in bulk. Keep the extraction literal; the value is in the counting, not in the model’s interpretation.
What counts as a signal: A request cluster that recurs across months and across people who do not know each other. You already have relationships with everyone in that cluster, which makes it the cheapest market to reach on this entire list.
Use AI as a systematic generator, not as an oracle
A model asked for startup ideas returns the average of the internet. A model given real corpora and a structural task returns combinations you would not have reached by hand. The distinction is inputs: generation is cheap and generation is not validation, so every output from this section re-enters the list above as a hypothesis to check.
Feed real startup corpora to a model and ask for the adjacent gaps
Load a body of primary material — YC company directories, funding announcements, accelerator batch pages — and ask for the adjacent problems those companies create or leave behind rather than for imitations of them. Every funded company generates work for somebody, and that downstream work is usually unclaimed.
Where to look: Public startup directories and batch listings pasted in bulk into Claude, with an explicit instruction to name second-order needs rather than to summarise or restate.
What counts as a signal: A gap you can immediately restate as somebody’s actual job, and then find people complaining about using methods 6 to 15. If you cannot find the complaint, the model invented the gap.
Extract a proven business model and transplant it to an overlooked industry
Most successful vertical software is a known model applied to an industry that has not had it yet — marketplace, subscription, usage-based, managed service. Have the model strip a successful company down to its mechanism, then apply that mechanism to industries with old software and real budgets.
Where to look: Any company you admire plus a list of unglamorous industries: waste management, freight brokerage, agricultural supply, funeral services, industrial maintenance.
What counts as a signal: A transplant where the target industry has the same underlying structure — fragmented supply, repeat purchase, coordination cost — that made the model work originally. If the structure differs, the analogy is decorative.
Simulate personas to generate hypotheses, then go and verify them
Ask a model to play ten distinct professionals and list their daily frustrations. Treat everything it produces as fiction: it is generating plausible-sounding problems from training data, not reporting real ones. Its actual value is coverage — it will name roles and rituals you would never have thought to investigate.
Where to look: Any capable model, prompted for specific roles in specific industries and specific company sizes rather than for generic personas.
What counts as a signal: A simulated frustration you can then find stated by a real named human in a real thread. Nothing from this method counts until it has been confirmed against a primary source.
How to run an idea hunt without drowning in tabs
Thirty-eight methods is a menu, not a checklist. Running all of them is how idea hunts die — you accumulate hundreds of observations, none of them comparable, and stop before the counting that would have made them useful. This is the loop that turns any three of them into one decision.
- Pick a market before you pick a method
Choose one industry, profession, or community you can reach and describe accurately. Every method below works better aimed at a defined group than run broadly, because the whole point is spotting repetition, and repetition is only visible inside a bounded population.
- Run three methods from different sections for two weeks
Combine one complaint-mining method, one community method, and one money-trail method. Different sources make different errors, so a problem that shows up in all three is far more likely to be real than one found five times in a single source.
- Log raw evidence, not summaries
Keep a sheet with one row per observation: a link, the exact words the person used, the date, and the source. Never paraphrase at capture time — the literal phrasing is what you will need later for positioning, ad copy, and search targeting.
- Cluster and count
Group observations by the job that failed, not by the feature requested, then count distinct people per cluster. A cluster with ten unrelated people beats a cluster with forty observations from one loud community.
- Score the top clusters against the four tests, then go and talk to ten people
Run frequency, budget, urgency, and reachability on your top three clusters. Take the winner to ten conversations with real practitioners and ask what they currently spend to make the problem go away. What they already pay is your price; what they say they would pay is not evidence.
One rule for mining communities
Half the methods above send you into communities that people built for each other, not for you. Read, count, and learn the vocabulary — but do not turn a research pass into a promotion pass. Reddit’s own rules treat undisclosed self-promotion and vote manipulation as bannable, and a subreddit that catches you extracting from it will close the door on the exact market you were researching. If you are planning to post as well as read, run the Reddit preflight check first.
Frequently asked questions
- What is the best way to find a startup idea?
- Mine complaints from people who already pay for software in the space — one- and two-star reviews on G2 and Capterra, public feature-request boards, and churn discussions. These sources prove budget exists before you build anything, which is the part most idea-generation methods skip.
- How do I know if a startup idea is good?
- Run it through four tests: frequency (does the problem recur weekly rather than yearly), budget (is somebody already spending money here), urgency (does something break if it is not solved), and reachability (can you find a hundred of these people this month). An idea that fails reachability is not an idea, it is a hobby.
- How many startup ideas should I evaluate before choosing one?
- Generate widely and cut hard. A fortnight of running three sourcing methods across one defined market typically produces twenty to forty candidate problems; clustering and counting usually leaves three that are worth interviewing people about, and one worth building.
- Can AI find startup ideas for me?
- AI is a generator, not a validator. Asked cold for startup ideas it returns the average of the internet. Given real corpora — company directories, review exports, your own message history — and a structural task, it produces combinations and coverage you would not reach by hand. Every output still has to be confirmed against a primary source before it counts.
- Do I need an original idea to build a startup?
- No. The lowest-risk ideas are proven categories executed properly: an unbundled suite, a modernised legacy tool, a hosted version of an open-source project, or a vertical build for a niche the generalists ignore. Demand is already established, so the only question you are testing is whether you can be visibly better along one dimension.
You found the idea. Now find the demand.
Everything above answers what to build. The harder half is getting the first hundred people to know it exists — and the same instinct that made you good at reading communities for problems makes you good at reading them for distribution. The plays library is the other half of this guide.
- The plays library— reusable marketing plays with what to do, how to ship it, and how each one performs.
- The idea finder— tell it what you are marketing and get the plays matched to it, free.
- The launch plan— the whole tree, from positioning to advocacy, no gate.
Free, no signup.
Sources
- Paul Graham, How to Get Startup Ideas (2012)
- Clayton M. Christensen, Taddy Hall, Karen Dillon & David S. Duncan, Know Your Customers’ “Jobs to Be Done” (Harvard Business Review, 2016)
- Eric Ries, The Lean Startup (2011)
- Rob Walling, Start Small, Stay Small: A Developer’s Guide to Launching a Startup (2010)
- Mike Strives, 38 Ways to Find $1M Startup Ideas (X, 2026)
- Reddit, Reddit Rules (spam & manipulation policy) (Reddit, Inc.)
- Google, Search Central: SEO documentation & quality guidelines
Compiled by The Marketinque Team to our editorial standards.
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