Reddit Keyword Research: Choosing Keywords That Surface Buyers

Why SEO keyword research does not transfer

If you have done keyword research for search, your instincts will mislead you here. SEO keywords are the phrases someone types into a search box when they already know roughly what they want: short, deliberate, and volume-ranked. Reddit keywords are fragments of how people talk to each other when they are frustrated, confused, or asking peers for help.

Three differences matter:

• There is no volume metric to rank by. You cannot look up how many Reddit posts a month contain a phrase before you track it. You find out by tracking it for a week.
• Intent lives in the sentence, not the phrase. "Invoicing software" appears in a buying question, a job ad, a rant, and a five-year-old thread. The phrase is identical; only three words of context separate a lead from noise.
• Nobody optimizes their phrasing. A buyer writing a Reddit post is not trying to be findable. They describe the symptom, misspell your category, and never use your product's marketing vocabulary.

That last point is the whole opportunity. The phrases your competitors are not tracking are the ones that sound nothing like software terminology.

The four keyword groups

Build the list in four distinct groups. Keep them labeled, because they have different hit rates and you will want to prune them independently.

• 1. Brand terms. Your product name, its common misspellings, and the spaced or hyphenated variants. Low volume, near-perfect relevance — unless your name is a common English word, in which case this group needs the most filtering of all.
• 2. Competitor terms. Every competitor's name, including the incumbent everyone already uses and the free tool people default to. These threads carry the clearest switching intent in the entire dataset, and they double as positioning research.
• 3. Category terms. What the market calls this kind of software, in the market's words. Include the awkward descriptive versions people actually type, not only the tidy label your website uses.
• 4. Problem language. The symptom described with no tool vocabulary at all. Hardest to write, highest value, and the only group that reaches people before they start shopping.

A healthy starting list is roughly 5 brand terms, 5–10 competitor names, 10–15 category phrases, and 15–25 problem phrases. Fewer than that and you will miss things; many more and tuning becomes guesswork.

Mining problem language from real threads

Do not brainstorm this group. Harvest it.

Spend an afternoon reading fifty real threads in the communities where your buyers spend time, and copy the exact sentences where someone describes the pain your product removes. You are looking for the phrasing of a person who does not yet know a category exists for their problem: "I've got three spreadsheets that have to agree and they never do," "we're paying someone to copy numbers between two systems."

Then convert each sentence into a trackable fragment. Take the two or three words that are load-bearing and would appear in any similar complaint, and drop the rest. "Copy numbers between two systems" becomes a phrase worth tracking; the full sentence never repeats.

Three more sources for this group:

• Your own support inbox and sales calls. The words customers used before they knew your vocabulary are still in the transcripts.
• Competitor review sites. One-star and three-star reviews describe pain in unguarded language.
• Your churn interviews. The problem people left to go solve elsewhere is a problem someone else is currently posting about.

Precision, recall, and where to sit

Every keyword decision trades two things against each other. Precision is what share of your matches are worth reading. Recall is what share of the real opportunities you catch. You cannot maximize both with keywords alone.

The third row is the only stable configuration, and it is why the keyword layer should be deliberately generous. If something reads every match and scores it before a human sees it, a noisy keyword costs you compute rather than attention. MentionSpot works this way on purpose: keywords are a cheap pre-filter, then an LLM reads each surviving post and comment, scores relevance, and assigns a buyer-intent stage. You can afford to cast wide.

Without that second pass, you are forced into the first row, and the first row is why so many teams believe Reddit does not work for their category.

Writing keywords that do not backfire

Some specific rules that save weeks of tuning:

• Avoid single common words. A one-word product name that is also an English noun will match everything. Pair it with a category word instead, and rely on scoring for the rest.
• Track phrases, not sentences. Two to four words is the sweet spot. Longer strings almost never match verbatim.
• Include the plural and the verb form where they differ meaningfully. People write "invoicing," "invoices," and "invoice tool" and mean the same thing.
• Add the misspellings that are common, not every possible one. Look at how your name appears in existing Reddit threads.
• Do not track your own domain expecting leads. That catches people who already found you, which is a support signal, not a lead signal.
• Watch for acronym collisions. Three-letter acronyms in software collide with sports teams, medical terms, and games. If yours does, always pair it.

Choosing where the keywords run

The same keyword performs completely differently depending on scope. Sitewide monitoring catches everything but pulls in unrelated contexts from thousands of communities. Subreddit-scoped monitoring is far more precise but misses buyers who asked their question somewhere you did not think of.

A practical split: run brand and competitor terms sitewide, because you want every mention of those regardless of where it appears, and run category and problem-language terms scoped to your chosen communities, because those phrases are ambiguous outside the right context.

Choosing those communities is its own exercise — start with the subreddits your existing customers name, then widen with an automated subreddit finder and the guide to the best subreddits for SaaS founders. Comments matter as much as posts here: the question gets asked in a post, but the tool recommendations, and the complaints about them, live in the comment thread.

The weekly tuning loop

Keyword lists are never finished. Run a fifteen-minute review once a week for the first month, then monthly:

• Which keywords produced nothing but unrelated matches? Cut or narrow them. This is the fastest precision win available.
• Which keywords produced no matches at all? Either the phrasing is wrong or the demand is not there. Try one variant before removing it.
• Which keywords produced the threads you actually replied to? Expand around them with near-variants — the phrasing that worked usually has siblings.
• What words appeared in good threads that you were not tracking? The threads you found by accident are your best source of new terms.

Classifying irrelevant matches explicitly rather than silently dropping them is what makes this loop possible. If unrelated results disappear without a trace, you have no way to tell a keyword that is failing from a keyword that is quiet.

Reading intent, not just matching words

Once the keyword layer is doing its job, the remaining work is judging what each matched conversation actually is. The same phrase appears in five completely different situations:

• Research. Someone forming an opinion about the problem before they know what to buy.
• Tool-seeking. An explicit request for a recommendation. The strongest signal on the platform.
• Competitor mention. Someone naming an alternative, often with the specific complaint that would make them switch.
• Pain venting. Frustration with no purchase framing yet — an emerging opportunity, not a lead today.
• Unrelated. The majority of raw keyword matches, once read in context.

These five stages are what MentionSpot assigns to every candidate that clears the keyword filter, and combining them with relevance, post age, and engagement is what produces a queue ordered by what deserves a reply today. The buyer-intent signals guide works through each stage in detail.

A worked example

Say you sell scheduling software for small home-services businesses — plumbers, electricians, HVAC contractors. A first keyword list might look like this:

• Brand: your product name, the spaced variant, and the two most likely misspellings.
• Competitor: the three named incumbents in the category, plus "Google Calendar" scoped to contractor communities, because that is what most of the market actually uses today.
• Category: "scheduling software," "dispatch software," "job scheduling app," "booking system," "field service app."
• Problem language: "double booked," "keep missing appointments," "customers no showing," "scheduling by text," "still using a paper calendar," "can't keep track of jobs."

Notice that the problem group contains no software words at all. Those phrases reach a contractor who is annoyed on a Tuesday evening and has not yet decided to buy anything — which is weeks before your competitors' brand terms would catch them, and long before they show up in anyone's search ads.