SaaS Keyword Research That Finds Buyers
SaaS keyword research starts with your own lost deals and support tickets, then checks volume last, because your buyers are not a mass market.
SaaS keyword research means building your term list from your own closed-lost deals, support tickets and sales calls before you open a keyword tool, then grouping what you find by the page type it should become. Volume is the last filter, not the first, because a B2B buyer pool is too small for mass-market sampling to mean much.
That is the research half of B2B SEO, narrowed to the one step every other page in that guide depends on: knowing which terms are actually worth building for.
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What makes SaaS keyword research different from any other kind?
The buyer pool is small and specific, and the tools were not built for that. A generic keyword tool samples a mass market: millions of people searching for millions of things. A B2B SaaS product might have a genuinely addressable market of a few thousand companies worldwide, each with one or two people who search for it in a given month.
That mismatch shows up as terms that look worthless by volume and are actually the entire opportunity. "Payroll software for construction companies with union reporting" might show ten searches a month in a tool and zero data in another. Both readings are wrong for what you need to know, which is whether the ten people typing that phrase have a budget and a deadline — and they almost always do, because nobody types a nine-word qualifier by accident.
Google's own starter guidance on SEO puts the same point more plainly: think about what someone would type to find your page, and write for that person, not for a number in a report [3]. In B2B SaaS, the person typing the long, specific phrase is usually closer to a deal than the person typing the short, popular one.
There is a second reason volume misleads in B2B SaaS specifically: most keyword tools sample consumer search behaviour far more heavily than professional software buyers, because consumers vastly outnumber them. A term with "for teams" or a named integration in it will consistently under-report against how much it actually matters to your pipeline, simply because the tool has fewer B2B searchers to sample from in the first place.
Where do you actually find the terms that convert?
In four places, in this order, before you ever open a keyword tool. Your last twenty closed-won and closed-lost deals: what did the prospect call the problem, which competitor came up, what objection killed it. Your support tickets: the exact words a confused or frustrated customer uses, which are rarely the words your homepage uses. Your sales call transcripts or notes, if you record them: the questions an evaluator asks before they will book a second call. And your own Search Console data, if the site has any history: the queries you already rank for on page two, which are proof of demand you have not captured yet [2].
Pull twenty won and twenty lost deals
Write down the competitor named, the integration asked about, the objection raised, and the champion's job title for each one. This is your first and best keyword list.
Read a month of support tickets
Note the exact phrases customers use to describe a problem, not the feature names your product team uses. The gap between the two is usually where new pages belong.
Check Search Console for near-misses
Sort the Performance report by impressions with low click-through, which surfaces queries you already rank for weakly [2]. Some of those are one rewritten paragraph away from ranking properly.
Only then, open a keyword tool
Use it to check volume and difficulty on the list you already built, and to find close variants of the terms you now know matter. Google Ads' Keyword Planner is a reasonable free option for the volume check [1].
| Source | What it tells you | How reliable for B2B intent |
|---|---|---|
| Closed-lost deals | Exact competitor and objection language | Highest — a real budget was attached |
| Support tickets | How customers describe problems in their own words | High — real users, real friction |
| Search Console near-misses | Demand you already attract but do not fully capture | High — proven, existing traffic |
| A generic keyword tool alone | Volume and difficulty across a mass market | Low on its own; useful as a second check |
How do you turn a keyword list into a page plan?
By sorting every term into one of four buckets, because each bucket is really a request for a different page type. Category terms — what you are — become your core product and feature pages. Comparison terms — you against a named rival — become comparison pages. Problem terms — the job the buyer is trying to do — become use-case and guide content. Question terms — what an evaluator asks before a demo — become FAQ-style pages and, later, blog posts.
This grouping is what turns a spreadsheet into a build plan, and it is also where most of the terms you found in the last section find a home: the competitor name from a lost deal is a comparison term, the confused customer's phrase from a support ticket is often a problem term. The page-type logic itself, and why the bottom four buckets should be built before the blog, is the full subject of bottom of funnel content.
Keep the bucket labels visible in whatever spreadsheet or board you plan pages from. A list of a hundred keywords with no bucket attached turns into a hundred blog post ideas by default, because a blog post is the easiest thing to assign a writer. A list where each row already says "comparison" or "problem" turns into the right page type by default instead, which is the entire point of doing the grouping before anyone starts writing.
Which keywords should you ignore, no matter what the volume says?
Two kinds. The first is anything your buyer would never type mid-evaluation: broad category terms with huge volume and no purchase intent behind them, the kind that bring traffic and never a demo. The second is anything you cannot honestly claim: a feature you do not have, an integration you have not shipped, a comparison you would lose on the facts. Ranking for either wastes the page and, worse, the click.
The keyword list that matters fits on one page and came out of your CRM. If your research phase produced a spreadsheet with thousands of rows and nobody can tell you which twenty came from an actual deal, you built a report, not a plan.
A services-flavoured version of this same discipline — starting from proposals instead of a product roadmap — is in SEO for B2B service companies, if your business sells expertise alongside or instead of software.
Does AI search change how you research keywords?
It changes what the resulting page has to do, not how you find the terms. Google and the major assistants increasingly answer a question by fanning it out into several related sub-questions and assembling an answer from what ranks for each one. Practically, that means a single page now has to earn several related queries, not just its head term, so your page plan should turn each keyword bucket into headings phrased as the actual sub-questions a buyer or an assistant would ask.
That is a reason to research more thoroughly, not differently. The deal-list, ticket and Search Console method above already surfaces the specific phrasing an assistant fans out to, because that phrasing came from real people asking real questions. A keyword list built entirely from a generic tool tends to miss it, because tools sample search volume, not conversation.
Practically, this means a page for "expense management software" should not stop at defining the category. It should also carry a clear answer to pricing, to the integrations question, to how it compares with the two tools buyers most often mention switching from, and to who the product is not a good fit for. Each of those is a sub-question your deal-list research already surfaced, and each is a candidate fan-out query an assistant might ask on the page's behalf.
Once the pages exist, SaaS SEO results shows what tracking their performance honestly looks like, using a clearly labelled hypothetical example so you know which numbers a real report should contain. And if you want the research and the page build handled end to end, that scoping work is the first month of our SEO services.
Questions people also ask
What keyword volume should a B2B SaaS company aim for?
Whatever it is. A term ten people a month type, all of them evaluators with a budget, is worth more than a term ten thousand people type out of curiosity. Sort by intent first and volume second, not the other way round.
Which tool should we use for SaaS keyword research?
Any keyword tool works for the mechanical step of checking volume and difficulty. None of them will hand you the list that matters, which comes from your own deals, tickets and sales calls first.
How many keywords does a B2B SaaS company actually need?
A few hundred well-chosen ones beats several thousand loosely related ones. Most SaaS sites already have enough keyword ideas sitting in their CRM; the work is turning that list into pages, not finding more terms.
Do AI assistants change how we should research keywords?
They change what a page has to do once it ranks: answer the fanned-out sub-questions completely enough to be quoted. The research method — deals first, tools second — does not change.
Sources
- About Keyword Planner — Google Ads (checked 22 September 2026)
- Search Console Overview — Google Search Central (checked 22 September 2026)
- SEO Starter Guide: The Basics — Google Search Central (checked 22 September 2026)
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Who wrote this
Jeremy ZavaCo-founder
Leads growth and client partnerships.
Works on: CRO, SEO
Reviewed before publishing by Ibrahem Helal, Co-founder.
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