How to mine ICP language for SaaS keyword research
Written by Olayinka Olayokun·Published ·Updated ·Verified
ICP language mining is the qualitative research practice of collecting the exact phrasing the ideal customer profile uses to describe a SaaS problem — pulled from sales calls, support tickets, reviews, and community discussions — before any keyword tool is opened.
Summary and key takeaways
The keywords that convert are the ones your ICP actually uses, and most of them are not in Ahrefs or Semrush. Pull a Slack history search, read 20 support tickets, scrape 50 G2 reviews of your competitors, and listen to 5 sales calls. The phrases that come up 3+ times across those sources are your real keyword universe — tool-based keyword research only refines what ICP mining surfaces.
- •Mine sales calls, support tickets, reviews, and Reddit before opening any keyword tool.
- •Phrases that recur 3+ times across independent sources are higher-converting than tool-derived keywords.
- •Buyers describe problems in jobs-to-be-done language, not category language — 'send invoices that get paid', not 'invoicing software'.
- •Competitor review sites (G2, Capterra) are the most underused source — buyers explain why they switched, in their own words.
- •Long-tail ICP phrases convert 2–5x better than head-term equivalents because intent is unambiguous.
In plain English ·ICP language mining surfaces the verbatim phrasing SaaS buyers use to describe problems — from sales calls, support tickets, Reddit, and review sites — before any keyword tool is consulted. The phrases that recur are higher-converting than tool-derived keywords because they match real buyer mental models.
- 3+ recurrences
- Times a phrase must appear across independent sources to enter the keyword universe SERPNAUT playbook
How this compares
| Source | Time investment | Typical yield | Best for |
|---|---|---|---|
| Sales call transcripts | 2 hours | 20–40 phrases | Objection / evaluation language |
| Support tickets | 1 hour | 15–30 phrases | Pain-point language |
| G2 / Capterra reviews | 2 hours | 40–80 phrases | Comparison + switching language |
| Reddit / niche forums | 1 hour | 20–50 phrases | Pre-purchase research language |
Part of the SaaS Keyword Research: an ICP-First Playbook guide
ICP language mining is the qualitative research practice of collecting the exact phrasing the ideal customer profile uses to describe a SaaS problem — pulled from sales calls, support tickets, reviews, and community discussions — before any keyword tool is opened.
Most SaaS keyword research starts with 'open Ahrefs, type the category name, sort by volume'. That returns the keywords your competitors already optimised for — and a list of suggestions defined by tool clustering, not buyer mental models. The result is content that ranks for the obvious queries and misses the queries that actually convert. ICP mining inverts the process: start with buyer language, then use tools to refine it.
What this chapter covers: verbatim, multi-source, job-framed, tool-preceding.
Sales calls: the highest-yield source
Pull Gong, Chorus, or Fathom transcripts from the last 90 days of discovery calls. Search for the problem keyword ('invoicing', 'topical authority', whatever applies). Read what prospects say when they describe the pain.
Pull Gong, Chorus, or Fathom transcripts from the last 90 days of discovery calls. Search for the problem keyword ('invoicing', 'topical authority', whatever applies). Read what prospects say when they describe the pain.
Capture the phrasing verbatim. 'I'm spending three hours on Sunday nights chasing payments' is a keyword; 'AR automation' is a category. Both might rank — but only the first one converts a Sunday-night freelancer.
Support tickets: the pain-language source
Open Intercom, Zendesk, or whatever ticketing tool you use. Filter the last 60 days for the topic area. Read the title and first message of each ticket.
Open Intercom, Zendesk, or whatever ticketing tool you use. Filter the last 60 days for the topic area. Read the title and first message of each ticket.
Customers describe problems with the words they were using right before they reached for help. That language overlaps heavily with the language they use right before they reached for Google — making support-ticket phrasing some of the highest-intent keyword material on your site.
Competitor review sites: the underused goldmine
G2, Capterra, TrustRadius, and Product Hunt all expose 'cons', 'reasons for switching', and 'features I wish had' fields. Read 50 reviews of each direct competitor.
G2, Capterra, TrustRadius, and Product Hunt all expose 'cons', 'reasons for switching', and 'features I wish had' fields. Read 50 reviews of each direct competitor.
The 'reason for switching' field in particular is verbatim buyer language at the highest commercial intent moment in their lifecycle. Phrases that appear in 5+ competitor reviews are nearly guaranteed converters — and they're publicly readable, so no privileged data access is required.
Reddit and niche forums: the pre-purchase research source
Find the 2–3 subreddits where your ICP hangs out. Search for the problem keyword. Read the top threads of the last year. Capture every variant of the question that gets asked.
Find the 2–3 subreddits where your ICP hangs out. Search for the problem keyword. Read the top threads of the last year. Capture every variant of the question that gets asked.
Reddit phrasing tends to be earlier in the buyer journey than support-ticket or sales-call phrasing — useful for top-of-funnel content but lower conversion intent. Mix it with later-funnel sources for a complete picture.
The checklist for this chapter
- ✓Pull 90 days of sales-call transcripts and search for the topic keyword
- ✓Read 50–100 support tickets in the topic area
- ✓Read 50 reviews of each top-3 competitor on G2 and Capterra
- ✓Search the 2–3 relevant subreddits for the topic and read top threads
- ✓Build a spreadsheet of every verbatim phrase, tagged by source
- ✓Shortlist phrases appearing 3+ times across independent sources
Where this chapter sits in the guide
every other step in keyword research — without ICP language, tool-derived keywords optimise for the wrong queries. Read the head term vs modifier mapping for saas chapter →
on-page entity coverage — the entities the ICP uses are the entities the page needs to mention. Read the related guide →
competitor keyword analysis — competitor analysis tells you what others rank for; ICP mining tells you what buyers actually search.
close-won customer interviews — phrases that converted customers say verbatim in onboarding are the highest-priority targets.
Quick answers about icp language mining for saas keyword research
- Where do I find ICP language?
- Five sources in order of yield: sales call transcripts (Gong/Chorus search for the problem keyword), support tickets (search Intercom/Zendesk for similar themes), competitor review sites (G2, Capterra, TrustRadius — read the 'cons' and 'why we switched' fields), Reddit threads in your ICP's subreddit, and your own onboarding survey if you have one.#
- What if the ICP phrases have low search volume?
- Good — that's usually the point. A phrase with 30 monthly searches and 90% commercial intent converts 5–10x better than a head term with 5000 monthly searches and mixed intent. Ranking for 20 ICP phrases with 30 monthly searches each beats ranking #4 for one head term.#
- Does this replace keyword tools?
- No — it precedes them. ICP mining tells you what to research; tools tell you the volume, difficulty, and adjacent variations. The mistake is opening Ahrefs first and letting tool-suggested keywords define the universe.#
- How long should ICP language mining take?
- 4–8 hours for a first pass: 2 hours of sales-call review, 1 hour of support-ticket search, 2 hours of competitor review-site reading, 1 hour of Reddit. The output is a spreadsheet of 50–150 verbatim phrases that becomes the input for everything else in the keyword research process.#
Questions about icp language mining for saas keyword research
- Substitute G2 reviews and Reddit threads — both are public and high-yield. Schedule 5–10 customer interviews specifically asking 'when you searched for this last, what did you type?' — the answers are gold.
- Voice-of-customer research surfaces buyer language that converts better than tool-derived keywords. Intercom — voice of the customer
- Jobs-to-be-done framing reveals problem-language different from category-language. Strategyn — Jobs to be done
This chapter is one node in the founder-led playbook. To see which nodes your specific URLs are bleeding traffic from, get a founder-grade SEO audit of your URLs. Same six disciplines, applied to the pages you actually own.
Olayinka Olayokun
Founder, SERPNAUT and Invoicemonk
Written by Olayinka Olayokun. I run SERPNAUT, a founder-led SEO service for B2B SaaS, and Invoicemonk, the SaaS I grew from zero to 300+ organic visits and a paying customer in 28 days using the same playbook. Everything below is what worked on my own URLs and on the audits I've shipped since.
More chapters in this guide
ICP language mining is the upstream step that makes the rest of keyword research worth doing. Skip it and the difficulty-band analysis, the head-term mapping, and the tooling all optimise for a keyword universe defined by a tool's clustering algorithm. Do it and every downstream step compounds, because every keyword you research is one a buyer might actually type.
See the full guide at saas keyword research: an icp-first playbook. The commercial bridge above is the canonical path from this chapter to your URLs.