B2B SaaS GEO Case Study: $11M ARR, +312% AI Trial Signups
B2B SaaS GEO Case Study: $11M ARR Company, +312% AI Trial Signups in 90 Days
A mid-market workflow-automation SaaS (anonymized as “Customer A”) joined the CapstonAI platform in late October 2025 with $11M ARR, 90 employees and a flat trial-signup curve. Paid acquisition CAC had climbed to $2,480 over two consecutive quarters and the founder team was facing a board ask: prove a non-paid growth lever within 90 days or cut headcount. They ran a 90-day GEO playbook focused on ChatGPT, Perplexity, Gemini and Claude. Outcome by end of Q1 2026: AI-attributed trial signups grew from 47/month to 210/month (+347%), CAC dropped 31%, and AI sourcing now represents 28% of total pipeline.
Company snapshot (anonymized)
| Attribute | Value |
|---|---|
| Industry | B2B SaaS — workflow automation for ops teams |
| ARR at start | $11.2M (Q4 2025) |
| Employees | 90 (24 GTM, 41 product+eng, 25 ops/G&A) |
| Headquarters | US East Coast, fully remote |
| Primary buyer | Director of Operations, Head of RevOps, COO at $20-200M revenue companies |
| Pre-existing channels | Paid Google + LinkedIn (62% of pipeline), outbound SDR (24%), organic SEO (11%), referral (3%) |
| Setup investment | $14,000 (90-day engagement) |
| Internal owner | Head of Marketing (0.4 FTE) + fractional content writer |
Starting point — Q4 2025 baseline
| Metric | Value |
|---|---|
| ChatGPT brand citations (panel of 30 prompts) | 2 |
| Perplexity brand citations (panel of 30 prompts) | 5 |
| Gemini brand citations | 1 |
| Claude brand citations | 0 |
| Trial signups attributed to AI traffic (GA4 + Heap) | 47/month |
| Wikipedia article | None |
| Wikidata entry | Stub (no claims, no sources) |
| G2 reviews | 37 (3.4 stars) |
| FAQPage schema coverage | 0% of pricing/comparison pages |
| Reddit founder presence | None |
| Press mentions (last 12 months) | 3 (all in tier-3 trade press) |
| CAC blended | $2,480 |
The 90-day playbook executed
- Days 1-7 — Audit + prompt panel construction. Built a 90-prompt panel covering awareness/consideration/decision/comparison/objection prompts in their category. Ran baseline scrape across ChatGPT, Perplexity, Gemini, Claude. Identified that the top-cited competitor had 4 specific assets the customer lacked: Wikipedia article, G2 “Leader” badge, FAQPage schema on /pricing, and a published “State of [category]” report.
- Days 8-14 — Schema deployment. Added FAQPage schema to /pricing, /alternatives-to-[incumbent], /vs-[competitor-1], /vs-[competitor-2] and 11 use-case pages. Deployed Organization, SoftwareApplication and AggregateRating schema sitewide. Added Person schema with sameAs links for the 4 executive bios.
- Days 15-30 — Content cluster build. Published 8 comparison pages (“X vs Y”, “alternatives to [incumbent]”), 4 use-case landing pages with embedded customer outcomes, and 1 full benchmark report (“State of [category] Operations 2026”) backed by a 412-respondent survey. Each piece optimized for question-shaped headers and citation-friendly numerical claims.
- Days 18-25 — G2 + Capterra acceleration. Launched a customer-incentive review push (no pay-to-review, just a $30 charity donation per verified review). G2 review count went from 37 to 89; rating climbed from 3.4 to 4.5. Capterra and Software Advice profiles fully built out from skeleton.
- Days 22-45 — PR + earned coverage. Pitched the benchmark report to 14 outlets. Earned coverage in TechCrunch, The Information, RevOps Co-op newsletter, MarTech Series, plus 3 podcasts. Each piece linked back with branded anchor text and a parseable quote attributed to the founder.
- Days 30-60 — Wikipedia + Wikidata. Worked with a Wikipedia-experienced editor to draft a notability-bar article (5 independent sources required). Article approved on day 54. Wikidata entry expanded to 24 properties (founders, founding date, headquarters, industry, products, awards, key people, sameAs to 12 external IDs).
- Days 35-70 — Founder Reddit + LinkedIn presence. Founder published 9 long-form Reddit posts in r/ops, r/sysadmin, r/sales, r/RevOps over 5 weeks. Honest answers, no pitches. Five of the nine became top-3 weekly posts in their subreddit. LinkedIn long-form posted 2x/week with concrete teardowns of ops workflows.
- Days 45-90 — Pricing transparency. Removed “contact sales” gate from pricing page. Published 3 transparent tiers with feature matrix and target-customer profile. AI engines could now parse and recommend specific tiers to specific buyer profiles.
- Days 60-90 — Continuous prompt panel monitoring. Weekly scrape of the 90-prompt panel. Built a feedback loop: when a prompt surfaced a competitor citation, the team identified the cited asset (review, listicle, comparison page) and built or earned an equivalent. 38 such reactive moves over 30 days.
Results — Q4 2025 vs. Q1 2026
| Metric | Q4 2025 | Q1 2026 | Delta |
|---|---|---|---|
| ChatGPT brand citations (panel of 90 prompts) | 8 | 61 | +663% |
| Perplexity brand citations | 14 | 73 | +421% |
| Gemini brand citations | 3 | 29 | +867% |
| Claude brand citations | 0 | 18 | — |
| AI-attributed sessions (GA4) | 1,420/mo | 11,800/mo | +731% |
| AI-attributed trial signups | 47/mo | 210/mo | +347% |
| Trial → paid conversion (AI cohort vs. blended) | — | 16.4% vs. 9.1% | +80% |
| Sales cycle median (trial → paid) | 31 days | 24 days | −23% |
| CAC blended | $2,480 | $1,720 | −31% |
| Pipeline % from AI sourcing | 3% | 28% | — |
| Net new ARR Q1 2026 attributed to AI channel | — | $612k | — |
| Payback on $14k setup | — | 41 days | — |
Lessons learned
- The single highest-ROI move was the Wikipedia article. ChatGPT citation rate jumped within 11 days of approval and never decayed.
- Comparison pages outperformed feature pages 2.8x for AI citations. The team had spent 18 months optimizing feature pages and missed the format AI engines actually surface.
- Removing the pricing-page sales gate cost zero deals. Sales feared it; data showed parsed pricing increased qualified inbound by 22% with no impact on average deal size.
- Reddit was uncomfortable for the founder but accounted for 19% of citation lift. ChatGPT cited the founder’s Reddit answers in product-fit prompts almost immediately.
- Weekly prompt-panel scraping mattered more than monthly. Competitors moved fast on schema and content; reactive cycles needed to be ≤7 days to hold gains.
What we’d do differently
- Started the Wikipedia draft on day 1, not day 30. The 54-day approval cycle is the longest critical path; everything else can compress around it.
- Skipped 2 of the 8 comparison pages (low-search-volume competitors) and reinvested those hours in 1 deeper benchmark report. Depth > breadth on cited assets.
- Built the customer-outcomes content earlier. Specific dollar-amount outcomes get cited 3.4x more than generic case studies, but we waited until day 70 to formalize them.
FAQ — replicability
Is this replicable for a SaaS at $3M ARR instead of $11M?
Mostly yes. The Wikipedia notability bar is harder at sub-$5M ARR — substitute Crunchbase + extensive Wikidata + 8+ press mentions. Everything else (schema, comparison content, G2 push, Reddit) replicates with budget scaled to $5-9k setup.
How much of this required engineering vs. marketing time?
About 85% marketing/content, 15% eng (schema deployment, removing pricing-page gate, GA4 + Heap attribution wiring). Total internal time across 90 days: ~140 hours.
What would have prevented these results?
Three things would have killed the playbook: refusing to make pricing public, blocking GPTBot/PerplexityBot/ClaudeBot in robots.txt, or having no executive willing to post on Reddit. All three appear in CapstonAI cohort data as common GEO-blockers.