GEOLOGY
SaaS

65% organic growth and 35% lower CAC for a B2B SaaS company

How a mid-size project management platform used Geology's SEO and GEO strategy to go from invisible on AI platforms to consistently recommended — while cutting customer acquisition costs by over a third.

About A Mid-Size B2B SaaS Company

A US-based B2B SaaS company offering a project management and team collaboration platform for mid-market businesses with 50–500 employees. The company had strong product-market fit and annual revenue in the $5M–$15M range, but was losing the organic visibility battle to better-known competitors.

The bigger problem was AI platforms. When buyers asked ChatGPT or Perplexity for project management recommendations, the client's product never appeared. Competitors with stronger content and citation signals were consistently getting the recommendation — and the signups that came with it.

The Challenge

The client needed to shift from a paid-acquisition-heavy model to sustainable organic growth. Blended CAC was climbing, and the marketing team knew that earning organic recommendations from both Google and AI platforms would be the most cost-effective way to drive qualified signups at scale.

The specific challenge was the "best of" problem. In SaaS, a disproportionate share of high-intent traffic flows through queries like "best project management tool" and "Asana vs Monday vs [competitor]." If your product doesn't appear in those results — on Google or in AI-generated responses — you're invisible to the buyers who are actually ready to choose.

The goal was to build enough content authority and citation signals to earn those recommendations consistently, across every platform where buyers research software.

Our Approach

1

Full SEO & GEO audit

We used AI-powered competitive analysis to benchmark the client's visibility across Google, ChatGPT, Perplexity, and Google AI Overviews — automatically running thousands of queries and mapping which competitors were being cited and why. The audit revealed a critical gap: competitors were being recommended by AI platforms for core product queries while our client was completely absent. The AI analysis mapped every "best project management tool" and "team collaboration software" response to understand exactly what it would take to earn those recommendations.

2

500+ keyword opportunity mapping

AI-assisted keyword research surfaced over 500 keyword opportunities across product-led, comparison, and educational intent clusters. The highest-value targets were "best X software" queries and "vs" comparisons — the exact queries where AI platforms make explicit brand recommendations. Our AI tools scored and prioritized these based on search volume, AI citation potential, and conversion likelihood to build a content roadmap that would move the needle on both traffic and signups.

3

Content engine at scale

Our AI content pipeline drafted 350+ content pieces including product comparisons, use case guides, integration tutorials, and thought leadership articles. AI handled the research, competitive analysis, and first drafts; human editors shaped the voice, verified technical accuracy, and added the nuance that makes content credible. Every piece was structured to serve double duty — ranking in Google while also providing the kind of authoritative, structured information that AI platforms prefer to cite. The comparison content was especially effective: detailed, fair-minded "vs" articles that AI models consistently reference when users ask for software recommendations.

4

Authority building & link acquisition

We acquired 45 high-quality backlinks from SaaS review sites, tech publications, and industry blogs. AI-powered prospecting identified the most impactful link targets and drafted personalized outreach communications that our team refined and sent. The link strategy focused on placements that AI platforms actively crawl and cite — software review aggregators, industry analyst sites, and tech publications with strong domain authority. Each placement was designed to build both traditional SEO authority and the citation signals that drive AI recommendations.

5

AI platform citation strategy

Beyond content and links, we implemented a targeted AI citation strategy. AI tools generated optimized landing page copy, structured data markup, and conversion-focused layouts that our team refined and deployed. This included the trust signals AI models weight heavily — verified reviews, feature comparisons, integration lists, and pricing transparency. We also built a competitor comparison hub that became the go-to reference for AI platforms generating software comparison responses.

6

Reddit & Quora thread activation

Our AI thread discovery engine identified the highest-value conversations on Reddit and Quora where users were comparing project management tools, asking for software recommendations, and discussing productivity workflows. By engaging authentically in subreddits like r/projectmanagement, r/SaaS, and Quora threads on team collaboration, we built the kind of real-user brand signals that AI platforms heavily weight when generating software recommendations. AI-powered reporting tracked thread performance and conversion attribution in real time. This wasn't drive-by link dropping — it was sustained, helpful participation that positioned the client as a credible option alongside established competitors.

Results

65%
Organic traffic growth in 6 months
200+
New page 1 keyword rankings
2.5x
Increase in AI platform visibility
35%
Reduction in blended CAC

We used to throw money at ads just to keep up with competitors who were getting recommended by AI for free. Six months later, we're the ones being recommended — and our CAC dropped by 35%. Best plot twist of the year.

R.K.
CMO

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