The Challenge
This AI productivity SaaS for Mac started at absolute zero — 0 DR, no backlinks, no organic presence. The productivity tools market is one of the most crowded SaaS verticals, with entrenched players who've been building domain authority for years.
But the bigger challenge was beyond competition: the market itself was changing. Over our 36-month engagement, ChatGPT launched, Google introduced AI Overviews, Perplexity emerged as an AI-native search engine. The content strategy couldn't just work for today's Google — it needed to evolve as the world of search evolved.
The Flywheel: 36 Months of Compound Growth
This is our longest-running case study and the clearest example of the flywheel in action. Over 36 months, we turned the crank dozens of times — each cycle building on data and authority from the previous one. What makes this case unique is how the flywheel adapted as search shifted from pure Google to Google + LLMs:
The PikaSEO Flywheel — 36-Month Evolution
Phase 1 — Foundation (Month 1-6)
Research the audience, publish at velocity, build initial authority
We started with deep competitive research — understanding why Mac productivity users search the way they do. They don't search for "productivity app" — they search for "best writing app for Mac," "Notion alternatives for Mac," and "how to automate workflows on macOS." We mapped 500+ keywords and started publishing at high velocity: listicles, alternatives pages, free tools, and feature-specific landing pages. Every page was designed to be genuinely useful — the kind of content Mac-focused blogs and newsletters would naturally reference.
Phase 2 — Data-Driven Scaling (Month 6-18)
First-party data reveals winners — scale them, cut the rest
By month 6, GSC was generating rich first-party data. We could see which content types earned the most backlinks (free tools, by a wide margin), which drove highest conversion rates (alternatives pages), and which keywords were generating unexpected impressions. We doubled down on what worked. Pages that underperformed were rewritten or redirected. Each cycle of the flywheel was informed by real data, not guesses.
Phase 3 — The LLM Shift (Month 18-30)
Adapt content for a world where ChatGPT and Perplexity are search engines
Midway through, the search landscape changed. ChatGPT launched and started being used to discover software tools. Perplexity emerged. Google introduced AI Overviews. We adapted in real time: restructuring pages with cleaner semantic HTML, adding factual extractable claims that LLMs could cite, ensuring comprehensive coverage. The content that was already well-researched started getting cited by these platforms — the SEO → AEO pipeline formed organically because the content quality was already there.
Phase 4 — Compound Returns (Month 30-36)
The flywheel hits escape velocity
By year 3, every new page ranked faster because of built-up authority. Every new backlink had more impact because the link profile was strong. LLM citation traffic was growing alongside Google traffic — a new channel that cost nothing extra. Monthly content investment stayed roughly flat, but returns were compounding month over month. That's the flywheel: same effort, exponentially more results over time.
Understanding the Dynamic LLM Market
This case study spans the most transformative period in search history. What worked for Google in month 1 wasn't sufficient by month 18 when LLMs entered the picture:
What LLMs understand
LLMs parse content differently than Google's crawler. They understand semantic relationships, factual claims, and narrative structure. They can extract "X is best for Y because Z" and cite it directly. They prefer comprehensive, authoritative content. They also have training data cutoffs, so freshness matters — updated content gets cited in newer model versions.
How to write for both worlds
Content that's genuinely high-quality works across both Google and LLMs. Well-researched comparisons, honest reviews, and comprehensive guides satisfy Google's E-E-A-T requirements and give LLMs citable, authoritative content. The key additions for LLM readability: clean semantic HTML, explicit factual claims, and regular updates to stay within training windows.
What to Write and How to Write It
Over 36 months, we refined our understanding of what makes content compound in value:
What to write (evolving with data)
Content strategy isn't set-and-forget. We adjusted every quarter based on first-party data. In year 1, listicles and free tools drove the most value. By year 2, alternatives pages became priority as the domain gained authority. By year 3, we targeted AEO-specific opportunities — queries where LLM citations could drive a new traffic channel.
How to write (quality that compounds)
High-value content gets referenced by other websites — that's what builds domain rating organically. Every page should be the best available resource on its topic. Not the longest, not the most keyword-optimized — the most useful. Honest opinions, real data, genuine recommendations. That standard meant Mac blogs and tech publications linked naturally. Each backlink made every future page rank faster.
Making Content Technically Readable
Content quality alone isn't enough if Google and LLMs can't read it. We ensured every page was technically optimized for both traditional crawlers and the new generation of AI systems:
The Content Types That Compounded
Free Tools
Productivity utilities that attracted backlinks on autopilot — the #1 DR builder. Other sites linked because the tools were genuinely useful. Over 36 months, these drove a significant portion of authority growth.
Listicles & Reviews
"Best productivity apps for Mac" with honest, well-researched analysis. Specific opinions, real screenshots, genuine tradeoffs. These became the reference lists in the Mac productivity niche.
Alternatives Pages
"Best Notion alternatives for Mac" — high-intent pages that converted better than any other content type and earned natural backlinks from review sites.
Programmatic SEO
Template-driven pages across use cases, integrations, and platform-specific queries. Thousands of long-tail pages that grew in value as domain authority increased.
Landing Pages
Feature-specific pages targeting product-aware keywords like "AI writing assistant for Mac." Captured the most conversion-ready traffic.
SEO → AEO Content
Content optimized for AI citation — structured comparison tables, clear recommendation statements, comprehensive coverage designed to be the source LLMs reference for productivity tool questions.
The Results
36 months of compound growth produced results that accelerated over time — not linearly, but exponentially:
- ✓ 5M+ impressions through compound growth — each year bigger than the last
- ✓ SEO → AEO pipeline established — now cited by Google AI Overviews, ChatGPT, and Gemini
- ✓ Domain authority grew from 0 to established presence competing with entrenched players
- ✓ Organic traffic became the primary growth channel with zero ad spend
- ✓ Content strategy successfully adapted across the Google → Google+LLM transition
- ✓ Same monthly content investment, exponentially growing returns
Key Takeaway
SEO is a compounding investment — but only if you adapt. Over 36 months, the search landscape changed fundamentally with the rise of LLMs. But the flywheel framework — research what your audience searches for, publish high-value content, measure what resonates with first-party data, double down on winners — works regardless of whether the search engine is Google, ChatGPT, or whatever comes next. Write content so genuinely useful that it becomes the reference source — for humans, for Google, and for LLMs. That's how 0 DR becomes 5M+ impressions and an SEO → AEO pipeline.