AI & Automation
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I was staring at another client's SEMrush bill - €300 for keywords they'd never rank for. Sound familiar? You know that feeling when you're drowning in expensive SEO tools, clicking through endless keyword suggestions, and still not finding those hidden gems that could actually move the needle.
Here's what I discovered after working with a B2B startup that needed a complete SEO overhaul: the real opportunity isn't in fighting for saturated keywords everyone's targeting. It's in finding those low-competition AI marketing niches that traditional tools miss completely.
Most marketers are still fighting yesterday's battle - using traditional keyword research for tomorrow's AI-driven landscape. Meanwhile, there's an entire category of search terms emerging around AI marketing that most tools haven't caught up with yet.
In this playbook, you'll learn exactly how I:
Replaced multiple expensive SEO subscriptions with one AI research tool
Found 200+ untapped AI marketing keywords in my client's niche
Built a systematic approach for discovering low-competition opportunities
Validated keyword potential without traditional volume data
Created content that ranks faster because nobody else is targeting these terms
Plus, I'll share the exact workflow I use to stay ahead of the curve while everyone else fights over the same saturated keywords. For SaaS companies especially, this approach can completely transform your organic acquisition strategy.
Industry Reality
What every SEO expert has already heard
Walk into any SEO conference and you'll hear the same tired advice: "Use Ahrefs for keyword research, check competition scores, target keywords with decent volume but manageable difficulty." Everyone's following the same playbook.
The standard approach looks like this:
Start with seed keywords - Usually your main product categories
Expand through keyword tools - Let SEMrush or Ahrefs suggest variations
Filter by metrics - Volume above 100, difficulty below 30
Create content clusters - Build topic clusters around these keywords
Wait for results - Hope your content ranks in 6-12 months
This methodology made sense five years ago. Today? You're competing with every agency following the exact same process, targeting the exact same keywords their tools suggest.
The real problem isn't the tools themselves - they're great at what they do. But they're historical data machines, not crystal balls. They show you what people searched for last month, not what they'll search for next month. In the rapidly evolving AI marketing space, this backwards-looking approach leaves massive gaps.
Traditional tools excel at showing established search patterns. They struggle with emerging trends, especially in niches like AI marketing automation where the terminology and search behavior is changing monthly. While you're fighting for "AI marketing tools" (keyword difficulty: 85), there are dozens of specific AI marketing applications that nobody's optimizing for yet.
Here's the uncomfortable truth: if your keyword research looks like everyone else's, your results will too.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I started working with this B2B startup, they had the classic problem: a great product with zero organic visibility. Their previous agency had followed the textbook approach - targeting broad, competitive keywords that would take years to rank for.
The client was a SaaS platform helping businesses implement AI workflows. Their existing keyword strategy focused on terms like "AI automation software" and "business process automation" - keywords with massive competition and established players already dominating the first page.
My first instinct was to fire up the usual suspects: SEMrush, Ahrefs, maybe cross-reference with Google autocomplete. After hours of research, I had the same list their previous agency had built. Lots of high-volume, high-competition terms that would require months of content and link building to even compete.
Then I remembered my own frustration with these tools. Yes, they're comprehensive, but they're also expensive and often overkill. More importantly, they're reactive - they tell you what's already popular, not what's emerging.
That's when I decided to experiment with a different approach. I had a dormant Perplexity Pro account that I'd barely used. On a whim, I decided to test their research capabilities for keyword discovery instead of traditional SEO tools.
The difference was immediate and shocking. Instead of generic keyword lists, Perplexity understood the context and nuances of AI marketing. It didn't just suggest keywords - it researched the actual problems businesses were trying to solve with AI marketing and the specific language they used to describe these challenges.
Within an hour, I had discovered semantic keyword clusters around specific AI marketing applications that traditional tools had completely missed. Terms like "AI workflow template for ecommerce" and "automated customer segmentation AI" - searches that real people were making but that didn't show up in conventional keyword databases yet.
Here's my playbook
What I ended up doing and the results.
Here's the exact system I developed that replaced multiple expensive SEO tool subscriptions and consistently finds untapped keyword opportunities:
Phase 1: AI-Powered Discovery
Instead of starting with traditional keyword tools, I begin with Perplexity's research feature. But here's the key - I don't ask for keywords. I research the actual problems and use cases.
My research queries look like: "What specific AI marketing challenges are SaaS startups facing in 2025?" or "What AI marketing workflows are ecommerce businesses trying to implement?" This approach uncovers the language real businesses use to describe their needs.
Perplexity doesn't just give me a list of terms. It provides context, identifies emerging trends, and reveals the specific pain points that businesses are actively searching for solutions to solve.
Phase 2: Semantic Expansion
Once I have the core problems and use cases, I use AI to expand these into searchable terms. I feed the research back into the system with prompts like: "Based on these AI marketing challenges, what specific searches would a marketing director make when looking for solutions?"
This reveals long-tail keywords that capture search intent perfectly but have virtually no competition because they're too specific for broad keyword tools to identify.
Phase 3: Validation Without Volume
Here's where my approach gets unconventional. Instead of relying on search volume data (which doesn't exist for emerging terms anyway), I validate keywords through problem-solution fit.
I ask: Does this keyword represent a real business problem? Is there a clear commercial intent? Would someone searching this term be willing to pay for a solution? If yes to all three, it's worth targeting regardless of what traditional metrics say.
Phase 4: Competitive Gap Analysis
For each promising keyword, I manually check the search results. I'm looking for gaps - searches where the first page results don't directly address the query or where the content is generic and doesn't provide specific solutions.
In the AI marketing space, I consistently find searches where the results are blog posts about AI in general, not specific solutions for the exact problem being searched.
Phase 5: Content Opportunity Mapping
The final step is mapping each keyword to a content opportunity that provides genuine value. Rather than creating generic "everything you need to know" content, I build specific resources that directly solve the problem indicated by the search.
For example, instead of targeting "AI marketing automation," I target "AI workflow template for abandoned cart recovery" and create an actual downloadable template, not just an article about the concept.
Research Method
Using Perplexity's research capabilities to understand real business problems instead of starting with traditional keyword tools
Validation Approach
Focusing on problem-solution fit rather than search volume data for emerging AI marketing terms
Content Strategy
Creating specific resources that solve exact problems rather than generic informational content
Competitive Analysis
Manually checking search results to identify gaps where current content doesn't address specific queries
The results from this approach have been consistently better than traditional keyword research:
Cost Reduction: Replaced €300+ monthly tool subscriptions with a single Perplexity Pro account. The ROI comparison isn't even close - we're talking about 90% cost reduction for better results.
Keyword Discovery: Found over 200 untapped keywords in the AI marketing space that traditional tools hadn't identified. These weren't just variations of existing terms - they were entirely new search patterns emerging as businesses adopt AI marketing.
Content Performance: Articles targeting these AI-specific keywords started ranking within weeks instead of months. When there's no competition, ranking happens fast.
Quality Over Quantity: Instead of targeting hundreds of competitive keywords, we focused on 50 highly specific terms with clear commercial intent. The result was higher-quality traffic that actually converted.
The most surprising outcome was discovering that search volume data is often misleading for emerging technologies. Many of our best-performing keywords showed "0" search volume in traditional tools but were driving consistent, high-intent traffic.
This approach works because it aligns with how people actually search for AI marketing solutions - they're specific about their problems and use language that's too nuanced for broad keyword tools to capture effectively.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from building keyword strategies with AI instead of traditional tools:
Search volume data is becoming less reliable for emerging technologies. Focus on intent and problem-solution fit instead of historical data.
AI research tools understand context better than keyword tools. They can identify semantic relationships and emerging trends that traditional tools miss.
Competition matters more than volume. A keyword with 50 monthly searches and no competition beats one with 500 searches and 100 competitors.
Specificity wins in the AI marketing space. "AI email automation workflow" outperforms "AI marketing" every time.
Manual validation is still essential. No tool - AI or traditional - replaces human judgment about content quality and search intent.
Speed to market matters. In rapidly evolving spaces like AI marketing, getting content live fast beats perfect optimization.
Cost-effectiveness scales. As you build expertise with AI research tools, the time and cost savings compound dramatically.
The biggest mindset shift was realizing that in emerging markets like AI marketing, being first matters more than being perfect. Traditional SEO wisdom says wait for data. In fast-moving spaces, waiting means missing the opportunity entirely.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies targeting AI marketing keywords:
Focus on specific workflow and integration terms rather than broad AI marketing concepts
Target problem-specific searches like "AI lead scoring for B2B SaaS" instead of generic terms
Create actual tools and templates, not just educational content
For your Ecommerce store
For ecommerce stores leveraging AI marketing:
Target conversion-focused terms like "AI product recommendation engine" and "automated personalization workflows"
Focus on platform-specific integrations like "Shopify AI automation tools"
Create guides that solve specific ecommerce AI implementation challenges