How to find low-competition keywords that actually convert

Low-competition keywords with no buyer intent = traffic without revenue. Here is the process to find ones that actually convert.

Start with commercial intent modifiers

Search Ahrefs↗or Semrushfor your category + these modifiers: "best", "vs", "review", "alternative", "comparison", "cost", "price", "pricing", "worth it".

Filter for KD under 25

KD over 25 is rarely winnable for new sites. Under 15 is best for under-6-month-old sites.

Check the SERP manually

Low KD doesn't always mean easy. Manually check: are top 10 results established sites or can you realistically match them?

Look for queries with weak SERPs

Weak SERP signals:

• →No big-domain sites in top 5 (no Forbes, CNN, Wikipedia)
• →Thin content in top 5 (under 1000 words on most pages)
• →Forum results ranking (Reddit↗, Quora) mean weak brand competition
• →Mixed intent (informational + commercial blending)

Validate with clicks data

High search volume with low clicks usually means SERP features stealing clicks (featured snippets, AI Overviews). Favor queries with high clicks-per-search ratio.

Frequently asked questions

Is this approach right for early-stage companies?

Most frameworks in this space assume a certain level of operational maturity, dedicated team members, established measurement infrastructure, some history of experimentation to build on. Pre-seed and seed-stage companies often lack these prerequisites and need a lighter-weight adaptation. For brands doing under $3M in annual revenue, focus on three or four of the principles that matter most for your specific business model rather than trying to implement the full framework at once. Rigor matters more than coverage at this stage.

How does this work for B2B versus B2C businesses?

The underlying principles around low competition keywords apply across both contexts, but execution differs meaningfully. B2B seotypically has longer sales cycles, multiple stakeholders per deal, and consideration periods measured in months rather than minutes. Measurement frameworks need longer windows. Attributionbecomes more complex. The same core strategic logic applies, but the tactical implementation looks different. We've worked extensively in both contexts and can flex the approach accordingly.

What changes when we integrate this with existing systems?

Every implementation requires integration work, systems don't exist in isolation. Analytics platforms, CRM, email systems, ad accounts, BI tooling all need to talk to each other for this to work at scale. Plan for 2-4 weeks of integration work at the start of any implementation. Shortcutting this phase creates data quality issues that compound and undermine the entire program over 6-12 months. We've seen teams skip integration work to move faster, only to spend 6 months later reconciling measurement discrepancies that could have been prevented upfront.

When should we reconsider the approach?

Every 6 months, run a structured review against the principles outlined here. Ask whether the market has shifted meaningfully, whether your business model has evolved, whether competitive dynamics have changed. Frameworks should evolve with context. A rigid commitment to any specific approach, including ours, eventually becomes the problem rather than the solution. The teams that outperform long-term are the ones that update their operating model based on evidence, not the ones that defend past decisions.