Keyword research from Google autosuggest

Why autosuggest is the best free keyword research source

Most keyword research tools (Semrush, Ahrefs, Ubersuggest) charge between $99 and $449 per month for keyword data that ultimately comes from the same underlying source: Google's own search activity. Google autosuggest gives you direct access to that source, free, and in real time.

Three reasons autosuggest beats paid tools for early-stage research:

• Real query data, not estimates: every suggestion in autosuggest is a query that real users actually search. Paid tools display volume estimates, while autosuggest displays validated reality.
• Intent signals built in: the order of suggestions reflects intent strength. Top suggestions are what most users want; bottom suggestions are what fewer users want.
• Trending keywords surface immediately: a query rising in popularity appears in autosuggest within hours. Paid tools take weeks to update their databases.

Method 1: Manual letter-by-letter mining

The simplest extraction method: type your seed keyword into Google search, then iterate through the alphabet to capture every suggestion variation.

Example workflow with seed "best CTR":

• Type "best CTR a" → capture suggestions ("best CTR analysis", "best CTR ad", "best CTR app")
• Type "best CTR b" → capture suggestions ("best CTR bot", "best CTR blog")
• Continue through z, then digits 0-9, then question words (what, how, why, when)

This method takes 30 to 60 minutes per seed keyword but produces 50 to 200 high-intent variations. Best for small-scale research where you need depth over breadth.

Question variations to always test

• "how to": surfaces tutorial intent ("how to use [keyword]", "how to find [keyword]")
• "what is": surfaces educational intent ("what is [keyword]", "what is the best [keyword]")
• "why": surfaces problem-aware intent ("why is [keyword] important", "why does [keyword] matter")
• "vs": surfaces comparison intent ("[keyword] vs [competitor]", "[keyword] vs alternative")
• "best": surfaces commercial intent ("best [keyword] for...", "best [keyword] to use")

Method 2: Free autosuggest scraping tools

For larger research projects, manual letter-by-letter mining doesn't scale. Free tools that automate the extraction:

For the highest-leverage approach, combine Keyword Sheeter (bulk extraction) with manual filtering, then validate top picks in a paid tool like Ahrefs or Semrush.

Method 3: Direct Google API access

Technical users can pull autosuggest data directly through Google's undocumented suggestion endpoint. The endpoint returns JSON with up to 10 suggestions per query.

Combining this with a script that iterates seed variations gives you scalable extraction. Be respectful with rate limits (1 request per second) to avoid IP blocks.

Filtering raw suggestions for SEO value

A typical extraction produces hundreds of suggestions, most of which are noise. Apply this filtering hierarchy:

Filter 1: Remove navigational queries

Suggestions like "facebook login" or "youtube" are dominated by single-brand monopolies. Remove anything that looks like brand+navigation.

Filter 2: Keep commercial intent

Prioritize queries with modifiers signaling buying intent: "best", "review", "vs", "alternative", "pricing", "cost", "buy".

Filter 3: Keep informational with high traffic potential

Question-based queries ("how to", "what is", "why does") often have lower direct conversion but higher traffic volume. Keep these for content marketing strategy.

Filter 4: Validate volumes in a paid tool

Take your filtered list (typically 30 to 80 keywords from 200+ raw suggestions) and bulk-check volumes in Ahrefs or Semrush. Discard anything under 50 monthly searches unless it's clearly high-intent.

From autosuggest to ranking strategy

Once you have a filtered list of high-value autosuggest keywords, convert them into a ranking strategy:

• Cluster the keywords by intent: tutorial, comparison, transactional, informational
• Map clusters to content formats: tutorials become guides, comparisons become "vs" pages, transactional become product/landing pages
• Prioritize by competitive gap: keywords with low difficulty and high autosuggest visibility are your fastest wins
• Build the content with the right structure: H1 matching the seed query, H2s addressing each related autosuggest variation

Combined with click signal automation (driving CTR on the keywords you're trying to rank for), this autosuggest-driven keyword strategy compounds: you find the right keywords through autosuggest, write content that matches search intent, then accelerate ranking with automated click signals.