Type a few words into Google’s search box and pause, and it offers to finish your sentence. Those autocomplete suggestions aren’t random — they’re drawn from real, aggregated search behavior, which makes them one of the most honest keyword data sources available. The problem has always been coverage: a single seed keyword only surfaces a handful of suggestions at a time. The A-Z method is a way of systematically forcing far more of that data out into the open.
The Core Mechanic
Instead of querying just your seed keyword and taking whatever four or five suggestions Google offers, the A-Z method appends every letter of the alphabet to the seed, one at a time — seed + “a,” seed + “b,” seed + “c,” and so on through “z” — and queries the suggest data for each combination separately. Each query returns its own small batch of suggestions, and because you’re running twenty-six separate queries instead of one, you end up with a far wider set of real, currently-searched phrases than a single lookup could ever surface.
The appeal is straightforward: this is genuine search data, not a modeled estimate. Every phrase that comes back is something people have actually typed into Google in enough volume to trigger an autocomplete suggestion. That makes it a strong source for long-tail phrasing — the specific, often oddly-worded searches that a broad keyword tool sorted by volume would never surface, because their individual volume is too low to rank highly on its own, even though collectively long-tail terms make up the majority of all search traffic.
Why the Alphabet, Specifically
The choice of the alphabet as the expansion mechanism isn’t arbitrary — it’s a brute-force way of forcing Google’s suggestion engine to reveal variations it wouldn’t otherwise surface unprompted. Google’s default suggestions for a bare seed keyword tend to cluster around the single most common completion. Appending a letter forces the engine to complete around that constraint instead, and different letters pull out genuinely different directions — questions, modifiers, related terms, comparisons — that a plain seed query alone would never expose.
What You Actually Get Back
Run the full alphabet against a decent seed keyword and you typically get somewhere in the range of dozens to well over a hundred raw suggestions, depending on how much search volume exists around that topic. That’s the raw output — before any filtering. It includes genuine long-tail variations you’ll want to use, but (as covered in more detail in a companion piece on filtering) it can also include suggestions that have drifted away from your original topic entirely, because Google’s fallback behavior when it has nothing good to complete for a specific seed+letter combination is to surface unrelated trending or spelling-correction suggestions instead.
Where This Fits Relative to Other Keyword Research
It’s worth being clear about what the A-Z Suggest method is good for versus what it isn’t. It’s not a competitor analysis tool — it has no idea what your competitors do or don’t rank for, and it won’t tell you whether a term is realistically winnable. What it’s genuinely good at is surfacing the raw universe of phrasing around a topic that real people actually search for, especially the long-tail variety that competitor-based gap analysis structurally can’t see (because gap analysis can only show you what a competitor has already targeted — it’s blind to anything nobody’s written about yet).
Used together, the two approaches complement each other well: competitor gap analysis for validated, proven topics; A-Z Suggest expansion for genuinely untapped long-tail phrasing that hasn’t been targeted by anyone in your comparison set yet.
Choosing a Good Seed Keyword
The quality of what comes back depends heavily on the seed you start with. A seed that’s too broad (a single generic word) tends to produce noisy, unfocused suggestions across unrelated sub-topics. A seed that’s too narrow (an already-specific long-tail phrase) often has too little underlying search volume for the alphabet expansion to find much beyond the seed itself. The sweet spot is usually a two- or three-word phrase that names a specific topic or product category clearly enough to anchor the suggestions, without being so specific that there’s nothing left to expand.
A Practical Workflow
- Start with a seed phrase that names a clear, specific topic — not a single word, not an already-long-tail sentence.
- Run the A-Z expansion and scan the full output rather than stopping at the first page of results.
- Discard anything that’s clearly drifted off-topic — a filtered version of this process is covered in a companion article, but a manual skim works too for smaller lists.
- Group what’s left by apparent intent (how-to, comparison, definition, local, pricing) — that grouping often reveals two or three distinct content angles from a single seed.
- Cross-check the strongest candidates against your competitor gap report to see whether anyone’s already covered them — if not, that’s a genuinely untapped opportunity.
The A-Z Suggest method won’t replace competitor analysis or volume-and-competition checks, but it fills a gap neither of those can: surfacing the long tail of real, current search phrasing that nobody’s competitive footprint has caught up to yet.
