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    <title>DEV Community: JinHan</title>
    <description>The latest articles on DEV Community by JinHan (@jinhan).</description>
    <link>https://dev.to/jinhan</link>
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      <title>DEV Community: JinHan</title>
      <link>https://dev.to/jinhan</link>
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      <title>I mined 636 Google Autocomplete queries before building a niche site — here's what people actually search for</title>
      <dc:creator>JinHan</dc:creator>
      <pubDate>Fri, 07 Aug 2026 12:31:44 +0000</pubDate>
      <link>https://dev.to/jinhan/i-mined-636-google-autocomplete-queries-before-building-a-niche-site-heres-what-people-actually-1nm4</link>
      <guid>https://dev.to/jinhan/i-mined-636-google-autocomplete-queries-before-building-a-niche-site-heres-what-people-actually-1nm4</guid>
      <description>&lt;p&gt;Most niche-site builders pick a keyword, check a volume number, and start writing. Before building my latest project, I tried something cheaper and more honest: I scripted Google's free Autocomplete API and harvested every real query pattern around one product niche — car laptop desks.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The method (free, ~300 requests)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Google's suggest endpoint (&lt;code&gt;suggestqueries.google.com/complete/search&lt;/code&gt;) returns what real users type. For each seed term (&lt;code&gt;car laptop desk&lt;/code&gt;, &lt;code&gt;car laptop stand&lt;/code&gt;, &lt;code&gt;laptop desk for car&lt;/code&gt;...), I appended &lt;code&gt;a&lt;/code&gt;–&lt;code&gt;z&lt;/code&gt; and &lt;code&gt;0&lt;/code&gt;–&lt;code&gt;9&lt;/code&gt; suffixes and collected everything — 296 queries later I had 636 unique search phrases, each a real thing people type.&lt;/p&gt;

&lt;p&gt;Simple scoring: +3 if it contains a year (fresh demand), +2 for question words (content opportunity), +1 for 4+ words (long tail).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What surprised me&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;
&lt;strong&gt;The top query wasn't about products at all.&lt;/strong&gt; "Is it legal to have a laptop mounted in your car" appeared across 17 different seed expansions. Nobody selling desks answers it — that's a content gap you could drive a truck through.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;"3D print" showed up 12 times.&lt;/strong&gt; A meaningful slice of this market wants to print a stand, not buy one. A product-only site would never serve them; a guide on materials that survive hot cars (PLA softens at dashboard temperatures) does.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Intent clusters map to site architecture.&lt;/strong&gt; Position words (passenger seat / back seat / steering wheel / cup holder) and type words (stand / table / tray / mount) each had full suggest trees — so the site's URL structure became exactly those clusters. The keyword data designed the sitemap.&lt;/li&gt;
&lt;li&gt;
&lt;strong&gt;Navigational noise is a filter, not a target.&lt;/strong&gt; "near me", "amazon", "officeworks" queries tell you what NOT to build pages for.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;The result&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The data became &lt;a href="https://carlaptopdesk.com/" rel="noopener noreferrer"&gt;CarLaptopDesk.com&lt;/a&gt; — position pages, type pages, and guides answering the legality/heat/DIY questions the autocomplete surfaced. Whether it ranks is now Google's call, but at least every page maps to a query someone actually typed.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Takeaway&lt;/strong&gt;: before trusting a paid tool's volume estimates, spend 20 minutes with the autocomplete API. It won't give you numbers, but it gives you the &lt;em&gt;shape&lt;/em&gt; of demand — and shape is what decides your information architecture.&lt;/p&gt;

&lt;p&gt;Happy to share the harvesting script if anyone wants it — it's ~80 lines of Python with rate limiting.&lt;/p&gt;

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      <category>seo</category>
      <category>webdev</category>
      <category>sideprojects</category>
      <category>datascience</category>
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