Distance.to Case Study: Generating Millions of Pages

Distance.to ranks for millions of search queries using a simple concept: people search for distances between places. By combining a geographic database with a single template, they generated 1.29 million indexed pages capturing long-tail traffic at scale.

The Programmatic SEO Model

No human wrote 1.29 million articles. The site stores structured data about global locations: countries, states, cities, districts. A template defines how distance pages look. When someone visits or a crawler requests a specific route, the system pulls the two locations from the database, calculates the distance, and renders the page dynamically.

This approach is called programmatic SEO. One template times hundreds of thousands of data combinations equals millions of unique pages, each targeting a specific search query like “distance from Berlin to Paris.”

The Math of Combinations

With roughly 200 countries, country-to-country combinations alone produce around 40,000 unique distance queries. Adding states and cities multiplies this exponentially. Every permutation represents a real search that real users make.

Each individual page might attract only a few visits per day. But a few visits across a million pages aggregates into tremendous traffic. Distance.to reportedly receives over 12 million monthly visits from this strategy.

Implementation Architecture

The site uses server-side rendering to ensure crawlers receive complete HTML. When a user requests /germany/france, the server fetches both locations, calculates distance and travel time, generates a map visualization, and returns the fully rendered page.

A discovery mechanism exposes pages to crawlers. Internal linking from country pages to city pages, combined with a sitemap covering the full URL space, ensures Google finds and indexes the entire catalog.

Extending with Languages

Subdomains like it.distance.to and nl.distance.to multiply the page count again. The same location database renders in Italian, Dutch, and dozens of other languages. Each language version captures search traffic from that language’s speakers.

The strategy requires identifying any domain with combinatorial data: locations, dates, product comparisons, name combinations. If users search for specific pairings, programmatic generation can capture that demand at scale impossible for manual content creation.