Vertical Search: What It Is, Examples and How to Rank in Each Vertical

Vertical search is older than most marketers think: the reference definition on Wikipedia describes engines that focus on a specific segment of online content, defined by topic, media type or genre. What has changed is its weight. Buyers search for products on Amazon, for videos on YouTube, for restaurants on Maps, and now ask ChatGPT or Gemini, which themselves query specialized sources. This guide explains how vertical search works, gives concrete examples and lists what to optimize in each vertical.

What is vertical search?

A vertical search engine indexes a limited segment of content and organizes it with structured data specific to that segment: price and availability for products, address and opening hours for places, duration and transcript for videos, salary and location for jobs. A general (or horizontal) search engine tries to index the whole web and answer any question.

The precision of vertical search comes from those structured records. A shopper on a marketplace filters by price, size and rating, which no generic list of links can offer.

What are examples of vertical search engines?

Vertical search engines fall into a few families:

• Google's own verticals: Images, Videos, News, Shopping, Maps, Flights, Hotels, Scholar, Books, plus the jobs experience built on structured data.
• Marketplaces: Amazon, eBay, Etsy, Walmart, where search is the main entry point to products. See our guides to Amazon SEO and Etsy SEO.
• Video and visual platforms: YouTube, TikTok, Pinterest, which many people use as search engines for tutorials, ideas and reviews.
• Travel: Booking.com, Expedia, Airbnb, Kayak, Skyscanner, Tripadvisor.
• Local and reviews: Google Maps, Apple Maps, Yelp, specialist directories.
• Jobs and professionals: Indeed, LinkedIn, Glassdoor.
• Real estate, health, academia: Zillow or national property portals, medical directories, Google Scholar and PubMed.

A business does not need to be present everywhere. It needs to be present in the verticals where its buyers start their search.

How does Google mix vertical results into web search?

Google announced universal search on its official blog on May 16, 2007 in a post titled "Universal search: The best answer is still the best answer". Since then, one results page can combine web links with images, videos, news, products and a map. These blocks are the SERP features you see today: the local pack, image and video carousels, product grids, top stories.

The consequence for SEO is direct: on many queries, the first position is no longer a blue link. A restaurant query shows a map before any website, a "how to" query often shows videos, a product query shows a shopping grid. Winning those queries means feeding the vertical that Google displays.

How do you rank in each vertical?

Each vertical publishes, at least in part, how it ranks results. The table summarizes official statements and the levers that follow from them.

Three official sources are worth bookmarking: Google's image SEO best practices, the Google Business Profile page on local ranking, which states that local results are mainly based on relevance, distance and popularity, and the YouTube help page on how search works, which names relevance, engagement and quality as the three elements its system prioritizes. Our guides to image SEO, video SEO and Google Shopping go deeper into each vertical.

What do all verticals have in common?

Behind different interfaces, the same principle applies: vertical engines rank structured data, not only prose. A product page without price, availability and reviews in its markup, a business profile with a vague category, a video without a transcript: each is invisible to the filters that users apply. Clean structured data, consistent across platforms, is the first lever in every vertical.

How do AI assistants change vertical search?

ChatGPT, Gemini, Perplexity and Google's AI Mode act as a layer above the verticals. To answer "which hotel near the station" or "best running shoes under 150 dollars", they draw on maps, marketplaces, review sites and videos, then summarize. Agents go one step further: agentic search queries several sources on its own, compares offers and can prepare an action.

Two consequences follow. First, the data you publish in each vertical (listings, reviews, product attributes, transcripts) becomes raw material for AI answers. Second, the platforms that AI assistants quote most often gain weight: our studies of AI citations from Amazon and Tripadvisor show how often vertical platforms appear as sources. Measuring whether your brand appears in those answers is now part of the job, which is what AI mention tracking in Sorank does.

Which verticals should you prioritize?

The answer depends on the sector. The table below gives a starting point for common business types; check it against the results pages of your own queries.

Then follow five steps:

1. Look at the results page of your main queries. If Google shows a map, videos or a product grid, that vertical is your priority, whatever your web rankings.
2. List where your buyers start. A retailer starts with Shopping and marketplaces, a local service with Maps, a software company with web search, YouTube and AI assistants.
3. Fix the data first. Product feed, business profile, structured data on your pages: the cheapest gains come from complete, accurate records.
4. Produce the formats each vertical needs. Real photos, short videos with transcripts, answers to frequent questions.
5. Keep your website as the hub. Verticals change their rules; your site, with its content and links, remains the source that every vertical and every AI can check.

The most robust strategy combines both: a website that ranks in horizontal search and is cited by AI assistants, plus complete profiles in the two or three verticals that matter for your market. Sorank handles the first part on autopilot, from keyword research to publishing articles; see the overview of Sorank. In short, vertical search rewards businesses that publish complete, structured and consistent data wherever their customers search.