The Ultimate Pagination - SEO Guide

Pagination on websites is a system to organize content. The idea is to divide the content on separate pages. Pagination is normally displayed as arrows, a series of page numbers or "previous" and "next" buttons.

Pagination has an effect on site structure as well as usability. This makes pagination interesting for SEO as we can use it to improve crawlability and optimize our most important pages.

What is Pagination?

The main objective of pagination is to make content more accessible. This especially refers to content buried deep on websites with a lot of levels. Pagination is also often used for large documents, like very long articles that are split into smaller chunks.

When to use Pagination?

If a website contains so many elements that they cannot or should not be displayed on a single page, then pagination is used. Reasons for splitting the list are often technical limitations and/or usability issues. A typical use case is a category in an online shop which lists products on multiple pages.

Pagination is also used to divide very long articles into smaller chunks. This makes sense, when each pagination page is supposed to focus on a specific topic e.g for usability or SEO reasons. However sometimes this is simply done to generate more page impressions e.g. when the main source of revenue is advertising.

Pagination can be used to make a website more accessible. Pagination can help to reduce the levels of a website. With a smart pagination users and bots need fewer clicks to reach content that was previously hidden in a deep structure. A flatter hierarchy can boost the crawlability of a website. This can have a positive effect for search engine optimization.

When not to use Pagination?

Pagination should not be used if it is possible to display the content on one page without technical and usability issues. In most cases it is easier to scroll down a single page than clicking through multiple pages.

Pagination should also be avoided for articles when there is the risk that the paginated pages generate less traffic in total than the content on a single page would. A comprehensive article that covers a topic from many perspectives can rank in search engines for interesting keyword combinations. If the article is split into several pagination pages, fewer keyword combinations would be possible.

You should also avoid using pagination for lists if there is the risk that the pagination pages could be considered thin content by search engines. This is usually true if you list only a few items on each page.

What are the Requirements for a Pagination in Terms of Usability and Website Structure?

Pagination is always a compromise between structure, usability and design.

Pagination needs to be user friendly. It should help users to navigate the website. This means a pagination should always make sure that users know where they are, where they can go and how to get back. It should not take users to irrelevant content and of course it needs to work from a technical point of view. An additional challenge is to design the pagination for limited space while keeping the navigation efficient. This is achieved when the pagination minimizes the number of clicks to reach each element.

What is the relation between Pagination Pages and Item Pages?

Relation between Item Pages and Pagination Pages

A pagination comprises of pagination pages and item pages. Imagine an online shop with a category that lists a number of products spread on so called pagination pages. Each pagination page shows a fixed maximum number of items. The products are the items. The individual product pages are the item pages.

Pagination pages and item pages form an internal link structure. Depending on the kind of pagination, some pages will become more relevant than others. These are the pages that should be optimized for search engines. The relevant pages could be pagination pages as well as item pages and they should contain the most important products.

In order to find the most important pages within our internal link structure we can use mathematical metrics like PageRank and CheiRank and combine them into 2D-Rank. PageRank is a metric to show the authority of a page, while CheiRank indicates the best hubs. Combined as 2D-Rank we can find the most relevant pages.

Testing different Types of Pagination

Most paginations follow a certain pattern. While researching the topic, we identified that paginations are often built using the following patterns:

Previous - next

First / Last

Neighbours

Fixed Steps

Fixed Block

Logarithmic Pagination

Ghostblock

We tested all these different types of paginations in order to identify the effects on PageRank and CheiRank distribution as well as the impact on clickpaths.

Analyze your site structure and pagination depth with Audisto Our software will help you understand and optimize the technical site structure. We calculate PageRank, CheiRank and 2D-Rank by category or complete cross-domain linkgraph. Simulate changes before release. Book a Demo

Test Setup

We built a wizard that allows us to simulate different types of paginations.

Pagination Wizard

The wizard includes the following settings:

Number of pagination pages

Number of items per pagination page

Link first and/or last pagination page

Number of neighbors

Fixed steps

Logarithmic Pagination

Fixed Blocksize

Ghostblock

To ensure comparability between the paginations, we always used 20 items per pagination page and 100 pagination pages. This results in 100 pagination pages and 2,000 item pages and a total of 2,100 pages.

For every pagination we took three assumptions that are important because they affect the linking structure and therefore the test results. We assumed:

Previous and next page are always linked.

Every pagination page links to the first pagination page.

Every item page links to the first pagination page.

We consider all three assumptions as best practice and a well known pattern to users that must be applied when building paginations.

Linking the previous and next page is a fundamental property of paginations.

The first pagination page often equals the first page of a category. It simply does not make sense if users cannot get back and we found no example where this was not possible. If you look at item pages, the first pagination page would usually be linked within the breadcrumb navigation.

The goal was to understand the different effects on the depth of the site (levels), PageRank and CheiRank distribution. For each pagination we wanted to answer the following questions:

How deep is the site structure?

Which pages are relevant for SEO?

How many pages are relevant for SEO?

Which pages are the best authorities (PageRank)?

Which pages are the best hubs (CheiRank)?

Once we generated a pagination, we used our crawler for analysis.

Clusters of Pages in Audisto

With our software we were able to define clusters. Having clusters for the pagination pages and item pages allowed us to analyze the two types of pages separately. The cluster dashboard provides an overview of PageRank and CheiRank distribution as well as the maximum depth for each pagination type.

Accessible Pages by Level in Audisto

For each cluster we also get a report with more detailed information of the distribution across the different levels.

Level Distribution of Basic Pagination

The left graph shows the number of levels for pagination pages. The right graph shows the number of levels for item pages. The pages are defined on the X-axis and the levels are defined on the Y-axis. When you look at the graph you should always keep in mind that we start counting levels on the first pagination page with level 0. This way the levels are identical with the number of clicks needed to reach a page from the first pagination page.

PageRank and CheiRank Distribution for Basic Pagination

The left graph shows the PageRank and CheiRank distribution for the pagination pages. The right graph shows the PageRank and CheiRank distribution for the item pages.

With our tests we aimed to find out, which and how many pages are relevant for SEO. To answer this questions we decided to count all pages that have a PageRank value above a certain threshold as relevant for SEO.

The idea behind this is, that when search engines crawl the web they have to decide which pages are relevant for crawling and indexing and which are not. Search engines use metrics like PageRank to evaluate the importance of pages within a website structure. All pages that exceed a certain threshold value would be considered relevant for crawling and indexing.

For a search engine determining a good threshold is a matter of resources needed for crawling and indexing. As SEOs we need to use a good threshold to compare different site structures.

The smaller the number of pages, the higher is the average PageRank per page. When distributing Pagerank across a small number of pages the average PageRank per page can be quite high. In our test setup we analyzed 2,100 pages with a total of 100% PageRank. An even distribution would mean calculation the PageRank with the following formula:

100% / 2,100 pages = ~0.0476% / page

However, PageRank values are not evenly distributed (e.g. the first pagination page usually has more than 40%). We need a good threshold to see true differences between the different types of paginations. In order to get good results we chose a threshold of 0.00001% which is below average. In our test results we call this a noteworthy amount of PageRank.

Test Results

Before we take a detailed look at the different types of paginations, we would like to point out some general observations.

The graphs for pagination pages and item pages often look similar. This is especially true for the levels and the PageRank distribution.

Because of our test setup where the first pagination page is always linked from all other pages, the first pagination page always has a very high PageRank.

As result of each pagination page linking 20 item pages the level distribution for the item pages looks similar to a staircase.

All tested paginations showed that there is virtually no CheiRank on the item pages. Since the CheiRank indicates hubs this means only pagination pages are good hubs.

Basic Pagination

Example of Basic Pagination

The basic pagination fulfills only our three assumptions and always links the first page, the previous page and the next page.

Level Distribution of Basic Pagination

This type of pagination generates 100 levels. The pagination is extremely deep since users can only click through the pagination pages one by one.

PageRank and CheiRank Distribution for Basic Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 100 51 49 100 0 4 60

The basic pagination generates 4 pagination pages and 60 item pages with a noteworthy amount of PageRank. The relevant pages are the first pagination pages and the first item pages.

The last pagination pages are the best hubs. This is because users can always reach pages at the beginning of the pagination with just a few clicks via the first pagination page. However, to reach the last pagination pages it can take as many as 100 clicks.

This pagination is not useful if you want to bring large amounts of pages into an index of a search engine. This is mainly because this pagination produces only 64 out of 2,100 pages with a noteworthy PageRank. On the other hand this pagination might be useful if you want to have most of the internal PageRank on a very small number of pages.

First Pages

Example of First Pages Pagination

This pagination always links to the first five pagination pages from all other pagination pages.

Level Distribution of First Pages Pagination

This type of pagination generates 97 levels. The pagination is still extremely deep. However, compared to the basic pagination, we lifted many more pages onto level one and two. While we only had one pagination page on level one in the basic pagination, we now have four pagination pages on level one. And while we only had 20 item pages on level two, we now have 80 item pages on level two.

PageRank and CheiRank Distribution for First Pages Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 97 55 45 100 0 7 120

The pagination generates 7 pagination pages and 120 item pages with a noteworthy amount of PageRank. The relevant pages are still the first pagination pages and the first item pages.

Like the basic pagination, the best hubs are still the last pagination pages.

This pagination brings more pages into an index of a search engine than the basic pagination. However, with 127 relevant pages it is only a small improvement compared to 64 relevant pages of the basic pagination.

First and Last Page

Example of First and Last Page Pagination

This pagination always links to the first and last pagination page. Everything else is identical to the basic pagination.

Level Distribution of First and Last Page Pagination

This type of pagination generates 51 levels. The deepest levels are now in the middle. Users can now click through the pagination from both ends. This is why the level depth is cut in half.

PageRank and CheiRank Distribution for First and Last Page Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 51 52 48 100 0 7 100

The pagination generates 7 pagination pages and 100 item pages with a noteworthy amount of PageRank. The relevant pages are now the first and last pagination pages and the first and last item pages.

The best hubs are now in the middle of the pagination pages.

Even with the improvement that this pagination is only half as deep as the basic pagination, it is often not useful. This is because the last pagination pages and the last item pages gain relevancy within the link structure. However, in most cases we have sorted listings and the last pages are the most irrelevant pages. In shops for example, products are sorted in a way to maximize revenue. The best products would be on the first pages and not at the end.

Analyze your site structure and pagination depth with Audisto Our software will help you understand and optimize the technical site structure. We calculate PageRank, CheiRank and 2D-Rank by category or complete cross-domain linkgraph. Simulate changes before release. Book a Demo

Neighbors

Example of Neighbors Pagination

This pagination always links the first pagination page and five neighbors.

Level Distribution of Neighbors Pagination

This pagination generates 21 levels. The depth level is a lot lower compared to the basic pagination, because we now have five pagination pages and 100 item pages on each level.

PageRank and CheiRank Distribution for Neighbors Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 21 56 44 100 0 19 260

This pagination generates 19 pagination pages and 260 item pages with a noteworthy amount of PageRank. As with the basic pagination, the relevant pagination and item pages are in the front.

Like the basic pagination, the best hubs are still the last pagination pages.

This pagination is an improvement compared to the basic pagination. There are now 279 pages out of 2,100 with a noteworthy PageRank. The most relevant pages are at the front.

First, last and neighbors

Example of First, Last and Neighbors Pagination

This pagination always links the first and last as well as five neighboring pagination pages.

Level Distribution of First, Last and Neighbors Pagination

This pagination generates 12 levels. The level depth is so low because there are up to twelve pagination pages linked. Each level now has a lot more pagination pages and item pages. The middle pages are again on the deepest level because the last page is linked and users can click through the pagination pages from both ends.

PageRank and CheiRank Distribution for First, Last and Neighbors Basic Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 12 57 43 100 0 32 360

The pagination generates 32 pagination pages and 360 item pages with a noteworthy amount of PageRank. The relevant pages are now the first and last pagination pages and the first and last item pages.

The best hubs are now in the middle of the pagination pages.

Out of 2,100 pages we now have 392 relevant pages. However, as with the "first and last page" variant there is still the problem that there are irrelevant pages at the end of the pagination that get noteworthy PageRank.

Fixed Steps

Example of Fixed Steps Pagination

This pagination uses fixed steps and links every 20th page within the pagination. In addition it always links the first, last, previous and next pagination page.

Level Distribution of Fixed Steps Pagination

This pagination generates 12 levels. The level structure is so low, because of the combination of having the last page linked as well as the fixed steps. This type of pagination is basically a concatenation of multiple "first and last page" paginations.

PageRank and CheiRank Distribution for Fixed Steps Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 12 57 43 100 0 27 340

The pagination generates 27 pagination pages and 340 item pages with a noteworthy amount of PageRank. The fixed steps distribute the relevant pages across the whole pagination.

The best hubs are also distributed. They are now in the middle between the steps of the pagination pages.

Out of 2,100 pages we now have 367 relevant pages. Because of the fact that the relevant pages are distributed throughout the whole pagination, it is a bad idea to use this kind of pagination for sorted listings.

Fixed Block

Example of Fixed Block Pagination

This pagination is a fixed block of ten consecutive pagination pages. The first, previous and next pagination pages are always linked as well. The pages within the block remain the same until users reach the last page of the block. It then changes to the next 10 consecutive pages. In the example, the first block would change to pages 11 to 20 when users click from page 10 to page 11.

For each page the first page of the block can be calculated with the following formula:

floor((current page - 1) / block size)

Level Distribution of Fixed Block Pagination

This pagination generates 20 levels. The distribution on the levels alternates between one and nine pagination pages. This is why there is a little edge in the graph for the item pages.

PageRank and CheiRank Distribution for Fixed Block Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 20 59 41 100 0 20 220

The pagination generates 20 pagination pages and 220 item pages with a noteworthy amount of PageRank. The PageRank is concentrated on the first 20 pagination pages and the first 220 item pages.

Like the basic pagination, the best hubs are located towards the back. Users can always reach pages at the beginning of the pagination with just a few clicks via the first pagination page. However, to reach the last pagination pages it takes 19 clicks.

Since the pages are located in the beginning of the pagination, it is useful for sorted lists.

Analyze your site structure and pagination depth with Audisto Our software will help you understand and optimize the technical site structure. We calculate PageRank, CheiRank and 2D-Rank by category or complete cross-domain linkgraph. Simulate changes before release. Book a Demo

Logarithmic Pagination

Example of Logarithmic Pagination

This is a logarithmic pagination using 10 steps. The first, last, previous and next pagination page are also linked. The steps are calculated in a way, that the gaps are smaller around the current page get bigger towards both ends. The goal is to minimize the number of levels.

Level Distribution of Logarithmic Pagination

This pagination only generates 4 levels. Compared to the other types of paginations it is much harder to comprehend the page distribution on the levels.

PageRank and CheiRank Distribution for Logarithmic Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 4 62 38 99 1 100 1,720

The logarithmic pagination distributes a noteworthy amount of PageRank to 100 pagination and 1,720 item pages. However, it still generates peaks and the positions of the most relevant pages are hard to guess. Peaks are a disadvantage for sorted lists.

Because of the fact that the first and last page is always linked, the best hubs are at the beginning and at the end. However the hubs are still much better distributed than in the previous tested paginations.

With this pagination, almost all pages become relevant. This is why the logarithmic pagination is useful for lists with lots of similar or equal items, compared to lists where only a fraction of items are important. Since the pagination pages are seemingly random, this type of pagination can be hard to understand for users.

Ghostblock

Example of Ghostblock Pagination

This type of pagination uses a set of two pagination blocks. The first block is a fixed block of ten consecutive pages exactly as used in the "Fixed Block" pagination.

For each page the first page of the block can be calculated with the following formula:

=floor((current page - 1), block size) + 1

Note: In most programming languages / programms (e.g. Google Sheets / Microsoft Excel) the floor function has two parameters where the second parameter is the number to whose multiples value will be rounded. In this example the two parameters are separated by comma.

The second block is also a fixed block of 10 consecutive pages and is related to the current page. The ghostblock changes, as soon as users click on a different page of the first block. The first number of the ghostblock is calculated using the following formula:

=current page * 10 + 1

This is a real example from a popular news site and we called this block "ghostblock" because of how it was designed. The numbers of the second block are designed in such a light grey, that it was almost invisible.

Level Distribution of Ghostblock Pagination

With the ghostblock, this type of pagination only generates 3 levels. This means all 2,100 pages are distributed on a very small number of levels.

PageRank and CheiRank Distribution for Ghostblock Pagination

Level Item Pages % PageRank Pagination Page % PageRank Item Pages % CheiRank Pagination Pages % CheiRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages 3 64 36 95 5 100 2,000

The ghostblock pagination is able to distribute a noteworthy amount of PageRank across all pagination and item pages. There is a big improvement as the most relevant pages are at the front of the pagination. There are no peaks towards the end of the pagination.

With this pagination all of our 2,100 pages become relevant. The ghostblock pagination is the only pagination in our test where the first pagination pages are the best hubs (CheiRank) as well as the best authorities (PageRank). This means we have exceptionally strong pagination pages in the beginning of our pagination.

This pagination is useful for both, sorted lists and lists with similar or equal items.

However this pagination has two downsides in terms of usability. The fixed block pagination and the ghostblock are hard to understand if the user wants to access pages beyond the first fixed block.

Summary Table

This table provides a quick overview for levels, PageRank distribution and the number of SEO-relevant pages for pagination pages and item pages. The table does not include data of other small variants for the different kinds of paginations we tested. We varied the number of pages linked within the pagination. For example, we not just tested with 5 neighbors, but also with 3 and 10. The results only varied in intensity. There were very small differences in depths of levels, PageRank and CheiRank distribution as well as the numbers of SEO-relevant pages.

The table also does not include data for CheiRank distribution, because all tested variants have more than 98.5% of the CheiRank on the pagination pages. The hubs always lie within the pagination pages.

Kind of Pagination Level Item Pages % PageRank Pagination Page % PageRank Item Pages # SEO-Relevant Pagination Pages # SEO-Relevant Item Pages Basic Pagination 100 51 49 4 60 First Pages 97 55 45 7 120 First and Last Page 51 52 48 7 100 Neighbors 21 56 44 19 260 First, Last and Neighbors 12 57 43 32 360 Fixed Steps 12 57 43 27 340 Fixed Block 20 59 41 20 220 Logarithmic Pagination 4 62 38 100 1,720 Ghostblock 3 64 36 100 2,000

Recommendations

In order to choose the right pagination the following question has to be answered:

Are all items of the list equally important or is only a small number of the items important?

For lists with equally important items you should choose the logarithmic pagination or the Ghostblock pagination because those two distribute a noteworthy amount of PageRank across all pagination pages and item pages.

For lists with only a small number of important items you should use the "Link first Pages", "Neighbors", "Fixed Block" pagination because they distributes a noteworthy amount of PageRank only to the first pages of the pagination and to the first item pages.

Analyze your site structure and pagination depth with Audisto Our software will help you understand and optimize the technical site structure. We calculate PageRank, CheiRank and 2D-Rank by category or complete cross-domain linkgraph. Simulate changes before release. Book a Demo

Technical Concepts and Markup for Pagination

Indicate Paginated Content

rel=next and rel=prev

Rel=next and rel=prev provide a powerful hint for search engines to identify pagination pages. Sequential link types like rel=next and rel=prev may be used with <link> , <a> and <area> elements within HTML or within the Link Header in a HTTP-Header. Typically <link> and <a> elements within HTML are used for paginations.

Within HTML-Markup <link> elements are used in the <head> . The <a> and <area> elements are used in the <body> .

Apart from the first and last page of your pagination series, each page should contain the rel=next and rel=prev markup, linking to the next and previous page of the series.

The first page only contains rel=next and the last page only the rel=prev tag.

You can use relative or absolute URLs

You can use <base> to resolve relative paths

To markup a pagination in the <head> section you can use:

Page 1: <link rel="next" href="/?page=2" /> Page 2: <link rel="prev" href=/?page=1" /> <link rel="next" href="/?page=3" /> Page 3: <link rel="prev" href="/?page=2" /> <link rel="next" href="/?page=4" /> Page 4: <link rel="prev" href="/?page=3" />

To markup a basic pagination with normal links in the <body> you can use:

Page1: <a rel="next" href="/?page=2">Next</a> Page 2: <a rel="prev" href="/?page=1">Previous</a> <a rel="next" href="/?page=3">Next</a> Page 3: <a rel="prev" href="/?page=2">Previous</a> <a rel="next" href="/?page=4">Next</a> Page 4: <a rel="prev" href="/?page=3">Previous</a>

This is an example for an area markup:

<img src="/image.jpg" alt="Gallery Image" usemap="#imagemap" width="800" height="600"> <map name="imagemap"> <area rel="prev" shape="rect" coords="0,0,400,600" alt="Previous" href="/?page=1"> <area rel="next" shape="rect" coords="400,0,800,600" alt="Next" href="/?page=3"> </map>

This is an example for a Link Header in the HTTP-Header:

Link: </?page=1>;rel="previous";,</?page=3>;rel="next";

Note: Only one Link Header is allowed in the HTTP-Header. Multiple links are separated with an ",".

Note: Google does not use the information from rel=next and rel=prev anymore, however it can still be used by other search engines, screen readers and so on.

Many search engines provide tools for webmasters like Google Search Console or Bing Webmaster Tools. These tools often allow to specify how the search engine should handle certain URL parameters.

Configuration of URL Parameters in old version of Google Search Console

The screenshot shows how a "page" parameter is configured in order to specify pagination pages in an old version of Google Search Console.

Using parameter settings in webmaster tools to indicate pagination is only possible when parameters are used in the URL. Therefore we would always prefer a URL like

http://example.com/directory/?page=1

over

http://example.com/directory/page-1.html

View-All Page

Another option to organize paginated content is adding a View-All page as suggested by Google. A View-All page lists the content of all paginated pages on a single document. This could be an article in full length or a list of all items.

Google claims to be able to detect View-All pages and then favor them over paginated pages in their search results. In order to provide a strong signal, it is possible to use canonical tags referencing the View-All page. Google would then consolidate ranking signals to the View-All page which usually allows the View-All page to rank better than the paginated pages. The whole process is described in an article in Google's Webmasters Blog.

Tests showed that users tend to prefer View-All pages with short load times. If you can provide a great View-All page, there is no need for additional paginated pages.

The main reason to use both, View-All pages and paginated pages is to show more advertisement. This usually means still directing the users towards the paginated version where more page impressions and ad impressions are generated. The result is usually more revenue from advertisement.

View-All pages are great but now you still got pagination pages, which provide no additional value to the user especially in terms of user experience. Even worse: They have a negative impact on the crawl budget and if Google gets it wrong they might also be seen as duplicate content and the user signals and ranking signals might not get consolidated to the View-All page. Unfortunately there is no way to be sure if user and ranking signals were consolidated properly. Improper consolidation could harm the website and result in a loss of revenue.

We highly suggest not to use pagination and View-All pages in combination. You should choose the one variant that helps to achieve your goals.

Limit Number of Results

Redistribute Pagerank

Imagine a shop with parent categories and subcategories. We usually see pagination pages within the parent categories and subcategories. The parent categories often list all products from all subcategories but all products can still be reached using the pagination pages in the more specific subcategories.

A pagination should improve the accessibility of all items but if we compare the number of pagination pages generated by parent categories and subcategories we can observe that the parent categories have many more pagination pages and generate a deeper level architecture. So the pagination in the more specific subcategories improves the accessibility and not the pagination in the parent categories. In addition more specific listings usually have higher conversion rates.

We can use this knowledge for optimization. If we cap the number of pagination pages in the parent categories this will not have a negative impact on the accessibility. On the other hand we reduce the number of pages with little or no value and redistribute the PageRank to all the other pages within our internal link structure.

Remove Performance Bottlenecks

Having a large number of pagination pages leads to longer load times for the last pagination pages. The reason is that in order to display the last pagination page, all items have to be sorted first and then all items not on the last page get skipped. The bigger the set of results, the slower it becomes towards the end. This could be a major performance issue that has a negative impact on the crawl budget and crawl rate.

The load time can be capped by limiting the number of items displayed within a pagination. This is a common approach for sorted lists like pagination pages in shops and pagination pages within search result sets. All major search engines like Google and Bing limit the number of displayed results in order to ensure low latencies.

For more information about crawl budget and crawl rate optimization read our guide.

Common mistakes with pagination

Link rel=canonical in Pagination

In the context of pagination there are two ways to implement rel=canonical correctly. A self referencing canonical tag on each pagination page or in conjunction with a View-All page.

Some webmasters use the canonical tag to reference the first pagination page from all other pagination pages. However, this isn't good practice. Even Google advises against it, since paginated pages aren't duplicate content pages. In order to mark a set of paginated pages, you should use rel=next and rel=prev.

If you want to know more about rel=canonical, please check our Canonical Guide.

How Audisto can help to detect problems with pagination

The Audisto Crawler allows to gather data for different types of pages like pagination pages and item pages. With this data you can perform a detailed analysis of your pagination and gather valuable insights for website optimization.

Clusters of Pages in Audisto

Accessible Pages by Level in Audisto

We have detailed information about the cluster feature available.

When working with a development environment you can also build different types of paginations in order to test the impact on the website structure.

With our URL-Rewriting feature you can also simulate what would happen if specific pages were removed from the website.

For any further questions contact us.