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5 Top Enterprise Local SEO Challenges & How To Solve Them

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5 Top Enterprise Local SEO Challenges & How To Solve Them

Local SEO can be challenging for enterprise brands because it means knowing how to do “national” SEO, Google Business Profile, and then learning how Google handles your priority search queries in various markets.

That means there are an infinite number of challenges in local SEO for enterprise search marketers. So what are the most common challenges in enterprise local SEO? Let’s find out.

1. Knowing When To Prioritize Local vs. National SEO

One of the biggest challenges enterprises face is knowing when to focus on a “local” SEO strategy instead of a “national” SEO strategy and vice versa.

This is understandable as it’s not always immediately apparent if your priorities are better served by one or the other. It can be challenging to tell if your target top keywords have local intent.

But, it’s vital to success with your overall strategy because it will significantly impact how well your initiatives serve your business goals.

Understanding which terms Google regards as local can help you develop your keyword strategy and determine how to approach and support your SEO investment.

You could lose a lot of traffic due to poor site design or keyword strategy.

Understanding Local Search Intent

So, what do we mean by local search intent, exactly?

By understanding search intent, you know what kind of features will appear in search results and what content you should prioritize.

For this discussion, there are four main types of search intents to focus on:

  1. Search queries with national intent.
  2. Search queries with semi-national.
  3. Search queries with local intent.
  4. Search queries with hyper-local intent.

You can tell what type of intent your target search queries fall into by the features shown on the SERPs, for example:

Queries With National Search Intent

SERPs feature no state/city-specific pages and no map pack (example).

Screenshot from search for [newspaper article], Google, September 2022

The fact that there are no “local’ results in this SERP probably means Google sees zero local intent for these queries.

The minute a large portion of searchers starts to redo this query with location info such as “Pleasanton newspaper article,” the SERPs will likely shift to results that have some local results, which brings us to…

Queries With Semi-National Search Intent

SERPs feature no state/city-specific pages but a map pack (example).

SERPs feature no state/city-specific pages but a map pack.Screenshot from search for [bank], Google, September 2022

Semi-national queries like [bank] might include a map pack because there’s an equal amount of local and national clicks. This could be because some users are looking for a bank branch close to them, but others are looking for the bank’s main home page.

Queries With Local Search Intent

SERPs feature partial to full state/city-specific pages and a map pack (example).

SERPs feature partial to full state/city-specific pages and a map pack.Screenshot from search for [plumber], Google, September 2022

For a term like [plumber], Google will feature a map pack of nearby plumbers, and the remainder of the page one results are filled with location pages. Google predicts that the user intends to find a plumber near their location.

Queries With Hyper-Local Search Intent

Hyper-local keywords are where the searcher’s location is the most significant and significantly impacts SERP results (example).

Queries With Hyper-Local Search IntentScreenshot from search for [Auto insurance near me], Google, September 2022

In the case of hyper-local intent queries, the distance between the user and business matters most. You can see that the map pack dominates the SERP real estate for this query. So, Google likely thinks [Auto insurance near me] requires hyper-local results to be helpful for the user.

How To Identify Search Intent

  1. Analyze current SERP outcomes across different geos.
  2. Examine the SERP for a map element.
  3. Check for state or city-specific pages.
  4. Review the titles and URLs.
  5. Analyze consistency and make an intent determination.

How To Build A Strategy For Different Types Of Search Intent

National Strategy

Nationally focused strategies will need a ton of content och authority.

Your main website should be where you invest the most of your SEO budget if you’ve determined that your target keywords are in queries with little to no local intent. This will help you get that ranking by generating backlinks.

Semi-Local Strategy

Semi-local keywords will require the bulk of focus to build the content and authority of your main site with one additional point of focus. Because semi-local keywords generate a map pack, you must optimize your Google Business Profile listings.

Local Strategy

Your site structure will become significantly more important if you’ve determined that Google treats your keyword as local. You can increase the volume of searches if you create a directory of state or city pages.

Hyper-Local Strategy

When your priority keywords are hyper-local, creating a directory of state and city-level pages is preferable and optimizing them for near-me keywords with special location pages is preferable.

The layers will likely look different depending on your vertical, but broadly, they might resemble this:

  • Locator index page.
  • State page.
  • City page.
  • Location page.

2. Having A Single Source Of Truth For Location Data

With the advent of local listings management companies such as Yext and Uberall, this is no longer a problem.

However, we still run into multi-location businesses that don’t have a “single source of truth” for all of their location information.

If you don’t have this yet, put it in place.

3. Optimizing Store Locators

Many brands outsource their store locators to third-party vendors. There’s nothing wrong with this in theory, but there are a few ways we have seen this go wrong:

Search-Only Store Locators

For SEO, an effective store locator should be a basic linked set of state, city, and location pages that a bot or user can easily click around to get to every page. But many brands often build their store locators a locator page with a search box to find your location.

A few years ago, we looked at the locators for the top 100 U.S. retailers and found those with search-only locators ranked for ~50% fewer keywords than those with a linkable state > city > location architecture.

So, make sure your locator architecture is built this way.

Location Page Content

Often, brands budget for building a locator on their site but leave nothing for the content.

There’s nothing wrong with a basic location page with the business name, address, phone number, product/service categories, etc. But a location page with unique, beefed-up content relevant to the location and topics you are trying to rank for can improve SEO performance.

This is where your location managers can come in handy. We often see successful brands use surveys of their location managers to get unique local content.

Other sources might include local customer reviews, syndicated local point of interest data, and popular products in the specific market.

Priority Categories

Most ecommerce queries show local results near the top of the SERPs these days.

We often see brands winning in Local Packs linking from their location pages to their key categories.

Think of it as signaling to Google that your locations are relevant for these categories.

4. Google Business Profile Management And Optimization

Google Business Profile (GBP) really shouldn’t be a challenge – I mean, it’s just a simple set of yellow pages listings for your locations – but there are a million ways it can go wrong for businesses.

Here are just a few challenges and opportunities with GBP.

Beware Of Duplicate Listings

Amazingly, duplicate listings are still a thing with GBP, but I just talked to a service area business that was having problems ranking. It was pretty easy to see they had duplicate GBP listings.

The minute they deleted the duplicate listings, their rankings went up by 15 positions for the main keyword they were targeting. So, keep an eye on those.

Monitor Your GBP listings

Your GBP listings are in a constant state of flux. Users are adding photos and reviews.

Google can overwrite your data if it trusts data from another party more than it trusts you.

GBP is not a “set it and forget it” thing. Create a system to monitor changes to your GBP pages regularly.

While you can see many changes via the GBP Dashboard, it won’t catch everything. That’s one of the reasons we built this free, open-source tool to monitor image changes to your GBP.

Scale GBP Posts

GBP Posts are short announcements you can attach to your GBP. These can be an inexpensive way to generate high-converting visits to your site. Posts can include text, photos, or videos.

The challenge we often see is that businesses are often not set up to produce content for each location. If you want to do GBP Posts for multiple locations, implement a system for creating GBP-ready marketing collateral for new promotions so they can be posted.

This often involves creating a GBP-sized version (400 x 300) of approved marketing images and copy for GBP as part of each new promotion.

You’ll also want to ensure you tag links from your GBP posts with tracking parameters to measure performance.

5. Building A Local Search Presence For SABs And Marketplaces

Not every local enterprise brand has locations.

There are plenty of local marketplace brands like Yelp, DoorDash, and Zillow, and service area businesses (SABs) like plumbers and roofers that target local search queries but are not eligible to appear in Local Packs. This is because they have no physical locations in their target markets.

And this means they are missing out on many potential clicks and revenue.

This won’t work for every brand, but for those with a suitable business model, creating a “store within a store” at a partner brand’s location is a great way to get additional local pack visibility.

FedEx OnSite services located in Walgreens is a good example of how this can work:

Building A Local Search Presence For SABs And MarketplacesScreenshot from Google search, September 2022

And, of course, if the value of the leads is high enough, you may want to consider opening up physical locations in certain areas to try to rank well in the Local Packs.

As I said at the top, there are an infinite number of local SEO tactics enterprise brands can deploy.

As you deploy new tactics, make sure you test, measure, and iterate like any other marketing channel.

More Resources:


Featured Image: GaudiLab/Shutterstock



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SEO

Ranking Factors & The Myths We Found

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Ranking Factors & The Myths We Found

Yandex is the search engine with the majority of market share in Russia and the fourth-largest search engine in the world.

On January 27, 2023, it suffered what is arguably one of the largest data leaks that a modern tech company has endured in many years – but is the second leak in less than a decade.

In 2015, a former Yandex employee attempted to sell Yandex’s search engine code on the black market for around $30,000.

The initial leak in January this year revealed 1,922 ranking factors, of which more than 64% were listed as unused or deprecated (superseded and best avoided).

This leak was just the file labeled kernel, but as the SEO community and I delved deeper, more files were found that combined contain approximately 17,800 ranking factors.

When it comes to practicing SEO for Yandex, the guide I wrote two years ago, for the most part, still applies.

Yandex, like Google, has always been public with its algorithm updates and changes, and in recent years, how it has adopted machine learning.

Notable updates from the past two-three years include:

  • Vega (which doubled the size of the index).
  • Mimicry (penalizing fake websites impersonating brands).
  • Y1 update (introducing YATI).
  • Y2 update (late 2022).
  • Adoption of IndexNow.
  • A fresh rollout and assumed update of the PF filter.

On a personal note, this data leak is like a second Christmas.

Since January 2020, I’ve run an SEO news website as a hobby dedicated to covering Yandex SEO and search news in Russia with 600+ articles, so this is probably the peak event of the hobby site.

I’ve also spoken twice at the Optimization conference – the largest SEO conference in Russia.

This is also a good test to see how closely Yandex’s public statements match the codebase secrets.

In 2019, working with Yandex’s PR team, I was able to interview engineers in their Search team and ask a number of questions sourced from the wider Western SEO community.

You can read the interview with the Yandex Search team here.

Whilst Yandex is primarily known for its presence in Russia, the search engine also has a presence in Turkey, Kazakhstan, and Georgia.

The data leak was believed to be politically motivated and the actions of a rogue employee, and contains a number of code fragments from Yandex’s monolithic repository, Arcadia.

Within the 44GB of leaked data, there’s information relating to a number of Yandex products including Search, Maps, Mail, Metrika, Disc, and Cloud.

What Yandex Has Had To Say

As I write this post (January 31st, 2023), Yandex has publicly stated that:

the contents of the archive (leaked code base) correspond to the outdated version of the repository – it differs from the current version used by our services

And:

It is important to note that the published code fragments also contain test algorithms that were used only within Yandex to verify the correct operation of the services.

So, how much of this code base is actively used is questionable.

Yandex has also revealed that during its investigation and audit, it found a number of errors that violate its own internal principles, so it is likely that portions of this leaked code (that are in current use) may be changing in the near future.

Factor Classification

Yandex classifies its ranking factors into three categories.

This has been outlined in Yandex’s public documentation for some time, but I feel is worth including here, as it better helps us understand the ranking factor leak.

  • Static factors – Factors that are related directly to the website (e.g. inbound backlinks, inbound internal links, headers, and ads ratio).
  • Dynamic factors – Factors that are related to both the website and the search query (e.g. text relevance, keyword inclusions, TF*IDF).
  • User search-related factors – Factors relating to the user query (e.g. where is the user located, query language, and intent modifiers).

The ranking factors in the document are tagged to match the corresponding category, with TG_STATIC and TG_DYNAMIC, and then TG_QUERY_ONLY, TG_QUERY, TG_USER_SEARCH, and TG_USER_SEARCH_ONLY.

Yandex Leak Learnings So Far

From the data thus far, below are some of the affirmations and learnings we’ve been able to make.

There is so much data in this leak, it is very likely that we will be finding new things and making new connections in the next few weeks.

These include:

  • PageRank (a form of).
  • At some point Yandex utilized TF*IDF.
  • Yandex still uses meta keywords, which are also highlighted in its documentation.
  • Yandex has specific factors for medical, legal, and financial topics (YMYL).
  • It also uses a form of page quality scoring, but this is known (ICS score).
  • Links from high-authority websites have an impact on rankings.
  • There’s nothing new to suggest Yandex can crawl JavaScript yet outside of already publicly documented processes.
  • Server errors and excessive 4xx errors can impact ranking.
  • The time of day is taken into consideration as a ranking factor.

Below, I’ve expanded on some other affirmations and learnings from the leak.

Where possible, I’ve also tied these leaked ranking factors to the algorithm updates and announcements that relate to them, or where we were told about them being impactful.

MatrixNet

MatrixNet is mentioned in a few of the ranking factors and was announced in 2009, and then superseded in 2017 by Catboost, which was rolled out across the Yandex product sphere.

This further adds validity to comments directly from Yandex, and one of the factor authors DenPlusPlus (Den Raskovalov), that this is, in fact, an outdated code repository.

MatrixNet was originally introduced as a new, core algorithm that took into consideration thousands of ranking factors and assigned weights based on the user location, the actual search query, and perceived search intent.

It is typically seen as an early version of Google’s RankBrain, when they are indeed two very different systems. MatrixNet was launched six years before RankBrain was announced.

MatrixNet has also been built upon, which isn’t surprising, given it is now 14 years old.

In 2016, Yandex introduced the Palekh algorithm that used deep neural networks to better match documents (webpages) and queries, even if they didn’t contain the right “levels” of common keywords, but satisfied the user intents.

Palekh was capable of processing 150 pages at a time, and in 2017 was updated with the Korolyov update, which took into account more depth of page content, and could work off 200,000 pages at once.

URL & Page-Level Factors

From the leak, we have learned that Yandex takes into consideration URL construction, specifically:

  • The presence of numbers in the URL.
  • The number of trailing slashes in the URL (and if they are excessive).
  • The number of capital letters in the URL is a factor.
Screenshot from author, January 2023

The age of a page (document age) and the last updated date are also important, and this makes sense.

As well as document age and last update, a number of factors in the data relate to freshness – particularly for news-related queries.

Yandex formerly used timestamps, specifically not for ranking purposes but “reordering” purposes, but this is now classified as unused.

Also in the deprecated column are the use of keywords in the URL. Yandex has previously measured that three keywords from the search query in the URL would be an “optimal” result.

Internal Links & Crawl Depth

Whilst Google has gone on the record to say that for its purposes, crawl depth isn’t explicitly a ranking factor, Yandex appears to have an active piece of code that dictates that URLs that are reachable from the homepage have a “higher” level of importance.

Yandex factorsScreenshot from author, January 2023

This mirrors John Mueller’s 2018 statement that Google gives “a little more weight” to pages found more than one click from the homepage.

The ranking factors also highlight a specific token weighting for webpages that are “orphans” within the website linking structure.

Clicks & CTR

In 2011, Yandex released a blog post talking about how the search engine uses clicks as part of its rankings and also addresses the desires of the SEO pros to manipulate the metric for ranking gain.

Specific click factors in the leak look at things like:

  • The ratio of the number of clicks on the URL, relative to all clicks on the search.
  • The same as above, but broken down by region.
  • How often do users click on the URL for the search?

Manipulating Clicks

Manipulating user behavior, specifically “click-jacking”, is a known tactic within Yandex.

Yandex has a filter, known as the PF filter, that actively seeks out and penalizes websites that engage in this activity using scripts that monitor IP similarities and then the “user actions” of those clicks – and the impact can be significant.

The below screenshot shows the impact on organic sessions (сессии) after being penalized for imitating user clicks.

Image Source: Russian Search NewsImage from Russian Search News, January 2023

User Behavior

The user behavior takeaways from the leak are some of the more interesting findings.

User behavior manipulation is a common SEO violation that Yandex has been combating for years. At the 2020 Optimization conference, then Head of Yandex Webmaster Tools Mikhail Slevinsky said the company is making good progress in detecting and penalizing this type of behavior.

Yandex penalizes user behavior manipulation with the same PF filter used to combat CTR manipulation.

Dwell Time

102 of the ranking factors contain the tag TG_USERFEAT_SEARCH_DWELL_TIME, and reference the device, user duration, and average page dwell time.

All but 39 of these factors are deprecated.

Yandex factorsScreenshot from author, January 2023

Bing first used the term Dwell time in a 2011 blog, and in recent years Google has made it clear that it doesn’t use dwell time (or similar user interaction signals) as ranking factors.

YMYL

YMYL (Your Money, Your Life) is a concept well-known within Google and is not a new concept to Yandex.

Within the data leak, there are specific ranking factors for medical, legal, and financial content that exist – but this was notably revealed in 2019 at the Yandex Webmaster conference when it announced the Proxima Search Quality Metric.

Metrika Data Usage

Six of the ranking factors relate to the usage of Metrika data for the purposes of ranking. However, one of them is tagged as deprecated:

  • The number of similar visitors from the YandexBar (YaBar/Ябар).
  • The average time spent on URLs from those same similar visitors.
  • The “core audience” of pages on which there is a Metrika counter [deprecated].
  • The average time a user spends on a host when accessed externally (from another non-search site) from a specific URL.
  • Average ‘depth’ (number of hits within the host) of a user’s stay on the host when accessed externally (from another non-search site) from a particular URL.
  • Whether or not the domain has Metrika installed.

In Metrika, user data is handled differently.

Unlike Google Analytics, there are a number of reports focused on user “loyalty” combining site engagement metrics with return frequency, duration between visits, and source of the visit.

For example, I can see a report in one click to see a breakdown of individual site visitors:

MetrikaScreenshot from Metrika, January 2023

Metrika also comes “out of the box” with heatmap tools and user session recording, and in recent years the Metrika team has made good progress in being able to identify and filter bot traffic.

With Google Analytics, there is an argument that Google doesn’t use UA/GA4 data for ranking purposes because of how easy it is to modify or break the tracking code – but with Metrika counters, they are a lot more linear, and a lot of the reports are unchangeable in terms of how the data is collected.

Impact Of Traffic On Rankings

Following on from looking at Metrika data as a ranking factor; These factors effectively confirm that direct traffic and paid traffic (buying ads via Yandex Direct) can impact organic search performance:

  • Share of direct visits among all incoming traffic.
  • Green traffic share (aka direct visits) – Desktop.
  • Green traffic share (aka direct visits) – Mobile.
  • Search traffic – transitions from search engines to the site.
  • Share of visits to the site not by links (set by hand or from bookmarks).
  • The number of unique visitors.
  • Share of traffic from search engines.

News Factors

There are a number of factors relating to “News”, including two that mention Yandex.News directly.

Yandex.News was an equivalent of Google News, but was sold to the Russian social network VKontakte in August 2022, along with another Yandex product “Zen”.

So, it’s not clear if these factors related to a product no longer owned or operated by Yandex, or to how news websites are ranked in “regular” search.

Backlink Importance

Yandex has similar algorithms to combat link manipulation as Google – and has since the Nepot filter in 2005.

From reviewing the backlink ranking factors and some of the specifics in the descriptions, we can assume that the best practices for building links for Yandex SEO would be to:

  • Build links with a more natural frequency and varying amounts.
  • Build links with branded anchor texts as well as use commercial keywords.
  • If buying links, avoid buying links from websites that have mixed topics.

Below is a list of link-related factors that can be considered affirmations of best practices:

  • The age of the backlink is a factor.
  • Link relevance based on topics.
  • Backlinks built from homepages carry more weight than internal pages.
  • Links from the top 100 websites by PageRank (PR) can impact rankings.
  • Link relevance based on the quality of each link.
  • Link relevance, taking into account the quality of each link, and the topic of each link.
  • Link relevance, taking into account the non-commercial nature of each link.
  • Percentage of inbound links with query words.
  • Percentage of query words in links (up to a synonym).
  • The links contain all the words of the query (up to a synonym).
  • Dispersion of the number of query words in links.

However, there are some link-related factors that are additional considerations when planning, monitoring, and analyzing backlinks:

  • The ratio of “good” versus “bad” backlinks to a website.
  • The frequency of links to the site.
  • The number of incoming SEO trash links between hosts.

The data leak also revealed that the link spam calculator has around 80 active factors that are taken into consideration, with a number of deprecated factors.

This creates the question as to how well Yandex is able to recognize negative SEO attacks, given it looks at the ratio of good versus bad links, and how it determines what a bad link is.

A negative SEO attack is also likely to be a short burst (high frequency) link event in which a site will unwittingly gain a high number of poor quality, non-topical, and potentially over-optimized links.

Yandex uses machine learning models to identify Private Blog Networks (PBNs) and paid links, and it makes the same assumption between link velocity and the time period they are acquired.

Typically, paid-for links are generated over a longer period of time, and these patterns (including link origin site analysis) are what the Minusinsk update (2015) was introduced to combat.

Yandex Penalties

There are two ranking factors, both deprecated, named SpamKarma and Pessimization.

Pessimization refers to reducing PageRank to zero and aligns with the expectations of severe Yandex penalties.

SpamKarma also aligns with assumptions made around Yandex penalizing hosts and individuals, as well as individual domains.

Onpage Advertising

There are a number of factors relating to advertising on the page, some of them deprecated (like the screenshot example below).

Yandex factorsScreenshot from author, January 2023

It’s not known from the description exactly what the thought process with this factor was, but it could be assumed that a high ratio of adverts to visible screen was a negative factor – much like how Google takes umbrage if adverts obfuscate the page’s main content, or are obtrusive.

Tying this back to known Yandex mechanisms, the Proxima update also took into consideration the ratio of useful and advertising content on a page.

Can We Apply Any Yandex Learnings To Google?

Yandex and Google are disparate search engines, with a number of differences, despite the tens of engineers who have worked for both companies.

Because of this fight for talent, we can infer that some of these master builders and engineers will have built things in a similar fashion (though not direct copies), and applied learnings from previous iterations of their builds with their new employers.

What Russian SEO Pros Are Saying About The Leak

Much like the Western world, SEO professionals in Russia have been having their say on the leak across the various Runet forums.

The reaction in these forums has been different to SEO Twitter and Mastodon, with a focus more on Yandex’s filters, and other Yandex products that are optimized as part of wider Yandex optimization campaigns.

It is also worth noting that a number of conclusions and findings from the data match what the Western SEO world is also finding.

Common themes in the Russian search forums:

  • Webmasters asking for insights into recent filters, such as Mimicry and the updated PF filter.
  • The age and relevance of some of the factors, due to author names no longer being at Yandex, and mentions of long-retired Yandex products.
  • The main interesting learnings are around the use of Metrika data, and information relating to the Crawler & Indexer.
  • A number of factors outline the usage of DSSM, which in theory was superseded by the release of Palekh in 2016. This was a search algorithm utilizing machine learning, announced by Yandex in 2016.
  • A debate around ICS scoring in Yandex, and whether or not Yandex may provide more traffic to a site and influence its own factors by doing so.

The leaked factors, particularly around how Yandex evaluates site quality, have also come under scrutiny.

There is a long-standing sentiment in the Russian SEO community that Yandex oftentimes favors its own products and services in search results ahead of other websites, and webmasters are asking questions like:

Why does it bother going to all this trouble, when it just nails its services to the top of the page anyway?

In loosely translated documents, these are referred to as the Sorcerers or Yandex Sorcerers. In Google, we’d call these search engine results pages (SERPs) features – like Google Hotels, etc.

In October 2022, Kassir (a Russian ticket portal) claimed ₽328m compensation from Yandex due to lost revenue, caused by the “discriminatory conditions” in which Yandex Sorcerers took the customer base away from the private company.

This is off the back of a 2020 class action in which multiple companies raised a case with the Federal Antimonopoly Service (FAS) for anticompetitive promotion of its own services.

Fler resurser:


Featured Image: FGC/Shutterstock



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SEO

Google uppdaterar Search Console Video Indexing Report

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Google Updates Search Console Video Indexing Report

Google’s updated Search Console Video indexing report now includes daily video impressions and a sitemap filter feature.

  • Google has updated the Search Console Video indexing report to provide more comprehensive insights into video performance in search results.
  • The updated report includes daily video impressions, which are grouped by page, and a new sitemap filter feature to focus on the most important video pages.
  • These updates are part of Google’s ongoing efforts to help website owners and content creators understand and improve the visibility of their videos in search results.



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Bing förnyar krypsystemet för att förbättra effektiviteten

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Bing Revamps Crawl System To Enhance Efficiency

According to a recent study by Bing, most websites have XML sitemaps, with the “lastmod” tag being the most critical component of these sitemaps.

The “lastmod” tag indicates the last time the webpages linked by the sitemap were modified and is used by search engines to determine how often to crawl a site and which pages to index.

However, the study also revealed that a significant number of “lastmod” values in XML sitemaps were set incorrectly, with the most prevalent issue being identical dates on all sitemaps.

Upon consulting with web admins, Microsoft discovered that the dates were set to the date of sitemap generation rather than content modification.

To address this issue, Bing is revamping its crawl scheduling stack to better utilize the information provided by the “lastmod” tag in sitemaps.

This will improve crawl efficiency by reducing unnecessary crawling of unchanged content and prioritizing recently updated content.

The improvements have already begun on a limited scale and are expected to roll out by June fully.

Additionally, Microsoft has updated sitemap.org for improved clarity by adding the following line:

“Note that the date must be set to the date the linked page was last modified, not when the sitemap is generated.”

How To Use The Lastmod Tag Correctly

To correctly set the “lastmod” tag in a sitemap, you should include it in the <url> tag for each page in the sitemap.

The date should be in W3C Datetime format, with the most commonly used formats being YYYY-MM-DD or YYYY-MM-DDThh:mm:ssTZD.

The date should reflect the last time the page was modified and should be updated regularly to ensure that search engines understand the relevance and frequency of updates.

Here’s an example code snippet:

<?xml version=”1.0″ encoding=”UTF-8″?>

<urlset xmlns=”http://www.sitemaps.org/schemas/sitemap/0.9″>

   <url>

      <loc>http://www.example.com/</loc>

      <lastmod>2023-01-23</lastmod>      

   </url>

Google’s Advice: Use Lastmod Tag After Significant Changes Only

Google’s crawlers also utilize the “lastmod” tag, and the suggestions on using it by both major search engines are similar.

Google Search Advocate John Mueller recently discussed the lastmod tag in the January edition of Google’s office-hours Q&A sessions.

It’s worth noting that Google recommends only using the “lastmod” tag for substantial modifications, which was not mentioned in Microsoft’s blog post.

Changing the date in the lastmod tag after minor edits can be viewed as an attempt to manipulate search snippets.

In Summary

Microsoft’s recent study and efforts to improve the utilization of the “lastmod” tag in sitemaps will result in more efficient and effective webpage crawling.

Publishers are encouraged to regularly update their sitemaps and lastmod tags to ensure that their pages are correctly indexed and easily accessible by search engines.


Featured Image: mundissima/Shutterstock

Source: Microsoft



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