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A Comprehensive Guide To Marketing Attribution Models

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A Comprehensive Guide To Marketing Attribution Models

We all know that customers interact with a brand through multiple channels and campaigns (online and offline) along their path to conversion.

Surprisingly, within the B2B sector, the average customer is exposed to a brand 36 times before converting into a customer.

With so many touchpoints, it is difficult to really pin down just how much a marketing channel or campaign influenced the decision to buy.

This is where marketing attribution comes in.

Marketing attribution provides insights into the most effective touchpoints along the buyer journey.

In this comprehensive guide, we simplify everything you need to know to get started with marketing attribution models, including an overview of your options and how to use them.

What Is Marketing Attribution?

Marketing attribution is the rule (or set of rules) that says how the credit for a conversion is distributed across a buyer’s journey.

How much credit each touchpoint should get is one of the more complicated marketing topics, which is why so many different types of attribution models are used today.

6 Common Attribution Models

There are six common attribution models, and each distributes conversion value across the buyer’s journey differently.

Don’t worry. We will help you understand all of the models below so you can decide which is best for your needs.

Note: The examples in this guide use Google Analytics 4 cross-channel rules-based models.

Cross-channel rules-based means that it ignores direct traffic. This may not be the case if you use alternative analytics software.

1. Last Click

The last click attribution model gives all the credit to the marketing touchpoint that happens directly before conversion.

Last Click helps you understand which marketing efforts close sales.

For example, a user initially discovers your brand by watching a YouTube Ad for 30 seconds (engaged view).

Later that day, the same user Googles your brand and clicks through an organic search result.

The following week this user is shown a retargeting ad on Facebook, clicks through, and signs up for your email newsletter.

The next day, they click through the email and convert to a customer.

Under a last-click attribution model, 100% of the credit for that conversion is given to email, the touchpoint that closed the sale.

2. First Click

The first click is the opposite of the last click attribution model.

All of the credit for any conversion that may happen is awarded to the first interaction.

The first click helps you to understand which channels create brand awareness.

It doesn’t matter if the customer clicked through a retargeting ad and later converted through an email visit.

If the customer initially interacted with your brand through an engaged YouTube view, Paid Video gets full credit for that conversion because it started the journey.

3. Linear

Linear attribution provides a look at your marketing strategy as a whole.

This model is especially useful if you need to maintain awareness throughout the entire buyer journey.

Credit for conversion is split evenly among all the channels a customer interacts with.

Let’s look at our example: Each of the four touchpoints (Paid Video, Organic, Paid Social, and Email) all get 25% of the conversion value because they’re all given equal credit.

4. Time Decay

Time Decay is useful for short sales cycles like a promotion because it considers when each touchpoint occurred.

The first touch gets the least amount of credit, while the last click gets the most.

Using our example:

  • Paid Video (YouTube engaged view) would get 10% of the credit.
  • Organic search would get 20%.
  • Paid Social (Facebook ad) gets 30%.
  • Email, which occurred the day of the conversion, gets 40%.

Note: Google Analytics 4 distributes this credit using a seven-day half-life.

5. Position-Based

The position-based (U-shaped) approach divides credit for a sale between the two most critical interactions: how a client discovered your brand and the interaction that generated a conversion.

With position-based attribution modeling, Paid Video (YouTube engaged view) and Email would each get 40% of the credit because they were the first and last interaction within our example.

Organic search and the Facebook Ad would each get 10%.

6. Data-Driven (Cross-Channel Linear)

Google Analytics 4 has a unique data-driven attribution model that uses machine learning algorithms.

Credit is assigned based on how each touchpoint changes the estimated conversion probability.

It uses each advertiser’s data to calculate the actual contribution an interaction had for every conversion event.

Best Marketing Attribution Model

There isn’t necessarily a “best” marketing attribution model, and there’s no reason to limit yourself to just one.

Comparing performance under different attribution models will help you to understand the importance of multiple touchpoints along your buyer journey.

Model Comparison In Google Analytics 4 (GA4)

If you want to see how performance changes by attribution model, you can do that easily with GA4.

To access model comparison in Google Analytics 4, click “Advertising” in the left-hand menu and then click “Model comparison” under “Attribution.”

Screenshot from GA4, July 2022

By default, the conversion events will be all, the date range will be the last 28 days, and the dimension will be the default channel grouping.

Start by selecting the date range and conversion event you want to analyze.

GA4 model comparison_choose event and date rangeScreenshot from GA4, July 2022

You can add a filter to view a specific campaign, geographic location, or device using the edit comparison option in the top right of the report.

GA4 Model comparison filterScreenshot from GA4, July 2022

Select the dimension to report on and then use the drown-down menus to select the attribution models to compare.

GA4 model comparison_select dimensionScreenshot from GA4, July 2022

GA4 Model Comparison Example

Let’s say you’re asked to increase new customers to the website.

You could open Google Analytics 4 and compare the “last-click” model to the “first-click” model to discover which marketing efforts start customers down the path to conversion.

GA4 model comparison_increase new customersScreenshot from GA4, July 2022

In the example above, we may choose to look further into the email and paid search further because they appear to be more effective at starting customers down the path to conversion than closing the sale.

How To Change Google Analytics 4 Attribution Model

If you choose a different attribution model for your company, you can edit your attribution settings by clicking the gear icon in the bottom left-hand corner.

Open Attribution Settings under the property column and click the Reporting attribution model drop-down menu.

Here you can choose from the six cross-channel attribution models discussed above or the “ads-preferred last click model.”

Ads-preferred gives full credit to the last Google Ads click along the conversion path.

edit GA4 attribution settingsScreenshot from GA4, July 2022

Please note that attribution model changes will apply to historical and future data.

Final Thoughts

Determining where and when a lead or purchase occurred is easy. The hard part is defining the reason behind a lead or purchase.

Comparing attribution modeling reports help us to understand how the entire buyer journey supported the conversion.

Looking at this information in greater depth enables marketers to maximize ROI.

Got questions? Let us know on Twitter eller Linkedin.

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Featured Image: Andrii Yalanskyi/Shutterstock



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13 Best High Ticket Affiliate Marketing Programs 2023

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13 Best High Ticket Affiliate Marketing Programs 2023

Are you looking for more ways to generate income for yourself or your business this year?

With high-ticket affiliate marketing programs, you earn money by recommending your favorite products or services to those who need them.

Affiliate marketers promote products through emails, blog posts, social media updates, YouTube videos, podcasts, and other forms of content with proper disclosure.

While not all affiliate marketers make enough to quit their 9-to-5, any additional income in the current economy can come in handy for individuals and businesses.

How To Get Started With Affiliate Marketing

Here’s a simple summary of how to get started with affiliate marketing.

  • Build an audience. You need websites with traffic, email lists with subscribers, or social media accounts with followers to promote a product – or ideally, a combination of all three.
  • Find products and services you can passionately promote to the audience you have built. The more you love something and believe in its efficacy, the easier it will be to convince someone else to buy it.
  • Sign up for affiliate and referral programs. These will be offered directly through the company selling the product or service, or a third-party affiliate platform.
  • Fill out your application and affiliate profile completely. Include your niche, monthly website traffic, number of email subscribers, and social media audience size. Companies will use that information to approve or reject your application.
  • Get your custom affiliate or referral link and share it with your audience, or the segment of your audience that would benefit most from the product you are promoting.
  • Look for opportunities to recommend products to new people. You can be helpful, make a new acquaintance, and earn a commission.
  • Monitor your affiliate dashboard and website analytics for insights into your clicks and commissions.
  • Adjust your affiliate marketing tactics based on the promotions that generate the most revenue.

Now, continue reading about the best high-ticket affiliate programs you can sign up for in 2023. They offer a high one-time payout, recurring commissions, or both.

The Best High-Ticket Affiliate Marketing Programs

What makes them these affiliate marketing programs the “best” is subjective, but I chose these programs based on their payout amounts, number of customers, and average customer ratings. Customer ratings help determine whether a product is worth recommending. You can also use customer reviews to help you market the products or services when you highlight impressive results customers gain from using the product or service, and the features customers love most.

1. Smartproxy

Smartproxy allows customers to access business data worldwide for competitor research, search engine results page (SERP) scraping, price aggregation, and ad verification.

836 reviewers gave it an average rating of 4.7 out of five stars.

Earn up to $2,000 per customer that you refer to Smartproxy using its affiliate program.

2. Thinkific

Thinkific is an online course creation platform used by over 50,000 instructors in over 100 million courses.

669 reviewers gave it an average rating of 4.6 out of five stars.

Earn up to $1,700 per referral per year through the Thinkific affiliate program.

3. BigCommerce

BigCommerce is an ecommerce provider with open SaaS, headless integrations, omnichannel, B2B, and offline-to-online solutions.

648 reviewers gave it an average rating of 8.1 out of ten stars.

Earn up to $1,500 for new enterprise customers, or 200% of the customer’s first payment by signing up for the BigCommerce affiliate program.

4. Teamwork

Teamwork, project management software focused on maximizing billable hours, helps everyone in your organization become more efficient – from the founder to the project managers.

1,022 reviewers gave it an average rating of 4.4 out of five stars.

Earn up to $1,000 per new customer referral with the Teamwork affiliate program.

5. Flywheel

Flywheel provides managed WordPress hosting geared towards agencies, ecommerce, and high-traffic websites.

36 reviewers gave it an average rating of 4.4 out of five stars.

Earn up to $500 per new referral from the Flywheel affiliate program.

6. Teachable

Teachable is an online course platform used by over 100,000 entrepreneurs, creators, and businesses of all sizes to create engaging online courses and coaching businesses.

150 reviewers gave it a 4.4 out of five stars.

Earn up to $450 (average partner earnings) per month by joining the Teachable affiliate program.

7. Shutterstock

Shutterstock is a global marketplace for sourcing stock photographs, vectors, illustrations, videos, and music.

507 reviewers gave it an average rating of 4.4 out of five stars.

Earn up to $300 for new customers by signing up for the Shutterstock affiliate program.

8. HubSpot

HubSpot provides a CRM platform to manage your organization’s marketing, sales, content management, and customer service.

3,616 reviewers gave it an average rating of 4.5 out of five stars.

Earn an average payout of $264 per month (based on current affiliate earnings) with the HubSpot affiliate program, or more as a solutions partner.

9. Sucuri

Sucuri is a cloud-based security platform with experienced security analysts offering malware scanning and removal, protection from hacks and attacks, and better site performance.

251 reviewers gave it an average rating of 4.6 out of five stars.

Earn up to $210 per new sale by joining Sucuri referral programs for the platform, firewall, and agency products.

10. ADT

ADT is a security systems provider for residences and businesses.

588 reviewers gave it an average rating of 4.5 out of five stars.

Earn up to $200 per new customer that you refer through the ADT rewards program.

11. DreamHost

DreamHost web hosting supports WordPress and WooCommerce websites with basic, managed, and VPS solutions.

3,748 reviewers gave it an average rating of 4.7 out of five stars.

Earn up to $200 per referral and recurring monthly commissions with the DreamHost affiliate program.

12. Shopify

Shopify, a top ecommerce solution provider, encourages educators, influencers, review sites, and content creators to participate in its affiliate program. Affiliates can teach others about entrepreneurship and earn a commission for recommending Shopify.

Earn up to $150 per referral and grow your brand as a part of the Shopify affiliate program.

13. Kinsta

Kinsta is a web hosting provider that offers managed WordPress, application, and database hosting.

529 reviewers gave it a 4.3 out of five stars.

Earn $50 – $100 per new customer, plus recurring revenue via the Kinsta affiliate program.

Even More Affiliate Marketing Programs

In addition to the high-ticket affiliate programs listed above, you can find more programs to join with a little research.

  • Search for affiliate or referral programs for all of the products or services you have a positive experience with, personally or professionally.
  • Search for affiliate or referral programs for all of the places you shop online.
  • Search for partner programs for products and services your organization uses or recommends to others.
  • Search for products and services that match your audience’s needs on affiliate platforms like Shareasale, Awin, and CJ.
  • Follow influencers in your niche to see what products and services they recommend. They may have affiliate or referral programs as well.

A key to affiliate marketing success is to diversify the affiliate marketing programs you join.

It will ensure that you continue to generate an affiliate income, regardless of if one company changes or shutters its program.

Fler resurser:


Featured image: Shutterstock/fatmawati achmad zaenuri



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The Current State of Google PageRank & How It Evolved

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The Current State of Google PageRank & How It Evolved

PageRank (PR) is an algorithm that improves the quality of search results by using links to measure the importance of a page. It considers links as votes, with the underlying assumption being that more important pages are likely to receive more links.

PageRank was created by Google co-founders Sergey Brin and Larry Page in 1997 when they were at Stanford University, and the name is a reference to both Larry Page and the term “webpage.” 

In many ways, it’s similar to a metric called “impact factor” for journals, where more cited = more important. It differs a bit in that PageRank considers some votes more important than others. 

By using links along with content to rank pages, Google’s results were better than competitors. Links became the currency of the web.

Want to know more about PageRank? Let’s dive in.

Google still uses PageRank

In terms of modern SEO, PageRank is one of the algorithms comprising Experience Expertise Authoritativeness Trustworthiness (E-E-A-T).

Google’s algorithms identify signals about pages that correlate with trustworthiness and authoritativeness. The best known of these signals is PageRank, which uses links on the web to understand authoritativeness.

Källa: How Google Fights Disinformation

We’ve also had confirmation from Google reps like Gary Illyes, who said that Google still uses PageRank and that links are used for E-A-T (now E-E-A-T).

When I ran a study to measure the impact of links and effectively removed the links using the disavow tool, the drop was obvious. Links still matter for rankings.

PageRank has also been a confirmed factor when it comes to crawl budget. It makes sense that Google wants to crawl important pages more often.

Fun math, why the PageRank formula was wrong 

Crazy fact: The formula published in the original PageRank paper was wrong. Let’s look at why. 

PageRank was described in the original paper as a probability distribution—or how likely you were to be on any given page on the web. This means that if you sum up the PageRank for every page on the web together, you should get a total of 1.

Here’s the full PageRank formula from the original paper published in 1997:

PR(A) = (1-d) + d (PR(T1)/C(T1) + … + PR(Tn)/C(Tn))

Simplified a bit and assuming the damping factor (d) is 0.85 as Google mentioned in the paper (I’ll explain what the damping factor is shortly), it’s:

PageRank for a page = 0.15 + 0.85 (a portion of the PageRank of each linking page split across its outbound links)

In the paper, they said that the sum of the PageRank for every page should equal 1. But that’s not possible if you use the formula in the paper. Each page would have a minimum PageRank of 0.15 (1-d). Just a few pages would put the total at greater than 1. You can’t have a probability greater than 100%. Something is wrong!

The formula should actually divide that (1-d) by the number of pages on the internet for it to work as described. It would be:

PageRank for a page = (0.15/number of pages on the internet) + 0.85 (a portion of the PageRank of each linking page split across its outbound links)

It’s still complicated, so let’s see if I can explain it with some visuals.

1. A page is given an initial PageRank score based on the links pointing to it. Let’s say I have five pages with no links. Each gets a PageRank of (1/5) or 0.2.

PageRank example of five pages with no links yet

2. This score is then distributed to other pages through the links on the page. If I add some links to the five pages above and calculate the new PageRank for each, then I end up with this: 

PageRank example of five pages after one iteration

You’ll notice that the scores are favoring the pages with more links to them.

3. This calculation is repeated as Google crawls the web. If I calculate the PageRank again (called an iteration), you’ll see that the scores change. It’s the same pages with the same links, but the base PageRank for each page has changed, so the resulting PageRank is different.

PageRank example of five pages after two iterations

The PageRank formula also has a so-called “damping factor,” the “d” in the formula, which simulates the probability of a random user continuing to click on links as they browse the web. 

Think of it like this: The probability of you clicking a link on the first page you visit is reasonably high. But the likelihood of you then clicking a link on the next page is slightly lower, and so on and so forth.

If a strong page links directly to another page, it’s going to pass a lot of value. If the link is four clicks away, the value transferred from that strong page will be a lot less because of the damping factor.

Example showing PageRank damping factor
History of PageRank

The first PageRank patent was filed on January 9, 1998. It was titled “Method for node ranking in a linked database.” This patent expired on January 9, 2018, and was not renewed. 

Google first made PageRank public when the Google Directory launched on March 15, 2000. This was a version of the Open Directory Project but sorted by PageRank. The directory was shut down on July 25, 2011.

It was December 11, 2000, when Google launched PageRank in the Google toolbar, which was the version most SEOs obsessed over.

This is how it looked when PageRank was included in Google’s toolbar. 

PageRank 8/10 in Google's old toolbar

PageRank in the toolbar was last updated on December 6, 2013, and was finally removed on March 7, 2016.

The PageRank shown in the toolbar was a little different. It used a simple 0–10 numbering system to represent the PageRank. But PageRank itself is a logarithmic scale where achieving each higher number becomes increasingly difficult.

PageRank even made its way into Google Sitemaps (now known as Google Search Console) on November 17, 2005. It was shown in categories of high, medium, low, or N/A. This feature was removed on October 15, 2009.

Link spam

Over the years, there have been a lot of different ways SEOs have abused the system in the search for more PageRank and better rankings. Google has a whole list of link schemes that include:

  • Buying or selling links—exchanging links for money, goods, products, or services.
  • Excessive link exchanges.
  • Using software to automatically create links.
  • Requiring links as part of a terms of service, contract, or other agreement.
  • Text ads that don’t use nofollow or sponsored attributes.
  • Advertorials or native advertising that includes links that pass ranking credit.
  • Articles, guest posts, or blogs with optimized anchor text links.
  • Low-quality directories or social bookmark links.
  • Keyword-rich, hidden, or low-quality links embedded in widgets that get put on other websites.
  • Widely distributed links in footers or templates. For example, hard-coding a link to your website into the WP Theme that you sell or give away for free.
  • Forum comments with optimized links in the post or signature.

The systems to combat link spam have evolved over the years. Let’s look at some of the major updates.

Nofollow

On January 18, 2005, Google announced it had partnered with other major search engines to introduce the rel=“nofollow” attribute. It encouraged users to add the nofollow attribute to blog comments, trackbacks, and referrer lists to help combat spam.

Here’s an excerpt from Google’s official statement on the introduction of nofollow:

If you’re a blogger (or a blog reader), you’re painfully familiar with people who try to raise their own websites’ search engine rankings by submitting linked blog comments like “Visit my discount pharmaceuticals site.” This is called comment spam, we don’t like it either, and we’ve been testing a new tag that blocks it. From now on, when Google sees the attribute (rel=“nofollow”) on hyperlinks, those links won’t get any credit when we rank websites in our search results. 

Almost all modern systems use the nofollow attribute on blog comment links. 

SEOs even began to abuse nofollow—because of course we did. Nofollow was used for PageRank sculpting, where people would nofollow some links on their pages to make other links stronger. Google eventually changed the system to prevent this abuse.

In 2009, Google’s Matt Cutts confirmed that this would no longer work and that PageRank would be distributed across links even if a nofollow attribute was present (but only passed through the followed link).

Google added a couple more link attributes that are more specific versions of the nofollow attribute on September 10, 2019. These included rel=“ugc” meant to identify user-generated content and rel=“sponsored” meant to identify links that were paid or affiliate.

Algorithms targeting link spam

As SEOs found new ways to game links, Google worked on new algorithms to detect this spam. 

When the original Penguin algorithm launched on April 24, 2012, it hurt a lot of websites and website owners. Google gave site owners a way to recover later that year by introducing the disavow tool on October 16, 2012.

When Penguin 4.0 launched on September 23, 2016, it brought a welcome change to how link spam was handled by Google. Instead of hurting websites, it began devaluing spam links. This also meant that most sites no longer needed to use the disavow tool. 

Google launched its first Link Spam Update on July 26, 2021. This recently evolved, and a Link Spam Update on December 14, 2022, announced the use of an AI-based detection system called SpamBrain to neutralize the value of unnatural links. 

The original version of PageRank hasn’t been used since 2006, according to a former Google employee. The employee said it was replaced with another less resource-intensive algorithm.

They replaced it in 2006 with an algorithm that gives approximately-similar results but is significantly faster to compute. The replacement algorithm is the number that’s been reported in the toolbar, and what Google claims as PageRank (it even has a similar name, and so Google’s claim isn’t technically incorrect). Both algorithms are O(N log N) but the replacement has a much smaller constant on the log N factor, because it does away with the need to iterate until the algorithm converges. That’s fairly important as the web grew from ~1-10M pages to 150B+.

Remember those iterations and how PageRank kept changing with each iteration? It sounds like Google simplified that system.

What else has changed?

Some links are worth more than others

Rather than splitting the PageRank equally between all links on a page, some links are valued more than others. There’s speculation from patents that Google switched from a random surfer model (where a user may go to any link) to a reasonable surfer model (where some links are more likely to be clicked than others so they carry more weight).

Some links are ignored

There have been several systems put in place to ignore the value of certain links. We’ve already talked about a few of them, including:

  • Nofollow, UGC, and sponsored attributes.
  • Google’s Penguin algorithm.
  • The disavow tool.
  • Link Spam updates.

Google also won’t count any links on pages that are blocked by robots.txt. It won’t be able to crawl these pages to see any of the links. This system was likely in place from the start.

Some links are consolidated

Google has a canonicalization system that helps it determine what version of a page should be indexed and to consolidate signals from duplicate pages to that main version.

Canonicalization signals

Canonical link elements were introduced on February 12, 2009, and allow users to specify their preferred version.

Redirects were originally said to pass the same amount of PageRank as a link. But at some point, this system changed and no PageRank is currently lost.

A bit is still unknown

When pages are marked as noindex, we don’t exactly know how Google treats the links. Even Googlers have conflicting statements.

According to John Mueller, pages that are marked noindex will eventually be treated as noindex, nofollow. This means that the links eventually stop passing any value.

According to Gary, Googlebot will discover and follow the links as long as a page still has links to it.

These aren’t necessarily contradictory. But if you go by Gary’s statement, it could be a very long time before Google stops crawling and counting links—perhaps never.

Can you still check your PageRank?

There’s currently no way to see Google’s PageRank.

URL Rating (UR) is a good replacement metric for PageRank because it has a lot in common with the PageRank formula. It shows the strength of a page’s link profile on a 100-point scale. The bigger the number, the stronger the link profile.

Screenshot showing UR score from Ahrefs overview 2.0

Both PageRank and UR account for internal and external links when being calculated. Many of the other strength metrics used in the industry completely ignore internal links. I’d argue link builders should be looking more at UR than metrics like DR, which only accounts for links from other sites.

However, it’s not exactly the same. UR does ignore the value of some links and doesn’t count nofollow links. We don’t know exactly what links Google ignores and don’t know what links users may have disavowed, which will impact Google’s PageRank calculation. We also may make different decisions on how we treat some of the canonicalization signals like canonical link elements and redirects.

So our advice is to use it but know that it may not be exactly like Google’s system.

We also have Page Rating (PR) in Site Audit’s Page Explorer. This is similar to an internal PageRank calculation and can be useful to see what the strongest pages on your site are based on your internal link structure.

Page rating in Ahrefs' Site Audit

How to improve your PageRank

Since PageRank is based on links, to increase your PageRank, you need better links. Let’s look at your options.

Redirect broken pages

Redirecting old pages on your site to relevant new pages can help reclaim and consolidate signals like PageRank. Websites change over time, and people don’t seem to like to implement proper redirects. This may be the easiest win, since those links already point to you but currently don’t count for you.

Here’s how to find those opportunities:

I usually sort this by “Referring domains.”

Best by links report filtered to 404 status code to show pages you may want to redirect

Take those pages and redirect them to the current pages on your site. If you don’t know exactly where they go or don’t have the time, I have an automated redirect script that may help. It looks at the old content from archive.org and matches it with the closest current content on your site. This is where you likely want to redirect the pages.

Internal links

Backlinks aren’t always within your control. People can link to any page on your site they choose, and they can use whatever anchor text they like.

Internal links are different. You have full control over them.

Internally link where it makes sense. For instance, you may want to link more to pages that are more important to you.

We have a tool within Site Audit called Internal Link Opportunities that helps you quickly locate these opportunities. 

This tool works by looking for mentions of keywords that you already rank for on your site. Then it suggests them as contextual internal link opportunities.

For example, the tool shows a mention of “faceted navigation” in our guide to duplicate content. As Site Audit knows we have a page about faceted navigation, it suggests we add an internal link to that page.

Example of an internal link opportunity

External links

You can also get more links from other sites to your own to increase your PageRank. We have a lot of guides around link building already. Some of my favorites are:

Slutgiltiga tankar

Even though PageRank has changed, we know that Google still uses it. We may not know all the details or everything involved, but it’s still easy to see the impact of links.

Also, Google just can’t seem to get away from using links and PageRank. It once experimented with not using links in its algorithm and decided against it.

So we don’t have a version like that that is exposed to the public but we have our own experiments like that internally and the quality looks much much worse. It turns out backlinks, even though there is some noise and certainly a lot of spam, for the most part are still a really really big win in terms of quality of search results.

We played around with the idea of turning off backlink relevance and at least for now backlinks relevance still really helps in terms of making sure that we turn the best, most relevant, most topical set of search results.

Källa: YouTube (Google Search Central)

If you have any questions, message me på Twitter.



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Chrome 110 Changes How Web Share API Embeds Third Party Content

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Chrome 110 Changes How Web Share API Embeds Third Party Content

Chrome 110, scheduled to roll out on February 7, 2023, contains a change to how it handles the Web Share API that improves privacy and security by requiring a the Web Share API to explicitly allow third-party content.

This might not be something that an individual publisher needs to act on.

It’s probably more relevant on the developer side where they are making things like web apps that use the Web Share API.

Nevertheless, it’s good to know what it is for the rare situation when it might be useful for diagnosing why a webpage doesn’t work.

The Mozilla developer page describes the Web Share API:

“The Web Share API allows a site to share text, links, files, and other content to user-selected share targets, utilizing the sharing mechanisms of the underlying operating system.

These share targets typically include the system clipboard, email, contacts or messaging applications, and Bluetooth or Wi-Fi channels.

…Note: This API should not be confused with the Web Share Target API, which allows a website to specify itself as a share target”

allow=”web-share” Attribute

An attribute is an HTML markup that modifies an HTML element in some way.

For example, the nofollow attribute modifies the <a> anchor element, by signaling the search engines that the link is not trusted.

The <iframe> is an HTML element and it can be modified with the allow=”web-share” attribute

An <iframe> allows a webpage to embed HTML, usually from another website.

Iframes are everywhere, such as in advertisements and embedded videos.

The problem with an iframe that contains content from another site is that it creates the possibility of showing unwanted content or allow malicious activities.

And that’s the problem that the allow=”web-share” attribute solves by setting a permission policy for the iframe.

This specific permission policy (allow=”web-share”) tells the browser that it’s okay to display 3rd party content from within an iframe.

Google’s announcement uses this example of the attribute in use:

<iframe allow="web-share" src="https://third-party.example.com/iframe.html"></iframe>

Google calls this a “a potentially breaking change in the Web Share API.

The announcement warns:

“If a sharing action needs to happen in a third-party iframe, a recent spec change requires you to explicitly allow the operation.

Do this by adding an allow attribute to the <iframe> tag with a value of web-share.

This tells the browser that the embedding site allows the embedded third-party iframe to trigger the share action.”

Read the announcement at Google’s Chrome webpage:

New requirements for the Web Share API in third-party iframes

Featured image by Shutterstock/Krakenimages.com



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