Using Python to recover SEO site traffic (Part three) Search Engine Watch

Using Python to recover SEO site traffic (Part three)

Using Python to recover SEO site traffic (Part three)

When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.

This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.

As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.

Let’s get started.

URL matching vs content matching

When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.

Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.

How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.

Example of using DOM elements to organize pages by their content

For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.

We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.

A scientist’s bottom-up approach

In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.

BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.

When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”

One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.

Hamlet’s observation and a simpler solution

For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.

Hamlet's observation for a simpler approach based on domain-level observationsHamlet's observation for a simpler approach by testing the quantity and size of images

I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.

I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.

Now let’s get to the fun part and get to code some machine learning code in Python!

Collecting training data

We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.

In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.

What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).

Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.

Here we load the raw data already collected.

Feature engineering

Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.

Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.

It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.

We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.

Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.

Here is what our processed data set looks like.

Example view of processed data for "binning"

Adding ground truth labels

As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.

We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:

Model training and grid search

Finally, the good stuff!

All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.

We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!

Evaluating performance

Graph for evaluating image performances through a linear pattern

After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.

In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.

Graph of the confusion matrix to evaluate image performance

When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.

Here is the code to put together the confusion matrix:

Finally, here is the code to plot the model evaluation:

Resources to learn more

You might be thinking that this is a lot of work to just tell page groups, and you are right!

Screenshot of a query on custom PageTypes and DataLayer

Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!

I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.

If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:

Got any tips or queries? Share it in the comments.

Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter .

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How to perfectly balance affiliate marketing and SEO Search Engine Watch

How to perfectly balance affiliate marketing and SEO

How to perfectly balance affiliate marketing and SEO

In all my years as an SEO consultant, I can’t begin to count the number of times I saw clients who were struggling to make both SEO and affiliate marketing work for them.

When their site rankings dropped, they immediately started blaming it on the affiliate links. Yet what they really needed to do was review their search marketing efforts and make them align with their affiliate marketing efforts.

Both SEO and affiliate marketing have the same goal of driving relevant, high-quality traffic to a site so that those visits eventually turn into sales. So there’s absolutely no reason for them to compete against each other. Instead, they should work together in perfect balance so that the site generates more revenue. SEO done right can prove to be the biggest boon for your affiliate marketing efforts.

It’s crucial that you take a strategic approach to align these two efforts.

Four ways to balance your affiliate marketing and SEO efforts

1. Find a niche that’s profitable for you

One of the reasons why affiliate marketing may clash with SEO is because you’re trying to sell too many different things from different product categories. So it’s extremely challenging to align your SEO efforts with your affiliate marketing because it’s all over the place.

This means that you’ll have a harder time driving a targeted audience to your website. While your search rankings may be high for a certain product keyword, you may be struggling to attract visitors and customers for other products.

Instead of trying to promote everything and anything, pick one or two profitable niches to focus on. This is where it gets tricky. While you may naturally want to focus on niches in which you have a high level of interest and knowledge, they may not always be profitable. So I suggest you conduct some research about the profitability of potential niches.

To conduct research, you can check resources that list the most profitable affiliate programs. You can also use platforms like ClickBank to conduct this research. While you can use other affiliate platforms for your research, this is a great place to start. First, click on the “Affiliate Marketplace” button at the top of the ClickBank homepage.

Snapshot of ClickBank

You’ll see a page that gives you the option to search for products. On your left, you can see the various affiliate product categories available. Click on any of the categories that pique your interest.

Snapshot of affiliate program categories available on ClickBank

On the search results page, you’ll see some of the affiliate marketing programs available on the platform. The page also displays various details about the program including the average earning per sale.

Then filter the search results by “Gravity,” which is a metric that measures how well a product sells in that niche.

snapshot of the search results page and filters

You should ideally look for products with a Gravity score of 50 or higher. Compare the top Gravity scores of each category to see which is the most profitable. You can additionally compare the average earnings per sale for products in different categories.

2. Revise your keyword strategy

Since you’re already familiar with search marketing, I don’t need to tell you about the importance of keyword planning. That being said, I would recommend that you revise your existing keyword strategy after you’ve decided on a niche to focus on and the products you want to sell.

The same keyword selection rules apply even in this process. You would want to work with keywords that have a significant search volume yet aren’t too competitive. And you will need to focus on long-tail keywords for more accuracy. While you should still use the Google Keyword Planner, I suggest you try out other tools as well for fresh keyword ideas.

Among the free tools, Google Trends is an excellent option. It gives you a clear look at the changes in interest for your chosen search term. You can filter the result by category, time frame, and region. It also gives you a breakdown of how the interest changes according to the sub-region.

Snapshot of tracking users' changing interest in a particular search

The best part about this tool is that if you scroll down, you can also see some of the related queries. This will give you insights into some of the other terms related to your original search term with rising popularity. So you can get some quick ideas for trending and relevant keywords to target.

Snapshot of related queries in Google Trends

AnswerThePublic is another great tool for discovering long-tail keyword ideas. This tool gives you insights into some of the popular search queries related to your search term. So you’ll be able to come up with ideas for keywords to target as well as topic ideas for fresh content.

Getting long-tail keywords and topic ideas on AnswerThePublic

3. Optimize your website content

High-quality content is the essence of a successful SEO strategy. It also serves the purpose of educating and converting visitors for affiliate websites. So it’s only natural that you will need to optimize the content on your website. You can either create fresh content or update your existing content, or you can do both.

Use your shortlisted keywords to come up with content ideas. These keywords have a high search volume, so you know that people are searching for content related to them. So when you create content optimized with those keywords, you’ll gain some visibility in their search results. And since you’re providing them with the content they need, you will be driving them to your site.

You can also update your existing content with new and relevant keywords. Perhaps to add more value, you can even include new information such as tips, stats, updates, and more. Whatever you decide to do, make sure the content is useful for your visitors. It shouldn’t be too promotional but instead, it needs to be informative.

4. Build links to boost site authority and attract high-quality traffic

You already know that building high-quality backlinks can improve the authority of your site and therefore, your search rankings. So try to align your link-building efforts with your affiliate marketing by earning backlinks from sites that are relevant to the products you’re promoting.

Of course, you can generate more social signals by trying to drive more content shares. But those efforts aren’t always enough. Especially if you want to drive more revenue.

I suggest you try out guest posting, as it can help you tap into the established audience of a relevant, authoritative site. This helps you drive high-quality traffic to your site. It also boosts your page and domain authority since you’re getting a link back from a high authority site.

Although Matt Cutts said in 2014 that guest posting for SEO is dead, that’s not true if you plan your approach. The problem is when you try to submit guest posts just for the sake of getting backlinks. Most reputable sites don’t allow that anymore.

To get guest posting right, you need to make sure that you’re creating content that has value. So it needs to be relevant to the audience of your target site, and it should be helpful to them somehow. Your guest posts should be of exceptional quality in terms of writing, readability, and information.

Not only does this improve your chances of getting accepted, but it also helps you gain authority in the niche. Plus, you will get to reach an engaged and relevant audience and later direct them to your site depending on how compelling your post is.

Bottom line

SEO and affiliate marketing can work in perfect alignment if you strategically balance your efforts. These tips should help you get started with aligning the two aspects of your business. You will need some practice and experimentation before you can perfectly balance them. You can further explore more options and evolve your strategy as you get better at the essentials.

Shane Barker is a Digital Strategist, Brand and Influencer Consultant. He can be found on Twitter .

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How you can use it for SEO Search Engine Watch

Google Dataset Search How you can use it for SEO

Google Dataset Search How you can use it for SEO

Back in September 2018, Google launched its Dataset Search tool, an engine which focuses on delivering results of hard data sources (research, reports, graphs, tables, and others) in a more efficient manner than the one which is currently offered by Google Search.

The service promises to enable easy access to the internet’s treasure trove of data. As Google’s Natasha Noy says,

“Scientists, data journalists, data geeks, or anyone else can find the data required for their work and their stories, or simply to satisfy their intellectual curiosity.”

For SEOs, it certainly has potential as a new research tool for creating our own informative, trustworthy, and useful content. But what of its prospects as a place to be visible, or as a ranking signal itself?

Google Dataset Search: As a research tool

As a writer who has been using Google to search for data since about a decade, I’d agree that finding hard statistics on search engines is not always massively straightforward.

Often, data which isn’t the most recent ranks better than newer research. This makes sense in an SEO sense, that which was published months or years prior has had a long time to earn authority and traffic. But usually I need the freshest stats, and even search results pointing to data on a page that has been published recently doesn’t necessarily mean that the data contained in that page is from that date.

Additionally, big publications (think news sites like the BBC) frequently rank better than the domain where the data was originally published. Again, this is unsurprising in the context of search engines. The BBC et al. have far more traffic, authority, inbound links, and changing content than most research websites, even .gov sites. But that doesn’t mean to say that the user looking for hard data wants to see BBC’s representation of that data.

Another key issue we find when researching hard data on Google concerns access to content. All too regularly, after a bit of browsing in the SERPs I find myself clicking through only to find that the report with the data I need is behind a paywall. How annoying.

On the surface, Google Dataset Search sets out to solve these issues.

Example of Google Dataset Search result

A quick search for “daily weather” (Google seems keen to use this kind of .gov data to exemplify the usefulness of the tool) shows how the service differs from a typical search at Google.com.

Results rank down the left-hand side of the page with the rest of the SERP real estate given over to more information about whichever result you have highlighted (position one is default). This description portion of the page includes:

  • Key URL links to landing pages
  • Key dates such as the time period the data covers, when the dataset was last updated and/or when it was first published
  • Who provides the data
  • The license for the data
  • Its relevant geolocation
  • A description of what the data is

By comparison, a search for the same keyphrase on Google in incognito mode prioritizes results for weather forecasts from Accuweather, the BBC, and the Met Office. So to have a search engine which focuses on pure, recorded data, is immediately useful.

Most results (though not all) make it clear to the user as to when the data is from and what the original source is. And by virtue of the source being included in the Dataset Search SERPs, we can be quite sure that a click through to the site will provide us access to the data we need.

Google Dataset Search: As a place to increase your visibility

As detailed on Google’s launch post for the service, Dataset Search is dependent on webmasters marking up their datasets with the Schema.org vocabulary.

Broadly speaking, Schema.org is a standardized way for developers to make information on their websites easy to crawl and understandable by search engines. SEOs might be familiar with the vocabulary if they have marked up their video content or other non-text objects on their sites. For example, whether they have sought to optimize their business for local search.

There are ample guidelines and sources to assist you with dataset markup (Schema.org homepage, Schema.org dataset markup list, Google’s reference on dataset markup, and Google’s webmaster forum are all very useful). I would argue that if you are lucky enough to produce original data, it is absolutely worth considering making it crawlable and accessible for Google.

If you are thinking about it, I’d also argue that it is important to start ranking in Google Dataset Search now. Traffic to the service might not be massive currently, but the competition to start ranking well is only going to get more difficult. The more webmasters and developers see potential in the service, the more it will be used.

Additionally, dataset markup will not only benefit your ranking in Dataset Search it will also increase your visibility for relevant data-centric queries in Google too. An important point as we see tables and stats incorporated more frequently and more intuitively in elements of the SERPs such as the Knowledge Graph.

In short:

  • Getting the most out of your data is straightforward to do.
  • The sooner you do, the more likely you are to have a head-start on visibility in Dataset Search before your competitors.
  • And it is good best-practice for visibility in increasingly data-intuitive everyday search.

Google Dataset Search: As a ranking signal

There is a good reason to believe that being indexed in Dataset Search will be a ranking signal in its own right.

Google Scholar, which indexes scholarly literature such as journals and books has been noted by Google to provide a valuable signal about the importance and prominence of a dataset.

With that in mind, it makes sense to think a dataset that is well-optimized with clear markup and is appearing in Dataset Search would send a strong signal to Google. This would signal that the respective site is a trusted authority as a source of that type of data.

Thoughts for the future

It is early days for Google Dataset Search. But for SEO, the service is already certainly showing its potential.

As a research tool, its usefulness really depends on the community of research houses who are marking up their data for the benefit of the ecosystem. I expect the number of contributors to the service will grow quickly making for a diverse and comprehensive data tool.

I also expect that the SERPs may change considerably. They certainly work better for these kinds of queries than Google’s normal search pages. But I had some bugbears. For example, which URL am I expected to click on if a search result has more than one? Can’t all results have publication dates and the time period the data covers? Could we see images of graphs/tables in the SERPs?

But when it comes to potential as a place for visibility and a ranking signal, if you are a business that collects data and research (or you are thinking about producing this type of content), now is the time to ensure your datasets are marked up with Schema.org to beat your competitors in ranking on Google Dataset Search. This dataset best practice will also stand you in good stead as Google’s main search engine gets increasingly savvy with how it presents the world’s data.

Luke Richards is a writer for Search Engine Watch and ClickZ. You can follow Luke on Twitter at .

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Common technical SEO issues and fixes for aggregators and finance brands Search Engine Watch

Common technical SEO issues and fixes, for aggregators and finance brands

Common technical SEO issues and fixes, for aggregators and finance brands

As most of you know the aggregator market is a competitive one, with the popularity in comparison sites rising.

Comparison companies are some of the most well-known and commonly used brands today. With external marketing and advertising efforts at an all-time high, people turn to the world wide web for these services. So, who is championing the online market?

In an investigation we carried out, we found brands such as MoneySuperMarket and MoneySavingExpert are the kings of the organic market.

graph on comparison websites

It’s hard to remember a world without comparison sites. It turns out that comparison websites have been around for quite some time. In fact, some of the most popular aggregator domain names have been available since 1999.

Yes 1999, two years after Google launched.

Think back to 1999 and how SEO has adapted. Now think about what websites have had to consider, keeping up with high customer demands, new functionality, site migrations, introduction to JavaScript, site speed, the list is endless.

The lifespan and longevity of these sites mean that over time issues start to build up, especially in the technical SEO department. Many of us SEOs are aware of the benefits that come with spending time on technical SEO issues — not to mention the great return on investment.

As comparison sites are so popular and relied upon by users, simple technical issues can result in a poor user experience damaging customer relationships. Or worse, users seeking assistance elsewhere.

Running comparison crawls have identified the common technical SEO issues across the market leaders. Find out what these issues are and how they will be harming their SEO — and see if they correlate with your own website.

1. Keyword cannibalization

When developing and creating new pages it is easy to forget about keyword cannibalization. Duplicating templates can easily leave metadata and headings unchanged, all confusing search engines on which page to rank for that keyword.

Here is an example from GoCompare.

Example of Keyword cannibalization in the h1 and h2 tags

The page on the left has the cannibalizing first heading. This is because the page’s h1 is situated in the top banner. This should target the long-tail opportunity “how to make your own electricity at home” which has been placed in an h2 tag directly under the banner.

The best course of action here would be to tweak the template, removing the banner and placing the call to action in the article body and placing the targeted keyword in a first heading tag.

Comparison sites are prime candidates for keyword cannibalization with the duplication of templates, services, and offers which results in cannibalization issues sitewide.

The fix

Run a crawl of your domain, gathering all the duplicated first headings tags, you can use tools such as Sitebulb for this. Decipher between which is the original page and which is the duplicate, then gather your keyword data to find a better keyword alternative for that duplicate page.

Top tip

Talk to your SEO expert when creating new pages, they will be able to provide recommendations on URL structure, first headings, and titles. It is worth having an SEO at the start of the planning process when rolling out new pages.

2. Internal redirects

Numerous changes can result in internal redirects, primary causes are redundant pages, upgrades to a site’s functionality, and furthermore, the dreaded site migration.

When Google urged sites to accelerate to HTTPs in January 2017, with the ideal methodology to 301 redirect HTTP pages to HTTPs, it’s painful to think about the mass number of internal redirects.

Here’s an example.

Example of internal redirects

Comparison sites specifically need to be aware of this. Just like ecommerce sites, products and services become unavailable. The normal behavior seems to be to then to redirect that product either to an alternative page or, in most cases, back to the parent directory.

This can then cause internal redirects across the site that need immediate attention.

The fix

To tackle this issue, gather all the internal redirected URLs from your crawler.

Once you’ve done this find the link on the parent page by inspecting the page on Google Developer tools.

Find where the link is and recommend to your development team that it changes the href attribute target within the link anchor to the final destination of the redirect.

3. Cleaning up the sitemap

With loads of changes happening across aggregator sites all the time, it is likely that the sitemap gets neglected.

However, it’s imperative you don’t allow this to happen! Search engines such as Google might ignore sitemaps that return “invalid” URLs.

Here’s an example.

Snapshot of the 404 error

Usually, a site’s 400/500 status code pages are on the development teams’ radar to fix. However, it isn’t always best practice as that these pages still sit in the sitemap. As they might be set live, orphaned and no indexed, or redirected elsewhere, that leaves some less severe issues within the Sitemap file.

Aggregators currently have to deal with sites changing product ranges, releasing new and, even, discontinuing services on a regular basis. New pages, therefore, have to be set up, redirects are then applied and sometimes issues are missed.

The fix

First, you need to identify errors within the sitemap. Search Console is perfect for this. Go to the coverage section, and filter with the drop down. Select your sitemaps with “Filter to Sitemaps” to inspect the errors that are within these.

Snapshot of canonical errors and redirects

If your sitemap has 400 or 500 status code pages, then this is more of a priority, if it has the odd redirect or canonical issue, focus on sorting these out first.

Top tip

Check your sitemap weekly or even more frequently. It is also a great way of checking your broken pages across the site.

4. Subdomains are causing index bloat

Behind any great comparison site is a quotation functionality. This allows users to place personal information about a quote and being able to revisit previously saved data kind of like a shopping cart on most ecommerce websites.

However, these are usually hosted on subdomains and can get indexed, which you don’t really want. These are mostly thin content pages, a useless page in Google index equaling index bloat.

Here’s an example.

Example of subdomains

The fix

The solution is to add the “noindex” meta attribute to the quotation domains to stop them from being indexed. You can also include the subdomains in your robots.txt file to stop them from being crawled. Just make sure they aren’t in the search engines’ index before you place them in the file as they won’t drop out of the SERPs.

5. Spreading link equity to irrelevant pages

Internal linking is important. However, passing link equity thinly across pages can cause a loss in value. Think of a pyramid, and how the homepage spreads equity to the directory and then down to the subdirectories through keyword targeted anchor text.

Example of how authority pages spread link equity across a website

These pages where equity is passed should hold the value and only link out to relevant pages that might be of relevance.

As comparison sites target a range of products and opportunities it is important to include them within the site architecture, but not spread the equity thinly.

How do we do this?

1. Consider the architecture of your site. For example:

“Fixed rate mortgages” has different yearly offerings, most sites sit these under a mortgage subdirectory, but this could easily have its own directory. This would benefit the site architecture as it lowers the click depth for those important pages and stops the thin spread of equity.

2. Only link to what is relevant.

Let’s take the below example. The targeted keyword here is “bad credit mortgages.” Money.co.uk then supplies a load of internal links at the bottom of the page that aren’t relevant to the keyword intent. Therefore, the equity is spread to these pages resulting in the page losing value.

Example of linking to relevant pages for link equity

 

The fix

Review the internal linking structure. You can do this by running pages through Screaming Frog, which identifies pages that have a click depth greater than two and evaluates the outgoing links. If there are a lot, this could be a good indicator that pages might be spreading the equity thinly. Manually evaluate the pages to find there the links are going to and remove any that might be irrelevant spreading equity unnecessarily.

6. Orphaned pages

Following on from the above point, pages that are orphaned, or poorly linked to, will receive low equity. Comparison sites are prime candidates for this.

MoneySuperMarket has several orphaned pages, especially located in the blog section of the site.

Example of orphaned pages

The fix

Use Sitebulb to crawl the site and discover orphaned pages. Spend time evaluating these, it might be that these pages should be orphaned. However, if they are present in the sitemap that indicates either one of two problems given below.

  • The pages should be linked to through the internal architecture
    or
  • The page shouldn’t be indexable or in the sitemap

If the pages are redundant, make them “no indexable.” However, if they should be linked to, evaluate your site’s internal architecture to work out a perfect linking strategy for these pages.

Top tip

It is very easy for blog posts to get orphaned, using methods such as topic clustering can help benefit your content marketing efforts while making sure your pages aren’t being orphaned.

Last ditch tips

A lot of these issues occur across a range of different sites and many sectors, as comparison sites undergo a lot of changes and development work with a vast product range and loads to aggregate. It is very hard to keep up-to-date with SEO tech issues.

Be vigilant and delegate resources sensibly. SEO tech issues shouldn’t be ignored, actively monitor and run crawls and checks after any site development work has been rolled out, this can save your organic performance and keep your technical SEO game strong.

Tom Wilkinson is Search & Data Lead at Zazzle Media.

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