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How to Estimate It and Source Data

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How to Estimate It and Source Data

Total addressable market (TAM) is an estimation of how much you could earn if you could sell your product or service to every possible customer in your market.

The basic formula for calculating TAM is:

TAM = (Total Number of Potential Customers) × (Average Annual Revenue per Customer)

Understanding TAM helps you figure out the size of your market and the amount of money you could make if you captured all of it.

TAM is also a key metric for startup investors. It shows whether a business idea has a big enough opportunity. Investors often look for a TAM that is “just right” — not too big or too small. A TAM that’s too large might mean the market is crowded with tough competition, while a TAM that’s too small could mean limited room for growth.

In this guide, you’ll learn how to estimate TAM using three methods, where people often make mistakes, and how to refine your estimations to make them plausible to investors or stakeholders and actionable for your business.

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There are three approaches to calculating TAM. Depending on the available market data, your business model, and your stakeholders/investors, you should consider using the top-down, bottom-up, or value theory approach.

1. Top-down approach

The top-down approach starts with broad market data and narrows it down to estimate the market size for your specific product or service.

This approach is useful when there’s reliable, broad industry data available.

How to use

  1. Estimate the overall market size in which your product operates, usually obtained from industry reports or research.
  2. Apply a percentage that represents the portion of the market your product can realistically capture.

Example

If the global smartphone market is valued at $500 billion, and you are launching a new smartphone accessory, you might estimate that your product could target 5% of the market, which gives you a TAM of $25 billion.

2. Bottom-up approach

The bottom-up approach builds the TAM by starting with specific, individual data related to your business and scaling it up.

TAM: bottom-up approach.TAM: bottom-up approach.

This method is great when you have detailed knowledge of your customer base and pricing. As far as I know, investors prefer this method, which offers the most accurate and actionable TAM estimation.

 

A few birds in the hand is worth billions in the TAM. Early-stage (pre-Series-B) startups shouldn’t worry too much about calculating a precise TAM. As long as it’s in the right ballpark for their thesis, investors care a lot more about the traction you can show with paying customers. That’s why bottom-up is far more convincing than hand-wavy top-down methods that only rely on finding a big enough pie to claim as your market. 

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Rob ChengRob Cheng

How to use

  1. Estimate how many potential customers there are in your target market. You can do this by using sources like industry reports, census data, or research from trusted organizations (more data sources at the end of the article).
  2. Multiply this number by the average revenue you expect to earn from each customer (ARPU – Average Revenue Per User).

Tip

To calculate ARPU, consider the pricing of your product or service, how frequently customers will purchase, and the churn rate.

For example, if you charge $100 per month for a subscription service, your monthly churn rate is 5%; on average, a customer might stay subscribed for around 6-7 months, meaning your average revenue per customer would be around $600-700.

Example

Let’s say you have subscription-based software that helps small businesses manage their finances. You identify that 2 million small businesses could benefit from your software. If your ARPU is $600, your TAM would be 2 million customers × $600 = $1.2 billion.

3. Value theory approach

The value theory approach estimates TAM based on the value your product provides to customers and how much they might be willing to pay for it.

TAM: value-based approach. TAM: value-based approach.

This approach is especially useful if you’re introducing a product or service that disrupts existing markets; traditional market size calculations may not accurately reflect the potential.

How to use

  1. Assess the value or cost savings that your product delivers to the customer.
  2. Estimate how much customers would be willing to pay for that value and scale it across the entire market.

Example

Suppose you have developed a new energy-efficient lighting system that saves companies $10,000 per year in energy costs.

If 100,000 companies could use your lighting system, and each is willing to pay $5,000 for it (because they’ll save $10,000), your TAM would be 100,000 companies × $5,000 = $500 million.

There’s also a fourth option — a middle ground mentioned by quite a few people who offered their insights for this article.

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I’d say the best method to estimate TAM is usually a combination of top-down and bottom-up approaches. The top-down method gives you a big picture view using industry reports and market research, while bottom-up lets you build from the ground up using your own data and customer insights. This combined approach helps balance out the weaknesses of each method. 

Aaron WhittakerAaron Whittaker

You may encounter the TAM, SAM, and SOM terminology and need to apply it if an investor requests it.

People who prefer this approach treat TAM as a “pie in the sky” number and further refine it with SAM and SOM portions of it.

  • TAM (Total Addressable Market) is the total market if you could sell to everyone, everywhere. Your biggest possible opportunity.
  • SAM (Service Addressable Market) is the portion of the TAM you can actually target based on where you operate and who your product is for. For example, if you’re a local coffee shop in New York City, your SAM might be coffee drinkers in NYC, not every coffee drinker worldwide.
  • SOM (Service Obtainable Market) is the realistic piece of the SAM that you can actually win over, considering the competition and your strengths. Continuing with the coffee shop example, your SOM might be the number of customers you can realistically attract in your neighborhood, given factors like nearby competitors, your unique offerings, and marketing efforts.

TAM is typically used to make a compelling story about the potential for growth, so it’s easy to be over-optimistic and make mistakes that could make your TAM look better.

Here’s an example. I used a tool that calculates TAM automatically based on a URL to find the market size for netflix.com. The tool told me that there are 7B people who “need it (…) even if they’re not willing or able to make a purchase” and 6.3B ready to make a purchase. Something that I find hard to believe since there are an estimated 5.3B people with internet access worldwide.

Also, the way that the tool defines my potential customers doesn’t sound convincing to me, either, let alone logical.

Example of mistakes in calculating TAM.Example of mistakes in calculating TAM.

Other mistakes you should avoid:

  • Falling into the “everything trap”. This is when businesses assume that their product or service could appeal to everyone in the market, leading them to calculate TAM based on an overly broad audience.
  • Sizing the problem instead of the market. This happens when businesses focus on the total number of people who might benefit from their solution without considering how many are realistically willing to pay for it.
  • Overlooking market trends and dynamics. The market can grow or contract, consumer preferences can change, government regulations can influence the market, etc.

The basic data sources for TAM calculations are industry reports you can find on platforms like Statista and census data (like the US census data). However, there are other places where you can look for more detailed data.

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Explore the market using search data

Search data is information about what people are looking for online. It can help you understand what customers want, where interest is growing, and what regions are most active.

Google Trends provides some of that data for free. For example, you can check if interest in a plant-based diet is still strong and where in the US you could find the most customers.

Using Google Trends for TAM.Using Google Trends for TAM.

But that’s how far this tool goes. You don’t know what terms are “inside” the topic or how popular a keyword is (the numbers in Google Trends are relative). Also, sometimes Google won’t have the data, just like for the term “baby food subscription”.

Using Google Trends for TAM.Using Google Trends for TAM.

Alternatively, you can use Ahrefs. I’m sure you’ll find more search terms there and a lot more data points. Let me take you through three examples.

Gauge demand with search volume

Search volume is an estimation of the average monthly number of searches for a keyword over the latest known 12 months of data.

High search volumes suggest a larger potential market. Low search volumes, suggest a smaller market (or that you will need to be more creative to find customers).

For example, while Google Trends didn’t have any data on “baby food subscription”, Ahrefs’ Keywords Explorer shows that there are an estimated 1.2K searches per month in the US of that term. Plus, it shows you the forecast for that keyword.

Example of keyword data useful for calculating TAM. Example of keyword data useful for calculating TAM.

If you’d be planning to start a new business in this niche, you’d need compelling arguments to justify a high TAM estimate, because the current demand for this type of service appears to be relatively low.

Learn what people want and how they look for it

Keyword research can tell you what people want in which countries. All you need to know is a few broad terms related to your product.

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For example, for plant-based products, you could just type in “plant-based, vegan” and then go to the Matching terms report to see the popularity of certain types of products. You can also see if the demand for these products has grown or fallen over the last three months.

A selection of keywords with growth metrics. A selection of keywords with growth metrics.

So, if you find that the demand for most vegan products has increased, you could assume that your TAM is going to expand in the near future because more people seem to be interested.

You can also use the tool to automatically translate these keywords and see what search terms people use to find the same products around the world and how popular they are.

Automatic keyword translations in Ahrefs. Automatic keyword translations in Ahrefs.

And if you’re unsure what keywords people could use to find a product or service like yours, just use the AI suggestion feature.

Using AI in Keywords Explorer to find more ways people could look for a product or service online. Using AI in Keywords Explorer to find more ways people could look for a product or service online.

Learn from your competitors

By studying the keywords your competitors are targeting, you can uncover untapped niches or areas where demand is high but competition is lower.

For example, say you’re a SaaS company offering a project management tool. If you used Ahrefs’ Site Explorer, you would find that one of your competitors ranks for terms like “engineering project management software”. This could indicate a niche market with unique needs, where there’s considerable demand but less competition.

Using competitive keyword research to find  new niches. Using competitive keyword research to find  new niches.

While you’re at it, go to the Organic Competitors tab to see who else competes for the same audience. Chances are, you may find some new potential competitors.

Identifying organic competitors to refine TAM. Identifying organic competitors to refine TAM.

Use S-1 filings and quarterly reports from public companies

Public companies’ quarterly reports (10-Q) and S-1/F-1 filings offer rich data for estimating TAM. They provide detailed breakdowns of revenue by product line, geographic region, and market segment, along with insights into market share and growth potential.

For example, if a company generates $500 million from a particular service and claims 10% of the market, you can estimate the TAM at $5 billion.

Both reports can also provide guidance on future growth trends, helping forecast your TAM’s evolution.

You can use AI like ChatGPT to analyze the documents for you (they can be quite complex). Here’s a sample analysis of an over 500-page F-1 filing by an Esports company.

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AI used to analyze an S1 document. AI used to analyze an S1 document.

Interview potential customers

While reports give you big numbers, talking to real people gives you the practical insights needed to adjust those estimates.

  • By speaking directly to customers, you can gauge whether they actually need your product and how likely they are to adopt it.
  • Interviews help you narrow down the customer segments most interested in your solution. Maybe not everyone is a fit, but if certain industries or company sizes show more interest, you can focus your TAM on those segments.
  • Asking customers what they’d actually pay for your product gives you real data. If you know what your target customers are willing to spend, you can multiply that by the number of similar customers to estimate your revenue potential and refine your TAM.

Use PitchBook for investment and market data

PitchBook offers broader market data and investment trends. It provides reliable information on market valuations, funding rounds, and industry growth, which helps you gauge the overall size and growth potential of a market.

PitchBook also helps identify key players, making it easier to estimate how much of the market is currently being captured and what remains untapped.

For example, based on Stripe’s post-valuation of $152 billion and an assumed 30% market share, Stripe’s TAM would be approximately $506.67 billion (TAM = valuation/market share).

Example of PitchBook data useful for estimating TAM.Example of PitchBook data useful for estimating TAM.

Other tools for SaaS companies

If you’re in SaaS, there are a couple more sources of data you may find especially useful: BuiltWith and Latka SaaS Database.

BuiltWith is a tool that shows you what technologies websites are using. This tool is great for identifying your ideal customer because you can see which companies use certain tools or platforms that align with your product.

Sidenote.

The Ideal customer profile (ICP) is a detailed description of the type of company or person who would benefit most from your product or service. It’s helpful mostly for a bottom-up approach to calculate market size, as it helps you focus on the specific segments of the market that are most relevant to your business. 

Enter a competitor into BuiltWith, and look for the list of their customers. For example, here are some of the sites that use Salesforce. You can sort the list by employees or traffic to find the size of the company you think you could get on board. 

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Example of BuiltWith data useful for estimating TAM.Example of BuiltWith data useful for estimating TAM.

The next one is Latka SaaS Database. If you can’t find a SaaS company on PitchBook or BuiltWith, there’s a chance you will find it on Latka. It’s a SaaS-specific database that tracks metrics like revenue, customer growth, churn rates, and funding for thousands of companies.

Example of Latka's data useful for estimating TAM.Example of Latka's data useful for estimating TAM.

Knowing your competitors’ revenue and the number of customers they serve can help you better estimate the size of your potential market.

  • Use competitors’ ARPU or ACV (Annual Contract Value) to estimate your own future metrics.
  • Use the competitor’s revenue or valuation and apply a market share estimation to calculate TAM.

Final thoughts

Remember, TAM is ultimately an estimation. It’s natural to be slightly off, and you’ll probably need to reevaluate every year, after significant changes in the market or after introducing new products.

 

Generally, TAM calculations are not very accurate. At best, you’re relying on partially known variables (number of potential customers and average lifetime customer value). Industries also change so quickly that TAM calculations can become irrelevant within a matter of months.

James OliverJames Oliver

What’s perhaps more important than the exact number is the methodology behind your TAM calculation. A well-thought-out approach demonstrates how seriously you take the business and the effort you’ve put into understanding the market.

Got questions or comments? Find me on LinkedIn.

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How to Revive an Old Blog Article for SEO

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Step-by-Step: How to Optimize Old Blog Posts for SEO

Quick question: What do you typically do with your old blog posts? Most likely, the answer is: Not much.

If that’s the case, you’re not alone. Many of us in SEO and content marketing tend to focus on continuously creating new content, rather than leveraging our existing blog posts.

However, here’s the reality—Google is becoming increasingly sophisticated in evaluating content quality, and we need to adapt accordingly. Just as it’s easier to encourage existing customers to make repeat purchases, updating old content on your website is a more efficient and sustainable strategy in the long run.

Ways to Optimize Older Content 

Some of your old content might not be optimized for SEO very well, rank for irrelevant keywords, or drive no traffic at all. If the quality is still decent, however, you should be able to optimize it properly with little effort. 

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Refresh Content 

If your blog post contains a specific year or mentions current events, it may become outdated over time. If the rest of the content is still relevant (like if it’s targeting an evergreen topic), simply updating the date might be all you need to do.

Rewrite Old Blog Posts 

When the content quality is low (you might have greatly improved your writing skills since you’ve written the post) but the potential is still there, there’s not much you can do apart from rewriting an old blog post completely. 

This is not a waste—you’re saving time on brainstorming since the basic structure is already in place. Now, focus on improving the quality.

Delete Old Blog Posts 

You might find a blog post that just seems unusable. Should you delete your old content? It depends. If it’s completely outdated, of low quality, and irrelevant to any valuable keywords for your website, it’s better to remove it. 

Once you decide to delete the post, don’t forget to set up a 301 redirect to a related post or page, or to your homepage.

Promote Old Blog Posts 

Sometimes all your content needs is a bit of promotion to start ranking and getting traffic again. Share it on your social media, link to it from a new post – do something to get it discoverable again to your audience. This can give it the boost it needs to attract organic links too.

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Which Blog Posts Should You Update?

Deciding when to update or rewrite blog posts is a decision that relies on one important thing: a content audit. 

Use your Google Analytics to find out which blog posts used to drive tons of traffic, but no longer have the same reach. You can also use Google Search Console to find out which of your blog posts have lost visibility in comparison to previous months. I have a guide on website analysis using Google Analytics and Google Search Console you can follow.

If you use keyword tracking tools like SE Ranking, you can also use the data it provides to come up with a list of blog posts that have dropped in the rankings. 

Make data-driven decisions to identify which blog posts would benefit from these updates – i.e., which ones still have the chance to recover their keyword rankings and organic traffic. 

With Google’s helpful content update, which emphasizes better user experiences, it’s crucial to ensure your content remains relevant, valuable, and up-to-date.

How To Update Old Blog Posts for SEO

Updating articles can be an involved process. Here are some tips and tactics to help you get it right.

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Author’s Note: I have a Comprehensive On-Page SEO Checklist you might also be interested in following while you’re doing your content audit.

Conduct New Keyword Research

Updating your post without any guide won’t get you far. Always do your keyword research to understand how users are searching for your given topic. 

Proper research can also show you relevant questions and sections that can be added to the blog post you’re updating or rewriting. Make sure to take a look at the People Also Ask (PAA) section that shows up when you search for your target keyword. Check out other websites like Answer The Public, Reddit, and Quora to see what users are looking for too. 

Look for New Ranking Opportunities

When trying to revive an old blog post for SEO, keep an eye out for new SEO opportunities (e.g., AI Overview, featured snippets, and related search terms) that didn’t exist when you first wrote your blog post. Some of these features can be targeted by the new content you will add to your post, if you write with the aim to be eligible for it. 

Rewrite Headlines and Meta Tags

If you want to attract new readers, consider updating your headlines and meta tags. 

Your headlines and meta tags should fulfill these three things:

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  1. Reflect the rewritten and new content you’ve added to the blog post.
  2. Be optimized for the new keywords it’s targeting (if any).
  3. Appeal to your target audience – who may have changed tastes from when the blog post was originally made. 

Remember that your meta tags in particular act like a brief advertisement for your blog post, since this is what the user first sees when your blog post is shown in the search results page. 

Take a look at your blog post’s click-through rate on Google Search Console – if it falls below 2%, it’s definitely time for new meta tags. 

Replace Outdated Information and Statistics

Updating blog content with current studies and statistics enhances the relevance and credibility of your post. By providing up-to-date information, you help your audience make better, well-informed decisions, while also showing that your content is trustworthy.

Tighten or Expand Ideas

Your old content might be too short to provide real value to users – or you might have rambled on and on in your post. It’s important to evaluate whether you need to make your content more concise, or if you need to elaborate more. 

Keep the following tips in mind as you refine your blog post’s ideas:

  • Evaluate Helpfulness: Measure how well your content addresses your readers’ pain points. Aim to follow the E-E-A-T model (Experience, Expertise, Authoritativeness, Trustworthiness).
  • Identify Missing Context: Consider whether your content needs more detail or clarification. View it from your audience’s perspective and ask if the information is complete, or if more information is needed.
  • Interview Experts: Speak with industry experts or thought leaders to get fresh insights. This will help support your writing, and provide unique points that enhance the value of your content.
  • Use Better Examples: Examples help simplify complex concepts. Add new examples or improve existing ones to strengthen your points.
  • Add New Sections if Needed: If your content lacks depth or misses a key point, add new sections to cover these areas more thoroughly.
  • Remove Fluff: Every sentence should contribute to the overall narrative. Eliminate unnecessary content to make your post more concise.
  • Revise Listicles: Update listicle items based on SEO recommendations and content quality. Add or remove headings to stay competitive with higher-ranking posts.

Improve Visuals and Other Media

No doubt that there are tons of old graphics and photos in your blog posts that can be improved with the tools we have today. Make sure all of the visuals used in your content are appealing and high quality. 

Update Internal and External Links

Are your internal and external links up to date? They need to be for your SEO and user experience. Outdated links can lead to broken pages or irrelevant content, frustrating readers and hurting your site’s performance.

You need to check for any broken links on your old blog posts, and update them ASAP. Updating your old blog posts can also lead to new opportunities to link internally to other blog posts and pages, which may not have been available when the post was originally published.

Optimize for Conversions

When updating content, the ultimate goal is often to increase conversions. However, your conversion goals may have changed over the years. 

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So here’s what you need to check in your updated blog post. First, does the call-to-action (CTA) still link to the products or services you want to promote? If not, update it to direct readers to the current solution or offer.

Second, consider where you can use different conversion strategies. Don’t just add a CTA at the end of the post. 

Last, make sure that the blog post leverages product-led content. It’s going to help you mention your products and services in a way that feels natural, without being too pushy. Being subtle can be a high ROI tactic for updated posts.

Key Takeaway

Reviving old blog articles for SEO is a powerful strategy that can breathe new life into your content and boost your website’s visibility. Instead of solely focusing on creating new posts, taking the time to refresh existing content can yield impressive results, both in terms of traffic and conversions. 

By implementing these strategies, you can transform old blog posts into valuable resources that attract new readers and retain existing ones. So, roll up your sleeves, dive into your archives, and start updating your content today—your audience and search rankings will thank you!

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How Compression Can Be Used To Detect Low Quality Pages

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Compression can be used by search engines to detect low-quality pages. Although not widely known, it's useful foundational knowledge for SEO.

The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.

Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.

What Is Compressibility?

In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.

TL/DR Of Compression

Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.

This is a simplified explanation of how compression works:

  • Identify Patterns:
    A compression algorithm scans the text to find repeated words, patterns and phrases
  • Shorter Codes Take Up Less Space:
    The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size.
  • Shorter References Use Less Bits:
    The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.

A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.

Research Paper About Detecting Spam

This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.

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Marc Najork

One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.

Dennis Fetterly

Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.

Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.

Detecting Spam Web Pages Through Content Analysis

Although the research paper was authored in 2006, its findings remain relevant to today.

Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.

Section 4.6 of the research paper explains:

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“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”

The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.

They write:

“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.

…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”

High Compressibility Correlates To Spam

The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.

Figure 9: Prevalence of spam relative to compressibility of page.

The researchers concluded:

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“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”

But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:

“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.

Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:

95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.

More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”

The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.

Insight Into Quality Rankings

The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.

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The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.

The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.

This is the part that every SEO and publisher should be aware of:

“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.

For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”

So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.

Combining Multiple Signals

The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.

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The researchers explained that they tested the use of multiple signals:

“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”

These are their conclusions about using multiple signals:

“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”

Key Insight:

Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.

What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.

Takeaways

We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.

Here are the key points of this article to keep in mind:

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  • Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
  • Groups of web pages with a compression ratio above 4.0 were predominantly spam.
  • Negative quality signals used by themselves to catch spam can lead to false positives.
  • In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
  • When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
  • Combing quality signals improves spam detection accuracy and reduces false positives.
  • Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.

Read the research paper, which is linked from the Google Scholar page of Marc Najork:

Detecting spam web pages through content analysis

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New Google Trends SEO Documentation

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Google publishes new documentation for how to use Google Trends for search marketing

Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.

The new guide has six sections:

  1. About Google Trends
  2. Tutorial on monitoring trends
  3. How to do keyword research with the tool
  4. How to prioritize content with Trends data
  5. How to use Google Trends for competitor research
  6. How to use Google Trends for analyzing brand awareness and sentiment

The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.

Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.

To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.

The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.

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Google explains:

“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”

Read the new Google Trends documentation:

Get started with Google Trends

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