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TF-IDF: The best content optimization tool SEOs aren’t using

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TF-IDF, short for term frequency–inverse document frequency, identifies the most important terms used in a given document. It is also one of the most ignored content optimization tools used by SEOs today.

TF-IDF fills in the gaps of standard keyword research. The saturation of target keywords on-page doesn’t determine relevance – anyone can practice keyword stuffing. Search marketers can use TF-IDF to uncover the specific words top-ranking pages use to give target keywords context, which is how search engines understand relevance.

Why should SEOs care about TF-IDF?

Conducting a TF-IDF analysis shows you the most important words used in the top 10 pages for a given keyword. You’ll see the exact terms that search engines consider highly relevant for your keyword and then compare your own content with competitors.

Now, I’m not suggesting you throw out your other keyword research tools—they are still very useful in the beginning stages when choosing your target keyword. However, they simply do not provide the semantic keywords necessary to fully represent a topic.

Let’s compare a keyword research tool’s semantic abilities with TF-IDF:

Keyword: ‘how to make coffee’

Say you’re writing a guide about how to make coffee. Here’s what Ahrefs would suggest including:

These tools provide excellent keyword variations but do not offer any keywords to improve topical relevance.

On the other hand, a TF-IDF tool would provide these insights:

In the top 10 pages about how to make coffee, the most weighted words include:

One glance at these words reveals the topic without a mention of the word coffee. That’s because TF-IDF provides a list of semantically related keywords, or “context” keywords, as one can think of them, that search engines are statistically expecting to see in relation to the topic of “how to make coffee.”

The exclusion of these words from an article about making coffee would absolutely indicate a lack of relevance to search engines… which means you can say goodbye to your chances of high rankings. Traditional keyword research just doesn’t provide this type of insight. 

But some may ask: what about E-A-T? Won’t a good reputation be enough to override the content?

The answer is: No, not really.

In his presentation on technical content optimization, Mike King of iPullRank offers an excellent “David and Goliath” example of the importance of content relevance:

Moz, arguably one of the most relevant sites for SEO-related keywords, ranks #20 for “what does seo stand for.”

Moz’s page (URL rating of 56 and 2.54k backlinks):

Alpine Web Design, the “David” in this situation, ranks #2 for the same keyword.

Alpine’s page: (URL rating of 15 and 75 backlinks)

From an authority and UX perspective, Moz is the clear winner. But TF-IDF analysis tells a different side of the story:

Moz:

Alpine:

As you can see, Moz’s page does not adequately represent many contextual keywords that Google finds relevant for the term “what does SEO stand for.” A significantly higher URL rating and backlink profile couldn’t save it.

How to implement TF-IDF with free tools

The advantages of adding TF-IDF to your content strategy are clear. Fortunately, several free tools exist to simplify this process:

1. Seobility’s TF-IDF tool

Personally, this is my favorite tool. It’s the only one I’ve found that’s completely free, no download or sign-up necessary. You get three TF-IDF checks per day to start, five with free sign-up or 50 with the premium plan.

You also gain access to their text editing tool so you can optimize your content with the tool’s suggestions.

2. Ryte’s content success tool

Ryte’s TF-IDF tool is another excellent choice. You can sign up for Ryte for free and get 10 TF-IDF analyses per month, which includes keyword recommendations and topic inspiration.

This tool also includes a text editor for easy content optimization.

3. Link Assistant’s website auditor

This tool is my honorable mention because it requires downloading to gain access. Once downloaded, you should get unlimited TF-IDF analyses.

If you do decide to download, this video explains how to navigate to the TF-IDF dashboard. 

Final word: TF-IDF is a tool, not the tool

It’s important to note: using TF-IDF is no substitution for having authoritative authors or reviewers, especially when it comes to YMYL topics.

This method of research should be used primarily to increase your understanding of the most weighted terms in a given document, and perhaps influence the variety of words used in your pages. It will never replace the expertise of a professional in the field.

Similarly, TF-IDF should not be taken at face value. You will be unsuccessful if you mimic the exact average of the weighted terms in your own content. Don’t force words in if they don’t make sense.

TF-IDF is just one method of content optimization, not the basket to put all your eggs in. If you get one thing out of this post, it would be to consider adding TF-IDF analysis to your toolbox when creating or updating content, not replacing your existing method of keyword research.


Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.


About The Author

Abby Reimer is a digital strategist at Uproer, where she develops SEO and content strategies for e-commerce and technology companies. Her career dream is to use public speaking and content to make SEO more accessible for marketers at all levels of expertise. She believes wholeheartedly that better search results are better for everyone.

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Google Search Console unparsable structured data report data issue

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Google has informed us that you may see a spike in errors in the unparsable structured data report within Google Search Console. This is a bug in the reporting system and you do not need to worry. The issue happened between January 13, 2020 and January 16, 2020.

The bug. Google wrote on the data anomalies page “Some users may see a spike in unparsable structured data errors. This was due to an internal misconfiguration that will be fixed soon, and can be ignored.” This was dated January 13, 2020 through January 16, 2020.

To be fixed. Google said they will fix the issue with the internal misconfiguration. It is, however, unclear if the data will be fixed or if you will see a spike in those errors between those date ranges.

Unparsable structured data report. The unparsable structured data report is accessible within Google Search Console by clicking here. The report aggregates structured data syntax errors. It puts all the parsing issues, including structured data syntax errors, that specifically prevented Google from identifying the feature type.

Why we care. The main thing here is that if you see a spike in errors in that report between January 13th and 16th, do not worry. It is a bug with the report and not an issue with your web site. Go back to the report in a few days and make sure that you do not see errors occurring after the 17th of January to be sure you have no technical issues.


About The Author

Barry Schwartz a Contributing Editor to Search Engine Land and a member of the programming team for SMX events. He owns RustyBrick, a NY based web consulting firm. He also runs Search Engine Roundtable, a popular search blog on very advanced SEM topics. Barry’s personal blog is named Cartoon Barry and he can be followed on Twitter here.



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Google rolls out organic ‘Popular Products’ listings in mobile search results

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Several years ago now, Google made the significant move to turn product search listings into an entirely paid product. Shopping campaigns, as they’re now called, have accounted for an increasing share of retail search budgets ever since. More recently, however, Google has been augmenting organic search results with product listings. It’s in a product search battle with Amazon, after all. On Thursday, the company announced the official rollout of “Popular Products” for apparel, shoe and similar searches in mobile results.

Organic product listings. Google has been experimenting with ways to surface product listings in organic search results, including Popular Products, which has been spotted for several months now. The section is powered by those organic feeds. Google says it identifies popular products from merchants to show them in a single spot, allowing users to filter by style, department and size type. The listings link to the retailers’ websites.

Popular Products is now live in Google mobile search results.

Why we care. This is part of a broader effort by Google to enhance product search experiences as it faces increasing competition from Amazon and other marketplaces as well as social platforms. Earlier this week, Google announced it has acquired Pointy, a hardware solution for capturing product and inventory data from small local merchants that can then be used in search results (and ads).

In the past few years, Google has also prompted retailers to adopt product schema markup on their sites by adding support for it in Search and Image search results. Then last spring, Google opened up Merchant Center to all retailers, regardless if they were running Shopping campaigns. Any retailer can submit their feed in real-time to Google to make their products eligible in search results.

Ad revenue was certainly at the heart of the shift to paid product listings, but prior to the move, product search on Google was often a terrible user experience with search listings often not matching what was on the landing page, from availability to pricing to even the very product. The move to a paid solution imposed quality standards that forced merchants to clean up their product data and provide it to Google in a structured manner in the form of product feeds through Google Merchant Center.


About The Author

Ginny Marvin is Third Door Media’s Editor-in-Chief, running the day to day editorial operations across all publications and overseeing paid media coverage. Ginny Marvin writes about paid digital advertising and analytics news and trends for Search Engine Land, Marketing Land and MarTech Today. With more than 15 years of marketing experience, Ginny has held both in-house and agency management positions. She can be found on Twitter as @ginnymarvin.



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Google buys Pointy to bring SMB store inventory online

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Google is acquiring Irish startup Pointy, the companies announced Tuesday. Pointy has solved a problem that vexed startups for more than a decade: how to bring small, independent retailer inventory online.

The terms of the deal were not disclosed, but Pointy had raised less than $20 million so it probably wasn’t an expensive buy for Google. But it could have a significant impact for the future of product search.

Complements local inventory feeds. This acquisition will help Google offer more local inventory data in Google My Business (GMB) listings, knowledge panels and ads especially. It complements Google Shopping Campaigns’ local inventory ads, which are largely utilized by enterprise merchants and first launched in 2013.

Numerous companies over the last decade tried to solve the challenge of how to bring small business product inventory online. However, most failed because the majority of SMB retailers lack sophisticated inventory management systems that can generate product feeds and integrate with APIs.

Pointy POS hardware

Source: Pointy

How Pointy works. The company created a simple way to get local store inventory online and then showcase that inventory in organic search results or paid search ads. It utilizes a low-cost hardware device that attaches to a point-of-sale barcode scanner (see image above). It’s compatible with multiple other POS systems, including Square.

Once the device is installed, it captures every product sold by the merchant and then creates a digital record of products, which can be pushed out in paid or organic results. (The company also helps small retailers set up local inventory ads using the data.) Pointy also creates local inventory pages for each store and product, which are optimized and can rank for product searches.

Pointy doesn’t actually understand real-time inventory. Cleverly, however, it uses machine learning algorithms to estimate this by measuring product purchase frequency. The system assumes local retailers are going to stock frequently purchased items. That’s an oversimplification, but is essentially how it works.

Pointy said it a blog post that it “serve[s] local retailers in almost every city and every town in the U.S. and throughout Ireland.”

Why we care. The Pointy acquisition will likely help Google in at least three ways:

  • Provide more structured, local inventory data for consumers to find in Search.
  • Generate more advertising revenue over time from independent retailers.
  • Help Google more effectively compete with Amazon in product search.

Notwithstanding the fact that e-commerce outperformed traditional retail over the holidays, most people spend the bulk of their shopping budgets offline and prefer to shop locally. Indeed, Generation Z prefers to shop in stores, according to an A.T. Kearney survey.

One of the reasons that people shop at Amazon is because they can find products they’re looking for. They often don’t know where to find a particular product locally. But if more inventory data becomes available, the more people may opt to buy from local stores instead.


About The Author

Greg Sterling is a Contributing Editor at Search Engine Land. He writes about the connections between digital and offline commerce. He previously held leadership roles at LSA, The Kelsey Group and TechTV. Follow him Twitter or find him on LinkedIn.



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