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Keyword Match Types Still Matter (Phrase & Exact Match Are Not Obsolete)

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2 years ago

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Keyword Match Types Still Matter (Phrase & Exact Match Are Not Obsolete)

A powerful tool for managing ad spend, keyword match types help advertisers tailor their ad campaigns to the most relevant audience, thus bringing in the right traffic and ultimately leading to higher conversion rates.

With the advent of more advanced targeting options, some marketers have begun to question whether keyword match types still matter.

This article will break down the historical background of match types and why they still play an important role in paid search campaigns today.

Quick Background To How We Got To Our Current State

When I started in this industry, Yahoo! was the dominant search engine. It had just two match types (standard and advanced), while Google had what we currently have (exact, phrase, and broad; although for seven years, they also had broad match modified).

When Bing (because I still refuse to call it Microsoft Advertising) separated completely from Yahoo! in the mid/late 2000s, it had the same setup as Google.

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Yahoo! would eventually move to the three-match types when it went from Overture to Panama (yes, I am old), and then again when it launched Gemini (God willing, that never comes back!).

Until recent years, there was always an emphasis that exact match was the most accurate to the query, followed by phrase, then broad match modified (while it was around), and broad (which was kind of a crapshoot).

But as things evolved, close match variants as a standalone function and broad match modified went the way of Old Yeller.

In addition to this, around 2018, exact match became much looser and started to feel like a combo for phrase match and broad match modified. Needless to say, the industry masses did not receive that info well.

As Google, quite possibly for the first time, used the term “keywordless AI” in February 2023, marketers are questioning the validity of match types moving forward.

People Didn’t Take Match Type Changes Well (I Saw This At A Google Event After The Announcement)

For years, big and sophisticated operations condemned or sparingly used broad match, often due to its lower Quality Score keywords.

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Advertisers almost always used exact and phrase match, duplicating the keywords in both match types and giving exact the highest bid, then phrase.

Some would also use broad, but giving it the lowest bid (to minimize risks), primarily to harvest insights from the Search Query Reports and make robust negative keyword lists (I still practice this today).

Pro-tip that is still relevant today: Never use Dynamic Keyword Insertion (DKI) in ad groups with broad match keywords.

I should note that, at the 2018 SMX West, James Svoboda of WebRanking blew my mind with a hybrid match type combining broad match modified with phrase match in a single keyword. Alas, that is no longer possible.

Remember, this history ignores “keywordless” search – Shopping (formerly PLA’s), Dynamic Search Ads, Local Service Ads, or Local Search; most shopper marketing platforms or niche/unique search engines, such as Yelp. Not to mention, it predates the questionable Performance Max.

Why Do Match Types Matter, If We’re Trending Toward Keywordless Search?

I’m glad you asked me that question, as I’ve been wearing my tinfoil hat for years on this.

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My only mildly proven claim is that big search (a new phrase I hope catches on) is trying to eliminate our control by getting away from the traditional keyword approach to make more money. I realize the band-aid hasn’t been ripped off (yet), and we still have some degree of control in keyword-focused search.

Therefore, focusing on match types is both relevant and important.

Some straightforward and simple answers (however, this is not all-encompassing for everyone, yet) are:

  • Shopping does not apply to all advertisers.
  • Not all advertisers have YouTube assets (and don’t want the engines creating the videos for them because they are a bit cringy).
  • Performance Max is expanding. It can be manipulated but still isn’t necessarily applicable to all advertisers.
  • Not all advertisers want to display imagery or placements in rotation (for various reasons).
  • Many advertisers want control of the spend and the ability to report based on where their ads appear.

The truth is, for various reasons, many advertisers just want to show for certain keywords in search and not much else. And “keywordless” efforts really just do not show that.

Screenshot by author, March 2023

There’s no true way to tell where your Performance Max ads show.

So, Why Do Phrase And Exact Match Still Matter?

Despite the neutering changes to them by big search, phrase and exact match still hold power.

When it comes to keyword-based search, exact match keywords still tend to hold the greatest relevancy (and thus Quality Score) to a search query.

Leading to a more cost-efficient cost per click (CPC) – with phrase match not far behind.

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Exact is cheaper, as QS actually populates more often than PhraseScreenshot by author, March 2023

Exact is cheaper, as Quality Score actually populates it more often than phrase.

The Single Keyword Ad Groups (SKAG) model is largely a dead model for bidding; the need and usage for curated and tight-knit ad groups are still very much necessary.

Typically, this can only be achieved via phrase and exact match, as broad match is more or less a game of Russian Roulette (while I can’t explain why, it is definitely a riskier gamble using broad match in Bing than in Google).

The next need for these match types and why they are so important is often overlooked: budget cannibalization.

Budget cannibalization, in its simplest terms, means this: You have a single pool of money that everyone can take from, with little to no restriction. So, instead of everyone getting an equal share of the funds, whoever takes it the fastest will get the most.

Keywordless search bids on a user query are relevant to the website – not a specific keyword you’re specifically looking to pay for.

This essentially means a high-volume search query can steal the budget from a mid/low-volume search query, preventing an advertiser from showing for both.

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Basically, think of “keywordless” and, to a degree, broad match as a mash-up of your brand and non-brand or high volume and low volume smashed together in a single ad group. Someone is going to get the short end of the stick.

So, while you may bid on “everything” with a keywordless search campaign, odds are the non-brand and/or high-volume queries account for the bulk of the spend.

Other potential queries you could show for (long tail, brand, mid/low volume, etc.) are not given an appropriate amount of budget to work with (or any at all).

Important note: Some of this is or will soon be controlled with campaign-level negatives in Performance Max (already applicable to shopping).

Thus, if you want to ensure your important keywords (i.e., brand, high volume/higher converting, etc.), a keyword-based search program consisting of phrase and/or exact match remains 100% necessary.

Making it a stand-alone campaign (I still love doing match-type isolation at the ad group level as well) ensures specific keywords or queries aren’t going to have to fight to get funds to trigger.

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They will have a separate stand-alone budget for it. (And before you ask, no, a shared campaign budget will not help you in this scenario, no matter how much you think it might.)

The Takeaway

When all is said and done, here is what should be truly taken away from this article:

  • Big search is pushing hard to a “keywordless” search advertising world.
  • Keywordless search, while streamlining, lacks control and transparency, leading to cannibalization.
  • Current-day broad match isn’t much better than “keywordless” search.
  • Phrase and exact match comprised ad groups are the only way to be sure you are bidding on your intended query and minimizing the lack of transparency.
  • Lastly, because I failed to mention it anywhere earlier: The most important match type of all is negative match.

More Resources:

Featured Image: Just dance/Shutterstock



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How Compression Can Be Used To Detect Low Quality Pages

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5 days ago

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October 27, 2024

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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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6 days ago

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October 27, 2024

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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

Featured Image by Shutterstock/Luis Molinero

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All the best things about Ahrefs Evolve 2024

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1 week ago

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October 25, 2024

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All the best things about Ahrefs Evolve 2024

Hey all, I’m Rebekah and I am your Chosen One to “do a blog post for Ahrefs Evolve 2024”.

What does that entail exactly? I don’t know. In fact, Sam Oh asked me yesterday what the title of this post would be. “Is it like…Ahrefs Evolve 2024: Recap of day 1 and day 2…?” 

Even as I nodded, I couldn’t get over how absolutely boring that sounded. So I’m going to do THIS instead: a curation of all the best things YOU loved about Ahrefs’ first conference, lifted directly from X.

Let’s go!

OUR HUGE SCREEN

CONFERENCE VENUE ITSELF

It was recently named the best new skyscraper in the world, by the way.

 

OUR AMAZING SPEAKER LINEUP – SUPER INFORMATIVE, USEFUL TALKS!

 

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GREAT MUSIC

 

AMAZING GOODIES

 

SELFIE BATTLE

Some background: Tim and Sam have a challenge going on to see who can take the most number of selfies with all of you. Last I heard, Sam was winning – but there is room for a comeback yet!

 

THAT BELL

Everybody’s just waiting for this one.

 

STICKER WALL

AND, OF COURSE…ALL OF YOU!

 

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–

There’s a TON more content on LinkedIn – click here – but I have limited time to get this post up and can’t quite figure out how to embed LinkedIn posts so…let’s stop here for now. I’ll keep updating as we go along!



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