SEO
Google’s “Information Gain” Patent For Ranking Web Pages
Google was recently granted a patent on ranking web pages, which may offer insights into how AI Overviews ranks content. The patent describes a method for ranking pages based on what a user might be interested in next.
Contextual Estimation Of Link Information Gain
The name of the patent is Contextual Estimation Of Link Information Gain, it was filed in 2018 and granted in June 2024. It’s about calculating a ranking score called Information Gain that is used to rank a second set of web pages that are likely to be of interest to a user as a slightly different follow-up topic related to a previous question.
The patent starts with general descriptions then adds layers of specifics over the course of paragraphs. An analogy can be that it’s like a pizza. It starts out as a mozzarella pizza, then they add mushrooms, so now it’s a mushroom pizza. Then they add onions, so now it’s a mushroom and onion pizza. There are layers of specifics that build up to the entire context.
So if you read just one section of it, it’s easy to say, “It’s clearly a mushroom pizza” and be completely mistaken about what it really is.
There are layers of context but what it’s building up to is:
- Ranking a web page that is relevant for what a user might be interested in next.
- The context of the invention is an automated assistant or chatbot
- A search engine plays a role in a way that seems similar to Google’s AI Overviews
Information Gain And SEO: What’s Really Going On?
A couple of months ago I read a comment on social media asserting that “Information Gain” was a significant factor in a recent Google core algorithm update. That mention surprised me because I’d never heard of information gain before. I asked some SEO friends about it and they’d never heard of it either.
What the person on social media had asserted was something like Google was using an “Information Gain” score to boost the ranking of web pages that had more information than other web pages. So the idea was that it was important to create pages that have more information than other pages, something along those lines.
So I read the patent and discovered that “Information Gain” is not about ranking pages with more information than other pages. It’s really about something that is more profound for SEO because it might help to understand one dimension of how AI Overviews might rank web pages.
TL/DR Of The Information Gain Patent
What the information gain patent is really about is even more interesting because it may give an indication of how AI Overviews (AIO) ranks web pages that a user might be interested next. It’s sort of like introducing personalization by anticipating what a user will be interested in next.
The patent describes a scenario where a user makes a search query and the automated assistant or chatbot provides an answer that’s relevant to the question. The information gain scoring system works in the background to rank a second set of web pages that are relevant to a what the user might be interested in next. It’s a new dimension in how web pages are ranked.
The Patent’s Emphasis on Automated Assistants
There are multiple versions of the Information Gain patent dating from 2018 to 2024. The first version is similar to the last version with the most significant difference being the addition of chatbots as a context for where the information gain invention is used.
The patent uses the phrase “automated assistant” 69 times and uses the phrase “search engine” only 25 times. Like with AI Overviews, search engines do play a role in this patent but it’s generally in the context of automated assistants.
As will become evident, there is nothing to suggest that a web page containing more information than the competition is likelier to be ranked higher in the organic search results. That’s not what this patent talks about.
General Description Of Context
All versions of the patent describe the presentation of search results within the context of an automated assistant and natural language question answering. The patent starts with a general description and progressively becomes more specific. This is a feature of patents in that they apply for protection for the widest contexts in which the invention can be used and become progressively specific.
The entire first section (the Abstract) doesn’t even mention web pages or links. It’s just about the information gain score within a very general context:
“An information gain score for a given document is indicative of additional information that is included in the document beyond information contained in documents that were previously viewed by the user.”
That is a nutshell description of the patent, with the key insight being that the information gain scoring happens on pages after the user has seen the first search results.
More Specific Context: Automated Assistants
The second paragraph in the section titled “Background” is slightly more specific and adds an additional layer of context for the invention because it mentions links. Specifically, it’s about a user that makes a search query and receives links to search results – no information gain score calculated yet.
The Background section says:
“For example, a user may submit a search request and be provided with a set of documents and/or links to documents that are responsive to the submitted search request.”
The next part builds on top of a user having made a search query:
“Also, for example, a user may be provided with a document based on identified interests of the user, previously viewed documents of the user, and/or other criteria that may be utilized to identify and provide a document of interest. Information from the documents may be provided via, for example, an automated assistant and/or as results to a search engine. Further, information from the documents may be provided to the user in response to a search request and/or may be automatically served to the user based on continued searching after the user has ended a search session.”
That last sentence is poorly worded.
Here’s the original sentence:
“Further, information from the documents may be provided to the user in response to a search request and/or may be automatically served to the user based on continued searching after the user has ended a search session.”
Here’s how it makes more sense:
“Further, information from the documents may be provided to the user… based on continued searching after the user has ended a search session.”
The information provided to the user is “in response to a search request and/or may be automatically served to the user”
It’s a little clearer if you put parentheses around it:
Further, information from the documents may be provided to the user (in response to a search request and/or may be automatically served to the user) based on continued searching after the user has ended a search session.
Takeaways:
- The patent describes identifying documents that are relevant to the “interests of the user” based on “previously viewed documents” “and/or other criteria.”
- It sets a general context of an automated assistant “and/or” a search engine
- Information from the documents that are based on “previously viewed documents” “and/or other criteria” may be shown after the user continues searching.
More Specific Context: Chatbot
The patent next adds an additional layer of context and specificity by mentioning how chatbots can “extract” an answer from a web page (“document”) and show that as an answer. This is about showing a summary that contains the answer, kind of like featured snippets, but within the context of a chatbot.
The patent explains:
“In some cases, a subset of information may be extracted from the document for presentation to the user. For example, when a user engages in a spoken human-to-computer dialog with an automated assistant software process (also referred to as “chatbots,” “interactive personal assistants,” “intelligent personal assistants,” “personal voice assistants,” “conversational agents,” “virtual assistants,” etc.), the automated assistant may perform various types of processing to extract salient information from a document, so that the automated assistant can present the information in an abbreviated form.
As another example, some search engines will provide summary information from one or more responsive and/or relevant documents, in addition to or instead of links to responsive and/or relevant documents, in response to a user’s search query.”
The last sentence sounds like it’s describing something that’s like a featured snippet or like AI Overviews where it provides a summary. The sentence is very general and ambiguous because it uses “and/or” and “in addition to or instead of” and isn’t as specific as the preceding sentences. It’s an example of a patent being general for legal reasons.
Ranking The Next Set Of Search Results
The next section is called the Summary and it goes into more details about how the Information Gain score represents how likely the user will be interested in the next set of documents. It’s not about ranking search results, it’s about ranking the next set of search results (based on a related topic).
It states:
“An information gain score for a given document is indicative of additional information that is included in the given document beyond information contained in other documents that were already presented to the user.”
Ranking Based On Topic Of Web Pages
It then talks about presenting the web page in a browser, audibly reading the relevant part of the document or audibly/visually presenting a summary of the document (“audibly/visually presenting salient information extracted from the document to the user, etc.”)
But the part that’s really interesting is when it next explains using a topic of the web page as a representation of the the content, which is used to calculate the information gain score.
It describes many different ways of extracting the representation of what the page is about. But what’s important is that it’s describes calculating the Information Gain score based on a representation of what the content is about, like the topic.
“In some implementations, information gain scores may be determined for one or more documents by applying data indicative of the documents, such as their entire contents, salient extracted information, a semantic representation (e.g., an embedding, a feature vector, a bag-of-words representation, a histogram generated from words/phrases in the document, etc.) across a machine learning model to generate an information gain score.”
The patent goes on to describe ranking a first set of documents and using the Information Gain scores to rank additional sets of documents that anticipate follow up questions or a progression within a dialog of what the user is interested in.
The automated assistant can in some implementations query a search engine and then apply the Information Gain rankings to the multiple sets of search results (that are relevant to related search queries).
There are multiple variations of doing the same thing but in general terms this is what it describes:
“Based on the information gain scores, information contained in one or more of the new documents may be selectively provided to the user in a manner that reflects the likely information gain that can be attained by the user if the user were to be presented information from the selected documents.”
What All Versions Of The Patent Have In Common
All versions of the patent share general similarities over which more specifics are layered in over time (like adding onions to a mushroom pizza). The following are the baseline of what all the versions have in common.
Application Of Information Gain Score
All versions of the patent describe applying the information gain score to a second set of documents that have additional information beyond the first set of documents. Obviously, there is no criteria or information to guess what the user is going search for when they start a search session. So information gain scores are not applied to the first search results.
Examples of passages that are the same for all versions:
- A second set of documents is identified that is also related to the topic of the first set of documents but that have not yet been viewed by the user.
- For each new document in the second set of documents, an information gain score is determined that is indicative of, for the new document, whether the new document includes information that was not contained in the documents of the first set of documents…
Automated Assistants
All four versions of the patent refer to automated assistants that show search results in response to natural language queries.
The 2018 and 2023 versions of the patent both mention search engines 25 times. The 2o18 version mentions “automated assistant” 74 times and the latest version mentions it 69 times.
They all make references to “conversational agents,” “interactive personal assistants,” “intelligent personal assistants,” “personal voice assistants,” and “virtual assistants.”
It’s clear that the emphasis of the patent is on automated assistants, not the organic search results.
Dialog Turns
Note: In everyday language we use the word dialogue. In computing they the spell it dialog.
All versions of the patents refer to a way of interacting with the system in the form of a dialog, specifically a dialog turn. A dialog turn is the back and forth that happens when a user asks a question using natural language, receives an answer and then asks a follow up question or another question altogether. This can be natural language in text, text to speech (TTS), or audible.
The main aspect the patents have in common is the back and forth in what is called a “dialog turn.” All versions of the patent have this as a context.
Here’s an example of how the dialog turn works:
“Automated assistant client 106 and remote automated assistant 115 can process natural language input of a user and provide responses in the form of a dialog that includes one or more dialog turns. A dialog turn may include, for instance, user-provided natural language input and a response to natural language input by the automated assistant.
Thus, a dialog between the user and the automated assistant can be generated that allows the user to interact with the automated assistant …in a conversational manner.”
Problems That Information Gain Scores Solve
The main feature of the patent is to improve the user experience by understanding the additional value that a new document provides compared to documents that a user has already seen. This additional value is what is meant by the phrase Information Gain.
There are multiple ways that information gain is useful and one of the ways that all versions of the patent describes is in the context of an audio response and how a long-winded audio response is not good, including in a TTS (text to speech) context).
The patent explains the problem of a long-winded response:
“…and so the user may wait for substantially all of the response to be output before proceeding. In comparison with reading, the user is able to receive the audio information passively, however, the time taken to output is longer and there is a reduced ability to scan or scroll/skip through the information.”
The patent then explains how information gain can speed up answers by eliminating redundant (repetitive) answers or if the answer isn’t enough and forces the user into another dialog turn.
This part of the patent refers to the information density of a section in a web page, a section that answers the question with the least amount of words. Information density is about how “accurate,” “concise,” and “relevant”‘ the answer is for relevance and avoiding repetitiveness. Information density is important for audio/spoken answers.
This is what the patent says:
“As such, it is important in the context of an audio output that the output information is relevant, accurate and concise, in order to avoid an unnecessarily long output, a redundant output, or an extra dialog turn.
The information density of the output information becomes particularly important in improving the efficiency of a dialog session. Techniques described herein address these issues by reducing and/or eliminating presentation of information a user has already been provided, including in the audio human-to-computer dialog context.”
The idea of “information density” is important in a general sense because it communicates better for users but it’s probably extra important in the context of being shown in chatbot search results, whether it’s spoken or not. Google AI Overviews shows snippets from a web page but maybe more importantly, communicating in a concise manner is the best way to be on topic and make it easy for a search engine to understand content.
Search Results Interface
All versions of the Information Gain patent are clear that the invention is not in the context of organic search results. It’s explicitly within the context of ranking web pages within a natural language interface of an automated assistant and an AI chatbot.
However, there is a part of the patent that describes a way of showing users with the second set of results within a “search results interface.” The scenario is that the user sees an answer and then is interested in a related topic. The second set of ranked web pages are shown in a “search results interface.”
The patent explains:
“In some implementations, one or more of the new documents of the second set may be presented in a manner that is selected based on the information gain stores. For example, one or more of the new documents can be rendered as part of a search results interface that is presented to the user in response to a query that includes the topic of the documents, such as references to one or more documents. In some implementations, these search results may be ranked at least in part based on their respective information gain scores.”
…The user can then select one of the references and information contained in the particular document can be presented to the user. Subsequently, the user may return to the search results and the references to the document may again be provided to the user but updated based on new information gain scores for the documents that are referenced.
In some implementations, the references may be reranked and/or one or more documents may be excluded (or significantly demoted) from the search results based on the new information gain scores that were determined based on the document that was already viewed by the user.”
What is a search results interface? I think it’s just an interface that shows search results.
Let’s pause here to underline that it should be clear at this point that the patent is not about ranking web pages that are comprehensive about a topic. The overall context of the invention is showing documents within an automated assistant.
A search results interface is just an interface, it’s never described as being organic search results, it’s just an interface.
There’s more that is the same across all versions of the patent but the above are the important general outlines and context of it.
Claims Of The Patent
The claims section is where the scope of the actual invention is described and for which they are seeking legal protection over. It is mainly focused on the invention and less so on the context. Thus, there is no mention of a search engines, automated assistants, audible responses, or TTS (text to speech) within the Claims section. What remains is the context of search results interface which presumably covers all of the contexts.
Context: First Set Of Documents
It starts out by outlining the context of the invention. This context is receiving a query, identifying the topic, and ranking a first group of relevant web pages (documents) and selecting at least one of them as being relevant and either showing the document or communicating the information from the document (like a summary).
“1. A method implemented using one or more processors, comprising: receiving a query from a user, wherein the query includes a topic; identifying a first set of documents that are responsive to the query, wherein the documents of the set of documents are ranked, and wherein a ranking of a given document of the first set of documents is indicative of relevancy of information included in the given document to the topic; selecting, based on the rankings and from the documents of the first set of documents, a most relevant document providing at least a portion of the information from the most relevant document to the user;”
Context: Second Set Of Documents
Then what immediately follows is the part about ranking a second set of documents that contain additional information. This second set of documents is ranked using the information gain scores to show more information after showing a relevant document from the first group.
This is how it explains it:
“…in response to providing the most relevant document to the user, receiving a request from the user for additional information related to the topic; identifying a second set of documents, wherein the second set of documents includes at one or more of the documents of the first set of documents and does not include the most relevant document; determining, for each document of the second set, an information gain score, wherein the information gain score for a respective document of the second set is based on a quantity of new information included in the respective document of the second set that differs from information included in the most relevant document; ranking the second set of documents based on the information gain scores; and causing at least a portion of the information from one or more of the documents of the second set of documents to be presented to the user, wherein the information is presented based on the information gain scores.”
Granular Details
The rest of the claims section contains granular details about the concept of Information Gain, which is a ranking of documents based on what the user already has seen and represents a related topic that the user may be interested in. The purpose of these details is to lock them in for legal protection as part of the invention.
Here’s an example:
The method of claim 1, wherein identifying the first set comprises:
causing to be rendered, as part of a search results interface that is presented to the user in response to a previous query that includes the topic, references to one or more documents of the first set;
receiving user input that that indicates selection of one of the references to a particular document of the first set from the search results interface, wherein at least part of the particular document is provided to the user in response to the selection;
To make an analogy, it’s describing how to make the pizza dough, clean and cut the mushrooms, etc. It’s not important for our purposes to understand it as much as the general view of what the patent is about.
Information Gain Patent
An opinion was shared on social media that this patent has something to do with ranking web pages in the organic search results, I saw it, read the patent and discovered that’s not how the patent works. It’s a good patent and it’s important to correctly understand it. I analyzed multiple versions of the patent to see what they had in common and what was different.
A careful reading of the patent shows that it is clearly focused on anticipating what the user may want to see based on what they have already seen. To accomplish this the patent describes the use of an Information Gain score for ranking web pages that are on topics that are related to the first search query but not specifically relevant to that first query.
The context of the invention is generally automated assistants, including chatbots. A search engine could be used as part of finding relevant documents but the context is not solely an organic search engine.
This patent could be applicable to the context of AI Overviews. I would not limit the context to AI Overviews as there are additional contexts such as spoken language in which Information Gain scoring could apply. Could it apply in additional contexts like Featured Snippets? The patent itself is not explicit about that.
Read the latest version of Information Gain patent:
Contextual estimation of link information gain
Featured Image by Shutterstock/Khosro
SEO
16 Essential Paid SEO Tools That Are Worth the Money
SEO professionals know the value of investing in the right tools. While free SEO tools have their place, some paid options offer more features and deeper insights.
This article examines 16 paid-for SEO tools that we think you may find are worth the investment, depending on your needs. We’ll highlight key features, discuss use cases, and offer tips to make the most of them.
Our Top 16 Paid SEO Tools
1. Semrush
Semrush is an all-in-one SEO toolkit ideal for handling large amounts of data.
In practice, we’ve used Semrush for keyword research. The Keyword Magic Tool helped surface long-tail phrases with lower competition.
It also assists with competitor analysis, making it possible to reverse engineer top-ranking pages.
Semrush is a valuable tool for SEO professionals due to its comprehensive features and extensive data. Its keyword database is among the largest in the industry, providing users with a wealth of information for content and optimization strategies.
One of Semrush’s key strengths lies in its competitive analysis capabilities. You access data on rivals’ top-performing pages, target keywords, and backlink profiles.
The platform’s intuitive design ensures that even those new to SEO can navigate its various tools and features, making it accessible to professionals at different experience levels.
Potential Drawbacks:
- Higher price point compared to other tools.
- It can be overwhelming for beginners due to the wealth of data and features.
Tips And Tricks:
- Use the Keyword Magic Tool’s “Questions” filter to find long-tail keyword opportunities.
- Set up weekly site audits to stay on top of technical SEO issues.
- Leverage the “Backlink Gap” tool to uncover link building opportunities based on competitor analysis.
Best Hack:
- Create a custom “Content Template” in Semrush’s Content Marketing Toolkit. Enter your target keyword, and Semrush will analyze top-ranking pages to provide recommendations.
Price Range:
- $139.95 – $499.95 per month.
2. Ahrefs
Ahrefs is known for its extensive backlink database, which is updated every 15 minutes.
We find Ahrefs shines at link building, and regularly use Site Explorer to analyze competitors’ backlinks and identify new opportunities.
Screenshot from Ahrefs, September 2024We also appreciate the “Link Intersect” feature, which shows sites that link to multiple competitors but not to you (yet).
Ahrefs has established itself as a go-to SEO tool, renowned for its backlink database. This resource is considered one of the best in the industry.
The platform’s Site Explorer feature offers a comprehensive view of any website’s SEO performance. This tool allows for in-depth analysis of various metrics, helping professionals understand both their sites and those of competitors.
Ahrefs’ Keywords Explorer is another standout feature, offering robust keyword research capabilities. This tool lets you uncover keyword opportunities, analyze search trends, and refine content strategies.
Potential Drawbacks:
- Steeper learning curve compared to some other tools.
- Higher price point.
Tips And Tricks:
- Use Content Explorer to find top-performing content in your niche and analyze what makes it successful.
- Filter for followed links in Site Explorer to focus on the most valuable backlink opportunities.
- Set up Rank Tracker to monitor your target keywords and get alerted to position changes.
Best Hack:
- Leverage Ahrefs’ “Content Gap” feature to find keywords your competitors rank for but you don’t. Plug in multiple competitors to surface keyword opportunities you may be missing.
Price Range:
- Starter plan for $29 per month.
- $129 – $449 per month.
3. Moz Pro
Moz Pro is an SEO platform offering keyword research, link analysis, site audits, rank tracking, and on-page optimization.
We’ve used Moz Pro for on-page SEO. The On-Page Grader provides actionable recommendations, ensuring pages are optimized for search engines and users.
Screenshot from Moz Pro, September 2024Moz Pro has carved out a niche in the SEO industry by offering a user-friendly platform catering to beginners and experienced professionals.
One of Moz Pro’s standout features is its comprehensive on-page optimization toolkit. It provides insights and recommendations for improving your website’s content, structure, and overall SEO performance.
The platform is also known for providing reliable and accurate data. Moz Pro’s metrics and reports are widely trusted within the industry.
Potential Drawbacks:
- Some features aren’t as comprehensive as specialized tools (e.g., backlink data vs. Ahrefs).
- Monthly limits on certain features like keyword queries.
Tips And Tricks:
- Use MozBar (free Chrome extension) for on-the-go metrics while browsing the web.
- Leverage the “SERP Analysis” feature in Keyword Explorer to understand the competitive landscape for target keywords.
- Set up custom reports to track key metrics like keyword rankings, link growth, and site crawl issues.
Best Hack:
- Run your site through Moz Pro’s “Site Crawl” and prioritize the identified issues by “High,” “Medium,” and “Low” impact. Focus on resolving the “High” impact issues first.
Price Range:
4. Majestic
Majestic is renowned for its backlink database, making it a go-to tool for link analysis and acquisition.
We’ve used Majestic for backlink audits. The “Backlinks” tab in Site Explorer shows your site’s link profile, allowing you to identify potentially toxic links and spot opportunities.
Screenshot from Majestic, September 2024Majestic has made a name in the SEO industry primarily due to its extensive backlink database. This resource provides current link data and offers historical information.
The platform stands out with its proprietary link quality metrics: Trust Flow and Citation Flow. These unique indicators offer a nuanced view of a website’s link profile quality.
Majestic also excels in comparative analysis. Its tools let you compare link profiles of multiple websites side by side.
Potential Drawbacks:
- User interface can be less intuitive compared to some other tools.
- Primarily focused on link data.
Tips and Tricks:
- Use the “Clique Hunter” to find sites that link to multiple competitors but not to you.
- Analyze linking sites’ “Topical Trust Flow” to ensure relevance and authority.
- Set up the “Link Alerts” feature to be notified of new backlinks to your site (or your competitors).
Best Hack:
- Use Majestic’s “Link Context” feature to see a backlink’s exact placement and surrounding text. This can provide valuable insights and help you craft pitches for outreach emails.
Price Range:
- $49.99 – $399.99 per month.
5. Screaming Frog SEO Spider
Screaming Frog is a tool for crawling websites and uncovering technical SEO issues.
It’s ideal for site audits. By crawling a site and exporting the data to Excel, you can spot and fix technical SEO issues at scale.
Screenshot from Screaming Frog, September 2024Screaming Frog is recognized for its powerful site crawling capabilities. Its speed and efficiency make it an invaluable asset for technical SEO audits and routine site checks.
The software excels in delivering comprehensive analysis of on-page SEO elements. It examines factors such as meta tags, headings, content, and internal linking structure.
One of Screaming Frog’s key strengths is its high level of customization. You can tailor crawl settings, configure custom extraction rules, and set up specific filters to focus on particular aspects of the website.
Potential Drawbacks:
- Requires some technical knowledge to leverage its capabilities fully.
- It can be resource-intensive for very large sites.
Tips And Tricks:
- Use the “Custom Extraction” feature to pull specific data points at scale.
- Integrate with Google Analytics to pull performance metrics into your crawl data.
- Leverage the “Visualization” feature to generate sitemaps and spot orphaned pages.
Best Hack:
- Set up a custom “Search” filter to identify pages with specific issues (e.g., missing meta descriptions, duplicate page titles). Export this list and use it as a prioritized action plan for your on-page optimizations.
Price Range:
6. Serpstat
Serpstat is an all-in-one SEO platform with features for keyword research, competitor analysis, backlink analysis, and site audits.
We found Serpstat effective for keyword clustering and topic modeling. By analyzing semantically related keywords and their search intent, you can create targeted content that better answers user queries.
Screenshot from Serpstat, September 2024Why It’s Good For SEO:
- Broad feature set covering multiple aspects of SEO.
- Strong keyword research capabilities, including related questions and search suggestions.
- Affordable pricing compared to some other all-in-one platforms.
Potential Drawbacks:
- User interface can be overwhelming initially due to the many features.
- Some advanced features (e.g., API access) are limited to higher-tier plans.
Tips And Tricks:
- Use the “Tree View” in Keyword Research to visualize keyword relationships and create topic clusters.
- Leverage the “Missing Keywords” feature in Competitor Analysis to identify quick-win opportunities.
- Set up regular “Site Audits” to monitor your technical SEO health over time.
Best Hack:
- Use Serpstat’s “Content Marketing” tool to analyze top-ranking pages to inform your own content creation and optimization.
Price Range:
7. CognitiveSEO
CognitiveSEO is a tool suite focusing on backlink analysis and link building.
In practice, we found CognitiveSEO helpful for link profile cleanups. The “Unnatural Link Detection” feature uses machine learning to identify potentially toxic links, saving time in the disavow process.
Screenshot from CognitiveSEO, September 2024Serpstat’s all-in-one approach allows users to manage multiple SEO tasks within a single tool, from keyword research to competitor analysis and rank tracking.
The platform’s keyword research capabilities are noteworthy. Serpstat provides extensive data on search terms, including related questions and search suggestions.
One of Serpstat’s key selling points is its competitive pricing structure. The tool offers a robust feature set at a more affordable price than other all-in-one platforms.
Potential Drawbacks:
- The primary focus is on link analysis (less comprehensive for technical SEO or content optimization).
- Higher price point compared to some other specialized link analysis tools.
Tips And Tricks:
- Use the “Link Velocity” graph to spot unnatural spikes in link acquisition that could trigger a manual review.
- Leverage the “Link Juice” metric to prioritize high-impact link opportunities.
- Set up alerts for new and lost links to your site (and competitors).
Best Hack:
- Take advantage of CognitiveSEO’s “Link Explorer” bookmarklet. When browsing the web, you can quickly analyze any page’s backlink profile without manually entering the URL into the tool.
Price Range:
- $129.99 – $499 per month.
8. Advanced Web Ranking
Advanced Web Ranking (AWR) is a rank-tracking and reporting tool that helps you monitor your search engine positions across multiple locations and devices.
AWR is used extensively for tracking and reporting client rank. The tool’s ability to track rankings at a granular level (e.g., by city or zip code) and generate custom-branded reports help demonstrate SEO progress and value.
Screenshot from Advanced Web Rankng, September 2024AWR is known for its precise and dependable rank tracking capabilities, allowing users to monitor their SEO performance confidently.
AWR excels in tracking rankings across various geographical locations and search engines.
The platform’s integration with Google Analytics and Google Search Console allows users to create comprehensive reports that combine ranking data with website traffic and performance metrics.
Potential Drawbacks:
- Primarily focused on rank tracking.
- Higher price point compared to some other rank tracking tools.
Tips And Tricks:
- Use the “Keyword Groups” feature to organize your tracked keywords by theme or strategy.
- Set up automated reports to inform clients or stakeholders of ranking progress.
- Leverage the “Competitor Benchmarking” feature to identify opportunities and threats.
Best Hack:
- Use AWR’s landing page report to identify which pages drive the most organic traffic and rankings. This can help prioritize your SEO efforts and replicate success across other pages.
Price Range:
9. Mangools
Mangools is a suite of SEO tools designed for simplicity and ease of use.
While it may lack some of the advanced features of other platforms, its intuitive interface makes it a great option for beginners or those who prefer a more streamlined toolkit.
Mangools is particularly useful for local SEO. KWFinder’s location-based keyword suggestions and SERP analysis make it easy to identify local search opportunities and optimize for regional keywords.
Screenshot from Mangools, September 2024Mangools offers a suite of straightforward features designed to be accessible to users of all skill levels.
One of Mangools’ standout offerings is KWFinder, its keyword research tool. KWFinder discovers and analyzes keywords, helping users identify valuable content creation and optimization opportunities.
Mangools sets itself apart with its competitive pricing structure and flexible plans. The platform offers various subscription options to suit different needs and budgets.
Potential Drawbacks:
- Fewer advanced features compared to more comprehensive SEO platforms.
- Limited data for certain tools (e.g., backlink database not as extensive as some competitors).
Tips And Tricks:
- Use KWFinder’s “Autocomplete” and “Questions” features to uncover long-tail keyword opportunities.
- Leverage SERPChecker’s “Domain Strength” metric to gauge the competitiveness of a SERP.
- Set up automated reports in SERPWatcher to keep stakeholders informed of ranking progress.
Best Hack:
- Utilize Mangools’ “SEO Browser Extension” to get quick, on-the-fly metrics while browsing the web. You can see key data points like search volume, CPC, and SERP snapshot for any keyword, right from your browser.
Price Range:
10. Conductor
Conductor is an enterprise SEO and content marketing platform that expanded its capabilities in 2023 by acquiring European competitor Searchmetrics. The combined company provides a comprehensive SEO solution on a global scale.
Screenshot from Conductor, October 2024.The core Conductor platform offers tools for keyword research, rank tracking, site auditing, and performance reporting.
Searchmetrics bolstered Conductor’s feature set with additional capabilities such as content optimization insights and backlink data.
In practice, the unified platform can assist throughout the full optimization workflow.
Potential Drawbacks:
- Higher price point befitting an enterprise-grade platform.
- Initial learning curve due to the breadth of features.
Tips and Tricks:
- Leverage Searchmetrics’ content optimization tools to reverse-engineer top results.
- Set up Insights Streams for automated issue monitoring and alerts.
- Use the unified platform to seamlessly move between keyword research, content planning, technical audits, and reporting.
Best Hack:
- Connect Conductor’s API to your business intelligence tools, surfacing SEO insights across the organization to better inform marketing and product decisions.
Price Range:
- Varies by usage (free trial available).
11. Yoast SEO (Premium)
Yoast SEO is a popular WordPress plugin that helps optimize your website’s content for search engines and readability. While the plugin offers a free version, the Premium version includes additional features.
Yoast is invaluable for optimizing blog posts and landing pages.
The Premium version’s ability to optimize for multiple keywords and provide internal linking suggestions has helped us create more comprehensive content.
Screenshot from Yoast SEO, September 2024Due to its seamless integration, Yoast SEO has become a staple tool for WordPress users. This plugin offers on-page optimization features directly within the WordPress interface.
One of Yoast SEO’s key strengths is its dual focus on SEO and content readability. The tool provides real-time feedback on SEO factors while offering suggestions to improve readability.
The plugin offers specific, easy-to-implement suggestions for improving various aspects of on-page SEO, making it easy to use regardless of skill level.
Potential Drawbacks:
- Limited to WordPress websites.
- Some advanced SEO tasks may require additional tools.
Tips And Tricks:
- Use the “Readability Analysis” to ensure your content is engaging and easy to read.
- Leverage the “Internal Linking Suggestions” to boost your site’s link equity and topical relevance.
- Optimize for semantic variations by using the “Multiple Focus Keywords” feature.
Best Hack:
- Use Yoast’s “Schema” feature to add structured data markup to your content. This can help your pages stand out in the search results with rich snippets.
Price Range:
12. Woorank
Woorank is a web-based SEO audit and monitoring tool that provides insights and recommendations for improving your website’s search visibility.
Woorank excels at quick, high-level SEO audits. The tool’s user-friendly interface and actionable recommendations make it easy to identify and prioritize key SEO tasks, even for newcomers.
Screenshot from Woorank, September 2024Woorank presents complex SEO concepts in a digestible format, providing straightforward recommendations you can readily understand and implement.
The tool provides a holistic view of a website’s SEO performance, allowing you to identify and address various aspects of your strategy.
Woorank’s pricing structure is designed to accommodate different needs and budgets. The platform offers flexible plans, making it accessible to small businesses and individual users.
Potential Drawbacks:
- Audit insights may not be as in-depth as some other specialized audit tools.
- Keyword data and backlink analysis may be less comprehensive than standalone tools.
Tips And Tricks:
- Use the “Priorities” feature to focus on the highest-impact optimization opportunities first.
- Leverage the “Keyword Tool” to identify long-tail keywords and content gaps.
- Set up weekly or monthly reports to track progress over time.
Best Hack:
- Utilize Woorank’s “Page-Level Analysis” to examine specific pages on your site to uncover opimization opportunities.
Price Range:
- $19.99 – $199.99 per month.
13. Ubersuggest
Ubersuggest is an affordable, all-in-one SEO tool that provides keyword suggestions, competitor analysis, and content ideas.
The tool aims to make SEO accessible to businesses of all sizes.
Screenshot from Ubersuggest, September 2024Ubersuggest has gained popularity in the SEO community largely due to its competitive pricing structure.
Despite its affordability, Ubersuggest delivers comprehensive SEO data and insights. Users can access information on keywords, backlinks, content ideas, and competitor analysis, all within a single platform.
One of Ubersuggest’s strengths lies in its ability to provide actionable recommendations. The tool doesn’t just present data; it offers specific suggestions for improving various aspects of SEO performance.
Potential Drawbacks:
- Data may not be as extensive or up-to-date as some other tools.
- Limited advanced features compared to more comprehensive platforms.
Tips And Tricks:
- Use the “Keyword Ideas” feature to uncover long-tail opportunities and content gaps.
- Leverage the “Top Pages” analysis to identify competitor content that’s performing well.
- Check the “SEO Analyzer” for quick, actionable insights on improving your pages.
Best Hack:
- Utilize Ubersuggest’s “Content Ideas” feature to generate blog post topics and outlines. The tool analyzes top-ranking content for your target keywords and provides suggestions for headings, word count, and related keywords to include.
Price Range:
14. Raven Tools
Raven Tools is an all-in-one SEO and digital marketing platform offering research, analysis, and reporting tools.
Raven Tools is particularly useful for SEO reporting. The custom report builder lets you pull data from multiple sources (e.g., Google Analytics, Google Search Console, social media) into a single, branded report, saving time and effort.
Screenshot from Raven Tools, September 2024Raven Tools covers various aspects of online marketing, including pay-per-click advertising, social media management, and content marketing. This all-in-one approach allows you to manage multiple facets of your digital strategy within a single platform.
Raven Tools integrates with a wide range of third-party tools and platforms. By centralizing information from different tools, Raven Tools helps streamline workflows and improve efficiency.
Potential Drawbacks:
- Some advanced features (e.g., competitor analysis) may not be as robust as standalone tools.
- Reporting features may be overkill for smaller teams or clients.
Tips And Tricks:
- Use the “Site Auditor” to regularly monitor your site for technical SEO issues and prioritize fixes.
- Set up “Automated Reports” to inform clients or stakeholders of key metrics and progress.
- Leverage the “Research Central” tool to access key SEO metrics while working on the platform.
Best Hack:
- Take advantage of Raven Tools’ integration of Google Analytics goals. You can report how your SEO efforts impact bottom-line metrics like conversions and revenue by syncing your GA goals with Raven.
Price Range:
15. Lumar
Lumar, previously known as Deepcrawl, focuses on website crawling and monitoring.
The tool identifies various technical SEO issues, including broken links, redirect chains, indexability problems, and other factors impacting search engine performance.
It also excels in accurately rendering and crawling JavaScript-heavy websites.
The platform offers customizable data extraction and reporting features, allowing you to tailor your analyses and reports to specific needs.
Screenshot from Lumar, October 2024.In practice, Lumar is considered invaluable for large-scale technical SEO audits. The automated crawling surfaces issues that would be difficult to catch manually, while monitoring ensures new problems are flagged as they pop up.
Potential Drawbacks:
- More narrow focus on technical issues compared to all-in-one SEO platforms.
- Higher pricing for an enterprise-grade crawling solution.
Tips and Tricks:
- Set up custom checks and extractions to hone in on your biggest tech SEO concerns.
- Integrate with analytics tools to correlate technical issues with performance impacts.
- Use the automated monitoring to validate fixes were implemented correctly.
Best Hack:
- Take advantage of Lumar’s data extraction capabilities. You can extract key page elements like titles, meta descriptions, etc. to cross-reference against SEO checklists or revise optimizations.
Price Range:
16. Sitebulb
Sitebulb is a desktop SEO tool that excels at in-depth technical audits and site crawls. It’s a go-to for agencies and freelancers needing detailed website analysis.
It extracts key page elements, integrates with analytics and rank tracking tools, and offers flexible reporting options.
Screenshot from Sitebulb, October 2024.Sitebulb’s real strength is its ability to present complex site issues in an accessible format, making it invaluable for pros conducting thorough website audits.
Its user-friendly interface coupled with powerful analysis capabilities makes it a standout choice for technical SEO work.
Potential Drawbacks:
- Desktop software requires local installation.
- More narrow specialization in technical crawling vs all-in-one SEO platforms.
Tips and Tricks:
- Leverage Sitebulb’s data extraction for exports of key on-page elements.
- Integrate with third-party tools for added context (e.g., rankings, analytics).
- Use the Project Comparison mode to track changes between crawls over time.
Best Hack:
- Take advantage of Sitebulb’s custom extraction hooks to pull in data from your own databases or APIs, enriching the crawl analysis.
Price Range:
- $13.50 – $245 per month.
What To Avoid When Choosing A Paid SEO Tool
When picking a paid SEO tool, keep an eye out for these potential issues:
- Don’t fall for tools that do everything but excel at nothing. Focus on those that nail the features crucial to your SEO game plan.
- Your tool should be easy to grasp. It’ll slow you down if it’s a pain to learn or navigate. Take advantage of free trials to test-drive before buying.
- The tool’s value lies in its data quality. Be skeptical of inconsistent or outdated info. Look for transparency about data sources and update schedules.
- Your SEO tool should be compatible with your other marketing tools. To streamline your workflow, check for compatibility with platforms like Google Analytics, Search Console, or WordPress.
- Even top-notch tools can be tricky sometimes. Aim for providers with responsive, knowledgeable support teams. Read reviews or ask fellow SEO pros for their take.
By sidestepping these pitfalls and identifying tools that fit your needs, you’ll make a smart investment.
Remember, tools are just part of the equation – your SEO know-how is what really drives results. Combine solid tools with your expertise, and you’ll be set for improved rankings and traffic.
Why Paid SEO Tools Might Be Worth The Investment
Paid tools offer key advantages:
- Paid tools have larger databases and provide more comprehensive data on keywords, backlinks, and competitor insights.
- Many paid tools include features like site audits, rank tracking, and content optimization that can elevate your SEO.
- Paid tools can reduce your time on manual SEO work by automating tasks.
- With access to the same tools as top SEO professionals, you can gain a competitive advantage and level the playing field.
The Right Tool Depends On Your Needs
Paid SEO tools can provide a competitive advantage over free tools. The right one depends on your needs, budget, and focus areas.
All-in-one suites like Semrush, Ahrefs, and Moz are ideal for agencies and in-house teams that need a comprehensive solution.
Specialized tools like Ahrefs, Majestic, and CognitiveSEO are valuable for focused link analysis and acquisition.
Technical SEO specialists should consider site crawling tools like Screaming Frog, while content teams may benefit from tools like Semrush and Searchmetrics.
With the many quality options available, investing in paid tools is often well worth the cost for serious SEO professionals.
More resources:
Featured Image: Nagy-Bagoly Arpad/Shutterstock
SEO
Reddit Makes Game-Changing Updates to Keyword Targeting
In a big move for digital advertisers, Reddit has just introduced a new Keyword Targeting feature, changing the game for how marketers reach their target audiences.
This addition brings fresh potential for PPC marketers looking to tap into Reddit’s highly engaged user base.
With millions of communities and conversations happening every day, Reddit is now offering advertisers a more precise way to get in front of users at the perfect moment.
The best part? They’re leveraging AI to make the process even more powerful.
Let’s break down why this is such an exciting development for digital advertisers.
Keyword Targeting for Conversation and Feed Placements
Reddit has always been about its vibrant communities, or “subreddits,” where users connect over shared interests and discuss a wide range of topics.
Until now, keyword targeting has only been available on conversation placements. Starting today, advertisers can use keyword targeting in both feed and conversation placements.
The targeting update allows advertisers to place ads directly within these conversations, ensuring they reach people when they’re actively engaged with content that’s related to their products or services.
For PPC marketers, this level of targeting means a higher chance of delivering ads to users who are in the right mindset.
Instead of serving ads to users scrolling passively through a general feed, Reddit is giving you the tools to place your ads into specific conversations, where users are already discussing topics related to your industry.
According to Reddit, advertisers who use keyword targeting have seen a 30% increase in conversion volumes. This is a significant lift for marketers focused on performance metrics, such as conversion rates and cost per acquisition.
Scaling Performance with AI-Powered Optimization
While precision is key, Reddit knows that advertisers also need scale.
Reddit mentioned two AI-powered solutions to help balance keyword targeting and scalability within the platform:
- Dynamic Audience Expansion
- Placement Expansion
Dynamic Audience Expansion
This feature works in tandem with keyword targeting to help advertisers broaden their reach, without sacrificing relevance.
Reddit’s AI does the heavy lifting by analyzing signals like user behavior and ad creative performance to identify additional users who are likely to engage with your ad. In essence, it’s expanding your audience in a smart, data-driven way.
For PPC marketers, this means more exposure without having to rely solely on manually selecting every keyword or interest.
You set the initial parameters, and Reddit’s AI expands from there. This not only saves time but also ensures that your ads reach a broader audience that’s still relevant to your goals.
Reddit claims campaigns using Dynamic Audience Expansion have seen a 30% reduction in cost per action (CPA), making it a must-have for marketers focused on efficiency and budget optimization.
Placement Expansion
Another standout feature is Reddit’s multi-placement optimization. This feature uses machine learning to determine the most effective places to show your ads, whether in the feed or within specific conversation threads.
This multi-placement strategy ensures your ads are delivered in the right context to maximize user engagement and conversions.
For PPC marketers, ad placement is a critical factor in campaign success. With Reddit’s AI optimizing these placements, you can trust that your ads will appear where they have the highest likelihood of driving action—whether that’s getting users to click, convert, or engage.
Introducing AI Keyword Suggestions
Reddit’s new AI Keyword Suggestions tool helps with this by analyzing Reddit’s vast conversation data to recommend keywords you might not have thought of.
It allows you to discover new, high-performing keywords related to your campaign, expanding your reach to conversations you might not have considered. And because it’s powered by AI, the suggestions are always based on real-time data and trends happening within Reddit’s communities.
This can be particularly helpful for marketers trying to stay ahead of trending topics or those who want to ensure they’re tapping into conversations with high engagement potential.
As conversations on Reddit shift, so do the keywords that drive those discussions. Reddit’s AI Keyword Suggestions help keep your targeting fresh and relevant, ensuring you don’t miss out on key opportunities.
New Streamlined Campaign Management
Reddit has also made strides in simplifying the campaign setup and management process. They’ve introduced a unified flow that allows advertisers to combine multiple targeting options within a single ad group.
You can now mix keywords, communities, and interests in one campaign, expanding your reach without overcomplicating your structure.
From a PPC perspective, this is huge. Simplifying campaign structure means you can test more variations, optimize faster, and reduce time spent on manual adjustments.
In addition, Reddit has enhanced its reporting capabilities with keyword-level insights, allowing you to drill down into what’s working and what’s not, giving you more control over your campaigns.
The Takeaway for PPC Marketers
For marketers working with Google Ads, Facebook, or Microsoft Advertising, this new update from Reddit should be on your radar.
The combination of keyword targeting, AI-driven audience expansion, and multi-placement optimization makes Reddit a serious contender in the digital advertising space.
If you’re looking to diversify your PPC campaigns, drive higher conversions, and optimize costs, Reddit’s new offerings provide a unique opportunity.
You can read the full announcement from Reddit here.
SEO
What The Google Antitrust Verdict Could Mean For The Future Of SEO
In August 2024, Google lost its first major antitrust case in the U.S. Department of Justice vs. Google.
While we all gained some interesting insights about how Google’s algorithm works (hello, NavBoost!), understanding the implications of this loss for Google as a business is not the easiest to unravel. Hence, this article.
There’s still plenty we don’t know about Google’s future as a result of this trial, but it’s clear there will be consequences ahead.
Even though Google representatives have said they will appeal the decision, both sides are already working on proposals for how to restore competition, which will be decided by August 2025.
My significant other is a corporate lawyer, and this trial has been a frequent topic at the dinner table over the course of the last year.
We come from different professional backgrounds, but we have been equally invested in the outcome – both for our respective careers and industries. His perspective has helped me better grasp the potential legal and business outcomes that could be ahead for Google.
I will break that down for you in this article, along with what that could mean for the SEO industry and Search at-large.
Background: The Case Against Google
In August 2024, Federal Judge Amit Mehta ruled that Google violated the U.S. antitrust law by maintaining an illegal monopoly through exclusive agreements it had with companies like Apple to be the world’s default search engine on smartphones and web browsers.
During the case, we learned that Google paid Apple $20 billion in 2022 to be the default search engine on its Safari browser, thus making it impossible for other search engines like DuckDuckGo or Bing to compete.
This case ruling also found Google guilty of monopolizing general search text advertising because Google was able to raise prices on ad products higher than what would have been possible in a free market.
Those ads are sold via Google Ads (formerly AdWords) and allow marketers to run ads against search keywords related to their business.
Note: There is a second antitrust case still underway about whether Google has created illegal monopolies with open web display ad technology as well. Closing arguments will be heard for that in November 2024 with a verdict to follow
Remedies Proposed By The DOJ
On Oct. 8, 2024, the DOJ filed proposed antitrust remedies for Google. Until this point, there has been plenty of speculation about potential solutions.
Now, we know that the DOJ will be seeking remedies in four “categories of harm”:
- Search Distribution and Revenue Sharing.
- Accumulation and Use of Data.
- Generation and Display of Search Results.
- Advertising Scale and Monetization.
The following sections highlight potential remedies the DOJ proposed in that filing.
Ban On Exclusive Contracts
In order to address Google’s search distribution and revenue sharing, it is likely that we will see a ban on exclusive contracts going forward for Google.
In the Oct. 8 filing, the DOJ outlined exploring limiting or prohibiting default agreements, pre-installation agreements, and other revenue-sharing agreements related to search and search-related products.
Given this is what the case was centered around, it seems most likely that we will see some flavor of this outcome, and that could provide new incentives for innovation around search at Apple.
Apple Search Engine?
Judge Mehta noted in his judgment that Apple had periodically considered building its own search technology, but decided against it when an analysis in 2018 concluded Apple would lose more than $12 billion in revenue during the first five years if they broke up with Google.
If Google were no longer able to have agreements of this nature, we may finally see Apple emerge with a search engine of its own.
According to a Bloomberg report in October 2023, Apple has been “tinkering” with search technology for years.
It has a large search team dedicated to a next-generation search engine for Apple’s apps called “Pegasus,” which has already rolled out in some apps.
And its development of Spotlight to help users find things across their devices has started adding web results to this tool pointing users to sites that answer search queries.
Apple already has a web crawler called Applebot that finds sites it can provide users in Siri and Spotlight. It has also built its own search engines for some of its services like the App Store, Maps, Apple TV, and News.
Apple purchased a company called Laserlike in 2019, which is an AI-based search engine founded by former Google employees. Apple’s machine learning team has been seeking new engineers to work on search technologies as well.
All of these could be important infrastructure for a new search engine.
Implications For SEO
If users are given more choices in their default search engine, some may stray away from Google, which could cut its market share.
However, as of now, Google is still thought of as the leader in search quality, so it’s hard to gauge how much would realistically change if exclusive contracts were banned.
A new search engine from Apple would obviously be an interesting development. It would be a new algorithm to test, understand, and optimize for.
Knowing that users are hungry for another quality option, people would likely embrace Apple in this space, and it could generate a significant amount of users, if the results are high enough quality. Quality is really key.
Search is the most used tool on smartphones, tablets, and computers. Apple has the users that Google needs.
Without Apple’s partnership with Google, Apple has the potential to disrupt this space. It can offer a more integrated search experience than any other company out there. And its commitment to privacy is appealing to many long-time Google users.
The DOJ would likely view this as a win as well because Apple is one of the few companies large enough to fully compete across the search space with Google.
Required Sharing Of Data To Competitors
Related to the accumulation and use of data harm Google has caused, the DOJ is considering a remedy that forces Google to license its data to competitors like Bing or DuckDuckGo.
The antitrust ruling found that Google’s contracts ensure that Google gets the most user data, and that data streams also keep its competitors from improving their search results to compete better.
In the Oct. 8 filing, the DOJ is considering forcing Google to make: 1) the indexes, data, fees, and models used for Google search, including those used in AI-assisted search features, and 2) Google search results, features, and ads, including the underlying ranking signals available via API.
Believe it or not, this solution has precedent, although certainly not at the same scale as what is being proposed for Google.
The DOJ required AT&T to provide royalty-free licenses to its patents in 1956, and required Microsoft to make some of its APIs available to third parties for free after they lost an antitrust case in 1999.
Google has argued that there are user privacy concerns related to data sharing. The DOJ’s response is that it is considering prohibiting Google from using or retaining data that cannot be shared with others because of privacy concerns.
Implications For SEO
Should Google be required to do any of this, it would be an unprecedented victory for the open web. It is overwhelming to think of the possibilities if any of these repercussions were to come to fruition.
We would finally be able to see behind the curtain of the algorithm and ranking signals at play. There would be a true open competition to build rival search engines.
If Google were no longer to use personalized data, we might see the end of personalized search results based on your search history, which has pros and cons.
I would also be curious what would happen to Google Discover since that product provides content based on your browsing history.
The flip side of this potential outcome is that it will be easier than ever to gamify search results again, at least in the short term.
If everyone knew what makes pages rank in Google, we would be back in the early days of SEO, when we could easily manipulate rank.
But if others take the search algorithm and build upon it in different ways, maybe that wouldn’t be as big of a concern in the long term.
Opting Out Of SERP Features
The DOJ filing briefly touched on one intriguing remedy for the harm Google has caused regarding the generation and display of search results.
The DOJ lawyers are proposing that website publishers receive the ability to opt out of Google features or products they wish to.
This would include Google’s AI Overviews, which they give as an example, but it could also include all other SERP features where Google relies on websites and other content created by third parties – in other words, all of them.
Because Google has held this monopoly, publishers have had virtually no bargaining power with Google in regards to being included in SERP features without risking complete exclusion from Google.
This solution would help publishers have more control over how they show up in the search results.
Implications For SEO
This could be potentially huge for SEO if the DOJ does indeed move forward with requiring Google to allow publishers to opt out of any and all features and products they wish without exclusion in Google’s results altogether.
There are plenty of website publishers who do not want Google to be able to use their content to train its AI products, and wish to opt out of AI Overviews.
When featured snippets first came about, there was a similar reaction to those.
Based on the query, featured snippets and AI Overviews have the ability to help or harm website traffic numbers, but it’s intriguing to think there could be a choice in the matter of inclusion.
Licensing Of Ad Feeds
To address advertising scale and monetization harm caused by Google, the DOJ filing provided a few half-baked solutions related to search text advertising.
Because Google holds a 91% market share of search in the U.S., other search engines have struggled to monetize through advertising.
One solution is to require Google to license or syndicate its ad feed independent of its search results. This way, other search engines could better monetize by utilizing Google’s advertising feed.
It is also looking at remedies to provide more transparent and detailed reporting to advertisers about search text ad auctions and monetization, and the ability to opt out of Google search features like keyword expansion and broad match that advertisers don’t want to partake in.
Implications For SEO
I don’t see obvious implications for SEO, but there are plenty for our friends in PPC.
While licensing the Google ad feed is intriguing in order to help other search engines monetize, it doesn’t get at the issue of Google overcharging advertisers in their auctions.
More thought and creativity might be needed here to find a solution that would make sense for both creating more competition in search and fairness for advertisers.
They are certainly on the right track with more transparency in reporting and allowing advertisers to opt out of programs they don’t want to be part of.
Breaking Up Of Google
The DOJ lawyers are also considering “structural remedies” like forcing Google to sell off parts of its business, like the Chrome browser or the Android operating system.
Divesting Android is the remedy that has been discussed the most. It would be another way to prevent Google from having a position of power over device makers and requiring them to enter into agreements for access to other Google product apps like Gmail or Google Play.
If the DOJ forced Google to sell Chrome, that would just be another way to force them to stop using the data from it to inform the search algorithm.
There are behavioral remedies already mentioned that could arguably accomplish the same thing, and without the stock market-shattering impact of a forced breakup.
That said, depending on the outcome of the U.S. election, we could see a DOJ that feels empowered to take bigger swings, so this may still be on the table.
The primary issue with this remedy is that Google’s revenue largely comes from search advertising. So, if the goal is to reduce its market share, would breaking up smaller areas of the business really accomplish that?
Implications For SEO
If Android became a stand-alone business, I don’t see implications for SEO because it isn’t directly related to search.
Also, Apple controls so much of the relevant mobile market that spinning Android off would have little to no effect in regards to addressing monopolistic practices.
If Chrome were sold, Google would lose the valuable user signals that inform Navboost in the algorithm.
That would have some larger implications for the quality of its results since we know, through trial testimony, that those Chrome user signals are heavily weighted in the algorithm.
How much of an impact that would have on the results may only be known inside Google, or maybe not even there, but it could be material.
Final Thoughts
There is so much to be decided in the year (potentially years) to come regarding Google’s fate.
While all of the recent headlines focus on the possibility of Google being broken up, I think this is a less likely outcome.
While divesting Chrome may be on the table, it seems like there are easier ways to accomplish the government’s goals.
And Android and Google Play are both free to customers and rely on open-source code, so mandating changes to them doesn’t seem the most logical way to solve monopolistic practices.
I suspect we’ll see some creative behavioral remedies instead. The banning of exclusive contracts feels like a no-brainer.
Of all the solutions out there, requiring Google to provide APIs of Google search results, ranking signals, etc. is by far the most intriguing idea.
I cannot even imagine a world where we have access to that information right now. And I can only hope that we do see the emergence of an Apple search engine. It feels long overdue for it to enter this space and start disrupting.
Even with Google appealing Mehta’s decision, the remedy proposals will continue ahead.
In November, the DOJ will file a more refined framework, and then Google will propose its own remedies in December.
More resources:
Featured Image: David Gyung/Shutterstock
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