If you have to choose between Facebook and Google Ads for promoting your SMB, Facebook is your best bet.

And in the debate between Microsoft Advertising and Google Ads, Google provides greater reach at a lower cost.

These are among the key findings of a new study by Cambridge University MBA students and ad platform Adzooma.

Participants analyzed how imagery, sentiment, and language impacted click-through rates (CTRs) on three top online ad platforms.

The study also considered whether landing page quality had an impact on conversion rates, and revealed new CPC and CPM benchmarks for Facebook, Microsoft, and Google Ads.

Which Factors Moved the Needle Most for Ad Performance

Researchers found that each of the major online ads platforms has its strengths:

• Facebook Ads was the most cost-effective channel on average.
• Microsoft Ads was most responsive to increasing ad spend.
• Google Ads had the greatest reach for the lowest cost.

In the evaluation of how factors impacted CPM and CPC, they found that an advertiser’s choice of channel has a significant impact on both metrics:

How CPM and CPC Differ on Facebook, Microsoft, and Google Ads

Upon determining this connection between channel and cost KPIs, researchers went looking for the average CPM and CPC across Google, Facebook and Microsoft Ads:

Microsoft Advertising had the highest median CPM and CPC, but researchers advise it is best for targeting affluent customers.

This is because 54% of Bing users are over the age of 45, and a third of them have a household income of over $100,000.

How Increasing Ad Spend Impacts ROAS

It’s a common problem in digital advertising—spending too much is wasteful, yet spending too little may not generate the return you need.

In this portion of the study, researchers used regression analysis to determine whether increasing ad spend also increases the number of impressions and clicks.

Here’s what they found:

Microsoft Advertising showed the greatest ROAS for increasing budget in terms of impressions, a good metric for brand awareness campaigns.

However, all three platforms performed similarly in terms of increased clicks, with Microsoft and Google Ads just slightly ahead of Facebook Ads.

How Sentiment Impacts Ad Performance on Google and Microsoft Ads

Researchers found ads with positive sentiment performed best on Microsoft Ads, where CTRs were:

• 4.2% for positive ads
• 3.6% for neutral ads
• 3.3 for negative ads

The opposite was true on Google Ads, where negative sentiment won the day with CTRs of:

• 6.5% for negative ads
• 5.7% for neutral and positive ads

Study participant Sunil Grewal, formerly of Amazon, said:

“Based on the analysis, the users of different advertising platforms have different responses to ad copy sentiment.”

This is important, he explained, because advertisers can optimize their return on ad spend (ROAS) by refining ads to capitalize on the impact of sentiment.

Landing Page Quality Had Greatest Impact on Conversion, Yet Few Excel Here

Srishti Warman, formerly an associate VP at Barclays Bank and an MBA Star winner at the Women of the Future Awards, was responsible for the study’s landing page analysis.

She explains:

“The task was to identify the key parameters that lead to a higher conversion e.g. faster page speed, better optimisation for mobile and web viewing, page responsiveness, etc. This analysis would eventually help understand not only which companies scale quickest within verticals, but critically, why.”

Warman found that although landing page best practices had the greatest impact on conversion rate, not a single one of those tested had perfect score for best practices in Lighthouse.

These best practices included using HTTPS, displaying images with correct aspect ratios, serving images with appropriate resolution, and avoiding a request for location on page loading.

Only 5% of landing pages had a perfect score for website performance checks.

That means that 95% of businesses are leaving money on the table.

Warman also noted that she was surprised to see that optimization best practices outperformed page loading speed as the most important factor in driving conversions.

In addition to the primary landing page factors studied—best practices, SEO, performance, and accessibility—Warman identified content quality, CTAs, and the PPC to landing page journey as impactful for improving conversion.

Key Takeaways from Facebook Ads v. Microsoft Advertising v. Google Ads Comparison

• Facebook is, on average, the most cost-effective channel for SMBs.
• Ads with positive sentiment perform better on Microsoft, and negative sentiment performs better on Google Ads.
• Best practices were the most important conversion element for landing pages, followed by SEO (titles and meta descriptions, valid robots.txt, ensuring links are crawlable, etc.).
• Very few advertisers are maximizing their conversion opportunities, leaving plenty of opportunity to improve ROAS through landing page optimization.

Methodology:

Study participants were given access to campaign data from the Adzooma platform, selected at random and spanning thousands of accounts and a variety of industries.

Participants used Lighthouse to evaluate landing page quality, as well as SEMrush, Screaming Frog, Adzooma, and Google Analytics.

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Sources:

Image source: All screenshots by author, February 2021

Structured data is one of the most effective ways to increase the visibility of your website content and increase the sustainability of your SEO as Google implements regular updates to the SERP environment. Over the last five years, many of Google’s most game-changing SERP features have been driven by the use of structured data from across the web. Google for Jobs, Google Shopping, featured snippets, how-to instructions, recipe cards, knowledge panels, and other rich snippets all serve content from sites with structured data.

So, when we think about how small and medium-sized enterprises (SMEs) can compete in today’s ever-evolving SERP landscape, time and time again, well-implemented structured data is what makes the difference.

In this blog I’ll explain the following:

• What is structured data?
• Why should SMEs prioritize structured data implementation?
• How do you identify which structured data is best for your SEO strategy?
• Strategies for adding schema to your site
• How to measure and demonstrate the impact of structured data

In my experience, well-implemented structured data is effective for websites of all sizes and in all verticals. For my own clients, schema implementation has enabled growth, improved performance on search and created opportunities to reach new audiences.

• For an established national retail brand with a thriving social media following, schema optimisations contributed to a 50% increase in organic conversions within a month. And six months on, the improvements we made to product markup, organisation, and blog schema had helped drive a 230% increase in organic conversion value.
• For a local recruitment site, we combined niche targeted schema and sitemap optimization to increase both organic clicks and impressions by over 80% each, within four months.
• For an international B2B retailer, improving existing schema formed the basis for marketing strategy that enabled us to grow our tally of ranking keywords by an average of 70% across three countries, increase our revenue by 35% when compared to the previous period, and create new marketing channels, within 6 months.
• For a professional services provider, we built E-A-T centric schema optimisations into a blog and were able to improve average ranking position by 30% in 3 months and by 43% within a year.

Though the target markets, objectives and audiences differed in each case, I was able to use schema markup as a strategic underpinning of a wider SEO and marketing strategy. This is because schema has become a fundamental element of scalable SEO.

What is structured data in SEO?

On a website, structured data is a means of defining content with a uniform set of names and values, so that bots and machines can better read, index, understand, and serve the content of your site. While the phrase “structured data” can include elements like open graph for social media, microdata, or indeed any set of data that is organized uniformly (think of your CRM), generally when SEOs talk about structured data, we’re referring to structured data markup in JSON-LD as specified by Schema.org and recommended by Google.

Why is Schema.org structured data so strategically important?

Schema.org has become structured data HQ, because its framework — sets of vocabularies and relationships — was created and is maintained through a cross-platform partnership between Google, Microsoft, Yandex, and other major search engines. They regularly create new schema types and relationships aimed at making the information on the web more easily accessible to users.

Schema.org breaks down content into common vocabulary of predefined @types, which each have predefined properties, and can then be expressed using a common Javascript notation (JSON-LD). Like entries to the Oxford English Dictionary, the team behind Schema.Org are constantly adding new @types and properties in order to keep pace with user needs. At present, there are 778 types, but that number will continue to grow. Each new type brings more clarity, consistency, and ease of access to the information on the web — something that’s brilliant for search engines, and great for your traffic.

What does that mean in practice?

Sometimes when I’m explaining structured data to clients, I describe it as a means of essentially turning your beautiful website into a spreadsheet for robots. They can prioritize and process the critical information about the content of the page without having to understand the layout of your particular WordPress theme, reams of CSS, or navigate your Joomla configuration.

This means that information a bot has on a page can be more consistent and resilient, even if the content changes day-to-day. So, in the example of a retailer with seasonal specials and campaigns that change the front end home page layout, structured data tells Google the same information about the page in the same way every time:

Why should SMEs prioritize structured data implementation?

Simply put, structured data gives you the chance to jump the queue on the SERP.

When we look at the ways in which Google has enhanced its SERPs over the last few years, what we see consistently is the use of JSON-LD structured data in combination with Google APIs to create new features and new channels for content. Rich snippet SERP features like Google for Jobs, Google Shopping, featured snippets, how-to instructions, recipe cards, knowledge panels, and other monumental changes to the SERP have all been driven or improved by the creation and utilization of structured data frameworks.

Users love these features because they’re multi-media search enhancements, and are impossible to miss as they often take up the entire viewport on mobile:

Not a plain blue link in sight.

In many cases, your content cannot be included in these attractive rich snippets without structured data. So, if you literally want to get ahead of the competition, structured data needs to be a component in your SEO strategy.

What are the other advantages of structured data for small businesses?

Along with increased visibility, structured data implementation offers the following advantages for small businesses:

1. You will likely outpace your local competitors. SMEs are likely to feature within local pack search results with other small businesses. Here, competition for keywords is fierce, but many have yet to incorporate structured data into their sites. This is an opportunity to increase your visibility, gain more market share, and therefore increase conversions.
2. Schema markup is a fully scalable optimization. While some SEO tasks like content creation can require pages to be optimized one-by-one, Schema markup can be built into the structure of the page. This means that once it’s set up, every new product listing page, for instance, would already have the optimization as you expand the site, whether you stock six products or six thousand. The time this saves is especially significant for small marketing teams.
3. Schema markup implementation can be carried out as a single project within a few months. This can be a win for clients and SEOs, because in many cases, there is a clear demarcation before and after, followed by solid results which help to build confidence in further SEO activities, give clear ROI and satisfy clients looking for quick wins.

How do you identify which structured data is best for SEO strategy?

With almost 800 types of schema markup available to add to a website, it can be difficult to decide which are the best for your page, but to start, you can introduce or improve some new elements to help you better perform online and complement your existing content or e-commerce SEO strategy.

Does your site pass the schema markup need-to-have checklist?

There are certain sets of schema markup that apply to almost every site, and others — like Product and Job Postings — that are niche critical to effective SEO. As a general rule of thumb, every time I get a new client, I run through the following initial checks:

• Do the homepage and about pages have Organisation or Local Business schema?
• Do the blogs have schema for Articles or Blog Postings?
• Do the team pages have Person schema?
• If it’s an e-commerce site, does it have the niche critical Product schema?
• If it is a recruiter site, does it have the niche critical Job Postings schema?
• If the business is in another niche with dedicated Google SERP features, does it have the necessary markup?

If you answered “no” to any of these questions and the site doesn’t have the appropriate markup, then you should add schema markup to your site.

If the answer to these questions is “yes”, then it’s important to test the quality of the implementation before moving on to the next step. To do this, take a look at Search Console’s Rich Results Report to review pages at scale, or use their Structured Data Testing Tool and Rich Results Test to inspect individual pages. If you see errors, they should be addressed.

How to Find Address Schema Errors

First, take a look at Search Console’s Rich Results Report to review pages at scale and identify which content is being read as Valid, Valid with Errors or Error.

Valid: If your markup is ‘Valid’, then it is being crawled and indexed correctly. These pages are unlikely to require further action.

Errors: Pages with markup that is identified with an ‘Error’ tend to have incorrect syntax, so you should review the individual page and correct the code as soon as possible. When the changes are complete, use the Validate Fix button, to request reassessment.

Valid with Warning: If your content is showing as ‘Valid a Warning’, then you are likely displaying schema markup with a Missing field. These warnings do not make the page or the markup invalid, but they can make the page less competitive, because the content is less targeted. Review your content to ensure that your schema is reflecting as much of the on page content as possible in order to reduce these errors, and therefore increase the performance of your schema markup.

Structured Data Testing Tool and Rich Results Test allow you to troubleshoot improvements to structured data on individual pages. Each of these tools you can enter the URL in question and you will receive itemised information on any errors or warnings.

The missing fields highlighted here correspond to properties within the Event schema type. So, to improve this markup, you would look up the definitions of the relevant properties on schema.org and, where applicable, use their example HTML to guide your optimizations.

In this instance, to improve the performance of my schema, I may need to build new performer fields into the CMS, or to work with the dev team to add the content from existing CMS data fields into the schema regex.

In either case you will be making improvements that help you better target and serve users.

Strategies for adding schema to your site

If you need to add schema to your site there are a few options for implementation.

Adding schema to single static pages

For some single pages with largely static content, adding markup types like Local Business, Organization, or a single FAQ page, can be a straightforward process of generating the code and placing it into the HTML of the page. Major CMS platforms like Shopify and WordPress have plugins to assist with generating the markup for these pages which is easy to implement. Those with custom CMS configurations can use tools like the Schema Markup Generator to generate the JSON-LD, then pass it onto the development team to push it live.

Adding scalable schema for bulk implementation

Bulk schema implementation is almost essential for high volume content creators. This applies to e-commerce shops, but also to those who regularly post standard format content like recipes, blogs, articles, job vacancies, events, training courses, etc.

For these pages, the most effective way to get the most out of the schema on your site is to automate the process by building it into the structure of your site. In most instances, this involves a four phase approach, working in coordination with your developers and clients.

• Start with your sitemap. You should have a dynamic XML sitemap that helps Google index pages as they are created, and includes the elements that are listed within the structured data. For an e-commerce site, this means having a sitemap that includes the product listings pages, but also the image sitemap and customer service pages.

• Organize your content. For bulk implementation, you should work with your content, and try to assign a type and property to each standard field for content on your page. Treat it like a formula that can be applied to each page with the same format. If you don’t have fields that correspond with the required properties for your schema type, add them. If you have a bulk of information, try to ensure that you have properties that support the USPs of your content. Then work with your developers to update your template so that the schema generates consistently across the site.

• Connect with relevant Google APIs. Once your sitemap is solid, your content is optimised and your markup is in place. check to see if the schema you’re using has an API and, if it does, get your site connected to it. Some of the most-used rich results connect directly with dedicated Google APIs, which further integrate with PPC tools to round out your marketing mix.

Examples of Schema Types with Dedicated Google APIs

• Lodging schema connects with the Hotel API and can be used with Google Hotels PPC
• Job Posting schema connects with the Indexing API to enable inclusion in Google for Jobs
• Product schema connects with Content API to enable inclusion in Google Shopping search results and ad listings.

How to measure and demonstrate the impact of schema markup

Measuring Schema Enable Rich Results in Search Console

The impact of schema markup which generates rich results, can be easily monitored and measured in Search Console. Within the Enhancements tab, you can monitor the quality of your implementation and any current or recent errors.

To monitor impressions, rankings, clicks and CTR, visit the Search Appearance tab under Performance. This tab provides historic data that can be compared to earlier configurations of the site.

Within Google Analytics, your tracking and monitoring will depend upon your implementation. For instance, google-jobs-apply clicks may show as a separate source from standard search results within Organic. But I’ve also seen Google Shopping clicks show as part of the (other) channel. In either case, annotate your implementation dates to monitor relevant content for changes in clicks, impressions, and conversions.

For many small businesses, Search Console data should be sufficient but there are also tools which can help you drill down further into the data.

In summary

Taking a strategic, integrated approach to structured data implementation helps SMEs to stay competitive in today’s search environment because of its scalability, versatility and measurability. Furthermore, the applicability of schema markup as the underpinning of a cohesive content and advertising strategy, brings much needed efficiencies for SME marketers who want to make the most out of their content.

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This patent is about presenting data with search results and deciding when to show that data in a knowledge panel with those search results.

The Web provides access to a wide variety of resources, such as image files, audio files, video files, and web pages.

A search system may identify resources in response to queries submitted by users and provide information about the resources in a manner that is useful to the users.

The users can then navigate through, e.g., click on, the search results to acquire information of interest to the users.

Users of search systems are often searching for information regarding a specific entity.

For example, users may want to learn about a singer that they just heard on the radio.

Conventionally, the user would submit a search query identifying the singer and select from a list of search results determined to be relevant to the search query.

One innovative aspect of the subject matter described in this patent can be shown in methods that include:

• Receiving a query
• Obtaining search results responsive to a received query
• Identifying the first set of factual entities referenced by the received query
• Selecting from among the first set of factual entities a particular factual entity for which a knowledge panel is eligible to be provided with the search results, the selection is based on a measure of topicality between each identified factual entity in the first set and one or more of the obtained search results
• Determining that the knowledge panel for the particular factual entity is to be provided with the search results, the determination is based, at least in part, on the content of the knowledge panel and characteristics of the search results, the knowledge panel including at least one content item, for the selected factual entity, received from a first resource and at least one content item, for the selected factual entity, received from a second resource different than the first resource
• Providing a knowledge panel

Additional features may include:

• Selecting, from among the first set of factual entities, a particular factual entity for which a knowledge panel is eligible to be provided with the search results can include identifying a second set of factual entities, each factual entity of the second set being a factual entity determined to be topical to the obtained search results
• Identifying one or more factual entities included in the set of factual entities and the second set of factual entities
• Selecting one of the one or more factual entities as the identified factual entity

Identifying the second set of factual entities, each factual entity of the second set being a factual entity determined to be topical to the obtained search results.

These can include:

• Identifying the third set of factual entities, each factual entity of the third set being a factual entity referenced in at least one of the obtained search results
• Identifying a proper subset of the obtained search results, the proper subset including a particular number of higher-ranked obtained search results; for each factual entity of the third set of factual entities
• Determining, relative to the factual entity, a partial topicality score for each search result in the proper subset of obtained search results, the partial topicality score for each search result is a measure of topical relatedness between the search result and the factual entity
• Selecting from the third set of factual entities the second set of factual entities based on the partial topicality scores

Selecting the second set of factual entities based on the partial topicality scores can include, for each factual entity of the third set:

• Determining an overall topicality score for the factual entity based on the partial topicality scores for the factual entity
• Selecting the second set of factual entities based on the overall topicality scores
• Determining the overall topicality score for the factual entity based on the partial topicality scores for the factual entity can include determining a weighted sum of the partial topicality scores for the factual entity

Selecting a Particular Factual Entity

Factual Information about entities may be chosen for a knowledge panel based on:

• Determining a topicality score for the search result concerning each factual entity of the first set of factual entities, with the topicality score for a search result concerning a factual entity being a measure of topical relatedness of the search result for the factual entity
• Comparing each topicality score to a topicality threshold to identify a proper subset of the topicality scores that exceed satisfy the topicality threshold
• Determining which one of the proper subsets of topicality scores corresponds to the highest-ranking search result for the proper subset of topicality scores
• Selecting the factual entity that corresponds to the one partial topicality score as the particular factual entity.

Determining That Information for a Factual Entity Should Be Provided With Search Results

This Decision may include:

• Identifying a click-through rate for each search result with respect to a received query
• Determining that the identified click-through rates do not satisfy a threshold click-through rate
• Determining to provide the knowledge panel in response to determining that the identified click-through rates do not satisfy the threshold click-through rate

Determining That Information for a Factual Entity is to be Provided with Search Results

This can include:

• Identifying a top ranked search result for the received query
• Identifying a click-through rate for the top ranked search result with respect to a received query
• Determining that the identified click-through rate does not exceed satisfy a click-through rate threshold
• Determining to provide the knowledge panel in response to determining that the identified click-through rate does not exceed satisfy the click-through rate threshold

Determining That Information for a Factual Entity will be shown with Search Results

These may include:

• Identifying content for inclusion in the knowledge panel
• Determining that the identified content includes at least a threshold amount of content
• Determining to provide the knowledge panel in response to determining that the identified content includes at least a threshold amount of content

Determining That Information for a Factual Entity is to be shown with Search Results

These can include:

• Determining whether the knowledge panel includes links to query refinements for the received query
• Determining to provide the knowledge panel in response to determining that the knowledge panel includes links to query refinements for the received query

Advantages of Following the Process in the Triggering Knowledge Panels Patent

• Knowledge panels can improve users’ search experiences, in particular for queries directed to learning, browsing, or discovery. For example, the knowledge panel supplies users with basic factual information from multiple different sources or a summary of information about a particular entity referenced in a search query
• Knowledge panels can assist users in navigating to multiple different network locations that each includes related content seamlessly and naturally
• Knowledge panels can supply new content that may not otherwise be encountered by a user without the user selecting several different search results and visiting multiple different network locations. Knowledge panels can also help users obtain information faster than they would if the users were required to click through multiple search results to obtain the information from multiple different network locations

This Triggering knowledge Panels patent can be found at:

Triggering knowledge panels
Inventors: John R. Provine
Assignee: Google LLC
US Patent: 10,922,326
Granted: February 16, 2021
Filed: March 14, 2013

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing knowledge panels with search results.

In one aspect, a method includes receiving a query.

Search results that are responsive to the received query are obtained.

The first set of factual entities referenced by the received query are identified.

A particular factual entity is selected from the first set.

It is determined that a knowledge panel for the particular factual entity is to be provided with the search results based, at least in part, on the content of the knowledge panel and characteristics of the search results.

The knowledge panel can include at least one content item, for the selected factual entity, received from a first resource and at least one content item, for the selected factual entity, received from a second resource different than the first resource.

The knowledge panel is provided.

What is a Knowledge Panel?

A search may return one or more knowledge panels in a response to a search query.

A knowledge panel is a user interface element that provides a collection of information or other content related to an entity that is referenced in a search query.

An entity could be a person, place, country, landmark, animal, historical event, organization, business, sports team, sporting event, movie, song, album, game, work of art, or any other entity.

An entity may also be a concept, subject, or topic.

When it appears in a search result, a knowledge panel shows a summary of information for a specific entity.

A knowledge panel for a singer may include:

• The name of the singer
• An image of the singer
• A description of the singer
• One or more facts about the singer
• Content that identifies songs and albums recorded by the singer
• Links to searches related to the singer
• Other types of information and content

A knowledge panel for an entity typically will include content that is obtained from multiple disparate sources or multiple different domains. A knowledge panel for a person could include an image from an official website for the person as well as facts from an online encyclopedia not affiliated with the official website for the person.

When a query is received, the search system can determine whether to provide a knowledge panel with search results for the search query. This may be based on whether the query is determined to reference a known entity.

When the system decides whether the query refers to a known entity based on comparing the received query, or terms of the received query, to an entity index that identifies known entities and their aliases.

If there is a match based on the comparison, the received query is determined to refer to the known entity, and the system may provide a knowledge panel for the matching entity.

A query may refer to multiple entities or one or more terms that can be considered an alias of multiple entities. For Instance, the term “phoenix” may refer to a city in Arizona or a mythical bird. For a query that matches, or is otherwise relevant to, multiple known entities, the system can determine whether to show a knowledge panel for one of the matching entities, for example, if one entity is the most likely entity for the query or to provide a knowledge panel that includes content for multiple entities.

If a query includes “Phoenix, Ariz.” or the query was received from a device located in Arizona, the system may determine that the city in Arizona is the most likely entity being referenced by the search query and provide a knowledge panel for the city of Phoenix. If the query includes only “phoenix” the system may provide a knowledge panel that includes content for the city of Phoenix and the mythical bird.

A Disambiguation Knowledge Panel

Another example of a knowledge panel that may include content for multiple entities is a disambiguation knowledge panel.

A disambiguation knowledge panel is a knowledge panel that includes content for two or more different known entities and/or user interface elements that enable a searcher to specify which of the known entities is of interest to the user.

A “phoenix” disambiguation knowledge panel could include content items including an artistic rendering of the mythical bird and a map representing Phoenix, Ariz. Each of these content items can be linked to more information about the respective entities and/or include instructions that upon user interaction with one of the content items request a knowledge panel for the entity represented by the content item.

Very often, a disambiguation knowledge panel includes the same or a similar amount of content for each entity referenced by the disambiguation knowledge panel.

A disambiguation knowledge panel may include titles for the entities, a brief description of the entities, and links that request resources with additional information or that initiate searches for each entity. The area of the knowledge panel that is designated for each entity may also be the same or similar in size.

A Dominant Entity Knowledge Panel

Another type of knowledge panel including content for multiple entities is a dominant entity knowledge panel.

A dominant entity knowledge panel includes content for multiple entities but has a greater amount of content for one or more of the matching entities.

Though more than one entity may match a query, a particular entity may more closely match the query and maybe more topical to results for the query. It could also be more relevant to the query than the other entities.

And Google may show a dominant entity knowledge panel with more content for the particular query, with also less content for each of the other matching entities.

A dominant entity knowledge panel may show a title, image, description, set of facts, links to resources related to the particular entity, and additional content for the particular entity.

That same dominant entity knowledge panel may also show, for each other matching entity, only a title, image, and link to additional content for the entity.

The system may also consider how well an entity matches the query or search results identified for the query to decide which entity to show a knowledge panel for. The system may select an entity closely matching the query and one that is well represented by the search results for the query.

Deciding Between Entitities

The search system may identify the first set of entities that each matches the query. It may also identify a second set of entities that are considered topical to the search results.

The system may then select between entities that are included in both the first and second sets.
This can help to ensure that the selected entity for which to provide a knowledge panel both matches the search query and is also on-topic for the query.

Other Factors may be used to decide whether to show a knowledge panel for a query, such as click-through rates or other performance measures of search results for the received query and/or content available to include in a knowledge panel for an entity referenced by the received query.

The system may think about whether search results for the query have:

• A low engagement rate, e.g., a low click-through rate
• Whether the query is non-navigational, e.g., the click-through rate of a high ranked search result for the query is high, whether there is sufficient content, for the entity referenced by the query, to be included in a knowledge panel
• Whether the knowledge panel for the entity includes links to common search refinements that users would otherwise have to enter manually

Deciding What Data a Knowledge Panel Contains?

A search service may include an apparatus attached to the search system.

The search system can decide whether to provide a knowledge panel for a search query. If it determines a knowledge panel should be shown, the knowledge panel apparatus can generate the knowledge panel and provide the generated knowledge panel.

The knowledge panel apparatus may be provided to the searcher’s device, bypassing the search system.

A knowledge panel is usually shown for queries determined to refer to a particular factual entity, such as a person, place, country, landmark, animal, historical event, organization, business, sports team, sporting event, movie, song, album, game, work of art, or any other entity. A factual entity can also be a concept, subject, or topic.

A knowledge panel for an entity may include content from multiple disparate resources related to the particular entity. A knowledge panel may include factual information relevant to the entity.

A knowledge panel for an actor may include biographical information for the actor, as well as content associated with shows that the actor has appeared in. The knowledge panel may include a summary of information related to the entity.

A knowledge panel for a nation may include:

• A map of the nation
• The flag of the nation
• The official language of the nation
• Other facts and content related to the nation

Each of these units of information may be from different resources than the other discrete units of information.

A knowledge panel can be much larger and consume more area of a search interface than standard search results. The knowledge panel may span the length or height of three or more standard search results to accommodate the content items in the knowledge panel and to draw attention to the knowledge panel.

A knowledge panel may be displayed in a knowledge panel area. That knowledge panel area may be presented with or alongside a search results area that presents search results. The knowledge panel area may consume a larger area than the area consumed by two or more of the search results presented in the search results area.

The knowledge panel apparatus includes or is communicably coupled to one or more data storage units that include:

• A content item store
• A knowledge panel store
• An entity index

The content item store may store content items that can be inserted. These can be content items are discrete units of content, such as data files, and can be in the form of text, images, videos, graphics, audio, tables, or other types of content.

The knowledge panel store may store knowledge panels for entities and/or knowledge panel templates that can be populated with content to generate a knowledge panel. In general, a knowledge panel template specifies types of content items to include in the knowledge panel and includes placeholders for content items of the specified type. For example, a knowledge panel template may include placeholders for a title, one or more images, a description, a set of facts, and/or other types of content items.

The entity index can include an index of known entities. For each of those entities, this can include data identifying aliases of the entity (a celebrity may have several nicknames or stage names for which a user may enter as a query for the celebrity.) Each of the nicknames or stage names can be included as aliases for the entity in the entity index.

That entity index can also include an index of content items for each entity. The knowledge panel apparatus may identify content items to include in knowledge panels for each entity and may store the content items in the content item store. The knowledge panel apparatus can also index the content items in the entity index for use in subsequent retrieval of the content items.

Knowledge Panel Templates

Depending on the type of entity that might be identified in an entity found in a query, factual information associated with that entity could be shown. The patent provides examples to make that clearer.

One example shown is for a knowledge patent for a famous actor. That panel may show the Famous Actor being credited with movie appearances and music.

The knowledge panel may include a set of images of movie posters for movies in which a Famous Actor appeared. Those images may be taken from an online movie database and/or include a link to the online movie database.

The knowledge panel could also include a set of images for albums released by a Famous Actor. Those images may be obtained from an online music database or an online music service and include a link to the source of the images. A table may be used to provide information about movies, albums, or songs related to a Famous Actor. The knowledge panel could also include a set of images for related search queries. Those images could each include a link that initiates a search for the subject of the image.

Deciding what to show for an Entity?

Google may look at Topicality Scores to decide whether to show a knowledge panel If an entity in a query is most likely to be the entity described in the query, there is more reason to show a knowledge panel.

Google may also consider performance measures, such as click-through rates to decide whether to show more information for an identified entity. This would be especially true if the knowledge panel would make it more likely to help a searcher find the information for which the user is searching for.

The apparatus may also consider the click-through rate for a highly ranked identified search result (such as a top-ranked search result, or one of the top ten search results.)

If the click-through rate for a high-ranked search result is relatively high (such as greater than a threshold or at least a threshold amount greater than each of the other search results,.)

A knowledge panel may not be shown if the content item store does not contain sufficient content to populate a knowledge panel for the entity, e.g., fewer than a threshold number of content items, the knowledge panel apparatus may determine that a knowledge panel for the entity will not be provided in response to the search query.

The apparatus determines to show a knowledge panel, at least in part, on the type of content that is available for the entity. So the apparatus may have templates that include placeholders for certain types of content to include in the knowledge panels. If that particular type of content that is specified by the template is not available for the entity, then the apparatus may decide to not provide a knowledge panel for the entity.

If a determination is made to not provide a panel for an entity, then it may show search results without a panel.

Why Show a Knowledge Panel When An Entity Appears in a Query

If Google can recognize that an entity shows in a query, it can provide more information about that entity that a searcher may not have been aware of including information that fits into a specific query template that may help a searcher understand better what they are looking for.

Google can show disambiguation panels when there is more than one entity that may have appeared in a query, and the possible entities may have been searched for more or less. Google may also decide that one of the entities possibly in the query is much more dominant than another of the entities.

As a searcher, it can be possible to learn more about what you may be searching for by learning from the search results that a search engine might show you. When a search engine decides to show you a knowledge panel you can learn a lot more about the entity in your query. That can make it possible to refine your query or to search for something else than what you may have thought was possibly a helpful query term or phrase. This can be true when the highest-ranking results for a query have low click-through rates, and the KP may show query revisions or suggestions that you can click through to potentially find answers to your query, which would make a KP very useful.

Last Updated 2/18/2021

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