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Monthly Archives: May 2015

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Report: Apple Using Camera-Equipped Minivans To Capture Map Data

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By Greg Sterling

It’s a very safe bet that Google has invested hundreds of millions of dollars into Google Maps and Street View. While many people regard Street View as eye candy, it has actually served a more important function: capturing real-world geo-data for Google Maps.

Now Apple appears to following in Google’s tire tracks.

According to an article in 9to5 Mac the mystery of the earlier Apple minivan sightings has been solved. Speculation at the time was that these vans were either capturing Street View-like images or that this was part of Apple’s nascent effort to build a self-driving vehicle. The answer is neither, says 9to5 Mac, although Street View imagery is potentially part of the equation.

Self-Driving car (Apple?)

The 9t05 Mac article asserts these vans are helping Apple “develop[ ] its first entirely in-house mapping database to reduce its reliance on TomTom” and that a Street View-like body of images will potentially be a byproduct of the process. This is all part of a broad set of Apple Maps upgrades reportedly coming (or to be announced) at WWDC in June.

In addition to reducing its TomTom dependence, Apple is reportedly doing so with Yelp-provided images and content. Apple has quietly begun diversifying review sources, for both US and international business locations, adding content from TripAdvisor and

Assuming the 9to5 Mac report is accurate it suggests that Apple has come to the same conclusion that Google did years ago. In order to have the highest quality mapping product you have to own key parts of the stack, including the base data.

If this is in fact the answer to the minivan mystery I hope it doesn’t mean the end of the iCar fantasy.

The post Report: Apple Using Camera-Equipped Minivans To Capture Map Data appeared first on Search Engine Land.



SearchCap: Google Maps Offline, App Indexing & Structured Data

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By Barry Schwartz

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

From Search Engine Land:

Recent Headlines From Marketing Land, Our Sister Site Dedicated To Internet Marketing:

Search News From Around The Web:




SEM / Paid Search

Search Marketing

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Google Maps Breakthrough: Search And Navigation Without A Connection

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By Greg Sterling

Google continues to add features and capabilities that keep its maps that much ahead of the competition. Coming out of I/O yesterday Google made a major announcement: Google Maps search and voice guided, turn-by-turn navigation will be available offline.

This is intended for anyone without a data connection or with a weak or inconsistent one. So it will be great for travelers who don’t want to pay international data charges or don’t want to get a local SIM card. But more importantly it’s for developing markets and places in the world where data connections are unreliable.

Offline turn by turn directions will rely on GPS. And while there have previously been offline digital maps, including a modest prior effort by Google and a more extensive one by Nokia, this is a breakthrough. That’s partly because of its scope and the fact that it doesn’t require the user to download the map or a section of the map onto the phone.

Google said during the keynote that offline maps will be available “later this year.” The company also told me this morning in email that the offline functionality will equally be coming to Google Maps for iOS.

Below is the full keynote video from yesterday. The discussion of offline Google Maps functionality starts at 2:11:47.

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Structured Data & The SERPs: What Google’s Patents Tell Us About Ranking In Universal Search

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By Barbara Starr

The use of structured data is now increasingly apparent in many aspects of search — but perhaps nowhere is it more evident than in today’s search engine results pages.

Search engine results pages have evolved considerably over the years. We’ve seen a shift from the classic “10 blue links” to an information-rich display that blends many different types of results. In addition to the standard organic search results we all know and love, we’re also seeing knowledge panels, image results, local packs, Google news, and more — each of which has its own unique algorithm for determining placement within these areas.

Google’s shift towards these “blended” search results that include Knowledge Graph-based information has had a marked effect on the search engine optimization (SEO) community. Not only do we need to start incorporating structured data into our SEO strategies, but we need to have an understanding of what factors determine which content gets displayed in different areas of the search engine results pages.

Today, I’m going to delve into some Google patents to help give you a better understanding of how the search giant is thinking about the display of search results based on structured data and context.

Ranking & Ordering Via Entity Metrics

A recent patent of Google’s, “Ranking search results based on entity metrics,” discusses the ways in certain metrics might be used by a search system (e.g. Google Search) to rank and order results.

The patent starts out by describing how a search engine algorithm works: It looks at a variety of metrics (what we typically refer to as “ranking factors”), then computes a relevance score based on a weighted sum of these metrics to determine placement within search results.

The patent also notes that “ranking search results may be distinct from ordering search results for presentation.” In other words, ranking is an internal measurement based on relevancy, whereas ordering refers to how search results are presented on a page.

So, what does this have to do with structured and entity search?

Well, the patent then goes on to describe how, in some instances, search results are based on information found within “data structures.”

In some implementations, search results are retrieved from a data structure. In some implementations, the data structure also contains data regarding relationships between topics, links, contextual information, and other information related to the search results that the system may use to determine the ranking metrics. For example, the data structure may contain an unordered list of movies, along with the awards and reviews for each respective movie. The search system may use the awards and reviews to determine a ranking of the list, and may present the search results using that ranking.

In other words, information from various external data sources (such as Wikidata, a repository of structured data that helps to power Google’s Knowledge Graph) as well as structured data within your website could be used to determine search engine results page placement.

Entity-specific metrics might be used to enhance and refine this ranking/ordering process. In particular, the patent discusses four entity metrics: a relatedness metric, a notable entity type metric, a contribution metrics, and a prize metric. (Note: The patent also indicates that these 4 metrics are illustrative examples, meaning that others may also potentially be used.)

The four illustrative entity metrics are described as follows:

1. Relatedness Metric

The relatedness metric looks at the co-occurrence of an entity and its “entity type” on web pages. An “entity type” is generally a categorization or defining characteristic of an entity — for example, George Washington is an entity, of the entity type “US Presidents.”

[W]here the search query contains the entity reference ‘Empire State Building,’ which is determined to be of the entity type ‘Skyscraper,’ the co-occurrence of the text ‘Empire State Building’ and ‘Skyscraper’ in webpages may determine the relatedness metric.

In other words, when you type in a search query, Google may determine that a web page is more or less related to that query based on what other, related words are included on the page.

2. Notable Entity Type Metric

The notable entity type metric refers to the fact that an entity may be categorized under many different entity types, some of which are more “notable” than others — for example, Barack Obama could be categorized as an Author, Politician, Public Speaker and Celebrity, but he is most notable for being a U.S. President.


The notable entity type metric also takes into account that multiple entities can be of the same entity type, so one in particular may be the most relevant to a searcher. For example, both George Washington and Barack Obama are of the entity type U.S. Presidents — but a Google search for “us president” yields a direct answer containing Barack Obama.


In some implementations, the value of the notable entity type metric is a global popularity metric divided by a notable entity type rank. The notable entity type rank indicates the position of an entity type in a notable entity type list.

3. Contribution Metric (And Fame Metric)

In some cases, the contribution metric is based on critical reviews, fame rankings, and other information. In some implementations, rankings are weighted such that the highest values contribute most heavily to the metric.

It is no surprise that Google may have discovered the power and potential of something like a contribution metric and then applied that to other domains leveraging context. These are a couple of other interesting tidbits regarding reviews that the patent provides which are stated as follows:

  • “[I]nformation for determining a contribution metric may include social media, news sources, research publications, books, magazines, professional and user reviews on commerce websites, e.g. Amazon product reviews, professional and user reviews on dedicated reviewing sites, e.g. restaurant reviews on Yelp, user reviews on industry or domain specific sites, e.g. movie reviews on IMDB, any other suitable source of information, or any combination thereof.”
  • “[T]he search system may combine professional critic reviews and user reviews of restaurants, giving more weight to the professional reviews and less weight to the user reviews.”

The Fame Metric

A sub-metric of the contribution metric, the fame metric takes into account all the contributions of a particular entity. “For example, the fame metric of a movie actor may include a summation of the contribution metrics of that actor’s movies.”

Check out the search engine results page below for actor Tom Hanks. You can see below that the “contributions” involved in calculating this fame metric (in this case, his movies) are displayed prominently in the Knowledge Graph Panel in its own dedicated area, as mapped to the knowledge panel template in Google’s patent, “Providing Knowledge Panels With Search Results.”


A screenshot of the Google search results page for “tom hanks.”


FIG. 5B is a screen shot of an example search interface in which a knowledge panel is presented with search results. From Google’s patent, “Providing Knowledge Panels With Search Results”

4. Prize Metric

The prize metric is based on an entity’s awards and prizes. For example, a movie may have been awarded a variety of awards such as Oscars and Golden Globes, each with a particular value. In some implementations, the prize metric is weighted such that the highest values contribute most heavily to the metric.

The patent provides strong evidence that semantic web technology is being used as background context for the definitions of the metrics and the environment in which they are framed.

Different Algorithms For Different Screen Areas

There are many interesting elements to the patent, and the last I wanted to address is Figure 3.0 below.

Figure 3 – Ranking Entity Metrics in Search Results Patent

At first glance, it looks very innocuous, like an image of standard search results with a bunch of links. You find those sort of diagrams in many search patents. However it is accompanied by a very intriguing explanation of the figure, part of which reads as follows:

It will be understood that the presentation of search results in user interface 300 is merely an example and that any suitable presentation of any suitable results may be used. In another example, results may be image thumbnail links, ordered horizontally based on score . In another example, search results may include elements of a map and the search system uses score -* to determine which elements to present on the map.

What is interesting here is that it seems that specific regions of the search results are defined or templated in some manner, and ranking/ordering for each varies by context or domain. (Have you noted those fine lines on the screen demarcating or separating results in your search results?) From an SEO point of view, this means that optimizing a company’s website or web presence will be based on targeting these templates, each of which may well have their own ranking algorithm based on context.

As further food for thought, I would like to close with the diagram below, which shows an image from a patent on context, “Maintaining Search Context,” compared to a Google search engine results page for “golden retriever.”

Figure 10 - "Maintaining Search Results Context Patent" - and Google search results mapping

Figure 10 from Google’s “Maintaining Search Context” patent, compared to Google search results for “golden retriever.”

FIG. 10 shows user interface 1000, [which] includes exemplary content displayed in response to receiving a search query “Dog Breeds.” In some implementations, the search system displays related entity area links in the related entity area 1002, […] including “Bernese Mountain Dog,” “Poodle,” Golden Retriever,” “German Shepherd,” and “Greyhound.” The search system displays search results related to the query “Dog Breeds” in a search result area 1026. The search system displays information related to the entity “Dogs” in an information area, for example information area 1030. Information area may include links to other types of entities such as information area links 1032 to entities of the type “dog breed” and information area links 1034 to entities of the type “Service Animals.”

As you can see, different areas of the screen correspond to different result sets for the same query, presumably each with their own distinct algorithm for ranking and ordering information.


With the increasing shift from keyword search to entity search — and with the increased growth and usage of Knowledge Graph Panels and other data-based displays — comes the corresponding shift in the direction of SEO.

Ordering of items and ranking of information driven by a need for a positive and personalized user experience means that different algorithms apply at different times. These algorithms are based not just on traditional ranking factors that assess relevance and authority, but also by how data may be optimally visually displayed for various device types and screen sizes.

The post Structured Data & The SERPs: What Google’s Patents Tell Us About Ranking In Universal Search appeared first on Search Engine Land.



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App Indexing: Why It Matters For The Future Of Search

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By Neil Patel

Until recently, not many people were talking about app indexing. That changed earlier this year when Google announced its forthcoming mobile friendly ranking algorithm, which went live on April 21, 2015.

Following Google announcement, SEOs and webmasters focused on getting their websites “mobile friendly” by the April 21st deadline. Yet “more mobile-friendly websites in search results” was only half of Google’s announcement. The other half was this: “More relevant app content in search results.”

Starting today, we will begin to use information from indexed apps as a factor in ranking for signed-in users who have the app installed. As a result, we may now surface content from indexed apps more prominently in search. To find out how to implement App Indexing, which allows us to surface this information in search results, have a look at our step-by-step guide on the developer site.

What exactly is app indexing? Why is it important? Should SEOs and marketers even care, especially if they don’t sell an Android app?

You’re about to find out. In today’s mobile search climate, app indexing signals a shift in the direction of search, and marketers need to pay attention.

What Is App Indexing?

According to Google, “App Indexing lets Google index apps just like websites. Deep links to your Android app appear in Google Search results, letting users get to your native mobile experience quickly, landing exactly on the right content within your app.”

What does this mean in practical terms?

If you perform a Google search on a mobile device, the search results will include not just web pages, but also relevant content that is within an app.

Let’s say you’re on a mobile device doing a Google search. The most relevant content for your movie query is located in the IMDb app. In addition to surfacing a link to the IMDb website, Google will show you an IMDb app result, like this:

app content in mobile search results

Image from Google

As Google once said, “Sometimes the best answer is on a website, and sometimes it’s in an app.”

Is Search App Indexing New?

Not really. Google introduced App Indexing back in October 2013. The idea was simple: By enabling deep linking within their apps, webmasters could allow Googlebot to index app content just like web page content. That way, Android users with your app installed would have the option to go directly to your app content from within Google search results (as shown above).

In other words:

Whether you’re searching for a movie, an apartment, restaurant, shoes, news article, book, recipe, or even a job, you can now go directly to the relevant content within apps that you’ve installed on your phone.

Though it isn’t new, there have been several major developments in app indexing capabilities since it was initially announced. For example, Google has expanded app indexing to include results from apps that are not installed on your phone (thereby assisting users with app discovery). And, just this week, the search giant announced that it would soon be bringing app indexing to iOS devices as well.

Bing Does It, Too

Google doesn’t have a leg up on the competition as far as this particular technology is concerned. Bing allows Windows phone users to access app content in search results, too.

What’s the biggest difference? Sheer numbers. Bing has a marginal share of global search, paralleled by a marginal share of the smartphone market.


Why Does This Even Matter?

So, let’s get down to the big question: Does this matter?

Yes. The fact of the matter is that mobile search now includes app results — and their inclusion in these search results only seems to be growing.

I think that this signals a more important trend in the evolution of mobile search. The indexing net is ever expanding. Could it be that, with the rise of wearable technology, Google can index even more information in algorithmic search results (e.g., location, health status, heart rate)? How will this information be available?

There are plenty of questions that surround the issue:

  • What else will be indexed in the future?
  • How will this affect the search results for businesses that do not have Android (and soon, iOS) apps?
  • Will creating an app enhance a business’s search presence?
  • How will this drive mobile usage upward? Obviously, Google doesn’t index the apps on my desktop. If it’s doing it for mobile, how will this shift overall usage in favor of mobile devices?

A few things are certain.

  1. This improves the search experience for mobile users. App indexing signals the a broadening of search potential. The more content that is indexed, the greater the user’s search experience and results.
  2. Google drives higher engagement with apps. As Google’s indexing page explains, “App Indexing helps you drive usage of your app through Google. Deep links to your app appear in Google Search results on Android so users can get to your native mobile experience quickly and easily.”

  3. Google remains in control. GigaOM explains that the expansion of in-app search will also help Google sell more ads and hold down the competition.

What Should You Do?

There are two main takeaways. The first is tactical. The second is strategic.

  1. Get your app indexed. If your business has an Android app, you need it to be indexed. There are plenty of ways to find out how to do this. The best source that I’ve found with step-by-step instructions is found in the Codelabs.

    Note: App indexing technology isn’t widely available to iOS app developers yet, but Google has outlined some first steps you can take to prepare here.

  2. Grow your mobile presence. On a strategic front, mobile should become the new obsession for marketers and technology workers, regardless of their specific function.

    Conversion specialists should be tuned into the conversion optimization potential for mobile. SEOs must identify the areas where mobile results can be improved. CTOs must strategize new ways to gain traction among mobile users. Developers must identify touch points between mobile apps and sites.

    At a minimum level, keep mobile front and center in your thinking. Mobile is the present and future of marketing.


Clearly, app indexing is s step forward in the dominance of mobile search and mobile usage.

SEOs, developers, webmasters, and marketers need to know this. More importantly, they need to understand that mobile search and marketing is not a static industry. It is in motion. We are moving towards a bigger mobile universe, an expansion of indexation potential, and a greater need to optimize mobile in every way possible.

How does Google’s expanded app indexing affect your business?

The post App Indexing: Why It Matters For The Future Of Search appeared first on Search Engine Land.