Tag: Google

Google injects Hire with AI to speed up common tasks

June 19, 2018 No Comments

Since Google Hire launched last year it has been trying to make it easier for hiring managers to manage the data and tasks associated with the hiring process, while maybe tweaking LinkedIn while they’re at it. Today the company announced some AI-infused enhancements that they say will help save time and energy spent on manual processes.

“By incorporating Google AI, Hire now reduces repetitive, time-consuming tasks, like scheduling interviews into one-click interactions. This means hiring teams can spend less time with logistics and more time connecting with people,” Google’s Berit Hoffmann, Hire product manager wrote in a blog post announcing the new features.

The first piece involves making it easier and faster to schedule interviews with candidates. This is a multi-step activity that involves scheduling appropriate interviewers, choosing a time and date that works for all parties involved in the interview and scheduling a room in which to conduct the interview. Organizing these kind of logistics tend to eat up a lot of time.

“To streamline this process, Hire now uses AI to automatically suggest interviewers and ideal time slots, reducing interview scheduling to a few clicks,” Hoffmann wrote.

Photo: Google

Another common hiring chore is finding keywords in a resume. Hire’s AI now finds these words for a recruiter automatically by analysing terms in a job description or search query and highlighting relevant words including synonyms and acronyms in a resume to save time spent manually searching for them.

Photo: Google

Finally, another standard part of the hiring process is making phone calls, lots of phone calls. To make this easier, the latest version of Google Hire has a new click-to-call function. Simply click the phone number and it dials automatically and registers the call in call a log for easy recall or auditing.

While Microsoft has LinkedIn and Office 365, Google has G Suite and Google Hire. The strategy behind Hire is to allow hiring personnel to work in the G Suite tools they are immersed in every day and incorporate Hire functionality within those tools.

It’s not unlike CRM tools that integrate with Outlook or GMail because that’s where sales people spend a good deal of their time anyway. The idea is to reduce the time spent switching between tools and make the process a more integrated experience.

While none of these features individually will necessarily wow you, they are making use of Google AI to simplify common tasks to reduce some of the tedium associated with every-day hiring tasks.

Enterprise – TechCrunch

Google Patent on Structured Data Focuses upon JSON-LD

June 18, 2018 No Comments

Ernest Hemingway Structure Data

In a search engine that answers questions based upon crawling and indexing facts found within structured data on a site, that search engine works differently than a search engine which looks at the words used in a query, and tries to return documents that contain the same words as the ones in the query; hoping that such a matching of strings might contain an actual answer to the informational need that inspired the query in the first place. Search using Structured Data works a little differently, as seen in this flowchart from a 2017 Google patent:

Flow Chart Showing Structured Data in a Search

In Schema, Structured Data, and Scattered Databases such as the World Wide Web, I talked about the Dipre Algorithm in a patent from Sergey Brin, as I described in the post, Google’s First Semantic Search Invention was Patented in 1999. That patent and algorithm described how the web might be crawled to collect pattern and relations information about specific facts. In that case, about books. In the Google patent on structured data, we see how Google might look for factual information set out in semi-structured data such as JSON-LD, to be able to answer queries about facts, such as, “What is a book, by Ernest Hemingway, published in 1948-1952.

This newer patent tells us that it might solve that book search in this manner:

In particular, for each encoded data item associated with a given identified schema, the system searches the locations in the encoded data item identified by the schema as storing values for the specified keys to identify encoded data items that store values for the specified keys that satisfy the requirements specified in the query. For example, if the query is for semi-structured data items that have a value “Ernest Hemingway” for an “author” key and that have values in a range of “1948-1952” for a “year published” key, the system can identify encoded data items that store a value corresponding to “Ernest Hemingway” in the location identified in the schema associated with the encoded data item as storing the value for the “author” key and that store a value in the range from “1948-1952” in the location identified in the schema associated with the encoded data item as storing the value for the “year published” key. Thus, the system can identify encoded data items that satisfy the query efficiently, i.e., without searching encoded data items that do not include values for each key specified in the received query and without searching locations in the encoded data items that are not identified as storing values for the specified keys.

It was interesting seeing Google come out with a patent about searching semi-structured data which focused upon the use of JSON-LD. We see them providing an example of JSON on one of the Google Developer’s pages at: Introduction to Structured Data

As it tells us on that page:

This documentation describes which fields are required, recommended, or optional for structured data with special meaning to Google Search. Most Search structured data uses schema.org vocabulary, but you should rely on the documentation on developers.google.com as definitive for Google Search behavior, rather than the schema.org documentation. Attributes or objects not described here are not required by Google Search, even if marked as required by schema.org.

The page then points us to the Structured Data Testing Tool, to be used as you prepare pages for use with Structured Data. It also tells us that for checking on Structured Data after it has been set up, the Structured Data Report in Google Search Console can be helpful, and is what I usually look at when doing site audits.

The Schema.org website has had a lot of JSON-LD examples added to it, and it was interesting to see this patent focus upon it. As they tell us about it in the patent, it seems that they like it:

Semi-structured data is self-describing data that does not conform to a static, predefined format. For example, one semi-structured data format is JavaScript Object Notation (JSON). A JSON data item generally includes one or more JSON objects, i.e., one or more unordered sets of key/value pairs. Another example semi-structured data format is Extensible Markup Language (XML). An XML data item generally includes one or more XML elements that define values for one or more keys.

I’ve used the analogy of how XML sitemaps are machine-readable, compared to HTML Sitemaps, and that is how JSON-LD shows off facts in a machine-readable way on a site, as opposed to content that is in HTML format. As the patent tells us that is the purpose behind this patent:

In general, this specification describes techniques for extracting facts from collections of documents.

The patent discusses schemas that might be on a site, and key/value pairs that could be searched, and details about such a search of semi-structured data on a site:

The aspect further includes receiving a query for semi-structured data items, wherein the query specifies requirements for values for one or more keys; identifying schemas from the plurality of schemas that identify locations for values corresponding to each of the one or more keys; for each identified schema, searching the encoded data items associated with the schema to identify encoded data items that satisfy the query; and providing data identifying values from the encoded data items that satisfy the query in response to the query. Searching the encoded data items associated with the schema includes: searching, for each encoded data item associated with the schema, the locations in the encoded data item identified by the schema as storing values for the specified keys to identify whether the encoded data item stores values for the specified keys that satisfy the requirements specified in the query.

The patent providing details of the use of JSON-LD to provide a machine readable set of facts on a site can be found here:

Storing semi-structured data
Inventors: Martin Probst
Assignee: Google Inc.
US Patent: 9,754,048
Granted: September 5, 2017
Filed: October 6, 2014


Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing semi-structured data. One of the methods includes maintaining a plurality of schemas; receiving a first semi-structured data item; determining that the first semi-structured data item does not match any of the schemas in the plurality of schemas; and in response to determining that the first semi-structured data item does not match any of the schemas in the plurality of schemas: generating a new schema, encoding the first semi-structured data item in the first data format to generate the first new encoded data item in accordance with the new schema, storing the first new encoded data item in the data item repository, and associating the first new encoded data item with the new schema.

Take Aways

By using Structured Data such as in Schema Vocabulary in JSON-LD formatting, you make sure that you provide precise facts in key/value pairs that provide an alternative to the HTML-based content on the pages of a site. Make sure that you follow the Structured Data General Guidelines from Google when you add it to a site. That page tells us that pages that don’t follow the guidelines may not rank as highly, or may become ineligible for rich results appearing for them in Google SERPs.

And if you are optimizing a site for Google, it also helps to optimize the same site for Bing, and it is good to see that Bing seems to like JSON-LD too. It has taken a while for Bing to do that (see Aaron Bradle’s post, An Open Letter to Bing Regarding JSON-LD.) It appears that Bing has listened a little, adding some capacity to check on JSON-LD after it is deployed: Bing announces Bing AMP viewer & JSON-LD support in Bing Webmaster Tools. The Bing Markup Validator does not yet help with JSON-LD, but Bing Webmaster Tools now helps with debugging JSON-LD. I like using this Structured Data Linter myself.

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Up and Running with AdWords Add-on for Google Sheets

June 14, 2018 No Comments

In this article we will set up for the AdWords add-on for Google Sheets. By the end you’ll set up your own reports and automate the data gathering portion of your workflow.

Read more at PPCHero.com
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Test and Build for Mobile with Google Optimize

June 12, 2018 No Comments
From buying new shoes to booking weekend getaways, mobile can make life more convenient for consumers — and create big wins for marketers. While 40% of consumers will leave a web page that takes longer than three seconds to load, 89% of people are likely to recommend a brand after a positive brand experience on mobile.1 That’s why getting your mobile site in shape is more important than ever.

To create the seamless and responsive mobile site that consumers expect, you need the right tools, like Google Optimize. Optimize makes it easy to test different elements of your site to find the winning combination for the best mobile site possible. Now it’s even easier with our new responsive visual editor – and be sure to read on and learn how two of our clients found mobile success with Optimize 360, our enterprise version.

New! Preview your mobile site on any screen size 

While almost everyone has a mobile device, there are so many variations and screen sizes that it’s hard to take a one-size-fits-all approach to optimizing your mobile site. Now, once you’ve created your test page, you can use the new responsive editor to immediately preview what it looks like on any screen size. Or, if you want to see how it appears on a specific device, like a Nexus 7 or iPad, we’ve added more devices that you can select to preview. Learn more about the visual editor here.

Turn ideas to tests quickly 

The responsive visual editor in Optimize is just one solution to help marketers succeed on mobile. Our enterprise version, Optimize 360, makes it easy to make improvements to mobile sites efficiently and rapidly.

Dutch airline carrier Transavia Airlines turned to Optimize 360 to try out different ideas on its mobile site. In fact, the team runs about 10 A/B tests each month on the site, all without having to spend significant time or effort. And the best part? Time spent on analyzing the success of site tests has fallen by 50%. This allows Transavia to focus more on testing to improve its mobile site. Learn more in the full case study.

The path to mobile excellence starts with the customer journey 

Need some help determining what should test on your mobile site? Google Analytics 360 is a great place to start. You’ll be able to analyze any customer interaction, from search to checkout, to figure out which points of your purchase process need help. Then, once you’ve determined where your site needs work, using Optimize 360 to take action is simple, since it’s natively integrated with Analytics 360.

This is exactly how fashion retailer Mango used Analytics 360 and Optimize 360 to tackle its mobile site: After discovering that mobile visits to its online store had skyrocketed 50% year over year, Mango decided to dig a little deeper. In Analytics 360 Mango discovered that while many consumers browsed product listing pages, few were taking the next step to add products to their shopping cart. To reduce steps to checkout, Mango used Optimize 360 to include an “Add” button to product listing pages. This increased the number of users adding products to their carts by 49%. Find out more in the full case study.

Ready to optimize your own mobile site? 

Start testing new mobile experiences with the responsive visual editor in Optimize. This update is one that can help marketers do more on mobile — because whether it’s changing a button or fine-tuning a homepage with quick A/B tests, we’ve learned that small tweaks can make a big impact.

And, if you haven’t already, sign up for a free Optimize account and give it a try.

1 Google / Purchased: “How Brand Experiences Inspire Consumer Action” April 2017. US Smartphone Owners 18+ = 2010, Brand Experiences = 17,726.

Google Analytics Blog

Google to Offer Combined Content (Paid and Organic) Search Results

June 12, 2018 No Comments

Combined Content Search Results

Google Introduces Combined Content Results

This new patent is about “Combined content. What does that mean exatchly? When Google patents talk about paid search, they refer to those paid results as “content” rather than as advertisements. This patent is about how Google might combine paid search results with organic results in certain instances.

The recent patent from Google (Combining Content with Search Results) tells us about how Google might identify when organic search results might be about specific entities, such as brands. It may also recognize when paid results are about the same brands, whether they might be products from those brands.

In the event that a set of search results contains high ranking organic results from a specific brand, and a paid search result from that same brand, the process described in the patent might allow for the creation of a combined content result of the organic result with the paid result.

Merging Local and Organic Results in the Past

When I saw this new patent, it brought back memories of when Google found a way to merge organic search results with local search results. The day after I wrote about that, in the following post, I received a call from a co-worker who asked me if I had any idea why a top ranking organic result for a client might have disappeared from Google’s search results.

I asked her what the query term was, and who the client was. I performed the search, and noticed that our client was ranking highly for that query term in a local result, but their organic result had disappeared. I pointed her to the blog post I wrote the day before, about Google possibly merging local and organic results, with the organic result disappearing, and the local result getting boosted in rankings. It seemed like that is what happened to our client, and I sent her a link to my post, which described that.

How Google May Diversify Search Results by Merging Local and Web Search Results

Google did merge that client’s organic listing with their local listing, but it appeared that was something that they ended up not doing too often. I didn’t see them do that too many more times.

I am wondering, will Google start merging together paid search results with organic search results? If they would do that for local and organic results, which rank things in different ways, it is possible that they might with organic and paid. The patent describes how.

The newly granted patent does tell us about how paid search works in Search results at Google:

Content slots can be allocated to content sponsors as part of a reservation system, or in an auction. For example, content sponsors can provide bids specifying amounts that the sponsors are respectively willing to pay for presentation of their content. In turn, an auction can be run, and the slots can be allocated to sponsors according, among other things, to their bids and/or the relevance of the sponsored content to content presented on a page hosting the slot or a request that is received for the sponsored content. The content can be provided to a user device such as a personal computer (PC), a smartphone, a laptop computer, a tablet computer, or some other user device.

Combined Content – Combining Paid and Organic Results

Here is the process behind this new patent involving merging paid results (content) and organic results:

  1. A search query is received.
  2. Search results responsive to the query are returned, including one associated with a brand.
  3. Content items (paid search results) based at least in part on the query, are returned for delivery along with the search results responsive to the query.
  4. This approach includes looking to see if eligible content items are associated with a same brand as the brand associated in the organic search results.
  5. If there is a paid result and an organic result that are associated with each othte, it may combine the organi search result and the eligible content item into a combined content item, and provide the combined content item as a search result responsive to the request.

When Google decides whether the eligible content item is associated with the same brand as an organi result, it is a matter of determining that one content item is sponsored by an owner of the brand.

A combined result (of the paid and the organic results covering the same brand) includes combining what the patent is referring to as “a visual universal resource locator (VisURL),”

That combined item would include:

  • A title
  • Text from the paid result
  • A link to a landing page from the paid result into the combined content item
  • The combine items may also includ other information associated with the brand, such as:

  • A map to retail locations associated with brand retail presence.
  • Retail location information associated with the brand.

In addition to the brand owner, the organic result that could be combine might be from a retailer associated with the brand.

It can involve designating content from the sponsored item that is included in the combined content item as sponsored content (so it may show that content from the paid result as being an ad.)

It may also include “monetizing interactions with material that is included from the at least one eligible content item that is included in the combined content item based on user interactions with the material.” Additional items shown could include an image or logo associated with the brand, or one or more products associated with the brand, or combine additional links relevant to the result.

Additional Brand Content in Search Results

The patent behind this approach of combining paid and organic results was this one, granted in April:

Combining content with a search result
Inventors: Conrad Wai, Christopher Souvey, Lewis Denizen, Gaurav Garg, Awaneesh Verma, Emily Kay Moxley, Jeremy Silber, Daniel Amaral de Medeiros Rocha and Alexander Fischer
Assignee: Google LLC
US Patent: 9,947,026
Granted: April 17, 2018
Filed: May 12, 2016


Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. A search query is received. Search results responsive to the query are identified, including identifying a first search result in a top set of search results that is associated with a brand. Based at least in part on the query, one or more eligible content items are identified for delivery along with the search results responsive to the query. A determination is made as to when at least one of the eligible content items is associated with a same brand as the brand associated with the first search result. The first search result and one of the determined at least one eligible content items are combined into a combined content item and providing the combined content item as a search result responsive to the request.

The patent does include details on things such as an “entity/brand determination engine,” which can be used to compare paid results with organic results, to see if they cover the same brand. This is one of the changes that indexing things instead of strings is bringing us.

The patent does have many other details, and until Google announces that they are introducing this, I suspect we won’t hear more details from them about it. Then again, they didn’t announce officially that they were merging organic and local results when they started doing that. Don’t be surprised if this becomes available at Google.

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How to Supercharge Your Google Shopping Ads

June 3, 2018 No Comments

Get tips for how to handle common issues and get the most out of your Google Shopping ad campaigns!

Read more at PPCHero.com
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Google Search Labeled the California GOP as Nazis, But It’s No Conspiracy

May 31, 2018 No Comments

No, Big Tech isn’t trying to defame conservatives. But Google did make a big mistake.
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Introducing Advanced Analysis in Google Analytics 360

May 19, 2018 No Comments
In our conversations with marketers, we consistently hear that they are looking to gain deeper insights into the customer journey and then turn those insights into better customer experiences. 

Today we’re excited to announce Advanced Analysis, a new tool in beta for Google Analytics 360 customers. Advanced Analysis offers more detailed analysis techniques and deeper exploration capabilities, so you can improve your understanding of how people interact with your site and use those insights to deliver better experiences and reach your business goals.

Our top priority is to help you discover business insights while respecting user privacy. So, as with all Analytics capabilities, data utilized in Advanced Analysis is treated confidentially and securely.

Three ways to support sophisticated analysis

Advanced Analysis offers three new powerful techniques to help surface actionable insights about how people use your site: Exploration, Funnel Analysis, and Segment Overlap. And you can build audiences using any of the techniques, making it seamless to take action on the learnings that come out of your analysis.

With the Exploration technique, deeper analysis can be done in just a few clicks. Easily drag and drop multiple variables (segments, dimensions, and metrics) into the analysis canvas and see instant visualizations of your data. Exploration allows you to view and compare multiple analysis tabs in a single view — helping you test and refine your insights as you go.

Create multiple tabs and compare your analyses.

Use the Funnel Analysis technique to understand the steps users take to complete actions on your site. For example, you can quickly see how users progress through your purchase process and identify steps where it can be improved. With the current Custom Funnels in Analytics 360, you can add up to 5 steps (e.g. Visited Site, Added Product to Cart, Started Checkout, Started Payment, Purchased), but Advanced Analysis lets you add up to 10 steps. These extra steps – along with the ability to add multiple segments and dimension breakdowns – give you a deeper look at how different groups of people interact with your site.

The Segment Overlap technique allows you to see how segments you’ve created in Analytics 360 intersect with one another. For example, suppose you ran a major display campaign last month that led to a lot of new first-time purchasers, and now you want to know if they’re sticking around to become repeat customers. Segment Overlap allows you to compare how much this group of first-time buyers overlaps with users who have made a purchase in the past month and with users who are now returning to your site.

See overlap between different audience segments.

Advanced Analysis in action

Let’s review an example of how you can use these techniques together to uncover helpful new insights and put them into action. Imagine you manage an ecommerce store that sells to people around the world. You want to know if there are opportunities to improve your site experience for international customers and drive more sales.

With Advanced Analysis, you can get those answers easily. Starting with Exploration, you organize your Analytics 360 data to show number of users and revenue by country. You realize that you have a lot of new users in India but no revenue — so there may be an opportunity to improve the checkout process and boost conversions.

Organize data by country to determine your top countries by traffic.

From there, you investigate further with the Funnels technique to compare conversion rates at each step of your purchase funnel for US and India users. In doing so, you see there is a steep drop in completion rate on the checkout step for the India group. This confirms what you suspected, that the checkout flow can be improved for these users.

With just two clicks, you build an audience of India users who have added a product to their cart but didn’t purchase. Once the audience is created, you can use Optimize 360 to test a new checkout experience for that group. And then, with just a few more clicks in Analytics 360, you can push that audience to AdWords or DoubleClick Bid Manager to run a remarketing campaign, taking advantage of the now optimized checkout flow.

Identify conversion rate drop off, and build a custom audience based on that segment.

For enterprises looking to better understand customer journeys, Advanced Analysis helps surface hard-to-find insights and makes it easy to put those insights into action. Advanced Analysis will be rolling out over the coming weeks as a beta to all Analytics 360 users.

Happy analyzing!

Posted by Dan Stone, Product Manager, Google Analytics 360

Google Analytics Blog

Google Kubeflow, machine learning for Kubernetes, begins to take shape

May 5, 2018 No Comments

Ever since Google created Kubernetes as an open source container orchestration tool, it has seen it blossom in ways it might never have imagined. As the project gains in popularity, we are seeing many adjunct programs develop. Today, Google announced the release of version 0.1 of the Kubeflow open source tool, which is designed to bring machine learning to Kubernetes containers.

While Google has long since moved Kubernetes into the Cloud Native Computing Foundation, it continues to be actively involved, and Kubeflow is one manifestation of that. The project was only first announced at the end of last year at Kubecon in Austin, but it is beginning to gain some momentum.

David Aronchick, who runs Kubeflow for Google, led the Kubernetes team for 2.5 years before moving to Kubeflow. He says the idea behind the project is to enable data scientists to take advantage of running machine learning jobs on Kubernetes clusters. Kubeflow lets machine learning teams take existing jobs and simply attach them to a cluster without a lot of adapting.

With today’s announcement, the project begins to move ahead, and according to a blog post announcing the milestone, brings a new level of stability, while adding a slew of new features that the community has been requesting. These include Jupyter Hub for collaborative and interactive training on machine learning jobs and Tensorflow training and hosting support, among other elements.

Aronchick emphasizes that as an open source project you can bring whatever tools you like, and you are not limited to Tensorflow, despite the fact that this early version release does include support for Google’s machine learning tools. You can expect additional tool support as the project develops further.

In just over 4 months since the original announcement, the community has grown quickly with over 70 contributors, over 20 contributing organizations along with over 700 commits in 15 repositories. You can expect the next version, 0.2, sometime this summer.

Enterprise – TechCrunch

Richer Google Analytics User Management

May 5, 2018 No Comments
Today we are introducing more powerful ways to manage access to your Analytics accounts: user groups inside Google Analytics, and enforceable user policies. These new features increase your ability to tightly manage who has access to your data, and amplify the impact of the user management features we launched last year.

User Groups

User groups can now be created from and used within Google Analytics, simplifying user management across teams of people. This is a big time saver if you find yourself repeatedly giving out similar permissions to many people, and simplifies granting permissions as individuals rotate into or out of a team.

To start with user groups, visit either Suite Home or Google Analytics, navigate to the user management section, and click the “+” button. You will then see an option to add new groups, which will walk you through creating a user group, adding people to it, and assigning permissions to the group. Here is a full list of steps to make a user group.

Google Analytics User Management page highlighting the new option to create a user group

Enforced User Policies

Google Analytics 360 Suite user policies let you define which users will have access to your Analytics accounts, and which do not. When a user violates a policy, you will be warned of this through the user management section in Google Analytics or Suite Home and have the option to remove that user from your organization.

We have enhanced these policies so you can choose to block policy-violating users from being added to your Analytics accounts. While policies aren’t enforced by default, you have the option to block violator additions.  When you create or edit your organization’s user policy, you will see a toggle switch like the one below:

User policy setup showcasing the new enforced policy option

User groups and enforced user policies are supported in Google Analytics today, and support for more products is coming, as we continue to plan features that help customers better manage access to their critical business data.

Posted by Matt Matyas, Product Manager Google Analytics 360 Suite

Google Analytics Blog