Search Results for: YT
One thing most SEOs are aware of is that search results at Google are sometimes personalized for searchers; but it’s not something that I’ve seen too much written about. So when I came across a patent that is about personalizing search results, I wanted to dig in, and see if it could give us more insights.
The patent was an updated continuation patent, and I love to look at those, because it is possible to compare changes to claims from an older version, to see if they can provide some details of how processes described in those patents have changed. Sometimes changes are spelled out in great detail, and sometimes they focus upon different concepts that might be in the original version of the patent, but weren’t necessarily focused upon so much.
One of the last continuation patents I looked at was one from Navneet Panda, in the post, Click a Panda: High Quality Search Results based on Repeat Clicks and Visit Duration In that one, we saw a shift in focus to involve more user behavior data such as repeat clicks by the same user on a site, and the duration of a visit to a site.
Personalizing search results
Inventors: Paul Tucker
Assignee: GOOGLE INC.
US Patent: 9,734,211
Granted: August 15, 2017
Filed: February 27, 2015
A system receives a search query from a user and performs a search of a corpus of documents, based on the search query, to form a ranked set of search results. The system re-ranks the set of search results based on preferences of the user, or a group of users, and provides the re-ranked search results to the user.
The older version of the patent is Personalizing search results, which was filed on September 16, 2013, and was granted on March 10, 2015.
A continuation patent has claims rewritten on it, that reflect changes in how a process that has been patented might have changed, using the filing date of the original version of the patent.
I like comparing the claims, since that is what usually changes in continuation patents. I noticed some significant changes from the older version to this newer version.
There is a lot more emphasis on “high quality” sites and “distrusted sites” in the new version of the patent, which can be seen in the first claim of the patent. It’s worth putting the old and the new first claim one after the other, and comparing the two.
The Old First Claim
1. A method comprising: identifying, by at least one of one or more server devices, a first set of documents associated with a user, documents, in the first set of documents, being assigned weights that reflect a relative quantification of an interest of the user in the documents in the first set of documents; receiving, by at least one of the one or more server devices, a search query from a client device associated with the user; identifying, by at least one of the one or more server devices and based on the search query, a second set of documents, each document from the second set of documents having a respective score; determining, by at least one of the one or more server devices, that a particular document, from the second set of documents, matches or links to one of the documents in the first set of documents; adjusting, by at least one of the one or more server devices, the respective score of the particular document, to form an adjusted score, based on the weight assigned to the one of the documents in the first set of documents; forming, by at least one of the one or more server devices, a list of documents in which documents from the second set of documents are ranked based on the respective scores, the particular document being ranked in the list based on the adjusted score; and providing, by at least one of the one or more server devices, the list of documents to the client device.
The New First Claim
This is newly granted this week:
1. A method, comprising: determining, by at least one of one or more server devices, preferences of a user or a group of users, wherein the preferences indicate a document bias set and weights assigned to the documents, wherein the weights include distrusted document weights; determining, by the at least one of the one or more server devices, a high quality document set obtained from a document ranking algorithm; creating, by at least one of the one or more server devices, an intersection set of documents which includes documents in both the document bias set and the high quality document set; receiving, by at least one of the one or more server devices, a search query from the user; performing, by at least one of the one or more server devices, a search of a corpus of documents, based on the search query, to form a ranked set of search result documents; determining, by at least one of the one or more server devices, at least one link from the intersection set of documents to at least one document in the ranked set of search result documents, the at least one document not in the intersection set of documents; re-ranking, by at least one of the one or more server devices, the set of search result documents based on the preferences of the user or the group of users, wherein re-ranking the set of search results comprises: identifying a link of the set of links from the intersection set of documents to the document of the set of search result documents, and based on identifying the link, adjusting a rank of the search result document based on the weight assigned to the document in the document bias set from where the identified link originated from; and providing, by at least one of the one or more server devices, the re-ranked search results to the user.
The changes I am seeing in these two different first claims involve what are being called “distrusted document weights” from a “document bias set”, and showing pages from “a high quality document set.” The newer claim makes it more clear that personalized results come from these two different sets of results. It’s possible that it doesn’t change how personalization actually works, but the increased clarity is good to see.
The Purpose of these Personalizing Search Results Patents
We are told that some sites are favored more than others, and some are disliked more than others, and those are are created from a query or browser history, to generate a document bias set:
FIG. 1 illustrates an overview of the re-ranking of search results based on a user’s or group’s document or site preferences. In accordance with this aspect of the invention, a document bias set F 105 may be generated that indicates the user’s or group’s preferred and/or disfavored documents. Bias set F 105 may be automatically collected from a query or browser history of a user. Bias set F 105 may also be generated by human compilation, or editing of an automatically generated set. Bias set F 105 may include a set of documents shared, or developed, by a group that may further include a community of users of common interest. Document bias set F 105 may include one or more designated documents (e.g., documents a, b, x, y and z) with associated weights (e.g. w.sup.a.sub.F, w.sup.b.sub.F, w.sup.x.sub.F, w.sup.y.sub.F and w.sup.z.sub.F). The weights may be assigned to each document (e.g., documents a, b, x, y and z) based on a user’s, or group’s, relative preferences among documents of bias set F 105. For example, bias set F 105 may include a user’s personal most-respected, or most-distrusted, document list, with the weights being assigned to each document in bias set F 105 based on a relative quantification of the user’s preference among each of the documents of the set.
This document bias set mention appears in both the older, and the newer version of the patent.
The patents also both refer to a high quality document set, and that is described in a way that seems to place a lot of attention on PageRank or a Hubs and Authority approach to ranking:
A high quality document set L 110 may be obtained from any existing document ranking algorithm. Such document ranking algorithms may include a link-based ranking algorithm, such as, for example, Google’s PageRank algorithm, or Kleinberg’s Hubs and Authorities ranking algorithm. The document ranking algorithm may provide a global ranking of document quality that may be used for ranking the results of searches performed by search engines. High quality document set L 110 may be derived from the highest-ranking documents in the web as ranked by an existing document ranking algorithm. In one implementation, for example, set L 110 may include the top percentage of the documents globally ranked by an existing document ranking algorithm (e.g., the highest ranked 20% of documents). In an implementation using PageRank, set L 110 may include documents having PageRank scores higher than a threshold value (e.g., documents with PageRank scores higher than 10,000,000). Set L 110 may include multiple documents (e.g., documents m, n, o, p, x, y and z) with associated weights (e.g., weights W.sup.m.sub.L, W.sup.n.sub.L, W.sup.o.sub.L, W.sup.p.sub.L, W.sup.x.sub.L, W.sup.y.sub.L and W.sup.Z.sub.L). The weights may be assigned to each document (e.g., documents m, n, o, p, x, y and z) based on a relative ranking of “quality” between the different documents of set L 110 produced by the document ranking algorithm.
Personalized results served to a searcher are results that come from both the document bias set, and the high quality document set (as the patent says, from an “intersection” between the two sets).
If you are interested in how personalized search may work at Google, spending some time with this new patent may provide some insights. Knowing about how two different sets of documents are involved in returning results is a good starting point.
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Chances are that some data is “hidden” in silos across your company. According to new research from Econsultancy in partnership with Google, 86% of senior executives agree: eliminating organizational silos is critical to expanding the use of data and analytics in decision-making.1
If teams don’t talk, or if your organization doesn’t have an integrated data strategy to harness marketing, customer, and advertising data, information and ideas won’t flow freely. Here are three ways to break down data silos and get your organization on the path to a more collaborative, data-driven culture.
1. Make data accessible — to everyone
If you have work to do to get your data house in order, you’re not alone: 61% of marketing decision-makers struggled to access or integrate data they needed last year.2
The first step to making data more accessible is to outline a data strategy that identifies data owners and key points of contact for each information source. Next, define how to integrate data and related technologies, and provide standards and processes related to data security and privacy. Include guidelines for sharing data internally.
Democratizing access to data and insights enables employees at all levels to check their gut — and that leads to better results. The same Econsultancy study found that marketing leaders are 1.6X as likely as their mainstream counterparts to strongly agree that open access to data leads to higher business performance.3
Watch our on-demand webinar featuring new research and best practices in marketing data and analytics strategy from Google and MIT Sloan School of Management.
2. Champion the value of data-driven insights over gut feelings
Once data is made available to marketing managers and business decision-makers, make sure you champion a data-first mindset with your team. Using data effectively is a key differentiator for marketers who are ahead of the curve.
While a documented data and analytics strategy can provide a guide for all employees, support from the top helps set the tone. Nearly two-thirds of leading organizations say that their executives treat data-driven insights as more valuable than gut instinct.4
C-suite buy-in and other champions across the company help reinforce a data-driven culture by giving teams stuck in silos a nudge to collaborate and share analytic insights. Even better, this environment should give teams the incentive to align or share goals since data is core to campaign plans and marketing strategy.
3. Educate stakeholders on how to interpret the data
Having access to data is great, but if employees don’t know how to use it, the insights will remain isolated and unused. Consider this: 75% of marketers agree that lack of education and training on data and analytics is the biggest barrier to more business decisions being made based on data insights.5
If a team is empowered with the right learnings, it will proactively integrate data rather than push it aside. Set up brown bag sessions or internal trainings, or provide employees access to self-paced learning modules.
Finally, consider pairing the “data evangelists” and data storytellers within your organization with different team members to identify areas of focus based on relevant business goals and the biggest opportunities.
1, 3, 4, 5 Google/Econsultancy, “The Customer Experience Is Written in Data”, U.S., n=677 marketing and measurement executives at companies with over $ 250M in revenues, primarily in North America; n=199 leading marketers who reported marketing significantly exceeded top business goal in 2016; n=478 mainstream marketers (remainder of sample); May 2017. 2 Google Surveys, U.S., “2016–2017 Marketing Analytics Challenges and Goals,” Base: 203, marketing executives who have analytics or data-driven initiatives, Dec. 2016.
2 Google Surveys, U.S., “2016–2017 Marketing Analytics Challenges and Goals,” Base: 203, marketing executives who have analytics or data-driven initiatives, Dec. 2016.
Posted by Casey Carey, Director of Platforms & Publisher Marketing, Google
Beginning in 2011, search marketers began to lose visibility over the organic keywords that consumers were using to find their websites, as Google gradually switched all of its searches over to secure search using HTTPS.
As it did so, the organic keyword data available to marketers in Google Analytics, and other analytics platforms, slowly became replaced by “(not provided)”. By 2014, the (not provided) issue was estimated to impact 80-90% of organic traffic, representing a massive loss in visibility for search marketers and website owners.
Marketers have gradually adjusted to the situation, and most have developed rough workarounds or ways of guessing what searches are bringing customers to their site. Even so, there’s no denying that having complete visibility over organic keyword data once more would have a massive impact on the search industry – as well as benefits for SEO.
One company believes that it has found the key to unlocking “(not provided)” keyword data. We spoke to Daniel Schmeh, MD and CTO at Keyword Hero, a start-up which has set out to solve the issue of “(not provided)”, and ‘Wizard of Moz’ Rand Fishkin, about how “(not provided)” is still impacting the search industry in 2017, and what a world without it might look like.
Content produced in association with Keyword Hero.
“(not provided)” in Google Analytics: How does it impact SEO?
“The “(not provided)” keyword data issue is caused by Google the search engine, so that no analytics program, Google Analytics included, can get the data directly,” explains Rand Fishkin, founder and former CEO of Moz.
“Google used to pass a referrer string when you performed a web search with them that would tell you – ‘This person searched for “red shoes” and then they clicked on your website’. Then you would know that when people searched for “red shoes”, here’s the behavior they showed on your website, and you could buy ads against that, or choose how to serve them better, maybe by highlighting the red shoes on the page better when they land there – all sorts of things.”
“You could also do analytics to understand whether visitors for that search were converting on your website, or whether they were having a good experience – those kinds of things.
“But Google began to take that away around 2011, and their reasoning behind it was to protect user privacy. That was quickly debunked, however, by folks in the industry, because Google provides that data with great accuracy if you choose to buy ads with them. So there’s obviously a huge conflict of interest there.
“I think the assumption at this point is that it’s just Google throwing their weight around and being the behemoth that they can be, and saying, ‘We don’t want to provide this data because it’s too valuable and useful to potential competitors, and people who have the potential to own a lot of the search ranking real estate and have too good of an idea of what patterns are going on.
“I think Google is worried about the quality and quantity of data that could be received through organic search – they’d prefer that marketers spend money on advertising with Google if they want that information.”
Where Google goes, its closest competitors are sure to follow, and Bing and Yandex soon followed suit. By 2013, the search industry was experiencing a near-total eclipse of visibility over organic keyword data, and found itself having to simply deal with the consequences.
“At this point, most SEOs use the data of which page received the visit from Google, and then try to reverse-engineer it: what keywords does that page rank for? Based on those two points, you can sort of triangulate the value you’re getting from visitors from those keywords to this page,” says Fishkin.
However, data analysis and processing have come a long way since 2011, or even 2013. One start-up believes that it has found the key to unlocking “(not provided)” keyword data and giving marketers back visibility over their organic keywords.
How to unlock “(not provided)” keywords in Google Analytics
“I started out as a SEO, first in a publishing company and later in ecommerce companies,” says Daniel Schmeh, MD and CTO of SEO and search marketing tool Keyword Hero, which aims to provide a solution to “(not provided)” in Google Analytics. “I then got into PPC marketing, building self-learning bid management tools, before finally moving into data science.
“So I have a pretty broad understanding of the industry and ecosystem, and was always aware of the “(not provided)” problem.
“When we then started buying billions of data points from browser extensions for another project that I was working on, I thought that this must be solvable – more as an interesting problem to work on than a product that we wanted to sell.”
Essentially, Schmeh explains, solving the problem of “(not provided)” is a matter of getting access to the data and engineering around it. Keyword Hero uses a wide range of data sources to deduce the organic keywords hidden behind the screen of “(not provided)”.
“In the first step, the Hero fetches all our users’ URLs,” says Schmeh. “We then use rank monitoring services – mainly other SEO tools and crawlers – as well as what we call “cognitive services” – among them Google Trends, Bing Cognitive Services, Wikipedia’s API – and Google’s search console, to compute a long list of possible keywords per URL, and a first estimate of their likelihood.
“All these results are then tested against real, hard data that we buy from browser extensions.
“This info will be looped back to the initial deep learning algorithm, using a variety of mathematical concepts.”
Ultimately, the process used by Keyword Hero to obtain organic keyword data is still guesswork, but very advanced guesswork.
“All in all, the results are pretty good: in 50 – 60% of all sessions, we attribute keywords with 100% certainty,” says Schmeh.
“For the remainder, at least 83% certainty is needed, otherwise they’ll stay (not provided). For most of our customers, 94% of all sessions are matched, though in some cases we need a few weeks to get to this matching rate.”
If the issue of “(not provided)” organic keywords has been around since 2011, why has it taken us this long to find a solution that works? Schmeh believes that Keyword Hero has two key advantages: One, they take a scientific approach to search, and two, they have much greater data processing powers compared with six years ago.
“We have a very scientific approach to SEO,” he says.
“We have a small team of world-class experts, mostly from Fraunhofer Institute of Technology, that know how to make sense of large amounts of data. Our background in SEO and the fact that we have access to vast amounts of data points from browser extensions allowed us to think about this as more of a data science problem, which it ultimately is.
“Processing the information – the algorithm and its functionalities – would have worked back in 2011, too, but the limiting factor is our capability to work with these extremely large amounts of data. Just uploading the information back into our customers’ accounts would take 13 hours on AWS [Amazon Web Services] largest instance, the X1 – something we could never afford.
“So we had to find other cloud solutions – ending up with things that didn’t exist even a year ago.”
A world without “(not provided)”: How could unlocking organic keyword data transform SEO?
If marketers and website owners could regain visibility over their organic keywords, this would obviously be a huge help to their efforts in optimizing for search and planning a commercial strategy.
But Rand Fishkin also believes it would have two much more wide-reaching benefits: it would help to prove the worth of organic SEO, and would ultimately lead to a better user experience and a better web.
“Because SEO has such a difficult time proving attribution, it doesn’t get counted and therefore businesses don’t invest in it the way they would if they could show that direct connection to revenue,” says Fishkin. “So it would help prove the value, which means that SEO could get budget.
“I think the thing Google is most afraid of is that some people would see that they rank organically well enough for some keywords they’re bidding on in AdWords, and ultimately decide not to bid anymore.
“This would cause Google to lose revenue – but of course, many of these websites would save a lot of money.”
And in this utopian world of keyword visibility, marketers could channel that revenue into better targeting the consumers whose behavior they would now have much higher-quality insights into.
“I think you would see more personalization and customization on websites – so for example, earlier I mentioned a search for ‘red shoes’ – if I’m an ecommerce website, and I see that someone has searched for ‘red shoes’, I might actually highlight that text on the page, or I might dynamically change the navigation so that I had shades of red inside my product range that I helped people discover.
“If businesses could personalize their content based on the search, it could create an improved user experience and user performance: longer time on site, lower bounce rate, higher engagement, higher conversion rate. It would absolutely be better for users.
“The other thing I think you’d see people doing is optimizing their content efforts around keywords that bring valuable visitors. As more and more websites optimized for their unique search audience, you would generally get a better web – some people are going to do a great job for ‘red shoes’, others for ‘scarlet sandals’, and others for ‘burgundy sneakers’. And as a result, we would have everyone building toward what their unique value proposition is.”
Daniel Schmeh adds that unlocking “(not provided)” keyword data has the ability to make SEO less about guesswork and more substantiated in numbers and hard facts.
“Just seeing simple things, like how users convert that use your brand name in their search phrase versus those who don’t, has huge impact on our customers,” he says. “We’ve had multiple people telling us that they have based important business decisions on the data.
“Seeing thousands of keywords again is very powerful for the more sophisticated, data-driven user, who is able to derive meaningful insights; but we’d really like the Keyword Hero to become a standard tool. So we’re working hard to make this keyword data accessible and actionable for all of our users, and will soon be offering features like keyword clustering – all through their Google Analytics interface.”
To find out more about how to unlock your “(not provided)” keywords in Google Analytics, visit the Keyword Hero website.
Filter controls: search
Filters give report viewers a powerful way to slice data by specific segments. But filters with hundreds or even thousands of possible values to choose from were previously difficult to use, requiring scrolling through very long lists of filter items. We recently added a search feature within the filter component, letting users quickly find and select or deselect specific items.
Filter controls: single-select
There are also scenarios when it only makes sense to filter a report on a single value, as filtering on multiple values would return confusing or nonsensical data. Report creators now have the ability to configure filters to allow for single-selection only.
New Combo charts allow users to create a line chart with a non-time-based dimension on the X-axis (previously only time-based dimensions were supported). The new component can plot a single dimension with up to 5 metrics, or 2 dimensions with a single metric. Learn more about Combo charts here.
Links in tabular data
Tables in Data Studio reports can now display clickable links! This feature introduces a new type of interactivity, as viewers can now be redirected to to relevant content outside the report. To use this feature, report owners must use a data source containing a column of URLs. Data Studio will detect this column and assign it to the URL field type (if automatic detection does not work data source owners can also set the field type to URL manually). Learn more about this here.
Submitting and voting for new features
The Data Studio team will continue to introduce new features and product enhancements. Have a feature request? You can view requests submitted by other users, upvote your favorites, or create new ones. Learn more here.
Posted by Alon Gotesman, Product Manager, Google Data Studio
Since the early 2010s, visual search has been offering users a novel alternative to keyword-based search results.
But with the sophistication of visual search tools increasing, and tech giants like Google and Microsoft investing heavily in the space, what commercial opportunities does it offer brands today?
Visual search 101
There are two types of visual search. The first compares metadata keywords for similarities (such as when searching an image database like Shutterstock).
The second is known as ‘content-based image retrieval’. This takes the colour, shape and texture of the image and compares it to a database, displaying entries according to similarity.
From a user perspective, this massively simplifies the process of finding products they like the look of. Instead of trying to find the words to describe the object, users can simply take a photo and see relevant results.
Visual search engines: A (very) brief history
The first product to really make use of this technology was ‘Google Goggles’. Released in 2010, it offered some fairly basic image-recognition capabilities. It could register unique objects like books, barcodes, art and landmarks, and provide additional information about them.
It also had the ability to understand and store text in an image – such as a photo of a business card. However, it couldn’t recognize general instances of objects, like trees, animals or items of clothing.
CamFind took the next step, offering an app where users could take photos of any object and see additional information alongside shopping results. My tests (featuring our beautiful office plant) yielded impressively accurate related images and web results.
More importantly for brands, it offers advertising based on the content of the image. However, despite the early offering, the app has yet to achieve widespread adoption.
A Pinterest-ing development
A newer player in the visual search arena, image-focused platform Pinterest has what CamFind doesn’t – engaged users. In fact, it reached 150m monthly users in 2016, 70m of which are in the US with a 60:40 split women to men.
So what do people use Pinterest for? Ben Silbermann, its CEO and co-founder, summed it up in a recent blog post:
“As a Pinner once said to me, “Pinterest is for yourself, not your selfies”—I love that. Pinterest is more of a personal tool than a social one. People don’t come to see what their friends are doing. (There are lots of other great places out there for that!) Instead, they come to Pinterest to find ideas to try, figure out which ones they love, and learn a little bit about themselves in the process.”
In other words, Pinterest is designed for discovery. Users are there to look for products and ideas, not to socialize. Which makes it inherently brand-friendly. In fact, 93% of Pinners said they use Pinterest to plan for purchases, and 87% said they’d bought something because of interest. Adverts are therefore less disruptive in this context than platforms like Facebook and Twitter, where users are focused on socializing, not searching.
Pinterest took their search functionality to the next level in February 2017 with an update offering users three new features:
Shop the Look allowed users to pick just one part of an image they were interested in to explore – like a hat or a pair of shoes.
Related Ideas gives users the ability to explore a tangent based on a single pin. For example, if I were interested in hideously garish jackets, I might click ‘more’ and see a collection of equally tasteless items.
Pinterest Lens was the heavyweight feature of this release. Linking to the functionality displayed in Shop the Look, it allowed users to take photos on their smartphone and see Pins that looked similar to the object displayed.
In practice, this meant a user might see a chair they were interested in purchasing, take a photo, and find similar styles – in exactly the same way as CamFind.
Pinterest Lens today
What does it mean for ecommerce brands?
Visual search engines have the potential to offer a butter-smooth customer journey – with just a few taps between snapping a picture of something and having it in a basket and checking out. Pinterest took a big step towards that in May this year, announcing they would be connecting their visual search functionality to Promoted Pins – allowing advertisers to get in front of users searching visually by surfacing adverts in the ‘Instant Ideas’ and the ‘More like this’ sections.
For retail brands with established Pinterest strategies like Target, Nordstrom, Walgreens and Lululemon, this is welcome news, as it presents a novel opportunity for brands to connect with users looking to purchase products.
Product images can be featured in visual search results
Nearly 2 million people Pin product-rich pins every day. The platform even offers the ability to include prices and other data on pins, which helps drive further engagement. Furthermore, it has the highest average order value of any major social platform at $ 50, and caters heavily to users on mobile (orders from mobile devices increased from 67% to 80% between 2013-2015).
But while Pinterest may have led the way in terms of visual search, it isn’t alone. Google and Bing have both jumped on the trend with Lens-equivalent products in the last year. Both Google Lens and Bing Visual Search (really, Microsoft? That’s the best you have?) function in an almost identical way to Pinterest Lens. Examples from Bing’s blog post on the product even show it being applied in the same contexts – picking out elements of a domestic scene and displaying shopping results.
One interesting question for ecommerce brands to answer will be how to optimize product images for these kinds of results.
Google Lens, announced at Google’s I/O conference in May to much furore, pitches itself as a tool to help users understand the world. By accessing Google’s vast knowledge base, the app can do things like identify objects, and connect to your WiFi automatically by snapping the code on the box.
Of course, this has a commercial application as well. One of the use cases highlighted by Google CEO Sundar Pichai was photographing a business storefront and having the Google Local result pop up, replete with reviews, menus and contact details.
The key feature here is the ability to connecting a picture taken with an action. It doesn’t take too much to imagine how brands might be able to use this functionality in interesting and engaging ways – for example, booking event tickets directly from an advert, as demonstrated at I/O:
Many marketers think we’re on the brink of a revolution when it comes to search. The growing popularity of voice search is arguably an indicator that consumers are moving away from keyword-based search and towards more intuitive methods.
It’s too soon to write off the medium entirely, of course – keywords are still by the far the easiest way to access most information. But visual search, along with voice, are certainly still useful additions to the roster of tools we might use to access information on the internet.
Ecommerce brands would be wise to keep close tabs on the progress of visual search tools; those that are prepared will have a significant competitive advance over those that aren’t.
This post was originally published on our sister site, ClickZ, and has been reproduced here for the enjoyment of our audience on Search Engine Watch.
Check out these quick video to see this feature in action
Try it out now!
We added the Data control to these templates so you test it out with your data: ￼ ￼
This feature is great if you:
Are an agency or large organization with access to many Google Analytics views and do not want to create a Data Studio report for each view. For example, if you have a set of charts and data you monitor every day, you can now build a report in Data Studio with those charts and data, add the Data Control, and quickly go between any of the views you have access to, allowing you to monitor your entire business very fast.
Are a large organization with many websites across: different brands, different regions, or different business units, and want to unify reporting and KPIs across your entire organization. Now you can build a template report in Data Studio, add the Data Control, and share the report across your organization. Every user will be able to see their data, in your curated report.
The data control is public for all users.
Read the Help Center for more details on how to use it.
If you build an exciting report, please submit to our gallery, so we can showcase it.
Posted By Nick Mihailovski, Product Manager, Data Studio
Google has released a new, feed-based mobile homepage in the US, with an international launch due in the next two weeks.
This is perhaps the most drastic and significant update of the Google.com homepage (the most visited URL globally) since Google’s launch in 1996.
The upgraded, dynamic entry point to the world’s biggest search engine will be available initially on mobile devices via both the Google website and its mobile apps, but will also be rolled out to desktop.
Let’s take a look at what’s changing and how, as well as what it might mean for marketers.
What’s different about the new homepage?
Google’s new homepage allows users to customize a news feed that updates based on their interests, location, and past search behaviors.
On the Google.com website (via a mobile device), there are now four icon-based options: Weather, Sports, Entertainment, and Food & Drink.
The ‘Weather’ and ‘Food & Drink’ options can be used straight away, as they take the user’s location data to provide targeted results. The ‘Sports’ and ‘Entertainment’ options require a little more customization before users can benefit from them fully. Without this, Google will just serve up popular and trending stories within each category.
In the example below, I tapped on the ‘Sports’ icon, then selected to follow a baseball team, the Boston Red Sox. Based on this preference, Google then knows to show me updates on this team on my homepage. The results varied in their media format, with everything from Tweets to GIFs and videos shown in my feed.
This means that rather than encountering the iconic search bar, Google logo, and the unadorned white interface we have all become accustomed to, each user’s feed will be unique. As I start to layer on more of the topics I am interested in, Google gains more information with which to tailor my feed.
On the Google mobile app, based on my selection above, my homepage looks as follows:
This is quite a big departure and is an experience we should expect the Google.com website to mirror soon. For now, the latter retains enough of the old aesthetic to be recognizable, but the app-based version is more overt in its positioning of suggested content.
The trusty search bar is still there, but users are encouraged to interact with their interests too. The interface is designed for tapping as well as typing.
Sashi Thakur, a Google engineer, has said of the launch,
“We want people to understand they’re consuming information from Google. It will just be without a query.”
It is essentially an extension of the functionality that has been available in Google’s Android app since December. Google will also continue to use push notifications to send updates on traffic, weather, and sports, based on the user’s set preferences.
Why is Google launching this product now?
Google has struggled to find a significant commercial hit to rival its hugely lucrative search advertising business. That business relies on search queries and user data, so anything that leads users to spend more time on Google will be of significant value.
The same motive has led to the increased presence of Google reservations, which now allow users to make appointments for a range of services from the search results page.
As Google stated in their official announcement, “The more you use Google, the better your feed will be.”
Users type a query when they have an idea of what they want to find; Google is pre-empting this by serving us content before we are even aware of what exactly we would like to know. By offering a service that will increase in accuracy in line with increased usage, Google hopes users will get hooked on a new mode of discovering information.
You’d be forgiven for wondering whether Google is trying to find its way into social media again. After the demise of the short-lived Google+ platform, Google has seen Facebook grow as a credible threat in the battle for digital advertising dollars.
Facebook’s algorithmic news feed has been a significant factor in its rise in popularity, and with Google Posts incorporated into this news feed, there are certainly elements reminiscent of a certain social network in Google’s new homepage initiative. Readers may also recall the launch of iGoogle in 2005, a similar attempt to add some personalization to the homepage.
That said, it seems more likely that these changes have been rolled out in response to recent launches from Amazon than as a direct challenge to Facebook.
Amazon has made an almost dizzying amount of product announcements and acquisitions of late. As a pure-play ecommerce company, their rapid growth will have been cause for consternation at Google and there is a need to respond.
Of particular interest in relation to the new Google feed is the very recent launch of Amazon Spark, a shoppable feed of curated content for Amazon Prime members. It is only available via the iOS app for now, but it will be launched on Android soon too.
Spark is a rival to Instagram in some ways, with its very visual feed and some early partnerships with social media influencers. It is also similar to Pinterest, as it encourages users to save their favorite images for later and clearly tries to tap into the ‘Discovery’ phase that Pinterest has made a play for recently.
Amazon has also launched its ‘Interesting Finds’ stream, which works in a noticeably Pinterest-esque fashion:
In Google’s announcement of the new homepage, they make use of the verbs “discover” and “explore”. Both Amazon and Pinterest have tried to shape and monetize these phases of the search-based purchase journey; Google evidently thinks its homepage needs to take on a new life if it is to compete.
Will it open new opportunities for marketers?
Almost certainly. We should view this as a welcome addition to the elements of current search strategies, with a host of new opportunities to get in front of target audiences.
Google is not launching this product because of any existential threat to its core search product, which still dominates Western markets:
The update should encourage a shift in user behavior. As people get used to the new experience, they will interact with Google in new ways and marketers need to be prepared for this.
From a paid perspective, we can expect to see new options open to advertisers, but not in the immediate future.
Amazon has two innate monetization mechanisms within Spark: users have to sign up to Prime (for an annual fee) to get access and, when they do, they are served a shoppable list of results. It comes as no surprise when we are on Amazon that we will be asked if we want to buy products.
That is not always the case on Google, where the initial purpose of the news feed is to gain traction with users and encourage them to spend more time within the site.
Options for sponsored content and (almost inevitably) paid ecommerce ads will come later, once a large and engaged user base has been established.
There are millions of people on Pinterest, searching, pinning, and sharing – so it’s important to recognize its potential for building awareness and filling the top of the funnel, particularly for ecommerce companies.
This blog will discuss a couple of recommended targeting types within Pinterest to help fill the top of the funnel and essentially build up your audience. From there, once your audience is built out, we’ll run through how to actually capitalize on these new users to drive sales.
Let’s jump in.
Use Pinterest to fill the funnel
Pinterest has some specific features that are highly effective for building your audience. These include:
You can leverage user intent by targeting specific keywords that users are searching within Pinterest.
For example, if you are a trendy clothing brand that sells sweaters, you may want to target “trendy sweaters” and have your ad (in Pinterest lingo, your promoted pin) show up in the search results and related pins.
Pinterest will determine a user’s interest based on the pins they have engaged with and saved. Your ad (promoted pin) will show up in the user’s home feed or relevant topics feed.
A Promoted Pin on Pinterest
This is similar to Facebook’s lookalike targeting; you can upload a customer list and Pinterest will target audiences similar in behaviors, traits, and characteristics as that customer list. Our recommendation is to start off with your top customers – for example, your highest-LTV or AOV audiences.
I would initially recommend prioritizing the Actalike and keyword targeting as they tend to be more effective at getting in front of highly relevant audiences. But by leveraging any or all of the targeting options, you’re discovering and engaging with new, relevant audiences and driving them to your site.
That said, make sure your expectations are aligned. You should not expect to see Pinterest as a lever for immediate purchases, but more as a longer-term play where you’re developing an awareness and building your audience to hit later via a few different methods below to actually drive the sale.
That said, let’s talk about how to…
Convert Pinterest engagement into sales
Now that you’ve engaged with your audiences via Pinterest, you should be capturing those audiences for remarketing purposes.
First, to be smart with your remarketing efforts and truly understand the value of Pinterest, you should make sure every link on your Pinterest ads include a tag that labels it as Pinterest. You can use UTM parameters or anything else, but essentially you want to make sure that you can identify these audiences that have come through from Pinterest and segment them out.
You can then create specific audiences within both Google and Facebook (for example) that have come in through Pinterest. (E.g. url contains ‘utm_source=pinterest). Now you can separate out these audiences, and as you use them in your retargeting strategies, you can understand if the Pinterest audiences you have built are actually converting into sales.
Speaking of converting, I’d recommend the following methods:
RLSA (remarketing for search ads)
Layer your Pinterest audiences onto existing search campaigns and add a higher bid modifier. These audiences have already visited your site and developed a familiarity with your brand. If they end up searching for your product, you want to make sure your ad appears high in the search results to remind them of your brand, pull them to your site, and entice them to convert.
One RLSA strategy I’d recommend is to create a separate “broad” RLSA campaign where you can bid on head terms, and broader but still relevant terms that you normally wouldn’t be able to afford.
For example, you typically may not bid on a term like “womens clothing” because it is so generic and has heavy competition, but given the user has already visited your site, you can create an RLSA campaign, layer your Pinterest audiences, and bid on the term.
The thought behind this is that by serving your ad on this more generic keyword, you are reminding them that you sell women’s clothing. Since the users have been to your site, they’ll have a sense of if it’s worth visiting. Essentially, this is way of getting in front of relevant eyes without doing significant harm to overall efficiency.
You can do this on both Facebook and GDN where ads include the product the user has visited on the site (as well as other relevant products). The usual segmentation caveats apply; you want to make sure you’re segmenting by time lapsed since the visit and depth of site pages reached and bid accordingly.
Remarketing for shopping
Make use of your audience list by layering it onto your shopping campaigns. Again, the goal here is to bid more aggressively so you can ensure your ad shows up for the audiences who have engaged with your Pinterest ad, visited the site, and developed familiarity with the brand. You’ll typically see higher CVRs for these types of audiences.
The main takeaway here: if you’re not investing in Pinterest, you’re missing out on engaging a robust, potentially high-ROI audience. The platform itself has come a long way in adding marketing-friendly features and reporting capabilities to position itself as a long-term player. Get on board now; the traffic’s not getting any cheaper.
For more on how to integrate Pinterest into your sales strategy, check out our visual guide to Pinterest advertising.
Science can be cute as hell when it wants to be – take the JEM Internal Ball Camera (“Int-Ball” for short). The device, created by the Japan Aerospace Exploration Agency (JAXA), was delivered to the International Space Station on June 4, 2017, and now JAXA is releasing its first video and images. The purpose of Int-Ball is to give scientists on the ground the ability to… Read More
What is semantic search? Broadly speaking, it’s a term that refers to a move towards more accurate search results by using various methods to better understand the intent and context behind a search.
Or as Alexis Sanders very eloquently explained it on the Moz Blog,
“The word “semantic” refers to the meaning or essence of something. Applied to search, “semantics” essentially relates to the study of words and their logic. Semantic search seeks to improve search accuracy by understanding a searcher’s intent through contextual meaning. […] Semantic search brings about an enhanced understanding of searcher intent, the ability to extract answers, and delivers more personalized results.”
Google is constantly making tweaks and changes to its documentation and features linked to semantic search. Many of these involve things like structured data and Schema.org, rich results, Knowledge Graph and so on, and the vast majority go unannounced and unnoticed – even though they can make a significant difference to the way we interact with search.
But there are some eagle-eyed members of the search community who keep tabs on changes to semantic search, and let the rest of us know what’s up. To aid in those efforts, I’m rounding up five recent important changes to semantic search on Google that you might not have noticed.
100% of the credit for these observations goes to the Semantic Search Marketing Google+ group (and specifically its founder Aaron Bradley), which is my source for all the latest news and updates on semantic search. If you want to keep in the loop, I highly recommend joining.
Videos and recipes are now accessible via image search
Earlier this week, Google made a telling addition to its documentation for videos, specifying that video rich results will now display in image search on mobile devices, “providing users with useful information about your video.”
A mobile image search for a phrase like “Daily Show Youtube” (okay, that one’s probably not going to happen organically, but I wanted to make the feature work) will fetch video thumbnails in among the grid of regular image results, which when selected, unfold into something like this:
You then need to select “Watch” or the title of the video to be taken to the video itself. (Selecting the image will only bring up the image in fullscreen and won’t redirect you to the video). So far, video rich results from YouTube and Wistia have been spotted in image search.
Google’s documentation for recipes also now features a similar addition: “Rich results can also appear in image search on mobile devices, providing users with useful information about your recipe.” So now you can do more than just stare at a mouthwatering picture of a lasagna in image search – you might be able to find out how it’s made.
Google’s documentation gives instructions on how to mark up your videos and recipes correctly, so that you can make sure your content gets pulled through into image search.
Rich cards are no more
RIP, rich cards. The term introduced by Google in May 2016 to describe the, well, card-style rich results that appear for specific searches have now been removed from Google Developers.
As identified by Aaron Bradley, Google has made changes to its ‘Mark Up Your Content Items’ on Google Developers to remove reference to “rich cards”. In most places, these have been changed to refer to “rich results”, the family of results which includes things like rich cards, rich snippets and featured snippets.
There’s no information as to why Google decided to retire the term; I think it’s usefully descriptive, but maybe Google decided there was no point making an arbitrary distinction between a “card” and a “non-card” rich result.
It may also have been aiming to slim down the number of similar-sounding terms it uses to describe search results with the addition of “enriched search results” to the mix – more on that later.
Google launches structured data-powered job postings in search results
Google has added another item to the list of things that will trigger a rich result in search: job postings.
This change was prefigured by the addition of a Jobs tab to Google’s ‘Early Access and partner-only features’ page, which is another good place to keep an eye out for upcoming developments in search.
— Aaron Bradley (@aaranged) February 9, 2017
Google also hinted at the addition during this year’s Google I/O, when it announced the launch of a new initiative called ‘Google for Jobs’. In a lengthy blog post published on the first day of the conference, Google CEO Sundar Pichai explained the advent of Google for Jobs as forming part of Google’s overall efforts towards “democratizing access to information and surfacing new opportunities”, tying it in with Google’s advances in AI and machine learning.
“For example, almost half of U.S. employers say they still have issues filling open positions. Meanwhile, job seekers often don’t know there’s a job opening just around the corner from them, because the nature of job posts—high turnover, low traffic, inconsistency in job titles—have made them hard for search engines to classify. Through a new initiative, Google for Jobs, we hope to connect companies with potential employees, and help job seekers find new opportunities.”
The new feature, which is U.S.-only for the time being, is being presented as an “enriched search experience”, which is another one of Google’s interesting new additions to semantic search that I’ve explored in full below.
And in a neat tie-in, reviews of employers are now due to be added in schema.org 3.3, including both individual text reviews and aggregate ratings of organizations in their role as employer.
Google introduces new “enriched search results”
Move over rich results – Google’s got an even better experience now. Introducing “enriched search results”, a “more interactive and enhanced class of rich results” being made available across Google.
How long have enriched search results been around? SEO By the Sea blogged about a Google patent for enriched search results as far back as 2014, and followed up with a post in 2015 exploring ‘enriched resources’ in more detail.
However, in the 2014 post Bill Slawski specifically identifies things like airline flights, weather inquiries and sports scores as triggering an enriched result, whereas in its Search Console Help topic on enriched search results, Google specifies that this experience is linked to job postings, recipes and events only.
According to Google:
“Enriched search results often include an immersive popup experience or other advanced interaction feature.”
Google also specifies that “Enriched search enables the user to search across the various properties of a structured data item; for instance, a user might search for chicken soup recipes under 200 calories, or recipes that take less than 1 hour of preparation time.”
Judging by this quote, enriched search results are a continuation of Google’s overall strategy to achieve two things: interpret and respond to more in-depth search queries, and make the SERP more of a one-stop-shop for anything that a searcher could need.
We’ve seen Google increasingly add interactive features to the SERP like new types of rich result, and Google Posts, while also improving its ability to interpret user intent and search context. (Which, as we established earlier, is the goal of semantic search). So in the recipe example given above, a user would be able to search for chicken soup recipes with under 200 calories, then view and follow the recipe in a pop-up, all without needing to click through to a recipe website.
Google makes a whole host of changes to its structured data developer guides
Finally, Google has made a wide-ranging set of changes to its structured data developer guides. I recommend reading Aaron Bradley’s post to Semantic Search Marketing for full details, but here are some highlights:
- Guides are now classified as covering the following topics: structured data, AMP, mobile friendly design
- Structured data has a new definition: it is now defined by Google as “a standardized format for providing information about a page and classifying the page content.” The old definition called it “a text-based organization of data that is included in a file and served from the web.” This one definitely seems a little clearer.
- Twice as many items now listed under “Technical guidelines”, including an explanation of what to do about duplicate content
- There is now less emphasis on the Structured Data Testing Tool, and more on post-publication analysis and testing – perhaps Google is trying to get users to do more of their own work on structured data markup, rather than relying on Google’s tool?
- All content types are now eligible to appear in a carousel.