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Monthly Archives: July 2013

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Bing Ads Adds Competitive “Auction Insights” To Web UI

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By Ginny Marvin

Bing Ads advertisers will now find competitive auction data in the web interface.

First launched earlier this year in Bing Ads Intelligence, Auction Insights is available from the Campaigns page on the web. Auction insights are available at the campaign, ad group and keyword level.

There are minimum data thresholds for insights to be available. If there is enough information, you’ll be able to see up to 25 advertisers competing with you in the auction. Competitors are listed by their display URL.

Metrics provided in Auction Insights include impression share, average position, overlap rate, position above rate and top of page rate.

The post Bing Ads Adds Competitive “Auction Insights” To Web UI appeared first on Search Engine Land.

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Twitter Search Now Indexes Every Public Tweet

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By Martin Beck

Twitter history will soon be more accessible. The company announced today that it is giving users the ability to search within the full archive of public tweets.

From a blog post by Twitter search infrastructure engineer Yi Zhuang:

Since that first simple Tweet over eight years ago, hundreds of billions of Tweets have captured everyday human experiences and major historical events. Our search engine excelled at surfacing breaking news and events in real time, and our search index infrastructure reflected this strong emphasis on recency. But our long-standing goal has been to let people search through every Tweet ever published.

Twitter search has long been incomplete because the company’s engineers focused on the real-time feed, about a week’s worth of public content on the service. Zhuang’s blog post goes into great detail about “how we built a search service that efficiently indexes roughly half a trillion documents and serves queries with an average latency of under 100ms.”

Twitter says the change will be rolling out on desktop and mobile app versions of Twitter in the next few days.

Read more coverage about the change on Marketing Land.

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The Vote Is In: European Parliament Is In Favor Of Breaking Up Google by @mattsouthern

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By Matt Southern

Earlier this week I reported on a story about European Parliament wanting to break up, or “unbundle”, Google search from the rest of its services. The vote was held today and EU Parliament voted in favor of the motion to break up Google, with 384 legislators for the motion, 174 against, and 56 abstaining from voting. Even though the motion was passed, EU Parliament does not have the power to actually put it into effect, that’s up to the European Commission. The most EU Parliament can hope to achieve with this vote is to put more pressure on the Commission […]

The post The Vote Is In: European Parliament Is In Favor Of Breaking Up Google by @mattsouthern appeared first on Search Engine Journal.

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Long Tail CTR Study: The Forgotten Traffic Beyond Top 10 Rankings

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By GaryMoyle

Posted by GaryMoyle

This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author’s views are entirely his or her own and may not reflect the views of Moz, Inc.

Search behavior is fundamentally changing, as users become more savvy and increasingly familiar with search technology. Google’s results have also changed significantly over the last decade, going from a simple page of 10 blue links to a much richer layout, including videos, images, shopping ads and the innovative Knowledge Graph.

We also know there are an increasing amount of touchpoints in a customer journey involving different channels and devices. Google’s
Zero Moment of Truth theory (ZMOT), which describes a revolution in the way consumers search for information online, supports this idea and predicts that we can expect the number of times natural search is involved on the path to a conversion to get higher and higher.

Understanding how people interact with Google and other search engines will always be important. Organic click curves show how many clicks you might expect from search engine results and are one way of evaluating the impact of our campaigns, forecasting performance and exploring changing search behavior.

Using search query data from Google UK for a wide range of leading brands based on millions of impressions and clicks, we can gain insights into the how CTR in natural search has evolved beyond those shown in previous studies by
Catalyst, Slingshot and AOL.

Our methodology

The NetBooster study is based entirely on UK top search query data and has been refined by day in order to give us the most accurate sample size possible. This helped us reduce anomalies in the data in order to achieve the most reliable click curve possible, allowing us to extend it way beyond the traditional top 10 results.

We developed a method to extract data day by day to greatly increase the volume of keywords and to help improve the accuracy of the
average ranking position. It ensured that the average was taken across the shortest timescale possible, reducing rounding errors.

The NetBooster study included:

  • 65,446,308 (65 million) clicks
  • 311,278,379 (311 million) impressions
  • 1,253,130 (1.2 million) unique search queries
  • 54 unique brands
  • 11 household brands (sites with a total of 1M+ branded keyword impressions)
  • Data covers several verticals including retail, travel and financial

We also looked at organic CTR for mobile, video and image results to better understand how people are discovering content in natural search across multiple devices and channels.

We’ll explore some of the most important elements in this article.

How does our study compare against others?

Let’s start by looking at the top 10 results. In the graph below we have normalized the results in order to compare our curve, like-for-like, with previous studies from Catalyst and Slingshot. Straight away we can see that there is higher participation beyond the top four positions when compared to other studies. We can also see much higher CTR for positions lower on the pages, which highlights how searchers are becoming more comfortable with mining search results.

A new click curve to rule them all

Our first click curve is the most useful, as it provides the click through rates for generic non-brand search queries across positions 1 to 30. Initially, we can see a significant amount of traffic going to the top three results with position No. 1 receiving 19% of total traffic, 15% at position No. 2 and 11.45% at position No. 3. The interesting thing to note, however, is our curve shows a relatively high CTR for positions typically below the fold. Positions 6-10 all received a higher CTR than shown in previous studies. It also demonstrates that searchers are frequently exploring pages two and three.

CTR-top-30-730px.jpg

When we look beyond the top 10, we can see that CTR is also higher than anticipated, with positions 11-20 accounting for 17% of total traffic. Positions 21-30 also show higher than anticipated results, with over 5% of total traffic coming from page three. This gives us a better understanding of the potential uplift in visits when improving rankings from positions 11-30.

This highlights that searchers are frequently going beyond the top 10 to find the exact result they want. The prominence of paid advertising, shopping ads, Knowledge Graph and the OneBox may also be pushing users below the fold more often as users attempt to find better qualified results. It may also indicate growing dissatisfaction with Google results, although this is a little harder to quantify.

Of course, it’s important we don’t just rely on one single click curve. Not all searches are equal. What about the influence of brand, mobile and long-tail searches?

Brand bias has a significant influence on CTR

One thing we particularly wanted to explore was how the size of your brand influences the curve. To explore this, we banded each of the domains in our study into small, medium and large categories based on the sum of brand query impressions across the entire duration of the study.

small-medium-large-brand-organic-ctr-730

When we look at how brand bias is influencing CTR for non-branded search queries, we can see that better known brands get a sizable increase in CTR. More importantly, small- to medium-size brands are actually losing out to results from these better-known brands and experience a much lower CTR in comparison.

What is clear is keyphrase strategy will be important for smaller brands in order to gain traction in natural search. Identifying and targeting valuable search queries that aren’t already dominated by major brands will minimize the cannibalization of CTR and ensure higher traffic levels as a result.

How does mobile CTR reflect changing search behavior?

Mobile search has become a huge part of our daily lives, and our clients are seeing a substantial shift in natural search traffic from desktop to mobile devices. According to Google, 30% of all searches made in 2013 were on a mobile device; they also predict mobile searches will constitute over 50% of all searches in 2014.

Understanding CTR from mobile devices will be vital as the mobile search revolution continues. It was interesting to see that the click curve remained very similar to our desktop curve. Despite the lack of screen real estate, searchers are clearly motivated to scroll below the fold and beyond the top 10.

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NetBooster CTR curves for top 30 organic positions


Position

Desktop CTR

Mobile CTR

Large Brand

Medium Brand

Small Brand
1 19.35% 20.28% 20.84% 13.32% 8.59%
2 15.09% 16.59% 16.25% 9.77% 8.92%
3 11.45% 13.36% 12.61% 7.64% 7.17%
4 8.68% 10.70% 9.91% 5.50% 6.19%
5 7.21% 7.97% 8.08% 4.69% 5.37%
6 5.85% 6.38% 6.55% 4.07% 4.17%
7 4.63% 4.85% 5.20% 3.33% 3.70%
8 3.93% 3.90% 4.40% 2.96% 3.22%
9 3.35% 3.15% 3.76% 2.62% 3.05%
10 2.82% 2.59% 3.13% 2.25% 2.82%
11 3.06% 3.18% 3.59% 2.72% 1.94%
12 2.36% 3.62% 2.93% 1.96% 1.31%
13 2.16% 4.13% 2.78% 1.96% 1.26%
14 1.87% 3.37% 2.52% 1.68% 0.92%
15 1.79% 3.26% 2.43% 1.51% 1.04%
16 1.52% 2.68% 2.02% 1.26% 0.89%
17 1.30% 2.79% 1.67% 1.20% 0.71%
18 1.26% 2.13% 1.59% 1.16% 0.86%
19 1.16% 1.80% 1.43% 1.12% 0.82%
20 1.05% 1.51% 1.36% 0.86% 0.73%
21 0.86% 2.04% 1.15% 0.74% 0.70%
22 0.75% 2.25% 1.02% 0.68% 0.46%
23 0.68% 2.13% 0.91% 0.62% 0.42%
24 0.63% 1.84% 0.81% 0.63% 0.45%
25 0.56% 2.05% 0.71% 0.61% 0.35%
26 0.51% 1.85% 0.59% 0.63% 0.34%
27 0.49% 1.08% 0.74% 0.42% 0.24%
28 0.45% 1.55% 0.58% 0.49% 0.24%
29 0.44% 1.07% 0.51% 0.53% 0.28%
30 0.36% 1.21% 0.47% 0.38% 0.26%

Creating your own click curve

This study will give you a set of benchmarks for both non-branded and branded click-through rates with which you can confidently compare to your own click curve data. Using this data as a comparison will let you understand whether the appearance of your content is working for or against you.

We have made things a little easier for you by creating an Excel spreadsheet: simply drop your own top search query data in and it’ll automatically create a click curve for your website.

Simply visit the NetBooster website and download our tool to start making your own click curve.

In conclusion

It’s been both a fascinating and rewarding study, and we can clearly see a change in search habits. Whatever the reasons for this evolving search behavior, we need to start thinking beyond the top 10, as pages two and three are likely to get more traffic in future.

We also need to maximize the traffic created from existing rankings and not just think about position.

Most importantly, we can see practical applications of this data for anyone looking to understand and maximize their content’s performance in natural search. Having the ability to quickly and easily create your own click curve and compare this against a set of benchmarks means you can now understand whether you have an optimal CTR.

What could be the next steps?

There is, however, plenty of scope for improvement. We are looking forward to continuing our investigation, tracking the evolution of search behavior. If you’d like to explore this subject further, here are a few ideas:

  • Segment search queries by intent (How does CTR vary depending on whether a search query is commercial or informational?)
  • Understand CTR by industry or niche
  • Monitor the effect of new Knowledge Graph formats on CTR across both desktop and mobile search
  • Conduct an annual analysis of search behavior (Are people’s search habits changing? Are they clicking on more results? Are they mining further into Google’s results?)

Ultimately, click curves like this will change as the underlying search behavior continues to evolve. We are now seeing a massive shift in the underlying search technology, with Google in particular heavily investing in entity- based search (i.e., the Knowledge Graph). We can expect other search engines, such as Bing, Yandex and Baidu to follow suit and use a similar approach.

The rise of smartphone adoption and constant connectivity also means natural search is becoming more focused on mobile devices. Voice-activated search is also a game-changer, as people start to converse with search engines in a more natural way. This has huge implications for how we monitor search activity.

What is clear is no other industry is changing as rapidly as search. Understanding how we all interact with new forms of search results will be a crucial part of measuring and creating success.

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Facebook Search Finally Lets You Search For Posts

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By Martin Beck

More than a year after promising to give users the ability to search for posts, Facebook is finally coming through.

The company today announced an update to Facebook Search that will make it possible to find that thread congratulating you on your new job, the hilarious comment you made about Walking Dead last month or the selfie you took with the bride at your best friend’s wedding last year.

That’s the type of information Facebook says users are most interested in getting from search — things they have seen before on Facebook. And that’s why the company is rolling out this update, which is more modest in ambition than Facebook’s previous moves to extend search to the content within posts.

The last time it announced a similar feature, in September 2013, it said users would be able to find public posts from people they didn’t follow. The promised update to Facebook’s Graph Search feature was never widely rolled out, but Facebook learned during testing that people were most interested in seeing posts from friends and pages they liked.

“Usually when you think about information retrieval, it’s all about matching content to queries or content to intent,” Facebook’s vice president of search Tom Stocky told Search Engine Land, “but what we found is that on Facebook people care as much if not more about who is posting the content as they do about what the actual content is.”

So that’s where Facebook is focusing now. The update, available only to English language users in the United States, will start rolling out today on the desktop and the iOS mobile app. It should reach all eligible users in the next couple weeks, Stocky said.

The search interface has been simplified. The Graph Search syntax — “Friends of friends who work at Foursquare and live in San Jose,” for example — is still functional, but users who type in a few words will be presented search suggestions, based on what Facebook believes they will be interested in. The results will be “personalized and unique” to each user, Stocky said, pulling from posts by friends, pages and people that he or she follows. The closer the connection, the higher a given piece of content will display in the results.

Facebook_Search_Screenshot copy

The news that Facebook Search won’t capture all public posts will disappoint journalists hoping to mine public conversation for sources and story leads, but Stocky said the results aren’t refined enough yet. Earlier tests of post searching, he said, only included about two days of posts and even then the results were inconsistent. And since then, they have included all Facebook posts in the index, a set of more than 1 trillion posts that grows by billions every day.

Stocky said his team is still working to bring public posts into the fold. “When we do eventually roll this out, we want to roll it out in the right way and we want to allow ranked content and show the most relevant information and get rid of a lot of that noise,” Stocky said. “It’s something that we are working on but we just don’t think it’s there yet.”

The post Facebook Search Finally Lets You Search For Posts appeared first on Search Engine Land.

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Google Launches Mobile Friendly Test Tool

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By Barry Schwartz

Wondering if your site will qualify for Google’s new mobile-friendly labels or be ready for a potential mobile-friendly ranking boost? Google has a new tool to help.

The new tool is at google.com/webmasters/tools/mobile-friendly and it basically gives you a pass or fail grade. Either it tells you that you are mobile friendly or you are not mobile friendly. The messages I was able to generate include:

  • Awesome! This page is mobile-friendly.
  • Not mobile-friendly

In each output, the yes, you are mobile friendly or no you are not, Google may or may not give more or less advice depending on the site.

For example, for this site, we got an “Awesome! This page is mobile-friendly,” but it did add that “this page uses 9 resources which are blocked by robots.txt. The results and screenshot may be incorrect.” It then listed out those resources it had issues with, so you as the webmaster can decide if it is something that needs addressing.

Here is a screen shot:

Here is a site that seemed to get no additional feedback from the tool, outside that it is mobile friendly:

google-mobile-friendly-test-tool-rustybrick

I then tested a site that I know what not mobile-friendly and Google explained what the issues were including (a) text too small to read, (b) mobile viewport not set and (c) links too close together. Here is a screen shot of that output:

google-mobile-friendly-test-tool-sc

Clearly, sites that do not pass, will not get a mobile-friendly label in the mobile results.

To try out the tool, go to google.com/webmasters/tools/mobile-friendly.

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