Tag Archive | "Behavior"

Digital Marketing News: Behavior & Analytics Studies, Facebook’s A/B Testing, & LinkedIn’s Carousel Ads

Perceived Influence Marketing Charts Graph

Perceived Influence Marketing Charts Graph

As Concerns Grow Over Internet Privacy, Most Say Search & Social Have Too Much Power
How Internet users perceive the influence a variety of popular online platforms have over their lives was among the subjects examined in a sizable new joint report by Ipsos, the Internet Society, and the Centre for International Governance Innovation, offering some surprising insight for digital marketers. Marketing Charts

Facebook Experiments with A/B Testing for Page Posts
Facebook has been trying out A/B testing of Facebook Page posts, a feature that if rolled out in earnest could eventually have significant implications for digital marketers. Social Media Today

CMOs Say Digital Marketing Is Most Effective: Nielsen Study
Accurately measuring digital marketing advertising spending’s return on investment remains a challenge, while the overall effectiveness of digital ad spend has grown, according to a fascinating new Nielsen study of chief marketing officers. Broadcasting & Cable

Snapchat Rolls Out Option to ‘Unsend’ Messages, New eCommerce Tools
Snapchat has added several e-commerce tools including an in-app ticket purchase solution, branded augmented-reality games, and has given its users the option to unsend messages. Social Media Today

People Are Changing the Way They Use Social Media
Trust of various social media platforms and how Internet users’ self-censorship has changed since 2013 are among the observations presented in the results of a broad new study conducted by The Atlantic. The Atlantic

Facebook launches tool to let users rate advertisers’ customer service
Facebook has added a feedback tool that lets users rate and review advertisers’ customer service, feedback the company says will help it find and even ban sellers with poor ratings. Marketing Land

2018 June 15 Statistics Image

Google’s about-face on GDPR consent tool is monster win for ad-tech companies
Google reversed its General Data Protection Regulation course recently, allowing publishers to work with an unlimited number of vendors, presenting new opportunities for advertising technology firms. AdAge

LinkedIn rolls out Sponsored Content carousel ads that can include up to 10 customized, swipeable cards
LinkedIn (client) has rolled out a variety of new ad types and more performance metrics for marketers, with its Sponsored Content carousel ads that allow up to 10 custom images. Marketing Land

Report: Facebook is Primary Referrer For Lifestyle Content, Google Search Dominates Rest
What people care about and where they look for relevant answers online are among the marketing-related insights revealed in a recent report from Web analytics firm Parse.ly. Facebook was many users’ go-to source for answers for lifestyle content, while Google was the top source for all other content types. MediaPost

Survey: 87% of mobile marketers see success with location targeting
Location targeting is widely-used and has performed well in the mobile marketing realm, helping increase conversion rates and how well marketers understand their audiences, according to new report data. Marketing Land

ON THE LIGHTER SIDE:

Marketoonist Short-Termism Cartoon

A lighthearted look at marketing short-termism, by Marketoonist Tom Fishburne — Marketoonist

‘The weird one wins’: MailChimp’s CMO on the company’s off-the-wall advertising — The Drum

TOPRANK MARKETING & CLIENTS IN THE NEWS:

  • Lee Odden — Why Content Marketing is Good for B2B Companies — Atomic Reach
  • Lee Odden — Top 2018 Influencers That Might Inspire Your Inner Marketer — Whatagraph
  • Lee Odden — Better than Bonuses: 4 Motivators that Matter More than Money — Workfront
  • Anne Leuman — What’s Trending: Marketing GOOOOOAAAALS! — LinkedIn (client)

Thanks for visiting, and please join us next week for a new selection of the latest digital marketing news, and in the meantime you can follow us at @toprank on Twitter for even more timely daily news. Also, don’t miss the full video summary on our TopRank Marketing TV YouTube Channel.

The post Digital Marketing News: Behavior & Analytics Studies, Facebook’s A/B Testing, & LinkedIn’s Carousel Ads appeared first on Online Marketing Blog – TopRank®.

Online Marketing Blog – TopRank®

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SearchCap: ASCI rankings, featured snippet quiz & consumer behavior

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

The post SearchCap: ASCI rankings, featured snippet quiz & consumer behavior appeared first on Search Engine Land.



Please visit Search Engine Land for the full article.


Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

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The State of Searcher Behavior Revealed Through 23 Remarkable Statistics

Posted by randfish

One of the marketing world’s greatest frustrations has long been the lack of data from Google and other search engines about the behavior of users on their platforms. Occasionally, Google will divulge a nugget of bland, hard-to-interpret information about how they process more than X billion queries, or how many videos were uploaded to YouTube, or how many people have found travel information on Google in the last year. But these numbers aren’t specific enough, well-sourced enough, nor do they provide enough detail to be truly useful for all the applications we have.

Marketers need to know things like: How many searches happen each month across various platforms? Is Google losing market share to Amazon? Are people really starting more searches on YouTube than Bing? Is Google Images more or less popular than Google News? What percent of queries are phrased as questions? How many words are in the average query? Is it more or less on mobile?

These kinds of specifics help us know where to put our efforts, how to sell our managers, teams, and clients on SEO investments, and, when we have this data over time, we can truly understand how this industry that shapes our livelihoods is changing. Until now, this data has been somewhere between hard and impossible to estimate. But, thanks to clickstream data providers like Jumpshot (which helps power Moz’s Keyword Explorer and many of our keyword-based metrics in Pro), we can get around Google’s secrecy and see the data for ourselves!

Over the last 6 months, Russ Jones and I have been working with Jumpshot’s Randy Antin, who’s been absolutely amazing — answering our questions late at night, digging in with his team to get the numbers, and patiently waiting while Russ runs fancy T-Distributions on large datasets to make sure our estimates are as accurate as possible. If you need clickstream data of any kind, I can’t recommend them enough.

If you’re wondering, “Wait… I think I know what clickstream data is, but you should probably tell me, Rand, just so I know that you know,” OK. :-) Clickstream monitoring means Jumpshot (and other companies like them — SimilarWeb, Clickstre.am, etc.) have software on the device that records all the pages visited in a browser session. They anonymize and aggregate this data (don’t worry, your searches and visits are not tied to you or to your device), then make parts of it available for research or use in products or through APIs. They’re not crawling Google or any other sites, but rather seeing the precise behavior of devices as people use them to surf or search the Internet.

Clickstream data is awesomely powerful, but when it comes to estimating searcher behavior, we need scale. Thankfully, Jumpshot can deliver here, too. Their US panel of Internet users is in the millions (they don’t disclose exact size, but it’s between 2–10) so we can trust these numbers to reliably paint a representative picture. That said, there may still be biases in the data — it could be that certain demographics of Internet users are more or less likely to be in Jumpshot’s panel, their mobile data is limited to Android (no iOS), and we know that some alternative kinds of searches aren’t captured by their methodology**. Still, there’s amazing stuff here, and it’s vastly more than we’ve been able to get any other way, so let’s dive in.

23 Search Behavior Stats

Methodology: All of the data was collected from Jumpshot’s multi-million user panel in October 2016. T-distribution scaling was applied to validate the estimates of overall searches across platforms. All other data is expressed as percentages. Jumpshot’s panel includes mobile and desktop devices in similar proportions, though no devices are iOS, so users on Macs, iPhones, and iPads are not included.

#1: How many searches are *really* performed on Google.com each month?

On the devices and types of queries Jumpshot can analyze, there were an average of 3.4 searches/day/searcher. Using the T-Distribution scaling analysis on various sample set sizes of Jumpshot’s data, Russ estimated that the most likely reality is that between 40–60 billion searches happen on Google.com in the US each month.

Here’s more detail from Russ himself:

“…All of the graphs are non-linear in shape, which indicates that as the samples get bigger we are approaching correct numbers but not in a simple % relationship… I have given 3 variations based on the estimated number of searches you think happen in the US annually. I have seen wildly different estimates from 20 billion to 100 billion, so I gave a couple of options. My gut is to go with the 40 billion numbers, especially since once we reach the 100MM line for 40 and 60B, there is little to no increase for 1 billion keywords, which would indicate we have reached a point where each new keyword is searched just 1 time.”

How does that compare to numbers Google’s given? Well, in May of 2016, Google told Search Engine Land they “processed at least 2 trillion searches per year.” Using our Jumpshot-based estimates, and assuming October of 2016 was a reasonably average month for search demand, we’d get to 480–720 billion annual searches. That’s less than half of what Google claims, but Google’s number is WORLDWIDE! Jumpshot’s data here is only for the US. This suggests that, as Danny Sullivan pointed out in the SELand article, Google could well be handling much, much more than 2 trillion annual searches.

Note that we believe our 40–60 billion/month number is actually too low. Why? Voice searches, searches in the Google app and Google Home, higher search use on iOS (all four of which Jumpshot can’t measure), October could be a lower-than-average month, some kinds of search partnerships, and automated searches that aren’t coming from human beings on their devices could all mean our numbers are undercounting Google’s actual US search traffic. In the future, we’ll be able to measure interesting things like growth or shrinkage of search demand as we compare October 2016 vs other months.

#2: How long is the average Google search session?

From the time of the initial query to the loading of the search results page and the selection of any results, plus any back button clicks to those SERPs and selection of new results, the all-in average was just under 1 minute. If that seems long, remember that some search sessions may be upwards of an hour (like when I research all the best ryokans in Japan before planning a trip — I probably clicked 7 pages deep into the SERPs and opened 30 or more individual pages). Those long sessions are dragging up that average.

#3: What percent of users perform one or more searches on a given day?

This one blew my mind! Of the millions of active, US web users Jumpshot monitored in October 2016, only 15% performed at least one or more searches in a day. 45% performed at least one query in a week, and 68% performed one or more queries that month. To me, that says there’s still a massive amount of search growth opportunity for Google. If they can make people more addicted to and more reliant on search, as well as shape the flow of information and the needs of people toward search engines, they are likely to have a lot more room to expand searches/searcher.

#4: What percent of Google searches result in a click?

Google is answering a lot of queries themselves. From searches like “Seattle Weather,” to more complicated ones like “books by Kurt Vonnegut” or “how to remove raspberry stains?“, Google is trying to save you that click — and it looks like they’re succeeding.

66% of distinct search queries resulted in one or more clicks on Google’s results. That means 34% of searches get no clicks at all. If we look at all search queries (not just distinct ones), those numbers shift to a straight 60%/40% split. I wouldn’t be surprised to find that over time, we get closer and closer to Google solving half of search queries without a click. BTW — this is the all-in average, but I’ve broken down clicks vs. no-clicks on mobile vs. desktop in #19 below.

#5: What percent of clicks on Google search results go to AdWords/paid listings?

It’s less than I thought, but perhaps not surprising given how aggressive Google’s had to be with ad subtlety over the last few years. Of distinct search queries in Google, only 3.4% resulted in a click on an AdWords (paid) ad. If we expand that to all search queries, the number drops to 2.6%. Google’s making a massive amount of money on a small fraction of the searches that come into their engine. No wonder they need to get creative (or, perhaps more accurately, sneaky) with hiding the ad indicator in the SERPs.

#6: What percent of clicks on Google search results go to Maps/local listings?

This is not measuring searches and clicks that start directly from maps.google.com or from the Google Maps app on a mobile device. We’re talking here only about Google.com searches that result in a click on Google Maps. That number is 0.9% of Google search clicks, just under 1 in 100. We know from MozCast that local packs show up in ~15% of queries (though that may be biased by MozCast’s keyword corpus).

#7: What percent of clicks on Google search results go to links in the Knowledge Graph?

Knowledge panels are hugely popular in Google’s results — they show up in ~38% of MozCast‘s dataset. But they’re not nearly as popular for search click activity, earning only ~0.5% of clicks.

I’m not totally surprised by that. Knowledge panels are, IMO, more about providing quick answers and details to searchers than they are about drawing the click themselves. If you see Knowledge Panels in your SERPs, don’t panic too much that they’re taking away your CTR opportunity. This made me realize that Keyword Explorer is probably overestimating the degree to which Knowledge Panels remove organic CTR (e.g. Alice Springs, which has only a Knowledge Panel next to 10 blue links, has a CTR opportunity of 64).

#8: What percent of clicks on Google search results go to image blocks?

Images are one of the big shockers of this report overall (more on that later). While MozCast has image blocks in ~11% of Google results, Jumpshot’s data shows images earn 3% of all Google search clicks.

I think this happens because people are naturally drawn to images and because Google uses click data to specifically show images that earn the most engagement. If you’re wondering why your perfectly optimized image isn’t ranking as well in Google Images as you hoped, we’ve got strong suspicions and some case studies suggesting it might be because your visual doesn’t draw the eye and the click the way others do.

If Google only shows compelling images and only shows the image block in search results when they know there’s high demand for images (i.e. people search the web, then click the “image” tab at the top), then little wonder images earn strong clicks in Google’s results.

#9: What percent of clicks on Google search results go to News/Top Stories results?

Gah! We don’t know for now. This one was frustrating and couldn’t be gathered due to Google’s untimely switch from “News Results” to “Top Stories,” some of which happened during the data collection period. We hope to have this in the summer, when we’ll be collecting and comparing results again.

#10: What percent of clicks on Google search results go to Twitter block results?

I was expecting this one to be relatively small, and it is, though it slightly exceeded my expectations. MozCast has tweet blocks showing in ~7% of SERPs, and Jumpshot shows those tweets earning ~0.23% of all clicks.

My guess is that the tweets do very well for a small set of search queries, and tend to be shown less (or shown lower in the results) over time if they don’t draw the click. As an example, search results for my name show the tweet block between organic position #1 and #2 (either my tweets are exciting or the rest of my results aren’t). Compare that to David Mihm, who tweeted very seldomly for a long while and has only recently been more active — his tweets sit between positions #4 and #5. Or contrast with Dr. Pete, whose tweets are above the #1 spot!

#11: What percent of clicks on Google search results go to YouTube?

Technically, there are rare occasions when a video from another provider (usually Vimeo) can appear in Google’s SERPs directly. But more than 99% of videos in Google come from YouTube (which violates anti-competitive laws IMO, but since Google pays off so many elected representatives, it’s likely not an issue for them). Thus, we chose to study only YouTube rather than all video results.

MozCast shows videos in 6.3% of results, just below tweets. In Jumpshot’s data, YouTube’s engagement massively over-performed its raw visibility, drawing 1.8% of all search clicks. Clearly, for those searches with video intent behind them, YouTube is delivering well.

#12: What percent of clicks on Google search results go to personalized Gmail/Google Mail results?

I had no guess at all on this one, and it’s rarely discussed in the SEO world because it’s so relatively difficult to influence and obscure. We don’t have tracking data via MozCast because these only show in personalized results for folks logged in to their Gmail accounts when searching, and Google chooses to only show them for certain kinds of queries.

Jumpshot, however, thanks to clickstream tracking, can see that 0.16% of search clicks go to Gmail or Google Mail following a query, only a little under the number of clicks to tweets.

#13: What percent of clicks on Google search results go to Google Shopping results?

The Google Shopping ads have become pretty compelling — the visuals are solid, the advertisers are clearly spending lots of effort on CTR optimization, and the results, not surprisingly, reflect this.

MozCast has Shopping results in 9% of queries, while clickstream data shows those results earning 0.55% of all search clicks.

#14: What percent of Google searches result in a click on a Google property?

Google has earned a reputation over the last few years of taking an immense amount of search traffic for themselves — from YouTube to Google Maps to Gmail to Google Books and the Google App Store on mobile, and even Google+, there’s a strong case to be made that Google’s eating into opportunity for 3rd parties with bets of their own that don’t have to play by the rules.

Honestly, I’d have estimated this in the 20–30 percent range, so it surprised me to see that, from Jumpshot’s data, all Google properties earned only 11.8% of clicks from distinct searches (only 8.4% across all searches). That’s still significant, of course, and certainly bigger than it was 5 years ago, but given that we know Google’s search volume has more than doubled in the last 5 years, we have to be intellectually honest and say that there’s vastly more opportunity in the crowded-with-Google’s-own-properties results today than there was in the cleaner-but-lower-demand SERPs of 5 years ago.

#15: What percent of all searches happen on any major search property in the US?

I asked Jumpshot to compare 10 distinct web properties, add together all the searches they receive combined, and share the percent distribution. The results are FASCINATING!

Here they are in order:

  1. Google.com 59.30%
  2. Google Images 26.79%
  3. YouTube.com 3.71%
  4. Yahoo! 2.47%
  5. Bing 2.25%
  6. Google Maps 2.09%
  7. Amazon.com 1.85%
  8. Facebook.com 0.69%
  9. DuckDuckGo 0.56%
  10. Google News 0.28%

I’ve also created a pie chart to help illustrate the breakdown:

Distribution of US Searches October 2016

If the Google Images data shocks you, you’re not alone. I was blown away by the popularity of image search. Part of me wonders if Halloween could be responsible. We should know more when we re-collect and re-analyze this data for the summer.

Images wasn’t the only surprise, though. Bing and Yahoo! combine for not even 1/10th of Google.com’s search volume. DuckDuckGo, despite their tiny footprint compared to Facebook, have almost as many searches as the social media giant. Amazon has almost as many searches as Bing. And YouTube.com’s searches are nearly twice the size of Bing’s (on web browsers only — remember that Jumpshot won’t capture searches in the YouTube app on mobile, tablet, or TV devices).

For the future, I also want to look at data for Google Shopping, MSN, Pinterest, Twitter, LinkedIn, Gmail, Yandex, Baidu, and Reddit. My suspicion is that none of those have as many searches as those above, but I’d love to be surprised.

BTW — if you’re questioning this data compared to Comscore or Nielsen, I’d just point out that Jumpshot’s panel is vastly larger, and their methodology is much cleaner and more accurate, too (at least, IMO). They don’t do things like group site searches on Microsoft-owned properties into Bing’s search share or try to statistically sample and merge methodologies, and whereas Comscore has a *global* panel of 2 million, Jumpshot’s *US-only* panel of devices is considerably larger.

#16: What’s the distribution of search demand across keywords?

Let’s go back to looking only at keyword searches on Google. Based on October’s searches, the top
1MM queries accounts for about 25% of all searches with the top 10MM queries accounting for
about 45% and the top 1BB queries accounting for close to 90%
. Jumpshot’s kindly illustrated this for us:

The long tail is still very long indeed, with a huge amount of search volume taking place in keywords outside the top 10 million most-searched-for queries. In fact, almost 25% of all search volume happens outside the top 100 million keywords!

I illustrated this last summer with data from Russ’ analysis based on Clickstre.am data, and it matches up fairly well (though not exactly; Jumpshot’s panel is far larger).

#17: How many words does the average desktop vs. mobile searcher use in their queries?

According to Jumpshot, a typical searcher uses about 3 words in their search query. Desktop users have a slightly higher query length due to having a slightly higher
share of queries of 6 words or more than mobile (16% for desktop vs. 14% for mobile).

I was actually surprised to see how close desktop and mobile are. Clearly, there’s not as much separation in query formation as some folks in our space have estimated (myself included).

#18: What percent of queries are phrased as questions?

For this data, Jumpshot used any queries that started with the typical “Who,” “What,” “Where,” “When,” “Why,” and “How,” as well as “Am” (e.g. Am I registered to vote?) and “Is” (e.g. Is
it going to rain tomorrow?). The data showed that
~8% of search queries are phrased as
questions
.

#19: What is the difference in paid vs. organic CTR on mobile compared to desktop?

This is one of those data points I’ve been longing for over many years. We’ve always suspected CTR on mobile is lower than on desktop, and now it’s confirmed.

For mobile devices, 40.9% of Google searches result in an organic click, 2% in a paid click, and 57.1% in no click at all. For desktop devices, 62.2% of Google searches result in an organic click, 2.8% in a paid click, and 35% in no click. That’s a pretty big delta, and one that illustrates how much more opportunity there still is in SEO vs. PPC. SEO has ~20X more traffic opportunity than PPC on both mobile and desktop. If you’ve been arguing that mobile has killed SEO or that SERP features have killed SEO or, really, that anything at all has killed SEO, you should probably change that tune.

#20: What percent of queries on Google result in the searcher changing their search terms without clicking any results?

You search. You don’t find what you’re seeking. So, you change your search terms, or maybe you click on one of Google’s “Searches related to…” at the bottom of the page.

I’ve long wondered how often this pattern occurs, and what percent of search queries lead not to an answer, but to another search altogether. The answer is shockingly big: a full 18% of searches lead to a change in the search query!

No wonder Google has made related searches and “people also ask” such a big part of the search results in recent years.

#21: What percent of Google queries lead to more than one click on the results?

Some of us use ctrl+click to open up multiple tabs when searching. Others click one result, then click back and click another. Taken together, all the search behaviors that result in more than one click following a single search query in a session combine for 21%. That’s 21% of searches that lead to more than one click on Google’s results.

#22: What percent of Google queries result in pogo-sticking (i.e. the searcher clicks a result, then bounces back to the search results page and chooses a different result)?

As SEOs, we know pogo-sticking is a bad thing for our sites, and that Google is likely using this data to reward pages that don’t get many pogo-stickers and nudge down those who do. Altogether, Jumpshot’s October data saw 8% of searches that followed this pattern of search > click > back to search > click a different result.

Over time, if Google’s successful at their mission of successfully satisfying more searchers, we’d expect this to go down. We’ll watch that the next time we collect results and see what happens.

#23: What percent of clicks on non-Google properties in the search results go to a domain in the top 100?

Many of us in the search and web marketing world have been worried about whether search and SEO are becoming “winner-take-all” markets. Thus, we asked Jumpshot to look at the distribution of clicks to the 100 domains that received the most Google search traffic (excluding Google itself) vs. those outside the top 100.

The results are somewhat relieving: 12.6% of all Google clicks go to the top 100 search-traffic-receiving domains. The other 87.4% are to sites in the chunky middle and long tail of the search-traffic curve.


Phew! That’s an immense load of powerful data, and over time, as we measure and report on this with our Jumpshot partners, we’re looking forward to sharing trends and additional numbers, too.

If you’ve got a question about searcher behavior or search/click patterns, please feel free to leave it in the comments. I’ll work with Russ and Randy to prioritize those requests and make the data available. It’s my goal to have updated numbers to share at this year’s MozCon in July.


** The following questions and responses from Jumpshot can illustrate some of the data and methodology’s limitations:

Rand: What search sources, if any, might be missed by Jumpshot’s methodology?
Jumpshot: We only looked at Google.com, except for the one question that asked specifically about Amazon, YouTube, DuckDuckGo, etc.

Rand: Do you, for example, capture searches performed in all Google apps (maps, search app, Google phone native queries that go to the web, etc)?
Jumpshot: Nothing in-app, but anything that opens a mobile browser — yes.

Rand: Do you capture all voice searches?
Jumpshot: If it triggers a web browser either on desktop or on mobile, then yes.

Rand: Is Google Home included?
Jumpshot: No.

Rand: Are searches on incognito windows included?
Jumpshot: Yes, should be since the plug-in is at the device level, we track any URL regardless.

Rand: Would searches in certain types of browsers (desktop or mobile) not get counted?
Jumpshot: From a browser perspective, no. But remember we have no iOS data so any browser being used on that platform will not be recorded.

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I Examined All My Successful Clients And Found One Behavior They All Share

Back in 2012 I wrote an email to a special group of people. The message was sent to all my past coaching members, everyone who had joined at least one of the three flagship courses I taught during 2007 to 2011. My question was simple: I wanted to know who…

The post I Examined All My Successful Clients And Found One Behavior They All Share appeared first on Entrepreneurs-Journey.com.

Entrepreneurs-Journey.com by Yaro Starak

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How Google May Use Searcher, Usage, & Clickstream Behavior to Impact Rankings – Whiteboard Friday

Posted by randfish

A recent patent from Google suggests a new kind of influence in the rankings that has immense implications for marketers. In today’s Whiteboard Friday, Rand discusses what it says, what that means, and adds a twist of his own to get us thinking about where Google might be heading.

How Google May Use Their Knowledge of Surfer & Searcher Behavior to Impact the Rankings - Whiteboard Friday

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week let’s chat about some things that Google is learning about web searchers and web surfers that may be impacting the rankings.

I was pretty psyched to see a patent a few weeks ago that had been granted actually to Google, so filed a while before that. That patent came from Navneet Panda who, as many in the SEO space may remember, is also the engineer for whom Panda, the Panda Update from Google, is named after. Bill Slawski did a great analysis of the patent on his website, and you can check that out, along with some of the other patent diagrams themselves. Patents can be a little confusing and weird, especially the language, but this one had some surprising clarity to it and some potentially obvious applications for web marketers too.

Deciphering searcher intent

So, in this case, Googlebot here — I’ve anthropomorphized him, my Googlebot there, nicely — is thinking about the queries that are being performed in Google search engine and basically saying, “Huh, if I see lots of people searching for things like ‘find email address,’ ‘email address tool,’ ‘email finder,’ and then I also see a lot of search queries similar to those but with an additional branded element, like ‘VoilaNorbert email tool’ or ‘Norbert email finder’ or ‘how to find email Norbert,’ or even things like ‘email site:voilanorbert.com,’” Googlebot might actually say, “Hmm, lots of searchers who look for these kinds of queries seem to be also looking for this particular brand.”

You can imagine this in tons and tons of ways. Lots of people searching for restaurants also search for Yelp. Lots of people searching for hotels also add in queries like “Trip Advisor.” Lots of people searching for homes to buy also add in Zillow. These brands that essentially get known and combined and perform very well in these non-branded searches, one of the ways that Google might be thinking about that is because they see a lot of branded search that includes the unbranded words around that site.

Google’s site quality patent

In Panda’s site quality patent — and Navneet Panda wasn’t the only author on this patent, but one of the ones we recognize — what’s described is essentially that this algorithm, well not algorithm, very simplistic equation. I’m sure much more than simplistic than what Google’s actually using if they are actually using this. Remember, when it comes to patents, they usually way oversimplify that type of stuff because they don’t want to get exactly what they’re doing out there in the public. But they have this equation that looks like this: Number of unique searchers for the brand or keyword X — so essentially, this is kind of a searches, searchers. They’re trying to identify only unique quantities of people doing it, looking at things like IP address and device and location and all of that to try and identify just the unique people who are performing this — divided by the number of unique searches for the non-branded version.

So branded divided by non-branded equals some sort of site quality score for keyword X. If a lot more people are performing a search for “Trip Advisor + California vacations” than are performing searches for just “California vacations,” then the site quality score for Trip Advisor when it comes to the keyword “California vacations” might be quite high.

You can imagine that if we take another brand — let’s say a brand that folks are less familiar with, WhereToGoInTheWorld.com — and there’s very, very few searches for that brand plus “California vacations,” and there’s lots of searches for the unbranded version, the site quality score for WhereToGoInTheWorld.com is going to be much lower. I don’t even think that’s a real website, but regardless.

Rand’s theory

Now, I want to add one more wrinkle on to this. I think one of the things that struck me as being almost obvious but not literally mentioned in this specific patent was my theory that this also applies to clickstream data. You can see this happening obviously already in personalization, personalized search, but I think it might be happening in non-personalized search as well, and that is essentially through Android and through Chrome, which I’ve drawn these lovely logos just for you. Google knows basically where everyone goes on the web and what everyone does on the web. They see this performance.

So they can look and see the clickstream for a lot of people’s process is a searcher goes and searches for “find email address tool,” and then they find this resource from Distilled and Distilled mentions Rob Ousbey’s account — I think it was from Rob Ousbey that that original resource came out — and they follow him and then they follow me and they see that I tweeted about VoilaNorbert. Voila, they make it to VoilaNorbert.com’s website, where their search ends. They’re no longer looking for this information. They’ve now found a source that sort of answers their desire, their intent. Google might go, “Huh, you know, why not just rank this? Why rank this one when we could just put this there? Because this seems to be the thing that is answering the searcher’s problem. It’s taking care of their issue.”

So what does this mean for us?

This is tough for marketers. I think both of these, the query formatting and the potential clickstream uses, suggest a world in which building up your brand association and building up the stream of traffic to your website that’s solving a problem not just for searchers, but for potential searchers and people with that issue, whether they search or not, is part of SEO. I think that’s going to mean that things like branding and things like attracting traffic from other sources, from social, from email, from content, from direct, from offline, and word-of-mouth, that all of those things are going to become part of the SEO equation. If we don’t do those things well, in the long term, we might do great SEO, kind of classic, old-school keywords and links and crawl and rankings SEO and miss out on this important piece that’s on the rise.

I’m looking forward to some great comments and your theories as well. We’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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The Future of User Behavior – Whiteboard Friday

Posted by willcritchlow

In the early days of search, Google used only your typed query to find the most relevant results. We’re now increasingly seeing SERPs that are influenced by all kinds of contextual information — the implicit queries.

In today’s Whiteboard Friday, Will Critchlow covers what exactly that means and how it might explain why we see “(not provided)” in our analytics more often than we’d like.









PRO Tip: Learn more about how Google ranks pages at Moz Academy.

For reference, here’s a still image of this week’s whiteboard:

Video Transcription

Hi, Moz fans. I’m Will Critchlow, one of the founders of Distilled, and I want to talk today about the future of user behavior, something that I’ve been talking about a MozCon this year. In particular, I want to talk about the implications of query enhancement. So I’m going to start by telling you what we mean by this phrase.

Old-school query, key phrase, this is what we’ve talked about for a long time. In SEO, something like “London tube stations,” a bunch of words strung together, that’s the entire query, and we would call it a query or a key phrase. But we’ve been defining this what we call the “new query” made up of two parts. The explicit query here in blue is London tube stations, again, in this example, exactly the same. What we’re calling the “implicit query” is essentially all of the other information that the search engine knows about you, and this what they know about you in general, what they know about you at this specific moment in time, and what they know about your recent history and any other factors they want to factor in.

So, in this particular case, I’ve said this is an iPhone user, they’re on the street, they’re in London. You can imagine how this information changes the kind of thing that you might be looking for when you perform a query like this or indeed any other.

This whole model is something that we’ve been kind of building out and thinking about a lot this year. Tom Anthony, one of my colleagues in London, presented this at a conference, and we’ve been working on it together. We came up with this kind of visual representation of what we think is happening over time. As people get used to this behavior, they see it in the search results, and they adapt to the information that they’re receiving back from the search engine.

So old school search results where everybody’s search result was exactly the same, if they performed a particular query, no matter where in the world they were, wherever in the country they were, whatever device they were on, whatever time of day it was, whatever their recent history, everybody’s was the same. In other words, the only information that the search engine is taking into account in this case is the old-style query, the explicit part.

Then, what we’ve seen is that there’s gradually been this implicit query information being added on top. You may not be able to see it from my brilliant hand-drawn diagram here, but my intention is that these blue bars are the same height out to here. So, at this point, there’s all of the explicit query information being passed over. In other words, I’m doing the same kind of search I’ve always done. But Google is taking into account this extra, implicit information about me, what it knows about me, what it knows about my device, what it knows about my history and so forth. Therefore, Google has more information here than they did previously. They can return better results.

That’s kind of what we’ve been talking about for a long time, I think, this evolution of better search results based on the additional information that the search engines have about us. But what we’re starting to see and what we’re certainly predicting is going to become more and more prevalent is that as the implicit information that search engines have grows, and, in particular, as their ability to use that information intelligently improves, then we’re actually going to see users start to give less explicit information over. In other words, they’re going to trust that the search engines are going to pull out the implicit information that they need. So I can do a much shorter, simpler query.

But what you see here is, again, to explain my hand-drawn diagram in case it’s not perfectly beautiful, the blue bars are declining here. In other words, I’m sending less and less explicit information over as time goes along. But actually, the total information that search engines have to work with, as time goes on, is actually increasing, because the implicit information they’re gathering is growing faster than the explicit information is declining.

I can give you a concrete example of this. So I vividly remember giving a talk about keyword research, and it was a few years ago. I was kind of mocking that business owner. We’ve all met these business owners who want to rank for the one-word key phrase. So I want to rank for restaurant or whatever. I say, “This is ridiculous. What in the world can you imagine somebody is possibly looking for when they do a search of ‘restaurant.’ ”

Back then, if you did a search like that, you got a kind of weird mix, because this is back in these days when there essentially no implicit information being taken in. You’ve got a mix of the most powerful websites of actual restaurants anywhere in your country plus some news, like a powerful page on a big domain, those kinds of things. Probably a Wikipedia entry. Why would a business owner want to rank for that stuff? That’s going to convert horribly poorly.

But my mind was changed powerfully when I caught myself. I was in Boston, and I caught myself doing a search for “breakfast.” I went to Google, typed in “breakfast,” hit Search. What was I thinking? What exactly was I hoping the outcome was going to be here? Well, actually, I’ve trained myself to believe that all of this other implicit information is going to be taken into account, and, in fact, it was. So, instead of getting that old-style Wikipedia entry, a news result, a couple of random restaurants from somewhere in the country, I got a local pack, and I got some local Boston news articles on the top 10 places to have breakfast in Boston. It was all customized to my exact location, so I got some stuff that was really near me, and I found a great place to have breakfast just around the corner from the hotel. So that worked.

I’ve actually noticed myself doing this more and more, and I imagine, given obviously the industry I work in, I’m pretty much an early adopter here. But I think we’re going to see all users adopt this style of searching more and more, and it’s really going to change how we as marketers have to think, because it doesn’t mean that you need to go out there and rank for the generic keyword “breakfast.” But it does mean that you need to take into account all of the possible ways that people might be searching for these things and the various different ways that Google might piece together a useful search result when somebody gives them such apparently unhelpful explicit information, in particular, obviously, in this case, local.

I kind of mentioned “not provided” down here. This is my one, I guess, non-
conspiracy theory view of what could be going on with the whole not provided thing, which is that actually, if Google’s model is looking more and more like this and less like this, and, in particular, as we get further over to this end, and of course, you can consider something like Google Now would be the extreme of this where is in fact no blue bar and pure orange, then actually the reliance on keywords goes away. Maybe the not provided thing is actually more of a strategic message for Google, kind of saying, “We’re not necessarily thinking in terms of keywords anymore. We’re thinking in terms of your need at a given moment in time.”

So, anyway, I hope that’s been a useful kind of rapid-fire run through over what I think is going to happen as people get used to the power of query enhancement. I’m Will Critchlow. Until next time, thanks.

Video transcription by Speechpad.com

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MarketingSherpa Email Summit 2013: Using buyer behavior in email campaigns

From MarketingSherpa Email Summit 2013, Loren McDonald, VP of Industry Relations, Silverpop, discussed to an audience how to use buyer behavior to improve email campaigns. Read on to learn Loren’s tactics to target audiences using a personal marketing approach.
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2013 Mobile Marketing Trends: 2 key data points to help you understand this growing behavior

Knowing the demographics, particularly age, of an audience is extremely important when it comes to mobile marketing. The MECLABS Business Intelligence team compiled data and resources to help marketers understand mobile marketing trends and to give marketers insight for the successful future of their mobile marketing efforts.
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New Exact & Phrase Matching Behavior: Early Findings

In a world where lots of search marketers are still reluctant to use broad match type due to its lack of relevance and control, Google has released two features to have more advertisers show their ads on all those very long tail queries: The broad modifier feature was rolled out in July 2010 in the…



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