Prioritizing Local Search Profile Listings: 2 Methods

Looking to start building those citations for local SEO? Columnist Lydia Jorden shares her strategy for determining which local business listing sites are worth investing in.

The post Prioritizing Local Search Profile Listings: 2 Methods appeared first on Search Engine Land.

Please visit Search Engine Land for the full article.

How to Use Server Log Analysis for Technical SEO

Posted by SamuelScott

It’s ten o’clock. Do you know where your logs are?

I’m introducing this guide with a pun on a common public-service announcement that has run on late-night TV news broadcasts in the United States because log analysis is something that is extremely newsworthy and important.

If your technical and on-page SEO is poor, then nothing else that you do will matter. Technical SEO is the key to helping search engines to crawl, parse, and index websites, and thereby rank them appropriately long before any marketing work begins.

The important thing to remember: Your log files contain the only data that is 100% accurate in terms of how search engines are crawling your website. By helping Google to do its job, you will set the stage for your future SEO work and make your job easier. Log analysis is one facet of technical SEO, and correcting the problems found in your logs will help to lead to higher rankings, more traffic, and more conversions and sales.

Here are just a few reasons why:

  • Too many response code errors may cause Google to reduce its crawling of your website and perhaps even your rankings.
  • You want to make sure that search engines are crawling everything, new and old, that you want to appear and rank in the SERPs (and nothing else).
  • It’s crucial to ensure that all URL redirections will pass along any incoming “link juice.”

However, log analysis is something that is unfortunately discussed all too rarely in SEO circles. So, here, I wanted to give the Moz community an introductory guide to log analytics that I hope will help. If you have any questions, feel free to ask in the comments!

What is a log file?

Computer servers, operating systems, network devices, and computer applications automatically generate something called a log entry whenever they perform an action. In a SEO and digital marketing context, one type of action is whenever a page is requested by a visiting bot or human.

Server log entries are specifically programmed to be output in the Common Log Format of the W3C consortium. Here is one example from Wikipedia with my accompanying explanations:

127.0.0.1 user-identifier frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
  • 127.0.0.1 — The remote hostname. An IP address is shown, like in this example, whenever the DNS hostname is not available or DNSLookup is turned off.
  • user-identifier — The remote logname / RFC 1413 identity of the user. (It’s not that important.)
  • frank — The user ID of the person requesting the page. Based on what I see in my Moz profile, Moz’s log entries would probably show either “SamuelScott” or “392388” whenever I visit a page after having logged in.
  • [10/Oct/2000:13:55:36 -0700] — The date, time, and timezone of the action in question in strftime format.
  • GET /apache_pb.gif HTTP/1.0 — “GET” is one of the two commands (the other is “POST”) that can be performed. “GET” fetches a URL while “POST” is submitting something (such as a forum comment). The second part is the URL that is being accessed, and the last part is the version of HTTP that is being accessed.
  • 200 — The status code of the document that was returned.
  • 2326 — The size, in bytes, of the document that was returned.

Note: A hyphen is shown in a field when that information is unavailable.

Every single time that you — or the Googlebot — visit a page on a website, a line with this information is output, recorded, and stored by the server.

Log entries are generated continuously and anywhere from several to thousands can be created every second — depending on the level of a given server, network, or application’s activity. A collection of log entries is called a log file (or often in slang, “the log” or “the logs”), and it is displayed with the most-recent log entry at the bottom. Individual log files often contain a calendar day’s worth of log entries.

Accessing your log files

Different types of servers store and manage their log files differently. Here are the general guides to finding and managing log data on three of the most-popular types of servers:

What is log analysis?

Log analysis (or log analytics) is the process of going through log files to learn something from the data. Some common reasons include:

  • Development and quality assurance (QA) — Creating a program or application and checking for problematic bugs to make sure that it functions properly
  • Network troubleshooting — Responding to and fixing system errors in a network
  • Customer service — Determining what happened when a customer had a problem with a technical product
  • Security issues — Investigating incidents of hacking and other intrusions
  • Compliance matters — Gathering information in response to corporate or government policies
  • Technical SEO — This is my favorite! More on that in a bit.

Log analysis is rarely performed regularly. Usually, people go into log files only in response to something — a bug, a hack, a subpoena, an error, or a malfunction. It’s not something that anyone wants to do on an ongoing basis.

Why? This is a screenshot of ours of just a very small part of an original (unstructured) log file:

Ouch. If a website gets 10,000 visitors who each go to ten pages per day, then the server will create a log file every day that will consist of 100,000 log entries. No one has the time to go through all of that manually.

How to do log analysis

There are three general ways to make log analysis easier in SEO or any other context:

  • Do-it-yourself in Excel
  • Proprietary software such as Splunk or Sumo-logic
  • The ELK Stack open-source software

Tim Resnik’s Moz essay from a few years ago walks you through the process of exporting a batch of log files into Excel. This is a (relatively) quick and easy way to do simple log analysis, but the downside is that one will see only a snapshot in time and not any overall trends. To obtain the best data, it’s crucial to use either proprietary tools or the ELK Stack.

Splunk and Sumo-Logic are proprietary log analysis tools that are primarily used by enterprise companies. The ELK Stack is a free and open-source batch of three platforms (Elasticsearch, Logstash, and Kibana) that is owned by Elastic and used more often by smaller businesses. (Disclosure: We at Logz.io use the ELK Stack to monitor our own internal systems as well as for the basis of our own log management software.)

For those who are interested in using this process to do technical SEO analysis, monitor system or application performance, or for any other reason, our CEO, Tomer Levy, has written a guide to deploying the ELK Stack.

Technical SEO insights in log data

However you choose to access and understand your log data, there are many important technical SEO issues to address as needed. I’ve included screenshots of our technical SEO dashboard with our own website’s data to demonstrate what to examine in your logs.

Bot crawl volume

It’s important to know the number of requests made by Baidu, BingBot, GoogleBot, Yahoo, Yandex, and others over a given period time. If, for example, you want to get found in search in Russia but Yandex is not crawling your website, that is a problem. (You’d want to consult Yandex Webmaster and see this article on Search Engine Land.)

Response code errors

Moz has a great primer on the meanings of the different status codes. I have an alert system setup that tells me about 4XX and 5XX errors immediately because those are very significant.

Temporary redirects

Temporary 302 redirects do not pass along the “link juice” of external links from the old URL to the new one. Almost all of the time, they should be changed to permanent 301 redirects.

Crawl budget waste

Google assigns a crawl budget to each website based on numerous factors. If your crawl budget is, say, 100 pages per day (or the equivalent amount of data), then you want to be sure that all 100 are things that you want to appear in the SERPs. No matter what you write in your robots.txt file and meta-robots tags, you might still be wasting your crawl budget on advertising landing pages, internal scripts, and more. The logs will tell you — I’ve outlined two script-based examples in red above.

If you hit your crawl limit but still have new content that should be indexed to appear in search results, Google may abandon your site before finding it.

Duplicate URL crawling

The addition of URL parameters — typically used in tracking for marketing purposes — often results in search engines wasting crawl budgets by crawling different URLs with the same content. To learn how to address this issue, I recommend reading the resources on Google and Search Engine Land here, here, here, and here.

Crawl priority

Google might be ignoring (and not crawling or indexing) a crucial page or section of your website. The logs will reveal what URLs and/or directories are getting the most and least attention. If, for example, you have published an e-book that attempts to rank for targeted search queries but it sits in a directory that Google only visits once every six months, then you won’t get any organic search traffic from the e-book for up to six months.

If a part of your website is not being crawled very often — and it is updated often enough that it should be — then you might need to check your internal-linking structure and the crawl-priority settings in your XML sitemap.

Last crawl date

Have you uploaded something that you hope will be indexed quickly? The log files will tell you when Google has crawled it.

Crawl budget

One thing I personally like to check and see is Googlebot’s real-time activity on our site because the crawl budget that the search engine assigns to a website is a rough indicator — a very rough one — of how much it “likes” your site. Google ideally does not want to waste valuable crawling time on a bad website. Here, I had seen that Googlebot had made 154 requests of our new startup’s website over the prior twenty-four hours. Hopefully, that number will go up!

As I hope you can see, log analysis is critically important in technical SEO. It’s eleven o’clock — do you know where your logs are now?

Additional resources

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

After seeing Rand’s “Mad Science Experiments in SEO” presented at last year’s MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO’s up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I’ve learned a thing or two about app store optimization and what goes into an app’s ranking. It’s been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website’s traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive’s recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I’ll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app’s ranking.

But first, a little context

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

  • Average app store rating
  • Rating/review volume
  • Download and install counts
  • Uninstalls (what retention and churn look like for the app)
  • App usage statistics (how engaged an app’s users are and how frequently they launch the app)
  • Growth trends weighted toward recency (how daily download counts changed over time and how today’s ratings compare to last week’s)
  • Keyword density of the app’s landing page (Ian did a great job covering this factor in a previous Moz post)

I’ve simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn’t say much. Over the next five studies, however, we’ll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I’ll stop here with the assumptions, but I’ve gone into far greater depth into how I’ve reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

  • They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
  • They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I’ll test in study #2)
  • They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
  • They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app’s current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they’re ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play’s Top 100 apps and very few blips within the App Store’s Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play’s 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these “lower-ranked” apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we’ll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it’s just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we’d see a downward trend in ratings. We’d expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

  1. The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
  2. The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play’s algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store’s calculation of an app’s current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I’m using the age of a current app’s version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I’m hypothesizing that the older the age of an app’s current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app’s rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app’s popularity on its ranking. In an ideal world, popularity would be measured by an app’s monthly active users (MAUs), but since few mobile app developers have released this information, I’ve settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app’s ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play’s Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store’s Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don’t have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I’ve applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

  1. Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
  2. Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store’s top charts.
  3. In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
  4. Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play’s top charts.
  5. The higher an app is rated, the less volatile its rankings are.
  6. The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
  7. An app’s update frequency is negatively correlated with Google Play’s ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app’s most recent ratings and reviews.
  8. The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
  9. In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
  10. Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores’ ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I’d now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It’s with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I’d hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app’s current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


Again, we’re oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let’s deconstruct the ranking formula together, one experiment at a time.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Has Google Gone Too Far with the Bias Toward Its Own Content?

Posted by ajfried

Since the beginning of SEO time, practitioners have been trying to crack the Google algorithm. Every once in a while, the industry gets a glimpse into how the search giant works and we have opportunity to deconstruct it. We don’t get many of these opportunities, but when we do—assuming we spot them in time—we try to take advantage of them so we can “fix the Internet.”

On Feb. 16, 2015, news started to circulate that NBC would start removing images and references of Brian Williams from its website.

This was it!

A golden opportunity.

This was our chance to learn more about the Knowledge Graph.

Expectation vs. reality

Often it’s difficult to predict what Google is truly going to do. We expect something to happen, but in reality it’s nothing like we imagined.

Expectation

What we expected to see was that Google would change the source of the image. Typically, if you hover over the image in the Knowledge Graph, it reveals the location of the image.

Keanu-Reeves-Image-Location.gif

This would mean that if the image disappeared from its original source, then the image displayed in the Knowledge Graph would likely change or even disappear entirely.

Reality (February 2015)

The only problem was, there was no official source (this changed, as you will soon see) and identifying where the image was coming from proved extremely challenging. In fact, when you clicked on the image, it took you to an image search result that didn’t even include the image.

Could it be? Had Google started its own database of owned or licensed images and was giving it priority over any other sources?

In order to find the source, we tried taking the image from the Knowledge Graph and “search by image” in images.google.com to find others like it. For the NBC Nightly News image, Google failed to even locate a match to the image it was actually using anywhere on the Internet. For other television programs, it was successful. Here is an example of what happened for Morning Joe:

Morning_Joe_image_search.png

So we found the potential source. In fact, we found three potential sources. Seemed kind of strange, but this seemed to be the discovery we were looking for.

This looks like Google is using someone else’s content and not referencing it. These images have a source, but Google is choosing not to show it.

Then Google pulled the ol’ switcheroo.

New reality (March 2015)

Now things changed and Google decided to put a source to their images. Unfortunately, I mistakenly assumed that hovering over an image showed the same thing as the file path at the bottom, but I was wrong. The URL you see when you hover over an image in the Knowledge Graph is actually nothing more than the title. The source is different.

Morning_Joe_Source.png

Luckily, I still had two screenshots I took when I first saw this saved on my desktop. Success. One screen capture was from NBC Nightly News, and the other from the news show Morning Joe (see above) showing that the source was changed.

NBC-nightly-news-crop.png

(NBC Nightly News screenshot.)

The source is a Google-owned property: gstatic.com. You can clearly see the difference in the source change. What started as a hypothesis in now a fact. Google is certainly creating a database of images.

If this is the direction Google is moving, then it is creating all kinds of potential risks for brands and individuals. The implications are a loss of control for any brand that is looking to optimize its Knowledge Graph results. As well, it seems this poses a conflict of interest to Google, whose mission is to organize the world’s information, not license and prioritize it.

How do we think Google is supposed to work?

Google is an information-retrieval system tasked with sourcing information from across the web and supplying the most relevant results to users’ searches. In recent months, the search giant has taken a more direct approach by answering questions and assumed questions in the Answer Box, some of which come from un-credited sources. Google has clearly demonstrated that it is building a knowledge base of facts that it uses as the basis for its Answer Boxes. When it sources information from that knowledge base, it doesn’t necessarily reference or credit any source.

However, I would argue there is a difference between an un-credited Answer Box and an un-credited image. An un-credited Answer Box provides a fact that is indisputable, part of the public domain, unlikely to change (e.g., what year was Abraham Lincoln shot? How long is the George Washington Bridge?) Answer Boxes that offer more than just a basic fact (or an opinion, instructions, etc.) always credit their sources.

There are four possibilities when it comes to Google referencing content:

  • Option 1: It credits the content because someone else owns the rights to it
  • Option 2: It doesn’t credit the content because it’s part of the public domain, as seen in some Answer Box results
  • Option 3: It doesn’t reference it because it owns or has licensed the content. If you search for “Chicken Pox” or other diseases, Google appears to be using images from licensed medical illustrators. The same goes for song lyrics, which Eric Enge discusses here: Google providing credit for content. This adds to the speculation that Google is giving preference to its own content by displaying it over everything else.
  • Option 4: It doesn’t credit the content, but neither does it necessarily own the rights to the content. This is a very gray area, and is where Google seemed to be back in February. If this were the case, it would imply that Google is “stealing” content—which I find hard to believe, but felt was necessary to include in this post for the sake of completeness.

Is this an isolated incident?

At Five Blocks, whenever we see these anomalies in search results, we try to compare the term in question against others like it. This is a categorization concept we use to bucket individuals or companies into similar groups. When we do this, we uncover some incredible trends that help us determine what a search result “should” look like for a given group. For example, when looking at searches for a group of people or companies in an industry, this grouping gives us a sense of how much social media presence the group has on average or how much media coverage it typically gets.

Upon further investigation of terms similar to NBC Nightly News (other news shows), we noticed the un-credited image scenario appeared to be a trend in February, but now all of the images are being hosted on gstatic.com. When we broadened the categories further to TV shows and movies, the trend persisted. Rather than show an image in the Knowledge Graph and from the actual source, Google tends to show an image and reference the source from Google’s own database of stored images.

And just to ensure this wasn’t a case of tunnel vision, we researched other categories, including sports teams, actors and video games, in addition to spot-checking other genres.

Unlike terms for specific TV shows and movies, terms in each of these other groups all link to the actual source in the Knowledge Graph.

Immediate implications

It’s easy to ignore this and say “Well, it’s Google. They are always doing something.” However, there are some serious implications to these actions:

  1. The TV shows/movies aren’t receiving their due credit because, from within the Knowledge Graph, there is no actual reference to the show’s official site
  2. The more Google moves toward licensing and then retrieving their own information, the more biased they become, preferring their own content over the equivalent—or possibly even superior—content from another source
  3. If feels wrong and misleading to get a Google Image Search result rather than an actual site because:
    • The search doesn’t include the original image
    • Considering how poor Image Search results are normally, it feels like a poor experience
  4. If Google is moving toward licensing as much content as possible, then it could make the Knowledge Graph infinitely more complicated when there is a “mistake” or something unflattering. How could one go about changing what Google shows about them?

Google is objectively becoming subjective

It is clear that Google is attempting to create databases of information, including lyrics stored in Google Play, photos, and, previously, facts in Freebase (which is now Wikidata and not owned by Google).

I am not normally one to point my finger and accuse Google of wrongdoing. But this really strikes me as an odd move, one bordering on a clear bias to direct users to stay within the search engine. The fact is, we trust Google with a heck of a lot of information with our searches. In return, I believe we should expect Google to return an array of relevant information for searchers to decide what they like best. The example cited above seems harmless, but what about determining which is the right religion? Or even who the prettiest girl in the world is?

Religion-and-beauty-queries.png

Questions such as these, which Google is returning credited answers for, could return results that are perceived as facts.

Should we next expect Google to decide who is objectively the best service provider (e.g., pizza chain, painter, or accountant), then feature them in an un-credited answer box? The direction Google is moving right now, it feels like we should be calling into question their objectivity.

But that’s only my (subjective) opinion.

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The Long Click and the Quality of Search Success

Posted by billslawski

“On the most basic level, Google could see how satisfied users were. To paraphrase Tolstoy, happy users were all the same. The best sign of their happiness was the “Long Click” — This occurred when someone went to a search result, ideally the top one, and did not return. That meant Google has successfully fulfilled the query.”

~ Steven Levy. In the Plex: How Google Thinks, Works, and Shapes our Lives

I often explore and read patents and papers from the search engines to try to get a sense of how they may approach different issues, and learn about the assumptions they make about search, searchers, and the Web. Lately, I’ve been keeping an eye open for papers and patents from the search engines where they talk about a metric known as the “long click.”

A recently granted Google patent uses the metric of a “Long Click” as the center of a process Google may use to track results for queries that were selected by searchers for long visits in a set of search results.

This concept isn’t new. In 2011, I wrote about a Yahoo patent in How a Search Engine May Measure the Quality of Its Search Results, where they discussed a metric that they refer to as a “target page success metric.” It included “dwell time” upon a result as a sign of search success (Yes, search engines have goals, too).

5543947f5bb408.24541747.jpg

Another Google patent described assigning web pages “reachability scores” based upon the quality of pages linked to from those initially visited pages. In the post Does Google Use Reachability Scores in Ranking Resources? I described how a Google patent that might view a long click metric as a sign to see if visitors to that page are engaged by the links to content they find those links pointing to, including links to videos. Google tells us in that patent that it might consider a “long click” to have been made on a video if someone watches at least half the video or 30 seconds of it. The patent suggests that a high reachability score on a page may mean that page could be boosted in Google search results.

554394a877e8c8.30299132.jpg

But the patent I’m writing about today is focused primarily upon looking at and tracking a search success metric like a long click or long dwell time. Here’s the abstract:

Modifying ranking data based on document changes

Invented by Henele I. Adams, and Hyung-Jin Kim

Assigned to Google

US Patent 9,002,867

Granted April 7, 2015

Filed: December 30, 2010

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for determining a weighted overall quality of result statistic for a document.

One method includes receiving quality of result data for a query and a plurality of versions of a document, determining a weighted overall quality of result statistic for the document with respect to the query including weighting each version specific quality of result statistic and combining the weighted version-specific quality of result statistics, wherein each quality of result statistic is weighted by a weight determined from at least a difference between content of a reference version of the document and content of the version of the document corresponding to the version specific quality of result statistic, and storing the weighted overall quality of result statistic and data associating the query and the document with the weighted overall quality of result statistic.

This patent tells us that search results may be be ranked in an order, according to scores assigned to the search results by a scoring function or process that would be based upon things such as:

  • Where, and how often, query terms appear in the given document,
  • How common the query terms are in the documents indexed by the search engine, or
  • A query-independent measure of quality of the document itself.

Last September, I wrote about how Google might identify a category associated with a query term base upon clicks, in the post Using Query User Data To Classify Queries. In a query for “Lincoln.” the results that appear in response might be about the former US President, the town of Lincoln, Nebraska, and the model of automobile. When someone searches for [Lincoln], Google returning all three of those responses as a top result could be said to be reasonable. The patent I wrote about in that post told us that Google might collect information about “Lincoln” as a search entity, and track which category of results people clicked upon most when they performed that search, to determine what categories of pages to show other searchers. Again, that’s another “search success” based upon a past search history.

There likely is some value in working to find ways to increase the amount of dwell time someone spends upon the pages of your site, if you are already having some success in crafting page titles and snippets that persuade people to click on your pages when they those appear in search results. These approaches can include such things as:

  1. Making visiting your page a positive experience in terms of things like site speed, readability, and scannability.
  2. Making visiting your page a positive experience in terms of things like the quality of the content published on your pages including spelling, grammar, writing style, interest, quality of images, and the links you share to other resources.
  3. Providing a positive experience by offering ideas worth sharing with others, and offering opportunities for commenting and interacting with others, and by being responsive to people who do leave comments.

Here are some resources I found that discuss this long click metric in terms of “dwell time”:

Your ability to create pages that can end up in a “long click” from someone who has come to your site in response to a query, is also a “search success” metric on the search engine’s part, and you both succeed. Just be warned that as the most recent patent from Google on Long Clicks shows us, Google will be watching to make sure that the content of your page doesn’t change too much, and that people are continuing to click upon it in search results, and spend a fair amount to time upon it.

(Images for this post are from my Go Fish Digital Design Lead Devin Holmes @DevinGoFish. Thank you, Devin!)

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Local Centroids are Now Individual Users: How Can We Optimize for Their Searches?

Posted by MiriamEllis

“Google is getting better at detecting location at a more granular level—even on the desktop.
The user is the new centroid.” – 
David Mihm

The history of the centroid

The above quote succinctly summarizes the current state of affairs for local business owners and their customers. The concept of a centroid—
a central point of relevance—is almost as old as local search. In 2008, people like Mike Blumenthal and Google Maps Manager Carter Maslan were sharing statistics like this:

“…research indicates that up to 80% of the variation in rank can be explained by distance from the centroid on certain searches.”

At that time, businesses located near town hall or a similar central hub appeared to be experiencing a ranking advantage.

Fast forward to 2013, and Mike weighed in again with 
an updated definition of “industry centroids”

“If you read their (Google’s) patents, they actually deal with the center of the industries … as defining the center of the search. So if all the lawyers are on the corner of Main and State, that typically defines the center of the search, rather than the center of the city… it isn’t even the centroid of the city that matters. It matters that you are near where the other people in your industry are.”

In other words, Google’s perception of a centralized location for auto dealerships could be completely different than that for medical practices, and that
neither might be located anywhere near the city center.

While the concepts of city and industry centroids may still play a part in some searches,
local search results in 2015 clearly indicate Google’s shift toward deeming the physical location of the desktop or mobile user a powerful factor in determining relevance. The relationship between where your customer is when he performs a search and where your business is physically located has never been more important.

Moreover, in this new, user-centric environment, Google has moved beyond simply detecting cities to detecting neighborhoods and even streets. What this means for local business owners is that
your hyperlocal information has become a powerful component of your business data. This post will teach you how to better serve your most local customers.

Seeing the centroid in action

If you do business in a small town with few competitors, ranking for your product/service + city terms is likely to cover most of your bases. The user-as-centroid phenomenon is most applicable in mid-to-large sized towns and cities with reasonable competition. I’ll be using two districts in San Francisco—Bernal Heights and North Beach—in these illustrations and we’ll be going on a hunt for pizza.

On a desktop, searching for “pizza north beach san francisco” or setting my location to this neighborhood and city while searching for the product, Google will show me something like this:

Performing this same search, but with “bernal heights” substituted, Google shows me pizzerias in a completely different part of the city:

local result bernal heights pizza san francisco

And, when I move over to my mobile device, Google narrows the initial results down to
just three enviable players in each district. These simple illustrations demonstrate Google’s increasing sensitivity to serving me nearby businesses offering what I want.

The physical address of your business is the most important factor in serving the user as centroid. This isn’t something you can control, but there are things you
can do to market your business as being highly relevant to your hyperlocal geography.

Specialized content for the user-centroid

We’ll break this down into four common business models to help get you thinking about planning content that serves your most local customers.

1. Single-location business

Make the shift toward viewing your business not just as “Tony’s Pizza in San Francisco”, but as “Tony’s Pizza
in North Beach, San Francisco”. Consider:

  • Improving core pages of your website or creating new pages to include references to the proud part you play in the neighborhood scene. Talk about the history of your area and where you fit into that.
  • Interview locals and ask them to share their memories about the neighborhood and what they like about living there.
  • Showcase your participation in local events.
  • Plan an event, contest or special for customers in your district.
  • Take pictures, label them with hyperlocal terms, post them on your site and share them socially.
  • Blog about local happenings that are relevant to you and your customers, such as a street market where you buy the tomatoes that top your pizzas or a local award you’ve won.
  • Depending on your industry, there will be opportunities for hyperlocal content specific to your business. For example, a restaurant can make sure its menu is in crawlable text and can name some favorite dishes after the neighborhood—The Bernal Heights Special. Meanwhile, a spa in North Beach can create a hyperlocal name for a service—The North Beach Organic Spa Package. Not only does this show district pride, but customers may mention these products and services by name in their reviews, reinforcing your local connection.

2. Multi-location business within a single city

All that applies to the single location applies to you, too, but you’ve got to find a way to scale building out content for each neighborhood.

  • If your resources are strong, build a local landing page for each of your locations, including basic optimization for the neighborhood name. Meanwhile, create blog categories for each neighborhood and rotate your efforts on a week by week basis. First week, blog about neighborhood A, next week, find something interesting to write about concerning neighborhood B. Over time, you’ll have developed a nice body of content proving your involvement in each district.
  • If you’re short on resources, you’ll still want to build out a basic landing page for each of your stores in your city and make the very best effort you can to showcase your neighborhood pride on these pages.

3. Multiple businesses, multiple cities

Again, scaling this is going to be key and how much you can do will depend upon your resources.

  • The minimum requirement will be a landing page on the site for each physical location, with basic optimization for your neighborhood terms.
  • Beyond this, you’ll be making a decision about how much hyperlocal content you can add to the site/blog for each district, or whether time can be utilized more effectively via off-site social outreach. If you’ve got lots of neighborhoods to cover in lots of different cities, designating a social representative for each store and giving him the keys to your profiles (after a training session in company policies) may make the most sense.

4. Service area businesses (SABs)

Very often, service area businesses are left out in the cold with various local developments, but in my own limited testing, Google is applying at least some hyperlocal care to these business models. I can search for a neighborhood plumber, just as I would a pizza:

local results plumber bernal heights san francisco

To be painstakingly honest, plumbers are going to have to be pretty ingenious to come up with a ton of engaging industry/neighborhood content and may be confined mainly to creating some decent service area landing pages that share a bit about their work in various neighborhoods. Other business models, like contractors, home staging firms and caterers should find it quite easy to talk about district architecture, curb appeal and events on a hyperlocal front.

While your SAB is still unlikely to beat out a competitor with a physical location in a given neighborhood, you still have a chance to associate your business with that area of your town with well-planned content.


Need creative inspiration for the writing projects ahead?
Don’t miss this awesome wildcard search tip Mary Bowling shared at LocalUp. Add an underscore or asterisk to your search terms and just look at the good stuff Google will suggest to you:

wildcard search content ideas

Does Tony’s patio make his business one of
Bernal Heights’ dog-friendly restaurants or does his rooftop view make his restaurant the most picturesque lunch spot in the district? If so, he’s got two new topics to write about, either on his basic landing pages or his blog.

Hop over to 
Whitespark’s favorite takeaways from Mike Ramsey’s LocalUp presentation, too.

Citations and reviews with the user centroid in mind

Here are the basics about citations, broken into the same four business models:

1. Single-location business

You get just one citation on each platform, unless you have multiple departments or practitioners. That means one Google+ Local page, one Yelp profile, one Best of the Web listing. etc. You do not get one citation for your city and another for your neighborhood. Very simple.

2. Multi-location business within a single city

As with the single location business, you are entitled to just one set of citations per physical location. That means one Google+ Local listing for your North Beach pizza place and another for your restaurant in Bernal Heights.

A regular FAQ here in the Moz Q&A Forum relates to how Google will differentiate between two businesses located in the same city. Here are some tips:

  • Google no longer supports the use of modifiers in the business name field, so you can no longer be Tony’s Pizza – Bernal Heights, unless your restaurant is actually named this. You can only be Tony’s Pizza.
  • Facebook’s policies are different than Google’s. To my understanding, Facebook won’t permit you to build more than one Facebook Place for the identical brand name. Thus, to comply with their guidelines, you must differentiate by using those neighborhood names or other modifiers. Given that this same rule applies to all of your competitors, this should not be seen as a danger to your NAP consistency, because apparently, no multi-location business creating Facebook Places will have 100% consistent NAP. The playing field is, then, even.
  • The correct place to differentiate your businesses on all other platforms is in the address field. Google will understand that one of your branches is on A St. and the other is on B St. and will choose which one they feel is most relevant to the user.
  • Google is not a fan of call centers. Unless it’s absolutely impossible to do so, use a unique local phone number for each physical location to prevent mix-ups on Google’s part, and use this number consistently across all web-based mentions of the business.
  • Though you can’t put your neighborhood name in the title, you can definitely include it in the business description field most citation platforms provide.
  • Link your citations to their respective local landing pages on your website, not to your homepage.

3. Multiple businesses, multiple cities

Everything in business model #2 applies to you as well. You are allowed one set of citations for each of your physical locations, and while you can’t modify your Google+ Local business name, you can mention your neighborhood in the description. Promote each location equally in all you do and then rely on Google to separate your locations for various users based on your addresses and phone numbers.

4. SABs

You are exactly like business model #1 when it comes to citations, with the exception of needing to abide by Google’s rules about hiding your address if you don’t serve customers at your place of business. Don’t build out additional citations for neighborhoods you serve, other cities you serve or various service offerings. Just create one citation set. You should be fine mentioning some neighborhoods in your citation descriptions, but don’t go overboard on this.

When it comes to review management, you’ll be managing unique sets of reviews for each of your physical locations. One method for preventing business owner burnout is to manage each location in rotation. One week, tend to owner responses for Business A. Do Business B the following week. In week three, ask for some reviews for Business A and do the same for B in week four. Vary the tasks and take your time unless faced with a sudden reputation crisis.

You can take some additional steps to “hyperlocalize” your review profiles:

  • Write about your neighborhood in the business description on your profile.
  • You can’t compel random customers to mention your neighborhood, but you can certainly do so from time to time when your write responses. “We’ve just installed the first soda fountain Bernal Heights has seen since 1959. Come have a cool drink on us this summer.”
  • Offer a neighborhood special to people who bring in a piece of mail with their address on it. Prepare a little handout for all-comers, highlighting a couple of review profiles where you’d love to hear how they liked the Bernal Heights special. Or, gather email addresses if possible and follow up via email shortly after the time of service.
  • If your business model is one that permits you to name your goods or service packages, don’t forget the tip mentioned earlier about thinking hyperlocal when brainstorming names. Pretty cool if you can get your customers talking about how your “North Beach Artichoke Pizza” is the best pie in town!

Investigate your social-hyperlocal opportunties

I still consider website-based content publication to be more than half the battle in ranking locally, but sometimes, real-time social outreach can accomplish things static articles or scheduled blog posts can’t. The amount of effort you invest in social outreach should be based on your resources and an assessment of how naturally your industry lends itself to socialization. Fire insurance salesmen are going to find it harder to light up their neighborhood community than yoga studios will. Consider your options:

Remember that you are investigating each opportunity to see how it stacks up not just to promoting your location in your city, but in your neighborhood.

Who are the people in your neighborhood?

Remember that Sesame Street jingle? It hails from a time when urban dwellers strongly identified with a certain district of hometown. People were “from the neighborhood.” If my grandfather was a Mission District fella, maybe yours was from Chinatown. Now, we’re shifting in fascinating directions. Even as we’ve settled into telecommuting to jobs in distant states or countries, Amazon is offering one hour home delivery to our neighbors in Manhattan. Doctors are making house calls again! Any day now, I’m expecting a milkman to start making his rounds around here. Commerce has stretched to span the globe and now it’s zooming in to meet the needs of the family next door.

If the big guys are setting their sights on near-instant services within your community, take note.
You live in that community. You talk, face-to-face, with your neighbors every day and know the flavor of the local scene better than any remote competitor can right now.

Now is the time to reinvigorate that old neighborhood pride in the way you’re visualizing your business, marketing it and personally communicating to customers that you’re right there for them.

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Reblogged 4 years ago from tracking.feedpress.it