Google Updates The Search Quality Rating Guidelines

Google has updated their search quality raters guidelines document on March 28, 2016 reducing it from 160 to 146 pages.

The post Google Updates The Search Quality Rating Guidelines appeared first on Search Engine Land.

Please visit Search Engine Land for the full article.

Reblogged 2 years ago from feeds.searchengineland.com

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.

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Reblogged 3 years ago from tracking.feedpress.it

I Can’t Drive 155: Meta Descriptions in 2015

Posted by Dr-Pete

For years now, we (and many others) have been recommending keeping your Meta Descriptions shorter than
about 155-160 characters. For months, people have been sending me examples of search snippets that clearly broke that rule, like this one (on a search for “hummingbird food”):

For the record, this one clocks in at 317 characters (counting spaces). So, I set out to discover if these long descriptions were exceptions to the rule, or if we need to change the rules. I collected the search snippets across the MozCast 10K, which resulted in 92,669 snippets. All of the data in this post was collected on April 13, 2015.

The Basic Data

The minimum snippet length was zero characters. There were 69 zero-length snippets, but most of these were the new generation of answer box, that appears organic but doesn’t have a snippet. To put it another way, these were misidentified as organic by my code. The other 0-length snippets were local one-boxes that appeared as organic but had no snippet, such as this one for “chichen itza”:

These zero-length snippets were removed from further analysis, but considering that they only accounted for 0.07% of the total data, they didn’t really impact the conclusions either way. The shortest legitimate, non-zero snippet was 7 characters long, on a search for “geek and sundry”, and appears to have come directly from the site’s meta description:

The maximum snippet length that day (this is a highly dynamic situation) was 372 characters. The winner appeared on a search for “benefits of apple cider vinegar”:

The average length of all of the snippets in our data set (not counting zero-length snippets) was 143.5 characters, and the median length was 152 characters. Of course, this can be misleading, since some snippets are shorter than the limit and others are being artificially truncated by Google. So, let’s dig a bit deeper.

The Bigger Picture

To get a better idea of the big picture, let’s take a look at the display length of all 92,600 snippets (with non-zero length), split into 20-character buckets (0-20, 21-40, etc.):

Most of the snippets (62.1%) cut off as expected, right in the 141-160 character bucket. Of course, some snippets were shorter than that, and didn’t need to be cut off, and some broke the rules. About 1% (1,010) of the snippets in our data set measured 200 or more characters. That’s not a huge number, but it’s enough to take seriously.

That 141-160 character bucket is dwarfing everything else, so let’s zoom in a bit on the cut-off range, and just look at snippets in the 120-200 character range (in this case, by 5-character bins):

Zooming in, the bulk of the snippets are displaying at lengths between about 146-165 characters. There are plenty of exceptions to the 155-160 character guideline, but for the most part, they do seem to be exceptions.

Finally, let’s zoom in on the rule-breakers. This is the distribution of snippets displaying 191+ characters, bucketed in 10-character bins (191-200, 201-210, etc.):

Please note that the Y-axis scale is much smaller than in the previous 2 graphs, but there is a pretty solid spread, with a decent chunk of snippets displaying more than 300 characters.

Without looking at every original meta description tag, it’s very difficult to tell exactly how many snippets have been truncated by Google, but we do have a proxy. Snippets that have been truncated end in an ellipsis (…), which rarely appears at the end of a natural description. In this data set, more than half of all snippets (52.8%) ended in an ellipsis, so we’re still seeing a lot of meta descriptions being cut off.

I should add that, unlike titles/headlines, it isn’t clear whether Google is cutting off snippets by pixel width or character count, since that cut-off is done on the server-side. In most cases, Google will cut before the end of the second line, but sometimes they cut well before this, which could suggest a character-based limit. They also cut off at whole words, which can make the numbers a bit tougher to interpret.

The Cutting Room Floor

There’s another difficulty with telling exactly how many meta descriptions Google has modified – some edits are minor, and some are major. One minor edit is when Google adds some additional information to a snippet, such as a date at the beginning. Here’s an example (from a search for “chicken pox”):

With the date (and minus the ellipsis), this snippet is 164 characters long, which suggests Google isn’t counting the added text against the length limit. What’s interesting is that the rest comes directly from the meta description on the site, except that the site’s description starts with “Chickenpox.” and Google has removed that keyword. As a human, I’d say this matches the meta description, but a bot has a very hard time telling a minor edit from a complete rewrite.

Another minor rewrite occurs in snippets that start with search result counts:

Here, we’re at 172 characters (with spaces and minus the ellipsis), and Google has even let this snippet roll over to a third line. So, again, it seems like the added information at the beginning isn’t counting against the length limit.

All told, 11.6% of the snippets in our data set had some kind of Google-generated data, so this type of minor rewrite is pretty common. Even if Google honors most of your meta description, you may see small edits.

Let’s look at our big winner, the 372-character description. Here’s what we saw in the snippet:

Jan 26, 2015 – Health• Diabetes Prevention: Multiple studies have shown a correlation between apple cider vinegar and lower blood sugar levels. … • Weight Loss: Consuming apple cider vinegar can help you feel more full, which can help you eat less. … • Lower Cholesterol: … • Detox: … • Digestive Aid: … • Itchy or Sunburned Skin: … • Energy Boost:1 more items

So, what about the meta description? Here’s what we actually see in the tag:

Were you aware of all the uses of apple cider vinegar? From cleansing to healing, to preventing diabetes, ACV is a pantry staple you need in your home.

That’s a bit more than just a couple of edits. So, what’s happening here? Well, there’s a clue on that same page, where we see yet another rule-breaking snippet:

You might be wondering why this snippet is any more interesting than the other one. If you could see the top of the SERP, you’d know why, because it looks something like this:

Google is automatically extracting list-style data from these pages to fuel the expansion of the Knowledge Graph. In one case, that data is replacing a snippet
and going directly into an answer box, but they’re performing the same translation even for some other snippets on the page.

So, does every 2nd-generation answer box yield long snippets? After 3 hours of inadvisable mySQL queries, I can tell you that the answer is a resounding “probably not”. You can have 2nd-gen answer boxes without long snippets and you can have long snippets without 2nd-gen answer boxes,
but there does appear to be a connection between long snippets and Knowledge Graph in some cases.

One interesting connection is that Google has begun bolding keywords that seem like answers to the query (and not just synonyms for the query). Below is an example from a search for “mono symptoms”. There’s an answer box for this query, but the snippet below is not from the site in the answer box:

Notice the bolded words – “fatigue”, “sore throat”, “fever”, “headache”, “rash”. These aren’t synonyms for the search phrase; these are actual symptoms of mono. This data isn’t coming from the meta description, but from a bulleted list on the target page. Again, it appears that Google is trying to use the snippet to answer a question, and has gone well beyond just matching keywords.

Just for fun, let’s look at one more, where there’s no clear connection to the Knowledge Graph. Here’s a snippet from a search for “sons of anarchy season 4”:

This page has no answer box, and the information extracted is odd at best. The snippet bears little or no resemblance to the site’s meta description. The number string at the beginning comes out of a rating widget, and some of the text isn’t even clearly available on the page. This seems to be an example of Google acknowledging IMDb as a high-authority site and desperately trying to match any text they can to the query, resulting in a Frankenstein’s snippet.

The Final Verdict

If all of this seems confusing, that’s probably because it is. Google is taking a lot more liberties with snippets these days, both to better match queries, to add details they feel are important, or to help build and support the Knowledge Graph.

So, let’s get back to the original question – is it time to revise the 155(ish) character guideline? My gut feeling is: not yet. To begin with, the vast majority of snippets are still falling in that 145-165 character range. In addition, the exceptions to the rule are not only atypical situations, but in most cases those long snippets don’t seem to represent the original meta description. In other words, even if Google does grant you extra characters, they probably won’t be the extra characters you asked for in the first place.

Many people have asked: “How do I make sure that Google shows my meta description as is?” I’m afraid the answer is: “You don’t.” If this is very important to you, I would recommend keeping your description below the 155-character limit, and making sure that it’s a good match to your target keyword concepts. I suspect Google is going to take more liberties with snippets over time, and we’re going to have to let go of our obsession with having total control over the SERPs.

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Everything You Need to Know About Mobile App Search

Posted by Justin_Briggs

Mobile isn’t the future. It’s the present. Mobile apps are not only changing how we interact with devices and websites, they’re changing the way we search. Companies are creating meaningful experiences on mobile-friendly websites and apps, which in turn create new opportunities to get in front of users.

I’d like to explore the growth of mobile app search and its current opportunities to gain visibility and drive engagement.

Rise of mobile app search

The growth of mobile device usage has driven a significant lift in app-related searches. This is giving rise to mobile app search as a vertical within traditional universal search.

While it has been clear for some time that mobile search is important, that importance has been more heavily emphasized by Google recently, as they continue to push
mobile-friendly labels in SERPs, and are likely increasing mobile-friendliness’s weight as a ranking factor.

The future of search marketing involves mobile, and it will not be limited to optimizing HTML webpages, creating responsive designs, and optimizing UX. Mobile SEO is a world where apps, knowledge graph, and conversational search are front and center.

For the
top 10 leading properties online, 34% of visitors are mobile-only (comScore data), and, anecdotally, we’re seeing similar numbers with our clients, if not more.

Mobile device and app growth

It’s also worth noting that
72% of mobile engagement relies on apps vs. on browsers. Looking at teen usage, apps are increasingly dominant. Additionally,
55% of teens use voice search more than once per day

If you haven’t read it, grab some coffee and read
A Teenagers View on Social Media, which is written by a 19-year old who gives his perspective of online behavior. Reading between the lines shows a number of subtle shifts in behavior. I noticed that every time I expected him say website, he said application. In fact, he referenced application 15 times, and it is the primary way he describes social networks.

This means that one of the fasting growing segments of mobile users cannot be marketed to by optimizing HTML webpages alone, requiring search marketers to expand their skills into app optimization.

The mobile app pack

This shift is giving rise to the mobile app pack and app search results, which are triggered on searches from mobile devices in instances of high mobile app intent. Think of these as being similar to local search results. Considering
mobile searcher behavior, these listings dominate user attention.

Mobile app search results and mobile app pack

As with local search, mobile app search can reorder traditional results, completely push them down, or integrate app listings with traditional web results.

You can test on your desktop using a
user-agent switcher, or by searching on your iOS or Android device. 

There are slight differences between iPhone and Android mobile app results:

iOS and Android mobile search result listing

From what I’ve seen, mobile app listings trigger more frequently, and with more results, on Android search results when compared to iOS. Additionally, iOS mobile app listings are represented as a traditional website result listing, while mobile app listings on Android are more integrated.

Some of the differences also come from the differences in app submission guidelines on the two major stores, the Apple App Store and Google Play.

Overview of differences in mobile app results

  1. Title – Google uses the app listing page’s HTML title (which is the app’s title). iOS app titles can exceed 55-62 characters, which causes wrapping and title truncation like a traditional result. Android app title requirements are shorter, so titles are typically shorter on Android mobile app listings.
  2. URL – iOS mobile app listings display the iTunes URL to the App Store as part of the search result.
  3. Icon – iOS icons are square and Android icons have rounded corners.
  4. Design – Android results stand out more, with an “Apps” headline above the pack and a link to Google Play at the end.
  5. App store content – The other differences show up in the copy, ratings, and reviews on each app store.

Ranking in mobile app search results

Ranking in mobile app search results is a
combination of App Store Optimization (ASO) and traditional SEO. The on-page factors are dependent upon your app listing, so optimization starts with having solid ASO. If you’re not familiar with ASO, it’s the process of optimizing your app listing for internal app store search.

Basics of ASO

Ranking in the Apple App Store and in Google Play is driven by two primary factors: keyword alignment and app performance. Text fields in the app store listing, such as title, description, and keyword list, align the app with a particular set of keywords. Performance metrics including download velocity, app ratings, and reviews determine how well the app will rank for each of those keywords. (Additionally, the Google Play algorithm may include external, web-based performance metrics like citations and links as ranking factors.)

App store ranking factors

Mobile app listing optimization

While I won’t explore ASO in-depth here, as it’s very similar to traditional SEO,
optimizing app listings is primarily a function of keyword targeting.

Tools like
Sensor Tower, MobileDevHQ, and App Annie can help you with mobile app keyword research. However, keep in mind that mobile app search listings show up in universal search, so it’s important to leverage traditional keyword research tools like the AdWords Tool or Google Trends.

While there are similarities with ASO, optimizing for these mobile app search listings on the web has some slight differences.

Differences between ASO & mobile app SEO targeting

  1. Titles – While the Apple App Store allows relatively long titles, they are limited to the preview length in organic search. Titles should be optimized with Google search in mind, in addition to optimizing for the app store. Additionally, several apps aggressively target keywords in their app title, but caution should be used as spamming keywords could influence app performance in Google.
  2. Description – The app description on the App Store may not be a factor in internal search, but it will impact external app search results. Leverage keyword targeting best practices when writing your iOS app description, as well as your Android app description.
  3. Device and platform keywords – When targeting for app store search, it is not as important to target terms related to the OS or device. However, these terms can help visibility in external search. Include device and OS terms, such as Android, Samsung Note, iOS, iPad, and iPhone.

App performance optimization

Outside of content optimization, Google looks at the performance of the app. On the Android side, they have access to the data, but for iOS they have to rely on publicly available information.

App performance factors

  • Number of ratings
  • Average rating score
  • Content and sentiment analysis of reviews
  • Downloads / installs
  • Engagement and retention
  • Internal links on app store

For iOS, the primary public metrics are ratings and reviews. However, app performance can be inferred using the App Store’s ranking charts and search results, which can be leveraged as proxies of these performance metrics.


The following objectives will have the greatest influence on your mobile app search ranking:

  1. Increase your average rating number
  2. Increase your number of ratings
  3. Increase downloads

For app ratings and reviews, leverage platforms like
Apptentive to improve your ratings. They are very effective at driving positive ratings. Additionally, paid tactics are a great way to drive install volume and are one area where paid budget capacity could directly influence organic results in Google. Anecdotally, both app stores use rating numbers (typically above or below 4 stars) to make decisions around promoting an app, either through merchandising spots or co-branded campaigns. I suspect this is being used as a general cut-off for what is displayed in universal results. Increasing your rating above 4 stars should improve the likelihood you’ll appear in mobile app search results.

Lastly, think of merchandising and rankings in terms of 
internal linking structures. The more visible you are inside of the app store, the more visibility you have in external search.

App web performance optimization

Lastly, we’re talking Google rankings, so factors like links, citations, and social shares matter. You should be
conducting content marketing, PR, and outreach for your app. Focus on merchandising your app on your own site, as well as increasing coverage of your app (linking to the app store page). The basics of link optimization apply here.

App indexation – drive app engagement

Application search is not limited to driving installs via app search results. With app indexing, you can leverage your desktop/mobile website visibility in organic search to drive engagement with those who have your app installed. Google can discover and expose content deep inside your app directly in search results. This means that when a user clicks on your website in organic search, it can open your app directly, taking them to that exact piece of content in your app, instead of opening your website.

App indexation fundamentally changes technical SEO, extending SEO from server and webpage setup to the setup and optimization of applications.

App indexation on Google

This also fundamentally changes search. Your most avid and engaged user may choose to no longer visit your website. For example, on my Note 4, when I click a link to a site of a brand that I have an app installed for, Google gives me the option not only to open in the app, but to set opening the app as a default behavior.

If a user chooses to open your site in your app, they may never visit your site from organic search again.

App indexation is currently limited to Android devices, but there is evidence to suggest that it’s already in the works and is
soon to be released on iOS devices. There have been hints for some time, but markup is showing up in the wild suggesting that Google is actively working with Apple and select brands to develop iOS app indexing.

URI optimization for apps

The first step in creating an indexable app is to set up your app to support deep links. Deep links are URIs that are understood by your app and will open up a specific piece of content. They are effectively URLs for applications.

Once this URI is supported, a user can be sent to deep content in the app. These can be discovered as alternates to your desktop site’s URLs, similar to how
separate-site mobile sites are defined as alternate URLs for the desktop site. In instances of proper context (on an Android device with the app installed), Google can direct a user to the app instead of the website.

Setting this up requires working with your app developer to implement changes inside the app as well as working with your website developers to add references on your desktop site.

Adding intent filters

Android has
documented the technical setup of deep links in detail, but it starts with setting up intent filters in an app’s Android manifest file. This is done with the following code.

<activity android:name="com.example.android.GizmosActivity"
android:label="@string/title_gizmos" >
<intent-filter android:label="@string/filter_title_viewgizmos">
<action android:name="android.intent.action.VIEW" />
<data android:scheme="http"
android:host="example.com"
android:pathPrefix="/gizmos" />
<category android:name="android.intent.category.DEFAULT" />
<category android:name="android.intent.category.BROWSABLE" />
</intent-filter>
</activity>

This dictates the technical optimization of your app URIs for app indexation and defines the elements used in the URI example above.

  • The <intent-filter> element should be added for activities that should be launchable from search results.
  • The <action> element specifies the ACTION_VIEW intent action so that the intent filter can be reached from Google Search.
  • The <data> tag represents a URI format that resolves to the activity. At minimum, the <data> tag must include the android:scheme attribute.
  • Include the BROWSABLE category. The BROWSABLE category is required in order for the intent filter to be accessible from a web browser. Without it, clicking a link in a browser cannot resolve to your app. The DEFAULT category is optional, but recommended. Without this category, the activity can be started only with an explicit intent, using your app component name.

Testing deep links

Google has created tools to help test your deep link setup. You can use
Google’s Deep Link Test Tool to test your app behavior with deep links on your phone. Additionally, you can create an HTML page with an intent:// link in it.

For example
:

<a href="intent://example.com/page-1#Intent;scheme=http;package=com.example.android;end;"> <a href="http://example.com/page-1">http://example.com/page-1></a>

This link would open up deep content inside the app from the HTML page.

App URI crawl and discovery

Once an app has deep link functionality, the next step is to
ensure that Google can discover these URIs as part of its traditional desktop crawling.

Ways to get apps crawled

  1. Rel=”alternate” in HTML head
  2. ViewAction with Schema.org
  3. Rel=”alternate” in XML Sitemap

Implementing all three will create clear signals, but at minimum you should add the rel=”alternate” tag to the HTML head of your webpages.

Effectively, think of the app URI as being similar to a mobile site URL when
setting up a separate-site mobile site for SEO. The mobile deep link is an alternative way to view a webpage on your site. You map a piece of content on your site to a corresponding piece of content inside the app.

Before you get started, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

#1: Rel=”alternate” in HTML head

On an example page, such as example.com/page-1, you would add the following code to the head of the document. Again, very similar to separate-site mobile optimization.

<html>
<head> 
... 
<link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
...
</head>
<body>
</body>
#2: ViewAction with Schema.org

Additionally, you can reference the deep link using Schema.org and JSON by using a 
ViewAction.

<script type="application/ld+json"> 
{ 
"@context": "http://schema.org", 
"@type": "WebPage", 
"@id": "http://example.com/gizmos", 
"potentialAction": { 
"@type": "ViewAction", 
"target": "android-app://com.example.android/http/example.com/gizmos" 
} 
} 
</script>
#3 Rel=”alternate” in XML sitemap

Lastly, you can reference the alternate URL in your XML Sitemaps, similar to using the rel=”alternate” for mobile sites.

<?xml version="1.0" encoding="UTF-8" ?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"> 
<url> 
<loc>http://example.com/page-1</loc> 
<xhtml:link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
</url> 
... 
</urlset>

Once these are in place, Google can discover the app URI and provide your app as an alternative way to view content found in search.

Bot control and robots noindex for apps

There may be instances where there is content within your app that you do not want indexed in Google. A good example of this might be content or functionality that is built out on your site, but has not yet been developed in your app. This would create an inferior experience for users. The good news is that we can block indexation with a few updates to the app.

First, add the following to your app resource directory (res/xml/noindex.xml).

<?xml version="1.0" encoding="utf-8"?> 
<search-engine xmlns:android="http://schemas.android.com/apk/res/android"> 
<noindex uri="http://example.com/gizmos/hidden_uri"/> 
<noindex uriPrefix="http://example.com/gizmos/hidden_prefix"/> 
<noindex uri="gizmos://hidden_path"/> 
<noindex uriPrefix="gizmos://hidden_prefix"/> 
</search-engine>

As you can see above, you can block an individual URI or define a URI prefix to block entire folders.

Once this has been added, you need to update the AndroidManifest.xml file to denote that you’re using noindex.html to block indexation.

<manifest xmlns:android="http://schemas.android.com/apk/res/android" package="com.example.android.Gizmos"> 
<application> 
<activity android:name="com.example.android.GizmosActivity" android:label="@string/title_gizmos" > 
<intent-filter android:label="@string/filter_title_viewgizmos"> 
<action android:name="android.intent.action.VIEW"/> 
... 
</activity> 
<meta-data android:name="search-engine" android:resource="@xml/noindex"/> 
</application> 
<uses-permission android:name="android.permission.INTERNET"/> 
</manifest>

App indexing API to drive re-engagement

In addition to URI discovery via desktop crawl, your mobile app can integrate
Google’s App Indexing API, which communicates with Google when users take actions inside your app. This sends information to Google about what users are viewing in the app. This is an additional method for deep link discovery and has some benefits.

The primary benefit is the ability to appear in
autocomplete. This can drive re-engagement through Google Search query autocompletions, providing access to inner pages in apps.

App auto suggest

Again, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

App actions with knowledge graph

The next, and most exciting, evolution of search is leveraging actions. These will be powerful when
combined with voice search, allowing search engines to take action on behalf of users, turning spoken language into executed actions.

App indexing allows you to take advantage of actions by allowing Google to not only launch an app, but execute actions inside of the app. Order me a pizza? Schedule my meeting? Drive my car? Ok, Google.

App actions work via entity detection and the application of the knowledge graph, allowing search engines to understand actions, words, ideas and objects. With that understanding, they can build an action graph that allows them to define common actions by entity type.

Here is a list of actions currently supported by Schema.org

For example, the PlayAction could be used to play a song in a music app. This can be achieve with the following markup.

<script type="application/ld+json">
{
"@context": "http://schema.org",
"@type": "MusicGroup",
"name": "Weezer", "potentialAction": {
"@type": "ListenAction",
"target": "android-app://com.spotify.music/http/we.../listen"
}
}
</script>
Once this is implemented, these app actions can begin to appear in search results and knowledge graph.

deep links in app search results

Overview of mobile app search opportunities

In summary, there are five primary ways to increase visibility and engagement for your mobile app in traditional organic search efforts.

Mobile apps in search results

The growth of mobile search is transforming how we define technical SEO, moving beyond front-end and back-end optimization of websites into the realm of structured data and application development. As app indexing expands to include iOS, I suspect the possibilities and opportunities associated with indexing applications, and their corresponding actions, to grow extensively. 

For those with Android apps, app indexing is a potential leapfrog style opportunity to get ahead of competitors who are dominant in traditional desktop search. Those with iOS devices should start by optimizing their app listings, while preparing to implement indexation, as I suspect it’ll be released for iOS this year.

Have you been leveraging traditional organic search to drive visibility and engagement for apps? Share your experiences in the comments below.

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Reblogged 3 years ago from tracking.feedpress.it

Leveraging Panda to Get Out of Product Feed Jail

Posted by MichaelC

This is a story about Panda, customer service, and differentiating your store from others selling the same products.

Many e-commerce websites get the descriptions, specifications, and imagery for products they sell from feeds or databases provided by the
manufacturers. The manufacturers might like this, as they control how their product is described and shown. However, it does their retailers
no good when they are trying to rank for searches for those products and they’ve got the exact same content as every other retailer. If the content
in the feed is thin, then you’ll have pages with…well….thin content. And if there’s a lot of content for the products, then you’ll have giant blocks of content that
Panda might spot as being the same as they’ve seen on many other sites. To throw salt on the wound, if the content is really crappy, badly written,
or downright wrong, then the retailers’ sites will look low-quality to Panda and users as well.

Many webmasters see Panda as a type of Google penalty—but it’s not, really. Panda is a collection of measurements Google
is taking of your web pages to try and give your pages a rating on how happy users are likely to be with those pages.
It’s not perfect, but then again—neither is your website.

Many SEO folks (including me) tend to focus on the kinds of tactical and structural things you can do to make Panda see
your web pages as higher quality: things like adding big, original images, interactive content like videos and maps, and
lots and lots and lots and lots of text. These are all good tactics, but let’s step back a bit and look at a specific
example to see WHY Panda was built to do this, and from that, what we can do as retailers to enrich the content we have
for e-commerce products where our hands are a bit tied—we’re getting a feed of product info from the manufacturers, the same
as every other retailer of those products.

I’m going to use a real-live example that I suffered through about a month ago. I was looking for a replacement sink
stopper for a bathroom sink. I knew the brand, but there wasn’t a part number on the part I needed to replace. After a few Google
searches, I think I’ve found it on Amazon:


Don’t you wish online shopping was always this exciting?

What content actually teaches the customer

All righty… my research has shown me that there are standard sizes for plug stoppers. In fact, I initially ordered a
“universal fit sink stopper.” Which didn’t fit. Then I found 3 standard diameters, and 5 or 6 standard lengths.
No problem…I possess that marvel of modern tool chests, a tape measure…so I measure the part I have that I need to replace. I get about 1.5″ x 5″.
So let’s scroll down to the product details to see if it’s a match:

Kohler sink stopper product info from hell

Whoa. 1.2 POUNDS? This sink stopper must be made of
Ununoctium.
The one in my hand weighs about an ounce. But the dimensions
are way off as well: a 2″ diameter stopper isn’t going to fit, and mine needs to be at least an inch longer.

I scroll down to the product description…maybe there’s more detail there, maybe the 2″ x 2″ is the box or something.

I've always wanted a sink stopper designed for long long

Well, that’s less than helpful, with a stupid typo AND incorrect capitalization AND a missing period at the end.
Doesn’t build confidence in the company’s quality control.

Looking at the additional info section, maybe this IS the right part…the weight quoted in there is about right:

Maybe this is my part after all

Where else customers look for answers

Next I looked at the questions and answers bit, which convinced me that it PROBABLY was the right part:

Customers will answer the question if the retailer won't...sometimes.

If I was smart, I would have covered my bets by doing what a bunch of other customers also did: buy a bunch of different parts,
and surely one of them will fit. Could there
possibly was a clearer signal that the product info was lacking than this?

If you can't tell which one to buy, buy them all!

In this case, that was probably smarter than spending another 1/2 hour of my time snooping around online. But in general, people
aren’t going to be willing to buy THREE of something just to make sure they get the right one. This cheap part was an exception.

So, surely SOMEONE out there has the correct dimensions of this part on their site—so I searched for the part number I saw on the Amazon
listing. But as it turned out, that crappy description and wrong weight and dimensions were on every site I found…because they came from
the manufacturer.

Better Homes and Gardens...but not better description.

A few of the sites had edited out the “designed for long long” bit, but apart from that, they were all the same.

What sucks for the customer is an opportunity for you

Many, many retailers are in this same boat—they get their product info from the manufacturer, and if the data sucks in their feed,
it’ll suck on their site. Your page looks weak to both users and to Panda, and it looks the same as everybody else’s page for that product…to
both users and to Panda. So (a) you won’t rank very well, and (b) if you DO manage to get a customer to that page, it’s not as likely to convert
to a sale.

What can you do to improve on this? Here’s a few tactics to consider.

1. Offer your own additional description and comments

Add a new field to your CMS for your own write-ups on products, and when you discover issues like the above, you can add your own information—and
make it VERY clear what’s the manufacturer’s stock info and what you’ve added (that’s VALUE-ADDED) as well. My client
Sports Car Market magazine does this with their collector car auction reports in their printed magazine:
they list the auction company’s description of the car, then their reporter’s assessment of the car. This is why I buy the magazine and not the auction catalog.

2. Solicit questions

Be sure you solicit questions on every product page—your customers will tell you what’s wrong or what important information is missing. Sure,
you’ve got millions of products to deal with, but what the customers are asking about (and your sales volume of course) will help you prioritize as well as
find the problems opportunities.

Amazon does a great job of enabling this, but in this case, I used the Feedback option to update the product info,
and got back a total
bull-twaddle email from the seller about how the dimensions are in the product description thank you for shopping with us, bye-bye.
I tried to help them, for free, and they shat on me.

3. But I don’t get enough traffic to get the questions

Don’t have enough site volume to get many customer requests? No problem, the information is out there for you on Amazon :-).
Take your most important products, and look them up on Amazon, and see what questions are being asked—then answer those ONLY on your own site.

4. What fits with what?

Create fitment/cross-reference charts for products.
You probably have in-house knowledge of what products fit/are compatible with what other products.
Just because YOU know a certain accessory fits all makes and models, because it’s some industry-standard size, doesn’t mean that the customer knows this.

If there’s a particular way to measure a product so you get the correct size, explain that (with photos of what you’re measuring, if it seems
at all complicated). I’m getting a new front door for my house. 

  • How big is the door I need? 
  • Do I measure the width of the door itself, or the width of the
    opening (probably 1/8″ wider)? 
  • Or if it’s pre-hung, do I measure the frame too? Is it inswing or outswing?
  • Right or left hinged…am I supposed to
    look at the door from inside the house or outside to figure this out? 

If you’re a door seller, this is all obvious stuff,
but it wasn’t obvious to me, and NOT having the info on a website means (a) I feel stupid, and (b) I’m going to look at your competitors’ sites
to see if they will explain it…and maybe I’ll find a door on THEIR site I like better anyway.

Again, prioritize based on customer requests.

5. Provide your own photos and measurements

If examples of the physical products are available to you, take your own photos, and take your own measurements.

In fact, take your OWN photo of YOURSELF taking the measurement—so the user can see exactly what part of the product you’re measuring.
In the photo below, you can see that I’m measuring the diameter of the stopper, NOT the hole in the sink, NOT the stopper plus the rubber gasket.
And no, Kohler, it’s NOT 2″ in diameter…by a long shot.

Don't just give the measurements, SHOW the measurements

Keep in mind, you shouldn’t have to tear apart your CMS to do any of this. You can put your additions in a new database table, just tied to the
core product content by SKU. In the page template code for the product page, you can check your database to see if you have any of your “extra bits” to display
alongside the feed content, and this way keep it separate from the core product catalog code. This will make updates to the CMS/product catalog less painful as well.

Fixing your content doesn’t have to be all that difficult, nor expensive

At this point, you’re probably thinking “hey, but I’ve got 1.2 million SKUs, and if I were to do this, it’d take me 20 years to update all of them.”
FINE. Don’t update all of them. Prioritize, based on factors like what you sell the most of, what you make the best margin on, what customers
ask questions about the most, etc. Maybe concentrate on your top 5% in terms of sales, and do those first. Take all that money you used to spend
buying spammy links every month, and spend it instead on junior employees or interns doing the product measurements, extra photos, etc.

And don’t be afraid to spend a little effort on a low value product, if it’s one that frequently gets questions from customers.
Simple things can make a life-long fan of the customer. I once needed to replace a dishwasher door seal, and didn’t know if I needed special glue,
special tools, how to cut it to fit with or without overlap, etc.
I found a video on how to do the replacement on
RepairClinic.com. So easy!
They got my business for the $10 seal, of course…but now I order my $50 fridge water filter from them every six months as well.

Benefits to your conversion rate

Certainly the tactics we’ve talked about will improve your conversion rate from visitors to purchasers. If JUST ONE of those sites I looked at for that damn sink stopper
had the right measurement (and maybe some statement about how the manufacturer’s specs above are actually incorrect, we measured, etc.), I’d have stopped right there
and bought from that site.

What does this have to do with Panda?

But, there’s a Panda benefit here too. You’ve just added a bunch of additional, unique text to your site…and maybe a few new unique photos as well.
Not only are you going to convert better, but you’ll probably rank better too.

If you’re NOT Amazon, or eBay, or Home Depot, etc., then Panda is your secret weapon to help you rank against those other sites whose backlink profiles are
stronger than
carbon fibre (that’s a really cool video, by the way).
If you saw my
Whiteboard Friday on Panda optimization, you’ll know that
Panda tuning can overcome incredible backlink profile deficits.

It’s go time

We’re talking about tactics that are time-consuming, yes—but relatively easy to implement, using relatively inexpensive staff (and in some
cases, your customers are doing some of the work for you).
And it’s something you can roll out a product at a time.
You’ll be doing things that really DO make your site a better experience for the user…we’re not just trying to trick Panda’s measurements.

  1. Your pages will rank better, and bring more traffic.
  2. Your pages will convert better, because users won’t leave your site, looking elsewhere for answers to their questions.
  3. Your customers will be more loyal, because you were able to help them when nobody else bothered.

Don’t be held hostage by other peoples’ crappy product feeds. Enhance your product information with your own info and imagery.
Like good link-building and outreach, it takes time and effort, but both Panda and your site visitors will reward you for it.

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Reblogged 3 years ago from tracking.feedpress.it

Local Search Expert Quiz: How Much Do You Know about Local SEO?

Posted by Cyrus-Shepard

How big is local SEO?

Our latest
Industry Survey revealed over 67% of online marketers report spending time on local search. We’ve witnessed demand for local SEO expertise grow as Google’s competitive landscape continues to evolve.

Last year, Moz introduced the
SEO Expert Quiz, which to date over 40,000 people have attempted to conquer. Today, we’re proud to announce the Local Search Expert Quiz. Written by local search expert Miriam Ellis, the quiz contains 40 questions and only takes less than 10 minutes to complete.

Ready to get started? When you are finished, we’ll automatically score your quiz and reveal the correct answers.

<a href=”http://mozbot.polldaddy.com/s/local-search-expert-quiz”>View Survey</a>

Rating your score

Keep in mind the Local Search Expert Quiz is
just for fun. That said, we’ve established the following guidelines to help judge your results.

  • 0-39% Newbie: Time to study up on your citation data!
  • 40-59% Beginner: Good job, but you’re not quite in the 7-pack yet.
  • 60-79% Intermediate: You’re getting close to the centroid!
  • 80-89% Pro: Let’s tackle multi-location!
  • 90-100% Guru: We all bow down to your local awesomeness

Resources to improve your performance

Want to learn more about local search? Here’s a collection of free learning resources to help up your performance (and possibly your income.)

  1. The Moz Local Learning Center
  2. Glossary of Local Search Terms and Definitions
  3. Guidelines for Representing Your Business on Google
  4. Local Search Ranking Factors
  5. Blumenthal’s Blog
  6. Local SEO Guide
  7. Whitespark Blog

You can also learn the latest local search tips and tricks by signing up for the LocalUp Advanced one-day conference or reading
local SEO posts on the Moz Blog.

Embed this Quiz

We created this quiz using
Polldaddy, and we’re making it available to embed on your own site. This isn’t a backlink play – we didn’t even include a link to our own site (but feel free to include one if you feel generous).

Here’s the embed code:

<iframe frameborder="0" width="100%" height="600" scrolling="auto" allowtransparency="true" src="http://mozbot.polldaddy.com/s/local-search-expert-quiz?iframe=1"><a href="http://mozbot.polldaddy.com/s/local-search-expert-quiz">View Survey</a></iframe>

How did you score on the quiz? Let us know in the comments below!

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www.findawineryvictoria.com.au is a local business directory for wineries

Reblogged 3 years ago from moz.com