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 4 years ago from tracking.feedpress.it

Exposing The Generational Content Gap: Three Ways to Reach Multiple Generations

Posted by AndreaLehr

With more people of all ages online than ever before, marketers must create content that resonates with multiple generations. Successful marketers realize that each generation has unique expectations, values and experiences that influence consumer behaviors, and that offering your audience content that reflects their shared interests is a powerful way to connect with them and inspire them to take action.

We’re in the midst of a generational shift, with
Millennials expected to surpass Baby Boomers in 2015 as the largest living generation. In order to be competitive, marketers need to realize where key distinctions and similarities lie in terms of how these different generations consume content and share it with with others.

To better understand the habits of each generation,
BuzzStream and Fractl surveyed over 1,200 individuals and segmented their responses into three groups: Millennials (born between 1977–1995), Generation X (born between 1965–1976), and Baby Boomers (born between 1946–1964). [Eds note: The official breakdown for each group is as follows: Millennials (1981-1997), Generation X (1965-1980), and Boomers (1946-1964)]

Our survey asked them to identify their preferences for over 15 different content types while also noting their opinions on long-form versus short-form content and different genres (e.g., politics, technology, and entertainment).

We compared their responses and found similar habits and unique trends among all three generations.

Here’s our breakdown of the three key takeaways you can use to elevate your future campaigns:

1. Baby Boomers are consuming the most content

However, they have a tendency to enjoy it earlier in the day than Gen Xers and Millennials.

Although we found striking similarities between the younger generations, the oldest generation distinguished itself by consuming the most content. Over 25 percent of Baby Boomers consume 20 or more hours of content each week. Additional findings:

  • Baby Boomers also hold a strong lead in the 15–20 hours bracket at 17 percent, edging out Gen Xers and Millennials at 12 and 11 percent, respectively
  • A majority of Gen Xers and Millennials—just over 22 percent each—consume between 5 and 10 hours per week
  • Less than 10 percent of Gen Xers consume less than five hours of content a week—the lowest of all three groups

We also compared the times of day that each generation enjoys consuming content. The results show that most of our respondents—over 30 percent— consume content between 8 p.m. and midnight. However, there are similar trends that distinguish the oldest generation from the younger ones:

  • Baby Boomers consume a majority of their content in the morning. Nearly 40 percent of respondents are online between 5 a.m. and noon.
  • The least popular time for most respondents to engage with content online is late at night, between midnight and 5 a.m., earning less than 10 percent from each generation
  • Gen X is the only generation to dip below 10 percent in the three U.S. time zones: 5 a.m. to 9 a.m., 6 to 8 p.m., and midnight to 5 a.m.

When Do We Consume Content

When it comes to which device each generation uses to consume content, laptops are the most common, followed by desktops. The biggest distinction is in mobile usage: Over 50 percent of respondents who use their mobile as their primary device for content consumption are Millennials. Other results reveal:

  • Not only do Baby Boomers use laptops the most (43 percent), but they also use their tablets the most. (40 percent of all primary tablet users are Baby Boomers).
  • Over 25 percent of Millennials use a mobile device as their primary source for content
  • Gen Xers are the least active tablet users, with less than 8 percent of respondents using it as their primary device

Device To Consume Content2. Preferred content types and lengths span all three generations

One thing every generation agrees on is the type of content they enjoy seeing online. Our results reveal that the top four content types— blog articles, images, comments, and eBooks—are exactly the same for Baby Boomers, Gen Xers, and Millennials. Additional comparisons indicate:

  • The least preferred content types—flipbooks, SlideShares, webinars, and white papers—are the same across generations, too (although not in the exact same order)
  • Surprisingly, Gen Xers and Millennials list quizzes as one of their five least favorite content types

Most Consumed Content Type

All three generations also agree on ideal content length, around 300 words. Further analysis reveals:

  • Baby Boomers have the highest preference for articles under 200 words, at 18 percent
  • Gen Xers have a strong preference for articles over 500 words compared to other generations. Over 20 percent of respondents favor long-form articles, while only 15 percent of Baby Boomers and Millennials share the same sentiment.
  • Gen Xers also prefer short articles the least, with less than 10 percent preferring articles under 200 words

Content Length PreferencesHowever, in regards to verticals or genres, where they consume their content, each generation has their own unique preference:

  • Baby Boomers have a comfortable lead in world news and politics, at 18 percent and 12 percent, respectively
  • Millennials hold a strong lead in technology, at 18 percent, while Baby Boomers come in at 10 percent in the same category
  • Gen Xers fall between Millennials and Baby Boomers in most verticals, although they have slight leads in personal finance, parenting, and healthy living
  • Although entertainment is the top genre for each generation, Millennials and Baby Boomers prefer it slightly more than than Gen Xers do

Favorite Content Genres

3. Facebook is the preferred content sharing platform across all three generations

Facebook remains king in terms of content sharing, and is used by about 60 percent of respondents in each generation studied. Surprisingly, YouTube came in second, followed by Twitter, Google+, and LinkedIn, respectively. Additional findings:

  • Baby Boomers share on Facebook the most, edging out Millennials by only a fraction of a percent
  • Although Gen Xers use Facebook slightly less than other generations, they lead in both YouTube and Twitter, at 15 percent and 10 percent, respectively
  • Google+ is most popular with Baby Boomers, at 8 percent, nearly double that of both Gen Xers and Millennials

Preferred Social PlatformAlthough a majority of each generation is sharing content on Facebook, the type of content they are sharing, especially visuals, varies by each age group. The oldest generation prefers more traditional content, such as images and videos. Millennials prefer newer content types, such as memes and GIFs, while Gen X predictably falls in between the two generations in all categories except SlideShares. Other findings:

  • The most popular content type for Baby Boomers is video, at 27 percent
  • Parallax is the least popular type for every generation, earning 1 percent or less in each age group
  • Millennials share memes the most, while less than 10 percent of Baby Boomers share similar content

Most Shared Visual ContentMarketing to several generations can be challenging, given the different values and ideas that resonate with each group. With the number of online content consumers growing daily, it’s essential for marketers to understand the specific types of content that each of their audiences connect with, and align it with their content marketing strategy accordingly.

Although there is no one-size-fits-all campaign, successful marketers can create content that multiple generations will want to share. If you feel you need more information getting started, you can review this deck of additional insights, which includes the preferred video length and weekend consuming habits of each generation discussed in this post.

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Reblogged 4 years ago from tracking.feedpress.it

How to Combat 5 of the SEO World’s Most Infuriating Problems – Whiteboard Friday

Posted by randfish

These days, most of us have learned that spammy techniques aren’t the way to go, and we have a solid sense for the things we should be doing to rank higher, and ahead of our often spammier competitors. Sometimes, maddeningly, it just doesn’t work. In today’s Whiteboard Friday, Rand talks about five things that can infuriate SEOs with the best of intentions, why those problems exist, and what we can do about them.

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

What SEO problems make you angry?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re chatting about some of the most infuriating things in the SEO world, specifically five problems that I think plague a lot of folks and some of the ways that we can combat and address those.

I’m going to start with one of the things that really infuriates a lot of new folks to the field, especially folks who are building new and emerging sites and are doing SEO on them. You have all of these best practices list. You might look at a web developer’s cheat sheet or sort of a guide to on-page and on-site SEO. You go, “Hey, I’m doing it. I’ve got my clean URLs, my good, unique content, my solid keyword targeting, schema markup, useful internal links, my XML sitemap, and my fast load speed. I’m mobile friendly, and I don’t have manipulative links.”

Great. “Where are my results? What benefit am I getting from doing all these things, because I don’t see one?” I took a site that was not particularly SEO friendly, maybe it’s a new site, one I just launched or an emerging site, one that’s sort of slowly growing but not yet a power player. I do all this right stuff, and I don’t get SEO results.

This makes a lot of people stop investing in SEO, stop believing in SEO, and stop wanting to do it. I can understand where you’re coming from. The challenge is not one of you’ve done something wrong. It’s that this stuff, all of these things that you do right, especially things that you do right on your own site or from a best practices perspective, they don’t increase rankings. They don’t. That’s not what they’re designed to do.

1) Following best practices often does nothing for new and emerging sites

This stuff, all of these best practices are designed to protect you from potential problems. They’re designed to make sure that your site is properly optimized so that you can perform to the highest degree that you are able. But this is not actually rank boosting stuff unfortunately. That is very frustrating for many folks. So following a best practices list, the idea is not, “Hey, I’m going to grow my rankings by doing this.”

On the flip side, many folks do these things on larger, more well-established sites, sites that have a lot of ranking signals already in place. They’re bigger brands, they have lots of links to them, and they have lots of users and usage engagement signals. You fix this stuff. You fix stuff that’s already broken, and boom, rankings pop up. Things are going well, and more of your pages are indexed. You’re getting more search traffic, and it feels great. This is a challenge, on our part, of understanding what this stuff does, not a challenge on the search engine’s part of not ranking us properly for having done all of these right things.

2) My competition seems to be ranking on the back of spammy or manipulative links

What’s going on? I thought Google had introduced all these algorithms to kind of shut this stuff down. This seems very frustrating. How are they pulling this off? I look at their link profile, and I see a bunch of the directories, Web 2.0 sites — I love that the spam world decided that that’s Web 2.0 sites — article sites, private blog networks, and do follow blogs.

You look at this stuff and you go, “What is this junk? It’s terrible. Why isn’t Google penalizing them for this?” The answer, the right way to think about this and to come at this is: Are these really the reason that they rank? I think we need to ask ourselves that question.

One thing that we don’t know, that we can never know, is: Have these links been disavowed by our competitor here?

I’ve got my HulksIncredibleStore.com and their evil competitor Hulk-tastrophe.com. Hulk-tastrophe has got all of these terrible links, but maybe they disavowed those links and you would have no idea. Maybe they didn’t build those links. Perhaps those links came in from some other place. They are not responsible. Google is not treating them as responsible for it. They’re not actually what’s helping them.

If they are helping, and it’s possible they are, there are still instances where we’ve seen spam propping up sites. No doubt about it.

I think the next logical question is: Are you willing to loose your site or brand? What we don’t see anymore is we almost never see sites like this, who are ranking on the back of these things and have generally less legitimate and good links, ranking for two or three or four years. You can see it for a few months, maybe even a year, but this stuff is getting hit hard and getting hit frequently. So unless you’re willing to loose your site, pursuing their links is probably not a strategy.

Then what other signals, that you might not be considering potentially links, but also non-linking signals, could be helping them rank? I think a lot of us get blinded in the SEO world by link signals, and we forget to look at things like: Do they have a phenomenal user experience? Are they growing their brand? Are they doing offline kinds of things that are influencing online? Are they gaining engagement from other channels that’s then influencing their SEO? Do they have things coming in that I can’t see? If you don’t ask those questions, you can’t really learn from your competitors, and you just feel the frustration.

3) I have no visibility or understanding of why my rankings go up vs down

On my HulksIncredibleStore.com, I’ve got my infinite stretch shorts, which I don’t know why he never wears — he should really buy those — my soothing herbal tea, and my anger management books. I look at my rankings and they kind of jump up all the time, jump all over the place all the time. Actually, this is pretty normal. I think we’ve done some analyses here, and the average page one search results shift is 1.5 or 2 position changes daily. That’s sort of the MozCast dataset, if I’m recalling correctly. That means that, over the course of a week, it’s not uncommon or unnatural for you to be bouncing around four, five, or six positions up, down, and those kind of things.

I think we should understand what can be behind these things. That’s a very simple list. You made changes, Google made changes, your competitors made changes, or searcher behavior has changed in terms of volume, in terms of what they were engaging with, what they’re clicking on, what their intent behind searches are. Maybe there was just a new movie that came out and in one of the scenes Hulk talks about soothing herbal tea. So now people are searching for very different things than they were before. They want to see the scene. They’re looking for the YouTube video clip and those kind of things. Suddenly Hulk’s soothing herbal tea is no longer directing as well to your site.

So changes like these things can happen. We can’t understand all of them. I think what’s up to us to determine is the degree of analysis and action that’s actually going to provide a return on investment. Looking at these day over day or week over week and throwing up our hands and getting frustrated probably provides very little return on investment. Looking over the long term and saying, “Hey, over the last 6 months, we can observe 26 weeks of ranking change data, and we can see that in aggregate we are now ranking higher and for more keywords than we were previously, and so we’re going to continue pursuing this strategy. This is the set of keywords that we’ve fallen most on, and here are the factors that we’ve identified that are consistent across that group.” I think looking at rankings in aggregate can give us some real positive ROI. Looking at one or two, one week or the next week probably very little ROI.

4) I cannot influence or affect change in my organization because I cannot accurately quantify, predict, or control SEO

That’s true, especially with things like keyword not provided and certainly with the inaccuracy of data that’s provided to us through Google’s Keyword Planner inside of AdWords, for example, and the fact that no one can really control SEO, not fully anyway.

You get up in front of your team, your board, your manager, your client and you say, “Hey, if we don’t do these things, traffic will suffer,” and they go, “Well, you can’t be sure about that, and you can’t perfectly predict it. Last time you told us something, something else happened. So because the data is imperfect, we’d rather spend money on channels that we can perfectly predict, that we can very effectively quantify, and that we can very effectively control.” That is understandable. I think that businesses have a lot of risk aversion naturally, and so wanting to spend time and energy and effort in areas that you can control feels a lot safer.

Some ways to get around this are, first off, know your audience. If you know who you’re talking to in the room, you can often determine the things that will move the needle for them. For example, I find that many managers, many boards, many executives are much more influenced by competitive pressures than they are by, “We won’t do as well as we did before, or we’re loosing out on this potential opportunity.” Saying that is less powerful than saying, “This competitor, who I know we care about and we track ourselves against, is capturing this traffic and here’s how they’re doing it.”

Show multiple scenarios. Many of the SEO presentations that I see and have seen and still see from consultants and from in-house folks come with kind of a single, “Hey, here’s what we predict will happen if we do this or what we predict will happen if we don’t do this.” You’ve got to show multiple scenarios, especially when you know you have error bars because you can’t accurately quantify and predict. You need to show ranges.

So instead of this, I want to see: What happens if we do it a little bit? What happens if we really overinvest? What happens if Google makes a much bigger change on this particular factor than we expect or our competitors do a much bigger investment than we expect? How might those change the numbers?

Then I really do like bringing case studies, especially if you’re a consultant, but even in-house there are so many case studies in SEO on the Web today, you can almost always find someone who’s analogous or nearly analogous and show some of their data, some of the results that they’ve seen. Places like SEMrush, a tool that offers competitive intelligence around rankings, can be great for that. You can show, hey, this media site in our sector made these changes. Look at the delta of keywords they were ranking for versus R over the next six months. Correlation is not causation, but that can be a powerful influencer showing those kind of things.

Then last, but not least, any time you’re going to get up like this and present to a group around these topics, if you very possibly can, try to talk one-on-one with the participants before the meeting actually happens. I have found it almost universally the case that when you get into a group setting, if you haven’t had the discussions beforehand about like, “What are your concerns? What do you think is not valid about this data? Hey, I want to run this by you and get your thoughts before we go to the meeting.” If you don’t do that ahead of time, people can gang up and pile on. One person says, “Hey, I don’t think this is right,” and everybody in the room kind of looks around and goes, “Yeah, I also don’t think that’s right.” Then it just turns into warfare and conflict that you don’t want or need. If you address those things beforehand, then you can include the data, the presentations, and the “I don’t know the answer to this and I know this is important to so and so” in that presentation or in that discussion. It can be hugely helpful. Big difference between winning and losing with that.

5) Google is biasing to big brands. It feels hopeless to compete against them

A lot of people are feeling this hopelessness, hopelessness in SEO about competing against them. I get that pain. In fact, I’ve felt that very strongly for a long time in the SEO world, and I think the trend has only increased. This comes from all sorts of stuff. Brands now have the little dropdown next to their search result listing. There are these brand and entity connections. As Google is using answers and knowledge graph more and more, it’s feeling like those entities are having a bigger influence on where things rank and where they’re visible and where they’re pulling from.

User and usage behavior signals on the rise means that big brands, who have more of those signals, tend to perform better. Brands in the knowledge graph, brands growing links without any effort, they’re just growing links because they’re brands and people point to them naturally. Well, that is all really tough and can be very frustrating.

I think you have a few choices on the table. First off, you can choose to compete with brands where they can’t or won’t. So this is areas like we’re going after these keywords that we know these big brands are not chasing. We’re going after social channels or people on social media that we know big brands aren’t. We’re going after user generated content because they have all these corporate requirements and they won’t invest in that stuff. We’re going after content that they refuse to pursue for one reason or another. That can be very effective.

You better be building, growing, and leveraging your competitive advantage. Whenever you build an organization, you’ve got to say, “Hey, here’s who is out there. This is why we are uniquely better or a uniquely better choice for this set of customers than these other ones.” If you can leverage that, you can generally find opportunities to compete and even to win against big brands. But those things have to become obvious, they have to become well-known, and you need to essentially build some of your brand around those advantages, or they’re not going to give you help in search. That includes media, that includes content, that includes any sort of press and PR you’re doing. That includes how you do your own messaging, all of these things.

(C) You can choose to serve a market or a customer that they don’t or won’t. That can be a powerful way to go about search, because usually search is bifurcated by the customer type. There will be slightly different forms of search queries that are entered by different kinds of customers, and you can pursue one of those that isn’t pursued by the competition.

Last, but not least, I think for everyone in SEO we all realize we’re going to have to become brands ourselves. That means building the signals that are typically associated with brands — authority, recognition from an industry, recognition from a customer set, awareness of our brand even before a search has happened. I talked about this in a previous Whiteboard Friday, but I think because of these things, SEO is becoming a channel that you benefit from as you grow your brand rather than the channel you use to initially build your brand.

All right, everyone. Hope these have been helpful in combating some of these infuriating, frustrating problems and that we’ll see some great comments from you guys. I hope to participate in those as well, and we’ll catch you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 4 years ago from tracking.feedpress.it

How Much Has Link Building Changed in Recent Years?

Posted by Paddy_Moogan

I get asked this question a lot. It’s mainly asked by people who are considering buying my link building book and want to know whether it’s still up to date. This is understandable given that the first edition was published in February 2013 and our industry has a deserved reputation for always changing.

I find myself giving the same answer, even though I’ve been asked it probably dozens of times in the last two years—”not that much”. I don’t think this is solely due to the book itself standing the test of time, although I’ll happily take a bit of credit for that 🙂 I think it’s more a sign of our industry as a whole not changing as much as we’d like to think.

I started to question myself and if I was right and honestly, it’s one of the reasons it has taken me over two years to release the second edition of the book.

So I posed this question to a group of friends not so long ago, some via email and some via a Facebook group. I was expecting to be called out by many of them because my position was that in reality, it hasn’t actually changed that much. The thing is, many of them agreed and the conversations ended with a pretty long thread with lots of insights. In this post, I’d like to share some of them, share what my position is and talk about what actually has changed.

My personal view

Link building hasn’t changed as much we think it has.

The core principles of link building haven’t changed. The signals around link building have changed, but mainly around new machine learning developments that have indirectly affected what we do. One thing that has definitely changed is the mindset of SEOs (and now clients) towards link building.

I think the last big change to link building came in April 2012 when Penguin rolled out. This genuinely did change our industry and put to bed a few techniques that should never have worked so well in the first place.

Since then, we’ve seen some things change, but the core principles haven’t changed if you want to build a business that will be around for years to come and not run the risk of being hit by a link related Google update. For me, these principles are quite simple:

  • You need to deserve links – either an asset you create or your product
  • You need to put this asset in front of a relevant audience who have the ability to share it
  • You need consistency – one new asset every year is unlikely to cut it
  • Anything that scales is at risk

For me, the move towards user data driving search results + machine learning has been the biggest change we’ve seen in recent years and it’s still going.

Let’s dive a bit deeper into all of this and I’ll talk about how this relates to link building.

The typical mindset for building links has changed

I think that most SEOs are coming round to the idea that you can’t get away with building low quality links any more, not if you want to build a sustainable, long-term business. Spammy link building still works in the short-term and I think it always will, but it’s much harder than it used to be to sustain websites that are built on spam. The approach is more “churn and burn” and spammers are happy to churn through lots of domains and just make a small profit on each one before moving onto another.

For everyone else, it’s all about the long-term and not putting client websites at risk.

This has led to many SEOs embracing different forms of link building and generally starting to use content as an asset when it comes to attracting links. A big part of me feels that it was actually Penguin in 2012 that drove the rise of content marketing amongst SEOs, but that’s a post for another day…! For today though, this goes some way towards explain the trend we see below.

Slowly but surely, I’m seeing clients come to my company already knowing that low quality link building isn’t what they want. It’s taken a few years after Penguin for it to filter down to client / business owner level, but it’s definitely happening. This is a good thing but unfortunately, the main reason for this is that most of them have been burnt in the past by SEO companies who have built low quality links without giving thought to building good quality ones too.

I have no doubt that it’s this change in mindset which has led to trends like this:

The thing is, I don’t think this was by choice.

Let’s be honest. A lot of us used the kind of link building tactics that Google no longer like because they worked. I don’t think many SEOs were under the illusion that it was genuinely high quality stuff, but it worked and it was far less risky to do than it is today. Unless you were super-spammy, the low-quality links just worked.

Fast forward to a post-Penguin world, things are far more risky. For me, it’s because of this that we see the trends like the above. As an industry, we had the easiest link building methods taken away from us and we’re left with fewer options. One of the main options is content marketing which, if you do it right, can lead to good quality links and importantly, the types of links you won’t be removing in the future. Get it wrong and you’ll lose budget and lose the trust if your boss or client in the power of content when it comes to link building.

There are still plenty of other methods to build links and sometimes we can forget this. Just look at this epic list from Jon Cooper. Even with this many tactics still available to us, it’s hard work. Way harder than it used to be.

My summary here is that as an industry, our mindset has shifted but it certainly wasn’t a voluntary shift. If the tactics that Penguin targeted still worked today, we’d still be using them.

A few other opinions…

I definitely think too many people want the next easy win. As someone surfing the edge of what Google is bringing our way, here’s my general take—SEO, in broad strokes, is changing a lot, *but* any given change is more and more niche and impacts fewer people. What we’re seeing isn’t radical, sweeping changes that impact everyone, but a sort of modularization of SEO, where we each have to be aware of what impacts our given industries, verticals, etc.”

Dr. Pete

 

I don’t feel that techniques for acquiring links have changed that much. You can either earn them through content and outreach or you can just buy them. What has changed is the awareness of “link building” outside of the SEO community. This makes link building / content marketing much harder when pitching to journalists and even more difficult when pitching to bloggers.

“Link building has to be more integrated with other channels and struggles to work in its own environment unless supported by brand, PR and social. Having other channels supporting your link development efforts also creates greater search signals and more opportunity to reach a bigger audience which will drive a greater ROI.

Carl Hendy

 

SEO has grown up in terms of more mature staff and SEOs becoming more ingrained into businesses so there is a smarter (less pressure) approach. At the same time, SEO has become more integrated into marketing and has made marketing teams and decision makers more intelligent in strategies and not pushing for the quick win. I’m also seeing that companies who used to rely on SEO and building links have gone through IPOs and the need to build 1000s of links per quarter has rightly reduced.

Danny Denhard

Signals that surround link building have changed

There is no question about this one in my mind. I actually wrote about this last year in my previous blog post where I talked about signals such as anchor text and deep links changing over time.

Many of the people I asked felt the same, here are some quotes from them, split out by the types of signal.

Domain level link metrics

I think domain level links have become increasingly important compared with page level factors, i.e. you can get a whole site ranking well off the back of one insanely strong page, even with sub-optimal PageRank flow from that page to the rest of the site.

Phil Nottingham

I’d agree with Phil here and this is what I was getting at in my previous post on how I feel “deep links” will matter less over time. It’s not just about domain level links here, it’s just as much about the additional signals available for Google to use (more on that later).

Anchor text

I’ve never liked anchor text as a link signal. I mean, who actually uses exact match commercial keywords as anchor text on the web?

SEOs. 🙂

Sure there will be natural links like this, but honestly, I struggle with the idea that it took Google so long to start turning down the dial on commercial anchor text as a ranking signal. They are starting to turn it down though, slowly but surely. Don’t get me wrong, it still matters and it still works. But like pure link spam, the barrier is a lot more lower now in terms what of constitutes too much.

Rand feels that they matter more than we’d expect and I’d mostly agree with this statement:

Exact match anchor text links still have more power than you’d expect—I think Google still hasn’t perfectly sorted what is “brand” or “branded query” from generics (i.e. they want to start ranking a new startup like meldhome.com for “Meld” if the site/brand gets popular, but they can’t quite tell the difference between that and https://moz.com/learn/seo/redirection getting a few manipulative links that say “redirect”)

Rand Fishkin

What I do struggle with though, is that Google still haven’t figured this out and that short-term, commercial anchor text spam is still so effective. Even for a short burst of time.

I don’t think link building as a concept has changed loads—but I think links as a signal have, mainly because of filters and penalties but I don’t see anywhere near the same level of impact from coverage anymore, even against 18 months ago.

Paul Rogers

New signals have been introduced

It isn’t just about established signals changing though, there are new signals too and I personally feel that this is where we’ve seen the most change in Google algorithms in recent years—going all the way back to Panda in 2011.

With Panda, we saw a new level of machine learning where it almost felt like Google had found a way of incorporating human reaction / feelings into their algorithms. They could then run this against a website and answer questions like the ones included in this post. Things such as:

  • “Would you be comfortable giving your credit card information to this site?”
  • “Does this article contain insightful analysis or interesting information that is beyond obvious?”
  • “Are the pages produced with great care and attention to detail vs. less attention to detail?”

It is a touch scary that Google was able to run machine learning against answers to questions like this and write an algorithm to predict the answers for any given page on the web. They have though and this was four years ago now.

Since then, they’ve made various moves to utilize machine learning and AI to build out new products and improve their search results. For me, this was one of the biggest and went pretty unnoticed by our industry. Well, until Hummingbird came along I feel pretty sure that we have Ray Kurzweil to thank for at least some of that.

There seems to be more weight on theme/topic related to sites, though it’s hard to tell if this is mostly link based or more user/usage data based. Google is doing a good job of ranking sites and pages that don’t earn the most links but do provide the most relevant/best answer. I have a feeling they use some combination of signals to say “people who perform searches like this seem to eventually wind up on this website—let’s rank it.” One of my favorite examples is the Audubon Society ranking for all sorts of birding-related searches with very poor keyword targeting, not great links, etc. I think user behavior patterns are stronger in the algo than they’ve ever been.

– Rand Fishkin

Leading on from what Rand has said, it’s becoming more and more common to see search results that just don’t make sense if you look at the link metrics—but are a good result.

For me, the move towards user data driving search results + machine learning advanced has been the biggest change we’ve seen in recent years and it’s still going.

Edit: since drafting this post, Tom Anthony released this excellent blog post on his views on the future of search and the shift to data-driven results. I’d recommend reading that as it approaches this whole area from a different perspective and I feel that an off-shoot of what Tom is talking about is the impact on link building.

You may be asking at this point, what does machine learning have to do with link building?

Everything. Because as strong as links are as a ranking signal, Google want more signals and user signals are far, far harder to manipulate than established link signals. Yes it can be done—I’ve seen it happen. There have even been a few public tests done. But it’s very hard to scale and I’d venture a guess that only the top 1% of spammers are capable of doing it, let alone maintaining it for a long period of time. When I think about the process for manipulation here, I actually think we go a step beyond spammers towards hackers and more cut and dry illegal activity.

For link building, this means that traditional methods of manipulating signals are going to become less and less effective as these user signals become stronger. For us as link builders, it means we can’t keep searching for that silver bullet or the next method of scaling link building just for an easy win. The fact is that scalable link building is always going to be at risk from penalization from Google—I don’t really want to live a life where I’m always worried about my clients being hit by the next update. Even if Google doesn’t catch up with a certain method, machine learning and user data mean that these methods may naturally become less effective and cost efficient over time.

There are of course other things such as social signals that have come into play. I certainly don’t feel like these are a strong ranking factor yet, but with deals like this one between Google and Twitter being signed, I wouldn’t be surprised if that ever-growing dataset is used at some point in organic results. The one advantage that Twitter has over Google is it’s breaking news freshness. Twitter is still way quicker at breaking news than Google is—140 characters in a tweet is far quicker than Google News! Google know this which is why I feel they’ve pulled this partnership back into existence after a couple of years apart.

There is another important point to remember here and it’s nicely summarised by Dr. Pete:

At the same time, as new signals are introduced, these are layers not replacements. People hear social signals or user signals or authorship and want it to be the link-killer, because they already fucked up link-building, but these are just layers on top of on-page and links and all of the other layers. As each layer is added, it can verify the layers that came before it and what you need isn’t the magic signal but a combination of signals that generally matches what Google expects to see from real, strong entities. So, links still matter, but they matter in concert with other things, which basically means it’s getting more complicated and, frankly, a bit harder. Of course, on one wants to hear that.”

– Dr. Pete

The core principles have not changed

This is the crux of everything for me. With all the changes listed above, the key is that the core principles around link building haven’t changed. I could even argue that Penguin didn’t change the core principles because the techniques that Penguin targeted should never have worked in the first place. I won’t argue this too much though because even Google advised website owners to build directory links at one time.

You need an asset

You need to give someone a reason to link to you. Many won’t do it out of the goodness of their heart! One of the most effective ways to do this is to develop a content asset and use this as your reason to make people care. Once you’ve made someone care, they’re more likely to share the content or link to it from somewhere.

You need to promote that asset to the right audience

I really dislike the stance that some marketers take when it comes to content promotion—build great content and links will come.

No. Sorry but for the vast majority of us, that’s simply not true. The exceptions are people that sky dive from space or have huge existing audiences to leverage.

You simply have to spend time promoting your content or your asset for it to get shares and links. It is hard work and sometimes you can spend a long time on it and get little return, but it’s important to keep working at until you’re at a point where you have two things:

  • A big enough audience where you can almost guarantee at least some traffic to your new content along with some shares
  • Enough strong relationships with relevant websites who you can speak to when new content is published and stand a good chance of them linking to it

Getting to this point is hard—but that’s kind of the point. There are various hacks you can use along the way but it will take time to get right.

You need consistency

Leading on from the previous point. It takes time and hard work to get links to your content—the types of links that stand the test of time and you’re not going to be removing in 12 months time anyway! This means that you need to keep pushing content out and getting better each and every time. This isn’t to say you should just churn content out for the sake of it, far from it. I am saying that with each piece of content you create, you will learn to do at least one thing better the next time. Try to give yourself the leverage to do this.

Anything scalable is at risk

Scalable link building is exactly what Google has been trying to crack down on for the last few years. Penguin was the biggest move and hit some of the most scalable tactics we had at our disposal. When you scale something, you often lose some level of quality, which is exactly what Google doesn’t want when it comes to links. If you’re still relying on tactics that could fall into the scalable category, I think you need to be very careful and just look at the trend in the types of links Google has been penalizing to understand why.

The part Google plays in this

To finish up, I want to briefly talk about the part that Google plays in all of this and shaping the future they want for the web.

I’ve always tried to steer clear of arguments involving the idea that Google is actively pushing FUD into the community. I’ve preferred to concentrate more on things I can actually influence and change with my clients rather than what Google is telling us all to do.

However, for the purposes of this post, I want to talk about it.

General paranoia has increased. My bet is there are some companies out there carrying out zero specific linkbuilding activity through worry.

Dan Barker

Dan’s point is a very fair one and just a day or two after reading this in an email, I came across a page related to a client’s target audience that said:

“We are not publishing guest posts on SITE NAME any more. All previous guest posts are now deleted. For more information, see www.mattcutts.com/blog/guest-blogging/“.

I’ve reworded this as to not reveal the name of the site, but you get the point.

This is silly. Honestly, so silly. They are a good site, publish good content, and had good editorial standards. Yet they have ignored all of their own policies, hard work, and objectives to follow a blog post from Matt. I’m 100% confident that it wasn’t sites like this one that Matt was talking about in this blog post.

This is, of course, from the publishers’ angle rather than the link builders’ angle, but it does go to show the effect that statements from Google can have. Google know this so it does make sense for them to push out messages that make their jobs easier and suit their own objectives—why wouldn’t they? In a similar way, what did they do when they were struggling to classify at scale which links are bad vs. good and they didn’t have a big enough web spam team? They got us to do it for them 🙂

I’m mostly joking here, but you see the point.

The most recent infamous mobilegeddon update, discussed here by Dr. Pete is another example of Google pushing out messages that ultimately scared a lot of people into action. Although to be fair, I think that despite the apparent small impact so far, the broad message from Google is a very serious one.

Because of this, I think we need to remember that Google does have their own agenda and many shareholders to keep happy. I’m not in the camp of believing everything that Google puts out is FUD, but I’m much more sensitive and questioning of the messages now than I’ve ever been.

What do you think? I’d love to hear your feedback and thoughts in the comments.

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Reblogged 4 years ago from tracking.feedpress.it

How We Fixed the Internet (Ok, an Answer Box)

Posted by Dr-Pete

Last year, Google expanded the Knowledge Graph to use data extracted (*cough* scraped) from the index to create answer boxes. Back in October, I wrote about a failed experiment. One of my posts, an odd dive
into Google’s revenue, was being answer-fied for the query “How much does Google make?”:

Objectively speaking, even I could concede that this wasn’t a very good answer in 2014. I posted it on Twitter, and
David Iwanow asked the inevitable question:

Enthusiasm may have gotten the best of us, a few more people got involved (like my former Moz colleague
Ruth Burr Reedy), and suddenly we were going to fix this once and for all:

There Was Just One Problem

I updated the post, carefully rewriting the first paragraph to reflect the new reality of Google’s revenue. I did my best to make the change user-friendly, adding valuable information but not disrupting the original post. I did, however, completely replace the old text that Google was scraping.

Within less than a day, Google had re-cached the content, and I just had to wait to see the new answer box. So, I waited, and waited… and waited. Two months later, still no change. Some days, the SERP showed no answer box at all (although I’ve since found these answer boxes are very dynamic), and I was starting to wonder if it was all a mistake.

Then, Something Happened

Last week, months after I had given up, I went to double-check this query for entirely different reasons, and I saw the following:

Google had finally updated the answer box with the new text, and they had even pulled an image from the post. It was a strange choice of images, but in fairness, it was a strange post.

Interestingly, Google also added the publication date of the post, perhaps recognizing that outdated answers aren’t always useful. Unfortunately, this doesn’t reflect the timing of the new content, but that’s understandable – Google doesn’t have easy access to that data.

It’s interesting to note that sometimes Google shows the image, and sometimes they don’t. This seems to be independent of whether the SERP is personalized or incognito. Here’s a capture of the image-free version, along with the #1 organic ranking:

You’ll notice that the #1 result is also my Moz post, and that result has an expanded meta description. So, the same URL is essentially double-dipping this SERP. This isn’t always the case – answers can be extracted from URLs that appear lower on page 1 (although almost always page 1, in my experience). Anecdotally, it’s also not always the case that these organic result ends up getting an expanded meta description.

However, it definitely seems that some of the quality signals driving organic ranking and expanded meta descriptions are also helping Google determine whether a query deserves a direct answer. Put simply, it’s not an accident that this post was chosen to answer this question.

What Does This Mean for You?

Let’s start with the obvious – Yes, the v2 answer boxes (driven by the index, not Freebase/WikiData)
can be updated. However, the update cycle is independent of the index’s refresh cycle. In other words, just because a post is re-cached, it doesn’t mean the answer box will update. Presumably, Google is creating a second Knowledge Graph, based on the index, and this data is only periodically updated.

It’s also entirely possible that updating could cause you to lose an answer box, if the new data weren’t a strong match to the question or the quality of the content came into question. Here’s an interesting question – on a query where a competitor has an answer box, could you change your own content enough to either replace them or knock out the answer box altogether? We are currently testing this question, but it may be a few more months before we have any answers.

Another question is what triggers this style of answer box in the first place? Eric Enge has an
in-depth look at 850,000 queries that’s well worth your time, and in many cases Google is still triggering on obvious questions (“how”, “what”, “where”, etc.). Nouns that could be interpreted as ambiguous also can trigger the new answer boxes. For example, a search for “ruby” is interpreted by Google as roughly meaning “What is Ruby?”:

This answer box also triggers “Related topics” that use content pulled from other sites but drive users to more Google searches. The small, gray links are the source sites. The much more visible, blue links are more Google searches.

Note that these also have to be questions (explicit or implied) that Google can’t answer with their curated Knowledge Graph (based on sources like Freebase and WikiData). So, for example, the question “When is Mother’s Day?” triggers an older-style answer:

Sites offering this data aren’t going to have a chance to get attribution, because Google essentially already owns the answer to this question as part of their core Knowledge Graph.

Do You Want to Be An Answer?

This is where things get tricky. At this point, we have no clear data on how these answer boxes impact CTR, and it’s likely that the impact depends a great deal on the context. I think we’re facing a certain degree of inevitability – if Google is going to list an answer, better it’s your answer then someone else’s, IMO. On the other hand, what if that answer is so complete that it renders your URL irrelevant? Consider, for example, the SERP for “how to make grilled cheese”:

Sorry, Food Network, but making a grilled cheese sandwich isn’t really that hard, and this answer box doesn’t leave much to the imagination. As these answers get more and more thorough, expect CTRs to fall.

For now, I’d argue that it’s better to have your link in the box than someone else’s, but that’s cold comfort in many cases. These new answer boxes represent what I feel is a dramatic shift in the relationship between Google and webmasters, and they may be tipping the balance. For now, we can’t do much but wait, see, and experiment.

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Reblogged 4 years ago from tracking.feedpress.it

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.

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 4 years ago from tracking.feedpress.it

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 4 years ago from tracking.feedpress.it