seo

Posted by Tom.Capper

In this post I’ll pull apart four of the most commonly used metrics in Google Analytics, how they are collected, and why they are so easily misinterpreted.

Average Time on Page

Average time on page should be a really useful metric, particularly if you’re interested in engagement with content that’s all on a single page. Unfortunately, this is actually its worst use case. To understand why, you need to understand how time on page is calculated in Google Analytics:

Time on Page: Total across all pageviews of time from pageview to last engagement hit on that page (where an engagement hit is any of: next pageview, interactive event, e-commerce transaction, e-commerce item hit, or social plugin). (Source)

If there is no subsequent engagement hit, or if there is a gap between the last engagement hit on a site and leaving the site, the assumption is that no further time was spent on the site. Below are some scenarios with an intuitive time on page of 20 seconds, and their Google Analytics time on page:

Scenario	Intuitive time on page	GA time on page
0s: Pageview 10s: Social plugin 20s: Click through to next page	20s	20s
0s: Pageview 10s: Social plugin 20s: Leave site	20s	10s
0s: Pageview 20s: Leave site	20s	0s

Google doesn’t want exits to influence the average time on page, because of scenarios like the third example above, where they have a time on page of 0 seconds (source). To avoid this, they use the following formula (remember that Time on Page is a total):

Average Time on Page: (Time on Page) / (Pageviews – Exits)

However, as the second example above shows, this assumption doesn’t always hold. The second example feeds into the top half of the average time on page faction, but not the bottom half:

Example 2 Average Time on Page: (20s+10s+0s) / (3-2) = 30s

There are two issues here:

1. Overestimation
   Excluding exits from the second half of the average time on page equation doesn’t have the desired effect when their time on page wasn’t 0 seconds—note that 30s is longer than any of the individual visits. This is why average time on page can often be longer than average visit duration. Nonetheless, 30 seconds doesn’t seem too far out in the above scenario (the intuitive average is 20s), but in the real world many pages have much higher exit rates than the 67% in this example, and/or much less engagement with events on page.
   
   
2. Ignored visits
   Considering only visitors who exit without an engagement hit, whether these visitors stayed for 2 seconds, 10 minutes or anything inbetween, it doesn’t influence average time on page in the slightest. On many sites, a 10 minute view of a single page without interaction (e.g. a blog post) would be considered a success, but it wouldn’t influence this metric.
   

Site Speed

If you’ve used the Site Speed reports in Google Analytics in the past, you’ve probably noticed that the numbers can sometimes be pretty difficult to believe. This is because the way that Site Speed is tracked is extremely vulnerable to outliers—it starts with a 1% sample of your users and then takes a simple average for each metric. This means that a few extreme values (for example, the occasional user with a malware-infested computer or a questionable wifi connection) can create a very large swing in your data.

The use of an average as a metric is not in itself bad, but in an area so prone to outliers and working with such a small sample, it can lead to questionable results.

Fortunately, you can increase the sampling rate right up to 100% (or the cap of 10,000 hits per day). Depending on the size of your site, this may still only be useful for top-level data. For example, if your site gets 1,000,000 hits per day and you’re interested in the performance of a new page that’s receiving 100 hits per day, Google Analytics will throttle your sampling back to the 10,000 hits per day cap—1%. As such, you’ll only be looking at a sample of 1 hit per day for that page.

Conversion Rate (by channel)

Obviously, conversion rate is not in itself a bad metric, but it can be rather misleading in certain reports if you don’t realise that, by default, conversions are attributed using a last non-direct click attribution model.

From Google Analytics Help:

“…if a person clicks over your site from google.com, then returns as “direct” traffic to convert, Google Analytics will report 1 conversion for “google.com / organic” in All Traffic.”

This means that when you’re looking at conversion numbers in your acquisition reports, it’s quite possible that every single number is different to what you’d expect under last click—every channel other than direct has a total that includes some conversions that occurred during direct sessions, and direct itself has conversion numbers that don’t include some conversions that occurred during direct sessions.

Exit Rate

Unlike some of the other metrics I’ve discussed here, the calculation behind exit rate is very intuitive—”for all pageviews to the page, Exit Rate is the percentage that were the last in the session.” The problem with exit rate is that it’s so often used as a negative metric: “Which pages had the highest exit rate? They’re the problem with our site!” Sometimes this might be true: Perhaps, for example, if those pages are in the middle of a checkout funnel.

Often, however, a user will exit a site when they’ve found what they want. This doesn’t just mean that a high exit rate is ok on informational pages like blog posts or about pages—it could also be true of product pages and other pages with a highly conversion-focused intent. Even on ecommerce sites, not every visitor has the intention of converting. They might be researching towards a later online purchase, or even planning to visit your physical store. This is particularly true if your site ranks well for long tail queries or is referenced elsewhere. In this case, an exit could be a sign that they found the information they wanted and are ready to purchase once they have the money, the need, the right device at hand or next time they’re passing by your shop.

Discussion

If you know of any other similarly misunderstood metrics, you have any questions or you have something to add to my analysis, tweet me at @THCapper or leave a comment below.

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Posted by randfish

A frequently asked question in the SEO world is whether or not branding plays a part in Google’s ranking algorithm. There’s a short answer with a big asterisk, and in today’s Whiteboard Friday, Rand explains what you need to know.

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

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week I’m going to try and answer a question that plagues a lot of marketers, a lot of SEOs and that we ask very frequently. That is: Is brand or branding a ranking factor in Google search engine?

Look, I think, to be fair, to be honest, that the technical answer to this question is no. However, I think when people say brand is powerful for SEO, that is a true statement. We’re going to try and reconcile these two things. How can brand not be a ranking factor and yet be a powerful influencer of higher rankings in SEO? What’s going to go on there?

What is a ranking factor, anyway?

Well, I’ll tell you. So when folks say ranking factor, they’re referring to something very technical, very specific, and that is an algorithmic input that Google measures directly and uses to determine rank position in their algorithm.

Okay, guess what? Brand almost certainly is not this.

Google doesn’t try and go out and say, “How well known is Coca-Cola versus Pepsi versus 7 Up versus Sprite versus Jones Cola? Hey, let’s rank Coca-Cola a little higher because they seem to have greater brand awareness, brand affinity than Pepsi.” That is not something that Google will try and do. That’s not something that’s in their algorithm.

However, a big however, many things that are in Google’s ranking algorithm correlate very well with brands.

Those things are probably used by Google in both direct and indirect ways.

So when you see sites that have done a great job of branding and also have good SEO best practices on them, you’ll notice kind of a correlation, like boy, it sure does seem like the brands have been performing better and better in Google’s rankings over the last four, five, or six years. I think this is due to two trends. One of those trends is that Google’s algorithmic inputs have started favoring things that brands are better at and that what I’d call generic sites or non-branded sites, or businesses that have not invested in brand affinity have not done well.

Those things are things like links, where Google is rewarding better links rather than just more links. They’re things around user and usage data, which Google previously didn’t use a whole lot of signals around that. Same story with user experience. Same story with things like pogo sticking, which is probably one of the ways that they’re measuring some of that stuff.

If we were to scatter plot it, we’d probably see something like this, where the better your brand performs as a brand, the higher and better it tends to perform in the rankings of Google search engine.

How does brand correlate to ranking signals?

Now, how is it that these brand signals that I’m talking about correlate more directly to ranking signals? Like why does this impact and influence? I think if we understand that, we can understand why we need to invest in brand and branding and where to invest in it as it relates to the web marketing kinds of things that we do for SEO.

One very clearly and very frankly is links. So when we talk about the links that Google wants to measure, wants to count today, those are organic, editorially earned links. They’re not manipulative. They weren’t bought. They tend not to be cajoled, they’re earned.

Because of that, one of the best ways that folks have been earning links is to get people to come to their website and then have some fraction, some percentage of those folks naturally link to them without having to do any extra effort. It’s basically like, “Hey, you made this great piece of content or this great product or great service or great data. Therefore, I’m going to reference it.” Granted, that’s a small percentage of people. There’s still only maybe two or three out of a hundred folks who might visit your website on the Internet who actually have the power or ability to link to you because they control content on the web as opposed to just social sharing.

But when that happens, in a lot of cases folks go and they say, “Hmm, yeah, this content’s good, but I’ve never heard of this brand before. I’m not sure if I should recommend it. It looks good, but I don’t know them.” Versus, “Oh, I love these folks. This is like one of my favorite companies or brands or products or experiences, and this content is great. I am totally going to link to it.” Because that happens, even if that difference is small, even if the percent goes from 1% to 2%, well now, guess what? For every hundred visits, you’re earning twice the links of your non-branded competitor.

Social signals

These are pretty much exactly the same thing. Folks who visit content, who have experiences with a company, with a product, or with a service, if they’re familiar and comfortable with the brand, if they want to evangelize that brand, then guess what? You’re going to get more social sharing per visit, per exposure than you would ordinarily, and that’s going to lead to a cycle of more social sharing which leads to visits which probably leads to links.

User and usage data

It’s also true that brand is going to impact user and usage data. So one of the most interesting patents, which we’ll probably be talking about in a future Whiteboard Friday, was brought up recently by Bill Slawski and looked at user and usage data. It was just granted to Google in the last month. It talked about how Google would look at the patterns of where web visitors would go and what their search experiences would be like. It would potentially say, “Hey, Google would like to reward sites that are getting organic traffic, not just from search, but traffic of all kinds on a particular topic.”

So if it turns out that lots of people who are researching a vacation to Costa Rica end up going to Oyster.com, well, Google might say, “Hey, you know what? We’ve seen this pattern over and over again. Let’s boost Oyster.com’s rankings because it seems like people who look for this kind of content end up on this site. Not necessarily directly through us, through Google. They might end up on it through social media, through organic web links, through direct visits, through e-mail marketing, whatever it is.”

When you’re unbranded, one of the few ways that you can get traffic is through unbranded search. Search is one of those few channels that does drive traffic, or historically anyway did drive traffic to a lot of non-branded, less branded sites. Brands tend to earn traffic from a wide variety of sources. If you can start earning traffic from lots of sources and have the retention and the experience to drive people back again and again, well, probably you’re going to benefit from some of these potential algorithmic shifts and future looking directions that Google’s got.

Click-through rates

Same story a little bit when it comes to click-through rate. Now, we know from experience and testing that click-through rate is or appears to have a very direct impact on rankings. If lots of people are performing a search and they click on your website in position number four or five, and they’re not clicking on position one, two, or three, you can bet that you’re going to be moving up those rankings very, very quickly.

Granted there is some manipulative services out there that try and automate this. Some of them work for a little while. Most of them get shut down pretty quick. I wouldn’t recommend investing in those. But I do recommend investing in brand, because when you have a recognizable brand, searchers are going to come here and they’re going to go, “Oh, that one, maybe I haven’t heard of it. That one, I’ve heard of it. That one, I haven’t heard of it.”

Guess what they’re clicking on? The one they’re already familiar with. The one they have a positive association with already. This is the power of brand advertising, and I think it’s one of the big reasons why you’ve seen case studies from folks like Seer Interactive, talking about how a radio ad campaign or a billboard ad campaign seemed to have a positive lift in their SEO work as well. This phenomenon is going to mean that you’re benefiting from every searcher who looks for something, even if you rank further down, if you’re the better known brand.

So is brand a ranking factor? No, it’s not. Is brand something that positively impacts SEO? Almost certainly in every niche, yes, it is.

All right. Looking forward to some great comments. I’ll try and jump in there and answer any questions that I can. If you have experiences you want to share, we’d love to hear from you. Hopefully, we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

UPDATE: Bill Slawski’s latest post – Brand Entities at Google: Crowdsourcing Their Identities in a Social Network – is a good addition to the topics covered here. Check it out!

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Posted by Trevor-Klein

Last week, we began posting short (< 2-minute) video tutorials that help you all get the most out of Moz’s tools. Each tutorial is designed to solve a use case that we regularly hear about from Moz community members—a need or problem for which you all could use a solution.

Today, we’ve got a brand-new roundup of the most recent videos:

• How to Examine and Analyze SERPs Using New MozBar Features
• How to Boost Your Rankings through On-Page Optimization
• How to Check Your Anchor Text Using Open Site Explorer
• How to Do Keyword Research with OSE and the Keyword Difficulty Tool
• How to Discover Keyword Opportunities in Moz Analytics

Let’s get right down to business!

Fix 1: How to Examine and Analyze SERPs Using New MozBar Features

The MozBar is a handy tool that helps you access important SEO metrics while you surf the web. In this Daily SEO Fix, Abe shows you how to use this toolbar to examine and analyze SERPs and access keyword difficulty scores for a given page—in a single click.

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Fix 2: How to Boost Your Rankings through On-Page Optimization

There are several on-page factors that influence your search engine rankings. In this Daily SEO Fix, Holly shows you how to use Moz’s On-Page Optimization tool to identify pages on your website that could use some love and what you can do to improve them.

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Fix 3: How to Check Your Anchor Text Using Open Site Explorer

Dive into OSE with Tori in this Daily SEO Fix to check out the anchor text opportunities for Moz.com. By highlighting all your anchor text you can discover other potential keyword ranking opportunities you might not have thought of before.

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Fix 4: How to Do Keyword Research with OSE and the Keyword Difficulty Tool

Studying your competitors can help identify keyword opportunities for your own site. In this Daily SEO Fix, Jacki walks through how to use OSE to research the anchor text for competitors websites and how to use the Keyword Difficulty Tool to identify potential expansion opportunities for your site.

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Fix 5: How to Discover Keyword Opportunities in Moz Analytics

Digesting organic traffic that is coming to your site is an easy way to surface potential keyword opportunities. In this Daily SEO Fix, Chiaryn walks through the keyword opportunity tab in Moz Analytics and highlights a quick tip for leveraging that tool.

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Looking for more?

We’ve got more videos in last week’s round-up! Check it out here.

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Don’t have a Pro subscription? No problem. Everything we cover in these Daily SEO Fix videos is available with a free 30-day trial.

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Posted by AlexApptentive

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

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:

• Android apps receive significantly more ratings than iOS apps, and thus I assumed the weight of each marginal rating impacted rankings less in Google Play than in the App Store
• Google Play does not record an average rating exclusive to the current version of an app, meaning ratings are not reset after each update; and thus, I assumed the less volatile Android ratings equated to less volatile Google Play rankings

Both of these assumptions will be tested in later analysis.

Results

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

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

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.

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

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.

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

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:

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.

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.

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

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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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