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:
- 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.
- 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:
- Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
- Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store's top charts.
- In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
- 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.
- The higher an app is rated, the less volatile its rankings are.
- 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.
- 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.
- The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
- 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.
- 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.
Hi Ikkie,
Glad you found this helpful! There has been very little research around this (and it doesn't help that the algorithms keep changing), but I'm definitely continuing to dive deeper into app store rankings to improve upon these findings.
I read something interesting today. Thank you for the remarkable insight Alex.
Thanks, Mark! Glad you found it insightful!
That's quite a detailed post about app ranking dynamics Alex, totally appreciated your efforts. I have a question here: according to your article the 'Rating' comes at second last place in Google play and the last spot at Apple Store which I have found quite contrary to the belief I had about the ratings and the app ranking correlation. Anyway it has cleared things greatly for me :)
Thanks for the kind words, Salman. This data is from a small sample of apps at the very top of the charts, so I can't quite remark on the impact of ratings for the average app. However, the distribution of ratings really isn't that great, with the majority of apps falling somewhere between 3 and 4 stars, so in an of itself, I don't think differences at these minutes levels will make a huge difference. I think both app stores take a page from Google's quality score and think about it from the consumer's perspective: Would you be more likely to download an 3-star app with 1,000 ratings or a 4-star app with 50 ratings (of which a good chunk likely comes from the developer's family and friends)?
In my research, where I've found star ratings to matter the most is at the point of download: 50% of consumers won't download a 3-star app and 85% won't download a 2-star app.
So both metrics are very important, but from a straight ranking perspective, I'd say count is more influential.
Hi Alex,
It is really a great post! Thanks for sharing your knowledge! It is really an awesome article!
Glad you found it helpful! :)
Great post!
I'd be interested to know about how semantics and reviews play into app rankings. At least from my experience, I always read the descriptions and reviews before downloading an app. I wonder how much CTA's and the semantics associated with positive/negative reviews would have on rankings.
Just a thought here.
Thanks! That's an interesting thought experiment. Keywords in the description definitely have a big influence on rankings. To the best of my knowledge, I don't believe the algorithm scans individual reviews (although I know someone who is investigating this). The semantics of reviews do, however, provide an indication of how customers discover and talk about your app. This is a great way to do keyword research and work those specific keywords into the rest of your app store listing.
I decided to focus more on the quantitative aspects of ASO in this post, but if you're interested in keywords and the differences between how Apple and Google handle keywords, I have a free app store ratings eBook that served as the inspiration of this post (the last section is all about traditional ASO).
Interesting reading, I can use this as input into shaping the satisfaction kpi for a business case i am writing at the moment.
Glad it helps! :)
Alex
You have highlighted the attributes of stars, reviews, downloads. Does the actual picture make a significant difference. ie have you seen downloads off the way the app is pictorialised?
Hi John,
I am not aware of any link between imagery and rankings, but imagery is tied to conversion and click-throughs. Your app icon, screenshots, and video are the first impression someone has of your app, and they certainly play a role in the download decision. If you're interested in learning more, I came across a recent case study on an app that saw a 700% increase in downloads as a result of ASO emphasizing a new app icon as the leading factor: https://blog.sensortower.com/blog/2015/04/14/aso-case-study-700-percent-increase-in-downloads-with-app-store-optimization/
Alex
You are a superstar thanks for info and the link.
Great, Alex. Amazing information and remarkable insight. Very, very useful.
Thanks Alex!
Thanks for the kind words, Javier. Glad you found it useful!
Hey Alex,
First of all congratulations for making at the main stage, you truly deserved it. :)
Coming to the post, I really like the way you assessed all these studies and it's interesting to see how both search engines reacts to top & lower apps.
I'd love to see more studies on this topic from you in future.
Well done!
Umar
Cheers, Umar!
Glad you enjoyed the post. I'm already doing more research around app store rankings. There are also some exciting new development in the way web and mobile search engines are indexing app content, which could make for some interesting studies!
All the best,
Alex
Hi Alex, thanks for sharing these very interesting insights! Do you also have any tips, tricks or ideas to increase the rating count for iOS and Android apps?
Hi Jochen,
As a disclaimer, I work with Apptentive, and we help companies do exactly that. I won't advertise here, but check us out if you'd like to request a demo or partake in the free trial.
Apart from working with us, I think the key is in proactively asking for ratings/reviews in a contextually relevant way. Subtle CTAs like asking for a rating in the app updates notes or Help section are what I'd recommend for a first step, followed by integrating or designing in-app rating prompts. When prompting, little things matter like where, when, and whom you prompt. I've found that waiting until after the customer has completed an action (say made an in-app transaction) to deliver a prompt will yield dramatically better results than bombarding them the moment they open the app. Likewise, segmenting your customers and prompting only your most engaged customers will result in more positive ratings while offering additional channels of customer support will circumvent some of the negative ratings. At the risk of tooting my own horn, I'd recommend checking out my free eBook, The Mobile Marketer's Guide to App Store Ratings & Reviews. The guide walks you through several strategies and includes brand new data from a recent consumer survey showing just how important ratings and reviews are to your app. :)
. "Mobile app monetization is a huge problem facing many app developers" Yep this is very true they have only have had this model for a short time. Monetization is new to many peeps and they are figuring it out on the FLY. There are quite a few similarities between monetizing an app and a website. Sure you have your in APP purchases but you also have many chances to monetize the traffic through ad networks, lead gen companies etc.
There has been a couple of these app dev companies that I have helped with the monetization process. To me digital traffic is digital traffic ...just a funnel in and a different traffic source
7x more Android devices < and they are cheap ((yea I have a droid)) .. don't like paying for much.. volume comes into play here.
App Maketing = get into top 100 and work your way up to the top 10.. cash a huge check .. pretty simple ..lol
Thanks for the insights! Monetization will always be a challenge, but the more people you get and keep in the funnel, via acquisition and retention marketing, the more revenue you can expect. At the same time, some of the most profitable apps in the market don't have a large volume of users or particularly high ratings/rankings, yet they've carved out a very specific niche and have found a way to deliver real value (think mHealth apps, which can have an average revenue per user in the thousands).
(think mHealth apps, which can have an average revenue per user in the thousands). 1000's?, Wow I need to check that out and see what they are doing...
Technically, LTV is probably more accurate than ARPU; but yes, there are some really incredible low-volume, high-value apps (particularly in mHealth, banking, and insurance) that are going so far as to replace traditional healthcare for their users that have a huge stake in their customer experience. Their users are sharing their most private financial or health data with these apps and are willing to pay for that exceptional quality and security.
Excellent post Alex,
As more and more people (including my clients) turn to Apps this kind of information is simple invaluable. It's a good reminder that it's not always about Google rankings.
Thanks for the awesome read.
Thanks David! Glad you liked it. I think even companies without a current mobile presence will need to increasingly study up on both app store optimization and mobile SEO, particularly when it comes to things like deep links or indexing mobile app content in Google or Bing SERPs.
Google is placing app downloads in the normal mobile search results on android. That could also be a new factor in the still virgin grounds of ASO. :-) lots of fun, and lots to discover (or reengineer)
Hi Alex Walz, You have done great job. Multi-billion dollar industry, People are working with it, but they don't know how to monetize more & more money by ranking on SERPs, you wrote some difficult important things, that's really attract me as a Apps Developer & optimizer. You deserve Special Thanks.
Hi Tafser,
Thanks for the kind remarks. Mobile app monetization is a huge problem facing many app developers, yet I think both discovery and monetization both come down to how well you engage and retain your current customers. An engaged and loyal customer is happier to both evangelize and spend money in your app.
Hi Alex!
Until today it seems that Apple App Store has always been above Google Play since this came out in 2008 or so. So I think that has changed today, because every time there are more Android devices. This shows the strength that has Google globally and that more and more users there
We'll see how it continues to evolve in the future.
Lot of research!
Hi Tino!
Thanks for the kind remarks. You hit the nail on the head when you said "globally." Worldwide, there are 7x more Android devices in use than iOS devices. Yet in the United States, Android only has a 9% market share lead over iOS.
It's also worth noting that the chart showing rating volume in the two stores isn't an indicator of active users. It's simply a lot easier to leave a rating or review in Google Play compared to the six-step process of rating an app in the App Store.
Very nice research although these arent exactly experiments.
Thanks, Russ. And true. I was just a little over inspired by seeing Rand's slideshare. A true experiment would definitely yield some interesting insights, though...
Hi Alex,
This is really a stunning post. Now many people, businessmen using the apps whether it is Apple app or it is android. But from my point of view Google play store is much better then Apple app store.
If we see most of the users using android phones (including me) because usage of android apps is much easier then apple apps. BTW you have given good thoughts over it and I really appreciate your post.
Thanks for sharing !!
Thanks, Shakti! I'm partial to Android as well, and the App Store seems excessively complicated in many regards. At the same time, its more volatile rankings come as good news to savvy indie developers and new apps that can quickly rise to the top, whereas it's much harder to launch a new app for Android.
Thanks Alex :) Much appreciated
Nice! Thanks for share.
Great post! I've been working with app store optimization a lot recently as I've been releasing a lot of my own apps. I've found that the number of good reviews actually has a really large impact on your apps ranking in search. I'm trying to start a small community for developers to post their apps and give each other honest reviews to help out with this (totally free too). Link is https://www.usertrue.com if interested!
Fascinating! Thanks for all the work you've put in here, a very interesting article.
Hello
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Excellent post Alex,
Well informative article!! I really thankful to you for sharing this article.
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