| Deconstructing the App Store Rankings Formula with a Little Mad Science Posted on: Thursday 28 May 2015 — 00:21 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:
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 volatilityThe 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:
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. HypothesisGoing 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
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 chartsTesting 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). HypothesisI 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 starsThe 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. HypothesisRanking = 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:
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 versionsThis next study looks at the relationship between the age of an app's current version, its rank and its ranking volatility. HypothesisRanking = 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 usersIn 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. HypothesisRanking = 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. SummaryTo 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:
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 algorithmRating Count > Installs > Trends > Rating Weight of factors in the Google Play ranking algorithmRating Count > Installs > Rating > Trends Again, we're oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies. I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments. Please share your thoughts in the comments below, and let's deconstruct the ranking formula together, one experiment at a time. Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read! |
| Posted on: Wednesday 27 May 2015 — 13:30 Posted by NoamC Today, we're excited to announce some new features and changes to the Moz Local dashboard. We've updated your dashboard to make it easier to manage and gauge the performance of your local search listings. New and improved dashboard
We spent a lot of time listening to customer feedback and finding areas where we weren't being as clear as we ought to. We've made great strides in improving Moz Local's dashboard (details below) to give you a lot more information at a glance. Geo Reporting
Our newest reporting view, geo reporting, shows you the relative strength of locations based on geography. The deeper the blue, the stronger the listings in that region. You can look at your scores broken down by state, or zoom in to see the score breakdown by county. Move your mouse over a region to see your average score there. Scores on the dashboard
We're more clearly surfacing the scores for each of your locations right in our dashboard. Now you can see each location's individual score immediately. Exporting reports
Use the new drop-down at the upper-right corner to download Moz Local reports in CSV format, so that you can access your historical listing data offline and use it to generate your own reports and visualizations. Search cheat sheet
If you want to take your search game to the next level, why not start with your Moz Local dashboard? A handy link next to the search bar shows you all the ways you can find what you're looking for. We're still actively addressing feedback and making improvements to Moz Local over time, and you can let us know what we're missing in the comments below. We hope that our latest updates will make your Moz Local experience better. But you don't have to take my word for it; head on over to Moz Local to see our new and improved dashboard and reporting experience today! 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! |
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