# ASO Stack 2019 — Introducing the Supporting Insights Row

- Link: https://phiture.com/asostack/aso-stack-2019-introducing-the-supporting-insights-row-329c87d56eb7/
- Published: 2019-07-10T14:19:08+01:00
- Author: Gabe Kwakyi

![The ASO Stack 2019](https://phiture.com/wp-content/uploads/2019/07/The-ASO-Stack-
2019-min.png)

In the early days of App Store Optimization, much of the focus pointed towards prospecting,
testing and iterating on the best keyword mix, along with running supporting A/B
tests. Yet as the nature of app discovery in the stores evolved so, too did the 
breadth of levers and factors that ASO practitioners needed to consider expanded.
The world of ASO in 2019 has continued to evolve, now beset by new dynamics such
as high competition, a need to look globally for growth, an increased focus beyond
keywords for app discovery, and a monetization push by both stores which has gradually
reduced the volume of organic visibility.

As put by [Thomas Petit](https://twitter.com/Thomasbcn) in the [ASO Conference](https://asostack.com/the-official-app-store-optimization-conference-berlin-recap-7eea1c299cec)
Berlin on May 22nd, if the current world of ASO had a relationship status, it would
be: “it’s complicated.”

![ASO in 2019](https://phiture.com/wp-content/uploads/2019/07/ASO-in-2019-min.png)

To reflect the nature of this increased complexity in ASO, we decided to revamp 
the bottom row of the ASO Stack to call attention to some of the most important 
of these complicated details. Creating this bottom row of supporting insights follows
the original goal of the ASO stack, which is to help give defined structure to what
may feel at first glance an overwhelmingly complicated set of strategies, and to
help ASO practitioners gain a handle on analyzing their ASO strategy in spite of
these rising complexities.

Whereas the top two rows of the stack focus on levers and tangible assets or optimization
techniques, the purpose of the supporting insights row is to leverage the tools 
row and custom analytics to inform an ASO team’s decision making, goal setting, 
and resource allocation, thus freeing up the top rows to achieve their best results.
Supporting insights describe underlying factors that, if left out of planning can
lead to impossible gaps between expectations and actual results; or, if properly
factored can be key to building support to align stakeholders around removing key
blockers to an app’s maximum ASO potential. Learning to master the calculation of
supporting insights can increase the accuracy of forecasting results, align paid
and organic marketing teams, prioritize the most important blockers, educate internal
teams and much more.

There are seven supporting insights groupings.

### ⌊supporting insights groupings⌉⌊supporting insights groupings⌉

### **Correlation Coefficients**

The algorithms of today’s “maturing” or “modern” ASO are influenced by a variety
of factors beyond submitted metadata and are continuously fine-tuned through algorithm
updates to take the latest factors deemed as relevant into account (such as excessive
app wakeups). This is especially true with respect to Android keyword and related/
recommended app lists, which are governed by Google’s vast content discovery algorithm
expertise. While both stores have expressed the fact that they study more signals
beyond downloads and conversion rate, Google has always exceeded Apple in proving
this to actually be the case.

In simple terms, studying your app’s correlation coefficients is a process concerned
with establishing a stack-ranked order of the factors which are found to have a 
significant influence on your app’s organic visibility, and the magnitude of the
relationship of each factor with organic visibility.

For instance, if your app’s conversion rate is hovering below your peer set’s benchmark,
this could be a significant factor in preventing your app from earning more visibility.
Or, if you experience a big increase in inorganic traffic, you may also see a significant
increase in your organic visibility.

The factors exerting an influence on ASO performance can be segmented into two main
camps of offensive and defensive factors.

**Offensive** factors power an app’s ability to capture a larger share of visibility,
including:

 * **Organic uplift** — this refers to the trend of inorganic downloads such as 
   those from paid marketing to improve an app’s organic rankings.
 * **Conversion rate** — an app’s conversion rate not only helps to turn more impressions
   into downloads but is a consideration when determining future rankings as Apple
   and Google’s algorithms desire to show apps which are able to convert visitors
   into downloaders.
 * **Ratings & reviews** — the volume of users leaving ratings and reviews is considered
   as a positive ranking factor, similar to organic uplift.

**Defensive** factors act as a potential penalty zone for apps, triggering a drag
on visibility when an app’s performance worsens beyond a certain level or that of
its peers. Defensive factors include:

 * **Vitals** — this refers to the data points on the development side, such as 
   crashes, App Not Respondings (ANRs, Android-only), excessive wakeups (Android-
   only), and others. In modern ASO, these factors can cause an app’s ranking to
   worsen or prevent improvements if they are at poor levels.
 * **Star rating** — an app’s star rating is a signal that ranking algorithms also
   consider. Per Google’s upcoming Play Store announcement, Google will also pay
   special attention to the most recent user ratings.
 * **Uninstalls / retention rate** — if your app churns the users that it acquires
   at a high rate (at worst via direct uninstall, but also via poor retention rates),
   this is a powerful negative signal that the users are not having a positive experience.
   Retention rate may also possibly act as an offensive factor.

⌊play store data⌉⌊play store data⌉

_Play Store data showing a worsening in store-sourced download volume after an increase
in crashes and uninstalls._

**Correlations** refer to the relationship between the above factors and an app’s
organic visibility. That is, are the offensive and defensive factors found to have
a significant relationship with an app’s organic visibility?

**Coefficient** refers to the calculated magnitude of the relationship between the
correlated variables. That is, for each change in a independent variable (e.g. inorganic
downloads), what was the observed change in the dependent variable (i.e. organic
downloads)?

While there are many ways to calculate correlation coefficients (and many more to
actually predict what organic visibility may be considering a host of factors), 
one of the most popular methods used to calculate correlation coefficients is a 
regression analysis.

⌊regression analysis ⌉⌊regression analysis ⌉

_A visual from a regression analysis showing variables found to have an insignificant
correlation (left), and variables with a significant correlation (right)._

The most commonly studied factor to date has been organic uplift, calculated in 
the form of “1 additional paid download is correlated with an uplift of X organic
downloads;” through regression analysis, the same calculation can apply to the other
factors as well (albeit with different potential interpretations, such as rate-based
factors). A few caveats to be aware of include:

 * Coefficients will differ from factor to factor, app to app, category to category,
   country to country, and store to store.
 * Coefficients can differ based on macro factors such as competitive pressure or
   seasonality.
 * Coefficients may experience different levels of magnitude depending on the level
   of scale involved. This is because of the non-linear nature of app store visibility
   and the fact that once an app reaches the top, there are no more rank gains to
   be had (although other factors such as word of mouth, changes in auto-fill results,
   and seasonality can extend yield beyond any one algorithm’s maximum rank).
 * One crucial challenge to using regression analysis to study coefficients is that
   it requires a sufficient number of observations, with significant variation in
   the variables in order to effectively assess the significance of the relationship.
   For example, if your app’s retention rate has remained unchanged for an entire
   year, it would be difficult to tell whether retention rate is a significant factor,
   because there would be no observations from periods of higher or lower retention
   rate that could indicate whether organic visibility had a linear relationship
   during those periods.

### **Share of voice**

With the maturation of the App Store (i.e. increased competition, more complex discovery
processes, wider access to data & trends, more aggressive monetization, etc.), managing
a modern ASO strategy should also include studying an app’s potential share of voice.

Share of voice refers to the share of impressions available to a particular app 
in a particular set of stores/countries. The purpose of studying share of voice 
is to inform your ASO team as to how much visibility your app can possibly achieve,
thus enabling a more accurate assessment of the growth potential for an app. Some
of the uses of share of voice include:

 * Planning growth targets for organic
 * Prioritizing resources (e.g. marketing budget for organic uplift)
 * Determining the contribution margin of organic to an overall growth strategy
 * Prioritizing ASO tactics (e.g. unlocking growth from featuring vs keywords vs
   top charts vs app lists)

To understand an app’s total **maximum** share of voice takes into account the which
countries and category(ies) an app operates within and the macro factors of those
markets, such total in-market users, new phone sales trends, localized store monetization
trends, and how many apps the total addressable userbase tends to download.

Typically more important than estimating total maximum share of voice is the total**
potential** share of voice, which takes into account competitive pressure and seasonality,
as well as other more micro-level factors such as offensive/defensive ASO factors
and featuring potential.

There are several dimensions to consider when estimating share of voice:

 * Operating system
 * Store (including Android 3rd party stores)
 * Country
 * Visibility source

 1. Keywords
 2. Top charts (by country / category)
 3. Features
 4. Related app lists
 5. Recommended app lists
 6. Collections/editorials (iOS)
 7. Trending searches (iOS)

One important note for share of voice (and for correlation coefficient analyses)
is that share of voice should assess the top-level visibility (i.e. rankings or 
impressions), rather than downloads. This is because downloads may be higher or 
lower for the same number of impressions due to an app’s conversion rate, which 
can fluctuate with time/optimizations/competition/etc. So analyzing share of voice(
or correlation coefficients) at the download level factors your app’s conversion
rate into the equation, rather than applying that as a second step and giving you
a sense of how many more downloads your app could receive if it had a better conversion
rate.

⌊downloads are the 3rd level of organic visibility⌉⌊downloads are the 3rd level 
of organic visibility⌉

_Downloads are the 3rd level of organic visibility._

Estimating share of voice can be difficult to due given the myriad, dynamic influences
on the maximum and potential share of voice, but a few methods include:

 * Using a Competitive Intelligence Tool such as App Annie to estimate total maximum
   share of voice.
 * Using a Keyword Research download estimation tool, such as AppTweak to estimate
   the total maximum number of keyword-sourced downloads available.
 * Using an ASO tool to estimate the number of downloads from an app that is heavily
   featured, is the #1 top-chart ranked app, or owns most of the #1 ranks in keywords
   or app lists, and setting this as the total **maximum** share of voice.
 * Using an ASO tool to estimate the number of downloads from an app that is a good
   competitor benchmark to beat and setting this as the total **potential** share
   of voice.

⌊AppTweak's organic download estimation tool⌉⌊AppTweak's organic download estimation
tool⌉

_AppTweak’s organic download estimation tool._

For example, if your app is the 15th ranked app in a category, has average keyword
ranks and has never been featured, then there is a large additional swath of visibility
that can be earned from the total **maximum** share of voice; but if your app faces
large competitive pressure, an inability to get featured and poor defensive factors,
then your app may have achieved its total **potential** share of voice. Or, if your
app is performing extremely well in a single market yet no effort has been invested
into localization, then spending effort optimizing the offensive/defensive ASO of
the app in other markets can push the total **potential** share of voice closer 
to the total **maximum** share of voice.

One major challenge with estimating share of voice is that it is difficult (or expensive)
to estimate the number of downloads in a market or the total share of an app’s downloads
sourced from organic sources-only. Inorganic downloads are often a large part of
an app’s downloads mix, and even apps which have a large share of organic downloads
may owe most of their downloads to a large brand awareness (more-so than the non-
branded visibility they receive), which may not be representative of your app’s 
situation. That said, these total download estimates can help establish the picture
of potential share of voice, given that if your app does not have the potential 
to earn the levels of total downloads that competitors drive, it is very difficult
to over-optimize the other offensive ASO factors enough to usurp the rank spots 
that more popular apps earn.

### **Organic Cannibalization**

The topic of organic cannibalization has been one of rising importance as both Apple
and Google increase the monetization rates of their stores.

**Cannibalization** refers to the downloads that an app would have received organically
due to users downloading the organic result, yet are earned by an ad for the app
because users download the app from the ad result. In other words, cannibalization
refers to the propensity for ads to take credit for downloads that should be attributed
to organic.

The inverse of cannibalization is **incrementality**, or downloads which were only
possible to be earned through an ad. An example of incremental downloads are those
that come from keyword ranks which the organic result does not rank in visible positions
for, or at all, such as competitive searches.

⌊expedia search on apple app store⌉⌊expedia search on apple app store⌉

_Examples of ad placements which can cannibalize downloads from the organic rankings._

⌊examples of ad placement on google play store⌉⌊examples of ad placement on google
play store⌉

_Examples of ad placements which can cannibalize downloads from the organic rankings._

One of the simplest, most popular methods to analyze cannibalization is to run an“
on/off test,” where all store ads are paused for a period, and the number of organic
downloads is compared during the “off” period and the “on” period.

⌊sample analysis of cannibalization⌉⌊sample analysis of cannibalization⌉

_Sample analysis of cannibalization using the on/off method_

When analyzing cannibalization for your app it is important to note that while cannibalization
is not desirable, some level of cannibalization is unavoidable, and store ads are
still important for maximizing visibility. The placement of non-organic app results
in the search engine results page for iOS in particular increases the need to run
store ads, to stay above the fold and earn downloads.

⌊tickets search on apple app store⌉⌊tickets search on apple app store⌉

_In this keyword search, there are no organic app results, leading to a lower potential
cannibalization level._

Note that while cannibalization most often focuses on store ads (i.e. Apple Search
Ads ad Google Ad Campaigns) because store ads show in the same view as organic results,
cannibalization is also possible in ads that are placed from outside of the store,
especially in the age of view-through impressions.

### **Feature Visibility**

Not all features are created equal; feature visibility refers to the difference 
in downloads that different feature types generate.

The following feature types are listed from the smallest to largest impact, with
App of the Day generating the most significant results (e.g. 22x or greater the 
number of downloads of a smaller or shared feature):

 * Icon collection (shared with other apps)
 * Banner collection (shared with other apps)
 * Featured banner (single app focus)
 * App of the Day collection (shared with other apps)
 * App of the Day (single app focus)

⌊games on apple app store⌉⌊games on apple app store⌉

_Featured banner for Minecraft_

⌊game picks on apple app store⌉⌊game picks on apple app store⌉

_Essential Game Picks icon collection_

⌊game of the day on apple app store⌉⌊game of the day on apple app store⌉

_App of the Day for Rad Trails!_

In addition to earning a temporary feature, some features earn a permanent element,
such as the Editor’s Choice badge or an App of the Day. Not only do App of the Day
results show below an app’s branded search (serving as competitive brand defense),
but App of the Day Stories can also be placed into non-brand results in high rank
locations.

⌊rad trails search on apple app store⌉⌊rad trails search on apple app store⌉

_App of the Day for Rad Trails after a branded search for Rad Trails!_

⌊peloton digital search on apple app store⌉⌊peloton digital search on apple app 
store⌉

_App of the Day collection including Peloton after a branded search for Peloton_

### **Macro Influences**

Per the Share of Voice section, macro influences affect the total maximum and potential
number of downloads that an app can possibly earn. Macro influences include factors
such as:

 * **Seasonality/holidays** — download trends shift throughout the year and can 
   spike during holidays by hundreds or thousands of percentage points.
 * **Active phones** — the total market of people able to download apps in a country(
   i.e. current active devices in-market, plus new phone sales). Especially of interest
   as of late is whether Apple can sell more phones in hot growth markets internationally
   as US sales cool.
 * **Keyword auto fill trends** — a lesser-considered influence of large download
   trends which involves studying the dynamic trends of which keywords appear in
   high-volume auto-fill results. Changes in the auto fill trends can cause shifts
   in download trends (positive or negative) as app names and non-brand keywords
   rankings change.
 * **UX / algorithm updates** — iOS 11 set plans in motion to push featuring to 
   become a larger share of app discovery for Apple, and Google’s store refresh 
   and push towards personalization has shifted the factors influencing ASO to more
   indirect, scaled factors difficult to track or optimize. Studying how these macro
   UX changes pan out is important to maintain and understanding of the avenues 
   to earning organic downloads and where to prioritize current and future efforts.
 * **Competitive pressure** — this refers to competitors’ performance in terms of
   offensive/defensive ASO factors, as well as incumbents departing the market and
   new entrants entering the market. Also with the advent of pre-install app rankings,
   new entrants can now announce their presence and begin capturing downloads before
   going live, pushing both more warning and more pressure for incumbents.

⌊apple reports record iphone sales⌉⌊apple reports record iphone sales⌉

_Annual iPhone sales showing multi-year stagnation. Image Source: [https://www.businessinsider.com/apple-iphone-sales-yearly-chart-2017-2](https://www.businessinsider.com/apple-iphone-sales-yearly-chart-2017-2)_

### **Search Term Data**

Now that Google Play has released organic search term data, these insights can be
mined for keyword research (including optimizing by APRU/retention), as well as 
analyzed to determine the impact of keyword optimization on actual search term trends.

On the iOS side, search term data can be proxied from Apple Search Ads campaigns.
If analyzing data on Search Match or Broad Match, be sure to navigate to the search
term tab rather than using the keywords tab.

⌊acquisition reports ⌉⌊acquisition reports ⌉

_Sample of Google Play search term data from a Google blog._

### **Benchmarks**

Per the correlation coefficients section, performance data is used to determine 
your app’s organic ranking visibility, and benchmarks are what this data is analyzed
against to determine the relative ranking strength of your app. In addition to reporting
on offensive and defensive raw data (e.g. conversion rate), be sure to report on
how your app stacks up to available benchmarks in order to get ahead of potential
problems or opportunities.

Google readily provides many benchmarks, but for iOS some ASO tools such as AppTweak
do provide [conversion rate benchmarks](https://www.apptweak.com/aso-blog/mobile-app-store-conversion-rate-benchmarks-per-category).
For rating/reviews volume and star rating, use you can use your competitor set’s
data as a benchmark, and for other metrics that do not have a reliable benchmark,
use your app’s historical average to assess whether your trends for those metrics
are getting better or worse, and whether this may be affecting your app’s organic
visibility.

⌊data on vitals vs benchamrks on google play console⌉⌊data on vitals vs benchamrks
on google play console⌉

_Data on vitals vs benchmarks from the Google Play Console._

Thanks for reading! Let us know if you have questions, comments or ideas on Supporting
Insights or other areas of the 2019 ASO Stack.

![ASO stack 2019](https://phiture.com/wp-content/uploads/2019/07/ASO-stack-2019-
min.png)