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WHY LOCALIZATION MATTERS:A Look at the Statistics Behind App Store Features
A P U B L I C AT I O N O F
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WHAT’S INSIDE?
INTRODUCTION:
Why we’re doing this
DATA AND METHODOLOGY
QUESTION 1:
What is the impact of localization in getting featured in the App Store?
QUESTION 2:
What types of apps are usually localized? Are there any patterns?
QUESTION 3:
How do people apply minimum viable localization (MVL) across the market?
CONCLUSION
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INTRODUCTION
Why We’re Doing This
CONCLUSION
QUESTION 3
QUESTION 2
QUESTION 1
DATA AND METHODOLOGY
Why Localization MattersIntroduction: Why we’re Doing This
Why Localization MattersIntroduction: Why we’re doing this
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So, you’re interested in localizing. But how much of a difference does it actually
make?
A common question we get is: what is the actual impact of localization for my
product?
There are a lot of great stories out there about wildly successful localization
strategies—and we love hearing these. It can be harder, though, to find good
quantitative analyses of localization. Anecdotes are great, but sometimes, you
need to find cold, hard, data to justify your business strategy.
This year, we decided to run the numbers ourselves.
Our team here at OneSky has investigated some of the data behind localization,
to help our readers and clients stay as informed as possible when making their
choices.
Why Localization MattersIntroduction: Why we’re doing this
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Now, let’s dive in to our research.
Note: If you want to get a sense of the data and stats we used for this project, scroll
to the very end for a break-down or drop us a line and we’ll walk you through our
whole process, stats and all.
For this project, we are using data for Apple’s App Store. If you’re interested in
statistics for Google Play and other app stores, let us know at content@oneskyapp.
com
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DO LOCALIZED APPS HAVE A HIGHER PROBABILITY OF GETTING FEATURED?
HOW DO PEOPLE APPLY MINIMUM VIABLE LOCALIZATION (MVL) ACROSS THE MARKET?
WHAT TYPES OF APPS ARE USUALLY LOCALIZED? ARE THERE ANY PATTERNS?
Here are 3 questions that are asked most frequently by our users, to estimate the ROI
of localization for their businesses:
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Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
QUESTION 1
What is the impact of localization in getting featured in the App Store?
CONCLUSION
QUESTION 3
QUESTION 2
INTRODUCTION
DATA AND METHODOLOGY
Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
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Has the app been featured in (1) the localized subgroup or (2) the unlocalized subgroup?
FALSE (Unfeatured)
FALSE (Unfeatured)
95.44%
99.36%
4.56%
0.64%
TRUE (Featured)
TRUE (Featured)
Localized subgroup
Unlocalized subgroup
With a chosen dataset of 1,500 apps, we determined what percentage of the apps
in our sample had been localized, and what percentage had been featured.
Then, we asked two questions:
For localized apps, what percentage had been featured in the iOS App Store?
For unlocalized apps, was that percentage different?
Has the app been localized?
Has the app been featured?
FALSE (Unlocalized)
FALSE (Unfeatured)
83.77%
98.72%
16.23%
1.28%
TRUE (Localized)
TRUE (Featured)
Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
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It turns out that all apps have a pretty slim chance of getting featured—but the
localized apps did far better.
While only approximately 0.64% of the unlocalized apps had been featured on the
homepage at some point, 4.56% of the localized apps had been successful.
In other words, you’re more than 7x more likely to be featured on the homepage
if you localize your app.
Localization is associated with being featured on the homepage.
PRELIMINARY CONCLUSION #1
Now let’s look at whether this is causation or just correlation.
This finding could indicate that localizing increases the likelihood of getting featured
on the homepage. But it could also just mean that the apps that are localizing are the
ones that already have a higher chance of being featured.
The second one is especially likely when it comes to wildly popular apps. Take
Snapchat, for example. Its incredible success means that it has a higher chance
of being featured and also that it had a much higher budget with which to fund
localization projects.
Before moving into the question of causation, though, we wanted to confirm that
we really were seeing a positive association here. So we flipped what we had done
earlier.
This time, we looked at what percentage of apps were localized (or not) within the
group of featured apps and within the group of unfeatured apps.
Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
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There is a positive relationship between the likelihood of localizing and being featured.
PRELIMINARY CONCLUSION #2
Here, again, localized apps really shone. Over 50% of apps that were featured had
been localized, while only 16% of “unfeatured” apps had localization history.
In other words, if you have been featured, you’re much more likely to have
localized than not.
Has the app been localized in (1) the featured subgroup or (2) the unfeatured subgroup?
FALSE (Unlocalized)
FALSE (Unlocalized)
42.11%
84.31%
57.89%
15.69%
TRUE (Localized)
TRUE (Localized)
Featured subgroup
Unlocalized subgroup
Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
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So, we’ve now firmly shown that the two variables—being featured and having been
localized—are positively related. But the big question remains: do these variables
actually affect each other?
To make sure that this finding wasn’t just a coincidence, we ran an independence
test.
The Independence Test
In short, the answer is yes: there is causation between localizing and getting
featured in the App Store.
The Short Explanation: For us to say with 95% confidence that the two variables
affect each other, we would need to see a p-value that is less than 0.05. As you can
see above, when we ran the test, the p-value was 3.411e-0.6—or much lower than that
0.05 threshold.
The Long Explanation: In an independence test, you have two hypotheses. The
null hypothesis assumes that the two variables tested are independent, and the
alternative hypothesis assumes that the two variables are dependent.
It is common to aim for a 95% confidence level at minimum; this means that, if the
p-value of the test statistics is greater than or equal to 0.05, then we know that
there is enough evidence to support the null hypothesis—i.e. the two variables are
independent.
Why Localization MattersQuestion 1: What is the impact of localization in getting featured in the App Store?
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FINAL CONCLUSION
This four-fold plot shows the dependency relationship between the two variables
(localizing and getting featured.) The larger quadrants represent the most common
associations in our dataset: in this case, having been localized and having been
featured (and the converse).
If the p-value is less than 0.05, then you
know the two variables are dependent.
When we ran the test, we got a p-value
of 3.411e-0.6, which is significantly smaller
than 0.05.
Therefore, we can reject the null
hypothesis and say the two variables—
localization and being featured—are
dependent on each other.
There is a strong, positive, dependent relationship between an app localizing and getting featured in the App Store.
If you localize, chances are much higher that you will be featured.
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Why Localization MattersQuestion 2: What are the types of apps more likely to be localized?
QUESTION 2
What are the types of apps more likely to be localized?
CONCLUSION
QUESTION 3
QUESTION 1
INTRODUCTION
DATA AND METHODOLOGY
Why Localization MattersQuestion 2: What are the types of apps more likely to be localized?
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We used our dataset to make the plot above, which shows the feature rate—the
percentage of apps featured on the homepage—by category.
Social Networking apps have the highest chance of being featured on the
homepage, followed by Finance and Education apps. The rate continues to drop
until you get to a bunch of categories—from Navigation to News to Music to
Health—for which our dataset did not include a single featured app.
The graph shows us that not all categories are created equal. Some seem more
likely to get featured. (Unfortunately, our dataset is not large enough to make
strong claims about this just yet.)
Why Localization MattersQuestion 2: What are the types of apps more likely to be localized?
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Our dataset was large enough to begin looking across categories and see whether
any patterns in localization emerged.
At first glance, we can already see that there seems to be a higher proportion of
localized apps in certain categories.
The Utilities category, for example, clearly holds many more localized apps
than the Sports category.
The Travel and Music categories may have about the same number of
unlocalized apps, but Travel outranks Music by far with its higher number of
localized apps.
Why Localization MattersQuestion 2: What are the types of apps more likely to be localized?
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There are many games out there—with a decent proportion of them being
localized.
What’s the actual percentage of localized apps in each category? Let’s dive into
the numbers.
Why Localization MattersQuestion 2: What are the types of apps more likely to be localized?
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As expected, there are some varying percentages here.
At least one out of every five Travel apps is localized—and the same goes for apps
in the Productivity, Entertainment, Utilities, and Games categories.
Navigation, on the other hand? Nope.
We don’t have enough data to compute a fully accurate localization rate. However, we can say that the rate of localization does differ across categories.
On average, certain categories of apps (like Travel and Productivity) localize far more than apps in other categories (like Reference and Navigation.)
FINAL CONCLUSION
Why Localization MattersQuestion 3: How do people apply minimum viable localization (MVL) across the market?
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Why Localization MattersQuestion 3: How do people apply minimum viable localization (MVL) across the market?
QUESTION 3
How do people apply minimum viable localization (MVL) across the market?
CONCLUSION
QUESTION 3
QUESTION 1
INTRODUCTION
DATA AND METHODOLOGY
Why Localization MattersQuestion 3: How do people apply minimum viable localization (MVL) across the market?
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When we were collecting our data, we didn’t just look at what languages were
available for the content of the app (i.e. the supported languages). We also looked
at what languages were used for the metadata.
We wanted to find out what ratio of the apps had utilized a minimum viable
localization strategy.
We took all the apps in our dataset that had multiple languages associated with
them, and split those into two categories:
Group 1: Any app that had both the app content and the metadata translated into
the same languages.
Group 2: Any app that had localized the metadata into more languages than the
content—meaning that the metadata might, be in Spanish, Russian, and English,
even though the app is only available in Russian and English.
Why Localization MattersQuestion 3: How do people apply minimum viable localization (MVL) across the market?
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Group 2 contains the apps that demonstrated MVL, since they tested the market
first using localized App Store Descriptions while the app itself remained in a non-
localized language.
While Group 2 only made up about 3.6% of our total dataset, it was just about
40%—or almost half—of all the localized samples.
A third group also emerged: these were apps that localized the app content but not
the metadata. So, while the app might be in Japanese and Korean, the metadata
was only in Korean. This category made up 15% of the apps from the sample.
When apps are localized, minimum viable localization (MVL) is a common choice.
FINAL CONCLUSION
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Why Localization MattersConclusion
Conclusion
QUESTION 3
QUESTION 1
QUESTION 2
INTRODUCTION
DATA AND METHODOLOGY
Why Localization MattersConclusion
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Here is a summary of our original research questions and findings:
1. Do localized apps have a higher probabilityof getting featured?Yes, there is a strong, positive, dependent relationship between an app having
been localized and being featured. If you localize, chances are much higher than
you will be featured.
2. What types of apps are usually localized?Are there any patterns?We don’t have enough data to compute a fully accurate localization rate. However,
we can say that the rate of localization does differ across categories. On average,
certain categories of apps localize far more than apps in other categories.
3. How do people apply minimum viablelocalization (MVL) across the market?When apps are localized, minimum viable localization (MVL) is a common choice.
But, as with any interesting question, the answers just inspire more questions.
With our dataset and methodology now established, we are ready to dive into more
questions and, in doing so, begin building a clearer picture of localization’s real
impact on your business.
If you’re interested in keeping up-to-date with our future research, let us know at
[email protected] and you’ll be the first to receive our updates.
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Why Localization MattersData and Methodology
QUESTION 3
QUESTION 1
QUESTION 2
INTRODUCTION
CONCLUSION
Data and Methodology
Why Localization MattersData and Methodology
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We began by limiting our scope, looking only at apps in the Apple App Store that
launched during 2014 or 2015. There are millions of apps out there—over 2,000,000
in Apple’s store alone—and we wanted a smaller dataset so we could really dig in.
We realized we needed to use a sample size of 1,500 apps. (If you’re interested in
how we got to this, drop us a line and we’ll walk you through the stats.)
But which ones? For our purposes, the best way to select the 1,500 apps was to
use a stratified random sampling method. In this method, you divide your data into
categories, and then choose a certain number of samples within each category,
based on how large that category is. This method ensures that each category in the
sample has the same representation as it would in the whole set; if you were to look
at a pie chart of each, they should be exactly the same.
And, luckily for us, Apple already divides its apps into categories. Check.
Statista’s “Most Popular Apple App Store Categories” survey, published in June
2016, provides a detailed distribution of the app categories currently on the iOS app
market. We used this survey to determine what percentage of samples should come
from each category, so our dataset would be representative of the app store as a
whole.
Why Localization MattersData and Methodology
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Why Localization MattersData and Methodology
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We ended up with a list that looked like this:
47
16
95
18
44
34
42
34
30
36
75
153
1 38
34
33
1 30
3 1
350
41
42
62
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BOOKS
CATALOGS
ENTERTAINMENT
NAVIGATION
HEALTH & FITNESS
REFERENCE
FOOD & DRINK
PHOTO & VIDEO
MEDICAL
SPORTS
UTILITIES
BUSINESS
EDUCATION
FINANCE
NEWS
LIFESTYLE
SOCIAL NETWORKING
GAMES
PRODUCTIVITY
MUSIC
TRAVEL
WEATHER
Why Localization MattersData and Methodology
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Now, we just needed to find the apps themselves. We built our dataset using three
main sources.
1. First, we went to the Explorer platform, powered by appFigures. W powered by
appFigures. We used this to find and filter a group of apps, all released in 2014 and
2015, and collected a number of data points for each.
2. We then went to App Annie to obtain the necessary information about these
apps’ feature histories. We collected data on the number of times each app had been
featured (both in its category, and on the homepage), as well as in which countries’
app stores.
3. The last stop was iTunes, where we manually checked the API queries in order to
access metadata-related information. This allowed us to determine the languages
into which the metadata for each app had been translated.
And then, of course, came the data cleaning. We manually recoded feature- and lan-
guage-related data so that they could be main indicators in the analysis; apps that
had been featured on the homepage were coded as “featured apps,” and apps with
localization history were coded as “localized apps.” We also estimated the MVL for
each app, based on the country of origin, the languages used, and the national app
stores in which it appeared.
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