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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

content@oneskyapp.com 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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