WHY LOCALIZATION MATTERS:

Transcription

1 WHY LOCALIZATION MATTERS: A Look at the Statistics Behind App Store Features A PUBLICATION OF 1

2 WHAT S INSIDE? INTRODUCTION: Why we re doing this 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 DATA AND METHODOLOGY 2

3 Introduction: Why we re Doing This INTRODUCTION Why We re Doing This QUESTION 1 QUESTION 2 QUESTION 3 CONCLUSION DATA AND METHODOLOGY 3

4 Introduction: Why we re doing this 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. 4

5 Introduction: Why we re doing this Here are 3 questions that are asked most frequently by our users, to estimate the ROI of localization for their businesses: 01 DO LOCALIZED APPS HAVE A HIGHER PROBABILITY OF GETTING FEATURED? WHAT TYPES OF APPS ARE USUALLY LOCALIZED? ARE THERE ANY PATTERNS? HOW DO PEOPLE APPLY MINIMUM VIABLE LOCALIZATION (MVL) ACROSS THE MARKET? 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 5

6 Question 1: What is the impact of localization in getting featured in the App Store? INTRODUCTION QUESTION 1 What is the impact of localization in getting featured in the App Store? QUESTION 2 QUESTION 3 CONCLUSION DATA AND METHODOLOGY 6

7 Question 1: What is the impact of localization in getting featured in the App Store? 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. Has the app been localized? FALSE (Unlocalized) TRUE (Localized) 83.77% 16.23% Has the app been featured? FALSE (Unfeatured) TRUE (Featured) 98.72% 1.28% 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 featured in (1) the localized subgroup or (2) the unlocalized subgroup? Localized subgroup FALSE (Unfeatured) TRUE (Featured) 95.44% 4.56% Unlocalized subgroup FALSE (Unfeatured) TRUE (Featured) 99.36% 0.64% 7

8 Question 1: What is the impact of localization in getting featured in the App Store? 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. PRELIMINARY CONCLUSION #1 Localization is associated with being featured on the homepage. 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. 8

9 Question 1: What is the impact of localization in getting featured in the App Store? Has the app been localized in (1) the featured subgroup or (2) the unfeatured subgroup? Featured subgroup FALSE (Unlocalized) TRUE (Localized) 42.11% 57.89% Unlocalized subgroup FALSE (Unlocalized) TRUE (Localized) 84.31% 15.69% 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. PRELIMINARY CONCLUSION #2 There is a positive relationship between the likelihood of localizing and being featured. 9

10 Question 1: What is the impact of localization in getting featured in the App Store? 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 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. 10

11 Question 1: What is the impact of localization in getting featured in the App Store? 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 Therefore, we can reject the null hypothesis and say the two variables localization and being featured are dependent on each other. 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). FINAL CONCLUSION 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. 11

12 Question 2: What are the types of apps more likely to be localized? INTRODUCTION QUESTION 1 QUESTION 2 What are the types of apps more likely to be localized? QUESTION 3 CONCLUSION DATA AND METHODOLOGY 12

13 Question 2: What are the types of apps more likely to be localized? 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.) 13

14 Question 2: What are the types of apps more likely to be localized? 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. 14

15 Question 2: What are the types of apps more likely to be localized? 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. 15

16 Question 2: What are the types of apps more likely to be localized? 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. FINAL CONCLUSION 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.) 16

17 Why Localization Matters Question 3: How do people Question apply minimum 3: How do viable people localization apply minimum (MVL) across viable the localization market? (MVL) across the market? INTRODUCTION QUESTION 1 QUESTION 3 QUESTION 3 How do people apply minimum viable localization (MVL) across the market? CONCLUSION DATA AND METHODOLOGY 17

18 Question 3: How do people apply minimum viable localization (MVL) across the market? 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. 18

19 Question 3: How do people apply minimum viable localization (MVL) across the market? 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 nonlocalized 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. FINAL CONCLUSION When apps are localized, minimum viable localization (MVL) is a common choice. 19

20 Conclusion INTRODUCTION QUESTION 1 QUESTION 2 QUESTION 3 Conclusion DATA AND METHODOLOGY 20

21 Conclusion Here is a summary of our original research questions and findings: 1. Do localized apps have a higher probability of 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 viable localization (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. 21

22 Data and Methodology INTRODUCTION QUESTION 1 QUESTION 2 QUESTION 3 CONCLUSION Data and Methodology 23

23 Data and Methodology We began by limiting our scope, looking only at apps in the Apple App Store that launched during 2014 or 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. 24

24 Data and Methodology 25

25 Data and Methodology We ended up with a list that looked like this: BOOKS 47 BUSINESS 153 CATALOGS 16 EDUCATION 138 ENTERTAINMENT 95 FINANCE 34 FOOD & DRINK 42 GAMES 350 HEALTH & FITNESS 44 LIFESTYLE 130 MEDICAL 30 MUSIC 42 NAVIGATION 18 NEWS 33 PHOTO & VIDEO 34 PRODUCTIVITY 41 REFERENCE 34 SOCIAL NETWORKING 31 SPORTS 36 TRAVEL 62 UTILITIES 75 WEATHER 16 26

26 Data and Methodology 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 language-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. 27

27 FREE ASSESSMENT Our Localization Experts will evaluate your product and show you how to win over users around the world. Talk to us. READ MORE Want to learn more and be updated with exciting news and insights about app localization? Subscribe to our Newsletter EXPLORE THE PLATFORM Explore the OneSky s platform to see how it really works and fit your product. Sign up for free A publication of