Analysis of Profitable App profile: App Store and Google Play Market

The project scope is aimed to identify mobile app profiles that are profitable for the App Store and Google Play markets. We’re working as a team of data analysts for a reknowned company that builds Android and iOS mobile apps, and our job is to enable our developer team to make data-driven decisions with respect to the kind of apps they build.

At our company, we only build apps that are free to download and install, and our main source of revenue consists of in-app ads. This means that our revenue for any given app is mostly influenced by the number of users that use our app. Our goal for this project is to analyze data to help our developers understand what kinds of apps are likely to attract more users.

As of September 2018, there were approximately 2 million iOS apps available on the App Store, and 2.1 million Android apps on Google Play.

Collecting data for over four million apps requires a significant amount of time and money, so we’ll try to analyze a sample of the data instead. To avoid spending resources on collecting new data ourselves, we should first try to see whether we can find any relevant existing data at no cost. Luckily, these are two data sets that seem suitable for our goals:

• A data set containing data about approximately ten thousand Android apps from Google Play — the data was collected in August 2018.
• A data set containing data about approximately seven thousand iOS apps from the App Store — the data was collected in July 2017.

Let’s start by opening the two data sets and then continue with exploring the data.

from csv import reader

### The Google Play data set ###
opened_file = open('googleplaystore.csv', encoding='utf8')
read_file = reader(opened_file)
android = list(read_file)
android_header = android[0]
android = android[1:]

### The App Store data set ###
opened_file = open('AppleStore.csv', encoding='utf8')
read_file = reader(opened_file)
ios = list(read_file)
ios_header = ios[0]
ios = ios[1:] 

To make it easier to explore the two data sets, we’ll first write a function named explore_data() that we can use repeatedly to explore rows in a more readable way. We’ll also add an option for our function to show the number of rows and columns for any data set.

def explore_data(dataset, start, end, rows_and_column = False):
    dataset_slice = dataset[start:end]
    for row in dataset_slice:
        print(row)
        print('\n') # adds a new empty line between rows.
    if rows_and_column:
        print('number of rows:', len(dataset))
        print('number of columns:', len(dataset[0]))
print(android_header)
print('\n')
explore_data(android, 0, 3, True) 

['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']


['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']


['Coloring book moana', 'ART_AND_DESIGN', '3.9', '967', '14M', '500,000+', 'Free', '0', 'Everyone', 'Art & Design;Pretend Play', 'January 15, 2018', '2.0.0', '4.0.3 and up']


['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']


number of rows: 10841
number of columns: 13

We see that the Google Play data set has 10841 apps and 13 columns. At a quick glance, the columns that might be useful for the purpose of our analysis are ‘App’, ‘Category’, ‘Reviews’, ‘Installs’, ‘Type’, ‘Price’ and ‘Genres’.

Now let’s take a look at the App Store data set.

print(ios_header)
print('\n')
explore_data(ios, 0, 3, True) 

['id', 'track_name', 'size_bytes', 'currency', 'price', 'rating_count_tot', 'rating_count_ver', 'user_rating', 'user_rating_ver', 'ver', 'cont_rating', 'prime_genre', 'sup_devices.num', 'ipadSc_urls.num', 'lang.num', 'vpp_lic']


['281656475', 'PAC-MAN Premium', '100788224', 'USD', '3.99', '21292', '26', '4', '4.5', '6.3.5', '4+', 'Games', '38', '5', '10', '1']


['281796108', 'Evernote - stay organized', '158578688', 'USD', '0', '161065', '26', '4', '3.5', '8.2.2', '4+', 'Productivity', '37', '5', '23', '1']


['281940292', 'WeatherBug - Local Weather, Radar, Maps, Alerts', '100524032', 'USD', '0', '188583', '2822', '3.5', '4.5', '5.0.0', '4+', 'Weather', '37', '5', '3', '1']


number of rows: 7197
number of columns: 16

We have 7197 ios apps and 16 columns in this data set. The columns that seems interesting are: ‘track_name’, ‘currency’, ‘price’, ‘rating_count_tot’, ‘rating_count_ver’, and ‘prime_genre’. Not all column names are self-explanatory in this case, but details about each column can be found in the data set documentation.

Deleting Wrong Data

The Google Play data set has a dedicated discussion section, and we can see that one of the discussions outlines an error for row 10472. Let’s print this row and compare it against the header and another row that is correct.

print(android[10472]) # Uncorrect row
print('\n')
print(android_header) # header
print('\n')
print(android[0]) # corrct row

['Life Made WI-Fi Touchscreen Photo Frame', '1.9', '19', '3.0M', '1,000+', 'Free', '0', 'Everyone', '', 'February 11, 2018', '1.0.19', '4.0 and up']


['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver', 'Android Ver']


['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']

The row 10472 corresponds to the app Life Made WI-Fi Touchscreen Photo Frame, and we can see that the rating is 19. This is clearly off because the maximum rating for a Google Play app is 5. As a consequence, we’ll delete this row.

print(len(android))
del(android[10472]) # Don't run this more than once
print(len(android))

10841
10840

Removing Duplicate Entries

Part one

If we explore the Google Play data set long enough, we’ll find that some apps have more than one entry. For instance, the application Instagram has four entries:

for app in android:
    name = app[0]
    if name == 'Instagram':
        print(app)    

['Instagram', 'SOCIAL', '4.5', '66577313', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66577446', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66577313', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']
['Instagram', 'SOCIAL', '4.5', '66509917', 'Varies with device', '1,000,000,000+', 'Free', '0', 'Teen', 'Social', 'July 31, 2018', 'Varies with device', 'Varies with device']

In total, there are 1,181 cases where an app occurs more than once:

duplicate_apps = []
unique_apps = []

for app in android:
    name = app[0]
    if name in unique_apps:
        duplicate_apps.append(name)
    else:
        unique_apps.append(name)
print('Number of Duplicate Apps:', len(duplicate_apps))
print('\n')
print('Examples of Duplicate Apps:', duplicate_apps[:15])

Number of Duplicate Apps: 1181


Examples of Duplicate Apps: ['Quick PDF Scanner + OCR FREE', 'Box', 'Google My Business', 'ZOOM Cloud Meetings', 'join.me - Simple Meetings', 'Box', 'Zenefits', 'Google Ads', 'Google My Business', 'Slack', 'FreshBooks Classic', 'Insightly CRM', 'QuickBooks Accounting: Invoicing & Expenses', 'HipChat - Chat Built for Teams', 'Xero Accounting Software']

We don’t want to count certain apps more than once when we analyze data, so we need to remove the duplicate entries and keep only one entry per app. One thing we could do is remove the duplicate rows randomly, but we could probably find a better way.

If you examine the rows we printed two cells above for the Instagram app, the main difference happens on the fourth position of each row, which corresponds to the number of reviews. The different numbers show that the data was collected at different times. We can use this to build a criterion for keeping rows. We won’t remove rows randomly, but rather we’ll keep the rows that have the highest number of reviews because the higher the number of reviews, the more reliable the ratings.

To do that, we will:

• Create a dictionary where each key is a unique app name, and the value is the highest number of reviews of that app
• Use the dictionary to create a new data set, which will have only one entry per app (and we only select the apps with the highest number of reviews)

Part Two

Let’s start building the dictionary.

reviews_max = {}

for app in android:
    name = app[0]
    n_reviews = float(app[3])
    
    if name in reviews_max and reviews_max[name] < n_reviews:
        reviews_max[name] = n_reviews
    
    elif name not in reviews_max:
        reviews_max[name] = n_reviews

In a previous code cell, we found that there are 1,181 cases where an app occurs more than once, so the length of our dictionary (of unique apps) should be equal to the difference between the length of our data set and 1,181.

print('Expected length: ', len(android) - 1181)
print('Actual length: ', len(reviews_max))

Expected length:  9659
Actual length:  9659

Now, let’s use the reviews_max dictionary to remove the duplicates. For the duplicate cases, we’ll only keep the entries with the highest number of reviews. In the code cell below:

• We start by initializing two empty lists, android_clean and already_added.
• We loop through the android data set, and for every iteration:
  • We isolate the name of the app and the number of reviews.
  • We add the current row (app) to the android_clean list, and the app name (name) to the already_added list if:
    • The number of reviews of the current app matches the number of reviews of that app as described in the reviews_max dictionary; and
    • The name of the app is not already in the already_added list. We need to add this supplementary condition to account for those cases where the highest number of reviews of a duplicate app is the same for more than one entry (for example, the Box app has three entries, and the number of reviews is the same). If we just check for reviews_max[name] == n_reviews, we’ll still end up with duplicate entries for some apps.

android_clean = []
already_added = []

for app in android:
    name = app[0]
    n_reviews = float(app[3])
    
    if (reviews_max[name] == n_reviews) and (name not in already_added):
        android_clean.append(app)
        already_added.append(name) # make sure this is inside the if block

Now let’s quickly explore the new data set, and confirm that the number of rows is 9,659.

explore_data(android_clean, 0, 3, True)

['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']


['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']


['Sketch - Draw & Paint', 'ART_AND_DESIGN', '4.5', '215644', '25M', '50,000,000+', 'Free', '0', 'Teen', 'Art & Design', 'June 8, 2018', 'Varies with device', '4.2 and up']


number of rows: 9659
number of columns: 13

We have 9659 rows, just as expected.

Removing Non-English Apps

Part One

If you explore the data sets enough, you’ll notice the names of some of the apps suggest they are not directed toward an English-speaking audience. Below, we see a couple of examples from both data sets:

print(ios[813][1])
print(ios[6731][1])

print(android_clean[4412][0])
print(android_clean[7940][0])

AliExpress Shopping App
Idle Armies
中国語 AQリスニング
لعبة تقدر تربح DZ

We’re not keeping these kind of apps, so we’ll remove them. One way to go about this is to remove each app whose name contains a symbol that is not commonly used in English text — English text usually includes letters from the English alphabet, numbers composed of digits from 0 to 9, punctuation marks (., !, ?, ;, etc.), and other symbols (+, *, /, etc.).

All these characters that are specific to English texts are encoded using the ASCII standard. Each ASCII character has a corresponding number between 0 and 127 associated with it, and we can take advantage of that to build a function that checks an app name and tells us whether it contains non-ASCII characters.

We built this function below, and we use the built-in ord() function to find out the corresponding encoding number of each character.

def is_english(string):
    
    for character in string:
        if ord(character) > 127:
            return False
    
    return True

print(is_english('Instagram'))
print(is_english('爱奇艺PPS -《欢乐颂2》电视剧热播'))

True
False

The function seems to work fine, but some English app names use emojis or other symbols (™, — (em dash), – (en dash), etc.) that fall outside of the ASCII range. Because of this, we’ll remove useful apps if we use the function in its current form.

print(is_english('Docs To Go™ Free Office Suite'))
print(is_english('Instachat 😜'))

print(ord('™'))
print(ord('😜'))

False
False
8482
128540

Part Two

To minimize the impact of data loss, we’ll only remove an app if its name has more than three non-ASCII characters:

def is_english(string):
    non_ascii = 0
    
    for character in string:
        if ord(character) > 127:
            non_ascii += 1
            
        if non_ascii > 3:
            return False
        else:
            return True
print(is_english('Docs To Go™ Free Office Suite'))
print(is_english('Instachat 😜'))                

True
True

The function is still not perfect, and very few non-English apps might get past our filter, but this seems good enough at this point in our analysis — we shouldn’t spend too much time on optimization at this point.

Below, we use the is_english() function to filter out the non-English apps for both data sets:

android_english = []
ios_english = []

for app in android_clean:
    name = app[0]
    if is_english(name):
        android_english.append(app)
        
for app in ios:
    name = app[1]
    if is_english(name):
        ios_english.append(app)
        
explore_data(android_english, 0, 3, True)
print('\n')
explore_data(ios_english, 0, 3, True)

['Photo Editor & Candy Camera & Grid & ScrapBook', 'ART_AND_DESIGN', '4.1', '159', '19M', '10,000+', 'Free', '0', 'Everyone', 'Art & Design', 'January 7, 2018', '1.0.0', '4.0.3 and up']


['U Launcher Lite – FREE Live Cool Themes, Hide Apps', 'ART_AND_DESIGN', '4.7', '87510', '8.7M', '5,000,000+', 'Free', '0', 'Everyone', 'Art & Design', 'August 1, 2018', '1.2.4', '4.0.3 and up']


['Sketch - Draw & Paint', 'ART_AND_DESIGN', '4.5', '215644', '25M', '50,000,000+', 'Free', '0', 'Teen', 'Art & Design', 'June 8, 2018', 'Varies with device', '4.2 and up']


number of rows: 9659
number of columns: 13


['281656475', 'PAC-MAN Premium', '100788224', 'USD', '3.99', '21292', '26', '4', '4.5', '6.3.5', '4+', 'Games', '38', '5', '10', '1']


['281796108', 'Evernote - stay organized', '158578688', 'USD', '0', '161065', '26', '4', '3.5', '8.2.2', '4+', 'Productivity', '37', '5', '23', '1']


['281940292', 'WeatherBug - Local Weather, Radar, Maps, Alerts', '100524032', 'USD', '0', '188583', '2822', '3.5', '4.5', '5.0.0', '4+', 'Weather', '37', '5', '3', '1']


number of rows: 7197
number of columns: 16

We can see that we are left with 9659 Android apps and 7197 iOS apps.

Isolating the Free Apps

As we mentioned in the introduction, we only build apps that are free to download and install, and our main source of revenue consists of in-app ads. Our data sets contain both free and non-free apps, and we’ll need to isolate only the free apps for our analysis. Below, we isolate the free apps for both our data sets:

android_final = []
ios_final = []

for app in android_english:
    price = app[7]
    if price == '0':
        android_final.append(app)
        
for app in ios_english:
    price = app[4]
    if price == '0':
        ios_final.append(app)
        
print(len(android_final))
print(len(ios_final)) 

8905
4056

We’re left with 8905 Android apps and 4056 iOS apps, which should be enough for our analysis.

Most Common Apps by Genre

Part One

As we mentioned in the introduction, our aim is to determine the kinds of apps that are likely to attract more users because our revenue is highly influenced by the number of people using our apps.

To minimize risks and overhead, our validation strategy for an app idea is comprised of three steps:

1. Build a minimal Android version of the app, and add it to Google Play.
2. If the app has a good response from users, we then develop it further.
3. If the app is profitable after six months, we also build an iOS version of the app and add it to the App Store.

Because our end goal is to add the app on both the App Store and Google Play, we need to find app profiles that are successful on both markets. For instance, a profile that might work well for both markets might be a productivity app that makes use of gamification.

Let’s begin the analysis by getting a sense of the most common genres for each market. For this, we’ll build a frequency table for the prime_genre column of the App Store data set, and the Genres and Category columns of the Google Play data set.

Part Two

We’ll build two functions we can use to analyze the frequency tables:

• One function to generate frequency tables that show percentages
• Another function that we can use to display the percentages in a descending order

def freq_table(dataset, index):
    table = {}
    total = 0
    
    for row in dataset:
        total += 1
        value = row[index]
        if value in table:
            table[value] += 1
        else:
            table[value] = 1
    table_percentages = {}
    for key in table:
        percentage = (table[key] / total) * 100
        table_percentages[key] = percentage
    return table_percentages

def display_table(dataset, index):
    table = freq_table(dataset, index)
    table_display = []
    for key in table:
        key_val_as_tuple = (table[key], key)
        table_display.append(key_val_as_tuple)
    
    table_sorted = sorted(table_display, reverse = True)
    for entry in table_sorted:
        print(entry[1], ':', entry[0])

Part Three

We start by examining the frequency table for the prime_genre column of the App Store data set.

display_table(ios_final,-5)

Games : 55.64595660749507
Entertainment : 8.234714003944774
Photo & Video : 4.117357001972387
Social Networking : 3.5256410256410255
Education : 3.2544378698224854
Shopping : 2.983234714003945
Utilities : 2.687376725838264
Lifestyle : 2.3175542406311638
Finance : 2.0710059171597637
Sports : 1.947731755424063
Health & Fitness : 1.8737672583826428
Music : 1.6518737672583828
Book : 1.6272189349112427
Productivity : 1.5285996055226825
News : 1.4299802761341223
Travel : 1.3806706114398422
Food & Drink : 1.0601577909270217
Weather : 0.7642998027613412
Reference : 0.4930966469428008
Navigation : 0.4930966469428008
Business : 0.4930966469428008
Catalogs : 0.22189349112426035
Medical : 0.19723865877712032

We can see that among the free English apps, more than a half (55.64%) are games. Entertainment apps are slightly above 8%, followed by photo and video apps, which are 4.11%. Only 3.25% of the apps are designed for education, whereas social networking apps which amount for 3.52% of the apps in our data set. The general impression is that App Store (at least the part containing free English apps) is dominated by apps that are designed for fun (games, entertainment, photo and video, social networking, sports, music, etc.), while apps with practical purposes (education, shopping, utilities, productivity, lifestyle, etc.) are more rare. However, the fact that fun apps are the most numerous doesn’t also imply that they also have the greatest number of users — the demand might not be the same as the offer. Let’s continue by examining the Genres and Category columns of the Google Play data set (two columns which seem to be related).

display_table(android_final, 1) # Category

FAMILY : 18.97810218978102
GAME : 9.70241437394722
TOOLS : 8.433464345873105
BUSINESS : 4.581695676586187
LIFESTYLE : 3.9303761931499155
PRODUCTIVITY : 3.885457608085345
FINANCE : 3.6833239752947784
MEDICAL : 3.5148792813026386
SPORTS : 3.3801235261089273
PERSONALIZATION : 3.312745648512072
COMMUNICATION : 3.2341381246490735
HEALTH_AND_FITNESS : 3.065693430656934
PHOTOGRAPHY : 2.9421673217293653
NEWS_AND_MAGAZINES : 2.829870859067939
SOCIAL : 2.6501965188096577
TRAVEL_AND_LOCAL : 2.3245367770915215
SHOPPING : 2.2459292532285233
BOOKS_AND_REFERENCE : 2.1785513756316677
DATING : 1.8528916339135317
VIDEO_PLAYERS : 1.7967434025828188
MAPS_AND_NAVIGATION : 1.4149354295339696
FOOD_AND_DRINK : 1.235261089275688
EDUCATION : 1.167883211678832
ENTERTAINMENT : 0.9545199326221224
LIBRARIES_AND_DEMO : 0.9320606400898372
AUTO_AND_VEHICLES : 0.9208309938236946
HOUSE_AND_HOME : 0.8197641774284109
WEATHER : 0.7973048848961257
EVENTS : 0.7074677147669848
PARENTING : 0.6513194834362718
ART_AND_DESIGN : 0.6513194834362718
COMICS : 0.6288601909039866
BEAUTY : 0.5951712521055587

The landscape seems significantly different on Google Play: there are not that many apps designed for fun, and it seems that a good number of apps are designed for practical purposes (family, tools, business, lifestyle, productivity, etc.). However, if we investigate this further, we can see that the family category (which accounts for almost 19% of the apps) means mostly games for kids. Even so, practical apps seem to have a better representation on Google Play compared to App Store. This picture is also confirmed by the frequency table we see for the Genres column:

display_table(android_final, -4)

Tools : 8.422234699606962
Entertainment : 6.086468276249298
Education : 5.390230207748456
Business : 4.581695676586187
Lifestyle : 3.9191465468837734
Productivity : 3.885457608085345
Finance : 3.6833239752947784
Medical : 3.5148792813026386
Sports : 3.4475014037057834
Personalization : 3.312745648512072
Communication : 3.2341381246490735
Action : 3.0881527231892196
Health & Fitness : 3.065693430656934
Photography : 2.9421673217293653
News & Magazines : 2.829870859067939
Social : 2.6501965188096577
Travel & Local : 2.313307130825379
Shopping : 2.2459292532285233
Books & Reference : 2.1785513756316677
Simulation : 2.0662549129702414
Dating : 1.8528916339135317
Arcade : 1.8416619876473892
Video Players & Editors : 1.7742841100505335
Casual : 1.7518248175182483
Maps & Navigation : 1.4149354295339696
Food & Drink : 1.235261089275688
Puzzle : 1.1229646266142617
Racing : 0.9882088714205502
Role Playing : 0.9320606400898372
Libraries & Demo : 0.9320606400898372
Strategy : 0.9208309938236946
Auto & Vehicles : 0.9208309938236946
House & Home : 0.8197641774284109
Weather : 0.7973048848961257
Events : 0.7074677147669848
Adventure : 0.6850084222346996
Comics : 0.617630544637844
Art & Design : 0.6064008983717013
Beauty : 0.5951712521055587
Parenting : 0.4941044357102751
Card : 0.4491858506457047
Trivia : 0.4267265581134195
Casino : 0.4267265581134195
Educational;Education : 0.39303761931499154
Board : 0.38180797304884895
Educational : 0.3705783267827063
Education;Education : 0.3481190342504211
Word : 0.2582818641212802
Casual;Pretend Play : 0.23582257158899494
Music : 0.2021336327905671
Racing;Action & Adventure : 0.16844469399213924
Puzzle;Brain Games : 0.16844469399213924
Entertainment;Music & Video : 0.16844469399213924
Casual;Brain Games : 0.13475575519371139
Casual;Action & Adventure : 0.13475575519371139
Arcade;Action & Adventure : 0.12352610892756878
Action;Action & Adventure : 0.10106681639528355
Educational;Pretend Play : 0.08983717012914093
Simulation;Action & Adventure : 0.07860752386299832
Parenting;Education : 0.07860752386299832
Entertainment;Brain Games : 0.07860752386299832
Board;Brain Games : 0.07860752386299832
Parenting;Music & Video : 0.06737787759685569
Educational;Brain Games : 0.06737787759685569
Casual;Creativity : 0.06737787759685569
Art & Design;Creativity : 0.06737787759685569
Education;Pretend Play : 0.05614823133071309
Role Playing;Pretend Play : 0.044918585064570464
Education;Creativity : 0.044918585064570464
Role Playing;Action & Adventure : 0.033688938798427846
Puzzle;Action & Adventure : 0.033688938798427846
Entertainment;Creativity : 0.033688938798427846
Entertainment;Action & Adventure : 0.033688938798427846
Educational;Creativity : 0.033688938798427846
Educational;Action & Adventure : 0.033688938798427846
Education;Music & Video : 0.033688938798427846
Education;Brain Games : 0.033688938798427846
Education;Action & Adventure : 0.033688938798427846
Adventure;Action & Adventure : 0.033688938798427846
Video Players & Editors;Music & Video : 0.022459292532285232
Sports;Action & Adventure : 0.022459292532285232
Simulation;Pretend Play : 0.022459292532285232
Puzzle;Creativity : 0.022459292532285232
Music;Music & Video : 0.022459292532285232
Entertainment;Pretend Play : 0.022459292532285232
Casual;Education : 0.022459292532285232
Board;Action & Adventure : 0.022459292532285232
Video Players & Editors;Creativity : 0.011229646266142616
Trivia;Education : 0.011229646266142616
Travel & Local;Action & Adventure : 0.011229646266142616
Tools;Education : 0.011229646266142616
Strategy;Education : 0.011229646266142616
Strategy;Creativity : 0.011229646266142616
Strategy;Action & Adventure : 0.011229646266142616
Simulation;Education : 0.011229646266142616
Role Playing;Brain Games : 0.011229646266142616
Racing;Pretend Play : 0.011229646266142616
Puzzle;Education : 0.011229646266142616
Parenting;Brain Games : 0.011229646266142616
Music & Audio;Music & Video : 0.011229646266142616
Lifestyle;Pretend Play : 0.011229646266142616
Lifestyle;Education : 0.011229646266142616
Health & Fitness;Education : 0.011229646266142616
Health & Fitness;Action & Adventure : 0.011229646266142616
Entertainment;Education : 0.011229646266142616
Communication;Creativity : 0.011229646266142616
Comics;Creativity : 0.011229646266142616
Casual;Music & Video : 0.011229646266142616
Card;Action & Adventure : 0.011229646266142616
Books & Reference;Education : 0.011229646266142616
Art & Design;Pretend Play : 0.011229646266142616
Art & Design;Action & Adventure : 0.011229646266142616
Arcade;Pretend Play : 0.011229646266142616
Adventure;Education : 0.011229646266142616

The difference between the Genres and the Category columns is not crystal clear, but one thing we can notice is that the Genres column is much more granular (it has more categories). We’re only looking for the bigger picture at the moment, so we’ll only work with the Category column moving forward. Up to this point, we found that the App Store is dominated by apps designed for fun, while Google Play shows a more balanced landscape of both practical and for-fun apps. Now we’d like to get an idea about the kind of apps that have most users.

Most Popular Apps by Genre on the App Store

One way to find out what genres are the most popular (have the most users) is to calculate the average number of installs for each app genre. For the Google Play data set, we can find this information in the Installs column, but for the App Store data set this information is missing. As a workaround, we’ll take the total number of user ratings as a proxy, which we can find in the rating_count_tot app.

Below, we calculate the average number of user ratings per app genre on the App Store:

genres_ios = freq_table(ios, -5)

for genre in genres_ios:
    total = 0
    len_genre = 0
    for app in ios_final:
        genre_app = app[-5]
        if genre_app == genre:
            n_ratings = float(app[5])
            total += n_ratings
            len_genre += 1
    avg_n_ratings = total / len_genre
    print(genre, ':', avg_n_ratings)

Games : 18924.68896765618
Productivity : 19053.887096774193
Weather : 47220.93548387097
Shopping : 18746.677685950413
Reference : 67447.9
Finance : 13522.261904761905
Music : 56482.02985074627
Utilities : 14010.100917431193
Travel : 20216.01785714286
Social Networking : 53078.195804195806
Sports : 20128.974683544304
Business : 6367.8
Health & Fitness : 19952.315789473683
Entertainment : 10822.961077844311
Photo & Video : 27249.892215568863
Navigation : 25972.05
Education : 6266.333333333333
Lifestyle : 8978.308510638299
Food & Drink : 20179.093023255813
News : 15892.724137931034
Book : 8498.333333333334
Medical : 459.75
Catalogs : 1779.5555555555557

On average, Reference apps have the highest number of user reviews, but this figure is heavily influenced by Bible and Dictionary, which have close to half a million user reviews together:

for app in ios_final:
    if app[-5] == 'Reference':
        print(app[1], ':', app[5]) # print name and number of ratings

Bible : 985920
Dictionary.com Dictionary & Thesaurus : 200047
Dictionary.com Dictionary & Thesaurus for iPad : 54175
Muslim Pro: Ramadan 2017 Prayer Times, Azan, Quran : 18418
Merriam-Webster Dictionary : 16849
Google Translate : 26786
Night Sky : 12122
WWDC : 762
Jishokun-Japanese English Dictionary & Translator : 0
教えて!goo : 0
VPN Express : 14
彩库宝典-【官方版】 : 0
New Furniture Mods - Pocket Wiki & Game Tools for Minecraft PC Edition : 17588
LUCKY BLOCK MOD ™ for Minecraft PC Edition - The Best Pocket Wiki & Mods Installer Tools : 4693
Guides for Pokémon GO - Pokemon GO News and Cheats : 826
Horror Maps for Minecraft PE - Download The Scariest Maps for Minecraft Pocket Edition (MCPE) Free : 718
City Maps for Minecraft PE - The Best Maps for Minecraft Pocket Edition (MCPE) : 8535
GUNS MODS for Minecraft PC Edition - Mods Tools : 1497
Real Bike Traffic Rider Virtual Reality Glasses : 8
無料で音楽や写真・カメラの裏技アプリ for iPhone7 : 0

However, this niche seems to show some potential. One thing we could do is take another popular book and turn it into an app where we could add different features besides the raw version of the book. This might include daily quotes from the book, an audio version of the book, quizzes about the book, etc. On top of that, we could also embed a dictionary within the app, so users don’t need to exit our app to look up words in an external app.

The same pattern applies to social networking apps, where the average number is heavily influenced by a few giants like Facebook, Pinterest, Skype, etc. Same applies to photo & video apps, where a few big players like Instagram, Snapchat, Youtube heavily influence the average number.

Our aim is to find popular genres, but Reference, social networking or music apps might seem more popular than they really are. The average number of ratings seem to be skewed by very few apps which have hundreds of thousands of user ratings, while the other apps may struggle to get past the 10,000 threshold. We could get a better picture by removing these extremely popular apps for each genre and then rework the averages, but we’ll leave this level of detail for later.

Music apps have 56,482 user ratings on average, but it’s actually the Pandora and Spotify which skew up the average rating:

for app in ios_final:
    if app[-5] == 'Music':
        print(app[1], ':', app[5])

Pandora - Music & Radio : 1126879
Shazam - Discover music, artists, videos & lyrics : 402925
iHeartRadio – Free Music & Radio Stations : 293228
Deezer - Listen to your Favorite Music & Playlists : 4677
Sonos Controller : 48905
NRJ Radio : 38
radio.de - Der Radioplayer : 64
Spotify Music : 878563
SoundCloud - Music & Audio : 135744
Sing Karaoke Songs Unlimited with StarMaker : 26227
SoundHound Song Search & Music Player : 82602
Ringtones for iPhone & Ringtone Maker : 25403
Coach Guitar - Lessons & Easy Tabs For Beginners : 2416
QQ音乐-来这里“发现・音乐” : 745
TuneIn Radio - MLB NBA Audiobooks Podcasts Music : 110420
Magic Piano by Smule : 131695
QQ音乐HD : 224
The Singing Machine Mobile Karaoke App : 130
Bandsintown Concerts : 30845
PetitLyrics : 0
edjing Mix:DJ turntable to remix and scratch music : 13580
Smule Sing! : 119316
Amazon Music : 106235
AutoRap by Smule : 18202
My Mixtapez Music : 26286
Certified Mixtapes - Hip Hop Albums & Mixtapes : 9975
Karaoke - Sing Karaoke, Unlimited Songs! : 28606
Napster - Top Music & Radio : 14268
Musi - Unlimited Music For YouTube : 25193
UE BOOM : 612
Spinrilla - Mixtapes For Free : 15053
Google Play Music : 10118
Piano - Play Keyboard Music Games with Magic Tiles : 1636
Bose SoundTouch : 3687
DatPiff : 2815
Sounds app - Music And Friends : 5126
Smart Music: Streaming Videos and Radio : 17
Free Piano app by Yokee : 13016
Simple Radio - Live AM & FM Radio Stations : 4787
Trebel Music - Unlimited Music Downloader : 2570
TIDAL : 7398
Acapella from PicPlayPost : 2487
Medly - Music Maker : 933
Amazon Alexa : 3018
Music Freedom - Unlimited Free MP3 Music Streaming : 1246
PlayGround • Music At Your Fingertips : 150
Musical Video Maker - Create Music clips lip sync : 320
Free Music Play - Mp3 Streamer & Player : 2496
LiveMixtapes : 555
AmpMe - A Portable Social Party Music Speaker : 1047
NOISE : 355
YouTube Music : 7109
Ringtones for iPhone with Ringtone Maker : 4013
Music Memos : 909
Musicloud - MP3 and FLAC Music Player for Cloud Platforms. : 2211
Bose Connect : 915
Cloud Music Player - Downloader & Playlist Manager : 319
Remixlive - Remix loops with pads : 288
Free Music -  Player & Streamer  for Dropbox, OneDrive & Google Drive : 46
Boom: Best Equalizer & Magical Surround Sound : 1375
MP3 Music Player & Streamer for Clouds : 329
Nicki Minaj: The Empire : 5196
SongFlip - Free Music Streamer : 5004
Blocs Wave - Make & Record Music : 158
Music and Chill : 135
Free Music - MP3 Streamer & Playlist Manager Pro : 13443
BOSS Tuner : 13

This idea seems to fit well with the fact that the App Store is dominated by practical apps. This suggests the market might be a bit saturated with pratical apps, which means a for-fun app might have more of a chance to stand out among the huge number of apps on the App Store.

Other genres that seem popular include weather, games, sports,food & drink or Finance. The games genre seem to overlap a bit with the app idea we described above, but the other genres don’t seem too interesting to us:

• Weather apps — people generally don’t spend too much time in-app, and the chances of making profit from in-app adds are low. Also, getting reliable live weather data may require us to connect our apps to non-free APIs.
• Food and drink — examples here include Starbucks, Dunkin’ Donuts, McDonald’s, etc. So making a popular food and drink app requires actual cooking and a delivery service, which is outside the scope of our company.
• Finance apps — these apps involve banking, paying bills, money transfer, etc. Building a finance app requires domain knowledge, and we don’t want to hire a finance expert just to build an app.

Now let’s analyze the Google Play market a bit.

Most Popular Apps by Genre on Google Play

For the Google Play market, we actually have data about the number of installs, so we should be able to get a clearer picture about genre popularity. However, the install numbers don’t seem precise enough — we can see that most values are open-ended (100+, 1,000+, 5,000+, etc.):

display_table(android_final, 5) # the Installs columns

1,000,000+ : 15.687815833801237
100,000+ : 11.577765300393038
10,000,000+ : 10.499719258843346
10,000+ : 10.252667040988209
1,000+ : 8.422234699606962
100+ : 6.917462099943853
5,000,000+ : 6.816395283548568
500,000+ : 5.53621560920831
50,000+ : 4.817518248175182
5,000+ : 4.525547445255475
10+ : 3.537338573834924
500+ : 3.2341381246490735
50,000,000+ : 2.2908478382930935
100,000,000+ : 2.1224031443009546
50+ : 1.9090398652442448
5+ : 0.7860752386299831
1+ : 0.5165637282425604
500,000,000+ : 0.26951151038742277
1,000,000,000+ : 0.22459292532285235
0+ : 0.044918585064570464
0 : 0.011229646266142616

One problem with this data is that is not precise. For instance, we don’t know whether an app with 100,000+ installs has 100,000 installs, 200,000, or 350,000. However, we don’t need very precise data for our purposes — we only want to get an idea which app genres attract the most users, and we don’t need perfect precision with respect to the number of users.

We’re going to leave the numbers as they are, which means that we’ll consider that an app with 100,000+ installs has 100,000 installs, and an app with 1,000,000+ installs has 1,000,000 installs, and so on.

To perform computations, however, we’ll need to convert each install number to float — this means that we need to remove the commas and the plus characters, otherwise the conversion will fail and raise an error. We’ll do this directly in the loop below, where we also compute the average number of installs for each genre (category).

categories_android = freq_table(android_final, 1)
for category in categories_android:
    total = 0
    len_category = 0
    for app in android_final:
        category_app = app[1]
        if category_app == category:
            n_installs = app[5]
            n_installs = n_installs.replace(',', '')
            n_installs = n_installs.replace('+', '')
            total += float(n_installs)
            len_category += 1
    avg_n_installs = total / len_category
    print(category, ':', avg_n_installs)         

ART_AND_DESIGN : 1952105.1724137932
AUTO_AND_VEHICLES : 647317.8170731707
BEAUTY : 513151.88679245283
BOOKS_AND_REFERENCE : 8587351.855670104
BUSINESS : 1708215.906862745
COMICS : 803234.8214285715
COMMUNICATION : 38322625.697916664
DATING : 854028.8303030303
EDUCATION : 1825480.7692307692
ENTERTAINMENT : 11640705.88235294
EVENTS : 253542.22222222222
FINANCE : 1387692.475609756
FOOD_AND_DRINK : 1924897.7363636363
HEALTH_AND_FITNESS : 4188821.9853479853
HOUSE_AND_HOME : 1331540.5616438356
LIBRARIES_AND_DEMO : 638503.734939759
LIFESTYLE : 1436126.94
GAME : 15551995.891203703
FAMILY : 3668870.823076923
MEDICAL : 120550.61980830671
SOCIAL : 23253652.127118643
SHOPPING : 7001693.425
PHOTOGRAPHY : 17772018.759541985
SPORTS : 3638640.1428571427
TRAVEL_AND_LOCAL : 13984077.710144928
TOOLS : 10787009.952063914
PERSONALIZATION : 5183850.806779661
PRODUCTIVITY : 16738957.554913295
PARENTING : 542603.6206896552
WEATHER : 5074486.197183099
VIDEO_PLAYERS : 24573948.25
NEWS_AND_MAGAZINES : 9401635.952380951
MAPS_AND_NAVIGATION : 3993339.603174603

On average, communication apps have the most installs: 38,322,625. This number is heavily skewed up by a few apps that have over one billion installs (WhatsApp, Facebook Messenger, Skype, Google Chrome, Gmail, and Hangouts), and a few others with over 100 and 500 million installs:

for app in android_final:
    if app[1] == 'COMMUNICATION' and (app[5] == '1,000,000,000+'
                                      or app[5] == '500,000,000+'
                                      or app[5] == '100,000,000+'):
        print(app[0], ':', app[5])

WhatsApp Messenger : 1,000,000,000+
imo beta free calls and text : 100,000,000+
Android Messages : 100,000,000+
Google Duo - High Quality Video Calls : 500,000,000+
Messenger – Text and Video Chat for Free : 1,000,000,000+
imo free video calls and chat : 500,000,000+
Skype - free IM & video calls : 1,000,000,000+
Who : 100,000,000+
GO SMS Pro - Messenger, Free Themes, Emoji : 100,000,000+
LINE: Free Calls & Messages : 500,000,000+
Google Chrome: Fast & Secure : 1,000,000,000+
Firefox Browser fast & private : 100,000,000+
UC Browser - Fast Download Private & Secure : 500,000,000+
Gmail : 1,000,000,000+
Hangouts : 1,000,000,000+
Messenger Lite: Free Calls & Messages : 100,000,000+
Kik : 100,000,000+
KakaoTalk: Free Calls & Text : 100,000,000+
Opera Mini - fast web browser : 100,000,000+
Opera Browser: Fast and Secure : 100,000,000+
Telegram : 100,000,000+
Truecaller: Caller ID, SMS spam blocking & Dialer : 100,000,000+
UC Browser Mini -Tiny Fast Private & Secure : 100,000,000+
Viber Messenger : 500,000,000+
WeChat : 100,000,000+
Yahoo Mail – Stay Organized : 100,000,000+
BBM - Free Calls & Messages : 100,000,000+

If we removed all the communication apps that have over 100 million installs, the average would be reduced roughly ten times:

under_100m = []

for app in android_final:
    n_installs = app[5]
    n_installs = n_installs.replace(',', '')
    n_installs = n_installs.replace('+', '')
    if (app[1] == 'COMMUNICATION') and (float(n_installs) < 100000000):
        under_100m.append(float(n_installs))

sum(under_100m) / len(under_100m)

3589717.245210728

We see the same pattern for the video players category, which is the winner with 55,088,19 installs. The market is dominated by apps like Youtube, Google Play Movies & TV, or MX Player. The pattern is repeated for social apps (where we have giants like Facebook, Instagram, Google+, etc.), photography apps (Google Photos and other popular photo editors), or productivity apps (Microsoft Word, Dropbox, Google Calendar, Evernote, etc.).

Again, the main concern is that these app genres might seem more popular than they really are. Moreover, these niches seem to be dominated by a few giants who are hard to compete against.

The game genre seems pretty popular, but previously we found out this part of the market seems a bit saturated, so we’d like to come up with a different app recommendation if possible.

The books and reference genre looks fairly popular as well, with an average number of installs of 8,767,811. It’s interesting to explore this in more depth, since we found this genre has some potential to work well on the App Store, and our aim is to recommend an app genre that shows potential for being profitable on both the App Store and Google Play.

Let’s take a look at some of the apps from this genre and their number of installs:

for app in android_final:
    if app[1] == 'BOOKS_AND_REFERENCE':
        print(app[0], ':', app[5])

E-Book Read - Read Book for free : 50,000+
Download free book with green book : 100,000+
Wikipedia : 10,000,000+
Cool Reader : 10,000,000+
Free Panda Radio Music : 100,000+
Book store : 1,000,000+
FBReader: Favorite Book Reader : 10,000,000+
English Grammar Complete Handbook : 500,000+
Free Books - Spirit Fanfiction and Stories : 1,000,000+
Google Play Books : 1,000,000,000+
AlReader -any text book reader : 5,000,000+
Offline English Dictionary : 100,000+
Offline: English to Tagalog Dictionary : 500,000+
FamilySearch Tree : 1,000,000+
Cloud of Books : 1,000,000+
Recipes of Prophetic Medicine for free : 500,000+
ReadEra – free ebook reader : 1,000,000+
Anonymous caller detection : 10,000+
Ebook Reader : 5,000,000+
Litnet - E-books : 100,000+
Read books online : 5,000,000+
English to Urdu Dictionary : 500,000+
eBoox: book reader fb2 epub zip : 1,000,000+
English Persian Dictionary : 500,000+
Flybook : 500,000+
All Maths Formulas : 1,000,000+
Ancestry : 5,000,000+
HTC Help : 10,000,000+
English translation from Bengali : 100,000+
Pdf Book Download - Read Pdf Book : 100,000+
Free Book Reader : 100,000+
eBoox new: Reader for fb2 epub zip books : 50,000+
Only 30 days in English, the guideline is guaranteed : 500,000+
Moon+ Reader : 10,000,000+
SH-02J Owner's Manual (Android 8.0) : 50,000+
English-Myanmar Dictionary : 1,000,000+
Golden Dictionary (EN-AR) : 1,000,000+
All Language Translator Free : 1,000,000+
Azpen eReader : 500,000+
URBANO V 02 instruction manual : 100,000+
Bible : 100,000,000+
C Programs and Reference : 50,000+
C Offline Tutorial : 1,000+
C Programs Handbook : 50,000+
Amazon Kindle : 100,000,000+
Aab e Hayat Full Novel : 100,000+
Aldiko Book Reader : 10,000,000+
Google I/O 2018 : 500,000+
R Language Reference Guide : 10,000+
Learn R Programming Full : 5,000+
R Programing Offline Tutorial : 1,000+
Guide for R Programming : 5+
Learn R Programming : 10+
R Quick Reference Big Data : 1,000+
V Made : 100,000+
Wattpad 📖 Free Books : 100,000,000+
Dictionary - WordWeb : 5,000,000+
Guide (for X-MEN) : 100,000+
AC Air condition Troubleshoot,Repair,Maintenance : 5,000+
AE Bulletins : 1,000+
Ae Allah na Dai (Rasa) : 10,000+
50000 Free eBooks & Free AudioBooks : 5,000,000+
Ag PhD Field Guide : 10,000+
Ag PhD Deficiencies : 10,000+
Ag PhD Planting Population Calculator : 1,000+
Ag PhD Soybean Diseases : 1,000+
Fertilizer Removal By Crop : 50,000+
A-J Media Vault : 50+
Al-Quran (Free) : 10,000,000+
Al Quran (Tafsir & by Word) : 500,000+
Al Quran Indonesia : 10,000,000+
Al'Quran Bahasa Indonesia : 10,000,000+
Al Quran Al karim : 1,000,000+
Al-Muhaffiz : 50,000+
Al Quran : EAlim - Translations & MP3 Offline : 5,000,000+
Al-Quran 30 Juz free copies : 500,000+
Koran Read &MP3 30 Juz Offline : 1,000,000+
Hafizi Quran 15 lines per page : 1,000,000+
Quran for Android : 10,000,000+
Al Quran Free - القرآن (Islam) : 50,000+
Surah Al-Waqiah : 100,000+
Hisnul Al Muslim - Hisn Invocations & Adhkaar : 100,000+
Satellite AR : 1,000,000+
Audiobooks from Audible : 100,000,000+
日本AV历史 : 10,000+
Kinot & Eichah for Tisha B'Av : 10,000+
AW Tozer Devotionals - Daily : 5,000+
Tozer Devotional -Series 1 : 1,000+
The Pursuit of God : 1,000+
AY Sing : 5,000+
Ay Hasnain k Nana Milad Naat : 10,000+
Ay Mohabbat Teri Khatir Novel : 10,000+
Arizona Statutes, ARS (AZ Law) : 1,000+
Oxford A-Z of English Usage : 1,000,000+
BD Fishpedia : 1,000+
BD All Sim Offer : 10,000+
Youboox - Livres, BD et magazines : 500,000+
Cъновник BG : 1,000+
B&H Kids AR : 10,000+
B y H Niños ES : 5,000+
Dictionary.com: Find Definitions for English Words : 10,000,000+
English Dictionary - Offline : 10,000,000+
Bible KJV : 5,000,000+
Borneo Bible, BM Bible : 10,000+
MOD Black for BM : 100+
BM Box : 1,000+
Anime Mod for BM : 100+
NOOK: Read eBooks & Magazines : 10,000,000+
NOOK Audiobooks : 500,000+
NOOK App for NOOK Devices : 500,000+
Browsery by Barnes & Noble : 5,000+
bp e-store : 1,000+
Brilliant Quotes: Life, Love, Family & Motivation : 1,000,000+
BR Ambedkar Biography & Quotes : 10,000+
BU Alsace : 100+
Catholic La Bu Zo Kam : 500+
Khrifa Hla Bu (Solfa) : 10+
Kristian Hla Bu : 10,000+
SA HLA BU : 1,000+
Learn SAP BW : 500+
Learn SAP BW on HANA : 500+
CA Laws 2018 (California Laws and Codes) : 5,000+
Bootable Methods(USB-CD-DVD) : 10,000+
cloudLibrary : 100,000+
SDA Collegiate Quarterly : 500+
Sabbath School : 100,000+
Cypress College Library : 100+
Stats Royale for Clash Royale : 1,000,000+
GATE 21 years CS Papers(2011-2018 Solved) : 50+
Learn CT Scan Of Head : 5,000+
Easy Cv maker 2018 : 10,000+
How to Write CV : 100,000+
CW Nuclear : 1,000+
CY Spray nozzle : 10+
BibleRead En Cy Zh Yue : 5+
CZ-Help : 5+
Modlitební knížka CZ : 500+
Guide for DB Xenoverse : 10,000+
Guide for DB Xenoverse 2 : 10,000+
Guide for IMS DB : 10+
DC HSEMA : 5,000+
DC Public Library : 1,000+
Painting Lulu DC Super Friends : 1,000+
Dictionary : 10,000,000+
Fix Error Google Playstore : 1,000+
D. H. Lawrence Poems FREE : 1,000+
Bilingual Dictionary Audio App : 5,000+
DM Screen : 10,000+
wikiHow: how to do anything : 1,000,000+
Dr. Doug's Tips : 1,000+
Bible du Semeur-BDS (French) : 50,000+
La citadelle du musulman : 50,000+
DV 2019 Entry Guide : 10,000+
DV 2019 - EDV Photo & Form : 50,000+
DV 2018 Winners Guide : 1,000+
EB Annual Meetings : 1,000+
EC - AP & Telangana : 5,000+
TN Patta Citta & EC : 10,000+
AP Stamps and Registration : 10,000+
CompactiMa EC pH Calibration : 100+
EGW Writings 2 : 100,000+
EGW Writings : 1,000,000+
Bible with EGW Comments : 100,000+
My Little Pony AR Guide : 1,000,000+
SDA Sabbath School Quarterly : 500,000+
Duaa Ek Ibaadat : 5,000+
Spanish English Translator : 10,000,000+
Dictionary - Merriam-Webster : 10,000,000+
JW Library : 10,000,000+
Oxford Dictionary of English : Free : 10,000,000+
English Hindi Dictionary : 10,000,000+
English to Hindi Dictionary : 5,000,000+
EP Research Service : 1,000+
FAHREDDİN er-RÂZİ TEFSİRİ : 1,000+
Hymnes et Louanges : 100,000+
EU Charter : 1,000+
EU Data Protection : 1,000+
EU IP Codes : 100+
EW PDF : 5+
BakaReader EX : 100,000+
EZ Quran : 50,000+
FA Part 1 & 2 Past Papers Solved Free – Offline : 5,000+
La Fe de Jesus : 1,000+
La Fe de Jesús : 500+
Le Fe de Jesus : 500+
Florida - Pocket Brainbook : 1,000+
Florida Statutes (FL Code) : 1,000+
English To Shona Dictionary : 10,000+
Greek Bible FP (Audio) : 1,000+
Golden Dictionary (FR-AR) : 500,000+
Fanfic-FR : 5,000+
Bulgarian French Dictionary Fr : 10,000+
Chemin (fr) : 1,000+
The SCP Foundation DB fr nn5n : 1,000+

The book and reference genre includes a variety of apps: software for processing and reading ebooks, various collections of libraries, dictionaries, tutorials on programming or languages, etc. It seems there’s still a small number of extremely popular apps that skew the average:

for app in android_final:
    if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000,000+'
                                            or app[5] == '500,000,000+'
                                            or app[5] == '100,000,000+'):
        print(app[0], ':', app[5])

Google Play Books : 1,000,000,000+
Bible : 100,000,000+
Amazon Kindle : 100,000,000+
Wattpad 📖 Free Books : 100,000,000+
Audiobooks from Audible : 100,000,000+

However, it looks like there are only a few very popular apps, so this market still shows potential. Let’s try to get some app ideas based on the kind of apps that are somewhere in the middle in terms of popularity (between 1,000,000 and 100,000,000 downloads):

for app in android_final:
    if app[1] == 'BOOKS_AND_REFERENCE' and (app[5] == '1,000,000+'
                                            or app[5] == '5,000,000+'
                                            or app[5] == '10,000,000+'
                                            or app[5] == '50,000,000+'):
        print(app[0], ':', app[5])

Wikipedia : 10,000,000+
Cool Reader : 10,000,000+
Book store : 1,000,000+
FBReader: Favorite Book Reader : 10,000,000+
Free Books - Spirit Fanfiction and Stories : 1,000,000+
AlReader -any text book reader : 5,000,000+
FamilySearch Tree : 1,000,000+
Cloud of Books : 1,000,000+
ReadEra – free ebook reader : 1,000,000+
Ebook Reader : 5,000,000+
Read books online : 5,000,000+
eBoox: book reader fb2 epub zip : 1,000,000+
All Maths Formulas : 1,000,000+
Ancestry : 5,000,000+
HTC Help : 10,000,000+
Moon+ Reader : 10,000,000+
English-Myanmar Dictionary : 1,000,000+
Golden Dictionary (EN-AR) : 1,000,000+
All Language Translator Free : 1,000,000+
Aldiko Book Reader : 10,000,000+
Dictionary - WordWeb : 5,000,000+
50000 Free eBooks & Free AudioBooks : 5,000,000+
Al-Quran (Free) : 10,000,000+
Al Quran Indonesia : 10,000,000+
Al'Quran Bahasa Indonesia : 10,000,000+
Al Quran Al karim : 1,000,000+
Al Quran : EAlim - Translations & MP3 Offline : 5,000,000+
Koran Read &MP3 30 Juz Offline : 1,000,000+
Hafizi Quran 15 lines per page : 1,000,000+
Quran for Android : 10,000,000+
Satellite AR : 1,000,000+
Oxford A-Z of English Usage : 1,000,000+
Dictionary.com: Find Definitions for English Words : 10,000,000+
English Dictionary - Offline : 10,000,000+
Bible KJV : 5,000,000+
NOOK: Read eBooks & Magazines : 10,000,000+
Brilliant Quotes: Life, Love, Family & Motivation : 1,000,000+
Stats Royale for Clash Royale : 1,000,000+
Dictionary : 10,000,000+
wikiHow: how to do anything : 1,000,000+
EGW Writings : 1,000,000+
My Little Pony AR Guide : 1,000,000+
Spanish English Translator : 10,000,000+
Dictionary - Merriam-Webster : 10,000,000+
JW Library : 10,000,000+
Oxford Dictionary of English : Free : 10,000,000+
English Hindi Dictionary : 10,000,000+
English to Hindi Dictionary : 5,000,000+

This niche seems to be dominated by software for processing and reading ebooks, as well as various collections of libraries and dictionaries, so it’s probably not a good idea to build similar apps since there’ll be some significant competition.

We also notice there are quite a few apps built around the book Quran, which suggests that building an app around a popular book can be profitable. It seems that taking a popular book (perhaps a more recent book) and turning it into an app could be profitable for both the Google Play and the App Store markets.

However, it looks like the market is already full of libraries, so we need to add some special features besides the raw version of the book. This might include daily quotes from the book, an audio version of the book, quizzes on the book, a forum where people can discuss the book, etc.

Conclusions

In this project, we analyzed data about the App Store and Google Play mobile apps with the goal of recommending an app profile that can be profitable for both markets.

We concluded that taking a popular book (perhaps a more recent book) and turning it into an app could be profitable for both the Google Play and the App Store markets. The markets are already full of libraries, so we need to add some special features besides the raw version of the book. This might include daily quotes from the book, an audio version of the book, quizzes on the book, a forum where people can discuss the book, etc.