What is App Revenue Data? Best Datasets, Databases & Statistics for 2025
On This Page:
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
On This Page:
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
What is App Revenue Data?
App Revenue Data is financial information generated by mobile applications. It includes the total income generated by an app through various revenue streams such as in-app purchases, subscriptions, advertisements, and app downloads. This data provides insights into the financial performance of an app, helping developers and businesses understand the revenue sources and make informed decisions to optimize monetization strategies.
Examples of App Revenue Data include the total revenue generated by a specific app, the revenue breakdown by country or region, and the revenue growth over a certain period of time. App Revenue Data is used by app developers, marketers, and investors to analyze the financial performance of apps, identify trends, and make informed decisions regarding app development, marketing strategies, and investment opportunities. In this page, you’ll find the best data sources for app revenue statistics, app revenue search, and checking app revenue.
Best App Revenue Databases & Datasets
Here is our curated selection of top App Revenue Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Mobile App Data, TV App Data, App Data, Google & Apple App store Data - Scrape all Publicly Available Mobile & TV related Data with 100% Accuracy
Factori US Firmographic data | Company data | B2B data | Stock Ticker, NAICS Code, Revenue, Employee Count, Credit, Contact, Address
Company Data | 6.7MM+ Total Companies | Company Name, Industry, Employees, Revenue, Website, Addresses + More
Firmographic Data | 4MM + US Private and Public Companies | Employees, Revenue, Website, Industry + More Firmographics
Real-Time Sourced & Verified B2B Company Data | Global Coverage (100M+) | 20+ Datapoints | Headcount, Funding, Industry, Geography, Revenue etc
Factori USA Consumer Graph Data | socio-demographic, location, interest and intent data | E-Commere |Mobile Apps | Online Services
Bumble, Match, Tinder Dating App Data | Consumer Transaction Data | US, EU, Asia, EMEA, LATAM, MENA, India | Granular & Aggregate Data available
Ads.txt / App-ads.txt for advertisement compliance
B2B Leads Data on SaaS-platform | Access & Export Millions of Leads via Success.ai's SaaS-Platform - Unbeatable Price for Top-Quality Data
Factori AI & ML Training Data | Consumer Data | USA | Machine Learning Data
Can't find the data you're looking for?
Let data providers come to you by posting your request
Post your request
Top App Revenue Data Providers & Companies
Popular Use Cases for App Revenue Data
App Revenue Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for App Revenue Data.
Main Attributes of App Revenue Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The email address of a company or contact. | View 19 datasets | |
| String | The first name of a contact. | View 14 datasets | |
| String | The last name (surname) of a contact. | View 14 datasets | |
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 13 datasets | |
| String | The name of a city. | View 11 datasets | |
| String | The country code in Alpha-2 format (ISO 3166) | View 10 datasets |
Frequently Asked Questions
Where Can I Buy App Revenue Data?
You can explore our data marketplace to find a variety of App Revenue Data tailored to different use cases. Our verified providers offer a range of solutions, and you can contact them directly to discuss your specific needs.
How is the Quality of App Revenue Data Maintained?
The quality of App Revenue Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is App Revenue Data Updated?
The update frequency for App Revenue Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is App Revenue Data Secure?
The security of App Revenue Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is App Revenue Data Delivered?
App Revenue Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for App Revenue Data.
How Much Does App Revenue Data Cost?
The cost of App Revenue Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of App Revenue Data for your needs.
What Are Similar Data Types to App Revenue Data?
App Revenue Data is similar to other data types, such as App Usage Data, App Store Data, Mobile Attribution Data, App Install Data, and App Market Data. These related categories are often used together for applications like App Intelligence.
