App download estimates explained: How to interpret app download data

Comparing the yearly seasonality of the Sports category on Google Play in the United States and India within AppTweak’s Market Intelligence

Travel apps and seasonality

As you can expect, app downloads for the travel category differ from one hemisphere to another. In the U.S. and the U.K., travel apps enjoy greater downloads during their summer months of June through September while those in Brazil see a similar increase during their summer between October and January.

Games vs. business apps seasonality

Seasonality can be inspected not just on a monthly basis, but by looking at the app download data by week to see if there are trends like weekdays versus weekends. Unsurprisingly, mobile gaming apps tend to earn more downloads during the weekend than the weekday while business apps see more downloads during the week.

Sports vs. Health & Fitness apps seasonality

Other categories that see these weekly trends include sports and health & fitness apps, and quite opposite trends at that. Sports apps downloads shoot up on the weekend, when most sporting events occur. Health & fitness apps, on the other hand, are most often downloaded at the beginning of the week when people reset their fitness and wellness goals.

More on our market seasonality feature

How are app download estimates calculated?

As most app stores don’t disclose the exact number of app downloads, app download estimates are calculated using a combination of app store signals, historical trends, and proprietary algorithms belonging to third-party app intelligence platforms like AppTweak.

Types of data used

To estimate the number of downloads an app receives, app store intelligence platforms combine the following data sources.

Public metadata: This data can include an app’s ranking on that store, the number of reviews, update frequency, category relevancy, and other visible signals.

Panel data (if available): Some tools may use opt-in panel data or integrate with a mobile measurement partner (MMP) to access anonymized install metrics. Then, this data is used to train and validate estimation models.

Proprietary machine learning algorithms: These algorithms serve to analyze patterns across hundreds of millions of data points, adjusting for category, country, seasonality and more. They then make fairly precise predictions about how many an app likely receives on a daily basis.

Common estimation models

Different app intelligence platforms utilize variations of the following modeling techniques.

Regression models: These models analyze the relationship between app rank and known download volumes. For instance, they can uncover that apps ranked in position five tend to receive around X number of downloads per day.

Time-series modeling: This type of modeling examines historical patterns in downloads and ranks to forecast future performance. This approach is incredibly useful to capture trends over time or during seasonal events.

Behavior-based inference: Some tools refine their estimations by analyzing user behavior patterns like engagement metrics, app updates, or feature releases.

Challenges in accuracy

Although download estimates can be highly informative, several factors can impact their precision.

1. Country-level data gaps 

In some regions, publicly available data is limited, making it hard to model downloads accurately for those regions.

2. Platform differentiators
Because user behavior, store ranking algorithms, and app visibility can differ significantly across app store platforms like the App Store and Google Play, impacting model consistency.

3. Sudden traffic spikes and seasonal events

Download estimates can be skewed by short-term spikes caused by UA campaigns or seasonal events. These surges are not necessarily anomalies, but they represent behavior that differs from typical download patterns. If not properly accounted for in the model, these spikes can distort estimated trends or even inflate perceived performance.

How AppTweak measures app download estimates

To truly dive into the data science driving AppTweak’s app store marketing intelligence platform, we asked our Head of Data Science, Jonathan Frische, to explain the methodology and algorithms used by our team.

As a pioneer in the app store marketing field, it was a priority for us to use deep learning to estimate the number of downloads an app drives every day in any store and country on iPhone, iPad, and Android.

One of the major advantages of using deep learning is producing a model that is both (1) cross-country and (2) cross-category. This means the model truly learns and understands the relationships between countries and categories, and can therefore benefit from the data of different categories in different parts of the world to estimate the overall downloads of one specific app.

Learn more about Atlas AI.

What AppTweak’s model analyzes

For each device and app type, we feed our deep-learning model with the data collected from the thousands of apps connected to AppTweak. Note that we’re continually retraining this model to maintain its accuracy, ensure it adapts to market changes, and reflects the most recent anonymized user behavior and app store dynamics.

Every day for each app, we define a set of features related to:

• The app’s category rank
• The date (seasonality)
• The country

Then, we map these features to the number of daily downloads the app is driving, allowing our model to learn the most relevant relationships between the different features and their related downloads.

This technique helps us identify unique insights such as the impact of Christmas across different categories and countries, or which countries have similar levels of competitiveness in terms of downloads.

Learn more about app seasonality trends for the holiday season

Once our model has a deep understanding of these relationships, we are ready to predict the number of downloads for any app for which we don’t have data – from any category, in any country, and at any point in the past. Thanks to the many metrics we tracked during the learning process, we are confident that this model can estimate the downloads of an app with extremely high accuracy.

AppTweak’s estimates based on a large data pool

As mentioned above, our ability to provide our users with accurate download and revenue estimates is a result of the large pool of mobile app data we have captured over the years.

Our model uses the data from apps worldwide to predict estimates in each specific country. Typically, to predict the number of downloads a financial app makes in Vietnam, the model benefits not only from all the data we have access to in Vietnam but also from the data we have for any financial app in the world.

As our pool of data grows significantly over the years as more and more users connect their data to AppTweak, this data is fed into our algorithms which are constantly improved over time.

Concrete examples of how marketers use app download data

Here are three real-life examples of brands utilizing app download data to inform their app marketing strategies.

Evaluate marketing action effectiveness: Binance

Download estimates can help assess the impact of nearly any marketing effort—from a user acquisition campaign to an App Store Optimization (ASO) update. By comparing estimated downloads before and after a change, marketers can understand which actions contributed to growth and which may need refinement.

Again, let’s take Binance, the world’s largest cryptocurrency exchange app, as an example. To improve conversion rates on high-priority crypto-related keywords, the Binance team implemented custom product pages and used app download estimates to measure the impact.

By comparing baseline installs with post-launch performance across 45 markets, Binance confirmed that its localized creatives led to a meaningful uplift in downloads, adding over 95,000 installs annually and helping the brand rank #1 for targeted keywords. Download estimates helped validate the effectiveness of their ASO strategy and optimize future efforts. Read the success story for more details.

Benchmark performance against the competition: Park+

By analyzing download estimates, marketers can gauge how their app stacks up against their competitors. This comparison helps in understanding market share, identifying growth opportunities, and setting realistic performance goals.

One example is Park+, an Indian auto services app that used AppTweak’s competitive insights and App competition monitoring tool to recover from a sharp drop in category ranking during the pandemic. By analyzing competitor strategies and identifying trending keywords like “FASTag” and “RTO,” the Park+ team refined its metadata and improved their app experience. These optimizations helped the app reach the #1 position in the “Auto & Vehicles” category on Google Play, driven largely by organic growth. Read the Park+ success story here.

Discover and adapt to market shifts: Bitcoin.com

Marketers need to be able to determine the impact of their major campaigns or metadata or creative changes, as well as local market shifts. To do so, they can perform an incrementality analysis.

For example, we analyzed the app download data of Bitcoin.com looking at three years of historical data to create a baseline forecast of expected downloads during and after the event: the 2024 U.S. presidential election. Our extrapolation model confirmed that Donald Trump’s election to the U.S. presidency was not just a temporary spike but a true market shift.

Conclusion: Make smarter decisions with app download estimates

App download estimates play a crucial role in today’s app marketing landscape. Whether you’re evaluating campaign effectiveness, benchmarking against competitors, or identifying new market opportunities, these estimates offer valuable context where real data is limited. When used correctly, they help you measure performance, validate strategic choices, and uncover where to optimize next.

To get the most value, always consider the estimation model behind the data and use it in combination with other ASO and market intelligence metrics. Check out our guide on How to build an effective ASO strategy to learn how to build a winning growth plan.

With a trusted provider like AppTweak—powered by deep learning and real app store connections—you can use download estimates with confidence and precision.

Ready to turn these insights into action? Request your demo with AppTweak now and we’ll show you how!

FAQs

Below we answer the most frequently asked questions about app download data and estimates.

What are app download estimates?

App download estimates are projected figures that reflect how many times an app has been installed from major app stores like the Apple App Store and Google Play. Since most platforms don’t disclose exact download counts, these estimates are generated using models from third-party tools that analyze publicly available signals such as app rankings, category performance, and historical trends.

How can I check how many downloads an app has?

To see how many downloads an app has, you can select the app in your AppTweak platform, go to analytics and see the download estimates, as well as revenue estimates, for that particular app.

Is there a tool for estimating app downloads?

Yes, AppTweak is one of the best ASO tools that also provides app download estimates.

How can I find app download statistics by country?

To view app download statistics by country, tools like AppTweak provide reliable estimates based on store intelligence and market data. In AppTweak, you can access country-level download insights under Market Intelligence, where you’ll find a market overview. These views allow you to analyze and compare estimated downloads across different countries, helping you uncover high-opportunity markets and assess local competitiveness.

How do tools like AppTweak estimate app downloads?

Platforms like AppTweak use machine learning models trained using anonymized data from thousands of apps connected to the platform. It combines this with public app store signals like category rankings, app metadata, and other observable trends to estimate downloads across stores and countries. By analyzing these elements across markets and devices, the algorithm can generate accurate, country-specific estimates, even when actual download numbers aren’t publicly available.