Software Alternatives, Accelerators & Startups

NumPy VS AppFollow

Compare NumPy VS AppFollow and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

AppFollow logo AppFollow

AppFollow is an integrated solution that makes monitoring, analyzing, and elevating your app's reputation easy.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • AppFollow Organic dashboard
    Organic dashboard //
    2024-04-25
  • AppFollow AI replies
    AI replies //
    2024-04-25
  • AppFollow Reply to reviews
    Reply to reviews //
    2024-04-25
  • AppFollow Agent Performance
    Agent Performance //
    2024-04-25
  • AppFollow Semantic analysis
    Semantic analysis //
    2024-04-25

Your app's reputation determines success. Apps with 4+ stars capture 80% of market revenue and get conversion rates that make competitors jealous. We built AppFollow as the reputation management platform that turns user feedback into measurable results.

AppFollow filters reviews for app teams who need to improve their product and increase sales. Better feedback management improves app ratings, better ratings boost conversion rates and trust, which then means more downloads and revenue. This loop is your competitive advantage.

Our AI suite does the heavy work: with its help, you can tag feedback by topic, summarize insights across thousands of reviews, translate languages, generate unique responses that sound human, and assist your team with complex cases. Automate routine replies and flag issues that need human attention.

Get the reporting you need. Executive reports deliver full summaries for leadership with granular analytics showing which keywords generate downloads. Reveal how competitors attract your users, identify which marketing channels work best, set up Slack alerts for critical feedback, and optimize the time your team spends on reputation management.

Track ASO performance and organic visibility. Monitor reviews across all app stores. See what drives rankings and conversion rates.

Major companies trust AppFollow to maintain their competitive edge. Easy Brain, Wargaming, Lazada, G5, Gameloft, Indeed, Standard Bank, and Opera rely on our platform. We integrate with App Store Connect, Google Play Console, Trustpilot, and all major app marketplaces, with platforms like Steam joining the list soon. We also connect with your existing tools like Zendesk and Slack.

Turn user feedback into business advantage.

AppFollow

$ Details
freemium
Release Date
2015 January
Startup details
Country
Finland
City
Helsinki
Founder(s)
Anatoly Sharifulin
Employees
50 - 99

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

AppFollow features and specs

  • Comprehensive Analytics
    AppFollow provides extensive app performance metrics and detailed analytics, which can help users understand their appโ€™s performance and user reviews in depth.
  • Review Management
    The platform offers robust review management tools, allowing users to monitor, analyze, and respond to user feedback directly from the dashboard, making customer interaction more streamlined.
  • Keyword Tracking
    AppFollow includes keyword tracking features that help users improve their app's visibility by identifying the most effective keywords for their appโ€™s ASO strategy.
  • Competitor Analysis
    Users can track competitors' apps and get insights into their performance and strategies. This helps in making informed decisions to stay ahead in the market.
  • Integrations
    AppFollow supports integration with various tools and platforms like Slack, Zendesk, and others, facilitating smoother workflow and collaboration.

Possible disadvantages of AppFollow

  • Pricing
    The service can be quite expensive, especially for startups and small businesses that might find the cost prohibitive.
  • Complexity
    The platform can be complex to navigate for new users, with a steep learning curve that might require additional time and resources to fully utilize.
  • Customization Limitations
    Some users have noted that there are limitations in customizing reports and dashboards, which might not cater to all specific business needs.
  • Limited Free Plan
    The free plan offers very limited functionalities, which may not be sufficient for users who need more comprehensive features and insights.
  • Support Response Time
    There have been instances where users reported slower response times from customer support, which can be a drawback in time-sensitive situations.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of AppFollow

Overall verdict

  • Overall, AppFollow is highly regarded, especially by app developers and marketing teams looking for a centralized solution to manage app performance and user feedback. It offers a broad range of tools that cater to various facets of app development and marketing, making it a versatile choice for those in need of detailed analytics and effective review management.

Why this product is good

  • AppFollow is considered a valuable tool for app developers and marketers because it provides comprehensive app tracking, analytics, and review management. It helps users monitor app store performance, gather user feedback, and optimize app visibility with features like keyword tracking and ASO tools. Users appreciate its user-friendly interface and integration capabilities with platforms such as Slack, Zendesk, and others.

Recommended for

  • Mobile app developers
  • Product managers
  • Marketing teams
  • ASO specialists
  • Customer support teams focusing on app feedback

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

AppFollow videos

ASO Tool for Keyword Research (AppFollow Review)

More videos:

  • Review - Intro to AppFollow Review Management Tools
  • Review - AppFollow and Slack Integration for App Review Management

Category Popularity

0-100% (relative to NumPy and AppFollow)
Data Science And Machine Learning
App Reviews
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Analytics
0 0%
100% 100

Questions & Answers

As answered by people managing NumPy and AppFollow.

What makes your product unique?

AppFollow's answer:

AppFollow uniquely combines AI-powered review analysis, reply automation, and app store optimization into one platform. We help mobile-first teams understand user feedback at scale and turn ratings and reviews into a real growth lever โ€” not just a support task.

Why should a person choose your product over its competitors?

AppFollow's answer:

AppFollow is built for teams that care about outcomes, not just data. Customers choose us because we:

  • Save time with smart automation

  • Reveal product and UX insights hidden in reviews

  • Help improve ratings faster with data-backed actions

In short: fewer tools, clearer decisions, better ratings.

What's the story behind your product?

AppFollow's answer:

AppFollow started with a simple problem: mobile teams were drowning in user feedback but couldnโ€™t act on it fast enough. What began as a way to track and respond to app store reviews quickly evolved into a full platform helping teams turn user voice into a competitive advantage.

Which are the primary technologies used for building your product?

AppFollow's answer:

AppFollow is built using modern cloud infrastructure and scalable web technologies, with a strong focus on AI/ML for text analysis, automation, and secure data processing. The platform is designed to handle large volumes of app store data reliably and in real time.

Who are some of the biggest customers of your product?

AppFollow's answer:

  • Easy Brain
  • Wargaming
  • Lazada
  • G5
  • Gameloft
  • Indeed
  • Standard Bank
  • Opera

How would you describe the primary audience of your product?

AppFollow's answer:

The platform is built for product managers, growth and ASO marketers, customer experience leaders, and app teams who manage large volumes of user feedback across app stores, regions, and languages. These teams rely on AppFollow to filter signal from noise, identify reputation risks early, and turn user feedback into faster product improvements and measurable business results.

AppFollow is especially valuable for organizations where:

  • A small change in star rating creates outsized financial impact

  • Review volume makes manual analysis impossible

  • Speed matters when bugs, crashes, or UX issues affect ratings

  • Reputation management must scale without increasing headcount

From fast-growing app publishers to international brands managing apps across dozens of markets, AppFollow serves teams that view reputation management as a growth engine, not a support task.

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and AppFollow

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

AppFollow Reviews

We have no reviews of AppFollow yet.
Be the first one to post

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (122)

View more

AppFollow mentions (0)

We have not tracked any mentions of AppFollow yet. Tracking of AppFollow recommendations started around Mar 2021.

What are some alternatives?

When comparing NumPy and AppFollow, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

appfigures - Cross-platform app store analytics for all of your mobile apps.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

AppTweak - The most comprehensive ASO & Apple Search Ads platform to optimize your apps' organic and paid performance in the app stores

OpenCV - OpenCV is the world's biggest computer vision library

Sensor Tower - Sensor Tower is a platform for app store optimization and app industry intelligence.