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• A Guide to App Analytics Tools that Drive Growth

  7 mars, par Daniel Crough — App Analytics

  Mobile apps are big business, generating £438 billion in global revenue between in-app purchases (38%) and ad revenue (60%). And with 96% of apps relying on in-app monetisation, the competition is fierce.

  To succeed, app developers and marketers need strong app analytics tools to understand their customers’ experiences and the effectiveness of their development efforts.

  This article discusses app analytics, how it works, the importance and benefits of mobile app analytics tools, key metrics to track, and explores five of the best app analytics tools on the market.

  What are app analytics tools ?

  Mobile app analytics tools are software solutions that provide insights into how users interact with mobile applications. They track user behaviour, engagement and in-app events to reveal what’s working well and what needs improvement.

  Insights gained from mobile app analytics help companies make more informed decisions about app development, marketing campaigns and monetisation strategies.

  What do app analytics tools do ?

  App analytics tools embed a piece of code, called a software development kit (SDK), into an app. These SDKs provide the essential infrastructure for the following functions :

  • Data collection : The SDK collects data within your app and records user actions and events, like screen views, button clicks, and in-app purchases.
  • Data filtering : SDKs often include mechanisms to filter data, ensuring that only relevant information is collected.
  • Data transmission : Once collected and filtered, the SDK securely transmits the data to an analytics server. The SDK provider can host this server (like Firebase or Amplitude), or you can host it on-premise.
  • Data processing and analysis : Servers capture, process and analyse large stores of data and turn it into useful information.
  • Visualisation and reporting : Dashboards, charts and graphs present processed data in a user-friendly format.

  

  Six ways mobile app analytics tools fuel marketing success and drive product growth

  Mobile app analytics tools are vital in driving product development, enhancing user experiences, and achieving business objectives.

  #1. Improving user understanding

  The better a business understands its customers, the more likely it is to succeed. For mobile apps, that means understanding how and why people use them.

  Mobile analytics tools provide detailed insights into user behaviours and preferences regarding apps. This knowledge helps marketing teams create more targeted messaging, detailed customer journey maps and improve user experiences.

  It also helps product teams understand the user experience and make improvements based on those insights.

  For example, ecommerce companies might discover that users in a particular area are more likely to buy certain products. This allows the company to tailor its offers and promotions to target the audience segments most likely to convert.

  #2 Optimising monetisation strategies for increased revenue and user retention

  In-app purchases and advertising make up 38% and 60% of mobile app revenue worldwide, respectively. App analytics tools provide insights companies need to optimise app monetisation by :

  • Analysing purchase patterns to identify popular products and understand pricing sensitivities.
  • Tracking in-app behaviour to identify opportunities for enhancing user engagement.

  App analytics can track key metrics like visit duration, user flow, and engagement patterns. These metrics provide critical information about user experiences and can help identify areas for improvement.

  How meaningful are the impacts ?

  Duolingo, the popular language learning app, reported revenue growth of 45% and an increase in daily active users (DAU) of 65% in its Q4 2023 financial report. The company attributed this success to its in-house app analytics platform.

  

  #3. Understanding user experiences

  Mobile app analytics tools track the performance of user interactions within your app, such as :

  • Screen views : Which screens users visit most frequently
  • User flow : How users navigate through your app
  • Session duration : How long users spend in your app
  • Interaction events : Which buttons, features, and functions users engage with most

  Knowing how users interact with your app can help refine your approach, optimise your efforts, and drive more conversions.

  #4. Personalising user experiences

  A recent McKinsey survey showed that 71% of users expect personalised app experiences. Product managers must stay on top of this since 76% of users get frustrated if they don’t receive the personalisation they expect.

  Personalisation on mobile platforms requires data capture and analysis. Mobile analytics platforms can provide the data to personalise the user onboarding process, deliver targeted messages and recommend relevant content or offers.

  Spotify is a prime example of personalisation done right. A recent case study by Pragmatic Institute attributed the company’s growth to over 500 million active daily users to its ability to capture, analyse and act on :

  • Search behaviour
  • Individual music preferences
  • Playlist data
  • Device usage
  • Geographical location

  The streaming service uses its mobile app analytics software to turn this data into personalised music recommendations for its users. Spotify also has an in-house analytics tool called Spotify Premium Analytics, which helps artists and creators better understand their audience.

  #5. Enhancing app performance

  App analytics tools can help identify performance issues that might be affecting user experience. By monitoring metrics like load time and app performance, developers can pinpoint areas that need improvement.

  Performance optimisation is crucial for user retention. According to Google research, 53% of mobile site visits are abandoned if pages take longer than three seconds to load. While this statistic refers to websites, similar principles apply to apps—users expect fast, responsive experiences.

  Analytics data can help developers prioritise performance improvements by showing which screens or features users interact with most frequently, allowing teams to focus their optimisation efforts where they’ll have the greatest impact.

  #6. Identifying growth opportunities

  App analytics tools can reveal untapped opportunities for growth by highlighting :

  • Features users engage with most
  • Underutilised app sections that might benefit from redesign
  • Common user paths that could be optimised
  • Moments where users tend to drop off

  This intelligence helps product teams make data-informed decisions about future development priorities, feature enhancements, and potential new offerings.

  For example, a streaming service might discover through analytics that users who create playlists have significantly higher retention rates. This insight could lead to development of enhanced playlist functionality to encourage more users to create them, ultimately boosting overall retention.

  Key app metrics to track

  Using mobile analytics tools, you can track dozens of key performance indicators (KPIs) that measure everything from customer engagement to app performance. This section focuses on the most important KPIs for app analytics, classified into three categories :

  • App performance KPIs
  • User engagement KPIs
  • Business impact KPIs

  While the exact metrics to track will vary based on your specific goals, these fundamental KPIs form the foundation of effective app analytics.

  

  App performance KPIs

  App performance metrics tell you whether an app is reliable and operating properly. They help product managers identify and address technical issues that may negatively impact user experiences.

  Some key metrics to assess performance include :

  • Screen load time : How quickly screens load within your app
  • App stability : How often your app crashes or experiences errors
  • Response time : How quickly your app responds to user interactions
  • Network performance : How efficiently your app handles data transfers

  User engagement KPIs

  Engagement KPIs provide insights into how users interact with an app. These metrics help you understand user behaviour and make UX improvements.

  Important engagement metrics include :

  • Returning visitors : A measure of how often users return to an app
  • Visit duration : How long users spend in your app per session
  • User flow : Visualisation of the paths users take through your app, offering insights into navigation patterns
  • Event tracking : Specific interactions users have with app elements
  • Screen views : Which screens are viewed most frequently

  Business impact KPIs

  Business impact KPIs connect app analytics to business outcomes, helping demonstrate the app’s value to the organisation.

  Key business impact metrics include :

  • Conversion events : Completion of desired actions within your app
  • Goal completions : Tracking when users complete specific objectives
  • In-app purchases : Monitoring revenue from within the app
  • Return on investment : Measuring the business value generated relative to development costs

  Privacy and app analytics : A delicate balance

  While app analytics tools can be a rich source of user data, they must be used responsibly. Tracking user in-app behaviour and collecting user data, especially without consent, can raise privacy concerns and erode user trust. It can also violate data privacy laws like the GDPR in Europe or the OCPA, FDBR and TDPSA in the US.

  With that in mind, it’s wise to choose user-tracking tools that prioritise user privacy while still collecting enough data for reliable analysis.

  Matomo is a privacy-focused web and app analytics solution that allows you to collect and analyse user data while respecting user privacy and following data protection rules like GDPR.

  The five best app analytics tools to prove marketing value

  In this section, we’ll review the five best app analytics tools based on their features, pricing and suitability for different use cases.

  Matomo — Best for privacy-compliant app analytics

  Matomo app analytics is a powerful, open-source platform that prioritises data privacy and compliance.

  

  It offers a suite of features for tracking user engagement and conversions across websites, mobile apps and intranets.

  Key features

  • Complete data ownership : Full control over your analytics data with no third-party access
  • User flow analysis : Track user journeys across different screens in your app
  • Custom event tracking : Monitor specific user interactions with customisable events
  • Ecommerce tracking : Measure purchases and product interactions
  • Goal conversion monitoring : Track completion of important user actions
  • Unified analytics : View web and app analytics in one platform for a complete digital picture

  Benefits

  • Eliminate compliance risks without sacrificing insights
  • Get accurate data with no sampling or data manipulation
  • Choose between self-hosting or cloud deployment
  • Deploy one analytics solution across your digital properties (web and app) for a single source of truth

  Pricing

  Plan	Price
  Cloud	Starts at £19/month
  On-Premise	Free

  Matomo is a smart choice for businesses that value data privacy and want complete control over their analytics data. It’s particularly well-suited for organisations in highly regulated industries, like banking.

  While Matomo’s app analytics features focus on core analytics capabilities, its privacy-first approach offers unique advantages. For organisations already using Matomo for web analytics, extending to mobile creates a unified analytics ecosystem with consistent privacy standards across all digital touchpoints, giving organisations a complete picture of the customer journey.

  Firebase — Best for Google services integration

  Firebase is the mobile app version of Google Analytics. It’s the most popular app analytics tool on the market, with over 99% of Android apps and 77% of iOS apps using Firebase.

  

  Firebase is popular because it works well with other Google services. It also has many features, like crash reporting, A/B testing and user segmentation.

  Pricing

  Plan	Price
  Spark	Free
  Blaze	Pay-as-you-go based on usage
  Custom	Bespoke pricing for high-volume enterprise users

  Adobe Analytics — Best for enterprise app analytics

  Adobe Analytics is an enterprise-grade analytics solution that provides valuable insights into user behaviour and app performance.

  

  It’s part of the Adobe Marketing Cloud and integrates easily with other Adobe products. Adobe Analytics is particularly well-suited for large organisations with complex analytics needs.

  Pricing

  Plan	Price
  Select	Pricing on quote
  Prime	Pricing on quote
  Ultimate	Pricing on quote

  While you must request a quote for pricing, Scandiweb puts Adobe Analytics at £2,000/mo–£2,500/mo for most companies, making it an expensive option.

  Apple App Analytics — Best for iOS app analysis

  Apple App Analytics is a free, built-in analytics tool for iOS app developers.

  

  This analytics platform provides basic insights into user engagement, app performance and marketing campaigns. It has fewer features than other tools on this list, but it’s a good place for iOS developers who want to learn how their apps work.

  Pricing

  Apple Analytics is free.

  Amplitude — Best for product analytics

  Amplitude is a product analytics platform that helps businesses understand user behaviour and build better products.

  

  It excels at tracking user journeys, identifying user segments and measuring the impact of product changes. Amplitude is a good choice for product managers and data analysts who want to make informed decisions about product development.

  Pricing

  Plan	Price
  Starter	Free
  Plus	From £49/mo
  Growth	Pricing on quote

  Choose Matomo’s app analytics to unlock growth

  App analytics tools help marketers and product development teams understand user experiences, improve app performance and enhance products. Some of the best app analytics tools available for 2025 include Matomo, Firebase and Amplitude.

  However, as you evaluate your options, consider taking a privacy-first approach to app data collection and analysis, especially if you’re in a highly regulated industry like banking or fintech. Matomo Analytics offers a powerful and ethical solution that allows you to gain valuable insights while respecting user privacy.

  Ready to take control of your app analytics ? Start your 21-day free trial.

• RTMP server with OpenCV (python)

  12 février 2024, par Overnout

  I'm trying to process an RTMP stream in Python, using OpenCV2 but I'm not able to get OpenCV to capture it (i.e. act as RTMP server).

  


  I can run FFmpeg/FFplay from the command line and receive the stream successfully.
What could cause OpenCV to fail opening the stream in listening mode ?

  


  Here is my code :

  


  import cv2

cap = cv2.VideoCapture("rtmp://0.0.0.0:8000/live", cv2.CAP_FFMPEG)

if not cap.isOpened():
    print("Cannot open video source")
    exit()


  


  And the output :

  


  [tcp @ 00000192c490d640] Connection to tcp://0.0.0.0:8000 failed: Error number -138 occurred
[rtmp @ 00000192c490d580] Cannot open connection tcp://0.0.0.0:8000 
Cannot open video source


  


  edit2 : Output with debug logging turned on :

  


  output of the python script with debug logging on:
[DEBUG:0@0.017] global videoio_registry.cpp:218 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Builtin backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[DEBUG:0@0.026] global videoio_registry.cpp:242 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Available backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ INFO:0@0.031] global videoio_registry.cpp:244 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Enabled backends(9, sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ WARN:0@0.037] global cap.cpp:132 cv::VideoCapture::open VIDEOIO(FFMPEG): trying capture filename='rtmp://192.168.254.101:8000/live' ...
[ INFO:0@0.040] global backend_plugin.cpp:383 cv::impl::getPluginCandidates Found 2 plugin(s) for FFMPEG
[ INFO:0@0.043] global plugin_loader.impl.hpp:67 cv::plugin::impl::DynamicLib::libraryLoad load C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll => OK
[ INFO:0@0.047] global backend_plugin.cpp:50 cv::impl::PluginBackend::initCaptureAPI Found entry: 'opencv_videoio_capture_plugin_init_v1'
[ INFO:0@0.049] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Capture plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.055] global backend_plugin.cpp:69 cv::impl::PluginBackend::initCaptureAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Capture plugin'
[ INFO:0@0.058] global backend_plugin.cpp:84 cv::impl::PluginBackend::initWriterAPI Found entry: 'opencv_videoio_writer_plugin_init_v1'
[ INFO:0@0.061] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Writer plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.065] global backend_plugin.cpp:103 cv::impl::PluginBackend::initWriterAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Writer plugin'
[tcp @ 00000266b2f0d0c0] Connection to tcp://192.168.254.101:8000 failed: Error number -138 occurred
[rtmp @ 00000266b2f0cfc0] Cannot open connection tcp://192.168.254.101:8000
[ WARN:0@5.630] global cap.cpp:155 cv::VideoCapture::open VIDEOIO(FFMPEG): can't create capture
[DEBUG:0@5.632] global cap.cpp:225 cv::VideoCapture::open VIDEOIO: choosen backend does not work or wrong. Please make sure that your computer support chosen backend and OpenCV built with right flags.
Cannot open video source
[ INFO:1@5.661] global plugin_loader.impl.hpp:74 cv::plugin::impl::DynamicLib::libraryRelease unload C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll


  


  Here is the output of cv2.getBuildInformation()

  


  General configuration for OpenCV 4.9.0 =====================================
  Version control:               4.9.0

  Platform:
    Timestamp:                   2023-12-31T11:21:12Z
    Host:                        Windows 10.0.17763 AMD64
    CMake:                       3.24.2
    CMake generator:             Visual Studio 14 2015
    CMake build tool:            MSBuild.exe
    MSVC:                        1900
    Configuration:               Debug Release

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (16 files):         + SSSE3 SSE4_1
      SSE4_2 (1 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (0 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (8 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (36 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe  (ver 19.0.24247.2)
    C++ flags (Release):         /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /O2 /Ob2 /DNDEBUG 
    C++ flags (Debug):           /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /Zi /Ob0 /Od /RTC1 
    C Compiler:                  C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe
    C flags (Release):           /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP   /O2 /Ob2 /DNDEBUG 
    C flags (Debug):             /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP /Zi /Ob0 /Od /RTC1 
    Linker flags (Release):      /machine:x64  /NODEFAULTLIB:atlthunk.lib /INCREMENTAL:NO  /NODEFAULTLIB:libcmtd.lib /NODEFAULTLIB:libcpmtd.lib /NODEFAULTLIB:msvcrtd.lib
    Linker flags (Debug):        /machine:x64  /NODEFAULTLIB:atlthunk.lib /debug /INCREMENTAL  /NODEFAULTLIB:libcmt.lib /NODEFAULTLIB:libcpmt.lib /NODEFAULTLIB:msvcrt.lib
    ccache:                      NO
    Precompiled headers:         YES
    Extra dependencies:          wsock32 comctl32 gdi32 ole32 setupapi ws2_32
    3rdparty dependencies:       libprotobuf ade ittnotify libjpeg-turbo libwebp libpng libtiff libopenjp2 IlmImf zlib ippiw ippicv

  OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
    Disabled:                    java world
    Disabled by dependency:      -
    Unavailable:                 python2 ts
    Applications:                -
    Documentation:               NO
    Non-free algorithms:         NO

  Windows RT support:            NO

  GUI:                           WIN32UI
    Win32 UI:                    YES
    VTK support:                 NO

  Media I/O: 
    ZLib:                        build (ver 1.3)
    JPEG:                        build-libjpeg-turbo (ver 2.1.3-62)
      SIMD Support Request:      YES
      SIMD Support:              NO
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         build (ver 1.6.37)
    TIFF:                        build (ver 42 - 4.2.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES (prebuilt binaries)
      avcodec:                   YES (58.134.100)
      avformat:                  YES (58.76.100)
      avutil:                    YES (56.70.100)
      swscale:                   YES (5.9.100)
      avresample:                YES (4.0.0)
    GStreamer:                   NO
    DirectShow:                  YES
    Media Foundation:            YES
      DXVA:                      YES

  Parallel framework:            Concurrency

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.11.0 [2021.11.0]
           at:                   D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/icv
    Intel IPP IW:                sources (2021.11.0)
              at:                D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/iw
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  OpenCL:                        YES (NVD3D11)
    Include path:                D:/a/opencv-python/opencv-python/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  Python 3:
    Interpreter:                 C:/hostedtoolcache/windows/Python/3.7.9/x64/python.exe (ver 3.7.9)
    Libraries:                   C:/hostedtoolcache/windows/Python/3.7.9/x64/libs/python37.lib (ver 3.7.9)
    numpy:                       C:/hostedtoolcache/windows/Python/3.7.9/x64/lib/site-packages/numpy/core/include (ver 1.17.0)
    install path:                python/cv2/python-3

  Python (for build):            C:\hostedtoolcache\windows\Python\3.7.9\x64\python.exe

  Java:                          
    ant:                         NO
    Java:                        YES (ver 1.8.0.392)
    JNI:                         C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include/win32 C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-install
-----------------------------------------------------------------


  


  edit : Receiving the stream with ffplay from command line :

  


  >ffplay.exe -i "rtmp://0.0.0.0:8000/live"  -listen 1 -f flv
ffplay version 2024-02-04-git-7375a6ca7b-full_build-www.gyan.dev Copyright (c) 2003-2024 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
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• Google Analytics 4 (GA4) vs Matomo

  7 avril 2022, par Erin

  Google announced that Universal Analytics’ days are numbered. Universal Analytics will be replaced by Google Analytics 4 (or GA4) on the 1st of July 2023. 

  If Google Analytics users want to compare year-on-year data, they have until July 2022 to get set up and start collecting data before the sun sets on Universal Analytics (or UA).

  But is upgrading to Google Analytics 4 the right move ? There’s a lot to consider, and many organisations are looking for an alternative to Google Analytics. So in this blog, we’ll compare GA4 to Matomo – the leading Google Analytics alternative. 

  In this blog, we’ll look at :

  What is Matomo ?

  Matomo is a powerful privacy-first web analytics platform that gives you 100% data ownership. First launched in 2007, Matomo is now the world’s leading open-source web analytics platform and is used by more than 1 million websites. 

  Matomo’s core values are based on ethical data collection and processing. Consistently more businesses and organisations from around the globe are adopting data-privacy-compliant web analytics solutions like Matomo. 

  Matomo offers both Cloud and On-Premise solutions (and a five-star rated WordPress plugin), making for an adaptable and flexible solution. 

  What is Google Analytics 4 ?

  Google Analytics 4 is the latest version of Google Analytics and represents a completely new approach to data-modelling than its predecessor, Universal Analytics. For an in-depth look at how GA4 and UA compare, check out this Google Analytics 4 vs Universal Analytics comparison. 

  Google Analytics 4 will soon be the only available version of analytics software from Google. So what’s the issue ? Surely, in 2022, Google makes it easy to migrate to their newest (and only) analytics platform ? Not quite.

  Google Analytics 4 vs Matomo

  Whilst the core purpose of GA4 and Matomo is similar (providing web analytics that help to optimise your website and grow your business), there are several key differences that organisations should consider before making the switch.

  Importing Historical Data from Universal Analytics

  Google Analytics 4

  Users assuming that historical data from Universal Analytics could be imported into Google Analytics 4 were faced with swift disappointment. Unfortunately, Google Analytics 4 does not have an option to import data from its predecessor, Universal Analytics. This means that businesses won’t be able to import and compare data from previous years.

  Matomo

  If you don’t want to start from scratch with your web analytics data, then Matomo is an ideal solution for data continuity. Matomo offers users the ability to import their historical Universal Analytics data. So you can keep all that valuable historical data you’ve collected over the years.

  

  User Interface

  Google Analytics 4

  GA4’s new user interface has been met with mixed reviews. Many claim that it’s overly complex and difficult to navigate. Some have even suggested that the tool has been designed specifically for enterprises with specialised analytics teams. 

  

  Matomo

  Matomo, on the other hand, is recognised for an easy to use interface, with a rating of 4.5 out of 5 stars for ease of use on Capterra. Matomo perfectly balances powerful features with a user-friendly interface so valuable insights are only a click away. There’s a reason why over 1 million websites are using Matomo. 

  

  Advanced Behavioural Analytics Features 

  Google Analytics 4

  While Google Analytics is undoubtedly robust in some areas (machine learning, for instance), what it really lacks is advanced behavioural analytics. Heatmaps, session recordings and other advanced tools can give you valuable insights into how users are engaging with your site. Well beyond pageviews and other metrics.

  Unfortunately, with this new generation of GA, Google still hasn’t introduced these features. So users have to manage subscriptions and tracking in third-party behavioural analytics tools like Hotjar or Lucky Orange, for example. This is inefficient, costly and time-consuming to manage. 

  

  Matomo 

  Meanwhile, Matomo is a one-stop shop for all of your web analytics needs. Not only do you get access to the metrics you’ve grown accustomed to with Universal Analytics, but you also get built-in behavioural analytics features like Heatmaps, Scroll Depth, Session Recordings and more. 

  Want to know if visitors are reaching your call to action at the bottom of the page ? Scroll Depth will answer that.

  Want to know why visitors aren’t clicking through to the next page ? Heatmaps will give you the insights you need.

  You get the picture – the full picture, that is. 

  

  Data Accuracy

  Google Analytics 4

  GA4 aims to make web and app analytics more privacy-centric by reducing the reliance on cookies to record certain events across platforms and devices. 

  However, when site and application visitors opt-out of cookie tracking, GA4 instead relies on machine learning to fill in the gaps. Data sampling could mean that your business is making business decisions based on inaccurate reports. 

  Matomo

  Data is the backbone of web analytics, so why make critical business decisions on sampled data ? With Matomo, you’re guaranteed 100% unsampled accurate data. So you can rest assured that any decisions you make are based on actual facts. 

  Compliance with Privacy Laws (GDPR, CCPA, etc.) 

  Google Analytics 4

  Google is making changes in an attempt to become compliant with privacy laws. However, even with GA4, users are still transferring data to the US. For this reason, both Austrian and French governments have ruled Google Analytics illegal under GDPR.

  The only possible workaround is “Privacy Shield 2.0”, but GDPR experts are still sceptical of this one. 

  Matomo

  If compliance with global privacy laws is a concern (and it should be), then Matomo is the clear winner here. 

  As an EU hosted web analytics tool, your data is stored in Europe, and no data is transferred to the US. On the other hand, if you choose to self-host, the data is stored in your country of choice.

  In addition, with cookieless tracking enabled, you can say goodbye to those pesky cookie consent screens. 

  Also, remember that under GDPR, and many other data privacy laws like CCPA and LGPD, end users have a legal right to access, amend and/or erase the personal data collected about them. 

  With Matomo you get 100% ownership of your web analytics data. This means that we don’t on-sell to third parties ; can’t claim ownership of the data ; and you can export your data at any time.

  

  Wrap up

  At the end of the day, the worst thing an organisation can do is nothing. Waiting until July 2023 to migrate to GA4 or another web analytics platform would be very disruptive and costly. Organisations need to consider their options now and start migrating in the next few months. 

  With all that said, moving to Google Analytics 4 could prove to be a costly and time-consuming operation. The global trend towards increased data privacy is a threat to platforms like Google Analytics which uses data for advertising and transfers data across borders.

  With Matomo, you get an easy to use all-in-one web analytics platform and keep your historical Universal Analytics data. Plus, you can future-proof your business by being compliant with global privacy laws and get access to advanced behavioural analytics features. 

  There’s a lot to weigh up here but fortunately, getting started with Matomo is easy. Try it free for 21-days (no credit card required) and see for yourself why over 1 million websites choose Matomo. 

  While this is the end of the road for Universal Analytics, it’s also an opportune time for organisations to find a better fit web analytics tool. 

  21 day free trial. No credit card required.

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now