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• "undefined reference to av···@···"ffmpeg error，when i cross compile opencv4.5.3 which include ffmpeg lib

  11 mai 2024, par caiping Peng

  everyone,It is sorry to bother you,but i need some help.
I'm working on an embedded deployment project,doing object detection work to real-time video stream. So I have to port my c++ inference prog to RKNN1808 platform. I compile this program with CMake tool,but I cant finish my work because opencv lib cant be compiled rightly.
To FFmpeg,my configure commend is following :

  


  ./configure --enable-cross-compile --cross-prefix=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu- --target-os=linux --arch=aarch64 --prefix=/usr/local/ffmpeg  --enable-shared


  


  then I am gonna show you the ffmpeg version :

  


  libavutil      56. 70.100
libavcodec     58.134.100
libavformat    58. 76.100
libavdevice    58. 13.100
libavfilter     7.110.100
libswscale      5.  9.100
libswresample   3.  9.100
libpostproc    55.  9.100


  


  next ,I use following commend to build cmake project :

  


  cmake -D CMAKE_BUILD_TYPE=RELEASE  -D CMAKE_C_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc -D CMAKE_CXX_COMPILER=/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ -D BUILD_SHARED_LIBS=ON -D CMAKE_CXX_FLAGS=-fPIC -D CMAKE_C_FLAGS=-fPIC -D CMAKE_EXE_LINKER_FLAGS=-lpthread -ldl -D ENABLE_PIC=ON -D WITH_1394=OFF -D WITH_ARAVIS=OFF -D WITH_ARITH_DEC=ON -D WITH_ARITH_ENC=ON -D WITH_CLP=OFF -D WITH_CUBLAS=OFF -D WITH_CUDA=OFF -D WITH_CUFFT=OFF -D WITH_FFMPEG=ON -D WITH_GSTREAMER=ON -D WITH_GSTREAMER_0_10=OFF -D WITH_HALIDE=OFF -D WITH_HPX=OFF -D WITH_IMGCODEC_HDR=ON -D WITH_IMGCODEC_PXM=ON -D WITH_IMGCODEC_SUNRASTER=ON -D WITH_INF_ENGINE=OFF -D WITH_IPP=OFF -D WITH_ITT=OFF -D WITH_JASPER=ON -D WITH_JPEG=ON -D WITH_LAPACK=ON -D WITH_LIBREALSENSE=OFF -D WITH_NVCUVID=OFF -D WITH_OPENCL=OFF -D WITH_OPENCLAMDBLAS=OFF -D WITH_OPENCLAMDFFT=OFF -D WITH_OPENCL_SVM=OFF -D WITH_OPENEXR=OFF -D WITH_OPENGL=OFF -D WITH_OPENMP=OFF -D WITH_OPENNNI=OFF -D WITH_OPENNNI2=OFF -D WITH_OPENVX=OFF -D WITH_PNG=OFF -D WITH_PROTOBUF=OFF -D WITH_PTHREADS_PF=ON -D WITH_PVAPI=OFF -D WITH_QT=OFF -D WITH_QUIRC=OFF  -D WITH_TBB=OFF -D WITH_TIFF=ON -D WITH_VULKAN=OFF -D WITH_WEBP=ON -D WITH_XIMEA=OFF -D CMAKE_INSTALL_PREFIX=../CrossCompileResult  -D WITH_GTK=OFF  -D BUILD_opencv_dnn=OFF ..


  


  following is the outpt about FFmpeg :

  


  --   Video I/O:
--     FFMPEG:                      YES
--       avcodec:                   YES (58.134.100)
--       avformat:                  YES (58.76.100)
--       avutil:                    YES (56.70.100)
--       swscale:                   YES (5.9.100)
--       avresample:                NO
--     GStreamer:                   NO
--     v4l/v4l2:                    YES (linux/videodev2.h)



  


  After building the cmake project,I compiled this project with comment 【make -j16】.After not so long time,I got the Error :

  


  [ 49%] Linking CXX executable ../../bin/opencv_annotation
[ 49%] Building CXX object modules/ts/CMakeFiles/opencv_ts.dir/src/ts_tags.cpp.o
[ 49%] Built target opencv_annotation
[ 49%] Linking CXX executable ../../bin/opencv_visualisation
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavcodec.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavformat.so.58, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libavutil.so.56, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
/home/midsummer/Tool/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/../lib/gcc/aarch64-linux-gnu/6.3.1/../../../../aarch64-linux-gnu/bin/ld: warning: libswscale.so.5, needed by ../../lib/libopencv_videoio.so.4.5.3, not found (try using -rpath or -rpath-link)
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_init_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_riff_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_get_mov_video_tags@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_decoder_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_transfer_data@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_malloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_open@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_alloc_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_sub_q@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_network_init@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_flush_buffers@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_find_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_receive_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_close_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_seek_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_freeContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_descriptor_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_scale@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_unref@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_parse_string@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_freep@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_find_stream_info@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_read_frame@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_free_context@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_default_get_format@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_init@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_register_all@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_get_buffer@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_sample_aspect_ratio@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_new_stream@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_constraints_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_unref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_write_trailer@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_rescale_ts@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_get_by_name@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_send_frame@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_config@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_buffer_ref@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_get@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_free@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_decoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_open_input@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_lockmgr_register@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_packet_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_create_derived@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_send_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_alloc@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_level@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_get_buffer_size@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_open2@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_is_encoder@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_guess_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_image_fill_arrays@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_receive_packet@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `sws_getCachedContext@LIBSWSCALE_5'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_tag@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_hwframe_constraints@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_ctx_create@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_iterate@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_log_set_callback@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_opt_set@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_codec_get_id@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avformat_write_header@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_parameters_copy@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_pix_fmt_to_codec_tag@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwframe_ctx_alloc@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_mallocz@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_find_input_format@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_dict_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_hw_frames_parameters@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_get_type_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avio_close@LIBAVFORMAT_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_frame_free@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_bsf_init@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_close@LIBAVCODEC_58'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `av_hwdevice_find_type_by_name@LIBAVUTIL_56'
../../lib/libopencv_videoio.so.4.5.3: undefined reference to `avcodec_get_context_defaults3@LIBAVCODEC_58'
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:89: bin/opencv_visualisation] Error 1
make[1]: *** [CMakeFiles/Makefile2:3357: apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
[ 49%] Linking CXX shared library ../../lib/libopencv_calib3d.so
[ 49%] Built target opencv_calib3d
[ 50%] Linking CXX static library ../../lib/libopencv_ts.a
[ 50%] Built target opencv_ts
make: *** [Makefile:163: all] Error 2



  


  I dont know what's wrong with it,It has confused me for a few days,I real hope someone can help me solve the prob.
I promise the the ffmpeg version match the version of opencv strictly,promising the PKG_CONFIG_PATH is right.

  


  I have tried many method like changing opencv version or ffmpeg version,recompiling the ffmpeg,changing PKG_CONFIG_PATH,coping ffmpeg pc file from /usr/local/ffmpeg/lib/pkgconfig to /usr/local/lib/pkgconfig.
I hope somebody can give some idea about how to solve this problem.

  


• 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.

• How to Choose a GDPR Compliant Web Analytics Solution

  2 mars 2022, par Matthieu Aubry — Privacy

  Since the launch of GDPR, one big question has lingered around with uncertainty – is Google Analytics GDPR compliant ? The current GDPR enforcement trend happening across the EU is certainly shedding some light on this question.

  Starting with the Austrian Data Protection Authority’s ruling on Google Analytics and more recently, CNIL (the French Data Protection Authority) has followed suit by also ruling Google Analytics illegal to use. Organisations with EU-based web visitors are now scrambling to find a compliant solution.

  The French Data Protection Authority (CNIL) has already started delivering formal notices to websites using Google Analytics, so now is the time to act. According to CNIL, organisations have two options :

  1. Ceasing use of the Google Analytics functionality (under the current conditions) 
  2. Use a compliant web analytics tool that does not transfer data outside the EU

  Getting started 

  For organisations considering migrating to a compliant web analytics tool, I’ve outlined below the things you need to consider when weighing up compliant web analytics tools. Once you’ve made a choice, I’ve also included a step-by-step guide to migrating away from Google Analytics. This guide is useful regardless of which GDPR compliant analytics provider you choose.

  Before getting started, I recommend that you document your findings against the following considerations while reviewing GDPR compliant Google Analytics alternatives. This document can then be shared with your Data Protection Officer (DPO) to get their final recommendation.

  10 key considerations when selecting a GDPR compliant web analytics tools

  Many tools will claim to be GDPR compliant so it’s important that you do your due diligence and review tools against the following considerations. 

  1. Where does the tool store data ? 

  The rulings in France and Austria were based on the fact that Google Analytics stores data in the US, which does not have an adequate level of data protection. Your safest option is to find a tool that legally stores data in the EU.

  You should be able to find out where the data is stored in the organisation’s privacy policy. Generally, data storage information can be found under sections titled “Subprocessors” and “Third-party services”. Check out the Matomo Privacy Policy as an example. 

  If you’re unable to easily find this information or it’s unclear, reach out to the organisation for more information.

  2. Does the tool offer anonymous tracking ?

  Anonymous tracking comes with many benefits, including :

  • The ability to track visitors without a cookie consent screen. Due to the privacy-respecting aspect of cookieless tracking, you don’t need to worry about the extra steps involved with compliant cookie banners.
  • More accurate data. When visitors deny tracking cookies, you lose out on valuable data. With anonymous tracking there is no data lost as you don’t need consent to track.
  • Simplified GDPR compliance. With this enabled, there are fewer steps you need to take to get GDPR compliant and stay GDPR compliant.

  For those reasons, it may be important for you to select a tool that offers anonymous tracking functionalities. The level of anonymous tracking you require will depend on your situation but you should look out for tools that allow you to :

  • Disable fingerprinting 
  • Disable user profiles 
  • Anonymise data
  • Cookieless tracking

  If you want to read more about data anonymization, check out this guide on data anonymization in web analytics.

  3. Does the tool integrate with my existing tech stack ?

  You’ll want to ensure that a new web analytics tool will play well with other tools in your tech stack including things like your CMS (content management system), eCommerce shop, etc. You should list out all the existing tools that currently integrate with your Google Analytics and check that the same integrations can be re-created with the new tool, via integrations or APIs.

  If not, it could become costly trying to connect your existing tech stack to a new solution.

  4. Does the tool offer the same features and insights you are currently using in Google Analytics ? Or more, if necessary ? 

  Just because you are moving to a new web analytics platform, doesn’t mean you have to give up the insights, reports and features you’ve grown accustomed to with Google Analytics. Ensuring that a new platform provides the same features and reports that you value the most will result in a smoother transition away from Google Analytics.

  It’s unlikely that a new tool will have all of the same features as Google Analytics, so I’d recommend listing out and prioritising your business-critical features and reports. 

  If I had to guess, you probably set up Google Analytics years ago because it was the default option. Now is your chance to make the most of this switch from Google Analytics and find a tool that offers additional reports and features that better aligns with your business. If time permits, I’d highly recommend that you consider other features or reports that you might have been missing out on while using Google Analytics.

  Check out this comparison of Google Analytics vs Matomo to see side-by-side feature comparison.

  5. Does the tool accept Google Analytics data imports ? 

  The historical data in Google Analytics is a critical asset for many businesses. Fortunately, some tools accept Google Analytics data imports so you don’t lose all of the data you’ve generated over time.

  However, it’s important to note that any data you import from Google Analytics to a new tool needs to be compliant data. I’ll cover this more below.

  6. Does the tool provide conversion tracking exports ? 

  Do you invest in paid advertising ? If you do, then tracking the conversions from people clicking on these paid ads is critical in assessing your return on investment. Since sending IP addresses or other personal information to the US is illegal under GDPR, we can only assume that this will also apply to advertising pixel/conversion tracking (e.g., Facebook pixel, Google Ads conversion tracking, etc). 

  As an example, Matomo offers conversion tracking exports so you can get a better understanding of ad performance while meeting privacy laws and without requiring consent from users. See how it works with Matomo’s conversion tracking exports. 

  7. How will you train up your in-house team ? Or can you hire a contractor ?

  This is a common concern of many, and rightfully so. You’ll want to confirm what resources are readily available so you can hit the ground running with your new web analytics tool. If you’d prefer to train up your in-house team, check the provider’s site for training resources, videos, guides, etc.

  If you’d rather hire an external contractor, we recommend heading to LinkedIn, reaching out to your community or asking the provider if they have any recommendations for contractors.

  In addition, check that the provider offers technical support or a forum, in case you have specific questions and need help.   

  8. Does the tool offer self-hosting ? (optional)

  For organisations that want full control over their data and storage location, an on-premise web analytics tool will be the preferred option. From a GDPR perspective, this is also the easiest option for compliance.

  Keep in mind that this requires resources, regular maintenance, technical knowledge and/or technical consultants. If you’re unsure which option is best for your organisation, check out our on-premise vs cloud web analytics comparison breakdown.

  Find out more about self-hosting Matomo.

  9. Is the tool approved by the CNIL for tracking without consent ?

  This is an important step for websites with French users. This step will help narrow down your selection of tools. The CNIL offers a programme to identify web analytics solutions that can be used without tracking consent. The CNIL’s list of recommended web analytics tools can act as your starting point for solutions to review.

  While this step is specific to sites with French users, it can also be helpful for websites with visitors from any other EU country.

  Benefits of consent-free tracking

  There are many benefits of tracking without consent.
  

  For one, it simplifies GDPR compliance and reduces the chances of GDPR breaches and fines. Cookie consent screens have recently been the target for EU Data Protection Authorities because many websites are unknowingly serving cookie consent screens that do not meet GDPR requirements. 

  Yet another benefit, and quite possibly the most important is more accurate data. Even if a website displays a user-friendly, lawful consent screen, the majority of users will either ignore or reject cookie consent. Legally website owners can’t track anything unless the visitor gives consent. So not having a cookie consent screen ensures that every visit is tracked and your web analytics data is 100% accurate. 

  Lastly, many visitors have grown fatigued and frustrated with invasive cookie consent screens. Not having one on your site creates a user-friendly experience, which will likely result in longer user sessions and lower bounce rates.

  10. Does the tool offer a Data Processing Agreement (DPA) ? 

  Technically, any GDPR compliant web analytics tool should offer a DPA but for the sake of completeness, I’ve added this as a consideration. Double check that any tools you are looking at provide this legally binding document. This should be located in the Privacy Policy of the web analytics provider, if not reach out to request it.

  As an example, here’s Matomo’s Data Processing Agreement which can be found in our Privacy Policy under Subprocessors. 

  That wraps up the key considerations. When it comes to compliance, privacy and customer data, Matomo leads the way. We are looking forward to helping you achieve GDPR compliance easily. Start your free 21-day trial of Matomo now – no credit card required.

  Try Matomo Cloud

  A step-by-step guide to migrating from Google Analytics

  Once you’ve identified a tool that suits your needs and your Data Protection Officer (DPO) has approved, you’re ready to get started. Here’s a simple step-by-step guide with all the important steps for you to follow :

  1. Before getting started, you should sign or download the Data Processing Agreement (DPA) offered by your new web analytics provider.

  2. Register for the new tool and configure it for compliance. The provider should offer guides on how to configure for GDPR compliance. This will include things like giving your users an easy way to opt-out of all tracking, turning on cookieless tracking or asking users for consent and anonymizing data and IP addresses, for instance.

  3. Inform your organisation about the change. Whether your colleagues use the tool or not, it’s important that you share information about the new tool with your staff. Let them know what the tool will be used for, who will use the tool and how it complies with GDPR. 

  4. Let your DPO know that you’ve removed Google Analytics and have implemented the new tool.

  5. Update your records of processing activities to include the new tool.

  6. Update your privacy policy. You’ll need to include details about the web analytics provider, where the data is stored, what data is being collected, how long the data will be stored and why the data is being collected. The web analytics tool should readily have this information for you.

  As an example, if you decide to use Matomo as your web analytics tool, we provide a Privacy Policy template for you to use on your site and a guide on how to complete your privacy policy under GDPR with Matomo. Note that these are only applicable if you are using Matomo.

  In addition, if the tool has an opt-out feature, you will also need to put the opt-out into the privacy policy (e.g., when using cookieless tracking).

  7. Now, the exciting part. Add the tracking code to your site by following the steps provided by the web analytics tool. 

  If you’re not comfortable with this step, the provider should offer steps to do this and you can share this with your web developer.

  8. Once added, login to your tool and check to see if traffic is being tracked.

  9. If your tool does not offer Google Analytics data imports or you do not need the historical data in your new tool, go to step 11. 

  To plan for your Google Analytics data migration, you’ll first need to establish what historical data is compliant with GDPR.

  For example, you shouldn’t import any data stored beyond the retention period established in your Privacy Policy or any personally identifiable information (PII) like IP addresses that aren’t anonymised. Discuss this further with your DPO.

  10. Once you’ve established what data you can legally import, then you can begin the import. Follow the steps provided by your new web analytics solution provider.

  11. Remove Google Analytics tracking code from your site. This will stop the collection of your visitors data by Google as well as slightly increase the page load speed.

  If you still haven’t made a choice yet, try Matomo free for 21-days and see why over 1 million websites choose Matomo.