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• 5 Key Benefits of Using a Tag Manager

  12 décembre 2021, par erin — Analytics Tips, Marketing

  Websites today have become very complex to manage, and as you continue to look for ways to optimise your website, you’ll want to consider using a Tag Manager. 

  A Tag Manager will help your marketing team seamlessly track how your visitors are engaging with your website’s elements. Without a Tag Manager, you are missing out on business-altering insights.

  In this blog, we’ll cover :

  Tag Manager overview 

  A Tag Manager (AKA Tag Management System or TMS) is a centralised system for implementing, managing and tracking events. A tag is just another word for a piece of code on a website that tracks a specific event. 

  An example of a tag tracking code might be Facebook pixels, ad conversions and other website activities such as signing up to a newsletter or PDF download. 

  Triggers are the actual actions that website visitors take that activate the tag. Examples of triggers are things like : 

  • A thank you page view to show that a visitor has completed a conversion action
  • Clicking a download or sign up button 
  • Scroll depth or how far down users are scrolling on your webpage 

  Each of these will give you insights into how your website is performing and how your users are engaging with your content. Going back to the scroll depth trigger example, this would be particularly helpful for validating bounce rate and finding out where users are dropping off on a page. Discover other ways to take advantage of tags and event tracking. 

  

  5 key benefits of a Tag Manager

  1. Removes the risks of website downtime 

  Tags are powerful for in-depth web analytics. However, tagging opens up the potential for non-technical team members to break the front-end of your website in a couple of clicks. 

  A Tag Manager reduces that risk. For example, Matomo Tag Manager lets you preview tags to see if they are firing before pushing them live. You can also give specific users restricted access so you can approve any tagging before it goes live. 

  Tag Managers protect the functionality of your website and ensure that there is no downtime.

  2. Your website will load faster 

  When it comes to the success of your website, page speed is one of the most important factors. 

  Each time you add a tag to your site, you run the risk of slowing down the page speed. This can quickly build up to a poor performing site and frustrate your visitors.

  You can’t track tags if visitors won’t even stay long enough for your site to load. In fact, 1 in 4 visitors would abandon a website that takes more than 4 seconds to load. According to Deloitte, just a 0.1 second difference in loading speed can affect every step of your customer journey. 

  A Tag Manager, on the other hand, is a lightweight option only requiring one single tag. Using a Tag Manager to track events can make all the difference to your website’s performance and user experience.

  3. Greater efficiency for marketing

  Time is critical in marketing. The longer it takes for a campaign to launch, the greater the chances are that you’re missing out on sales opportunities.

  Waiting for the IT team to tag a thank you page before setting an ad live is inefficient and impacts your bottom line.

  Equipping marketing with a Tag Manager means that they’ll be able to launch campaigns faster and more effectively.

  Check out our Marketer’s Guide to Successful Website Event Tracking for more.

  4. Control all of your tracking and marketing tags in one place 

  Keeping track of what tags are on your site and where they’re located is a complicated task if you aren’t using a Tag Manager. Unmanaged tags can quickly pile up and result in errors with your analytics, like counting conversions twice. 

  Using a Tag Manager to centralise your tags in one easy to manage place reduces the chances of human errors. Instead, your team will be able to quickly see what tags are already in place so they aren’t doubling up on tracking.

  5. Reduce work for the IT team 

  Let’s face it, the IT team has more critical tasks at hand than adding tags to the website. Freeing up your IT team to focus on higher priority tasks should always be a goal.

  Tagging, while crucial for marketing, has the potential to create a lot of extra work for your website developers. Inserting code for each individual tag is time-consuming and means you aren’t collecting data in the meantime.

  Rather than overloading your IT team, empower your marketing team with the ability to add tags with a few clicks. 

  How to choose a Tag Management System

  There are many tools to choose from and the default option tends to be Google Tag Manager (GTM). But before you implement GTM or any other Tag Management Solution, we highly recommend asking these questions :

  1. What are my goals for a Tag Manager ? Before purchasing a Tag Manager, or any tool for that matter, understanding your goals upfront is best practice.
  2. Does the solution offer Tag Manager training resources ? If online Tag Manager training and educational resources are available for the tool, then you’ll be able to hit the ground running and start to see an ROI instantly.
  3. Can I get online support ? In case you need any help with the tool, having access to online support is a big bonus. 
  4. Is it compliant with privacy regulations ? If your business is already compliant, in the process of becoming compliant or future-proofing your tech stack for looming privacy regulations, then researching this is crucial. 
  5. How much does it cost ? If it’s “free”, find out how and why. In most cases, free solutions are just vehicles for collecting data to advertise to your users. 
  6. What do others think about the Tag Manager ? Check out reviews on sites like Capterra or G2 to find out how other businesses rate the tool. 

  Google Tag Manager alternative

  As privacy becomes a greater concern globally for end-users and governments, many businesses are looking for alternatives to the world’s largest advertising company – Google.

  Matomo Tag Manager is more than a Google Tag Manager alternative. With Matomo Tag Manager, you get a GDPR, HIPAA, CCPA and PECR compliant, open source Tag Manager and your data is 100% yours to own.

  Plus, with Matomo Tag Manager you only need one single tracking code for all of your website and tag analytics. No matter what you are tracking (scrolls, clicks, downloads, Heatmaps, visits, etc.), you will only ever need one piece of code on your website and one tool to manage it all. 

  The takeaway 

  Tagging is powerful but can quickly become complicated, risky and time-consuming. Tag Managers reduce these obstacles allowing you to set tags and triggers effortlessly. It empowers marketing teams, streamlines processes and removes the reliance on IT.

  Ready to try Matomo Tag Manager ? Start your 21-day free trial now – no credit card required. 

• opencv does not find ffmpeg functions during compilation (make)

  17 avril 2022, par titicplusplus

  I am currently trying to compile OpenCV with CUDA.
So I downloaded opencv 4.5.5 and opencv_contrib and followed this tutorial : https://gist.github.com/raulqf/f42c718a658cddc16f9df07ecc627be7

  


  cd opencv-4.5.5/
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D WITH_CUDA=ON \
-D BUILD_opencv_cudacodec=OFF \
-D WITH_CUDNN=OFF \
-D OPENCV_DNN_CUDA=OFF \
-D CUDA_ARCH_BIN=7.5 \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_PC_FILE_NAME=opencv.pc \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.5.5/modules ../


  


  The cmake command generated these lines :

  


  -- General configuration for OpenCV 4.5.5 =====================================
--   Version control:               unknown
-- 
--   Extra modules:
--     Location (extra):            /mnt/704E048C4E044D72/build/opencv/opencv_contrib-4.5.5/modules
--     Version control (extra):     unknown
-- 
--   Platform:
--     Timestamp:                   2022-04-17T16:01:44Z
--     Host:                        Linux 5.4.0-107-lowlatency x86_64
--     CMake:                       3.16.3
--     CMake generator:             Unix Makefiles
--     CMake build tool:            /usr/bin/make
--     Configuration:               RELEASE
-- 
--   CPU/HW features:
--     Baseline:                    SSE SSE2 SSE3
--       requested:                 SSE3
--     Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX
--       requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
--       SSE4_1 (18 files):         + SSSE3 SSE4_1
--       SSE4_2 (2 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
--       FP16 (1 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
--       AVX (5 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
--       AVX2 (33 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2
--       AVX512_SKX (8 files):      + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX
-- 
--   C/C++:
--     Built as dynamic libs?:      YES
--     C++ standard:                11
--     C++ Compiler:                /usr/bin/c++  (ver 8.4.0)
--     C++ flags (Release):         -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
--     C++ flags (Debug):           -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Wsuggest-override -Wno-delete-non-virtual-dtor -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
--     C Compiler:                  /usr/bin/cc
--     C flags (Release):           -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
--     C flags (Debug):             -fsigned-char -ffast-math -W -Wall -Werror=return-type -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wuninitialized -Wno-comment -Wimplicit-fallthrough=3 -Wno-strict-overflow -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections  -msse -msse2 -msse3 -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
--     Linker flags (Release):      -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed  
--     Linker flags (Debug):        -Wl,--exclude-libs,libippicv.a -Wl,--exclude-libs,libippiw.a   -Wl,--gc-sections -Wl,--as-needed  
--     ccache:                      NO
--     Precompiled headers:         NO
--     Extra dependencies:          m pthread cudart_static dl rt nppc nppial nppicc nppicom nppidei nppif nppig nppim nppist nppisu nppitc npps cublas cufft -L/usr/lib/x86_64-linux-gnu
--     3rdparty dependencies:
-- 
--   OpenCV modules:
--     To be built:                 alphamat aruco barcode bgsegm bioinspired calib3d ccalib core cudaarithm cudabgsegm cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cvv datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hdf hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo plot python2 python3 quality rapid reg rgbd saliency sfm shape stereo stitching structured_light superres surface_matching text tracking ts video videoio videostab wechat_qrcode xfeatures2d ximgproc xobjdetect xphoto
--     Disabled:                    cudacodec world
--     Disabled by dependency:      -
--     Unavailable:                 java julia matlab ovis viz
--     Applications:                tests perf_tests apps
--     Documentation:               NO
--     Non-free algorithms:         YES
-- 
--   GUI:                           QT5
--     QT:                          YES (ver 5.12.8 )
--       QT OpenGL support:         YES (Qt5::OpenGL 5.12.8)
--     GTK+:                        YES (ver 3.24.20)
--       GThread :                  YES (ver 2.64.6)
--       GtkGlExt:                  NO
--     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGL.so /usr/lib/x86_64-linux-gnu/libGLU.so)
--     VTK support:                 NO
-- 
--   Media I/O: 
--     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
--     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 80)
--     WEBP:                        build (ver encoder: 0x020f)
--     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
--     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.1.0)
--     JPEG 2000:                   build (ver 2.4.0)
--     OpenEXR:                     /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2_3)
--     HDR:                         YES
--     SUNRASTER:                   YES
--     PXM:                         YES
--     PFM:                         YES
-- 
--   Video I/O:
--     DC1394:                      YES (2.2.5)
--     FFMPEG:                      YES
--       avcodec:                   YES (58.54.100)
--       avformat:                  YES (58.29.100)
--       avutil:                    YES (56.31.100)
--       swscale:                   YES (5.5.100)
--       avresample:                YES (4.0.0)
--     GStreamer:                   YES (1.16.2)
--     v4l/v4l2:                    YES (linux/videodev2.h)
-- 
--   Parallel framework:            TBB (ver 2020.1 interface 11101)
-- 
--   Trace:                         YES (with Intel ITT)
-- 
--   Other third-party libraries:
--     Intel IPP:                   2020.0.0 Gold [2020.0.0]
--            at:                   /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/icv
--     Intel IPP IW:                sources (2020.0.0)
--               at:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/build/3rdparty/ippicv/ippicv_lnx/iw
--     VA:                          NO
--     Lapack:                      NO
--     Eigen:                       YES (ver 3.3.7)
--     Custom HAL:                  NO
--     Protobuf:                    build (3.19.1)
-- 
--   NVIDIA CUDA:                   YES (ver 10.1, CUFFT CUBLAS FAST_MATH)
--     NVIDIA GPU arch:             75
--     NVIDIA PTX archs:
-- 
--   OpenCL:                        YES (no extra features)
--     Include path:                /mnt/704E048C4E044D72/build/opencv/opencv-4.5.5/3rdparty/include/opencl/1.2
--     Link libraries:              Dynamic load
-- 
--   Python 2:
--     Interpreter:                 /usr/bin/python2.7 (ver 2.7.18)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.18)
--     numpy:                       /usr/lib/python2.7/dist-packages/numpy/core/include (ver 1.16.5)
--     install path:                lib/python2.7/dist-packages/cv2/python-2.7
-- 
--   Python 3:
--     Interpreter:                 /usr/bin/python3 (ver 3.8.10)
--     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.8.so (ver 3.8.10)
--     numpy:                       /home/famillevincent/.local/lib/python3.8/site-packages/numpy/core/include (ver 1.22.3)
--     install path:                lib/python3.8/site-packages/cv2/python-3.8
-- 
--   Python (for build):            /usr/bin/python2.7
-- 
--   Java:                          
--     ant:                         NO
--     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
--     Java wrappers:               NO
--     Java tests:                  NO
-- 
--   Install to:                    /usr/local
-- -----------------------------------------------------------------
-- 
-- Configuring done
-- Generating done


  


  CMake have detected ffmpeg libraries, but when I run make -j8. I have this error :

  


  [ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/calibPipeline.cpp.o
Scanning dependencies of target opencv_cudafilters
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_transfer_data »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_config »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_hwframe_constraints »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_get_buffer »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_ctx_create »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_send_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_parameters_copy »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_find_type_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_alloc »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_codec_iterate »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_init »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwdevice_get_type_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_constraints_free »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_receive_frame »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_get_by_name »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « avcodec_get_hw_frames_parameters »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_bsf_send_packet »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_hwframe_ctx_create_derived »
/usr/bin/ld : ../../lib/libopencv_videoio.so.4.5.5 : référence indéfinie vers « av_packet_alloc »
collect2: error: ld returned 1 exit status
make[2]: *** [apps/visualisation/CMakeFiles/opencv_visualisation.dir/build.make:92 : bin/opencv_visualisation] Erreur 1
make[1]: *** [CMakeFiles/Makefile2:11412 : apps/visualisation/CMakeFiles/opencv_visualisation.dir/all] Erreur 2
make[1]: *** Attente des tâches non terminées....
[ 39%] Building CXX object apps/interactive-calibration/CMakeFiles/opencv_interactive-calibration.dir/frameProcessor.cpp.o


  


  So what can I do to compile opencv with cuda and ffmpeg ?
  
Thank you in advance for your answers.

  


  I use Ubuntu 20.04 with g++8

  


• 10 Key Google Analytics Limitations You Should Be Aware Of

  9 mai 2022, par Erin

  Google Analytics (GA) is the biggest player in the web analytics space. But is it as “universal” as its brand name suggests ?
  

  Over the years users have pointed out a number of major Google Analytics limitations. Many of these are even more visible in Google Analytics 4. 

  Introduced in 2020, Google Analytics 4 (GA4) has been sceptically received. As the sunset date of 1st, July 2023 for the current version, Google Universal Analytics (UA), approaches, the dismay grows stronger.

  To the point where people are pleading with others to intervene : 

  

  Main limitations of Google Analytics

  Google Analytics 4 is advertised as a more privacy-centred, comprehensive and “intelligent” web analytics platform. 

  According to Google, the newest version touts : 

  • Machine learning at its core provides better segmentation and fast-track access to granular insights 
  • Privacy-by-design controls, addressing restrictions on cookies and new regulatory demands 
  • More complete understanding of customer journeys across channels and devices 

  Some of these claims hold true. Others crumble upon a deeper investigation. Newly advertised Google Analytics capabilities such as ‘custom events’, ‘predictive insights’ and ‘privacy consent mode’ only have marginal improvements. 

  Complex setup, poor UI and lack of support with migration also leave many other users frustrated with GA4. 

  

  Let’s unpack all the current (and legacy) limitations of Google Analytics you should account for. 

  1. No Historical Data Imports 

  Google rushed users to migrate from Universal Analytics to Google Analytics 4. But they overlooked one important precondition — backwards compatibility. 

  You have no way to import data from Google Universal Analytics to Google Analytics 4. 

  Historical records are essential for analysing growth trends and creating benchmarks for new marketing campaigns. Effectively, you are cut short from past insights — and forced to start strategising from scratch. 

  At present, Google offers two feeble solutions : 

  • Run data collection in parallel and have separate reporting for GA4 and UA until the latter is shut down. Then your UA records are gone. 
  • For Ecommerce data, manually duplicate events from UA at a new GA4 property while trying to figure out the new event names and parameters. 

  Google’s new data collection model is the reason for migration difficulties. 

  In Google Analytics 4, all analytics hits types — page hits, social hits, app/screen view, etc. — are recorded as events. Respectively, the “‘event’ parameter in GA4 is different from one in Google Universal Analytics as the company explains : 

  

  This change makes migration tedious — and Google offers little assistance with proper events and custom dimensions set up. 

  2. Data Collection Limits 

  If you’ve wrapped your head around new GA4 events, congrats ! You did a great job, but the hassle isn’t over. 

  You still need to pay attention to new Google Analytics limits on data collection for event parameters and user properties. 

  

  These apply to :

  • Automatically collected events
  • Enhanced measurement events
  • Recommended events 
  • Custom events 

  When it comes to custom events, GA4 also has a limit of 25 custom parameters per event. Even though it seems a lot, it may not be enough for bigger websites. 

  You can get higher limits by upgrading to Google Analytics 360, but the costs are steep. 

  3. Limited GDPR Compliance 

  Google Analytics has a complex history with European GDPR compliance. 

  A 2020 ruling by the Court of Justice of the European Union (CJEU) invalidated the Privacy Shield framework Google leaned upon. This framework allowed the company to regulate EU-US data transfers of sensitive user data. 

  But after this loophole was closed, Google faced a heavy series of privacy-related fines :

  • French data protection authority, CNIL, ruled that  “the transfers to the US of personal data collected through Google Analytics are illegal” — and proceeded to fine Google for a record-setting €150 million at the beginning of 2022. 
  • Austrian regulators also deemed Google in breach of GDPR requirements and also branded the analytics as illegal. 

  Other EU-member states might soon proceed with similar rulings. These, in turn, can directly affect Google Analytics users, whose businesses could face brand damage and regulatory fines for non-compliance. In fact, companies cannot select where the collected analytics data will be stored — on European servers or abroad — nor can they obtain this information from Google.

  Getting a web analytics platform that allows you to keep data on your own servers or select specific Cloud locations is a great alternative. 

  Google also has been lax with its cookie consent policy and doesn’t properly inform consumers about data collection, storage or subsequent usage. Google Analytics 4 addresses this issue to an extent. 

  By default, GA4 relies on first-party cookies, instead of third-party ones — which is a step forward. But the user privacy controls are hard to configure without losing most of the GA4 functionality. Implementing user consent mode to different types of data collection also requires a heavy setup. 

  4. Strong Reliance on Sampled Data 

  To compensate for ditching third-party cookies, GA4 more heavily leans on sampled data and machine learning to fill the gaps in reporting. 

  In GA4 sampling automatically applies when you :

  • Perform advanced analysis such as cohort analysis, exploration, segment overlap or funnel analysis with not enough data 
  • Have over 10,000,000 data rows and generate any type of non-default report 

  Google also notes that data sampling can occur at lower thresholds when you are trying to get granular insights. If there’s not enough data or because Google thinks it’s too complex to retrieve. 

  In their words :

  

  Data sampling adds “guesswork” to your reports, meaning you can’t be 100% sure of data accuracy. The divergence from actual data depends on the size and quality of sampled data. Again, this isn’t something you can control. 

  Unlike Google Analytics 4, Matomo applies no data sampling. Your reports are always accurate and fully representative of actual user behaviours. 

  5. No Proper Data Anonymization 

  Data anonymization allows you to collect basic analytics about users — visits, clicks, page views — but without personally identifiable information (or PII) such as geo-location, assigns tracking ID or other cookie-based data. 

  This reduced your ability to :

  • Remarket 
  • Identify repeating visitors
  • Do advanced conversion attribution 

  But you still get basic data from users who ignored or declined consent to data collection. 

  By default, Google Analytics 4 anonymizes all user IP addresses — an upgrade from UA. However, it still assigned a unique user ID to each user. These count as personal data under GDPR. 

  For comparison, Matomo provides more advanced privacy controls. You can anonymize :

  • Previously tracked raw data 
  • Visitor IP addresses
  • Geo-location information
  • User IDs 

  This can ensure compliance, especially if you operate in a sensitive industry — and delight privacy-mindful users ! 

  6. No Roll-Up Reporting

  Getting a bird’s-eye view of all your data is helpful when you need hotkey access to main sites — global traffic volume, user count or percentage of returning visitors.

  With Roll-Up Reporting, you can see global-performance metrics for multiple localised properties (.co.nz, .co.uk, .com, etc,) in one screen. Then zoom in on specific localised sites when you need to. 

  

  7. Report Processing Latency 

  The average data processing latency is 24-48 hours with Google Analytics. 

  Accounts with over 200,000 daily sessions get data refreshes only once a day. So you won’t be seeing the latest data on core metrics. This can be a bummer during one-day promo events like Black Friday or Cyber Monday when real-time information can prove to be game-changing ! 

  Matomo processes data with lower latency even for high-traffic websites. Currently, we have 6-24 hour latency for cloud deployments. On-premises web analytics can be refreshed even faster — within an hour or instantly, depending on the traffic volumes. 

  8. No Native Conversion Optimisation Features

  Google Analytics users have to use third-party tools to get deeper insights like how people are interacting with your webpage or call-to-action.

  You can use the free Google Optimize tool, but it comes with limits : 

  • No segmentation is available 
  • Only 10 simultaneous running experiments allowed 

  There isn’t a native integration between Google Optimize and Google Analytics 4. Instead, you have to manually link an Optimize Container to an analytics account. Also, you can’t select experiment dimensions in Google Analytics reports.

  What’s more, Google Optimize is a basic CRO tool, best suited for split testing (A/B testing) of copy, visuals, URLs and page layouts. If you want to get more advanced data, you need to pay for extra tools. 

  Matomo comes with a native set of built-in conversion optimization features : 

  • Heatmaps 
  • User session recording 
  • Sales funnel analysis 
  • A/B testing 
  • Form submission analytics 

  

  9. Deprecated Annotations

  Annotations come in handy when you need to provide extra context to other team members. For example, point out unusual traffic spikes or highlight a leak in the sales funnel. 

  This feature was available in Universal Analytics but is now gone in Google Analytics 4. But you can still quickly capture, comment and share knowledge with your team in Matomo. 

  You can add annotations to any graph that shows statistics over time including visitor reports, funnel analysis charts or running A/B tests. 

  10. No White Label Option 

  This might be a minor limitation of Google Analytics, but a tangible one for agency owners. 

  Offering an on-brand, embedded web analytics platform can elevate your customer experience. But white label analytics were never a thing with Google Analytics, unlike Matomo. 

  Wrap Up 

  Google set a high bar for web analytics. But Google Analytics inherent limitations around privacy, reporting and deployment options prompt more users to consider Google Analytics alternatives, like Matomo. 

  With Matomo, you can easily migrate your historical data records and store customer data locally or in a designated cloud location. We operate by a 100% unsampled data principle and provide an array of privacy controls for advanced compliance. 

  Start your 21-day free trial (no credit card required) to see how Matomo compares to Google Analytics ! 

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