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• How to use Behavioural Analytics to Improve Website Performance

  20 septembre 2021, par Ben Erskine — Analytics Tips, Plugins, Heatmap
  

  User behavioural analytics (UBA) give your business unique insights into your customers. 

  Where traditional website metrics track what actions are completed or how many visitors you have, user behaviour shows the driving factors behind those actions. UBA tools such as website heatmap software provide an easy-to-read visualisation of this data. 

  Ultimately, user behaviour analysis improves website performance and conversions by boosting customer engagement, optimising positive customer experiences, and focusing on the most important part of your sales : the people who are actually buying from you. 

  What is user behaviour analytics ?

  User behaviour analytics (UBA) is data that shows how customers and website visitors interact with your brand online. 

  UBA is tracked using tools such as heatmaps, session recordings and data visualisation software. 

  Where traditional web analytics track metrics such as page views and bounce rates, behavioural analytics provide an even more in-depth picture of your website or funnel success. 

  For example, UBA tracks actions like 

  • How far users are scrolling down the page 
  • Which CTA’s and copy they are focusing on (or not focusing on) 
  • Which design elements, links or buttons they are interacting with 
  • What is happening in between each action

  Tracking user behaviour metrics help keep visitors on your website longer because they analyse where customers may be confused or unclear so you can fix it. 

  What’s the difference between data and behavioural analytics ?

  There are a few key differences between data and behavioural analytics. While data analytics are beneficial to improving website performance, using UBA creates a more customer-centric approach to funnel building. 

  The biggest difference between data and behavioural analytics ? Metric data shows which actions are happening. Behavioural analytics show you WHY they are happening. 

  For example, data can show you that a customer bounced or clicked away. Behaviour analytics show you that a page took a long time to load, they tried to click a link several times and then maybe got frustrated and clicked away. 

  Key differences between data analytics and behavioural analytics : 

  • What is happening versus what is driving it 
  • Track an action (e.g. click-through) versus tracking inaction (e.g. hover without clicking) 
  • Measuring completion of an action versus the flow of actions to complete action 
  • Source of traffic versus individual actions 
  • What happens when someone takes an action versus what happens in between taking action 

  Matomo heatmaps offer both website analytics and user behaviour for a comprehensive analysis.

  Why do behavioural analytics help improve website performance ?

  User behaviour is important because it doesn’t matter how many website visitors you have if they don’t convert. 

  If you have a lot of traffic on mobile devices, but a low CTR, heatmaps show you what is causing the low conversions. Perhaps there is a button that isn’t optimised for mobile scrolling, or a pop up that covers important copy. 

  Analysing the driving factors behind each decision means that you can increase sign-ups and conversions without losing money on website traffic that never actually buys. 

  

  How do heatmap tools show website user behaviour analytics ? 

  Heatmap tools provide a visual representation of user behaviour. 

  There are several key ways that heatmap tracking can improve website performance and therefore your overall conversions.

  Firstly, heatmaps show where to optimise website structure. It uses real visitor experiences to indicate whether customers have to scroll to reach important content, whether important messages are being missed, and whether CTAs are clear. 

  Secondly, heatmaps provide always-on UX and useability testing for your website, identifying user frustrations and optimising their experience over time.

  They also show valuable user experience insights for A/B versions of a landing page. Not only will you see the raw conversion data, but you will also understand why one page converts more than another.

  Ultimately, heatmaps increase ROI on marketing by optimising the traffic that you are sending to your website.

  

  5 ways heatmaps and user behaviour analytics improve website performance and conversions

  #1. Improve customer experience

  One of the most important uses for UBA is to improve your customer experience. 

  Imagine you had a physical store. If there was something blocking customers from getting to the counter you could easily see and fix the problem. 

  It is just as important for an online store to find and fix these “roadblocks”. 

  Not only does it reduce friction in the sales funnel and make it easy for customers to buy from you, it improves their overall experience. And when 86% of buyers are willing to pay more for a great customer experience, UBA should be one of your number one priorities for growing your bottom line. 

  #2. Improve customer engagement

  Customer engagement is any interaction between a customer/product user and your business. 

  User behaviour analytics increase engagement at each customer journey touch point. 

  Using data from heatmaps will improve customer engagement because it gives you insights into how you can make your website more user friendly. This reduces friction and increases customer loyalty by making sure customers :

  • See important content 
  • Are not distracted by unnecessary elements 
  • Can easily access information or pages no matter what device they are using 
  • Are clicking on important page elements that take them further through the customer journey 

  For example, say a customer is on a sales page. A heatmap might show that pop ups or design elements like links to another page are pulling their attention away from the primary focus (i.e. the sales copy). 

  #3. Focus on customer-centric approach 

  A customer-centric approach means putting your customers at the centre of everything that you do. There is a lot of competition for your customers’ hard earned dollars, so you need to stand out. A good product or service is not enough on its own anymore. 

  User behaviour analytics are at the heart of customer-centric strategies. Instead of guessing how customers interact with your online presence, tools like heatmaps give insight into exactly what customers need. 

  This matched with an effective customer feedback strategy gives a holistic and effective approach to improving your customer experiences. 
  

  #4. Capture customer data across multiple channels

  Most customers won’t convert on their very first visit to a website. They might interact with your business across many channels and research your product multiple times before purchasing. 

  Multi Channel Conversion Attribution, also known as Cross Channel Attribution, lets you assign a value to each visit prior to a conversion or prior to a sale. By applying different attribution models, you get a better view on which channels actually lead to a conversion.

  User behaviour analytics like the multi channel conversion attribution that Matomo offers can show you exactly where you should focus your money to acquire new customers. 

  #5. Track and measure business objectives

  User behaviour analytics like heatmaps can show you whether you are actually hitting your targets. 

  Setting goals helps track your website performance against business objectives. 

  These include objectives such as lead generation, online sales and increased brand exposure. Matomo has a specific function for tracking goals and measuring analytics.

  Using a combination of UBA and data metrics will produce the most effective conversions. 

  For example, a customer reaching the payment confirmation page is a common objective to measure conversions. However, it is only tracked if they actually complete the action. Measuring on-page customer activity with heatmaps shows why they do or do not convert so you can fix issues. 

  Final thoughts on user behaviour analytics 

  User behavioural analytics (UBA) provide a unique and in-depth insight into your customers and their needs. Unlike traditional data metrics that track completed actions, UBA like heatmaps show you what happens in between each action and help fix any critical issues. 

  Heatmaps are your secret weapon to improving website performance while staying customer-centric ! 

  Want to know how heatmap analytics increase conversions and improve customer experience without spending more on traffic or marketing ? Check out some of the other in depth guides below. 

  The Ultimate Guide to Heatmap Software

  10 Proven Ways Heatmap Software Improves Website Conversions

  Heatmap Video

  Session Recording Video

• Setting/Installing up OpenCV 2.4.6.1+ on Ubuntu 12.04.02

  30 mai 2016, par Damilola

  I had previously used OpenCV 2.4.5 with some certain configs and packages on Ubuntu 12.04.1 but had issues upgrading to OpenCV 2.4.6.1 on Ubuntu 12.04.2

  I would like to share some ideas (a compilation of noteworthy information gathered from several sources including SO, ubuntu.org, asklinux.org and many other ; and of course by trying several procedures)

  Below is what eventually got me through.

  NOTE : ensure you uninstall any previous installation of OpenCV, FFMpeg and other dependencies previously installed.

  STEP 1 (install ffmpeg and dependencies)

  ---

  # goto http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/
  
  # download the latest stable opencv such as 2.4.6.1 (http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/2.4.5/opencv-2.4.5.1.tar.gz/download) to current directory (such as home or ~/Document)
  
  # cd /opt
  
  # tar -xvf /OpenCV-2.4.6.1.tar.gz
  
  # cd OpenCV-2.4.6.1
  
  # create a foler under current dir (following previous step, this should be ), called prepare
  
  # cd prepare
  
  # Copy the following script to gedit and save as install.sh to current dir, this should be /prepare
  
  # Check corresponding url used in the script for latest versions of the package and replace as required
  
  # Open terminal and navigate to location used above
  
  # sudo chmod +x install.sh
  
  # ./install

  ---

  echo "Removing any pre-installed ffmpeg, x264, and other dependencies (not all the previously installed dependecies)"
  
  sudo apt-get remove ffmpeg x264 libx264-dev libvpx-dev librtmp0 librtmp-dev libopencv-dev
  
  sudo apt-get update
  
  
  
  arch=$(uname -m)
  
  if [ "$arch" == "i686" -o "$arch" == "i386" -o "$arch" == "i486" -o "$arch" == "i586" ]; then
  
  flag=0
  
  else
  
  flag=1
  
  fi
  
  
  
  echo "Installing Dependenices"
  
  sudo apt-get install autoconf automake make g++ curl cmake bzip2 python unzip \
  
    build-essential checkinstall git git-core libass-dev libgpac-dev \
  
    libsdl1.2-dev libtheora-dev libtool libva-dev libvdpau-dev libvorbis-dev libx11-dev \
  
    libxext-dev libxfixes-dev pkg-config texi2html zlib1g-dev
  
  
  
  echo "downloading yasm (assembler used by x264 and FFmpeg)"
  
  # use git or tarball (not both)
  
  wget http://www.tortall.net/projects/yasm/releases/yasm-1.2.0.tar.gz
  
  tar xzvf yasm-1.2.0.tar.gz
  
  cd yasm-1.2.0
  
  
  
  echo "installing yasm"
  
  ./configure
  
  make
  
  sudo make install
  
  cd ..
  
  
  
  echo 'READ NOTE BELOW which was extracted from http://wiki.serviio.org/doku.php?id=build_ffmpeg_linux'
  
  echo 'New version of x264 contains by default support of OpenCL. If not installed or without sense (example Ubuntu 12.04LTS on VMWare) add to configure additional option --disable-opencl. Without this option ffmpeg could not be configured (ERROR: libx264 not found).'
  
  
  
  echo "downloading x264 (H.264 video encoder)"
  
  # use git or tarball (not both)
  
  # git clone http://repo.or.cz/r/x264.git or
  
  git clone git://git.videolan.org/x264.git
  
  cd x264
  
  # wget ftp://ftp.videolan.org/pub/videolan/x264/snapshots/x264-snapshot-20130801-2245-stable.tar.bz2
  
  # tar -xvjf x264-snapshot-20130801-2245-stable.tar.bz2
  
  # cd x264-snapshot-20130801-2245-stable/
  
  
  
  echo "Installing x264"
  
  if [ $flag -eq 0 ]; then
  
  ./configure --enable-static --disable-opencl
  
  else
  
  ./configure --enable-shared --enable-pic --disable-opencl
  
  fi
  
  make
  
  sudo make install
  
  cd ..
  
  
  
  echo "downloading fdk-aac (AAC audio encoder)"
  
  # use git or tarball (not both)
  
  git clone --depth 1 git://github.com/mstorsjo/fdk-aac.git
  
  cd fdk-aac
  
  
  
  echo "installing fdk-aac"
  
  autoreconf -fiv
  
  ./configure --disable-shared
  
  make
  
  sudo make install
  
  cd ..
  
  
  
  echo "installing libmp3lame-dev (MP3 audio encoder.)"
  
  sudo apt-get install libmp3lame-dev
  
  
  
  echo "downloading libopus (Opus audio decoder and encoder.)"
  
  wget http://downloads.xiph.org/releases/opus/opus-1.0.3.tar.gz
  
  tar xzvf opus-1.0.3.tar.gz
  
  cd opus-1.0.3
  
  
  
  echo "installing libopus"
  
  ./configure --disable-shared
  
  make
  
  sudo make install
  
  cd ..
  
  
  
  echo "downloading libvpx VP8/VP9 video encoder and decoder)"
  
  # use git or tarball (not both)
  
  git clone --depth 1 http://git.chromium.org/webm/libvpx.git
  
  cd libvpx
  
  # wget http://webm.googlecode.com/files/libvpx-v1.1.0.tar.bz2 (this seems not to be update, but can still be used if the fedoraproject link below is not available))
  
  # wget http://pkgs.fedoraproject.org/repo/pkgs/libvpx/libvpx-v1.2.0.tar.bz2/400d7c940c5f9d394893d42ae5f463e6/libvpx-v1.2.0.tar.bz2
  
  # tar xvjf libvpx-v1.2.0.tar.bz2
  
  # cd libvpx-v1.2.0
  
  
  
  echo "installing libvpx"
  
  ./configure --disable-examples
  
  make
  
  sudo make install
  
  cd ..
  
  
  
  sudo ldconfig
  
  
  
  echo "downloading ffmpeg"
  
  # git clone http://repo.or.cz/r/ffmpeg.git
  
  git clone git://source.ffmpeg.org/ffmpeg.git
  
  cd ffmpeg/
  
  # wget http://ffmpeg.org/releases/ffmpeg-2.0.tar.bz2
  
  # tar -xvjf ffmpeg-2.0.tar.bz2
  
  # cd ffmpeg-2.0/
  
  
  
  echo "installing ffmpeg" 
  
  if [ $flag -eq 0 ]; then
  
  ./configure --enable-gpl --enable-libass --enable-libfdk-aac --enable-libopus --enable-libfaac --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libtheora --enable-libvorbis --enable-libx264 --enable-libxvid --enable-nonfree --enable-postproc --enable-version3 --enable-x11grab --enable-libvpx
  
  else
  
  ./configure --enable-gpl --enable-libass --enable-libfdk-aac --enable-libopus --enable-libfaac --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libtheora --enable-libvorbis --enable-libx264 --enable-libxvid --enable-nonfree --enable-postproc --enable-version3 --enable-x11grab --enable-libvpx --enable-shared
  
  fi
  
  
  
  make
  
  sudo make install
  
  hash -r
  
  
  
  cd .. # move up one level to prepare folder
  
  cd .. # move up one level to opencv folder
  
  
  
  echo "Checking to see if you're using your new ffmpeg"
  
  ffmpeg 2>&1 | head -n1
  
  
  
  sudo ldconfig

  STEP 2 (Install OpenCV and necessary packages)

  echo "Installing Dependenices"    
  
  sudo apt-get install libtiff4-dev libjpeg-dev libjasper-dev
  
  
  
  echo "installing Video I/O libraries, support for Firewire video cameras and video streaming libraries"
  
  sudo apt-get install libav-tools libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev v4l-utils v4l-conf
  
  
  
  echo "installing the Python development environment and the Python Numerical library"
  
  sudo apt-get install python-dev python-numpy
  
  
  
  echo "installing the parallel code processing library (the Intel tbb library)"
  
  sudo apt-get install libtbb-dev
  
  
  
  echo "installing the Qt dev library"
  
  sudo apt-get install libqt4-dev libgtk2.0-dev
  
  
  
  echo "installing other dependencies (if need be it would upgrade current version of the packages)"
  
  sudo apt-get install patch subversion ruby librtmp0 librtmp-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libvpx-dev libxvidcore-dev
  
  
  
  echo installing optional packages"
  
  sudo apt-get install libdc1394-utils libdc1394-22-dev libdc1394-22 libjpeg-dev libpng-dev libtiff-dev libjasper-dev

  STEP 3 (run ldconfig)

  # Open a new terminal window
  
  # Open /etc/ld.so.conf and check, 
  
  # if the paths "/usr/lib" and "/usr/local/lib" including the quote exist in the file. If not, add them manually or by
  
      sudo echo "/usr/local/lib" >> /etc/ld.so.conf
  
      sudo echo "/usr/lib" >> /etc/ld.so.conf
  
  # execute the following
  
      sudo ldconfig

  STEP 4a (Build & Install for OS Usage)

  # still ensure you haven't close the new terminal window open in STEP 3
  
  # execute the following
  
  mkdir os_build
  
  cd os_build
  
  cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local -DBUILD_NEW_PYTHON_SUPPORT=ON -DINSTALL_PYTHON_EXAMPLES=ON -DWITH_TBB=ON -DWITH_V4L=ON -DINSTALL_C_EXAMPLES=ON -DBUILD_EXAMPLES=ON -DWITH_QT=ON -DWITH_OPENGL=ON -DWITH_OPENCL=ON -DWITH_EIGEN=ON -DWITH_OPENEXR=ON ..
  
  
  
      make
  
      sudo make install
  
  
  
  # add the following to user environment variable ~/.bashrc
  
      export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib
  
      export PKG_CONFIG_PATH=${PKG_CONFIG_PATH}:/usr/local/lib/pkgconfig
  
  # execute the following
  
      sudo ldconfig
  
  # start to use and enjoy opencv, it should have been install into any of these locations
  
  #   /usr/local/include/opencv2, /usr/local/include/opencv, /usr/include/opencv, /usr/include/opencv2, /usr/local/share/opencv
  
  #   /usr/local/share/OpenCV, /usr/share/opencv, /usr/share/OpenCV, /usr/local/bin/opencv*, /usr/local/lib/libopencv*

  STEP 4b (Build for Java Usage) : OPTIONAL

  # still ensure you haven't close the new terminal window open in STEP 4
  
  # execute the following
  
      cd ..
  
      mkdir java_build
  
      cd java_build
  
      cmake -DCMAKE_BUILD_TYPE=RELEASE -DBUILD_SHARED_LIBS=OFF -DINSTALL_PYTHON_EXAMPLES=ON -DWITH_TBB=ON -DWITH_V4L=ON -DINSTALL_C_EXAMPLES=ON -DBUILD_EXAMPLES=ON -DWITH_QT=ON -DWITH_OPENGL=ON -DWITH_OPENCL=ON -DWITH_EIGEN=ON -DWITH_OPENEXR=ON ..
  
  
  
      make
  
  
  
  # You can check the "java_build/bin" directory to locate the jar and libopencv_java.so file for your development
  
  # As stated in the docs, the Java bindings dynamic library is all-sufficient, i.e. doesn’t depend on other OpenCV libs, but includes all the OpenCV code inside

  STEP 5 (install v4l : Note : installing v4l-utils after opencv installation works for Ubuntu 12.04.2 & OpenCV 2.4.6.1)

  # still ensure you haven't close the new terminal window open in STEP 3
  
  # goto http://www.linuxtv.org/downloads/v4l-utils
  
  # download the latest v4l such as v4l-utils-0.9.5.tar.bz2
  
  # copy the downloaded file to the current terminal dir (following previous step, this should be /prepare)
  
  # execute the following
  
      tar -xvjf v4l-utils-0.9.5.tar.bz2
  
      cd v4l-utils-0.9.5/
  
      ./configure
  
      make
  
      sudo make install
  
      cd ..
  
      cd .. # (to go to )
  
      sudo ldconfig

  Worth Noting

  # To check the path where opencv & other lib files are stored, do:
  
  
  
  
  
  pkg-config --cflags opencv
  
  
  
      (output will come as)
  
      -I/usr/include/opencv
  
  
  
  
  
  
  
  pkg-config --libs opencv
  
  
  
      (output will come as)
  
      -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ --ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann
  
  
  
  # The above paths are needed to compile your opencv programs, as given in the next illustration.
  
  
  
  # write a simple C program to test, by saving below program in a file named DisplayImage.c
  
  
  
  #include 
  
      #include <opencv2></opencv2>highgui/highgui.hpp>
  
  
  
      int main(int argc, char *argv[]) {
  
          IplImage* img=0; /* pointer to an image */
  
          printf("Hello\n");
  
  
  
          if(argv[1] != 0)
  
              img = cvLoadImage(argv[1], 0); // 1 for color
  
          else
  
              printf("Enter filename\n");
  
  
  
          if(img != 0) {
  
              cvNamedWindow("Display", CV_WINDOW_AUTOSIZE); // create a window
  
              cvShowImage("Display", img); // show image in window
  
              cvWaitKey(0); // wait until user hits a key
  
              cvDestroyWindow("Display");
  
          }
  
          else
  
              printf("File not found\n");
  
  
  
          return 0;
  
      }
  
  
  
  # write a simple C++ program to test, by saving below program in a file named DisplayImage.cpp
  
  
  
  #include 
  
  #include <opencv2></opencv2>opencv.hpp>
  
  #include <opencv2></opencv2>highgui/highgui.hpp>
  
  
  
  using namespace cv;
  
  
  
  int main( int argc, char** argv )
  
  {
  
    Mat image;
  
    image = imread( argv[1], 1 );
  
  
  
    if( argc != 2 || !image.data )
  
      {
  
        printf( "No image data \n" );
  
        return -1;
  
      }
  
  
  
    namedWindow( "Display Image", CV_WINDOW_AUTOSIZE );
  
    imshow( "Display Image", image );
  
  
  
    waitKey(0);
  
  
  
    return 0;
  
  }
  
  
  
  # To compile &amp; run :
  
  
  
  
  
  
  
  g++  `pkg-config --cflags --libs opencv` &amp;&amp; ./a.out img
  
  
  
  or
  
  
  
  
  
  
  
  g++ -I/usr/include/opencv -I/usr/local/include -lopencv_core -lopencv_highgui -lopencv_ml -lopencv_imgproc -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann -lopencv_nonfree  &amp;&amp; ./a.out img
  
  
  
  where “img” is the name of any image with extension within the same folder .
  
  You should be able to see “Hello” and the image in a different window.
  
  
  
  If this runs, Congrats! now you can run any C/C++ program with opencv lib.
  
  
  
  
  
  # Now lets simplify the above big command by making a shortcut for it:
  
  go to your local home directory(cd /home/) and open the .bashrc file using gedit(the file will be hidden). Append the following to the file:
  
  
  
  
  
  
  
  alias gcv="g++ -I/usr/include/opencv -I/usr/local/include -lopencv_core -lopencv_highgui -lopencv_ml -lopencv_imgproc -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann -lopencv_nonfree"
  
  
  
  and save. Close the terminal and open it again.(as this process requires relogin of the terminal)
  
  
  
  # Now, go to directory containing a sample program &amp; do
  
  
  
  
  
  
  
  gcv  &amp;&amp; ./a.out
  
  
  
  or
  
  
  
  
  
  
  
  gcv 
  
      ./a.out input_img.jpg

  As you can see the commands now become similar to $cc filename.c, $./a.out which are used normally for compiling and executing C/C++ programs.

  ---

  Some ways to check whether all lib files are installed-

  apt-cache search opencv

  returns :

  libcv-dev - Translation package for libcv-dev
  
  libcv2.3 - computer vision library - libcv* translation package
  
  libcvaux-dev - Translation package for libcvaux-dev
  
  libcvaux2.3 - computer vision library - libcvaux translation package
  
  libhighgui-dev - Translation package for libhighgui-dev
  
  libhighgui2.3 - computer vision library - libhighgui translation package
  
  libopencv-calib3d-dev - development files for libopencv-calib3d
  
  libopencv-calib3d2.3 - computer vision Camera Calibration library
  
  libopencv-contrib-dev - development files for libopencv-contrib
  
  libopencv-contrib2.3 - computer vision contrib library
  
  libopencv-core-dev - development files for libopencv-core
  
  libopencv-core2.3 - computer vision core library
  
  libopencv-dev - development files for opencv
  
  libopencv-features2d-dev - development files for libopencv-features2d
  
  libopencv-features2d2.3 - computer vision Feature Detection and Descriptor Extraction library
  
  libopencv-flann-dev - development files for libopencv-flann
  
  libopencv-flann2.3 - computer vision Clustering and Search in Multi-Dimensional spaces library
  
  libopencv-gpu-dev - development files for libopencv-gpu
  
  libopencv-gpu2.3 - computer vision GPU Processing library
  
  libopencv-highgui-dev - development files for libopencv-highgui
  
  libopencv-highgui2.3 - computer vision High-level GUI and Media I/O library
  
  libopencv-imgproc-dev - development files for libopencv-imgproc
  
  libopencv-imgproc2.3 - computer vision Image Processing library
  
  libopencv-legacy-dev - development files for libopencv-legacy
  
  libopencv-legacy2.3 - computer vision legacy library
  
  libopencv-ml-dev - development files for libopencv-ml
  
  libopencv-ml2.3 - computer vision Machine Learning library
  
  libopencv-objdetect-dev - development files for libopencv-objdetect
  
  libopencv-objdetect2.3 - computer vision Object Detection library
  
  libopencv-video-dev - development files for libopencv-video
  
  libopencv-video2.3 - computer vision Video analysis library
  
  opencv-doc - OpenCV documentation and examples
  
  python-opencv - Python bindings for the computer vision library

• A Guide to Ethical Web Analytics in 2024

  17 juin 2024, par Erin

  User data is more valuable and sought after than ever. 

  Ninety-four percent of respondents in Cisco’s Data Privacy Benchmark Study said their customers wouldn’t buy from them if their data weren’t protected, with 95% saying privacy was a business imperative. 

  Unfortunately, the data collection practices of most businesses are far from acceptable and often put their customers’ privacy at risk. 

  But it doesn’t have to be this way. You can ethically collect valuable and insightful customer data—you just need the right tools.

  In this article, we show you what an ethical web analytics solution can look like, why Google Analytics is a problem and how you can collect data without risking your customers’ privacy.

  What is ethical web analytics ?

  Ethical web analytics put user privacy first. These platforms prioritise privacy and transparency by only collecting necessary data, avoiding implicit user identification and openly communicating data practices and tracking methods. 

  Ethical tools adhere to data protection laws like GDPR as standard (meaning businesses using these tools never have to worry about fines or disruptions). In other words, ethical web analytics refrain from exploiting and profiting from user behaviour and data. 

  Unfortunately, most traditional data solutions collect as much data as possible without users’ knowledge or consent.

  Why does digital privacy matter ?

  Digital privacy matters because companies have repeatedly proven they will collect and use data for financial gain. It also presents security risks. Unsecured user data can lead to identity theft, cyberattacks and harassment. 

  Big tech companies like Google and Meta are often to blame for all this. These companies collect millions of user data points — like age, gender, income, political beliefs and location. Worse still, they share this information with interested third parties.

  After public outrage over data breaches and other privacy scandals, consumers are taking active steps to disallow tracking where possible. IAPP’s Privacy and Consumer Trust Report finds that 68% of consumers across 19 countries are somewhat or very concerned about their digital privacy. 

  There’s no way around it : companies of all sizes and shapes need to consider how they handle and protect customers’ private information. 

  Why should you use an ethical web analytics tool ?

  When companies use ethical web analytics tools they can build customer trust, boost their brand reputation, improve data security practices and future proof their website tracking solution. 

  Boost brand reputation

  The fallout from a data privacy scandal can be severe. 

  Just look at what happened to Facebook during the Cambridge Analytica data scandal. The eponymous consulting firm harvested 50 million Facebook profiles and used that information to target people with political messages. Due to the instant public backlash, Facebook’s stock tanked, and use of the “delete Facebook” hashtag increased by 423% in the following days.

  That’s because consumers care about data privacy, according to Deloitte’s Connected Consumer Study :

  • Almost 90 percent agree they should be able to view and delete data companies collect 
  • 77 percent want the government to introduce stricter regulations
  • Half feel the benefits they get from online services outweigh data privacy concerns.

  If you can prove you buck the trend by collecting data using ethical methods, it can boost your brand’s reputation. 

  Build trust with customers

  At the same time, collecting data in an ethical way can help you build customer trust. You’ll go a long way to changing consumer perceptions, too. Almost half of consumers don’t like sharing data, and 57% believe companies sell their data. 

  This additional trust should generate a positive ROI for your business. According to Cisco’s Data Privacy Benchmark Study, the average company gains $180 for every $100 they invest in privacy. 

  Improve data security

  According to IBM’s Cost of a Data Breach report, the average cost of a data breach is nearly $4.5 million. This kind of scenario becomes much less likely when you use an ethical tool that collects less data overall and anonymises the data you do collect. 

  Futureproof your web analytics solution

  The obvious risk of not complying with privacy regulations is a fine — which can be up to €20 million, or 4% of worldwide annual revenue in the case of GDPR.

  It’s not just fines and penalties you risk if you fail to comply with privacy regulations like GDPR. For some companies, especially larger ones, the biggest risk of non-compliance with privacy regulations is the potential sudden need to abandon Google Analytics and switch to an ethical alternative.

  If Data Protection Authorities ban Google Analytics again, as has happened in Austria, France, and other countries, businesses will be forced to drop everything and make an immediate transition to a compliant web analytics solution.

  When an organisation’s entire marketing operation relies on data, migrating to a new solution can be incredibly painful and time-consuming. So, the sooner you switch to an ethical tool, the less of a headache the process will be. 

  The problem with Google Analytics

  Google Analytics (GA) is the most popular analytics platform in the world, but it’s a world away from being an ethical tool. Here’s why :

  You don’t have data ownership

  Google Analytics is attractive to businesses of all sizes because of its price. Everyone loves getting something for free, but there’s still a cost — your and your customers’ data.

  That’s because Google combines the data you collect with information from the millions of other websites it tracks to inform its advertising efforts. It may also use your data to train large language models like Gemini. 

  It has a rocky history with GDPR laws

  Google and EU regulators haven’t always got along. For example, the German Data Protection Authority is investigating 200,000 pending cases against websites using GA. The platform has also been banned and added back to the EU-US Data Privacy Framework several times over the past few years. 

  You can use GA to collect data about EU customers right now, but there’s no guarantee you’ll be able to do so in the future. 

  It requires a specific setup to remain compliant

  While you can currently use GA in a GDPR-compliant way — owing to its inclusion in the EU-US Data Privacy Framework — you have to set it up in a very specific way. That’s because the platform’s compliance depends on what data you collect, how you inform users and the level of consent you acquire. You’ll still need to include an extensive privacy policy on your website. 

  What does ethical web analytics look like ?

  An ethical web analytics solution should put user privacy first, ensure compliance with regulations like GDPR, give businesses 100% control of the data they collect and be completely transparent about data collection and storage practices. 

  

  100% data ownership

  You don’t fully control customer data when you use Google Analytics. The search giant uses your data for its own advertising purposes and may also use it to train large language models like Gemini. 

  When you choose an ethical web analytics alternative like Matomo, you can ensure you completely own your data.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  Respects user privacy

  It’s possible to track and measure user behaviour without collecting personally identifiable information (PII). Just look at the ethical web analytics tools we’ve reviewed below. 

  These platforms respect user privacy and conform to strict privacy regulations like GDPR, CCPA and HIPAA by incorporating some or all of the following features :

  • Opt-out mechanisms to let users refuse tracking
  • IP addresses anonymisation and other data anonymisation techniques
  • DoNotTrack options
  • Shorter expiration dates for tracking cookies

  In Matomo’s case, it’s all of the above. Better still, you can check our privacy credentials yourself. Our software’s source code is open source on GitHub and accessible to anyone at any time. 

  Compliant with government regulations

  While Google’s history with data regulations is tumultuous, an ethical web analytics platform should follow even the strictest privacy laws, including GDPR, HIPAA, CCPA, LGPD and PECR.

  But why stop there ? Matomo has been approved by the French Data Protection Authority (CNIL) as one of the few web analytics tools that French sites can use to collect data without tracking consent. So you don’t need an annoying consent banner popping up on your website anymore. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Complete transparency 

  Ethical web analytics tools will be upfront about their data collection practices, whether that’s in the U.S., EU, or on your own private servers. Look for a solution that refrains from collecting personally identifiable information, shows where data is stored, and lets you alter tracking methods to increase privacy even further. 

  Some solutions, like Matomo, will increase transparency further by providing open source software. Anyone can find our source code on GitHub to see exactly how our platform tracks and stores user data. This means our code is regularly examined and reviewed by a community of developers, making it more secure, too.

  Ethical web analytics solutions

  There are several options for an ethical web analytics tool. We list three of the best providers below. 

  Matomo

  Matomo is an open source web analytics tool and privacy-focused Google Analytics alternative used by over one million sites globally. 

  

  Matomo is fully compliant with prominent global privacy regulations like GDPR, CCPA and HIPAA, meaning you never have to worry about collecting consent when tracking user behaviour. 

  The data you collect is completely accurate since Matomo doesn’t use data sampling and is 100% yours. We don’t share data with third parties but can prove it. Our product source code is publicly available on GitHub. As a community-led project, you can download and install it yourself for free. 

  With Matomo, you get a full range of web analytics capabilities and behavioural analytics. That includes your standard metrics (think visitors, traffic sources, bounce rates, etc.), advanced features to analyse user behaviour like A/B Testing, Form Analytics, Heatmaps and Session Recordings. 

  Migrating to Matomo is easy. You can even import historical Google Analytics data to generate meaningful insights immediately. 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Fathom

  Fathom Analytics is a lightweight privacy-focused analytics solution that launched in 2018. It aims to be an easy-to-use Google Analytics alternative that doesn’t compromise privacy. 

  

  Like Matomo, Fathom complies with all major privacy regulations, including GDPR and CCPA. It also provides 100% accurate, unsampled reports and doesn’t share your data with third parties. 

  While Fathom provides fairly comprehensive analytics reports, it doesn’t have some of Matomo’s more advanced features. That includes e-commerce tracking, heatmaps, session recordings, and more. 

  Plausible

  Plausible Analytics is another open source Google Analytics alternative that was built and hosted in the EU. 

  

  Launched in 2019, Plausible is a newer player in the privacy-focused analytics market. Still, its ultra-lightweight script makes it an attractive option for organisations that prioritise speed over everything else. 

  Like Matomo and Fathom, Plausible is GDPR and CCPA-compliant by design. Nor is there any cap on the amount of data you collect or any debate over whether the data is accurate (Plausible doesn’t use data sampling) or who owns the data (you do). 

  Matomo makes it easy to migrate to an ethical web analytics alternative

  There’s no reason to put your users’ privacy at risk, especially when there are so many benefits to choosing an ethical tool. Whether you want to avoid fines, build trust with your customers, or simply know you’re doing the right thing, choosing a privacy-focused, ethical solution like Matomo is taking a massive step in the right direction. 

  Making the switch is easy, too. Matomo is one of the few options that lets you import historical Google Analytics data, so starting from scratch is unnecessary. 

  Get started today by trying Matomo for free for 21-days. No credit card required. 

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