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• trying to make OpenCV 3.2.0 work with virtualenv

  24 juillet 2017, par lollercoaster

  I’m on Ubuntu 16.04 with Python 2.7 and virtualenv & virtualenvwrapper.

  By following this guide I managed to get the following script working with my system Python2.7 which has cv2 globally installed.

  I used this script to install it :

  ######################################
  
  # INSTALL OPENCV ON UBUNTU OR DEBIAN #
  
  ######################################
  
  
  
  # |         THIS SCRIPT IS TESTED CORRECTLY ON         |
  
  # |----------------------------------------------------|
  
  # | OS             | OpenCV       | Test | Last test   |
  
  # |----------------|--------------|------|-------------|
  
  # | Ubuntu 16.04.2 | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 8.8     | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 9.0     | OpenCV 3.2.0 | OK   | 25 Jun 2017 |
  
  
  
  # 1. KEEP UBUNTU OR DEBIAN UP TO DATE
  
  
  
  sudo apt-get -y update
  
  sudo apt-get -y upgrade
  
  sudo apt-get -y dist-upgrade
  
  sudo apt-get -y autoremove
  
  
  
  
  
  # 2. INSTALL THE DEPENDENCIES
  
  
  
  # Build tools:
  
  sudo apt-get install -y build-essential cmake
  
  
  
  # GUI (if you want to use GTK instead of Qt, replace 'qt5-default' with 'libgtkglext1-dev' and remove '-DWITH_QT=ON' option in CMake):
  
  sudo apt-get install -y qt5-default libvtk6-dev
  
  
  
  # Media I/O:
  
  sudo apt-get install -y zlib1g-dev libjpeg-dev libwebp-dev libpng-dev libtiff5-dev libjasper-dev libopenexr-dev libgdal-dev
  
  
  
  # Video I/O:
  
  sudo apt-get install -y libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev yasm libopencore-amrnb-dev libopencore-amrwb-dev libv4l-dev libxine2-dev
  
  
  
  # Parallelism and linear algebra libraries:
  
  sudo apt-get install -y libtbb-dev libeigen3-dev
  
  
  
  # Python:
  
  sudo apt-get install -y python-dev python-tk python-numpy python3-dev python3-tk python3-numpy
  
  
  
  # Documentation:
  
  sudo apt-get install -y doxygen
  
  
  
  # UI stuff
  
  sudo apt-get install libgtk-3-dev libatlas-base-dev gfortran
  
  
  
  
  
  # 3. INSTALL THE LIBRARY (YOU CAN CHANGE '3.2.0' FOR THE LAST STABLE VERSION)
  
  sudo apt-get install -y unzip wget
  
  
  
  # opencv contrib
  
  wget https://github.com/opencv/opencv_contrib/archive/3.2.0.zip -O opencv_contrib-3.2.0.zip
  
  unzip opencv_contrib-3.2.0.zip
  
  rm opencv_contrib-3.2.0.zip
  
  
  
  # opencv
  
  wget https://github.com/opencv/opencv/archive/3.2.0.zip
  
  unzip 3.2.0.zip
  
  rm 3.2.0.zip
  
  mv opencv-3.2.0 OpenCV-3.2.0
  
  cd OpenCV-3.2.0
  
  
  
  mkdir build
  
  cd build
  
  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig
  
  
  
  
  
  # 4. EXECUTE SOME OPENCV EXAMPLES AND COMPILE A DEMONSTRATION
  
  
  
  # To complete this step, please visit 'http://milq.github.io/install-opencv-ubuntu-debian'.

  The following script below works great with that system-wide installation :

  import cv2
  
  
  
  img = cv2.imread('some_img.jpg')

  Though this one doesn’t - even the system Python can’t read videos for some reason...

  import cv2
  
  
  
  video_capture = cv2.VideoCapture(0)
  
  ret, frame = video_capture.read()
  
  print ret  # always False

  but I want it to work with my virtualenv. So I recompiled OpenCV with :

  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      -D OPENCV_EXTRA_MODULES_PATH=/home/me/code/myproject/opencv_contrib-3.2.0/modules \
  
      -D PYTHON_EXECUTABLE=~/.envs/myenv/bin/python \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig

  Here’s the CMake log :

  -- Found VTK ver. 6.2.0 (usefile: /usr/lib/cmake/vtk-6.2/UseVTK.cmake)
  
  -- Caffe:   NO
  
  -- Protobuf:   YES
  
  -- Glog:   NO
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Module opencv_sfm disabled because the following dependencies are not found: Glog/Gflags
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Checking for modules 'tesseract;lept'
  
  --   No package 'tesseract' found
  
  --   No package 'lept' found
  
  -- Tesseract:   NO
  
  -- Check contents of vgg_generated_48.i ...
  
  -- Check contents of vgg_generated_64.i ...
  
  -- Check contents of vgg_generated_80.i ...
  
  -- Check contents of vgg_generated_120.i ...
  
  -- Check contents of boostdesc_bgm.i ...
  
  -- Check contents of boostdesc_bgm_bi.i ...
  
  -- Check contents of boostdesc_bgm_hd.i ...
  
  -- Check contents of boostdesc_binboost_064.i ...
  
  -- Check contents of boostdesc_binboost_128.i ...
  
  -- Check contents of boostdesc_binboost_256.i ...
  
  -- Check contents of boostdesc_lbgm.i ...
  
  -- 
  
  -- General configuration for OpenCV 3.2.0 =====================================
  
  --   Version control:               817bd7b-dirty
  
  -- 
  
  --   Extra modules:
  
  --     Location (extra):            /home/me/code/myproject/opencv_contrib-3.2.0/modules
  
  --     Version control (extra):     817bd7b-dirty
  
  -- 
  
  --   Platform:
  
  --     Timestamp:                   2017-07-20T18:25:26Z
  
  --     Host:                        Linux 4.8.0-58-generic x86_64
  
  --     CMake:                       3.5.1
  
  --     CMake generator:             Unix Makefiles
  
  --     CMake build tool:            /usr/bin/make
  
  --     Configuration:               Release
  
  -- 
  
  --   C/C++:
  
  --     Built as dynamic libs?:      YES
  
  --     C++ Compiler:                /usr/bin/c++  (ver 5.4.0)
  
  --     C++ flags (Release):         -fsigned-char -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 -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C++ flags (Debug):           -fsigned-char -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 -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     C Compiler:                  /usr/bin/cc
  
  --     C flags (Release):           -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C flags (Debug):             -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     Linker flags (Release):
  
  --     Linker flags (Debug):
  
  --     ccache:                      NO
  
  --     Precompiled headers:         NO
  
  --     Extra dependencies:          Qt5::Test Qt5::Concurrent Qt5::OpenGL /usr/lib/x86_64-linux-gnu/libwebp.so /usr/lib/x86_64-linux-gnu/libjasper.so /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 /usr/lib/libgdal.so dc1394 xine avcodec-ffmpeg avformat-ffmpeg avutil-ffmpeg swscale-ffmpeg Qt5::Core Qt5::Gui Qt5::Widgets /usr/lib/x86_64-linux-gnu/hdf5/serial/lib/libhdf5.so /usr/lib/x86_64-linux-gnu/libpthread.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so vtkRenderingOpenGL vtkImagingHybrid vtkIOImage vtkCommonDataModel vtkCommonMath vtkCommonCore vtksys vtkCommonMisc vtkCommonSystem vtkCommonTransforms vtkCommonExecutionModel vtkDICOMParser vtkIOCore /usr/lib/x86_64-linux-gnu/libz.so vtkmetaio /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so vtkImagingCore vtkRenderingCore vtkCommonColor vtkFiltersExtraction vtkFiltersCore vtkFiltersGeneral vtkCommonComputationalGeometry vtkFiltersStatistics vtkImagingFourier vtkalglib vtkFiltersGeometry vtkFiltersSources vtkInteractionStyle vtkRenderingLOD vtkFiltersModeling vtkIOPLY vtkIOGeometry /usr/lib/x86_64-linux-gnu/libjsoncpp.so vtkFiltersTexture vtkRenderingFreeType /usr/lib/x86_64-linux-gnu/libfreetype.so vtkftgl vtkIOExport vtkRenderingAnnotation vtkImagingColor vtkRenderingContext2D vtkRenderingGL2PS vtkRenderingContextOpenGL /usr/lib/libgl2ps.so vtkRenderingLabel dl m pthread rt /usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so tbb
  
  --     3rdparty dependencies:       libprotobuf
  
  -- 
  
  --   OpenCV modules:
  
  --     To be built:                 core flann hdf imgproc ml photo reg surface_matching video viz dnn freetype fuzzy imgcodecs shape videoio highgui objdetect plot superres ts xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib cvv datasets rgbd stereo tracking videostab xfeatures2d ximgproc aruco optflow phase_unwrapping stitching structured_light java python2 python3
  
  --     Disabled:                    world contrib_world
  
  --     Disabled by dependency:      -
  
  --     Unavailable:                 cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cnn_3dobj matlab sfm
  
  -- 
  
  --   GUI: 
  
  --     QT 5.x:                      YES (ver 5.5.1)
  
  --     QT OpenGL support:           YES (Qt5::OpenGL 5.5.1)
  
  --     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so)
  
  --     VTK support:                 YES (ver 6.2.0)
  
  -- 
  
  --   Media I/O: 
  
  --     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.8)
  
  --     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver )
  
  --     WEBP:                        /usr/lib/x86_64-linux-gnu/libwebp.so (ver encoder: 0x0202)
  
  --     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.2.54)
  
  --     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 - 4.0.6)
  
  --     JPEG 2000:                   /usr/lib/x86_64-linux-gnu/libjasper.so (ver 1.900.1)
  
  --     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 2.2.0)
  
  --     GDAL:                        /usr/lib/libgdal.so
  
  --     GDCM:                        NO
  
  -- 
  
  --   Video I/O:
  
  --     DC1394 1.x:                  NO
  
  --     DC1394 2.x:                  YES (ver 2.2.4)
  
  --     FFMPEG:                      YES
  
  --       avcodec:                   YES (ver 56.60.100)
  
  --       avformat:                  YES (ver 56.40.101)
  
  --       avutil:                    YES (ver 54.31.100)
  
  --       swscale:                   YES (ver 3.1.101)
  
  --       avresample:                NO
  
  --     GStreamer:                   NO
  
  --     OpenNI:                      NO
  
  --     OpenNI PrimeSensor Modules:  NO
  
  --     OpenNI2:                     NO
  
  --     PvAPI:                       NO
  
  --     GigEVisionSDK:               NO
  
  --     Aravis SDK:                  NO
  
  --     UniCap:                      NO
  
  --     UniCap ucil:                 NO
  
  --     V4L/V4L2:                    NO/YES
  
  --     XIMEA:                       NO
  
  --     Xine:                        YES (ver 1.2.6)
  
  --     gPhoto2:                     NO
  
  -- 
  
  --   Parallel framework:            TBB (ver 4.4 interface 9002)
  
  -- 
  
  --   Other third-party libraries:
  
  --     Use IPP:                     9.0.1 [9.0.1]
  
  --          at:                     /home/me/code/myproject/OpenCV-3.2.0/build/3rdparty/ippicv/ippicv_lnx
  
  --     Use IPP Async:               NO
  
  --     Use VA:                      NO
  
  --     Use Intel VA-API/OpenCL:     NO
  
  --     Use Lapack:                  NO
  
  --     Use Eigen:                   YES (ver 3.2.92)
  
  --     Use Cuda:                    NO
  
  --     Use OpenCL:                  YES
  
  --     Use OpenVX:                  NO
  
  --     Use custom HAL:              NO
  
  -- 
  
  --   OpenCL:                        <dynamic loading="loading" of="of" opencl="opencl" library="library">
  
  --     Include path:                /home/me/code/myproject/OpenCV-3.2.0/3rdparty/include/opencl/1.2
  
  --     Use AMDFFT:                  NO
  
  --     Use AMDBLAS:                 NO
  
  -- 
  
  --   Python 2:
  
  --     Interpreter:                 /home/me/.envs/myenv/bin/python (ver 2.7.12)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.12)
  
  --     numpy:                       /home/me/.envs/myenv/local/lib/python2.7/site-packages/numpy/core/include (ver 1.13.1)
  
  --     packages path:               lib/python2.7/site-packages
  
  -- 
  
  --   Python 3:
  
  --     Interpreter:                 /usr/bin/python3 (ver 3.5.2)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.5m.so (ver 3.5.2)
  
  --     numpy:                       /usr/lib/python3/dist-packages/numpy/core/include (ver 1.11.0)
  
  --     packages path:               lib/python3.5/dist-packages
  
  -- 
  
  --   Python (for build):            /home/me/.envs/myenv/bin/python
  
  -- 
  
  --   Java:
  
  --     ant:                         /usr/bin/ant (ver 1.9.6)
  
  --     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
  
  --     Java wrappers:               YES
  
  --     Java tests:                  YES
  
  -- 
  
  --   Matlab:                        Matlab not found or implicitly disabled
  
  -- 
  
  --   Documentation:
  
  --     Doxygen:                     /usr/bin/doxygen (ver 1.8.11)
  
  -- 
  
  --   Tests and samples:
  
  --     Tests:                       YES
  
  --     Performance tests:           YES
  
  --     C/C++ Examples:              YES
  
  -- 
  
  --   Install path:                  /usr/local
  
  -- 
  
  --   cvconfig.h is in:              /home/me/code/myproject/OpenCV-3.2.0/build
  
  -- -----------------------------------------------------------------
  
  -- 
  
  </dynamic>

  Unfortunately, while this works and I can import cv2 in the shell, it cannot read video using the above script, probably due to incorrect compilation or linking of ffmpeg ? The confusing part is the system-wide installation of OpenCV works fine, even without ffmpeg installed !

  What am I doing wrong ? How can I get OpenCV working with a virtualenv ?

  ====

  EDIT : Running the C++ video writing example has this result :

  $ cd /home/me/code/myproject/OpenCV-3.2.0/build/bin
  
  $ ./cpp-tutorial-video-write ../../samples/data/vtest.avi R Y
  
  ------------------------------------------------------------------------------
  
  This program shows how to write video files.
  
  You can extract the R or G or B color channel of the input video.
  
  Usage:
  
  ./video-write  [ R | G | B] [Y | N]
  
  ------------------------------------------------------------------------------
  
  
  
  OpenCV: FFMPEG: tag 0xffffffff/'����' is not found (format 'avi / AVI (Audio Video Interleaved)')'
  
  
  
  (cpp-tutorial-video-write:19523): GStreamer-CRITICAL **: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed
  
  OpenCV Error: Unsupported format or combination of formats (Gstreamer Opencv backend does not support this codec.) in CvVideoWriter_GStreamer::open, file /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp, line 1388
  
  VIDEOIO(cvCreateVideoWriter_GStreamer(filename, fourcc, fps, frameSize, is_color)): raised OpenCV exception:
  
  
  
  /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp:1388: error: (-210) Gstreamer Opencv backend does not support this codec. in function CvVideoWriter_GStreamer::open
  
  
  
  Could not open the output video for write: ../../samples/data/vtest.avi

  And the opencv_test_videoio unit test reports the following : https://pastebin.com/q4mf224Q

  However, running the c++ video starter example DOES work, with the following command and output, I can see the webcam working and streaming video in the highgui interface :

  $ ./cpp-example-videocapture_starter 0
  
  VIDEOIO ERROR: V4L: device 0: Unable to query number of channels
  
  (ERROR)icvOpenAVI_XINE(): Unable to initialize video driver.
  
  GStreamer: Error opening bin: no element "0"
  
  press space to save a picture. q or esc to quit
  
  init done
  
  opengl support available

• Measuring success for your SEO content

  20 mars 2020, par Jake Thornton — Uncategorized
  With over a billion searches every day in search engines, it’s hard to underestimate the importance of having your business present on page one (ideally in positions 1 – 3) ranking for the keywords that impact your sales and conversions.

  "In 2019, Google received nearly 2.3 trillion searches and on page one alone, the first five organic results accounted for 67.60% of all the clicks."

  So how is your business performing when it comes to ranking in the crucial top three spots of search for your most important keywords ?

  Accurately measuring the success of your content

  Once you’ve done your keyword research, created compelling content, optimised it to be search-friendly, and hit ‘publish’, you then need to accurately measure the success of your efforts.

  4 tips for measuring the success of your SEO content

  1. Create a custom segment for "Visitors from Search Engines only"

  By creating this custom segment, you’ll be able to analyse the behavioural patterns of the visitors who found your website through a search engine. 

  This way you can use many of Matomo’s powerful features (Visitors, Behaviour, Acquisition, Ecommerce, Goals etc.) focused entirely on search engine visitors only.

  

  Once you’ve created this segment, you can begin to see key metrics like which entry pages are responsible for referring visitors to your website. For example : Visit Behaviour – Entry Pages, this is a great way to analyse your most effective SEO pages.You may be surprised at what pages currently bring in the most traffic.

  As well as discovering which content resonates with your search audience, you will also be able to create more content focused on your targeted audience. Do this by learning which locations your search visitors are from, which device they use, what time of the day they visited your website and much more.

  >> Learn more about creating custom segments

  2. Website visits, time on site, pages per session, and bounce rate.

  “The top four ranking factors are website visits, time on site, pages per session, and bounce rate.”

  These four metrics set the benchmark for your SEO success.

  First, you need to get as many of the ‘right’ users to see your content. If you feel you’ve exhausted channels such as social media, email and possibly paid posts ; think about who your ideal audience is. Where are they likely to hang out online ? Are there community groups or forum sites that are interested in what you’re writing about ? 

  Whatever the case, putting yourself out there and getting more traffic to your website will help show search engines that people are interested in your website. As a result, they’ll likely rank you higher for that.

  When we say getting more of the ‘right’ users, we mean users who are generally interested in the topic/subject you’re writing about and interested in the work you do. 

  

  This is important for the next three metrics – increasing users time on your website, increasing the amount of pages your users explore on your website, and reducing the overall bounce rate for users who leave your website in a matter of seconds.

  To evaluate these metrics, go to Behaviour – Pages in your Matomo and see how these metrics vary on previous posts or pages you’ve created. Which pages are already showing you the best results ? Why do they get the results ? Can you focus on creating more content like this ?

  Understanding what content is resonating with your users through these metrics is easy and is the starting point for measuring the success of your SEO content strategy.

  >> Learn more about the Behaviour feature

  3. Row Evolution

  The Row Evolution feature embedded within the Search Engine Keywords Performance plugin lets you see how your ranking positions have changed over time for your important keywords. It also lets you see how the incoming traffic, related to your keywords, has changed over time.

  

  This is valuable when measuring the changes you’ve made to your landing pages to see if it has a positive or negative effect on your ranking efforts. 

  This also lets you see how search engine algorithm changes affect your search rankings over time, and to see if the effects of these algorithm updates are temporary or long lasting.

  Row evolution allows you to report on keyword performance with ease. If you only check your insights once a week or once a fortnight, you’ll see how ranking positions for your important keywords have changed daily (or even weekly, monthly or yearly however you prefer.)

  >> Learn more about Row Evolution

  4. What results are you getting from the lesser known search engines ?

  "In 2019 (to date), Google accounted for just over 75% of all global desktop search traffic, followed by Bing at 9.97%, Baidu at 9.34%, and Yahoo at 2.77%."

  For most of us, we want to be ranking in the top three spots in Google Search because that’s where the majority of search users are. However, don’t shy away from opportunities you could be missing with lesser known search engines.

  If you sell a product aimed at 55-65 year olds who use a PC computer, chances are they are using Bing. If you have customers in China the majority will be using Baidu, or in our case at Matomo, many of our loyal users use a privacy-friendly search engine like DuckDuckGo or Qwant.

  Some of your ideal customers might be finding you through these alternative search engines, so be sure to measure the impact that these referrals may have on your conversions.

  

  Strategically including important keywords that impact your business

  While search is an important acquisition channel for most businesses, it’s also one of the most competitive.

  We recommend analysing your keyword and content performance regularly and alter content that isn’t performing as well as you’d like. You need to continually learn from the content that is successful, and focus on creating more content like this. 

  The final thing to remember with search keyword performance is to be patient. If you have had little success in the past with attracting customers through search, it can take time to build this reputation with search engines.

• Switch to Matomo for WordPress from Google Analytics

  10 mars 2020, par Joselyn Khor — Plugins, Privacy
  While Google Analytics may seem like a great plugin option on the WordPress directory, we’d like to present a new ethical alternative called Matomo for WordPress, which gives you 100% data ownership and privacy protection.

  Firstly what does Google Analytics offer in WordPress ?

  When you think of getting insights about visitors on your WordPress (WP) sites, the first thing that comes to mind might be Google Analytics. Why not right ? Especially when there are good free Google Analytics plugins, like Monster Insights and Site Kit. 

  These give you access to a great analytics platform, but the downside with Google Analytics is the lack of transparency around privacy and data ownership.

  

  Matomo Analytics for WordPress is an ethical alternative to Google Analytics for WordPress

  If you’re more interested in a privacy-respecting, GDPR compliant alternative, there’s now a new option on the WP plugins directory : Matomo Analytics – Ethical Stats. Powerful Insights. 

  It’s free and can be considered the #1 ethical alternative to Google Analytics in terms of features and capabilities. Why is it important to choose a web analytics platform that respects privacy ?

  

  Risk facing fines for non-GDPR compliance and privacy/data breaches

  In Europe there’s an overarching privacy law called GDPR which provides better privacy protection for EU citizens on the web. 

  Websites need to be GDPR compliant and follow rules governing how personal data is used or risk facing fines up to 4% of their yearly revenue for data/privacy breaches or non-compliance. Even if your website is based outside of Europe. If you have visitors from Europe, you can still be liable.

  

  In the US, there isn’t one main privacy law, there hundreds on both the federal and state levels to protect the personal data (or personally identifiable information) of US residents – like the California Consumer Privacy Act (CCPA). There are also industry-specific statutes related to data privacy like HIPAA.

  To protect your website from coming under fire for privacy breaches, best practise is to find platforms that are privacy and GDPR compliant by design. 

  When you own your own data – as with the case of Matomo – you have control over where data is stored, what you’re doing with it, and can better protect the privacy of your visitors.

  At this point you may be asking, “what’s the point of an analytics platform if you have to follow all these rules ?”

  The importance of analytics for your WordPress site

  • Figuring out how your audience behaves to increase conversions
  • Setting, tracking and measuring conversion goals
  • Being able to find insights to improve and optimize your site 
  • Making smarter, data-driven decisions so your company can thrive, rather than risk being left behind

  Analytics is used to answer questions like :

  • Where are your website visitors coming from (location) ?
  • How many people visit your website ?
  • Which are the most popular pages on your site ?
  • What sources of traffic are coming to your site (social, marketing campaigns, search) ?
  • Is your marketing campaign performing better this month compared to last ?

  Matomo can answer all of the above questions. BONUS : On top of that, with Matomo you get the peace of mind knowing you’re the only one who has access to those answers.

  

  Matomo Analytics vs Google Analytics on WordPress

  The top 5 most useful features in Matomo Analytics that’s comparable to GA

  1. Campaign measurement – traffic. Matomo also has a URL builder that lets you track which campaigns are working effectively
  2. Tracking goals. Matomo empowers you to set goals you can track. Being able to see this means you can accurately measure your return on investment (ROI) 
  3. Audience reports to learn about visitors. Matomo’s powerful visitors feature lets you learn who is visiting your site, what their journey is and the steps they take to conversion.
  4. In depth view of behaviour with Funnels in Matomo. This tracks the journey of your visitors from the moment they enter your site, to when they leave. Giving you insight into where and why you lose your visitors.
  5. Custom reports. Where you create your unique reports to fit your business goals.

  Other benefits of using Matomo :

  • No data sampling which means you get 100% accurate reporting
  • 100% data ownership
  • Free Tag Manager
  • Search engine keyword rankings
  • Unlimited websites
  • Unlimited team members
  • GDPR manager
  • API access
  • Hosted on your own servers so you have full control over where your data is stored

  Learn more about the differences in this comprehensive table.

  

  Matomo Analytics for WordPress is free !

  Matomo Analytics is the best free Google Analytics alternative on the WordPress Directory. In addition to having comparable features where you can do pretty much do everything you wanted to do in GA. Matomo Analytics for WordPress makes for an ethical choice because you can respect your visitor’s privacy, can become GDPR compliant, and maintain control over your own data.

  Google Analytics leads the market for good reasons. It’s a great free tool for those who want analytics, but there’s no clarity when it comes to grey areas like privacy and data ownership. If these are major concerns for you, Matomo offers complete peace of mind that you’re doing the best you can to stay ethical while growing your business and website.

  It’s just as easy to install in a few click !

  Install and see for yourself !