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• Consent Mode v2 : Everything You Need to Know

  7 mai 2024, par Alex — Analytics Tips

  Confused about Consent Mode v2 and its impact on your website analytics ? You’re not the only one. 

  Google’s latest update has left many scratching their heads about data privacy and tracking. 

  In this blog, we’re getting straight to the point. We’ll break down what Consent Mode v2 is, how it works, and the impact it has.

  What is Consent Mode ?

  What exaclty is Google Consent Mode and why is there so much buzz surrounding it ? This question has been frustrating analysts and marketers worldwide since the beginning of this year. 

  Consent Mode is the solution from Google designed to manage data collection on websites in accordance with user privacy requirements.

  This mode enables website owners to customise how Google tags respond to users’ consent status for cookie usage. At its core, Consent Mode adheres to privacy regulations such as GDPR in Europe and CCPA in California, without significant loss of analytical data.

  

  How does Consent Mode work ?

  Consent Mode operates by adjusting the behaviour of tags on a website depending on whether consent for cookie usage is provided or not. If a user does not consent to the use of analytical or advertising cookies, Google tags automatically switch to collecting a limited amount of data, ensuring privacy compliance.

  This approach allows for continued valuable insights into website traffic and user behavior, even if users opt out of most tracking cookies.

  What types of consent are available in Consent Mode ?

  As of 6 March 2024, Consent Mode v2 has become the current standard (and in terms of utilising Google Advertising Services, practically mandatory), indicating the incorporation of four consent types :

  1. ad_storage : allows for the collection and storage of data necessary for delivering personalised ads based on user actions.
  2. ad_user_data : pertains to the collection and usage of data that can be associated with the user for ad customisation and optimisation.
  3. ad_personalization : permits the use of user data for ad personalisation and providing more relevant content.
  4. analytics_storage : relates to the collection and storage of data for analytics, enabling websites to analyse user behaviour and enhance user experience.

  Additionally, in Consent Mode v2, there are two modes :

  1. Basic Consent Mode : in which Google tags are not used for personalised advertising and measurements if consent is not obtained.
  2. Advanced Consent Mode : allows Google tags to utilise anonymised data for personalised advertising campaigns and measurements, even if consent is not obtained.

  What is Consent Mode v2 ? (And how does it differ from Consent Mode v1 ?)

  Consent Mode v2 is an improved version of the original Consent Mode, offering enhanced customisation capabilities and better compliance with privacy requirements. 

  The new version introduces additional consent configuration parameters, allowing for even more precise control over which data is collected and how it’s used. The key difference between Consent Mode v2 and Consent Mode v1 lies in more granular consent management, making this tool even more flexible and powerful in safeguarding personal data.

  In Consent Mode v2, the existing markers (ad_storage and analytics_storage) are accompanied by two new markers :

  1. ad_user_data – does the user agree to their personal data being utilized for advertising purposes ?
  2. ad_personalization – does the user agree to their data being employed for remarketing ?

  In contrast to ad_storage and analytics_storage, these markers don’t directly affect how the tags operate on the site itself. 

  They serve as additional directives sent alongside the pings to Google services, indicating how user data can be utilised for advertising purposes.

  While ad_storage and analytics_storage serve as upstream qualifiers for data (determining which identifiers are sent with the pings), ad_user_data and ad_personalization serve as downstream instructions for Google services regarding data processing.

  How is the implementation of Consent Mode v2 going ?

  The implementation of Consent Mode v2 is encountering some issues and bugs (as expected). The most important thing to understand :

  1. Advanced Consent Mode v2 is essential if you have traffic and campaigns with Google Ads in the European Union.
  2. If you don’t have substantially large traffic, enabling Advanced Consent Mode v2 will likely result in a traffic drop in GA4 – because this version of consent mode (unlike the basic one) applies behavioural modelling to users who haven’t accepted the use of cookies. And modelling the behaviour requires time.

  The aspect of behavioural modelling in Consent Mode v2 implies the following : the data of users who have declined tracking options begin to be modelled using machine learning. 

  However, training the model requires a suitable data volume. As the Google’s documentation states :

  The property should collect at least 1,000 events per day with analytics_storage=’denied’ for at least 7 days. The property should have at least 1,000 daily users submitting events with analytics_storage=’granted’ for at least 7 of the previous 28 days.

  Largely due to this, the market’s response to the Consent Mode v2 implementation was mixed : many reported a significant drop in traffic in their GA4 and Google Ads reports upon enabling the Advanced mode. Essentially, a portion of the data was lost because Google’s models lacked enough data for training. 

  And from the very beginning of implementation, users regularly report about a few examples of that scenario. If your website doesn’t have enough traffic for behaviour modelling, after Consent Mode v2 switching you will face significant drop in your traffic in Google Ads and GA4 reports. There are a lot of cases of observing 90-95% drop in metrics of users and sessions.

  In a nutshell, you should be prepared for significant data losses if you are planning to switch to Google Consent Mode v2.

  How does Consent Mode v2 impact web analytics ? 

  The transition to Consent Mode v2 alters the methods of user data collection and processing. The main concerns arise from the potential loss of accuracy and completeness of analytical data due to restrictions on the use of cookies and other identifiers when user consent is absent. 

  With Google Consent Mode v2, the data of visitors who have not agreed to tracking will be modelled and may not accurately reflect your actual visitors’ behaviours and actions. So as an analyst or marketer, you will not have true insights into these visitors and the data acquired will be more generalised and less accurate.

  Google Consent Mode v2 appears to be a kind of compromise band-aid solution. 

  It tries to solve these issues by using data modelling and anonymised data collection. However, it’s critical to note that there are specific limitations inherent to the modelling mechanism.

  This complicates the analysis of visitor behavior, advertising campaigns, and website optimisation, ultimately impacting decision-making and resulting in poor website performance and marketing outcomes.

  Wrap up

  Consent Mode v2 is a mechanism of managing Google tag operations based on user consent settings. 

  It’s mandatory if you’re using Google’s advertising services, and optional (at least for Advanced mode) if you don’t advertise on Google Ads. 

  There are particular indications that this technology is unreliable from a GDPR perspective. 

  Using Google Consent Mode will inevitably lead to data losses and inaccuracies in its analysis. 

  In other words, it in some sense jeopardises your business.

• Error of "Built target opencv_imgproc" while compiling opencv2

  23 mars 2017, par Hong

  Following https://github.com/menpo/conda-opencv3, while I compile opencv, there is following error (please read the error at the end of the post). The only change I made is to enable ffmpeg by modifying "-DWITH_FFMPEG=1" in BUILD.SH. Any suggestion ?

  $conda build conda/
  
  BUILD START: opencv3-3.1.0-py27_0
  
  updating index in: /home/cocadas/anaconda2/conda-bld/linux-64
  
  updating index in: /home/cocadas/anaconda2/conda-bld/noarch
  
  
  
  The following NEW packages will be INSTALLED:
  
  
  
  bzip2:      1.0.6-3            
  
  cmake:      3.6.3-0            
  
  curl:       7.52.1-0           
  
  eigen:      3.2.7-0       menpo
  
  expat:      2.1.0-0            
  
  mkl:        2017.0.1-0         
  
  ncurses:    5.9-10             
  
  numpy:      1.12.1-py27_0      
  
  openssl:    1.0.2k-1           
  
  pip:        9.0.1-py27_1       
  
  python:     2.7.13-0           
  
  readline:   6.2-2              
  
  setuptools: 27.2.0-py27_0      
  
  sqlite:     3.13.0-0           
  
  tk:         8.5.18-0           
  
  wheel:      0.29.0-py27_0      
  
  xz:         5.2.2-1            
  
  zlib:       1.2.8-3            
  
  Source cache directory is: /home/cocadas/anaconda2/conda-bld/src_cache
  
  Found source in cache: opencv-3.1.0.tar.gz
  
  Extracting download
  
  Applying patch: u'/home/cocadas/conda-opencv3/conda/no_rpath.patch'
  
  patching file CMakeLists.txt
  
  patch unexpectedly ends in middle of line
  
  Hunk #1 succeeded at 397 with fuzz 1 (offset 11 lines).
  
  Package: opencv3-3.1.0-py27_0
  
  source tree in: /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0
  
  
  
  source /home/cocadas/anaconda2/bin/activate /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/_b_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl
  
  mkdir build
  
  cd build
  
  CMAKE_GENERATOR='Unix Makefiles'
  
  CMAKE_ARCH=-m64
  
  ++ uname -s
  
  SHORT_OS_STR=Linux
  
  '[' Linux == Linux ']'
  
  DYNAMIC_EXT=so
  
  TBB=
  
  OPENMP=-DWITH_OPENMP=1
  
  IS_OSX=0
  
  
  
  -- 3rdparty dependencies: zlib libjpeg libwebp libpng libtiff libjasper IlmImf
  
  --
  
  -- OpenCV modules:
  
  -- To be built: core flann hdf imgproc ml photo reg surface_matching video dnn fuzzy imgcodecs shape videoio highgui objdetect plot superres xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib datasets rgbd stereo structured_light tracking videostab xfeatures2d ximgproc aruco optflow sfm stitching python2
  
  -- Disabled: world contrib_world
  
  -- Disabled by dependency: -
  
  -- Unavailable: cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev java python3 ts viz cvv matlab
  
  --
  
  -- GUI:
  
  -- QT: NO
  
  -- GTK+ 3.x: YES (ver 3.18.9)
  
  -- GThread : YES (ver 2.48.2)
  
  -- GtkGlExt: NO
  
  -- OpenGL support: NO
  
  -- VTK support: NO
  
  --
  
  -- Media I/O:
  
  -- ZLib: build (ver 1.2.8)
  
  -- JPEG: build (ver 90)
  
  -- WEBP: build (ver 0.3.1)
  
  -- PNG: build (ver 1.6.19)
  
  -- TIFF: build (ver 42 - 4.0.2)
  
  -- JPEG 2000: build (ver 1.900.1)
  
  -- OpenEXR: build (ver 1.7.1)
  
  -- GDAL: NO
  
  --
  
  -- Video I/O:
  
  -- DC1394 1.x: NO
  
  -- DC1394 2.x: YES (ver 2.2.4)
  
  -- FFMPEG: YES
  
  -- codec: YES (ver 56.60.100)
  
  -- format: YES (ver 56.40.101)
  
  -- util: YES (ver 54.31.100)
  
  -- swscale: YES (ver 3.1.101)
  
  -- resample: NO
  
  -- gentoo-style: YES
  
  -- GStreamer: NO
  
  -- OpenNI: NO
  
  -- OpenNI PrimeSensor Modules: NO
  
  -- OpenNI2: NO
  
  -- PvAPI: NO
  
  -- GigEVisionSDK: NO
  
  -- UniCap: NO
  
  -- UniCap ucil: NO
  
  -- V4L/V4L2: Using libv4l1 (ver 1.10.0) / libv4l2 (ver 1.10.0)
  
  -- XIMEA: NO
  
  -- Xine: NO
  
  -- gPhoto2: NO
  
  --
  
  -- Parallel framework: OpenMP
  
  --
  
  -- Other third-party libraries:
  
  -- Use IPP: 9.0.1 [9.0.1]
  
  -- at: /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/3rdparty/ippicv/unpack/ippicv_lnx
  
  -- Use IPP Async: NO
  
  -- Use VA: NO
  
  -- Use Intel VA-API/OpenCL: NO
  
  -- Use Eigen: YES (ver 3.2.7)
  
  -- Use Cuda: NO
  
  -- Use OpenCL: NO
  
  -- Use custom HAL: NO
  
  --
  
  -- Python 2:
  
  
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:554:22: error: ‘H5Tclose’ was not declared in this scope
  
  H5Tclose( dstype );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:555:22: error: ‘H5Sclose’ was not declared in this scope
  
  H5Sclose( dspace );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:557:22: error: ‘H5Dclose’ was not declared in this scope
  
  H5Dclose( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: At global scope:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:466:50: warning: unused parameter ‘dslabel’ [-Wunused-parameter]
  
  void HDF5Impl::dsread( OutputArray Array, String dslabel,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: In member function ‘virtual void cv::hdf::HDF5Impl::dswrite(cv::InputArray, cv::String, const int*, const int*) const’:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:583:5: error: ‘hsize_t’ was not declared in this scope
  
  hsize_t dsdims[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:584:13: error: expected ‘;’ before ‘offset’
  
  hsize_t offset[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:588:7: error: ‘offset’ was not declared in this scope
  
  offset[d] = 0;
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:590:7: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = matrix.size[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:601:9: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = dims_counts[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:605:5: error: ‘hid_t’ was not declared in this scope
  
  hid_t dsdata = H5Dopen( m_h5_file_id, dslabel.c_str(), H5P_DEFAULT );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:608:11: error: expected ‘;’ before ‘dspace’
  
  hid_t dspace = H5Screate_simple( n_dims, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:614:9: error: ‘offset’ was not declared in this scope
  
  offset[d] = dims_offset[d];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:618:11: error: expected ‘;’ before ‘fspace’
  
  hid_t fspace = H5Dget_space( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:619:26: error: ‘fspace’ was not declared in this scope
  
  H5Sselect_hyperslab( fspace, H5S_SELECT_SET,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:619:34: error: ‘H5S_SELECT_SET’ was not declared in this scope
  
  H5Sselect_hyperslab( fspace, H5S_SELECT_SET,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:26: error: ‘offset’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:40: error: ‘dsdims’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:620:53: error: ‘H5Sselect_hyperslab’ was not declared in this scope
  
  offset, NULL, dsdims, NULL );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:623:11: error: expected ‘;’ before ‘dstype’
  
  hid_t dstype = GetH5type( matrix.type() );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:628:15: error: expected ‘;’ before ‘adims’
  
  hsize_t adims[1] = { channs };
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:7: error: ‘dstype’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:44: error: ‘adims’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:629:50: error: ‘H5Tarray_create’ was not declared in this scope
  
  dstype = H5Tarray_create( dstype, 1, adims );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:15: error: ‘dsdata’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:23: error: ‘dstype’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:633:31: error: ‘dspace’ was not declared in this scope
  
  H5Dwrite( dsdata, dstype, dspace, fspace,
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:634:15: error: ‘H5P_DEFAULT’ was not declared in this scope
  
  H5P_DEFAULT, matrix.data );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:634:40: error: ‘H5Dwrite’ was not declared in this scope
  
  H5P_DEFAULT, matrix.data );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:637:24: error: ‘H5Tclose’ was not declared in this scope
  
  H5Tclose( dstype );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:639:22: error: ‘H5Sclose’ was not declared in this scope
  
  H5Sclose( dspace );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:641:22: error: ‘H5Dclose’ was not declared in this scope
  
  H5Dclose( dsdata );
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:580:9: warning: unused variable ‘channs’ [-Wunused-variable]
  
  int channs = matrix.channels();
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp: In member function ‘virtual void cv::hdf::HDF5Impl::dsinsert(cv::InputArray, cv::String, const int*, const int*) const’:
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:670:5: error: ‘hsize_t’ was not declared in this scope
  
  hsize_t dsdims[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:671:13: error: expected ‘;’ before ‘offset’
  
  hsize_t offset[n_dims];
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:675:7: error: ‘offset’ was not declared in this scope
  
  offset[d] = 0;
  
  ^
  
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:676:7: error: ‘dsdims’ was not declared in this scope
  
  dsdims[d] = matrix.size[d];

  ......
  hsize_t foffset[1] = 0  ;
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1022:11 : error : expected ‘ ;’ before ‘dspace’
  hid_t dspace = H5Screate_simple( 1, dsddims, NULL ) ;
  ^
  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1025:26 : error : ‘dspace’ was not declared in this scope
  H5Sselect_hyperslab( dspace, H5S_SELECT_SET,
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  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1025:34 : error : ‘H5S_SELECT_SET’ was not declared in this scope
  H5Sselect_hyperslab( dspace, H5S_SELECT_SET,
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  /home/cocadas/anaconda2/conda-bld/opencv3_1490285248642/work/opencv-3.1.0/build/opencv_contrib/modules/hdf/src/hdf5.cpp:1026:26 : error : ‘foffset’ was not declared in this scope
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  execute(sys.argv[1:])
  
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  noverify=args.no_verify)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/api.py", line 97, in build
  
  need_source_download=need_source_download, config=config)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/build.py", line 1502, in build_tree
  
  config=config)
  
  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/build.py", line 1137, in build
  
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  File "/home/cocadas/anaconda2/lib/python2.7/site-packages/conda_build/utils.py", line 616, in check_call_env
  
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• 10 Customer Segments Examples and Their Benefits

  9 mai 2024, par Erin

  Now that companies can segment buyers, the days of mass marketing are behind us. Customer segmentation offers various benefits for marketing, content creation, sales, analytics teams and more. Without customer segmentation, your personalised marketing efforts may fall flat. 

  According to the Twilio 2023 state of personalisation report, 69% of business leaders have increased their investment in personalisation. There’s a key reason for this — customer retention and loyalty directly benefit from personalisation. In fact, 62% of businesses have cited improved customer retention due to personalisation efforts. The numbers don’t lie. 

  Keep reading to learn how customer segments can help you fine-tune your personalised marketing campaigns. This article will give you a better understanding of customer segmentation and real-world customer segment examples. You’ll leave with the knowledge to empower your marketing strategies with effective customer segmentation. 

  What are customer segments ?

  Customer segments are distinct groups of people or organisations with similar characteristics, needs and behaviours. Like different species of plants in a garden, each customer segment has specific needs and care requirements. Customer segments are useful for tailoring personalised marketing campaigns for specific groups.

  Personalised marketing has been shown to have significant benefits — with 56% of consumers saying that a personalised experience would make them become repeat buyers. 

  Successful marketing teams typically focus on these types of customer segmentation :

  

  1. Geographic segmentation : groups buyers based on their physical location — country, city, region or climate — and language.
  2. Purchase history segmentation : categorises buyers based on their purchasing habits — how often they make purchases — and allows brands to distinguish between frequent, occasional and one-time buyers. 
  3. Product-based segmentation : groups buyers according to the products they prefer or end up purchasing. 
  4. Customer lifecycle segmentation : segments buyers based on where they are in the customer journey. Examples include new, repeat and lapsed buyers. This segmentation category is also useful for understanding the behaviour of loyal buyers and those at risk of churning. 
  5. Technographic segmentation : focuses on buyers’ technology preferences, including device type, browser type, and operating system. 
  6. Channel preference segmentation : helps us understand why buyers prefer to purchase via specific channels — whether online channels, physical stores or a combination of both. 
  7. Value-based segmentation : categorises buyers based on their average purchase value and sensitivity to pricing, for example. This type of segmentation can provide insights into the behaviours of price-conscious buyers and those willing to pay premium prices. 

  Customer segmentation vs. market segmentation

  Customer segmentation and market segmentation are related concepts, but they refer to different aspects of the segmentation process in marketing. 

  Market segmentation is the broader process of dividing the overall market into homogeneous groups. Market segmentation helps marketers identify different groups based on their characteristics or needs. These market segments make it easier for businesses to connect with new buyers by offering relevant products or new features. 

  On the other hand, customer segmentation is used to help you dig deep into the behaviour and preferences of your current customer base. Marketers use customer segmentation insights to create buyer personas. Buyer personas are essential for ensuring your personalised marketing efforts are relevant to the target audience. 

  10 customer segments examples

  Now that you better understand different customer segmentation categories, we’ll provide real-world examples of how customer segmentation can be applied. You’ll be able to draw a direct connection between the segmentation category or categories each example falls under.

  One thing to note is that you’ll want to consider privacy and compliance when you are considering collecting and analysing types of data such as gender, age, income level, profession or personal interests. Instead, you can focus on these privacy-friendly, ethical customer segmentation types :

  1. Geographic location (category : geographic segmentation)

  The North Face is an outdoor apparel and equipment company that relies on geographic segmentation to tailor its products toward buyers in specific regions and climates. 

  For instance, they’ll send targeted advertisements for insulated jackets and snow gear to buyers in colder climates. For folks in seasonal climates, The North Face may send personalised ads for snow gear in winter and ads for hiking or swimming gear in summer. 

  The North Face could also use geographic segmentation to determine buyers’ needs based on location. They can use this information to send targeted ads to specific customer segments during peak ski months to maximise profits.

  2. Preferred language (category : geographic segmentation)

  Your marketing approach will likely differ based on where your customers are and the language they speak. So, with that in mind, language may be another crucial variable you can introduce when identifying your target customers. 

  Language-based segmentation becomes even more important when one of your main business objectives is to expand into new markets and target international customers — especially now that global reach is made possible through digital channels. 

  Coca-Cola’s “Share a Coke” is a multi-national campaign with personalised cans and bottles featuring popular names from countries around the globe. It’s just one example of targeting customers based on language.

  3. Repeat users and loyal customers (category : customer lifecycle segmentation)

  Sephora, a large beauty supply company, is well-known for its Beauty Insider loyalty program. 

  It segments customers based on their purchase history and preferences and rewards their loyalty with gifts, discounts, exclusive offers and free samples. And since customers receive personalised product recommendations and other perks, it incentivises them to remain members of the Beauty Insider program — adding a boost to customer loyalty.

  By creating a memorable customer experience for this segment of their customer base, staying on top of beauty trends and listening to feedback, Sephora is able to keep buyers coming back.

  

  4. New customers (category : customer lifecycle segmentation)

  Subscription services use customer lifecycle segmentation to offer special promotions and trials for new customers. 

  HBO Max is a great example of a real company that excels at this strategy : 

  They offer 40% savings on an annual ad-free plan, which targets new customers who may be apprehensive about the added monthly cost of a recurring subscription.

  This marketing strategy prioritises fostering long-term customer relationships with new buyers to avoid high churn rates. 

  5. Cart abandonment (category : purchase history segmentation)

  With a rate of 85% among US-based mobile users, cart abandonment is a huge issue for ecommerce businesses. One way to deal with this is to segment inactive customers and cart abandoners — those who showed interest by adding products to their cart but haven’t converted yet — and send targeted emails to remind them about their abandoned carts.

  E-commerce companies like Ipsy, for example, track users who have added items to their cart but haven’t followed through on the purchase. The company’s messaging often contains incentives — like free shipping or a limited-time discount — to encourage passive users to return to their carts. 

  Research has found that cart abandonment emails with a coupon code have a high 44.37% average open rate. 

  6. Website activity (category : technographic segmentation)

  It’s also possible to segment customers based on website activity. Now, keep in mind that this is a relatively broad approach ; it covers every interaction that may occur while the customer is browsing your website. As such, it leaves room for many different types of segmentation. 

  For instance, you can segment your audience based on the pages they visited, the elements they interacted with — like CTAs and forms — how long they stayed on each page and whether they added products to their cart. 

  Matomo’s Event Tracking can provide additional context to each website visit and tell you more about the specific interactions that occur, making it particularly useful for segmenting customers based on how they spend their time on your website. 

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  Get the web insights you need, while respecting user privacy.

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  Amazon segments its customers based on browsing behaviour — recently viewed products and categories, among other things — which, in turn, allows them to improve the customer’s experience and drive sales.

  7. Traffic source (category : channel segmentation) 

  You can also segment your audience based on traffic sources. For example, you can determine if your website visitors arrived through Google and other search engines, email newsletters, social media platforms or referrals. 

  In other words, you’ll create specific audience segments based on the original source. Matomo’s Acquisition feature can provide insights into five different types of traffic sources — search engines, social media, external websites, direct traffic and campaigns — to help you understand how users enter your website.

  You may find that most visitors arrive at your website through social media ads or predominantly discover your brand through search engines. Either way, by learning where they’re coming from, you’ll be able to determine which conversion paths you should prioritise and optimise further. 

  8. Device type (category : technographic segmentation)

  Device type is customer segmentation based on the devices that potential customers may use to access your website and view your content. 

  It’s worth noting that, on a global level, most people (96%) use mobile devices — primarily smartphones — for internet access. So, there’s a high chance that most of your website visitors are coming from mobile devices, too. 

  However, it’s best not to assume anything. Matomo can detect the operating system and the type of device — desktop, mobile device, tablet, console or TV, for example. 

  By introducing the device type variable into your customer segmentation efforts, you’ll be able to determine if there’s a preference for mobile or desktop devices. In return, you’ll have a better idea of how to optimise your website — and whether you should consider developing an app to meet the needs of mobile users.

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  Get the web insights you need, while respecting user privacy.

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  9. Browser type (category : technographic segmentation)

  Besides devices, another type of segmentation that belongs to the technographic category and can provide valuable insights is browser-related. In this case, you’re tracking the internet browser your customers use. 

  Many browser types are available — including Google Chrome, Microsoft Edge, Safari, Firefox and Brave — and each may display your website and other content differently. 

  So, keeping track of your customers’ preferred choices is important. Otherwise, you won’t be able to fully understand their online experience — or ensure that these browsers are displaying your content properly. 

  

  10. Ecommerce activity (category : purchase history, value based, channel or product based segmentation) 

  Similar to website activity, looking at ecommerce activity can tell your sales teams more about which pages the customer has seen and how they have interacted with them. 

  With Matomo’s Ecommerce Tracking, you’ll be able to keep an eye on customers’ on-site behaviours, conversion rates, cart abandonment, purchased products and transaction data — including total revenue and average order value.

  Considering that the focus is on sales channels — such as your online store — this approach to customer segmentation can help you improve the sales experience and increase profitability. 

  Start implementing these customer segments examples

  With ever-evolving demographics and rapid technological advancements, customer segmentation is increasingly complex. The tips and real-world examples in this article break down and simplify customer segmentation so that you can adapt to your customer base. 

  Customer segmentation lays the groundwork for your personalised marketing campaigns to take off. By understanding your users better, you can effectively tailor each campaign to different segments. 

  If you’re ready to see how Matomo can elevate your personalised marketing campaigns, try it for free for 21 days. No credit card required.

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