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Autres articles (64)
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Récupération d’informations sur le site maître à l’installation d’une instance
26 novembre 2010, parUtilité
Sur le site principal, une instance de mutualisation est définie par plusieurs choses : Les données dans la table spip_mutus ; Son logo ; Son auteur principal (id_admin dans la table spip_mutus correspondant à un id_auteur de la table spip_auteurs)qui sera le seul à pouvoir créer définitivement l’instance de mutualisation ;
Il peut donc être tout à fait judicieux de vouloir récupérer certaines de ces informations afin de compléter l’installation d’une instance pour, par exemple : récupérer le (...) -
Organiser par catégorie
17 mai 2013, parDans MédiaSPIP, une rubrique a 2 noms : catégorie et rubrique.
Les différents documents stockés dans MédiaSPIP peuvent être rangés dans différentes catégories. On peut créer une catégorie en cliquant sur "publier une catégorie" dans le menu publier en haut à droite ( après authentification ). Une catégorie peut être rangée dans une autre catégorie aussi ce qui fait qu’on peut construire une arborescence de catégories.
Lors de la publication prochaine d’un document, la nouvelle catégorie créée sera proposée (...) -
Selection of projects using MediaSPIP
2 mai 2011, parThe examples below are representative elements of MediaSPIP specific uses for specific projects.
MediaSPIP farm @ Infini
The non profit organizationInfini develops hospitality activities, internet access point, training, realizing innovative projects in the field of information and communication technologies and Communication, and hosting of websites. It plays a unique and prominent role in the Brest (France) area, at the national level, among the half-dozen such association. Its members (...)
Sur d’autres sites (4349)
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Buffered encoded images not saved
26 mars 2021, par xyfixI have an issue with the first 12 images not being saved to file. I have attached the relevant files in this issue. I have also attached a log file to show that the first 12 images aren't written to the file that is generated. The frame rate is 24 fps and the recording is 5 sec, so there should be 120 frames written to the output file. This can be seen in the 4th column. The lines in the log files are as follows :


image num [unique num from camera] [temp image num for recording seq] [time in ms]


The image class is actually a simple wrapper around OpenCV's mat class with some additional members. The output file that I currently get is around 10 MB and when I open it in VLC it doesn't run for 5 seconds but more like 1 - 2 seconds but I can see whatever I have recorded. Can anyone explain to me why the files not written and the duration isn't 5 secs (minus 12 frames missing) as expected . As you can see I have tried with "av_interleaved_write_frame" but that didn't help


xcodec.h


#ifndef XCODEC_H
#define XCODEC_H

#include "image/Image.h"

extern "C"
{
 #include "Codec/include/libavcodec/avcodec.h"
 #include "Codec/include/libavdevice/avdevice.h"
 #include "Codec/include/libavformat/avformat.h"
 #include "Codec/include/libavutil/avutil.h"
 #include "Codec/include/libavformat/avio.h"
 #include "Codec/include/libavutil/imgutils.h"
 #include "Codec/include/libavutil/opt.h"
 #include "Codec/include/libswscale/swscale.h"
}


class XCodec
{
public:

 XCodec(const char *filename);

 ~XCodec();

 void encodeImage( const Image& image );

 void encode( AVFrame *frame, AVPacket *pkt );

 void add_stream();

 void openVideoCodec();

 void write_video_frame(const Image &image);

 void createFrame( const Image& image );

 void close();

private:

 static int s_frameCount;

 int m_timeVideo = 0;

 std::string m_filename;


 AVCodec* m_encoder = NULL;

 AVOutputFormat* m_outputFormat = NULL;

 AVFormatContext* m_formatCtx = NULL;

 AVCodecContext* m_codecCtx = NULL;

 AVStream* m_streamOut = NULL;

 AVFrame* m_frame = NULL;

 AVPacket* m_packet = NULL;

};

#endif


xcodec.cpp


#include "XCodec.h"

#include <qdebug>


#define STREAM_DURATION 5.0
#define STREAM_FRAME_RATE 24
#define STREAM_NB_FRAMES ((int)(STREAM_DURATION * STREAM_FRAME_RATE))
#define STREAM_PIX_FMT AV_PIX_FMT_YUV420P /* default pix_fmt */
#define OUTPUT_CODEC AV_CODEC_ID_H264

int XCodec::s_frameCount = 0;

XCodec::XCodec( const char* filename ) :
 m_filename( filename ),
 m_encoder( avcodec_find_encoder( OUTPUT_CODEC ))
{
 av_log_set_level(AV_LOG_VERBOSE);

 int ret(0);


 // allocate the output media context
 ret = avformat_alloc_output_context2( &m_formatCtx, m_outputFormat, NULL, m_filename.c_str());

 if (!m_formatCtx)
 return;

 m_outputFormat = m_formatCtx->oformat;

 // Add the video stream using H264 codec
 add_stream();

 // Open video codec and allocate the necessary encode buffers
 if (m_streamOut)
 openVideoCodec();

 // Print detailed information about input and output
 av_dump_format( m_formatCtx, 0, m_filename.c_str(), 1);

 // Open the output media file, if needed
 if (!( m_outputFormat->flags & AVFMT_NOFILE))
 {
 ret = avio_open( &m_formatCtx->pb, m_filename.c_str(), AVIO_FLAG_WRITE);

 if (ret < 0)
 {
 char error[255];
 ret = av_strerror( ret, error, 255);
 fprintf(stderr, "Could not open '%s': %s\n", m_filename.c_str(), error);
 return ;
 }
 }
 else
 {
 return;
 }

 // Write media header
 ret = avformat_write_header( m_formatCtx, NULL );

 if (ret < 0)
 {
 char error[255];
 av_strerror(ret, error, 255);
 fprintf(stderr, "Error occurred when opening output file: %s\n", error);
 return;
 }

 if ( m_frame )
 m_frame->pts = 0;
}



XCodec::~XCodec()
{}

/* Add an output stream. */
void XCodec::add_stream()
{
 AVCodecID codecId = OUTPUT_CODEC;

 if (!( m_encoder ))
 {
 fprintf(stderr, "Could not find encoder for '%s'\n",
 avcodec_get_name(codecId));
 return;
 }

 // Get the stream for codec
 m_streamOut = avformat_new_stream(m_formatCtx, m_encoder);

 if (!m_streamOut) {
 fprintf(stderr, "Could not allocate stream\n");
 return;
 }

 m_streamOut->id = m_formatCtx->nb_streams - 1;

 m_codecCtx = avcodec_alloc_context3( m_encoder);

 switch (( m_encoder)->type)
 {
 case AVMEDIA_TYPE_VIDEO:
 m_streamOut->codecpar->codec_id = codecId;
 m_streamOut->codecpar->codec_type = AVMEDIA_TYPE_VIDEO;
 m_streamOut->codecpar->bit_rate = 400000;
 m_streamOut->codecpar->width = 800;
 m_streamOut->codecpar->height = 640;
 m_streamOut->codecpar->format = STREAM_PIX_FMT;
 m_streamOut->time_base = { 1, STREAM_FRAME_RATE };

 avcodec_parameters_to_context( m_codecCtx, m_streamOut->codecpar);

 m_codecCtx->gop_size = 12; /* emit one intra frame every twelve frames at most */
 m_codecCtx->max_b_frames = 1;
 m_codecCtx->time_base = { 1, STREAM_FRAME_RATE };
 m_codecCtx->framerate = { STREAM_FRAME_RATE, 1 };
 m_codecCtx->pix_fmt = STREAM_PIX_FMT;
 m_codecCtx->profile = FF_PROFILE_H264_HIGH;

 break;

 default:
 break;
 }

 if (m_streamOut->codecpar->codec_id == OUTPUT_CODEC)
 {
 av_opt_set( m_codecCtx, "preset", "ultrafast", 0 );
 }

/
// /* Some formats want stream headers to be separate. */
 if (m_formatCtx->oformat->flags & AVFMT_GLOBALHEADER)
 m_codecCtx->flags |= AV_CODEC_FLAG_GLOBAL_HEADER;


 int ret = avcodec_parameters_from_context( m_streamOut->codecpar, m_codecCtx );

 if (ret < 0)
 {
 char error[255];
 av_strerror(ret, error, 255);
 fprintf(stderr, "avcodec_parameters_from_context returned (%d) - %s", ret, error);
 return;
 }
}


void XCodec::openVideoCodec()
{
 int ret;

 /* open the codec */
 ret = avcodec_open2(m_codecCtx, m_encoder, NULL);

 if (ret < 0)
 {
 char error[255];
 av_strerror(ret, error, 255);
 fprintf(stderr, "Could not open video codec: %s\n", error);
 return;
 }

 /* allocate and init a re-usable frame */
// m_frame = av_frame_alloc();

}


void XCodec::encodeImage(const Image &image)
{
 // Compute video time from last added video frame
 m_timeVideo = image.timeStamp(); //(double)m_frame->pts) * av_q2d(m_streamOut->time_base);

 // Stop media if enough time
 if (!m_streamOut /*|| m_timeVideo >= STREAM_DURATION*/)
 return;


 // Add a video frame
 write_video_frame( image );

}


void XCodec::write_video_frame( const Image& image )
{
 int ret;

qDebug() << "image num " << image.uniqueImageNumber() << " " << s_frameCount;

 if ( s_frameCount >= STREAM_NB_FRAMES)
 {
 /* No more frames to compress. The codec has a latency of a few
 * frames if using B-frames, so we get the last frames by
 * passing the same picture again. */
 int p( 0 ) ;
 }
 else
 {
 createFrame( image );
 }

 // Increase frame pts according to time base
// m_frame->pts += av_rescale_q(1, m_codecCtx->time_base, m_streamOut->time_base);
 m_frame->pts = int64_t( image.timeStamp()) ;


 if (m_formatCtx->oformat->flags & 0x0020 )
 {
 /* Raw video case - directly store the picture in the packet */
 AVPacket pkt;
 av_init_packet(&pkt);

 pkt.flags |= AV_PKT_FLAG_KEY;
 pkt.stream_index = m_streamOut->index;
 pkt.data = m_frame->data[0];
 pkt.size = sizeof(AVPicture);

// ret = av_interleaved_write_frame(m_formatCtx, &pkt);
 ret = av_write_frame( m_formatCtx, &pkt );
 }
 else
 {
 AVPacket pkt;
 av_init_packet(&pkt);

 /* encode the image */
 ret = avcodec_send_frame(m_codecCtx, m_frame);

 if (ret < 0)
 {
 char error[255];
 av_strerror(ret, error, 255);
 fprintf(stderr, "Error encoding video frame: %s\n", error);
 return;
 }

 /* If size is zero, it means the image was buffered. */
 ret = avcodec_receive_packet(m_codecCtx, &pkt);

 if( !ret && pkt.size)
 {
qDebug() << "write frame " << m_frame->display_picture_number;
 pkt.stream_index = m_streamOut->index;

 /* Write the compressed frame to the media file. */
// ret = av_interleaved_write_frame(m_formatCtx, &pkt);
 ret = av_write_frame( m_formatCtx, &pkt );
 }
 else
 {
 ret = 0;
 }
 }

 if (ret != 0)
 {
 char error[255];
 av_strerror(ret, error, 255);
 fprintf(stderr, "Error while writing video frame: %s\n", error);
 return;
 }

 s_frameCount++;
}


void XCodec::createFrame( const Image& image /*, AVFrame *m_frame, int frame_index, int width, int height*/)
{
 /**
 * \note allocate frame
 */
 m_frame = av_frame_alloc();
 int ret = av_frame_make_writable( m_frame );

 m_frame->format = STREAM_PIX_FMT;
 m_frame->width = image.width();
 m_frame->height = image.height();
// m_frame->pict_type = AV_PICTURE_TYPE_I;
 m_frame->display_picture_number = image.uniqueImageNumber();

 ret = av_image_alloc(m_frame->data, m_frame->linesize, m_frame->width, m_frame->height, STREAM_PIX_FMT, 1);

 if (ret < 0)
 {
 return;
 }

 struct SwsContext* sws_ctx = sws_getContext((int)image.width(), (int)image.height(), AV_PIX_FMT_RGB24,
 (int)image.width(), (int)image.height(), STREAM_PIX_FMT, 0, NULL, NULL, NULL);

 const uint8_t* rgbData[1] = { (uint8_t* )image.getData() };
 int rgbLineSize[1] = { 3 * image.width() };

 sws_scale(sws_ctx, rgbData, rgbLineSize, 0, image.height(), m_frame->data, m_frame->linesize);

//cv::Mat yuv420p( m_frame->height + m_frame->height/2, m_frame->width, CV_8UC1, m_frame->data[0]);
//cv::Mat cvmIm;
//cv::cvtColor(yuv420p,cvmIm,CV_YUV420p2BGR);
//std::ostringstream ss;
//ss << "c:\\tmp\\YUVoriginal_" << image.uniqueImageNumber() << ".png";
//cv::imwrite( ss.str().c_str(), cvmIm);
}


void XCodec::close()
{
 /* reset the framecount */
 s_frameCount = 0 ;

 int ret( 0 );

 /* flush the encoder */
 while( ret >= 0 )
 ret = avcodec_send_frame(m_codecCtx, NULL);

 // Write media trailer
 if( m_formatCtx )
 ret = av_write_trailer( m_formatCtx );

 /* Close each codec. */
 if ( m_streamOut )
 {
 if( m_frame )
 {
 av_free( m_frame->data[0]);
 av_frame_free( &m_frame );
 }

 if( m_packet )
 av_packet_free( &m_packet );
 }

 if (!( m_outputFormat->flags & AVFMT_NOFILE))
 /* Close the output file. */
 ret = avio_close( m_formatCtx->pb);


 /* free the stream */
 avformat_free_context( m_formatCtx );

 fflush( stdout );
}
</qdebug>

image.h


#ifndef IMAGE_H
#define IMAGE_H

#include 

class Image 
{
public:

 Image();

 Image( const cv::Mat& mat );

 Image(const Image& other) = default;

 Image(Image&& other) = default;

 ~Image();


 inline const cv::Mat& matrix() const{ return m_matrix; }

 inline const int uniqueImageNumber() const{ return m_uniqueId; }

 inline const int timeStamp() const { return m_timeStamp; }

 inline const int width() const { return m_matrix.cols(); }
 
 inline const int height() const { return m_matrix.rows(); }

private:

 cv::Mat m_matrix;

 int m_timeStamp;

 int m_uniqueId;

};

#endif


logtxt


image num 1725 0 0
 image num 1727 1 40
 image num 1729 2 84
 image num 1730 3 126
 image num 1732 4 169
 image num 1734 5 211
 image num 1736 6 259
 image num 1738 7 297
 image num 1740 8 340
 image num 1742 9 383
 image num 1744 10 425
 image num 1746 11 467
 image num 1748 12 511
 image num 1750 13 553
 write frame 1750
 image num 1752 14 600
 write frame 1752
 image num 1753 15 637
 write frame 1753
 image num 1755 16 680
 write frame 1755
 image num 1757 17 723
 write frame 1757
 image num 1759 18 766
 write frame 1759
 image num 1761 19 808
 write frame 1761
 image num 1763 20 854
 write frame 1763
 image num 1765 21 893
 write frame 1765
 image num 1767 22 937
 write frame 1767
 image num 1769 23 979
 write frame 1769
 image num 1770 24 1022
 write frame 1770
 image num 1772 25 1064
 write frame 1772
 image num 1774 26 1108
 write frame 1774
 image num 1776 27 1150
 write frame 1776
 image num 1778 28 1192
 write frame 1778
 image num 1780 29 1235
 write frame 1780
 image num 1782 30 1277
 write frame 1782
 image num 1784 31 1320
 write frame 1784
 image num 1786 32 1362
 write frame 1786
 image num 1787 33 1405
 write frame 1787
 image num 1789 34 1450
 write frame 1789
 image num 1791 35 1493
 write frame 1791
 image num 1793 36 1536
 write frame 1793
 image num 1795 37 1578
 write frame 1795
 image num 1797 38 1621
 write frame 1797
 image num 1799 39 1663
 write frame 1799
 image num 1801 40 1709
 write frame 1801
 image num 1803 41 1748
 write frame 1803
 image num 1805 42 1791
 write frame 1805
 image num 1807 43 1833
 write frame 1807
 image num 1808 44 1876
 write frame 1808
 image num 1810 45 1920
 write frame 1810
 image num 1812 46 1962
 write frame 1812
 image num 1814 47 2004
 write frame 1814
 image num 1816 48 2048
 write frame 1816
 image num 1818 49 2092
 write frame 1818
 image num 1820 50 2133
 write frame 1820
 image num 1822 51 2175
 write frame 1822
 image num 1824 52 2221
 write frame 1824
 image num 1826 53 2277
 write frame 1826
 image num 1828 54 2319
 write frame 1828
 image num 1830 55 2361
 write frame 1830
 image num 1832 56 2405
 write frame 1832
 image num 1833 57 2447
 write frame 1833
 image num 1835 58 2491
 write frame 1835
 image num 1837 59 2533
 write frame 1837
 image num 1839 60 2576
 write frame 1839
 image num 1841 61 2619
 write frame 1841
 image num 1843 62 2662
 write frame 1843
 image num 1845 63 2704
 write frame 1845
 image num 1847 64 2746
 write frame 1847
 image num 1849 65 2789
 write frame 1849
 image num 1851 66 2831
 write frame 1851
 image num 1852 67 2874
 write frame 1852
 image num 1854 68 2917
 write frame 1854
 image num 1856 69 2959
 write frame 1856
 image num 1858 70 3003
 write frame 1858
 image num 1860 71 3045
 write frame 1860
 image num 1862 72 3088
 write frame 1862
 image num 1864 73 3130
 write frame 1864
 image num 1866 74 3173
 write frame 1866
 image num 1868 75 3215
 write frame 1868
 image num 1870 76 3257
 write frame 1870
 image num 1872 77 3306
 write frame 1872
 image num 1873 78 3347
 write frame 1873
 image num 1875 79 3389
 write frame 1875
 image num 1877 80 3433
 write frame 1877
 image num 1879 81 3475
 write frame 1879
 image num 1883 82 3562
 write frame 1883
 image num 1885 83 3603
 write frame 1885
 image num 1887 84 3660
 write frame 1887
 image num 1889 85 3704
 write frame 1889
 image num 1891 86 3747
 write frame 1891
 image num 1893 87 3789
 write frame 1893
 image num 1895 88 3832
 write frame 1895
 image num 1897 89 3874
 write frame 1897
 image num 1899 90 3917
 write frame 1899
 image num 1900 91 3959
 write frame 1900
 image num 1902 92 4001
 write frame 1902
 image num 1904 93 4044
 write frame 1904
 image num 1906 94 4086
 write frame 1906
 image num 1908 95 4130
 write frame 1908
 image num 1910 96 4174
 write frame 1910
 image num 1912 97 4216
 write frame 1912
 image num 1914 98 4257
 write frame 1914
 image num 1915 99 4303
 write frame 1915
 image num 1918 100 4344
 write frame 1918
 image num 1919 101 4387
 write frame 1919
 image num 1922 102 4451
 write frame 1922
 image num 1924 103 4494
 write frame 1924
 image num 1926 104 4541
 write frame 1926
 image num 1927 105 4588
 write frame 1927
 image num 1931 106 4665
 write frame 1931
 image num 1933 107 4707
 write frame 1933
 image num 1935 108 4750
 write frame 1935
 image num 1937 109 4794
 write frame 1937
 image num 1939 110 4836
 write frame 1939
 image num 1941 111 4879
 write frame 1941
 image num 1943 112 4922
 write frame 1943
 image num 1945 113 4965
 write frame 1945
 image num 1947 114 5007
 write frame 1947
 image num 1948 115 5050
 write frame 1948
 image num 1950 116 5093
 write frame 1950
 image num 1952 117 5136
 write frame 1952
 image num 1954 118 5178
 write frame 1954
 image num 1956 119 5221
 write frame 1956
 MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
0, 8-bit
Not writing 'clli' atom. No content light level info.
Not writing 'mdcv' atom. Missing mastering metadata.
 2 seeks, 41 writeouts


-
How to Choose a GDPR Compliant Web Analytics Solution
2 mars 2022, par Matthieu Aubry — PrivacySince the launch of GDPR, one big question has lingered around with uncertainty – is Google Analytics GDPR compliant ? The current GDPR enforcement trend happening across the EU is certainly shedding some light on this question.
Starting with the Austrian Data Protection Authority’s ruling on Google Analytics and more recently, CNIL (the French Data Protection Authority) has followed suit by also ruling Google Analytics illegal to use. Organisations with EU-based web visitors are now scrambling to find a compliant solution.
The French Data Protection Authority (CNIL) has already started delivering formal notices to websites using Google Analytics, so now is the time to act. According to CNIL, organisations have two options :
- Ceasing use of the Google Analytics functionality (under the current conditions)
- Use a compliant web analytics tool that does not transfer data outside the EU
Getting started
For organisations considering migrating to a compliant web analytics tool, I’ve outlined below the things you need to consider when weighing up compliant web analytics tools. Once you’ve made a choice, I’ve also included a step-by-step guide to migrating away from Google Analytics. This guide is useful regardless of which GDPR compliant analytics provider you choose.
Before getting started, I recommend that you document your findings against the following considerations while reviewing GDPR compliant Google Analytics alternatives. This document can then be shared with your Data Protection Officer (DPO) to get their final recommendation.
10 key considerations when selecting a GDPR compliant web analytics tools
Many tools will claim to be GDPR compliant so it’s important that you do your due diligence and review tools against the following considerations.
1. Where does the tool store data ?
The rulings in France and Austria were based on the fact that Google Analytics stores data in the US, which does not have an adequate level of data protection. Your safest option is to find a tool that legally stores data in the EU.
You should be able to find out where the data is stored in the organisation’s privacy policy. Generally, data storage information can be found under sections titled “Subprocessors” and “Third-party services”. Check out the Matomo Privacy Policy as an example.
If you’re unable to easily find this information or it’s unclear, reach out to the organisation for more information.
2. Does the tool offer anonymous tracking ?
Anonymous tracking comes with many benefits, including :
- The ability to track visitors without a cookie consent screen. Due to the privacy-respecting aspect of cookieless tracking, you don’t need to worry about the extra steps involved with compliant cookie banners.
- More accurate data. When visitors deny tracking cookies, you lose out on valuable data. With anonymous tracking there is no data lost as you don’t need consent to track.
- Simplified GDPR compliance. With this enabled, there are fewer steps you need to take to get GDPR compliant and stay GDPR compliant.
For those reasons, it may be important for you to select a tool that offers anonymous tracking functionalities. The level of anonymous tracking you require will depend on your situation but you should look out for tools that allow you to :
- Disable fingerprinting
- Disable user profiles
- Anonymise data
- Cookieless tracking
If you want to read more about data anonymization, check out this guide on data anonymization in web analytics.
3. Does the tool integrate with my existing tech stack ?
You’ll want to ensure that a new web analytics tool will play well with other tools in your tech stack including things like your CMS (content management system), eCommerce shop, etc. You should list out all the existing tools that currently integrate with your Google Analytics and check that the same integrations can be re-created with the new tool, via integrations or APIs.
If not, it could become costly trying to connect your existing tech stack to a new solution.
4. Does the tool offer the same features and insights you are currently using in Google Analytics ? Or more, if necessary ?
Just because you are moving to a new web analytics platform, doesn’t mean you have to give up the insights, reports and features you’ve grown accustomed to with Google Analytics. Ensuring that a new platform provides the same features and reports that you value the most will result in a smoother transition away from Google Analytics.
It’s unlikely that a new tool will have all of the same features as Google Analytics, so I’d recommend listing out and prioritising your business-critical features and reports.
If I had to guess, you probably set up Google Analytics years ago because it was the default option. Now is your chance to make the most of this switch from Google Analytics and find a tool that offers additional reports and features that better aligns with your business. If time permits, I’d highly recommend that you consider other features or reports that you might have been missing out on while using Google Analytics.
Check out this comparison of Google Analytics vs Matomo to see side-by-side feature comparison.
5. Does the tool accept Google Analytics data imports ?
The historical data in Google Analytics is a critical asset for many businesses. Fortunately, some tools accept Google Analytics data imports so you don’t lose all of the data you’ve generated over time.
However, it’s important to note that any data you import from Google Analytics to a new tool needs to be compliant data. I’ll cover this more below.
6. Does the tool provide conversion tracking exports ?
Do you invest in paid advertising ? If you do, then tracking the conversions from people clicking on these paid ads is critical in assessing your return on investment. Since sending IP addresses or other personal information to the US is illegal under GDPR, we can only assume that this will also apply to advertising pixel/conversion tracking (e.g., Facebook pixel, Google Ads conversion tracking, etc).
As an example, Matomo offers conversion tracking exports so you can get a better understanding of ad performance while meeting privacy laws and without requiring consent from users. See how it works with Matomo’s conversion tracking exports.
7. How will you train up your in-house team ? Or can you hire a contractor ?
This is a common concern of many, and rightfully so. You’ll want to confirm what resources are readily available so you can hit the ground running with your new web analytics tool. If you’d prefer to train up your in-house team, check the provider’s site for training resources, videos, guides, etc.
If you’d rather hire an external contractor, we recommend heading to LinkedIn, reaching out to your community or asking the provider if they have any recommendations for contractors.
In addition, check that the provider offers technical support or a forum, in case you have specific questions and need help.
8. Does the tool offer self-hosting ? (optional)
For organisations that want full control over their data and storage location, an on-premise web analytics tool will be the preferred option. From a GDPR perspective, this is also the easiest option for compliance.
Keep in mind that this requires resources, regular maintenance, technical knowledge and/or technical consultants. If you’re unsure which option is best for your organisation, check out our on-premise vs cloud web analytics comparison breakdown.
Find out more about self-hosting Matomo.
9. Is the tool approved by the CNIL for tracking without consent ?
This is an important step for websites with French users. This step will help narrow down your selection of tools. The CNIL offers a programme to identify web analytics solutions that can be used without tracking consent. The CNIL’s list of recommended web analytics tools can act as your starting point for solutions to review.
While this step is specific to sites with French users, it can also be helpful for websites with visitors from any other EU country.
Benefits of consent-free tracking
There are many benefits of tracking without consent.
For one, it simplifies GDPR compliance and reduces the chances of GDPR breaches and fines. Cookie consent screens have recently been the target for EU Data Protection Authorities because many websites are unknowingly serving cookie consent screens that do not meet GDPR requirements.
Yet another benefit, and quite possibly the most important is more accurate data. Even if a website displays a user-friendly, lawful consent screen, the majority of users will either ignore or reject cookie consent. Legally website owners can’t track anything unless the visitor gives consent. So not having a cookie consent screen ensures that every visit is tracked and your web analytics data is 100% accurate.
Lastly, many visitors have grown fatigued and frustrated with invasive cookie consent screens. Not having one on your site creates a user-friendly experience, which will likely result in longer user sessions and lower bounce rates.
10. Does the tool offer a Data Processing Agreement (DPA) ?
Technically, any GDPR compliant web analytics tool should offer a DPA but for the sake of completeness, I’ve added this as a consideration. Double check that any tools you are looking at provide this legally binding document. This should be located in the Privacy Policy of the web analytics provider, if not reach out to request it.
As an example, here’s Matomo’s Data Processing Agreement which can be found in our Privacy Policy under Subprocessors.
That wraps up the key considerations. When it comes to compliance, privacy and customer data, Matomo leads the way. We are looking forward to helping you achieve GDPR compliance easily. Start your free 21-day trial of Matomo now – no credit card required.
A step-by-step guide to migrating from Google Analytics
Once you’ve identified a tool that suits your needs and your Data Protection Officer (DPO) has approved, you’re ready to get started. Here’s a simple step-by-step guide with all the important steps for you to follow :
1. Before getting started, you should sign or download the Data Processing Agreement (DPA) offered by your new web analytics provider.
2. Register for the new tool and configure it for compliance. The provider should offer guides on how to configure for GDPR compliance. This will include things like giving your users an easy way to opt-out of all tracking, turning on cookieless tracking or asking users for consent and anonymizing data and IP addresses, for instance.
3. Inform your organisation about the change. Whether your colleagues use the tool or not, it’s important that you share information about the new tool with your staff. Let them know what the tool will be used for, who will use the tool and how it complies with GDPR.
4. Let your DPO know that you’ve removed Google Analytics and have implemented the new tool.
5. Update your records of processing activities to include the new tool.
6. Update your privacy policy. You’ll need to include details about the web analytics provider, where the data is stored, what data is being collected, how long the data will be stored and why the data is being collected. The web analytics tool should readily have this information for you.
As an example, if you decide to use Matomo as your web analytics tool, we provide a Privacy Policy template for you to use on your site and a guide on how to complete your privacy policy under GDPR with Matomo. Note that these are only applicable if you are using Matomo.
In addition, if the tool has an opt-out feature, you will also need to put the opt-out into the privacy policy (e.g., when using cookieless tracking).
7. Now, the exciting part. Add the tracking code to your site by following the steps provided by the web analytics tool.
If you’re not comfortable with this step, the provider should offer steps to do this and you can share this with your web developer.
8. Once added, login to your tool and check to see if traffic is being tracked.
9. If your tool does not offer Google Analytics data imports or you do not need the historical data in your new tool, go to step 11.
To plan for your Google Analytics data migration, you’ll first need to establish what historical data is compliant with GDPR.
For example, you shouldn’t import any data stored beyond the retention period established in your Privacy Policy or any personally identifiable information (PII) like IP addresses that aren’t anonymised. Discuss this further with your DPO.
10. Once you’ve established what data you can legally import, then you can begin the import. Follow the steps provided by your new web analytics solution provider.
11. Remove Google Analytics tracking code from your site. This will stop the collection of your visitors data by Google as well as slightly increase the page load speed.
If you still haven’t made a choice yet, try Matomo free for 21-days and see why over 1 million websites choose Matomo.
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Cohort Analysis 101 : How-To, Examples & Top Tools
13 novembre 2023, par Erin — Analytics TipsImagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.
The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.
With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.
Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024.
What is cohort analysis ?
By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.
Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.
Why does cohort analysis matter ?
In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.
An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories.
Types of cohort analysis
There are a few different types of notable cohorts :
- Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
- Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods.
- Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement.
- Demographic cohorts share common demographic characteristics like age, gender, education level and income.
- Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
- Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions.
- Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention.
- Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users).
Best practices for conducting a cohort analysis
So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.
1. Preparing for cohort analysis :
- First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
- Choosing the right time frame will help you compare short-term vs. long-term data trends.
2. Creating effective cohorts :
- Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise.
- It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.
3. Performing cohort analysis :
- Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates.
- Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ?
4. Visualising and interpreting data :
- Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
- Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean.
5. Continue improving :
- User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date.
- Encourage iterative analysis optimisation based on your findings.
The top cohort analysis tools for 2024
In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.
1. Matomo
Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time.
You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts.
Key features
- Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
- All core session and conversion metrics are also available in the Cohorts report.
- Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour.
- Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when.
- Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts.
- Create cohorts based on events or interactions with your website.
- Intuitive, colour-coded data visualisation, so you can easily spot patterns.
Pros
- No setup is needed if you use the JavaScript tracker
- You can fetch cohort without any limit
- 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling
Cons
- Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
- Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.
Price :
- Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
- Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.
2. Mixpanel
Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users.
Key features
- Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property.
- Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
- Create interactive dashboards, charts and graphs to visualise data.
- Mixpanel provides retention analysis tools to see how often users return to your product over time.
- Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc.
- Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.
Pros
- Easily export cohort analysis data for further analysis
- Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product
Cons
- With the free Mixpanel plan, you can’t save cohorts for future use
- Enterprise-level pricing is expensive
- Time-consuming cohort creation process
Price : Free basic version. The growth version starts at £16/month.
3. Amplitude
Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.
Key features
- Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property.
- Conduct behavioural, time-based and retention analyses.
- Create custom reports with custom data.
- Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.
Pros
- Highly customisable and flexible
- Quick and simple setup
Cons
- Steep learning curve — requires significant training
- Slow loading speed
- High price point compared to other tools
Price : Free basic version. Plus version starts at £40/month (billed annually).
4. Kissmetrics
Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation.
Key features
- Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
- The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points.
- Create and optimise conversion funnels.
- Customise events, user properties, funnels, segments, cohorts and more.
Pros
- Powerful data visualisation options
- Highly customisable
Cons
- Difficult to install
- Not well-suited for small businesses
- Limited integration with other tools
Price : Starting at £21/month for 10k events (billed monthly).
Improve your cohort analysis with Matomo
When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards.
If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.
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