Recherche avancée

Sur d’autres sites (3871)

• C# on linux : FFmpeg (FFMediaToolkit) MediaOutput..Video.AddFrame(FrameToImageData(ImageData)) causes program to exit with code 139

  19 mai 2021, par Jan Černý

  In my C# program I have instance of MediaOutput from FFMediaToolkit. It is initialized like this :

  


  MediaOutput buffer = MediaBuilder.CreateContainer(videoPath).WithVideo(new VideoEncoderSettings(width: width,
                height: height, framerate: frameRate,
                codec: VideoCodec.H264)
            ).Create();


  


  When I want to add frame to buffer I use this code :

  


  private static ImageData FrameToImageData(Bitmap bitmap) {
    Rectangle rect = new Rectangle(System.Drawing.Point.Empty, bitmap.Size);
    BitmapData bitLock = bitmap.LockBits(rect, ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);
    ImageData bitmapImageData = ImageData.FromPointer(bitLock.Scan0, ImagePixelFormat.Bgr24, bitmap.Size);
    bitmap.UnlockBits(bitLock);
    return bitmapImageData;
}

public void AddFrame(Bitmap frame) {
    buffer.Video.AddFrame(FrameToImageData(frame));
}


  


  But when code reaches buffer.Video.AddFrame(); it exits with code 139 without throwing any exception.

  


  I have two test files and only one is causing it. One is .png file 100x100 and it works fine. The other is .png file 1000x1000 and it makes program exit as soon as it reaches this method.

  


  What exit code 139 means in C# ?
  
How can I diagnose this problem when it is not throwing any exceptions ?
  
How can I fix it ?

  


  Thank you for help. If you need any more information, leave a comment and I will add it soon as possible.

  


  Edit1 :
  
This is my instel drivers :

  


  john@arch-thinkpad ~> yay -Qs intel
local/intel-gmmlib 21.1.1-1
    Intel Graphics Memory Management Library
local/intel-media-driver 21.1.3-1
    Intel Media Driver for VAAPI — Broadwell+ iGPUs
local/intel-media-sdk 21.1.3-1
    API to access hardware-accelerated video on Intel Gen graphics hardware platforms
local/intel-mkl 2020.4.304-1
    Intel Math Kernel Library
local/intel-ucode 20210216-1
    Microcode update files for Intel CPUs
local/intellij-idea-ultimate-edition 2021.1.1-1
    An intelligent IDE for Java, Groovy and other programming languages with advanced refactoring features intensely focused on developer productivity.
local/libmfx 21.1.3-1
    Intel Media SDK dispatcher library
local/libva-intel-driver 2.4.1-1
    VA-API implementation for Intel G45 and HD Graphics family
local/onednn 2.2.2-1
    oneAPI Deep Neural Network Library (oneDNN)
local/tbb 2020.3-1
    High level abstract threading library
local/xf86-video-intel 1:2.99.917+916+g31486f40-1 (xorg-drivers)
    X.org Intel i810/i830/i915/945G/G965+ video drivers


  


  EDIT2 :

  


  [17091.524781] Slimulator[20962]: segfault at 7fd84003a011 ip 00007fd8400cd348 sp 00007ffddd9d7fb8 error 4 in libswscale.so.5.9.100[7fd840062000+75000]
[17091.524791] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17091.524829] audit: type=1701 audit(1621440058.690:188): auid=1000 uid=1000 gid=1000 ses=1 pid=20962 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17091.546116] audit: type=1334 audit(1621440058.713:189): prog-id=61 op=LOAD
[17091.546209] audit: type=1334 audit(1621440058.713:190): prog-id=62 op=LOAD
[17091.546262] audit: type=1334 audit(1621440058.713:191): prog-id=63 op=LOAD
[17091.547395] audit: type=1130 audit(1621440058.713:192): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@6-20996-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17094.458151] audit: type=1131 audit(1621440061.623:193): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@6-20996-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17094.542823] audit: type=1334 audit(1621440061.710:194): prog-id=63 op=UNLOAD
[17094.542832] audit: type=1334 audit(1621440061.710:195): prog-id=62 op=UNLOAD
[17094.542836] audit: type=1334 audit(1621440061.710:196): prog-id=61 op=UNLOAD
[17295.099124] Slimulator[21147]: segfault at 7f555b1de011 ip 00007f555b271348 sp 00007fff48239f48 error 4 in libswscale.so.5.9.100[7f555b206000+75000]
[17295.099132] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17295.099197] audit: type=1701 audit(1621440262.267:197): auid=1000 uid=1000 gid=1000 ses=1 pid=21147 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17295.108536] audit: type=1334 audit(1621440262.277:198): prog-id=64 op=LOAD
[17295.108679] audit: type=1334 audit(1621440262.277:199): prog-id=65 op=LOAD
[17295.108752] audit: type=1334 audit(1621440262.277:200): prog-id=66 op=LOAD
[17295.109589] audit: type=1130 audit(1621440262.277:201): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@7-21181-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17297.322989] audit: type=1131 audit(1621440264.487:202): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@7-21181-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17297.401409] audit: type=1334 audit(1621440264.571:203): prog-id=66 op=UNLOAD
[17297.401421] audit: type=1334 audit(1621440264.571:204): prog-id=65 op=UNLOAD
[17297.401426] audit: type=1334 audit(1621440264.571:205): prog-id=64 op=UNLOAD
[17353.331142] Slimulator[21281]: segfault at 7f35f1fd3011 ip 00007f35f2066348 sp 00007ffe7d1288e8 error 4 in libswscale.so.5.9.100[7f35f1ffb000+75000]
[17353.331160] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17353.331214] audit: type=1701 audit(1621440320.498:206): auid=1000 uid=1000 gid=1000 ses=1 pid=21281 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17353.344382] audit: type=1334 audit(1621440320.511:207): prog-id=67 op=LOAD
[17353.344518] audit: type=1334 audit(1621440320.511:208): prog-id=68 op=LOAD
[17353.344566] audit: type=1334 audit(1621440320.511:209): prog-id=69 op=LOAD
[17353.345651] audit: type=1130 audit(1621440320.511:210): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@8-21378-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17356.180885] audit: type=1131 audit(1621440323.345:211): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@8-21378-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17356.261051] audit: type=1334 audit(1621440323.428:212): prog-id=69 op=UNLOAD
[17356.261055] audit: type=1334 audit(1621440323.428:213): prog-id=68 op=UNLOAD
[17356.261057] audit: type=1334 audit(1621440323.428:214): prog-id=67 op=UNLOAD
[17379.499165] Slimulator[21454]: segfault at 7f68418a1011 ip 00007f6841934348 sp 00007ffea9f22eb8 error 4 in libswscale.so.5.9.100[7f68418c9000+75000]
[17379.499174] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17379.499245] audit: type=1701 audit(1621440346.665:215): auid=1000 uid=1000 gid=1000 ses=1 pid=21454 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17379.509368] audit: type=1334 audit(1621440346.675:216): prog-id=70 op=LOAD
[17379.509448] audit: type=1334 audit(1621440346.675:217): prog-id=71 op=LOAD
[17379.509481] audit: type=1334 audit(1621440346.675:218): prog-id=72 op=LOAD
[17379.510098] audit: type=1130 audit(1621440346.675:219): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@9-21492-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17381.661151] audit: type=1131 audit(1621440348.828:220): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@9-21492-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17381.740919] audit: type=1334 audit(1621440348.908:221): prog-id=72 op=UNLOAD
[17381.740924] audit: type=1334 audit(1621440348.908:222): prog-id=71 op=UNLOAD
[17381.740926] audit: type=1334 audit(1621440348.908:223): prog-id=70 op=UNLOAD
[17389.743524] Slimulator[21565]: segfault at 7f95075a4011 ip 00007f9507637348 sp 00007ffccfab3f18 error 4 in libswscale.so.5.9.100[7f95075cc000+75000]
[17389.743535] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17389.743613] audit: type=1701 audit(1621440356.908:224): auid=1000 uid=1000 gid=1000 ses=1 pid=21565 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17389.753604] audit: type=1334 audit(1621440356.918:225): prog-id=73 op=LOAD
[17389.753783] audit: type=1334 audit(1621440356.918:226): prog-id=74 op=LOAD
[17389.753847] audit: type=1334 audit(1621440356.918:227): prog-id=75 op=LOAD
[17389.755847] audit: type=1130 audit(1621440356.921:228): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@10-21600-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17392.121917] audit: type=1131 audit(1621440359.288:229): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@10-21600-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17392.204160] audit: type=1334 audit(1621440359.371:230): prog-id=75 op=UNLOAD
[17392.204167] audit: type=1334 audit(1621440359.371:231): prog-id=74 op=UNLOAD
[17392.204169] audit: type=1334 audit(1621440359.371:232): prog-id=73 op=UNLOAD
[17409.596374] Slimulator[21674]: segfault at 7fddab4c5011 ip 00007fddab558348 sp 00007ffe55e75e28 error 4 in libswscale.so.5.9.100[7fddab4ed000+75000]
[17409.596383] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17409.596441] audit: type=1701 audit(1621440376.762:233): auid=1000 uid=1000 gid=1000 ses=1 pid=21674 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17409.606014] audit: type=1334 audit(1621440376.772:234): prog-id=76 op=LOAD
[17409.606096] audit: type=1334 audit(1621440376.772:235): prog-id=77 op=LOAD
[17409.606139] audit: type=1334 audit(1621440376.772:236): prog-id=78 op=LOAD
[17409.606845] audit: type=1130 audit(1621440376.772:237): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@11-21706-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17411.977651] audit: type=1131 audit(1621440379.145:238): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@11-21706-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17412.074091] audit: type=1334 audit(1621440379.242:239): prog-id=78 op=UNLOAD
[17412.074098] audit: type=1334 audit(1621440379.242:240): prog-id=77 op=UNLOAD
[17412.074101] audit: type=1334 audit(1621440379.242:241): prog-id=76 op=UNLOAD
[17431.213606] Slimulator[21785]: segfault at 7f218cdca011 ip 00007f218ce5d348 sp 00007ffffd122a98 error 4 in libswscale.so.5.9.100[7f218cdf2000+75000]
[17431.213616] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17431.213648] audit: type=1701 audit(1621440398.378:242): auid=1000 uid=1000 gid=1000 ses=1 pid=21785 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17431.223086] audit: type=1334 audit(1621440398.388:243): prog-id=79 op=LOAD
[17431.223210] audit: type=1334 audit(1621440398.388:244): prog-id=80 op=LOAD
[17431.223272] audit: type=1334 audit(1621440398.388:245): prog-id=81 op=LOAD
[17431.224003] audit: type=1130 audit(1621440398.392:246): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@12-21817-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17433.560362] audit: type=1131 audit(1621440400.725:247): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@12-21817-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17433.620924] audit: type=1334 audit(1621440400.788:248): prog-id=81 op=UNLOAD
[17433.620929] audit: type=1334 audit(1621440400.788:249): prog-id=80 op=UNLOAD
[17433.620931] audit: type=1334 audit(1621440400.788:250): prog-id=79 op=UNLOAD
[17636.068527] audit: type=1701 audit(1621440603.236:251): auid=1000 uid=1000 gid=1000 ses=1 pid=22189 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=6 res=1
[17636.075124] audit: type=1334 audit(1621440603.243:252): prog-id=82 op=LOAD
[17636.075299] audit: type=1334 audit(1621440603.243:253): prog-id=83 op=LOAD
[17636.075334] audit: type=1334 audit(1621440603.243:254): prog-id=84 op=LOAD
[17636.075947] audit: type=1130 audit(1621440603.246:255): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@13-22213-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17636.859400] audit: type=1131 audit(1621440604.030:256): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@13-22213-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17636.952582] audit: type=1334 audit(1621440604.123:257): prog-id=84 op=UNLOAD
[17636.952586] audit: type=1334 audit(1621440604.123:258): prog-id=83 op=UNLOAD
[17636.952587] audit: type=1334 audit(1621440604.123:259): prog-id=82 op=UNLOAD
[17683.442450] Slimulator[22349]: segfault at 7fce7b840011 ip 00007fce7b8d3348 sp 00007ffdf12fde88 error 4 in libswscale.so.5.9.100[7fce7b868000+75000]
[17683.442461] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17683.442489] audit: type=1701 audit(1621440650.613:260): auid=1000 uid=1000 gid=1000 ses=1 pid=22349 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17683.451485] audit: type=1334 audit(1621440650.620:261): prog-id=85 op=LOAD
[17683.451530] audit: type=1334 audit(1621440650.620:262): prog-id=86 op=LOAD
[17683.451561] audit: type=1334 audit(1621440650.620:263): prog-id=87 op=LOAD
[17683.452200] audit: type=1130 audit(1621440650.620:264): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@14-22400-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17685.702716] audit: type=1131 audit(1621440652.873:265): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@14-22400-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17685.789205] audit: type=1334 audit(1621440652.960:266): prog-id=87 op=UNLOAD
[17685.789209] audit: type=1334 audit(1621440652.960:267): prog-id=86 op=UNLOAD
[17685.789211] audit: type=1334 audit(1621440652.960:268): prog-id=85 op=UNLOAD
[17741.587367] audit: type=1701 audit(1621440708.757:269): auid=1000 uid=1000 gid=1000 ses=1 pid=22506 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=6 res=1
[17741.597924] audit: type=1334 audit(1621440708.767:270): prog-id=88 op=LOAD
[17741.597991] audit: type=1334 audit(1621440708.767:271): prog-id=89 op=LOAD
[17741.598017] audit: type=1334 audit(1621440708.767:272): prog-id=90 op=LOAD
[17741.598635] audit: type=1130 audit(1621440708.770:273): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@15-22533-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17743.536566] audit: type=1131 audit(1621440710.707:274): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@15-22533-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17743.645445] audit: type=1334 audit(1621440710.817:275): prog-id=90 op=UNLOAD
[17743.645456] audit: type=1334 audit(1621440710.817:276): prog-id=89 op=UNLOAD
[17743.645460] audit: type=1334 audit(1621440710.817:277): prog-id=88 op=UNLOAD
[17826.501073] Slimulator[22630]: segfault at 7efff17b2011 ip 00007efff1845348 sp 00007ffe58353908 error 4 in libswscale.so.5.9.100[7efff17da000+75000]
[17826.501081] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17826.501144] audit: type=1701 audit(1621440793.671:278): auid=1000 uid=1000 gid=1000 ses=1 pid=22630 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17826.508176] audit: type=1334 audit(1621440793.681:279): prog-id=91 op=LOAD
[17826.508254] audit: type=1334 audit(1621440793.681:280): prog-id=92 op=LOAD
[17826.508285] audit: type=1334 audit(1621440793.681:281): prog-id=93 op=LOAD
[17826.508907] audit: type=1130 audit(1621440793.681:282): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@16-22667-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17828.821665] audit: type=1131 audit(1621440795.994:283): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@16-22667-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17828.911512] audit: type=1334 audit(1621440796.084:284): prog-id=93 op=UNLOAD
[17828.911523] audit: type=1334 audit(1621440796.084:285): prog-id=92 op=UNLOAD
[17828.911528] audit: type=1334 audit(1621440796.084:286): prog-id=91 op=UNLOAD
[18255.197203] Slimulator[23128]: segfault at 7f44e8746011 ip 00007f44e87d9348 sp 00007ffdfc25d318 error 4 in libswscale.so.5.9.100[7f44e876e000+75000]
[18255.197213] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[18255.197237] audit: type=1701 audit(1621441222.370:287): auid=1000 uid=1000 gid=1000 ses=1 pid=23128 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[18255.205057] audit: type=1334 audit(1621441222.376:288): prog-id=94 op=LOAD
[18255.205138] audit: type=1334 audit(1621441222.376:289): prog-id=95 op=LOAD
[18255.205165] audit: type=1334 audit(1621441222.376:290): prog-id=96 op=LOAD
[18255.206164] audit: type=1130 audit(1621441222.380:291): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@17-23164-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18257.792719] audit: type=1131 audit(1621441224.967:292): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@17-23164-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18257.862155] audit: type=1334 audit(1621441225.037:293): prog-id=96 op=UNLOAD
[18257.862159] audit: type=1334 audit(1621441225.037:294): prog-id=95 op=UNLOAD
[18257.862161] audit: type=1334 audit(1621441225.037:295): prog-id=94 op=UNLOAD
[18312.879423] audit: type=1334 audit(1621441280.053:296): prog-id=97 op=LOAD
[18312.879566] audit: type=1334 audit(1621441280.053:297): prog-id=98 op=LOAD
[18312.879634] audit: type=1334 audit(1621441280.053:298): prog-id=99 op=LOAD
[18312.918107] audit: type=1130 audit(1621441280.090:299): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18342.984220] audit: type=1131 audit(1621441310.157:300): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18343.084948] audit: type=1334 audit(1621441310.260:301): prog-id=99 op=UNLOAD
[18343.084955] audit: type=1334 audit(1621441310.260:302): prog-id=98 op=UNLOAD
[18343.084959] audit: type=1334 audit(1621441310.260:303): prog-id=97 op=UNLOAD
[18527.198876] Slimulator[23504]: segfault at 7fd59543e011 ip 00007fd5954d1348 sp 00007ffcc5d59e78 error 4 in libswscale.so.5.9.100[7fd595466000+75000]
[18527.198885] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[18527.198911] audit: type=1701 audit(1621441494.371:304): auid=1000 uid=1000 gid=1000 ses=1 pid=23504 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[18527.207861] audit: type=1334 audit(1621441494.381:305): prog-id=100 op=LOAD
[18527.207959] audit: type=1334 audit(1621441494.381:306): prog-id=101 op=LOAD
[18527.207996] audit: type=1334 audit(1621441494.381:307): prog-id=102 op=LOAD
[18527.208682] audit: type=1130 audit(1621441494.381:308): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@18-23538-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18530.192509] audit: type=1131 audit(1621441497.364:309): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@18-23538-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18530.274214] audit: type=1334 audit(1621441497.448:310): prog-id=102 op=UNLOAD
[18530.274221] audit: type=1334 audit(1621441497.448:311): prog-id=101 op=UNLOAD
[18530.274224] audit: type=1334 audit(1621441497.448:312): prog-id=100 op=UNLOAD
[18788.989941] i915 0000:00:02.0: [drm] *ERROR* Atomic update failure on pipe B (start=1126979 end=1126980) time 153 us, min 1073, max 1079, scanline start 1072, end 1082
[18793.024933] audit: type=1334 audit(1621441760.199:313): prog-id=103 op=LOAD
[18793.025042] audit: type=1334 audit(1621441760.199:314): prog-id=104 op=LOAD
[18793.025082] audit: type=1334 audit(1621441760.199:315): prog-id=105 op=LOAD
[18793.061481] audit: type=1130 audit(1621441760.235:316): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18823.124309] audit: type=1131 audit(1621441790.299:317): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18823.193828] audit: type=1334 audit(1621441790.369:318): prog-id=105 op=UNLOAD
[18823.193847] audit: type=1334 audit(1621441790.369:319): prog-id=104 op=UNLOAD
[18823.193856] audit: type=1334 audit(1621441790.369:320): prog-id=103 op=UNLOAD
[19615.817516] Slimulator[24574]: segfault at 7fb94cbbd011 ip 00007fb94cc50348 sp 00007fffefd935c8 error 4 in libswscale.so.5.9.100[7fb94cbe5000+75000]
[19615.817524] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[19615.817544] audit: type=1701 audit(1621442582.996:321): auid=1000 uid=1000 gid=1000 ses=1 pid=24574 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[19615.825508] audit: type=1334 audit(1621442583.002:322): prog-id=106 op=LOAD
[19615.825620] audit: type=1334 audit(1621442583.002:323): prog-id=107 op=LOAD
[19615.825652] audit: type=1334 audit(1621442583.002:324): prog-id=108 op=LOAD
[19615.826425] audit: type=1130 audit(1621442583.006:325): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@19-24609-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[19617.863304] audit: type=1131 audit(1621442585.043:326): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@19-24609-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[19617.939124] audit: type=1334 audit(1621442585.119:327): prog-id=108 op=UNLOAD
[19617.939129] audit: type=1334 audit(1621442585.119:328): prog-id=107 op=UNLOAD
[19617.939132] audit: type=1334 audit(1621442585.119:329): prog-id=106 op=UNLOAD
[20633.629288] Slimulator[25687]: segfault at 7fed842b8011 ip 00007fed8434b348 sp 00007ffcbf0f45d8 error 4 in libswscale.so.5.9.100[7fed842e0000+75000]
[20633.629298] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[20633.629328] audit: type=1701 audit(1621443600.811:330): auid=1000 uid=1000 gid=1000 ses=1 pid=25687 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[20633.640886] audit: type=1334 audit(1621443600.824:331): prog-id=109 op=LOAD
[20633.640985] audit: type=1334 audit(1621443600.824:332): prog-id=110 op=LOAD
[20633.641017] audit: type=1334 audit(1621443600.824:333): prog-id=111 op=LOAD
[20633.641934] audit: type=1130 audit(1621443600.824:334): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@20-25722-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[20636.303870] audit: type=1131 audit(1621443603.488:335): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@20-25722-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[20636.410990] audit: type=1334 audit(1621443603.594:336): prog-id=111 op=UNLOAD
[20636.411006] audit: type=1334 audit(1621443603.594:337): prog-id=110 op=UNLOAD
[20636.411014] audit: type=1334 audit(1621443603.594:338): prog-id=109 op=UNLOAD


  


• Google Analytics Privacy Issues : Is It Really That Bad ?

  2 juin 2022, par Erin

  If you find yourself asking : “What’s the deal with Google Analytics privacy ?”, you probably have some second thoughts. 

  Your hunch is right. Google Analytics (GA) is a popular web analytics tool, but it’s far from being perfect when it comes to respecting users’ privacy. 

  This post helps you understand tremendous Google Analytics privacy concerns users, consumers and regulators expressed over the years.

  In this blog, we’ll cover :

  What Does Google Analytics Collect About Users ? 

  To understand Google Analytics privacy issues, you need to know how Google treats web users’ data. 

  By default, Google Analytics collects the following information : 

  • Session statistics — duration, page(s) viewed, etc. 
  • Referring website details — a link you came through or keyword used. 
  • Approximate geolocation — country, city. 
  • Browser and device information — mobile vs desktop, OS usage, etc. 

  Google obtains web analytics data about users via two means : an on-site Google Analytics tracking code and cookies.

  A cookie is a unique identifier (ID) assigned to each user visiting a web property. Each cookie stores two data items : unique user ID and website name. 

  With the help of cookies, web analytics solutions can recognise returning visitors and track their actions across the website(s).

  

  • First party cookies are generated by one website and collect user behaviour data from said website only. 
  • Third-party cookies are generated by a third-party website object (for example, an ad) and can track user behaviour data across multiple websites. 

  As it’s easy to imagine, third-party cookies are a goldmine for companies selling online ads. Essentially, they allow ad platforms to continue watching how the user navigates the web after clicking a certain link. 

  Yet, people have little clue as to which data they are sharing and how it is being used. Also, user consent to tracking across websites is only marginally guaranteed by existing Google Analytics controls. 

  Why Third-Party Cookie Data Collection By GA Is Problematic 

  Cookies can transmit personally identifiable information (PII) such as name, log in details, IP address, saved payment method and so on. Some of these details can end up with advertisers without consumers’ direct knowledge or consent.

  Regulatory frameworks such as General Data Protection Regulation (GDPR) in Europe and California Consumer Privacy Act (CCPA) emerged as a response to uncontrolled user behaviour tracking.

  Under regulatory pressure, Big Tech companies had to adapt their data collection process.

  Apple was the first to implement by-default third-party blocking in the Safari browser. Then added a tracking consent mechanism for iPhone users starting from iOS 15.2 and later. 

  Google, too, said it would drop third-party cookie usage after The European Commission and UK’s Competition and Markets Authority (CMA) launched antitrust investigations into its activity. 

  To shake off the data watchdogs, Google released a Privacy Sandbox — a set of progressive tech, operational and compliance changes for ensuring greater consumer privacy. 

  Google’s biggest promise : deprecate third-party cookies usage for all web and mobile products. 

  Originally, Google promised to drop third-party cookies by 2022, but that didn’t happen. Instead, Google delayed cookie tracking depreciation for Chrome until the second half of 2023. 

  Why did they push back on this despite hefty fines from regulators ?

  Because online ads make Google a lot of money.

  In 2021, Alphabet Inc (parent company of Google), made $256.7 billion in revenue, of which $209.49 billion came from selling advertising. 

  Lax Google Analytics privacy enforcement — and its wide usage by website owners — help Google make those billions from collecting and selling user data. 

  How Google Uses Collected Google Analytics Data for Advertising 

  Over 28 million websites (or roughly 85% of the Internet) have Google Analytics tracking codes installed. 

  Even if one day we get a Google Analytics version without cookies, it still won’t address all the privacy concerns regulators and consumers have. 

  Over the years, Google has accumulated an extensive collection of user data. The company’s engineers used it to build state-of-the-art deep learning models, now employed to build advanced user profiles. 

  Deep learning is the process of training a machine to recognise data patterns. Then this “knowledge” is used to produce highly-accurate predictive insights. The more data you have for model training — the better its future accuracy will be. 

  Google has amassed huge deposits of data from its collection of products — GA, YouTube, Gmail, Google Docs and Google Maps among others. Now they are using this data to build a third-party cookies-less alternative mechanism for modelling people’s preferences, habits, lifestyles, etc. 

  Their latest model is called Google Topics. 

  This comes only after Google’s failed attempt to replace cookie-based training with Federated Learning of Cohorts (FLoC) model. But the solution wasn’t offering enough user transparency and user controls among other issues.

  

  Google Topics promises to limit the granularity of data advertisers get about users. 

  But it’s still a web user surveillance method. With Google Topics, the company will continue collecting user data via Chrome (and likely other Google products) — and share it with advertisers. 

  Because as we said before : Google is in the business of profiting off consumers’ data. 

  Two Major Ways Google Takes Advantage of Customer Data

  Every bit of data Google collects across its ecosystem of products can be used in two ways :

  • For ad targeting and personalisation 
  • To improve Google’s products 

  The latter also helps the former. 

  Advanced Ad Personalisation and Targeting

  GA provides the company with ample data on users’ 

  • Recent and frequent searches 
  • Location history
  • Visited websites
  • Used apps 
  • Videos and ads viewed 
  • Personal data like age or gender 

  The company’s privacy policy explicitly states that :
  

  

  Google also admits to using collected data to “measure the effectiveness of advertising” and “personalise content and ads you see on Google.” 

  But there are no further elaborations on how exactly customers’ data is used — and what you can do to prevent it from being shared with third parties. 

  In some cases, Google also “forgets” to inform users about its in-product tracking.

  Journalists from CNBC and The New York Times independently concluded that Google monitors users’ Gmail activity. In particular, the company scans your inbox for recent purchases, trips, flights and bills notifications. 

  While Google says that this information isn’t sold to advertisers (directly), they still may use the “saved information about your orders in other Google services”. 

  Once again, this means you have little control or knowledge of subsequent data usage. 

  Improving Product Usability 

  Google has many “arms” to collect different data points — from user’s search history to frequently-travelled physical routes. 

  They also reserve the right to use these insights for improving existing products. 

  Here’s what it means : by combining different types of data points obtained from various products, Google can pierce a detailed picture of a person’s life. Even if such user profile data is anonymised, it is still alarmingly accurate. 

  Douglas Schmidt, a computer science researcher at Vanderbilt University, well summarised the matter : 

  “[Google’s] business model is to collect as much data about you as possible and cross-correlate it so they can try to link your online persona with your offline persona. This tracking is just absolutely essential to their business. ‘Surveillance capitalism’ is a perfect phrase for it.”

  Google Data Collection Obsession Is Backed Into Its Business Model 

  OK, but Google offers some privacy controls to users ? Yes. Google only sees and uses the information you voluntarily enter or permit them to access. 

  But as the Washington Post correspondent points out :

  “[Big Tech] companies get to set all the rules, as long as they run those rules by consumers in convoluted terms of service that even those capable of decoding the legalistic language rarely bother to read. Other mechanisms for notice and consent, such as opt-outs and opt-ins, create similar problems. Control for the consumer is mostly an illusion.”

  Google openly claims to be “one of many ad networks that personalise ads based on your activity online”. 

  The wrinkle is that they have more data than all other advertising networks (arguably combined). This helps Google sell high-precision targeting and contextually personalised ads for billions of dollars annually.

  Given that Google has stakes in so many products — it’s really hard to de-Google your business and minimise tracking and data collection from the company.
  

  They are also creating a monopoly on data collection and ownership. This fact makes regulators concerned. The 2021 antitrust lawsuit from the European Commission says : 

  “The formal investigation will notably examine whether Google is distorting competition by restricting access by third parties to user data for advertising purposes on websites and apps while reserving such data for its own use.”

  In other words : By using consumer data to its unfair advantage, Google allegedly shuts off competition.

  But that’s not the only matter worrying regulators and consumers alike. Over the years, Google also received numerous other lawsuits for breaching people’s privacy, over and over again. 

  Here’s a timeline : 

  • 2019 : UK citizens issued a class action suit against Google for imposing cookies to override users’ privacy settings in the Safari browser. 
  • 2020 : US citizens pushed for a $5 billion class-action suit for tracking their activity through browsers set in “private” mode.
  • 2022 : Another class-action lawsuit in the US for deceptive privacy controls and unconsented location data tracking by Google mobile apps. 
  • 2022 : Google reached a $100 million class-action settlement for breaching Illinois biometrics privacy laws in Google Photos. 

  Separately, Google has a very complex history with GDPR compliance. 

  How Google Analytics Contributes to the Web Privacy Problem 

  Google Analytics is the key puzzle piece that supports Google’s data-driven business model. 

  If Google was to release a privacy-focused Google Analytics alternative, it’d lose access to valuable web users’ data and a big portion of digital ad revenues. 

  Remember : Google collects more data than it shares with web analytics users and advertisers. But they keep a lot of it for personal usage — and keep looking for ways to share this intel with advertisers (in a way that keeps regulators off their tail).

  For Google Analytics to become truly ethical and privacy-focused, Google would need to change their entire revenue model — which is something they are unlikely to do.

  Where does this leave Google Analytics users ? 

  In a slippery territory. By proxy, companies using GA are complicit with Google’s shady data collection and usage practice. They become part of the problem.

  In fact, Google Analytics usage opens a business to two types of risks : 

  • Reputational. 77% of global consumers say that transparency around how data is collected and used is important to them when interacting with different brands. That’s why data breaches and data misuse by brands lead to major public outrages on social media and boycotts in some cases. 
  • Legal. EU regulators are on a continuous crusade against Google Analytics 4 (GA4) as it is in breach of GDPR. French and Austrian watchdogs ruled the “service” illegal. Since Google Analytics is not GDPR compliant, it opens any business using it to lawsuits (which is already happening).

  But there’s a way out.

  Choose a Privacy-Friendly Google Analytics Alternative 

  Google Analytics is a popular web analytics service, but not the only one available. You have alternatives such as Matomo. 

  Our guiding principle is : respecting privacy.

  Unlike Google Analytics, we leave data ownership 100% in users’ hands. Matomo lets you implement privacy-centred controls for user data collection.

  Plus, you can self-host Matomo On-Premise or choose Matomo Cloud with data securely stored in the EU and in compliance with GDPR.

  The best part ? You can try our ethical alternative to Google Analytics for free. No credit card required ! Start your free 21-day trial now. 

  21 day free trial. No credit card required.

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreeement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

  The deeper you go with your customer analytics, the better your insights will be.

  The result ? Your marketing performance soars to new heights.

  Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

  By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

  In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

  What is customer segmentation ?

  Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general. 

  Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

  These groups could be based on demographics like age or location or behaviours like recent purchases or website visits. 

  By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

  Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

  But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

  What is customer segmentation analytics ?

  Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

  

  With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

  One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

  In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country. 

  For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

  The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

  Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

  By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

  5 types of segmentation

  There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

  While you can segment your customers in almost endless ways, five common types the majority fall under are :

  

  Geographic

  Another way to segment is by geography.

  This is important because you could have drastically different interests, pain points and desires based on where you live.

  If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

  For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

  Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

  Geographic segmentation typically includes the following subtypes :

  • Cities (i.e., Austin, Paris, Berlin, etc.)
  • State (i.e., Massachusetts)
  • Country (i.e., Thailand)

  Psychographic

  Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

  Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

  While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

  Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons. 

  Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

  By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

  Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

  While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

  Technographic segmentation typically includes the following subtypes :

  • Smartphone type
  • Device type : smartphone, desktop, tablet
  • Apps
  • Video games

  Demographic

  The most common approach to segmentation is to split your customers up by demographics. 

  Demographic segmentation typically includes subtypes like language, job title, age or education.

  This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

  However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

  How to build website segmentation analytics

  With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

  • The device they used while visiting your site
  • What channel they entered your site from
  • What country they are located
  • Whether or not they visited a key page of your website
  • And more

  While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

  For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :

  

  The criteria you use to define these segments are based on the data collected within your web analytics platform.

  Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

  • Do returning visitors show different traits than first-time visitors ?
  • Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

  This information can inform your content strategy, user interface design and marketing efforts.

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

  This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

  • Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
  • Device type (i.e., smartphone, tablet, desktop)

  This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

  • Events (i.e., when someone clicks a specific URL on your website)
  • Goals (i.e., when someone stays on your site for a certain period)

  Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

  • Visitors arriving from specific traffic sources
  • Visitors arriving from specific advertising campaigns

  With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

  • Visitors who purchased vs. those who didn’t
  • Visitors who purchased a specific product

  This allows you to refine your website and marketing strategy for increased conversions and revenue.

  Leverage Matomo for your segmentation analytics

  By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

  If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

  Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.