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• Audio clipping after amerge in FFmpeg

  23 avril 2021, par gooey_duck

  Hoping someone out there can help me with this audio issue I'm having using FFmpeg. I've written a bash script using FFmpeg that processes as source a broadcast quality ProRes (HQ) file with four mono tracks of audio. Source audio is LPCM 24-bit, 48kHz signed little endian, and I am exporting the same. Track 1 is full mix left, track 2 is full mix right, track 3 is music/FX left and track 4 is music/FX right. The script takes the source video and trims the video asset using in and out points from a sidecar XML. It also adds a custom slate at the head that uses a separate slate-only MOV file and is joined together via concat. Custom text is added to the slate via the drawtext filter and, finally, audio tracks 3 and 4 are removed and tracks 1 and 2 are merged together into a single stereo interleaved track using amerge.

  


  All of this, seemingly, works like a charm. The problem I'm noticing occurs when I run the resulting export through our in-house QC software. This software detects audio signal clipping at multiple points throughout the file created by FFmpeg. When I create this exact same file via Adobe Premiere or another transcode system, our QC does not detect any clipping. Our QC tools also do not detect any clipping in the source file. This leads me to believe that the clipping is being introduced either by FFmpeg or by my implementation of it. I've tried multiple additional filters within FFmpeg, including pan, amix, volume, etc., but nothing seems to help.

  


  Anyone have any ideas ? Pasting the relevant section of my script for reference :

  


  ffmpeg \
-ss "$hour":"$min":"$sec""$mil_fin" \
-t "$hour_minus":"$min_b":"$sec_b""$mil_fin_b" \
-i "$vid" \
-i "$elements_path"Slate_HD.mov \
-filter_complex \
"[0:a:0] [0:a:1] amerge=inputs=2 [stereo]; \
[1:0] [1:1] [0:0] [stereo] concat=n=2:v=1:a=1 [v] [a]; \
[v]drawtext=enable='between(t,0,28)':fontfile="$fonts"Arial.ttf:fontsize=50:fontcolor=white\
:x=170:y=170:text='$title', \
drawtext=enable='between(t,0,28)':fontfile="$fonts"Arial.ttf:fontsize=50:fontcolor=white\
:x=170:y=170+50:text='Series $series_number Episode $episode_number', \
drawtext=enable='between(t,0,28)':fontfile="$fonts"Arial.ttf:fontsize=50:fontcolor=white\
:x=170:y=170+150:text='$transcode_date', \
yadif=0:-1:0 [o]" \
-map '[o]' -map '[a]' \
-timecode 09:59:30:00 \
-c:v prores_ks -profile:v 3 \
-c:a pcm_s24le \
-threads 3 \
"$output_name".mov


  


  Per suggestion, simplified audio only command line was run as follows :

  


  ffmpeg -i test.mov -i Slate_HD.mov -filter_complex "[0:a:0] [0:a:1] amerge=inputs=2 [stereo];[1:a][stereo]concat=n=2:a=1:v=0" -c:a pcm_s24le test_output.mov


  


  ...and the log from that command line :

  


  ffmpeg version 4.3.2 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple clang version 12.0.0 (clang-1200.0.32.29)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.3.2_3 --enable-shared --enable-pthreads --enable-version3 --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
Guessed Channel Layout for Input Stream #0.1 : mono
Guessed Channel Layout for Input Stream #0.2 : mono
Guessed Channel Layout for Input Stream #0.3 : mono
Guessed Channel Layout for Input Stream #0.4 : mono
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/input/test.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    creation_time   : 2021-04-22T18:58:58.000000Z
  Duration: 00:02:00.04, start: 0.000000, bitrate: 170739 kb/s
    Stream #0:0(eng): Video: prores (HQ) (apch / 0x68637061), yuv422p10le(tv, bt709/unknown/unknown, top coded first (swapped)), 1920x1080, 166061 kb/s, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 25 tbn, 25 tbc (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Video Media Handler
      encoder         : Apple ProRes 422 HQ
      timecode        : 00:00:00:00
    Stream #0:1(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:2(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:3(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:4(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:5(eng): Data: none (tmcd / 0x64636D74) (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Time Code Media Handler
      timecode        : 00:00:00:00
Input #1, mov,mp4,m4a,3gp,3g2,mj2, from '/input/Slate_HD.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    creation_time   : 2021-03-22T17:23:16.000000Z
  Duration: 00:00:30.00, start: 0.000000, bitrate: 77599 kb/s
    Stream #1:0(eng): Video: prores (HQ) (apch / 0x68637061), yuv422p10le(tv, bt709, progressive), 1920x1080, 75783 kb/s, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 25 tbn, 25 tbc (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Apple Video Media Handler
      encoder         : Apple ProRes 422 HQ
      timecode        : 00:00:00:00
    Stream #1:1(eng): Audio: pcm_s16le (sowt / 0x74776F73), 48000 Hz, stereo, s16, 1536 kb/s (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #1:2(eng): Data: none (tmcd / 0x64636D74), 0 kb/s (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Time Code Media Handler
      timecode        : 00:00:00:00
Stream mapping:
  Stream #0:1 (pcm_s24le) -> amerge:in0 (graph 0)
  Stream #0:2 (pcm_s24le) -> amerge:in1 (graph 0)
  Stream #1:1 (pcm_s16le) -> concat:in0:a0 (graph 0)
  concat (graph 0) -> Stream #0:0 (pcm_s24le)
  Stream #0:0 -> #0:1 (prores (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x7feadf81a600] using SAR=1/1
[libx264 @ 0x7feadf81a600] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7feadf81a600] profile High 4:2:2, level 4.0, 4:2:2, 10-bit
[libx264 @ 0x7feadf81a600] 264 - core 161 r3048 b86ae3c - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=81 qpstep=4 ip_ratio=1.40 aq=1:1.00
[Parsed_amerge_0 @ 0x7feade41dfc0] No channel layout for input 1
[Parsed_amerge_0 @ 0x7feade41dfc0] Input channel layouts overlap: output layout will be determined by the number of distinct input channels
Output #0, mov, to '/output/test_output.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    encoder         : Lavf58.45.100
    Stream #0:0: Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, stereo, s32, 2304 kb/s (default)
    Metadata:
      encoder         : Lavc58.91.100 pcm_s24le
    Stream #0:1(eng): Video: h264 (libx264) (avc1 / 0x31637661), yuv422p10le(top coded first (swapped)), 1920x1080 [SAR 1:1 DAR 16:9], q=-1--1, 0.04 fps, 12800 tbn, 25 tbc (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Video Media Handler
      timecode        : 00:00:00:00
      encoder         : Lavc58.91.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=   47 fps=0.0 q=0.0 size=       0kB time=00:00:00.02 bitrate=  15.0kbits/sframe=   88 fps= 80 q=40.0 size=     256kB time=00:00:01.17 bitrate=1791.7kbits/frame=  133 fps= 82 q=40.0 size=     768kB time=00:00:02.98 bitrate=2110.4kbits/frame=  164 fps= 76 q=40.0 size=    1280kB time=00:00:04.21 bitrate=2488.8kbits/frame=  185 fps= 70 q=40.0 size=    1792kB time=00:00:05.06 bitrate=2899.0kbits/frame=  195 fps= 61 q=40.0 size=    2048kB time=00:00:05.44 bitrate=3079.6kbits/frame=  206 fps= 55 q=40.0 size=    2560kB time=00:00:05.89 bitrate=3557.0kbits/frame=  218 fps= 51 q=40.0 size=    3072kB time=00:00:06.38 bitrate=3942.1kbits/frame=  230 fps= 47 q=40.0 size=    3584kB time=00:00:06.85 bitrate=4284.1kbits/frame=  242 fps= 44 q=40.0 size=    4096kB time=00:00:07.34 bitrate=4570.7kbits/frame=  251 fps= 42 q=37.0 size=    4352kB time=00:00:07.70 bitrate=4627.7kbits/frame=  265 fps= 41 q=40.0 size=    5376kB time=00:00:08.25 bitrate=5334.4kbits/frame=  272 fps= 39 q=40.0 size=    5632kB time=00:00:08.53 bitrate=5406.8kbits/frame=  283 fps= 37 q=40.0 size=    5888kB time=00:00:08.98 bitrate=5370.6kbits/frame=  296 fps= 37 q=40.0 size=    6400kB .04 bitrate=5549.0kbits/frame= 3001 fps= 21 q=-1.0 Lsize=  106530kB time=00:02:30.04 bitrate=5816.4kbits/s speed=1.07x    
video:64287kB audio:42199kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.041280%
[libx264 @ 0x7feadf81a600] frame I:33    Avg QP:33.56  size:113497
[libx264 @ 0x7feadf81a600] frame P:984   Avg QP:36.20  size: 35478
[libx264 @ 0x7feadf81a600] frame B:1984  Avg QP:37.44  size: 13696
[libx264 @ 0x7feadf81a600] consecutive B-frames: 11.5%  0.9%  0.9% 86.8%
[libx264 @ 0x7feadf81a600] mb I  I16..4:  3.4% 86.8%  9.7%
[libx264 @ 0x7feadf81a600] mb P  I16..4:  1.6% 13.7%  0.7%  P16..4: 44.3%  8.5%  7.0%  0.0%  0.0%    skip:24.2%
[libx264 @ 0x7feadf81a600] mb B  I16..4:  0.1%  1.1%  0.0%  B16..8: 47.6%  2.4%  0.3%  direct: 1.3%  skip:47.3%  L0:47.0% L1:51.3% BI: 1.7%
[libx264 @ 0x7feadf81a600] 8x8 transform intra:85.9% inter:88.3%
[libx264 @ 0x7feadf81a600] coded y,uvDC,uvAC intra: 67.9% 73.8% 13.9% inter: 20.8% 21.6% 0.6%
[libx264 @ 0x7feadf81a600] i16 v,h,dc,p: 48% 15%  4% 33%
[libx264 @ 0x7feadf81a600] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 11% 27%  6%  7%  7%  7%  8%  7%
[libx264 @ 0x7feadf81a600] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 28% 15% 14%  7%  9%  9%  8%  6%  5%
[libx264 @ 0x7feadf81a600] i8c dc,h,v,p: 57% 12% 24%  7%
[libx264 @ 0x7feadf81a600] Weighted P-Frames: Y:0.1% UV:0.1%
[libx264 @ 0x7feadf81a600] ref P L0: 69.1% 21.7%  9.2%  0.0%
[libx264 @ 0x7feadf81a600] ref B L0: 87.2% 10.4%  2.5%
[libx264 @ 0x7feadf81a600] ref B L1: 94.9%  5.1%
[libx264 @ 0x7feadf81a600] kb/s:4387.16
WU:~ user$ 
ffmpeg version 4.3.2 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple clang version 12.0.0 (clang-1200.0.32.29)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.3.2_3 --enable-shared --enable-pthreads --enable-version3 --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox
  libavutil      56. 51.100 / 56. 51.100
  libavcodec     58. 91.100 / 58. 91.100
  libavformat    58. 45.100 / 58. 45.100
  libavdevice    58. 10.100 / 58. 10.100
  libavfilter     7. 85.100 /  7. 85.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  7.100 /  5.  7.100
  libswresample   3.  7.100 /  3.  7.100
  libpostproc    55.  7.100 / 55.  7.100
Guessed Channel Layout for Input Stream #0.1 : mono
Guessed Channel Layout for Input Stream #0.2 : mono
Guessed Channel Layout for Input Stream #0.3 : mono
Guessed Channel Layout for Input Stream #0.4 : mono
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/input/test.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    creation_time   : 2021-04-22T18:58:58.000000Z
  Duration: 00:02:00.04, start: 0.000000, bitrate: 170739 kb/s
    Stream #0:0(eng): Video: prores (HQ) (apch / 0x68637061), yuv422p10le(tv, bt709/unknown/unknown, top coded first (swapped)), 1920x1080, 166061 kb/s, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 25 tbn, 25 tbc (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Video Media Handler
      encoder         : Apple ProRes 422 HQ
      timecode        : 00:00:00:00
    Stream #0:1(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:2(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:3(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:4(eng): Audio: pcm_s24le (in24 / 0x34326E69), 48000 Hz, mono, s32 (24 bit), 1152 kb/s (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #0:5(eng): Data: none (tmcd / 0x64636D74) (default)
    Metadata:
      creation_time   : 2021-04-22T18:58:58.000000Z
      handler_name    : Time Code Media Handler
      timecode        : 00:00:00:00
Input #1, mov,mp4,m4a,3gp,3g2,mj2, from '/input/Slate_HD.mov':
  Metadata:
    major_brand     : qt  
    minor_version   : 537199360
    compatible_brands: qt  
    creation_time   : 2021-03-22T17:23:16.000000Z
  Duration: 00:00:30.00, start: 0.000000, bitrate: 77599 kb/s
    Stream #1:0(eng): Video: prores (HQ) (apch / 0x68637061), yuv422p10le(tv, bt709, progressive), 1920x1080, 75783 kb/s, SAR 1:1 DAR 16:9, 25 fps, 25 tbr, 25 tbn, 25 tbc (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Apple Video Media Handler
      encoder         : Apple ProRes 422 HQ
      timecode        : 00:00:00:00
    Stream #1:1(eng): Audio: pcm_s16le (sowt / 0x74776F73), 48000 Hz, stereo, s16, 1536 kb/s (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Apple Sound Media Handler
      timecode        : 00:00:00:00
    Stream #1:2(eng): Data: none (tmcd / 0x64636D74), 0 kb/s (default)
    Metadata:
      creation_time   : 2021-03-22T17:23:16.000000Z
      handler_name    : Time Code Media Handler
      timecode        : 00:00:00:00
Stream mapping:
  Stream #0:1 (pcm_s24le) -> amerge:in0 (graph 0)
  Stream #0:2 (pcm_s24le) -> amerge:in1 (graph 0)
  Stream #1:1 (pcm_s16le) -> concat:in0:a0 (graph 0)
  concat (graph 0) -> Stream #0:0 (pcm_s24le)
  Stream #0:0 -> #0:1 (prores (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x7feadf81a600] using SAR=1/1
[libx264 @ 0x7feadf81a600] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7feadf81a600] profile High 4:2:2, level 4.0, 4:2:2, 10-bit
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[Parsed_amerge_0 @ 0x7feade41dfc0] No channel layout for input 1
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[libx264 @ 0x7feadf81a600] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 28% 15% 14%  7%  9%  9%  8%  6%  5%
[libx264 @ 0x7feadf81a600] i8c dc,h,v,p: 57% 12% 24%  7%
[libx264 @ 0x7feadf81a600] Weighted P-Frames: Y:0.1% UV:0.1%
[libx264 @ 0x7feadf81a600] ref P L0: 69.1% 21.7%  9.2%  0.0%
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WU:~ user$ 


  


• Exceeded GA’s 10M hits data limit, now what ?

  1er décembre 2021, par Joselyn Khor
  Exceeded Google Analytics’ 10M hits data limit, now what ?

  “Your data volume (1XXM hits) exceeds the limit of 10M hits per month as outlined in our Terms of Service. If you continue to exceed the limit, we will stop processing new data on XXX 21, 2019. Learn more about possible solutions.”

  Yikes. Alarm bells were ringing when a Google Analytics free user came to us faced with this notice. Let’s call him ‘Mark’. Mark had reached the limits on the data he could collect through Google Analytics and was shocked by the limited options available to fix the problem, without blowing the budget. The thoughts racing through his head were :

  • “What happens to all my data ?”
  • “What if Google starts charging USD150K now ?”

  Then he came across Matomo and decided to get in touch with our support team …

  “Can you fix this issue ?” he asked us.

  “Absolutely !” we said.

  We’ll get back to helping Mark in a minute. For now let’s go over why this was such a dilemma for him.

  In order to resolve this data limits issue, one of the solutions was for him to upgrade to Google Analytics 360, which meant shelling out USD150,000 per year for their 1 billion hits per month option. Going from free to USD150,000 was too much of a stretch for a growing company.

  “Your data volume (1XXM hits) exceeds the limit of 10M hits per month …”, what did this message mean ?

  With the free version, Mark could collect up to 10 million “hits” per month, per account. Going over meant Google Analytics could stop collecting any more data for free as outlined in their Terms.

  Google Analytics’ Terms of Service (2018, sec. 2) states, “Subject to Section 15, the Service is provided without charge to You for up to 10 million Hits per month per account.”[1]

  What is a "hit" in Google Analytics ?

  Data being sent to Google Analytics. It can be a transaction, event, social interaction or pageview - these all produce what Google calls a “hit”.

  

  And their Analytics Help Data Limits (n.d.) support page makes clear that : “If a property sends more hits per month to Analytics than allowed by the Analytics Terms of Service, there is no assurance that the excess hits will be processed. If the property’s hit volume exceeds this limit, a warning may be displayed in the user interface and you may be prevented from accessing reports.”[2]

  

  Possible solutions

  So the possible solutions given by Google Analytics’ Data Limits support page were (also shown in image below) :

  • To pay USD150K to upgrade to Google Analytics 360
  • To send fewer hits by setting up sampling
  • Or choose the slightly less relevant option to upgrade mobile app tracking to Google Analytics for Firebase.

  Without the means to pay, the free version was fast becoming inaccessible for Mark as he was facing a future where he risked no longer having access to up-to-date data used in his business’ reporting.

  Mark was facing a problem that potentially didn’t have a cost-effective solution.

  

  So what can you really do about it ?

  This is where we can help provide some assistance. If you’re reading this article, we’ll assume you can relate to Mark and share with you the advice on options we gave him.

  Options :

  One option posed by Google is for you to send fewer hits by auditing your data collection processes

  If you really don’t have the budget, you’ll need to reassess your data collection priorities and go over your strategies to see what is necessary to track, and what isn’t.

  • Make sure you know what you’re tracking and why. Look at what websites are being tracked by Google and into what properties.
  • Go through what data you’re tracking and decide what is or isn’t of value.
  • Set up data sampling, this however, will lead to inaccurate data.

  From here you can start to course correct. If you’ve found data you’re not using for analysis, get rid of these events/pageviews in your Google Analytics.

  But the limitations here are that eventually, you’re going to run out of irrelevant metrics and everything you’re tracking will be essential. So you’ll hit another brick wall and return to the same situation.

  Option 2 – Ignore and continue using the free version of Google Analytics

  With this option, you’ll have to bear the business risks involved by basing decisions off of analytics reports that may or may not be updated. In this case, you may still get contacted about exceeding the limits. As the free service is provided for only up to 10 million hits, once you’ve gone over them, you’re violating what’s stipulated in the Terms of Service. 

  There’s also the warning that “… you may be prevented from accessing reports” (Data limits, n.d.). So while we may not know for certain what Google Analytics will do, in this case it may be better to be safe rather than sorry by acting quickly to resolve it. 

  Option 3 -The Matomo solution – a privacy-friendly Google Analytics alternative

  Upgrade to a web analytics platform that can handle your demanding data requirements. Save money while continuing to gain valuable insights by moving over to Matomo Analytics (recommended)

  This is where you can save up to USD130,000 a year. As well as that, the transition from Google Analytics to the Matomo Cloud is a seamless experience as setup and maintenance is taken care of by our experts.

  For example, you can get up to 25M hits for USD3,241/month (or USD38,900/year) on the Essentials plan.

  Or even 25M hits for USD4,991/month (or USD59,900/year) on the Business plan – which offers additional web analytics and conversion optimization resources.

  Matomo Cloud is a great option if you’re looking for a secure, cost-effective and powerful analytics solution. You also get what Google Analytics could never offer you : full control and ownership of your own data and privacy. 

  Try Matomo free for 21 days – no credit card required.

  No need to worry about losing your Google Analytics data because …

  Now you can import your historic Google Analytics data directly into your Matomo with the Google Analytics Importer tool. Simply follow the step-by-step guide to get started for free.

  Along with savings you can get :

  • A solution for the data limits issue forever. You choose the right plan to suit your data needs and adapt as you continue growing
  • 100% accurate data (no data sampling)
  • 100% data ownership of all your information without signing away your data to a third party
  • Powerful web analytics and conversion optimization features
  • Matomo Tag Manager
  • Easy setup
  • Support from Matomo’s specialists

  Learn more about Matomo Cloud pricing.

  

  Or go for Matomo On-Premise

  If you have the in-house infrastructure to support self-hosting Matomo on your own servers then there’s also the option of Matomo On-Premise. Here you’ll get full security knowing the data is on your own servers. 

  Setup will also require technical knowledge. There will also be costs associated with acquiring your own servers, and keeping up with regular maintenance and updates. With On-Premise you get maximum flexibility, with no data limits whatsoever. But if you’re coming over from Google Analytics and don’t have the infrastructure and team to host On-Premise, the Matomo Cloud could be right for you.

  Learn more about Matomo On-Premise.

  Where do you go from here ?

  Getting 10 millions hits per month is no small feat, it’s actually pretty fantastic. But if it means having to shell out USD150,000 just to be able to continue with Google Analytics, we feel your problem could be fixed with Matomo Cloud. You could then put the rest of the money you save to better use.

  If you choose Matomo, you now have the option to : 

  • Raise your data limits for a fraction of Google Analytics 360’s price
  • Get a comprehensive range of analytics features for the most impactful insights to ensure your website continues excelling
  • Get data that’s not sampled – meaning 100% accuracy in your reports
  • Migrate your data easily with the help of Matomo’s support team

  We’ll have you covered. 

  By sharing with you the options and advice we gave to Mark, we hope you’ll be able to find a solution that makes your life easier and solves the issue of data restrictions forever.

  The team at Matomo is here to help you every step of the way to ensure a stress-free transition from Google Analytics if that is what works best for you.

  For next steps, check out our live online Matomo demo and start your free 21-day trial.

  References :

  [1] Terms of Service. (2018, July 24). In Google Analytics Terms of Service. Retrieved June 12, 2019, from https://www.google.com/analytics/terms/us.html

  [2] Data limits. (n.d.). In Analytics Help Data limits. Retrieved June 12, 2019, from https://support.google.com/analytics/answer/1070983?hl=en

• 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.