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• On-premise analytics demand grows as Google Analytics GDPR uncertainties continue

  7 janvier 2020, par Jake Thornton — Privacy

  The Google Analytics GDPR relationship is a complicated one. Website owners in states like Berlin in Germany are now required to ask users for consent to collect their data. This doesn’t make for the friendliest user-experience and often the website visitor will simply click “no.”

  The problem Google Analytics now presents website owners in the EU is with more visitors clicking “no”, the less accurate your data will become.

  Why do you need to ask your visitors for consent ?

  At this stage it’s simply because Google Analytics collects data for its own purposes. An example of this is using your visitor’s personal data for retargeting purposes across their advertising platforms like Google Ads and YouTube. 

  Google’s Privacy & Terms states : “when you visit a website that uses advertising services like AdSense, including analytics tools like Google Analytics, or embeds video content from YouTube, your web browser automatically sends certain information to Google. This includes the URL of the page you’re visiting and your IP address. We may also set cookies on your browser or read cookies that are already there. Apps that use Google advertising services also share information with Google, such as the name of the app and a unique identifier for advertising.”

  

  The rise of hosting web analytics on-premise

  Managing Google Analytics and GDPR can quickly become complicated, so there’s been an increase in website owners switching from cloud-hosted web analytics platforms, like Google Analytics, to more GDPR compliant alternatives, where you can host web analytics software on your own servers. This is called hosting web analytics on-premise.

  Hosting web analytics on your own servers means :

  No third-parties are involved

  The visitor data your website collects is stored on your own internal infrastructure. This means no third-parties are involved and there’s no risk of personal data being used in the way Google Analytics uses it e.g. sending personal data to its advertising platforms. 

  When you sign up with Google Analytics you sign away control of your user’s personal data. With on-premise website analytics, you own your data and are in full control.

  NOTE : Though Google Analytics uses personal data for its own purposes, not all cloud hosted web analytics platforms do this. As an example, Matomo Analytics Cloud hosted solution states that all personal data collected is not used for its own purposes and that Matomo has no rights in accessing or using this personal data. 

  

  You control where in the world your personal data is stored

  Google Analytics servers are based out of USA, Europe and Asia, so where your personal data will end up is uncertain and you don’t have the option to choose which location it goes to when using free Google Analytics.

  Different countries have different laws when it comes to accessing personal data. When you choose to host your web analytics on-premise, you can choose the location of your servers and where the personal data is stored.

  More flexibility

  With self-hosted web analytics platforms like Matomo On-Premise, you can extend the platform to do anything you want without the restrictions that cloud hosted platforms impose.

  You can :

  • Get full access to the source code of open-source solutions, like Matomo
  • Extend the platform however you want for your business
  • Get access to APIs
  • Have no data limitations or restrictions
  • Get RAW data access
  • Have control over security

  >> Read more about on-premise flexibility for web analytics here

  

  So what does the future look like for Google Analytics and GDPR ?

  It’s difficult to assess this right now. How exactly GDPR is enforced is still quite unclear. 

  What is clear however, is now website owners in Berlin using Google Analytics are lawfully required to ask their visitors for consent to collect personal data. It has been reported that Google Analytics has already received 200,000 complaints in Germany alone and it appears this trend is likely to continue across much of the EU.

  When using Google Analytics in the EU you must also ensure your privacy policy is updated so website visitors are aware that data is being collected through Google Analytics for its own purposes.

  Moving to a web analytics on-premise platform

  Matomo Analytics is the #1 open-source web analytics platform in the world and has been rated as an exceptional alternative to Google Analytics. Check the reviews on Capterra.

  Choosing Matomo On-Premise means you can control exactly where your data is stored, you have full flexibility to customise the platform to do what you want and it’s FREE.

  Matomo’s mission is to give control back to website owners and the team has designed the platform so that moving away from Google Analytics is seamless. Matomo offers most of your favourite Google Analytics features, a leaner interface to navigate, and the option to add free and paid premium features that Google Analytics can’t even offer you.

  And now you can import your historical Google Analytics data directly into your Matomo with the Google Analytics Importer plugin.

  DOWNLOAD MATOMO (FREE)

  And if you can’t host web analytics on your own servers ...

  Hosting web analytics on-premise is not an option for all businesses as you do need the internal infrastructure and technical knowledge to host your own platform.

  If you can’t self-host, then Matomo has a Cloud hosted solution you can easily install and operate like Google Analytics, which is hosted on Matomo’s servers in the EU. 

  The GDPR advantages of choosing Matomo Cloud over Google Analytics are :

  • Servers are secure and based in the EU (strict laws forbid outside access)
  • 100% data ownership – we never use data for our own purposes
  • You can export your data anytime and switch to Matomo On-Premise whenever you like
  • User-privacy protection
  • Advanced GDPR Manager and data anonymisation features which GA doesn’t offer

  

  Interested to learn more ?

  If you are wanting to learn more about why users are making the move from Google Analytics to Matomo, check out our Matomo Analytics vs Google Analytics comparison page.

  >> Matomo Analytics vs Google Analytics

• FFmpeg saturates memory + CPU usage drops to 0% during very basic conversion of PNG files to MP4 video

  7 août 2022, par mattze_frisch

  I have this Python function that runs ffmpeg with minimal options from the Windows command line :

  


  def run_ffmpeg(frames_path, ffmpeg_path=notebook_directory):
    '''
    This function runs ffmpeg.exe to convert PNG image files into a MP4 video.
    
    Parameters
    ----------
    frames_path : string
        Absolute path to the PNG files
    ffmpeg_path : string
        Absolute path to the FFmpeg executable (ffmpeg.exe)
    '''
    
    from subprocess import check_call
    
    
    check_call(
        [
            os.path.join(ffmpeg_path, 'ffmpeg'),
            '-y',    # Overwrite output files without asking
            '-report',    # Write logfile to current working directory
            '-framerate', '60',    # Input frame rate
            '-i', os.path.join(frames_path, 'frame%05d.png'),    # Path to input frames
            os.path.join(frames_path, 'video.mp4')    # Path to store output video
        ]
    )


  


  When running it from a Jupyter notebook over 2500 PNG files (RGBA, ca. 600-700 kB each, 9000 x 13934 pixels), CPU usage briefly peaks to 100% before dropping to 0%, while memory usage quickly saturates to 100% and stays there, slowing the system down almost to a freeze, so I need to terminate ffmpeg from the task manager :

  


  Screenshot

  


  The generated video file has a size of only 48 bytes and contains just a black frame when viewed in the VLC player.

  


  This is the ffmpeg log output :

  


  ffmpeg started on 2022-08-05 at 17:17:55
Report written to "ffmpeg-20220805-171755.log"
Log level: 48
Command line:
"C:\\Users\\Username\\Desktop\\folder\\ffmpeg" -y -report -framerate 60 -i "C:\\Users\\Username\\Desktop\\e\\frame%05d.png" "C:\\Users\\Username\\Desktop\\e\\video.mp4"
ffmpeg version 2022-07-14-git-882aac99d2-full_build-www.gyan.dev Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.1.0 (Rev2, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libmfx --enable-libshaderc --enable-vulkan --enable-libplacebo --ena  libavutil      57. 29.100 / 57. 29.100
  libavcodec     59. 38.100 / 59. 38.100
  libavformat    59. 28.100 / 59. 28.100
  libavdevice    59.  8.100 / 59.  8.100
  libavfilter     8. 45.100 /  8. 45.100
  libswscale      6.  8.100 /  6.  8.100
  libswresample   4.  8.100 /  4.  8.100
  libpostproc    56.  7.100 / 56.  7.100
Splitting the commandline.
Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
Reading option '-report' ... matched as option 'report' (generate a report) with argument '1'.
Reading option '-framerate' ... matched as AVOption 'framerate' with argument '60'.
Reading option '-i' ... matched as input url with argument 'C:\Users\Username\Desktop\e\frame%05d.png'.
Reading option 'C:\Users\Username\Desktop\e\video.mp4' ... matched as output url.
Finished splitting the commandline.
Parsing a group of options: global .
Applying option y (overwrite output files) with argument 1.
Applying option report (generate a report) with argument 1.
Successfully parsed a group of options.
Parsing a group of options: input url C:\Users\Username\Desktop\e\frame%05d.png.
Successfully parsed a group of options.
Opening an input file: C:\Users\Username\Desktop\e\frame%05d.png.
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00000.png' for reading
[file @ 0000000000425680] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042d800] Statistics: 668318 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00001.png' for reading
[file @ 000000000042dac0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042d6c0] Statistics: 668371 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00002.png' for reading
[file @ 000000000042d6c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042dac0] Statistics: 669177 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00003.png' for reading
[file @ 000000000042dac0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437a40] Statistics: 684594 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00004.png' for reading
[file @ 0000000000437a40] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437c00] Statistics: 703014 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00005.png' for reading
[file @ 0000000000437c00] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437d00] Statistics: 721604 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00006.png' for reading
[file @ 0000000000437cc0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000437f40] Statistics: 739761 bytes read, 0 seeks
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00007.png' for reading
[file @ 0000000000437f40] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 0000000000438040] Statistics: 757327 bytes read, 0 seeks
[image2 @ 000000000041ff80] Probe buffer size limit of 5000000 bytes reached
Input #0, image2, from 'C:\Users\Username\Desktop\e\frame%05d.png':
  Duration: 00:00:41.67, start: 0.000000, bitrate: N/A
  Stream #0:0, 8, 1/60: Video: png, rgba(pc), 9000x13934 [SAR 29528:29528 DAR 4500:6967], 60 fps, 60 tbr, 60 tbn
Successfully opened the file.
Parsing a group of options: output url C:\Users\Username\Desktop\e\video.mp4.
Successfully parsed a group of options.
Opening an output file: C:\Users\Username\Desktop\e\video.mp4.
[file @ 000000002081e3c0] Setting default whitelist 'file,crypto,data'
Successfully opened the file.
detected 12 logical cores
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00008.png' for reading
[file @ 00000000024ad980] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 00000000004379c0] Statistics: 767857 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00009.png' for reading
[file @ 000000000042d600] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 00000000004379c0] Statistics: 774848 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00010.png' for reading
[file @ 00000000004379c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 787178 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00011.png' for reading
[file @ 00000000004379c0] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 797084 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:0 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00012.png' for reading
[file @ 0000000000437a80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000042da00] Statistics: 802870 bytes read, 0 seeks
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'video_size' to value '9000x13934'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'pix_fmt' to value '26'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'time_base' to value '1/60'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'pixel_aspect' to value '29528/29528'
[graph 0 input from stream 0:0 @ 00000000208bf800] Setting 'frame_rate' to value '60/1'
[graph 0 input from stream 0:0 @ 00000000208bf800] w:9000 h:13934 pixfmt:rgba tb:1/60 fr:60/1 sar:29528/29528
[format @ 00000000025ef840] Setting 'pix_fmts' to value 'yuv420p|yuvj420p|yuv422p|yuvj422p|yuv444p|yuvj444p|nv12|nv16|nv21|yuv420p10le|yuv422p10le|yuv444p10le|nv20le|gray|gray10le'
[auto_scale_0 @ 00000000025efe40] w:iw h:ih flags:'' interl:0
[format @ 00000000025ef840] auto-inserting filter 'auto_scale_0' between the filter 'Parsed_null_0' and the filter 'format'
[AVFilterGraph @ 000000000042da00] query_formats: 4 queried, 3 merged, 1 already done, 0 delayed
[auto_scale_0 @ 00000000025efe40] picking yuv444p out of 13 ref:rgba alpha:1
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[auto_scale_0 @ 00000000025efe40] w:9000 h:13934 fmt:rgba sar:29528/29528 -> w:9000 h:13934 fmt:yuv444p sar:1/1 flags:0x0
[libx264 @ 000000002081d280] using mv_range_thread = 376
[libx264 @ 000000002081d280] using SAR=1/1
[libx264 @ 000000002081d280] frame MB size (563x871) > level limit (139264)
[libx264 @ 000000002081d280] DPB size (4 frames, 1961492 mbs) > level limit (1 frames, 696320 mbs)
[libx264 @ 000000002081d280] MB rate (29422380) > level limit (16711680)
[libx264 @ 000000002081d280] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 000000002081d280] profile High 4:4:4 Predictive, level 6.2, 4:4:4, 8-bit
[libx264 @ 000000002081d280] 264 - core 164 r3095 baee400 - H.264/MPEG-4 AVC codec - Copyleft 2003-2022 - 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=4 threads=18 lookahead_threads=3 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=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'C:\Users\Username\Desktop\e\video.mp4':
  Metadata:
    encoder         : Lavf59.28.100
  Stream #0:0, 0, 1/15360: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 9000x13934 [SAR 1:1 DAR 4500:6967], q=2-31, 60 fps, 15360 tbn
    Metadata:
      encoder         : Lavc59.38.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
Clipping frame in rate conversion by 0.000008
frame=    1 fps=0.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00013.png' for reading
[file @ 000000000a6a2180] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 810395 bytes read, 0 seeks
frame=    2 fps=0.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00014.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 818213 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00015.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 817936 bytes read, 0 seeks
frame=    4 fps=1.2 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00016.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 817014 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00017.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 828088 bytes read, 0 seeks
frame=    6 fps=1.5 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00018.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 831007 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00019.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 845203 bytes read, 0 seeks
frame=    8 fps=1.7 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00020.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 851548 bytes read, 0 seeks
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00021.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 847629 bytes read, 0 seeks
frame=   10 fps=1.8 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00022.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 860169 bytes read, 0 seeks
frame=   11 fps=1.4 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00023.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 857243 bytes read, 0 seeks
frame=   12 fps=1.2 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   0x    
cur_dts is invalid st:0 (0) [init:1 i_done:0 finish:0] (this is harmless if it occurs once at the start per stream)
[image2 @ 000000000041ff80] Opening 'C:\Users\Username\Desktop\e\frame00024.png' for reading
[file @ 000000001ec86c80] Setting default whitelist 'file,crypto,data'
[AVIOContext @ 000000000b38de80] Statistics: 835155 bytes read, 0 seeks


  


  What is the problem ?

  


  By the way, the color model of the image files was confirmed by doing

  


  from PIL import Image


img = Image.open('C:\\Users\\EPI-SMLM\\Desktop\\e\\frame00000.png')
img.mode
-------------------------------------------------------------------
C:\Program Files\Python38\lib\site-packages\PIL\Image.py:3035: DecompressionBombWarning: Image size (125406000 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.
  warnings.warn(

'RGBA'


  


  The "decompression bomb warning" appears to be a false alarm/bug.

  


  UPDATE : I can confirm that this happens even when there are only 50 image files, i.e. 50 x 700 kB = 35 MB in total size. ffmpeg still gobbles up all available memory (almost 60 GB of private bytes !!!).

  


  And it also happens if ffmpeg is run from the command line.

  


  This must be a bug !

  


• What Is Incrementality & Why Is It Important in Marketing ?

  26 mars 2024, par Erin

  Imagine this : you just launched your latest campaign and it was a major success.

  You blew last month’s results out of the water.

  You combined a variety of tactics, channels and ad creatives to make it work.

  Now, it’s time to build the next campaign.

  The only issue ?

  You don’t know what made it successful or how much your recent efforts impacted the results.

  You’ve been building your brand for years. You’ve built up a variety of marketing pillars that are working for you. So, how do you know how much of your campaign is from years of effort or a new tactic you just implemented ?

  The key is incrementality.

  This is a way to properly attribute the right weight to your marketing tactics.

  In this article, we break down what incrementality is in marketing, how it differs from traditional attribution and how you can calculate and track it to grow your business.

  What is incrementality in marketing ?

  Incrementality in marketing is growth that can be directly credited to a marketing effort above and beyond the success of the branding.

  It looks at how much a specific tactic positively impacted a campaign on top of overall branding and marketing strategies.

  

  For example, this could be how much a specific tactic, campaign or channel helped increase conversions, email sign-ups or organic traffic.

  The primary purpose of incrementally in marketing is to more accurately determine the impact a single marketing variable had on the success of a project.

  It removes every other factor and isolates the specific method to help marketers double down on that strategy or move on to new tactics.

  With Matomo, you can track conversions simply. With our last non-direct channel attribution system, you’ll be able to quickly see what channels are converting (and which aren’t) so you can gain insights into incrementality. 

  See why over 1 million websites choose Matomo today.

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  How incrementality differs from attribution

  In marketing and advertising, it’s crucial to understand what tactics and activities drive growth.

  Incrementality and attribution help marketers and business owners understand what efforts impact their results.

  But they’re not the same.

  Here’s how they differ :

  

  Incrementality explained

  Incrementality measures how much a specific marketing campaign or activity drives additional sales or growth.

  Simply put, it’s analysing the difference between having never implemented the campaign (or tactic or channel) in the first place versus the impact of the activity.

  In other words, how much revenue would you have generated this month without campaign A ?

  And how much additional revenue did you generate directly due to campaign A ?

  The reality is that dozens of factors impact revenue and growth.

  You aren’t just pouring your marketing into one specific channel or campaign at a time.

  Chances are, you’ve got your hands on several marketing initiatives like SEO, PPC, organic social media, paid search, email marketing and more.

  Beyond that, you’ve built a brand with a not-so-tangible impact on your recurring revenue.

  So, the question is, if you took away your new campaign, would you still be generating the same amount of revenue ?

  And, if you add in that campaign, how much additional revenue and growth did it directly create ?

  That is incrementality. It’s how much a campaign went above and beyond to add new revenue that wouldn’t have been there otherwise.

  So, how does attribution play into all of this ?

  Attribution explained

  Attribution is simply the process of assigning credit for a conversion to a particular marketing touchpoint.

  While incrementality is about narrowing down the overall revenue impact from a particular campaign, attribution seeks to point to a specific channel to attribute a sale.

  For example, in any given marketing campaign, you have a few marketing tactics.

  Let’s say you’re launching a limited-time product.

  You might have :

  • Paid ads via Facebook and Instagram
  • A blog post sharing how the product works
  • Organic social media posts on Instagram and TikTok
  • Email waitlist campaign building excitement around the upcoming product
  • SMS campaigns to share a limited-time discount

  So, when the time comes for the sale launch, and you generate $30,000 in revenue, what channel gets the credit ?

  Do you give credit to the paid ads on Facebook ? What about Instagram ? They got people to follow you and got them on the email waitlist.

  Do you give credit to email for reminding people of the upcoming sale ? What about your social media posts that reminded people there ?

  Or do you credit your SMS campaign that shared a limited-time discount ?

  Which channel is responsible for the sale ?

  This is what attribution is all about.

  It’s about giving credit where credit is due.

  The reason you want to attribute credit ? So you know what’s working and can double down your efforts on the high-impact marketing activities and channels.

  Leveraging incrementality and attribution together

  Incrementality and attribution aren’t competing methods of analysing what’s working.

  They’re complementary to one another and go hand in hand.

  You can (and should) use attribution and incrementality in your marketing to help understand what activities, campaigns and channels are making the biggest incremental impact on your business growth.

  Why it’s important to measure incrementality

  Incrementality is crucial to measure if you want to pour your time, money and effort into the right marketing channels and tactics.

  Here are a few reasons why you need to measure incrementality if you want to be successful with your marketing and grow your business :

  1. Accurate data

  If you want to be an effective marketer, you need to be accurate.

  You can’t blindly start marketing campaigns in hopes that you will sell many products or services.

  That’s not how it works.

  Sure, you’ll probably make some sales here and there. But to truly be effective with your work, you must measure your activities and channels correctly.

  Incrementality helps you see how each channel, tactic or campaign made a difference in your marketing.

  Matomo gives you 100% accurate data on your website activities. Unlike Google Analytics, we don’t use data sampling which limits how much data is analysed.

  

  2. Helps you to best determine the right tactics for success

  How can you plan your marketing strategy if you don’t know what’s working ?

  Think about it.

  You’ll be blindly sailing the seas without a compass telling you where to go.

  Measuring incrementality in your marketing tactics and channels helps you understand the best tactics.

  It shows you what’s moving the needle (and what’s not).

  Once you can see the most impactful tactics and channels, you can forge future campaigns that you know will work.

  3. Allows you to get the most out of your marketing budget

  Since incrementality sheds light on what’s moving your business forward, you can confidently implement your efforts on the right tactics and channels.

  Guess what happens when you start doubling down on the most impactful activities ?

  You start increasing revenue, decreasing ad spend and getting a higher return on investment.

  The result is that you will get more out of your marketing budget.

  Not only will you boost revenue, but you’ll also be able to boost profit margins since you’re not wasting money on ineffective tactics.

  4. Increase traffic

  When you see what’s truly working in your business, you can figure out what channels and tactics you should be working.

  Incrementality helps you understand not only what your best revenue tactics are but also what channels and campaigns are bringing in the most traffic.

  When you can increase traffic, you can increase your overall marketing impact.

  5. Increase revenue

  Finally, with increased traffic, the inevitable result is more conversions.

  More conversions mean more revenue.

  Incrementality gives you a vision of the tactics and channels that are converting the best.

  If you can see that your SMS campaigns are driving the best ROI, then you know that you’ll grow your revenue by pouring more into acquiring SMS leads.

  By calculating incrementality regularly, you can rest assured that you’re only investing time and money into the most impactful activities in terms of revenue generation.

  How to calculate and test incrementality in marketing

  Now that you understand how incrementality works and why it’s important to calculate, the question is : 

  How do you calculate and conduct incrementality tests ?

  Given the ever-changing marketing landscape, it’s crucial to understand how to calculate and test incrementally in your business.

  If you’re not sure how incrementality testing works, then follow these simple steps :

  

  Your first step to get an incrementality measurement is to conduct what’s referred to as a “holdout test.”

  It’s not a robust test, but it’s an easy way to get the ball rolling with incrementality.

  Here’s how it works :

  1. Choose your target audience.

  With Matomo’s segmentation feature, you can get pretty specific with your target audience, such as :

  • • Visitors from the UK
    • Returning visitors
    • Mobile users
    • Visitors who clicked on a specific ad

  2. Split your audience into two groups :

  1. • Control group (60% of the segment)
     • Test group (40% of the segment)

  3. Target the control group with your marketing tactic (the simpler the tactic, the better).

  4. Target the test group with a different marketing tactic.

  5. Analyse the results. The difference between the control and test groups is the incremental lift in results. The new marketing tactic is either more effective or not.

  6. Repeat the test with a new control group (with an updated tactic) and a new test group (with a new tactic).

  Matomo can help you analyse the results of your campaigns in our Goals feature. Set up business objectives so you can easily track different goals like conversions.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  Here’s an example of how this incrementality testing could look in real life.

  Imagine a fitness retailer wants to start showing Facebook ads in their marketing mix.

  The marketing manager decided to conduct a holdout test. If we match our example below with the steps above, this is how the holdout test might look.

  1. They choose people who’ve purchased free weights in the past as their target audience (see how that segmentation works ?).
  2. They split this segment into a control group and a test group.
  3. For this test, they direct their regular marketing campaign to the control group (60% of the segment). The campaign includes promoting a 20% off sale on organic social media posts, email marketing, and SMS.
  4. They direct their regular marketing campaign plus Facebook ads to the test group (40% of the segment).
  5. They ran the campaign for three weeks with the goal for sale conversions and noticed :

  • • The control group had a 1.5% conversion rate.
    • The test group (with Facebook ads) had a 2.1% conversion rate.
    • In this scenario, they could see the group who saw the Facebook ads convert better.
    • They created the following formula to measure the incremental lift of the Facebook ads :

  

  • • Here’s how the calculation works out : (2.1% – 1.5%) / 1.5% = 40%

  The Facebook ads had a positive 40% incremental lift in conversions during the sale.

  Incrementality testing isn’t a one-and-done process, though.

  While this first test is a great sign for the marketing manager, it doesn’t mean they should immediately throw all their money into Facebook ads.

  They should continue conducting tests to verify the initial test.

  Use Matomo to track incrementality today

  Incrementality can give you insights into exactly what’s working in your marketing (and what’s not) so you can design proven strategies to grow your business.

  If you want more help tracking your marketing efforts, try Matomo today.

  Our web analytics and behaviour analytics platform gives you firsthand data on your website visitors you can use to craft effective marketing strategies.

  Matomo provides 100% accurate data. Unlike other major web analytics platforms, we don’t do data sampling. What you see is what’s really going on in your website. That way, you can make more informed decisions for better results.

  At Matomo, we take privacy very seriously and include several advanced privacy protections to ensure you are in full control.

  As a fully compliant web analytics solution, we’re fully compliant with some of the world’s strictest privacy regulations like GDPR. With Matomo, you get peace of mind knowing you can make data-driven decisions while also being compliant. 

  If you’re ready to launch a data-driven marketing strategy today and grow your business, get started with our 21-day free trial now. No credit card required.

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