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• Custom Segmentation Guide : How it Works & Segments to Test

  13 novembre 2023, par Erin — Analytics Tips, Uncategorized

  Struggling to get the insights you’re looking for with premade reports and audience segments in your analytics ?

  Custom segmentation can help you better understand your customers, app users or website visitors, but only if you know what you’re doing.

  You can derive false insights with the wrong segments, leading your marketing campaigns or product development in the wrong direction.

  In this article, we’ll break down what custom segmentation is, useful custom segments to consider, how new privacy laws affect segmentation options and how to create these segments in an analytics platform.

  What is custom segmentation ?

  Custom segmentation is when you divide your audience (customers, users, website visitors) into bespoke segments of your own design, not premade segments designed by the analytics or marketing platform provider.

  To do this, you single out “custom segment input” — data points you will use to pinpoint certain users. For example, it could be everyone who has visited a certain page on your site.

  

  Segmentation isn’t just useful for targeting marketing campaigns and also for analysing your customer data. Creating segments is a great way to dive deeper into your data beyond surface-level insights.

  You can explore how various factors impact engagement, conversion rates, and customer lifetime value. These insights can help guide your higher-level strategy, not just campaigns.

  How custom segments can help your business

  As the global business world clamours to become more “data-driven,” even smaller companies collect all sorts of data on visitors, users, and customers.

  However, inexperienced organisations often become “data hoarders” without meaningful insights. They have in-house servers full of data or gigabytes stored by Google Analytics and other third-party providers.

  

  One way to leverage this data is with standard customer segmentation models. This can help you get insights into your most valuable customer groups and other standard segments.

  Custom segments, in turn, can help you dive deeper. They help you unlock insights into the “why” of certain behaviours. They can help you segment customers and your audience to figure out :

  • Why and how someone became a loyal customer
  • How high-order-value customers interact with your site before purchases
  • Which behaviours indicate audience members are likely to convert
  • Which traffic sources drive the most valuable customers

  This specific insight’s power led Gartner to predict that 70% of companies will shift focus from “big data” to “small and wide” by 2025. The lateral detail is what helps inform your marketing strategy. 

  You don’t need the same volume of data if you’re analysing and segmenting it effectively.

  Custom segment inputs : 6 data points you can use to create valuable custom segments 

  To help you get started, here are six useful data points you can use as a basis to create segments — AKA customer segment inputs :

  

  Visits to certain pages

  A basic data point that’s great for custom segments is visits to certain pages. Create segments for popular middle-of-funnel pages and compare their engagement and conversion rates. 

  For example, if a user visits a case study page, you can compare their likelihood to convert vs. other visitors.

  This is a type of behavioural segmentation, but it is the easiest custom segment to set up in terms of analysis and marketing efforts.

  Visitors who perform certain actions

  The other important type of behavioural segment is visitors or users who take certain actions. Think of things like downloading a file, clicking a link, playing a video or scrolling a certain amount.

  For instance, you can create a segment of all visitors who have downloaded a white paper. This can help you explore, for example, what drives someone to download a white paper. You can look at the typical user journey and make it easier for them to access the white paper — especially if your sales reps indicate many inbound leads mention it as a key driver of their interest.

  User devices

  Device-based segmentation lets you compare engagement and conversion rates on mobile, desktop and tablets. You can also get insights into their usage patterns and potential issues with certain mobile elements.

  

  This is one aspect of technographic segmentation, where you segment based on users’ hardware or software. You can also create segments based on browser software or even specific versions.

  Loyal or high-value customers

  The best way to get more loyal or high-value customers is to explore their journey in more detail. These types of segments can help you better understand your ideal customers and how they act on your site.

  You can then use this insight to alter your campaigns or how you communicate with your target audience.

  For example, you might notice that high-value customers tend to come from a certain source. You can then focus your marketing efforts on this source to reach more of your ideal customers.

  Visitor or customer source

  You need to track the results if you’re investing in marketing (like an influencer campaign or a sponsored post) outside platforms with their own analytics.

  

  Before you can create a reliable segment, you need to make sure that you use campaign tracking parameters to reliably track the source. You can use our free campaign tracking URL builder for that.

  Demographic segments — location (country, state) and more

  Web analytics tools, such as Matomo, use visitors’ IP addresses to pinpoint their location more accurately by cross-referencing with a database of known and estimated IP locations. In addition, these tools can detect a visitor’s location through the language settings in their browser. 

  This can help create segments based on location or language. By exploring these trends, you can identify patterns in behaviour, tailor your content to specific audiences, and adapt your overall strategy to better meet the preferences and needs of your diverse visitor base.

  How new privacy laws affect segmentation options

  Over the past few years, new legislation regarding privacy and customer data has been passed globally. The most notable privacy laws are the GDPR in the EU, the CCPA in California and the VCDPA in Virginia.

  

  For most companies, it can save a lot of work and future headaches to choose a GDPR-compliant web analytics solution not only streamlines operations, saving considerable effort and preventing future headaches, but also ensures peace of mind by guaranteeing the collection of compliant and accurate data. This approach allows companies to maintain compliance with privacy regulations while remaining firmly committed to a data-driven strategy.

  Create your very own custom segments in Matomo (while ensuring compliance and data accuracy)

  Crafting precise marketing messages and optimising ROI is crucial, but it becomes challenging without the right tools, especially when it comes to maintaining accurate data.

  That’s where Matomo comes in. Our privacy-friendly web analytics platform is GDPR-compliant and ensures accurate data, empowering you to effortlessly create and analyse precise custom segments.

  If you want to improve your marketing campaigns while remaining GDPR-compliant, start your 21-day free trial of Matomo. No credit card required.

• ffmpeg streaming fails to stream over internet to twitch.tv

  15 avril 2021, par josh joyer

  I did already streaming to twitch.tv with command :

  


  ffmpeg -stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic"
 -f dshow -i audio="realTek" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3"
 -f dshow -i video="USB2.0 PC CAMERA" 
-ac 1 -ar 11025 -acodec libmp3lame -c:v libx264 -b:v 100k -f flv -s 80x120 
rtmp://live.twitch.tv/app/live_streamingKey


  


  It was most advanced command that I used to stream online.

  


  (I do not know how to make enter in here so I put double enter)

  


  9stream.wav was played in loop as background music

  


  microphone was added

  


  stereoMix named realTek was the playback of system sounds

  


  volume was adjusted and all sounds mixed into one stream

  


  camera view was added

  


  THEN network flow was reduced by sending only one channel with low frequency of 11025 with lowest

  


  possible data size made by mp3 encoder and libx264 was used to encode video in png files.

  


  It was working fine SO I decided to make final version

  


  (this one worked with all sounds(background music,microphone,system sounds) and camera)

  


  Final version was about adding screen view and logo.

  


  I succeded writing everything to disc with command :

  


  ffmpeg 
-stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic" 
-f dshow -i audio="stereoMixRealtek" 
-i camera.png 
-f gdigrab -framerate 1 -i desktop 
-f dshow -framerate 15 -i video="USB2.0 PC CAMERA" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];
[a0][a1][a2]amix=inputs=3[aMix];
[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];
[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];
[vScreenCam][3:v]overlay=5:5[v]" 
-map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 output.mkv


  


  That was

  


  background music

  


  microphone

  


  system sounds

  


  logo picture

  


  screen view

  


  camera

  


  adjusting sound volume

  


  mixing sounds

  


  reducing size of screen view and camera view

  


  overlaying reduced camera view over reduced screen view

  


  adding logo

  


  choosing final view, final mixed sounds,

  


  reducing data size to one channel, reducing sample frequency,

  


  choosing mp3 codec to reduce final data size,

  


  choosing minimal framerate of one per second to reduce data size

  


  choosing libx264 codec for video.

  


  THEN I tried to use final command for network streaming with slight modification :

  


  ffmpeg 
-stream_loop -1 -i 9stream.wav 
-f dshow -i audio="mic" 
-f dshow -i audio="stereo mix" 
-i camera.png 
-f gdigrab -framerate 1 -i desktop 
-f dshow -framerate 15 -i video="USB2.0 PC CAMERA" 
-filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];
[a0][a1][a2]amix=inputs=3[aMix];
[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];
[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" 
-map "[v]" -map "[aMix]" 
-ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_streamingKey


  


  I added parameter
-b:v 100k to reduce video flow
-b:a 10k to reduce sound flow
-f flv to be good for twitch.tv otherwise it would not accept stream

  


  BUT ffmpeg is always stopping sending data with message like this :

  


  testosteron_@testosteron MINGW64 ~/Desktop/2021b/magisterka/ScreenRecorderXi/ScreenRecorderXi/bin
$ cmd
Microsoft Windows [Version 6.3.9600]
(c) 2013 Microsoft Corporation. Wszelkie prawa zastrze▒one.

C:\Users\testosteron_\Desktop\2021b\magisterka\ScreenRecorderXi\ScreenRecorderXi\bin>ffmpeg -stream_loop -1 -i 9stream.wav -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}" -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}" -i camera.png -f gdigrab -framerate 1 -i desktop -f dshow -framerate 15 -i video="USB2.0 PC CAMERA" -filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3[aMix];[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" -map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_674912043_oAwGnACTndHyeZnlA6scLegm8gaxwf
ffmpeg -stream_loop -1 -i 9stream.wav -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}" -f dshow -i audio="@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}" -i camera.png -f gdigrab -framerate 1 -i desktop -f dshow -framerate 15 -i video="USB2.0 PC CAMERA" -filter_complex "[0:a]volume=2[a0];[1:a]volume=1.5[a1];[2:a]volume=1.5[a2];[a0][a1][a2]amix=inputs=3[aMix];[4:v]scale=200:-1[v4];[5:v]scale=50:-1[v5];[v4][v5]overlay=(W-w)-5:(H-h)-5[vScreenCam];[vScreenCam][3:v]overlay=5:5[v]" -map "[v]" -map "[aMix]" -ac 1 -ar 11025 -c:a libmp3lame -r 1 -c:v libx264 -b:v 100k -b:a 10k -f flv rtmp://live.twitch.tv/app/live_674912043_oAwGnACTndHyeZnlA6scLegm8gaxwf
ffmpeg version git-2020-08-02-b48397e Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 10.2.1 (GCC) 20200726
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libsrt --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libgsm --enable-librav1e --disable-w32threads --enable-libmfx --enable-ffnvcodec --enable-cuda-llvm --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt --enable-amf
  libavutil      56. 57.100 / 56. 57.100
  libavcodec     58. 99.100 / 58. 99.100
  libavformat    58. 49.100 / 58. 49.100
  libavdevice    58. 11.101 / 58. 11.101
  libavfilter     7. 87.100 /  7. 87.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
Guessed Channel Layout for Input Stream #0.0 : stereo
Input #0, wav, from '9stream.wav':
  Metadata:
    encoder         : Lavf58.49.100
  Duration: 00:00:13.48, bitrate: 1411 kb/s
    Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 44100 Hz, stereo, s16, 1411 kb/s
Guessed Channel Layout for Input Stream #1.0 : stereo
Input #1, dshow, from 'audio=@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{5B4DB0B5-B645-4AFA-930D-4710AAF753DB}':
  Duration: N/A, start: 209609.948000, bitrate: 1411 kb/s
    Stream #1:0: Audio: pcm_s16le, 44100 Hz, stereo, s16, 1411 kb/s
Guessed Channel Layout for Input Stream #2.0 : stereo
Input #2, dshow, from 'audio=@device_cm_{33D9A762-90C8-11D0-BD43-00A0C911CE86}\wave_{ADECEC1D-C3CC-4BAE-8516-752251B8B63F}':
  Duration: N/A, start: 209610.502000, bitrate: 1411 kb/s
    Stream #2:0: Audio: pcm_s16le, 44100 Hz, stereo, s16, 1411 kb/s
Input #3, png_pipe, from 'camera.png':
  Duration: N/A, bitrate: N/A
    Stream #3:0: Video: png, rgba(pc), 32x32 [SAR 3779:3779 DAR 1:1], 25 tbr, 25 tbn, 25 tbc
[gdigrab @ 0000009a3f019700] Capturing whole desktop as 1280x1024x32 at (0,0)
[gdigrab @ 0000009a3f019700] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #4, gdigrab, from 'desktop':
  Duration: N/A, start: 1618506176.140738, bitrate: 41943 kb/s
    Stream #4:0: Video: bmp, bgra, 1280x1024, 41943 kb/s, 1 fps, 1000k tbr, 1000k tbn, 1000k tbc
Input #5, dshow, from 'video=USB2.0 PC CAMERA':
  Duration: N/A, start: 209613.583000, bitrate: N/A
    Stream #5:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 640x480, 15 fps, 15 tbr, 10000k tbn, 10000k tbc
[dshow @ 0000009a3f034900] real-time buffer [USB2.0 PC CAMERA] [video input] too full or near too full (101% of size: 3041280 [rtbufsize parameter])! frame dropped!
    Last message repeated 9 times
Stream mapping:
  Stream #0:0 (pcm_s16le) -> volume
  Stream #1:0 (pcm_s16le) -> volume
  Stream #2:0 (pcm_s16le) -> volume
  Stream #3:0 (png) -> overlay:overlay
  Stream #4:0 (bmp) -> scale
  Stream #5:0 (rawvideo) -> scale
  overlay -> Stream #0:0 (libx264)
  amix -> Stream #0:1 (libmp3lame)
Press [q] to stop, [?] for help
[dshow @ 0000009a3efd5b80] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)
[dshow @ 0000009a406fb280] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)
[libx264 @ 0000009a4082ddc0] using cpu capabilities: MMX2 SSE2Fast SSSE3 Cache64 SlowShuffle
[libx264 @ 0000009a4082ddc0] profile High, level 1.1, 4:2:0, 8-bit
[libx264 @ 0000009a4082ddc0] 264 - core 161 - H.264/MPEG-4 AVC codec - Copyleft 2003-2020 - 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=5 lookahead_threads=1 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=1 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=100 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, flv, to 'rtmp://live.twitch.tv/app/live_streamingKey':
  Metadata:
    encoder         : Lavf58.49.100
    Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p(progressive), 200x160, q=-1--1, 100 kb/s, 1 fps, 1k tbn, 1 tbc (default)
    Metadata:
      encoder         : Lavc58.99.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/100000 buffer size: 0 vbv_delay: N/A
    Stream #0:1: Audio: mp3 (libmp3lame) ([2][0][0][0] / 0x0002), 11025 Hz, mono, fltp, 10 kb/s (default)
    Metadata:
      encoder         : Lavc58.99.100 libmp3lame
frame=    1 fps=0.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    1 fps=1.0 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    1 fps=0.7 q=0.0 size=       0kB time=00:00:00.00 bitrate=N/A speed=   frame=    3 fps=1.5 q=0.0 size=       0kB time=00:00:03.08 bitrate=   1.0kbits/sframe=    4 fps=1.6 q=0.0 size=       0kB time=00:00:03.66 bitrate=   0.8kbits/sframe=    4 fps=1.3 q=0.0 size=       0kB time=00:00:03.66 bitrate=   0.8kbits/sframe=    5 fps=1.4 q=0.0 size=       0kB time=00:00:04.65 bitrate=   0.7kbits/sframe=    5 fps=1.2 q=0.0 size=       0kB time=00:00:04.65 bitrate=   0.7kbits/sframe=    6 fps=1.3 q=0.0 size=       0kB time=00:00:05.64 bitrate=   0.5kbits/sframe=    6 fps=1.2 q=0.0 size=       0kB time=00:00:05.64 bitrate=   0.5kbits/sframe=    7 fps=1.3 q=0.0 size=       0kB time=00:00:06.64 bitrate=   0.5kbits/sframe=    7 fps=1.2 q=0.0 size=       0kB time=00:00:06.64 bitrate=   0.5kbits/sframe=    8 fps=1.2 q=0.0 size=       0kB time=00:00:07.58 bitrate=   0.4kbits/sframe=    8 fps=1.1 q=0.0 size=       0kB time=00:00:07.58 bitrate=   0.4kbits/sframe=    9 fps=1.2 q=0.0 size=       0kB time=00:00:08.57 bitrate=   0.4kbits/sframe=    9 fps=1.1 q=0.0 size=       0kB time=00:00:08.57 bitrate=   0.4kbits/sframe=   10 fps=1.2 q=0.0 size=       0kB time=00:00:09.56 bitrate=   0.3kbits/sframe=   10 fps=1.1 q=0.0 size=       0kB time=00:00:09.56 bitrate=   0.3kbits/sframe=   11 fps=1.1 q=0.0 size=       1kB time=00:00:10.55 bitrate=   0.9kbits/sframe=   11 fps=1.1 q=0.0 size=       1kB time=00:00:10.55 bitrate=   0.9kbits/sframe=   12 fps=1.1 q=0.0 size=       2kB time=00:00:11.55 bitrate=   1.7kbits/sframe=   12 fps=1.1 q=0.0 size=       2kB time=00:00:11.55 bitrate=   1.7kbits/sframe=   13 fps=1.1 q=0.0 size=       4kB time=00:00:12.59 bitrate=   2.5kbits/sframe=   13 fps=1.1 q=0.0 size=       4kB time=00:00:12.59 bitrate=   2.5kbits/sframe=   14 fps=1.1 q=0.0 size=       5kB time=00:00:13.58 bitrate=   3.0kbits/sframe=   14 fps=1.1 q=0.0 size=       5kB time=00:00:13.58 bitrate=   3.0kbits/sframe=   15 fps=1.1 q=0.0 size=       6kB time=00:00:14.58 bitrate=   3.5kbits/sframe=   15 fps=1.1 q=0.0 size=       6kB time=00:00:14.58 bitrate=   3.5kbits/sframe=   16 fps=1.1 q=0.0 size=       8kB time=00:00:15.57 bitrate=   4.0kbits/sframe=   16 fps=1.1 q=0.0 size=       8kB time=00:00:15.57 bitrate=   4.0kbits/sframe=   17 fps=1.1 q=0.0 size=       9kB time=00:00:16.56 bitrate=   4.4kbits/sframe=   17 fps=1.1 q=0.0 size=       9kB time=00:00:16.56 bitrate=   4.4kbits/sframe=   18 fps=1.1 q=0.0 size=      10kB time=00:00:17.55 bitrate=   4.7kbits/sframe=   18 fps=1.0 q=0.0 size=      10kB time=00:00:17.55 bitrate=   4.7kbits/sframe=   19 fps=1.1 q=0.0 size=      11kB time=00:00:18.55 bitrate=   5.0kbits/sframe=   19 fps=1.0 q=0.0 size=      11kB time=00:00:18.55 bitrate=   5.0kbits/sframe=   20 fps=1.1 q=0.0 size=      13kB time=00:00:19.54 bitrate=   5.3kbits/sframe=   20 fps=1.0 q=0.0 size=      13kB time=00:00:19.54 bitrate=   5.3kbits/sframe=   21 fps=1.1 q=0.0 size=      14kB time=00:00:20.58 bitrate=   5.6kbits/sframe=   21 fps=1.0 q=0.0 size=      14kB time=00:00:20.58 bitrate=   5.6kbits/sframe=   22 fps=1.1 q=0.0 size=      15kB time=00:00:21.58 bitrate=   5.8kbits/sframe=   22 fps=1.0 q=0.0 size=      15kB time=00:00:21.58 bitrate=   5.8kbits/sframe=   23 fps=1.1 q=0.0 size=      17kB time=00:00:22.57 bitrate=   6.0kbits/sframe=   23 fps=1.0 q=0.0 size=      17kB time=00:00:22.57 bitrate=   6.0kbits/sframe=   24 fps=1.1 q=0.0 size=      18kB time=00:00:23.56 bitrate=   6.2kbits/sframe=   24 fps=1.0 q=0.0 size=      18kB time=00:00:23.56 bitrate=   6.2kbits/sframe=   25 fps=1.1 q=0.0 size=      19kB time=00:00:24.56 bitrate=   6.4kbits/sframe=   25 fps=1.0 q=0.0 size=      19kB time=00:00:24.56 bitrate=   6.4kbits/sframe=   26 fps=1.1 q=0.0 size=      20kB time=00:00:25.55 bitrate=   6.5kbits/sframe=   26 fps=1.0 q=0.0 size=      20kB time=00:00:25.55 bitrate=   6.5kbits/sframe=   27 fps=1.0 q=0.0 size=      22kB time=00:00:26.54 bitrate=   6.7kbits/sframe=   27 fps=1.0 q=0.0 size=      22kB time=00:00:26.54 bitrate=   6.7kbits/sframe=   28 fps=1.0 q=0.0 size=      23kB time=00:00:27.58 bitrate=   6.8kbits/sframe=   28 fps=1.0 q=0.0 size=      23kB time=00:00:27.58 bitrate=   6.8kbits/sframe=   29 fps=1.0 q=0.0 size=      24kB time=00:00:28.58 bitrate=   6.9kbits/sframe=   30 fps=1.1 q=0.0 size=      25kB time=00:00:29.00 bitrate=   7.0kbits/sframe=   30 fps=1.0 q=0.0 size=      25kB time=00:00:29.57 bitrate=   7.0kbits/sframe=   30 fps=1.0 q=0.0 size=      25kB time=00:00:29.57 bitrate=   7.0kbits/sframe=   31 fps=1.0 q=0.0 size=      27kB time=00:00:30.56 bitrate=   7.2kbits/sframe=   32 fps=1.1 q=0.0 size=      27kB time=00:00:30.56 bitrate=   7.2kbits/sframe=   32 fps=1.0 q=0.0 size=      28kB time=00:00:31.56 bitrate=   7.3kbits/sframe=   33 fps=1.1 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   33 fps=1.0 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bitrate=   7.8kbits/sframe=   40 fps=1.0 q=0.0 size=      38kB time=00:00:39.55 bitrate=   7.9kbits/sframe=   40 fps=1.0 q=0.0 size=      38kB time=00:00:39.55 bitrate=   7.9kbits/sframe=   41 fps=1.0 q=0.0 size=      39kB time=00:00:40.54 bitrate=   8.0kbits/sframe=   41 fps=1.0 q=0.0 size=      39kB time=00:00:40.54 bitrate=   8.0kbits/sframe=   42 fps=1.0 q=0.0 size=      41kB time=00:00:41.59 bitrate=   8.0kbits/sframe=   42 fps=1.0 q=0.0 size=      41kB time=00:00:41.59 bitrate=   8.0kbits/sframe=   43 fps=1.0 q=0.0 size=      42kB time=00:00:42.58 bitrate=   8.1kbits/sframe=   43 fps=1.0 q=0.0 size=      42kB time=00:00:42.58 bitrate=   8.1kbits/sframe=   44 fps=1.0 q=0.0 size=      43kB time=00:00:43.57 bitrate=   8.1kbits/sframe=   44 fps=1.0 q=0.0 size=      43kB time=00:00:43.57 bitrate=   8.1kbits/sframe=   45 fps=1.0 q=0.0 size=      45kB time=00:00:44.56 bitrate=   8.2kbits/sframe=   45 fps=1.0 q=0.0 size=      45kB time=00:00:44.56 bitrate=   8.2kbits/sframe=   46 fps=1.0 q=0.0 size=      46kB time=00:00:45.56 bitrate=   8.2kbits/sframe=   46 fps=1.0 q=0.0 size=      46kB time=00:00:45.56 bitrate=   8.2kbits/sframe=   47 fps=1.0 q=0.0 size=      47kB time=00:00:46.55 bitrate=   8.3kbits/sframe=   47 fps=1.0 q=0.0 size=      47kB time=00:00:46.55 bitrate=   8.3kbits/sframe=   48 fps=1.0 q=0.0 size=      48kB time=00:00:47.54 bitrate=   8.3kbits/sframe=   48 fps=1.0 q=0.0 size=      48kB time=00:00:47.54 bitrate=   8.3kbits/sframe=   49 fps=1.0 q=0.0 size=      50kB time=00:00:48.59 bitrate=   8.4kbits/sframe=   49 fps=1.0 q=0.0 size=      50kB time=00:00:48.59 bitrate=   8.4kbits/s[flv @ 0000009a40865940] Packets poorly interleaved, failed to avoid negative timestamp -3900 in stream 0.
Try -max_interleave_delta 0 as a possible workaround.
[flv @ 0000009a40865940] Packets are not in the proper order with respect to DTS
av_interleaved_write_frame(): Invalid argument
[flv @ 0000009a40865940] Failed to update header with correct duration.
[flv @ 0000009a40865940] Failed to update header with correct filesize.
frame=   50 fps=1.0 q=6.0 Lsize=      63kB time=00:00:49.11 bitrate=  10.5kbits/s speed=   1x
video:27kB audio:48kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
[libx264 @ 0000009a4082ddc0] frame I:1     Avg QP: 0.56  size: 27197
[libx264 @ 0000009a4082ddc0] frame P:15    Avg QP: 0.76  size:  2567
[libx264 @ 0000009a4082ddc0] frame B:34    Avg QP: 3.98  size:  1481
[libx264 @ 0000009a4082ddc0] consecutive B-frames:  8.0%  0.0% 12.0% 80.0%
[libx264 @ 0000009a4082ddc0] mb I  I16..4: 13.1% 13.8% 73.1%
[libx264 @ 0000009a4082ddc0] mb P  I16..4:  0.0%  0.1%  0.8%  P16..4: 17.5%  5.9%  4.2%  0.0%  0.0%    skip:71.5%
[libx264 @ 0000009a4082ddc0] mb B  I16..4:  0.0%  0.0%  0.3%  B16..8: 12.1%  4.2%  2.4%  direct: 6.3%  skip:74.7%  L0:42.9% L1:41.8% BI:15.4%
[libx264 @ 0000009a4082ddc0] final ratefactor: -7.50
[libx264 @ 0000009a4082ddc0] 8x8 transform intra:12.3% inter:14.5%
[libx264 @ 0000009a4082ddc0] coded y,uvDC,uvAC intra: 95.2% 96.9% 96.9% inter: 16.0% 14.9% 14.8%
[libx264 @ 0000009a4082ddc0] i16 v,h,dc,p: 26% 32% 32% 11%
[libx264 @ 0000009a4082ddc0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  8% 40% 14%  8%  1%  2%  1%  1% 25%
[libx264 @ 0000009a4082ddc0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 15% 45%  7%  4%  5%  3%  7%  3%  9%
[libx264 @ 0000009a4082ddc0] i8c dc,h,v,p: 36% 40% 18%  6%
[libx264 @ 0000009a4082ddc0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0000009a4082ddc0] ref P L0: 65.2%  2.2% 19.9% 12.7%
[libx264 @ 0000009a4082ddc0] ref B L0: 71.8% 23.0%  5.2%
[libx264 @ 0000009a4082ddc0] ref B L1: 88.2% 11.8%
[libx264 @ 0000009a4082ddc0] kb/s:17.86
Conversion failed!


  


  Main message from above was :

  


  [flv @ 0000009a40865940] Packets poorly interleaved, failed to avoid negative timestamp -3900 in stream 0.


  


  It was problem to stream 0 so it was mixed sounds stream BUT earlier it was fine with mixing

  


  and sending mix over internet BUT after I added screen view and scaling it failed to work.

  


  What is problem ?

  


  How to fix it ?

  


  Since I was able to do this to stream to disc I would assume that

  


  computer processing power is enough. Since I was able to stream over internet mixed sounds I

  


  would assume that it is not problem here. So the problem must be with sending

  


  screen view. BUT I put framerate 1 per second and downsized its resolution. I compressed

  


  sounds as much as I could. I added -b:a and -b:v commands to reduce network flow.

  


  WHAT ELSE COULD I DO TO FIX IT ?

  


• Privacy-friendly analytics : The benefits of an ethical, GDPR-compliant platform

  13 juin, par Joe

  Your visitors shouldn’t feel like you’re spying on them — even if you’re just trying to improve the user experience or track your marketing efforts. 

  While many analytics platforms make customers feel that way thanks to intrusive cookie consent banners and highly personalised ads, there is a growing movement towards ethical, privacy-friendly analytics.

  In this article, you’ll learn what privacy-friendly analytics is, why it matters, what to look for in a solution and which of the leading providers is right for you. 

  What is privacy-friendly analytics ? 

  Privacy-friendly analytics is a form of website analytics that collects and analyses data in a way that respects the user’s privacy. It’s a type of ethical web analytics.

  Privacy-friendly platforms limit personal data collection and anonymise individual user data while being transparent about collection and tracking methods. They help companies adhere to data protection laws (like GDPR, CCPA, and HIPAA) and new privacy laws (like OCPA, FDBR, and TDPSA) without configuring custom settings. 

  Why use privacy-friendly analytics ? 

  Millions of businesses choose privacy-friendly analytics platforms like Matomo. Here are a few reasons why : 

  Build trust with customers

  Research shows that the vast majority of consumers don’t trust companies with their data, believing that they prioritise profits over data protection. 

  Privacy-friendly analytics can help businesses prove they aren’t out to profit from consumer data and regain customer trust. This can ultimately boost revenue. According to Cisco’s Data Privacy Benchmark Study, organisations gain $180 for every $100 spent on privacy. 

  Comply with privacy regulations

  Data privacy regulations, such as GDPR, protect consumer privacy and establish strict rules governing how businesses can collect and use personal data.

  The cost of non-compliance is high. Under GDPR, fines can be up to €20 million, or 4% of worldwide annual revenue.

  Thanks to features like data anonymisation and the default use of first-party cookies, privacy-friendly analytics platforms can support and strengthen compliance efforts. 

  In fact, the French Data Protection Authority (CNIL) approved Matomo as one of the only web analytics tools to collect data without tracking consent.

  Minimise the impact of a breach

  According to IBM’s Cost of a Data Breach report, the average cost of a data breach is nearly $4.5 million. The more personally identifiable information (PII) is involved, the higher the fines and penalties. 

  A privacy-friendly analytics tool can reduce the potential impact of a breach by minimising the amount of personal information you hold. 

  Is Google Analytics privacy-friendly ?

  Google may be the best-known analytics platform, but it’s not the best choice for businesses that want to collect data responsibly and ethically. 

  Here are just a few of Google Analytics’s privacy issues :

  • It uses analytics data to run its advertising business.
  • It may train large language models like Gemini with analytics data.
  • It requires a specific setup to be GDPR compliant that isn’t available out of the box.

  Google Analytics’s ongoing issues with privacy laws like GDPR also raise doubt. The French and Austrian Data Protection Authorities have banned Google Analytics in the past, and there is no guarantee they won’t do so again. 

  What to look for in privacy-friendly analytics ?

  Several privacy-friendly analytics tools are available. To find the right one for your brand, look for the following features.

  Data ownership

  Choose a provider that gives you as much control over your users’ data as possible. Ideally, this will be via an on-site solution where you store data on your servers. For cloud-based options, ensure your analytics provider can’t access, use or sell it.

  With 100% data ownership, you have the power to protect your users’ privacy. You know where your customer data is stored and what’s happening to it without external influence.

  Open source

  The only genuinely privacy-friendly software is open-source software. Open-source software means anyone can review the code to ensure it does what it promises — in this case, maximising privacy. 

  Matomo is an open-source software company. Our source code is on GitHub, where everyone can see precisely how our platform tracks and stores user data. A community of developers also regularly examines and reviews our code to further strengthen security. 

  Data anonymisation 

  Privacy-friendly analytics should allow marketers to completely anonymise the data they collect. They achieve this through several techniques like IP anonymisation and pseudonymised user IDs that modify or remove personally identifiable data so it can’t be linked to individuals.

  

  Matomo’s data anonymisation settings 

  In Matomo, for example, you can anonymise the following things in the platform’s Privacy settings :

  • IP address
  • Location
  • User ID

  IP address anonymisation is enabled by default in Matomo.

  No data sampling 

  Data sampling involves extrapolating analytics reports from an incomplete data set. Google Analytics uses this practice and relies on estimates, leading to incomplete and potentially inaccurate results.

  Privacy-friendly analytics should provide 100% accurate insights without making assumptions about your users’ data.

  GDPR compliance

  Privacy-friendly web analytics platforms adhere to even the strictest privacy laws, including GDPR, HIPAA and CCPA, thanks to the following features :

  • Data anonymisation
  • Cookieless tracking
  • EU data storage
  • First-party cookies by default

  

  Matomo data subject access request settings
  (Image Source)

  Privacy-first platforms also make it easy for companies to fulfil data subject access requests. In Matomo, for example, a dedicated feature lets you find, download and delete all of the data you hold about specific individuals. 

  Cookieless tracking

  Cookieless tracking is a form of visitor tracking that uses methods other than cookies to identify individual users. It is more privacy-friendly because no personal data is collected, and users can withhold consent from cookie banners.

  Matomo uses the most privacy-friendly industry-leading cookieless tracking method, config_id, to anonymously track visitors without fingerprinting them. 

  Top 3 privacy-friendly analytics platforms

  We’ve shortlisted three of the leading privacy-friendly analytics platforms. Learn what they offer, what makes them different and how much they cost.

  Matomo

  Matomo is an open-source web analytics tool and privacy-focused Google Analytics alternative trusted by over one million sites in over 190 countries and over 50 languages. 

  

  Matomo dashboard

  Matomo prioritises privacy and keeping businesses compliant with global privacy regulations like GDPR, CCPA and HIPAA. The data you collect is 100% accurate and yours alone. We don’t share it or use it for other purposes. 

  Benefits

  • Matomo’s all-in-one solution offers traditional web and behavioural analytics, such as heatmaps and session recordings. It also includes a free, open-source tag manager. 
  • Matomo gives you the choice of where to store your user’s data. With Matomo Cloud, that’s in our European servers. With Matomo On-Premise, that’s on your servers.
  • Matomo is open-source. Hundreds of independent developers have reviewed our code, and you can view it yourself on GitHub.

  Pricing 

  Hosting Matomo On-Premise is free, while Matomo Cloud costs $26 per month. 

  Fathom

  Fathom Analytics is a simple, easy-to-use alternative to Google Analytics that puts a premium on privacy. 

  

  Fathom dashboard
  (Image Source)

  Fathom has made its platform as easy to use as possible. You can install Fathom on any website or CMS using a single line of code. It also means the platform won’t massively impact your site’s speed or SEO performance. 

  Benefits

  • Fathom complies with all major privacy regulations, including GDPR and CCPA.
  • Fathom doesn’t sample data. It also blocks bots and scrapers, so you only see human visitors.
  • Fathom anonymises IP addresses, so you don’t have to show cookie banners.

  Drawbacks

  • Fathom doesn’t offer many of Matomo’s advanced features like e-commerce tracking, heatmaps, and session recordings.
  • The premium version of Fathom is not open-source. 

  Pricing 

  From $15 per month.

  Plausible

  Plausible Analytics is an open-source, privacy-friendly analytics tool built and hosted in the EU.

  

  Plausible dashboard
  (Image Source)

  The platform launched in 2019 as a lightweight, easy-to-use alternative to Google Analytics. Its simplicity is a big selling point. Instead of dozens of menus, it presents the information you need on a single page.

  Benefits

  • Plausible boasts an ultra-lightweight script, which means it has a minimal impact on page loading times. 
  • Plausible is GDPR and CCPA-compliant by design, so there’s no need for cookie banners.
  • Plausible is an open-source software with the source code available on GitHub.

  Drawbacks

  • Plausible lacks advanced privacy controls like a GDPR manager.
  • It has none of Matomo’s advanced features like A/B testing, session recordings or heatmaps. 

  Pricing 

  From $9 per month

  Try Matomo for free

  Ready to try a privacy-friendly analytics solution ? Making the switch is easy with Matomo, as it’s one of the only platforms to import historical Google Analytics data. You can also try Matomo for free for 21 days — no credit card required.