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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 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   33 fps=1.0 q=0.0 size=      29kB time=00:00:32.55 bitrate=   7.4kbits/sframe=   34 fps=1.0 q=0.0 size=      31kB time=00:00:33.54 bitrate=   7.4kbits/sframe=   35 fps=1.1 q=0.0 size=      31kB time=00:00:33.96 bitrate=   7.5kbits/sframe=   35 fps=1.0 q=0.0 size=      32kB time=00:00:34.53 bitrate=   7.5kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   36 fps=1.0 q=0.0 size=      33kB time=00:00:35.58 bitrate=   7.6kbits/sframe=   37 fps=1.0 q=0.0 size=      34kB time=00:00:36.57 bitrate=   7.7kbits/sframe=   38 fps=1.0 q=0.0 size=      36kB time=00:00:37.56 bitrate=   7.8kbits/sframe=   38 fps=1.0 q=0.0 size=      36kB time=00:00:37.56 bitrate=   7.8kbits/sframe=   39 fps=1.0 q=0.0 size=      37kB time=00:00:38.56 bitrate=   7.8kbits/sframe=   39 fps=1.0 q=0.0 size=      37kB time=00:00:38.56 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 ?

  


• A Complete Guide to Metrics in Google Analytics

  11 janvier 2024, par Erin

  There’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.

  However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.

  What is a metric in Google Analytics ?

  In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app. 

  Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are : 

  • Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
  • Total Users : The cumulative count of individuals who accessed your site within a specified date range.
  • Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.

  

  Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.

  GA4 metrics vs. dimensions

  GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together : 

  • “Session duration” = metric, “device type” = dimension 
    • In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
  • “Bounce rate” = metric, “traffic source/medium” = dimension 
    • Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing. 
  • “Conversion rate” = metric, “Landing page” = dimension 
    • When the conversion rate data is segmented by landing page, you can better see the most effective landing pages. 

  You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.

  How do Google Analytics metrics work ?

  Before diving into the most important metrics you should track, let’s review how metrics in GA4 work. 

  

  1. Tracking code implementation

  The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.

  2. Data collection

  As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.

  3. Data processing algorithms

  When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.

  4. Segmentation and customisation

  As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.

  5. Report generation

  Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.

  What are the most important Google Analytics metrics to track ? 

  In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4. 

  

  1. Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in. 
  2. Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content. 
  3. Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate. 
  4. Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content. 
  5. Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches. 
  6. Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation. 
  7. Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers. 
  8. Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.

  Examples of how Matomo can elevate your web analytics

  Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.

  

  1. Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.

  

  1. Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.

  3. Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.

  

  4. Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.

     Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.

     See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.

     Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.

  Try Matomo for Free

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

  Start for free

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  Final thoughts

  Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience. 

  Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data. 

  Start your 21-day free trial of Matomo — no credit card required.

  Try Matomo for Free

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