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• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

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

  The result ? Your marketing performance soars to new heights.

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

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

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

  What is customer segmentation ?

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

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

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

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

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

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

  What is customer segmentation analytics ?

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

  

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

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

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

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

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

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

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

  5 types of segmentation

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

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

  

  Geographic

  Another way to segment is by geography.

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

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

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

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

  Geographic segmentation typically includes the following subtypes :

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

  Psychographic

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

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

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

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

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

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

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

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

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

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

  Technographic segmentation typically includes the following subtypes :

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

  Demographic

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

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

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

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

  How to build website segmentation analytics

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

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

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

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

  

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

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

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

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

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

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

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

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

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

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

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

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

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

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

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

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

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

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

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

  Leverage Matomo for your segmentation analytics

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

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

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

• 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 ?