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• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.

• FFmpeg on aws lambda - Invalid NAL unit size

  28 juin 2020, par Lina Sharifi Moghaddam

  I am trying to run below :

  



  1- generate mp4 from one png image and a mp3 file

  



  2- overlay another png image on top of the previous video

  



  A-On my local environment (mac) things work perfect :

  



  1A-

  



  ffmpeg -loop 1 -i images/01.png -i audio_123e4567-e89b-12d3-a456-426655440000.mp3 -c:v libx264 -pix_fmt yuv420p -shortest 01.mp4 -y
ffmpeg version 4.0.2 Copyright (c) 2000-2018 the FFmpeg developers
  built with Apple LLVM version 9.0.0 (clang-900.0.39.2)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.0.2 --enable-shared --enable-pthreads --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-gpl --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --enable-videotoolbox --disable-lzma
  libavutil      56. 14.100 / 56. 14.100
  libavcodec     58. 18.100 / 58. 18.100
  libavformat    58. 12.100 / 58. 12.100
  libavdevice    58.  3.100 / 58.  3.100
  libavfilter     7. 16.100 /  7. 16.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  1.100 /  5.  1.100
  libswresample   3.  1.100 /  3.  1.100
  libpostproc    55.  1.100 / 55.  1.100
[png_pipe @ 0x7ff1ba000000] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, png_pipe, from 'images/01.png':
  Duration: N/A, bitrate: N/A
    Stream #0:0: Video: png, rgba(pc), 3360x2100 [SAR 5669:5669 DAR 8:5], 25 tbr, 25 tbn, 25 tbc
[mp3 @ 0x7ff1ba00fa00] Estimating duration from bitrate, this may be inaccurate
Input #1, mp3, from 'audio_123e4567-e89b-12d3-a456-426655440000.mp3':
  Metadata:
    encoder         : Lavf57.71.100
  Duration: 00:00:05.38, start: 0.000000, bitrate: 48 kb/s
    Stream #1:0: Audio: mp3, 22050 Hz, mono, fltp, 48 kb/s
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
  Stream #1:0 -> #0:1 (mp3 (mp3float) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x7ff1ba021400] using SAR=1/1
[libx264 @ 0x7ff1ba021400] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7ff1ba021400] profile High, level 5.1
[libx264 @ 0x7ff1ba021400] 264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to '01.mp4':
  Metadata:
    encoder         : Lavf58.12.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p(progressive), 3360x2100 [SAR 1:1 DAR 8:5], q=-1--1, 25 fps, 12800 tbn, 25 tbc
    Metadata:
      encoder         : Lavc58.18.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
    Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 69 kb/s
    Metadata:
      encoder         : Lavc58.18.100 aac
frame=  191 fps= 19 q=-1.0 Lsize=     798kB time=00:00:07.52 bitrate= 868.8kbits/s speed=0.75x     
video:744kB audio:49kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.685866%
[libx264 @ 0x7ff1ba021400] frame I:1     Avg QP:13.38  size:706076
[libx264 @ 0x7ff1ba021400] frame P:48    Avg QP:13.63  size:   516
[libx264 @ 0x7ff1ba021400] frame B:142   Avg QP:23.33  size:   210
[libx264 @ 0x7ff1ba021400] consecutive B-frames:  0.5%  1.0%  0.0% 98.4%
[libx264 @ 0x7ff1ba021400] mb I  I16..4:  5.4% 81.4% 13.2%
[libx264 @ 0x7ff1ba021400] mb P  I16..4:  0.0%  0.0%  0.0%  P16..4:  0.8%  0.0%  0.0%  0.0%  0.0%    skip:99.2%
[libx264 @ 0x7ff1ba021400] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.0%  0.0%  0.0%  direct: 0.0%  skip:100.0%  L0:10.7% L1:89.3% BI: 0.0%
[libx264 @ 0x7ff1ba021400] 8x8 transform intra:81.3% inter:99.4%
[libx264 @ 0x7ff1ba021400] coded y,uvDC,uvAC intra: 97.1% 1.7% 1.4% inter: 0.1% 0.0% 0.0%
[libx264 @ 0x7ff1ba021400] i16 v,h,dc,p:  1% 21% 66% 11%
[libx264 @ 0x7ff1ba021400] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  9%  9% 45%  5%  7%  5%  5%  6%  8%
[libx264 @ 0x7ff1ba021400] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 18% 14% 18% 10% 11%  8%  7%  6%  8%
[libx264 @ 0x7ff1ba021400] i8c dc,h,v,p: 97%  3%  1%  0%
[libx264 @ 0x7ff1ba021400] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7ff1ba021400] ref P L0: 98.3%  0.1%  1.3%  0.3%
[libx264 @ 0x7ff1ba021400] ref B L0: 35.0% 65.0%
[libx264 @ 0x7ff1ba021400] ref B L1: 94.6%  5.4%
[libx264 @ 0x7ff1ba021400] kb/s:796.51
[aac @ 0x7ff1ba022c00] Qavg: 2267.321


  



  2A-

  



  ffmpeg -i 01.mp4 -i square.png  -filter_complex "[0:v][1:v] overlay=12:12:enable='between(t,1,3)' "  -c:a copy -y temp.mp4
ffmpeg version 4.0.2 Copyright (c) 2000-2018 the FFmpeg developers
  built with Apple LLVM version 9.0.0 (clang-900.0.39.2)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.0.2 --enable-shared --enable-pthreads --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-gpl --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --enable-videotoolbox --disable-lzma
  libavutil      56. 14.100 / 56. 14.100
  libavcodec     58. 18.100 / 58. 18.100
  libavformat    58. 12.100 / 58. 12.100
  libavdevice    58.  3.100 / 58.  3.100
  libavfilter     7. 16.100 /  7. 16.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  1.100 /  5.  1.100
  libswresample   3.  1.100 /  3.  1.100
  libpostproc    55.  1.100 / 55.  1.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '01.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf58.12.100
  Duration: 00:00:07.64, start: 0.000000, bitrate: 855 kb/s
    Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3360x2100 [SAR 1:1 DAR 8:5], 797 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
    Metadata:
      handler_name    : VideoHandler
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 73 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
Input #1, png_pipe, from 'square.png':
  Duration: N/A, bitrate: N/A
    Stream #1:0: Video: png, rgba(pc), 90x90 [SAR 1:1 DAR 1:1], 25 tbr, 25 tbn, 25 tbc
Stream mapping:
  Stream #0:0 (h264) -> overlay:main
  Stream #1:0 (png) -> overlay:overlay
  overlay -> Stream #0:0 (libx264)
  Stream #0:1 -> #0:1 (copy)
Press [q] to stop, [?] for help
[libx264 @ 0x7f8e22006e00] using SAR=1/1
[libx264 @ 0x7f8e22006e00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x7f8e22006e00] profile High, level 5.1
[libx264 @ 0x7f8e22006e00] 264 - core 152 r2854 e9a5903 - H.264/MPEG-4 AVC codec - Copyleft 2003-2017 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'temp.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf58.12.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 3360x2100 [SAR 1:1 DAR 8:5], q=-1--1, 25 fps, 12800 tbn, 25 tbc (default)
    Metadata:
      encoder         : Lavc58.18.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 73 kb/s (default)
    Metadata:
      handler_name    : SoundHandler
frame=  191 fps= 40 q=-1.0 Lsize=     780kB time=00:00:07.52 bitrate= 849.4kbits/s speed=1.56x    
video:726kB audio:49kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.701603%
[libx264 @ 0x7f8e22006e00] frame I:1     Avg QP:13.38  size:697836
[libx264 @ 0x7f8e22006e00] frame P:48    Avg QP:15.44  size:   284
[libx264 @ 0x7f8e22006e00] frame B:142   Avg QP:23.34  size:   218
[libx264 @ 0x7f8e22006e00] consecutive B-frames:  0.5%  1.0%  0.0% 98.4%
[libx264 @ 0x7f8e22006e00] mb I  I16..4:  6.1% 81.0% 13.0%
[libx264 @ 0x7f8e22006e00] mb P  I16..4:  0.0%  0.0%  0.0%  P16..4:  0.1%  0.0%  0.0%  0.0%  0.0%    skip:99.9%
[libx264 @ 0x7f8e22006e00] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.1%  0.0%  0.0%  direct: 0.0%  skip:99.9%  L0:49.9% L1:49.8% BI: 0.3%
[libx264 @ 0x7f8e22006e00] 8x8 transform intra:80.9% inter:78.2%
[libx264 @ 0x7f8e22006e00] coded y,uvDC,uvAC intra: 96.5% 1.7% 1.4% inter: 0.0% 0.0% 0.0%
[libx264 @ 0x7f8e22006e00] i16 v,h,dc,p:  2% 28% 63%  8%
[libx264 @ 0x7f8e22006e00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu:  9%  9% 43%  6%  7%  6%  6%  6%  8%
[libx264 @ 0x7f8e22006e00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 13% 19% 10% 10%  8%  7%  6%  8%
[libx264 @ 0x7f8e22006e00] i8c dc,h,v,p: 96%  3%  1%  0%
[libx264 @ 0x7f8e22006e00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7f8e22006e00] ref P L0: 64.8%  0.3% 13.8% 21.1%
[libx264 @ 0x7f8e22006e00] ref B L0: 34.0% 64.9%  1.0%
[libx264 @ 0x7f8e22006e00] ref B L1: 99.0%  1.0%
[libx264 @ 0x7f8e22006e00] kb/s:777.46


  



  B- When I try the same on aws lambda , I get encoding erros and the overlay command fails. (Tried two different ffmpeg build on lambda, local build and official static 64bit build )

  



  1B-

  



  ffmpeg version N-92107-g4901fa1 Copyright (c) 2000-2018 the FFmpeg developers
built with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-23)
configuration: --prefix=/home/centos/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/centos/ffmpeg_build/include --extra-ldflags=-L/home/centos/ffmpeg_build/lib --extra-libs=-lpthread --extra-libs=-lm --bindir=/home/centos/bin --enable-gpl --enable-libfdk_aac --enable-libfreetype --enable-libmp3lame --enable-libvpx --enable-libx264 --enable-nonfree
libavutil 56. 19.101 / 56. 19.101
libavcodec 58. 32.100 / 58. 32.100
libavformat 58. 18.104 / 58. 18.104
libavdevice 58. 4.105 / 58. 4.105
libavfilter 7. 33.100 / 7. 33.100
libswscale 5. 2.100 / 5. 2.100
libswresample 3. 2.100 / 3. 2.100
libpostproc 55. 2.100 / 55. 2.100
[png_pipe @ 0x7f85a5b6c740] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, png_pipe, from '/tmp/01.png':
Duration: N/A, bitrate: N/A
Stream #0:0: Video: png, rgba(pc), 3360x2100 [SAR 5669:5669 DAR 8:5], 25 tbr, 25 tbn, 25 tbc
[mp3 @ 0x7f85a5b6f300] Estimating duration from bitrate, this may be inaccurate
Input #1, mp3, from '/tmp/audio_123e4567-e89b-12d3-a456-426655440000.mp3':
Metadata:
encoder : Lavf57.71.100
Duration: 00:00:05.38, start: 0.000000, bitrate: 48 kb/s
Stream #1:0: Audio: mp3, 22050 Hz, mono, fltp, 48 kb/s
Stream mapping:
Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Stream #1:0 -> #0:1 (mp3 (mp3float) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0x7f85a5b84880] using SAR=1/1
[libx264 @ 0x7f85a5b84880] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7f85a5b84880] profile High, level 5.1, 4:2:0, 8-bit
[libx264 @ 0x7f85a5b84880] 264 - core 157 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:-3:-3 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=2.00:0.70 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-4 threads=3 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=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=18.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.20
Output #0, mp4, to '/tmp/01.mp4':
Metadata:
encoder : Lavf58.18.104
Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p(progressive), 3360x2100 [SAR 1:1 DAR 8:5], q=-1--1, 25 fps, 12800 tbn, 25 tbc
Metadata:
encoder : Lavc58.32.100 libx264
Side data:
cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 69 kb/s
Metadata:
encoder : Lavc58.32.100 aac
frame= 6 fps=0.0 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 12 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 18 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 24 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 30 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 36 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 42 fps= 11 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 47 fps=9.6 q=0.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 48 fps=7.9 q=23.0 size= 0kB time=00:00:00.00 bitrate=N/A speed= 0x 
frame= 51 fps=7.7 q=23.0 size= 1536kB time=00:00:00.04 bitrate=270958.1kbits/s speed=0.00697x 
frame= 51 fps=7.1 q=23.0 size= 1536kB time=00:00:00.04 bitrate=270958.1kbits/s speed=0.00645x 
frame= 53 fps=6.7 q=23.0 size= 1536kB time=00:00:00.13 bitrate=90319.4kbits/s speed=0.0176x 
frame= 55 fps=6.5 q=23.0 size= 1536kB time=00:00:00.23 bitrate=54191.6kbits/s speed=0.0273x 
frame= 57 fps=6.0 q=23.0 size= 1536kB time=00:00:00.32 bitrate=38708.4kbits/s speed=0.0344x 
frame= 60 fps=5.7 q=23.0 size= 1792kB time=00:00:00.41 bitrate=35124.1kbits/s speed=0.0397x 
frame= 62 fps=5.6 q=23.0 size= 1792kB time=00:00:00.55 bitrate=26343.1kbits/s speed=0.0505x 
frame= 64 fps=5.5 q=23.0 size= 1792kB time=00:00:00.60 bitrate=24316.7kbits/s speed=0.0522x 
frame= 66 fps=5.4 q=23.0 size= 1792kB time=00:00:00.65 bitrate=22579.8kbits/s speed=0.0532x 
frame= 67 fps=5.2 q=23.0 size= 1792kB time=00:00:00.74 bitrate=19757.3kbits/s speed=0.0582x 
frame= 69 fps=5.1 q=23.0 size= 1792kB time=00:00:00.78 bitrate=18595.1kbits/s speed=0.0579x 
frame= 71 fps=5.0 q=23.0 size= 1792kB time=00:00:00.88 bitrate=16637.7kbits/s speed=0.0616x 
frame= 73 fps=4.9 q=23.0 size= 1792kB time=00:00:00.92 bitrate=15805.9kbits/s speed=0.0627x 
frame= 75 fps=4.8 q=23.0 size= 2048kB time=00:00:01.02 bitrate=16421.6kbits/s speed=0.0654x 
frame= 77 fps=4.7 q=23.0 size= 2048kB time=00:00:01.16 bitrate=14451.0kbits/s speed=0.0709x 
frame= 80 fps=4.7 q=23.0 size= 2048kB time=00:00:01.20 bitrate=13895.2kbits/s speed=0.0713x 
frame= 81 fps=4.6 q=23.0 size= 2048kB time=00:00:01.25 bitrate=13380.6kbits/s speed=0.0707x 
frame= 83 fps=4.5 q=23.0 size= 2048kB time=00:00:01.39 bitrate=12042.5kbits/s speed=0.0757x 
frame= 85 fps=4.4 q=23.0 size= 2048kB time=00:00:01.43 bitrate=11654.0kbits/s speed=0.0748x 
frame= 88 fps=4.4 q=23.0 size= 2048kB time=00:00:01.53 bitrate=10947.7kbits/s speed=0.0771x 
frame= 89 fps=4.3 q=23.0 size= 2048kB time=00:00:01.57 bitrate=10625.7kbits/s speed=0.0764x 
frame= 92 fps=4.2 q=23.0 size= 2048kB time=00:00:01.71 bitrate=9764.2kbits/s speed=0.0789x 
frame= 95 fps=4.3 q=23.0 size= 2048kB time=00:00:01.81 bitrate=9263.5kbits/s speed=0.0811x 
frame= 96 fps=4.2 q=23.0 size= 2048kB time=00:00:01.85 bitrate=9031.9kbits/s speed=0.0809x 
frame= 97 fps=4.1 q=23.0 size= 2048kB time=00:00:01.90 bitrate=8811.6kbits/s speed=0.0811x 
frame= 99 fps=4.1 q=23.0 size= 2304kB time=00:00:01.99 bitrate=9452.0kbits/s speed=0.0831x 
frame= 102 fps=4.1 q=23.0 size= 2304kB time=00:00:02.08 bitrate=9031.9kbits/s speed=0.0849x 
frame= 102 fps=4.1 q=23.0 size= 2304kB time=00:00:02.08 bitrate=9031.9kbits/s speed=0.0831x 
frame= 104 fps=4.1 q=23.0 size= 2304kB time=00:00:02.22 bitrate=8467.4kbits/s speed=0.0868x 
frame= 106 fps=4.0 q=23.0 size= 2304kB time=00:00:02.27 bitrate=8294.6kbits/s speed=0.0859x 
frame= 109 fps=4.0 q=23.0 size= 2304kB time=00:00:02.36 bitrate=7969.3kbits/s speed=0.0873x 
frame= 109 fps=3.9 q=23.0 size= 2304kB time=00:00:02.41 bitrate=7816.0kbits/s speed=0.0872x 
frame= 112 fps=3.9 q=23.0 size= 2304kB time=00:00:02.50 bitrate=7526.6kbits/s speed=0.0877x 
frame= 114 fps=3.9 q=23.0 size= 2304kB time=00:00:02.64 bitrate=7130.4kbits/s speed=0.0904x 
frame= 117 fps=3.8 q=23.0 size= 2304kB time=00:00:02.69 bitrate=7007.5kbits/s speed=0.0884x 
frame= 119 fps=3.8 q=23.0 size= 2560kB time=00:00:02.83 bitrate=7403.2kbits/s speed=0.0913x 
frame= 120 fps=3.8 q=23.0 size= 2560kB time=00:00:02.83 bitrate=7403.2kbits/s speed=0.0898x 
frame= 122 fps=3.8 q=23.0 size= 2560kB time=00:00:02.92 bitrate=7168.1kbits/s speed=0.0913x 
frame= 124 fps=3.8 q=23.0 size= 2560kB time=00:00:02.97 bitrate=7056.1kbits/s speed=0.0911x 
frame= 125 fps=3.8 q=23.0 size= 2560kB time=00:00:03.06 bitrate=6842.3kbits/s speed=0.0924x 
frame= 127 fps=3.8 q=23.0 size= 2560kB time=00:00:03.11 bitrate=6740.2kbits/s speed=0.0923x 
frame= 128 fps=3.7 q=23.0 size= 2560kB time=00:00:03.15 bitrate=6641.1kbits/s speed=0.0919x 
frame= 131 fps=3.7 q=23.0 size= 2560kB time=00:00:03.25 bitrate=6451.3kbits/s speed=0.0923x 
frame= 133 fps=3.7 q=23.0 size= 2560kB time=00:00:03.34 bitrate=6272.1kbits/s speed=0.0929x 
frame= 135 fps=3.7 q=23.0 size= 2560kB time=00:00:03.48 bitrate=6021.2kbits/s speed=0.0954x 
frame= 138 fps=3.7 q=23.0 size= 2560kB time=00:00:03.52 bitrate=5942.0kbits/s speed=0.0945x 
frame= 139 fps=3.6 q=23.0 size= 2816kB time=00:00:03.57 bitrate=6451.3kbits/s speed=0.0937x 
frame= 142 fps=3.6 q=23.0 size= 2816kB time=00:00:03.71 bitrate=6209.4kbits/s speed=0.0951x 
frame= 145 fps=3.7 q=23.0 size= 2816kB time=00:00:03.80 bitrate=6057.9kbits/s speed=0.096x 
frame= 145 fps=3.6 q=23.0 size= 2816kB time=00:00:03.80 bitrate=6057.9kbits/s speed=0.0948x 
frame= 148 fps=3.6 q=23.0 size= 2816kB time=00:00:03.94 bitrate=5844.1kbits/s speed=0.0957x 
frame= 152 fps=3.6 q=23.0 size= 2816kB time=00:00:04.08 bitrate=5644.9kbits/s speed=0.0967x 
frame= 153 fps=3.6 q=23.0 size= 2816kB time=00:00:04.13 bitrate=5581.5kbits/s speed=0.0961x 
frame= 156 fps=3.5 q=23.0 size= 2816kB time=00:00:04.31 bitrate=5341.4kbits/s speed=0.0979x 
frame= 160 fps=3.6 q=23.0 size= 2816kB time=00:00:04.41 bitrate=5229.0kbits/s speed=0.0983x 
frame= 161 fps=3.5 q=23.0 size= 3072kB time=00:00:04.50 bitrate=5586.7kbits/s speed=0.0987x 
frame= 163 fps=3.5 q=23.0 size= 3072kB time=00:00:04.55 bitrate=5529.7kbits/s speed=0.0983x 
frame= 165 fps=3.5 q=23.0 size= 3072kB time=00:00:04.64 bitrate=5419.1kbits/s speed=0.0984x 
frame= 168 fps=3.5 q=23.0 size= 3072kB time=00:00:04.73 bitrate=5312.8kbits/s speed=0.0982x 
frame= 170 fps=3.5 q=23.0 size= 3072kB time=00:00:04.82 bitrate=5210.7kbits/s speed=0.0989x 
frame= 172 fps=3.5 q=23.0 size= 3072kB time=00:00:04.92 bitrate=5112.4kbits/s speed=0.0995x 
frame= 174 fps=3.5 q=23.0 size= 3072kB time=00:00:04.96 bitrate=5064.6kbits/s speed=0.0991x 
frame= 176 fps=3.5 q=23.0 size= 3072kB time=00:00:05.06 bitrate=4971.6kbits/s speed=0.0993x 
frame= 178 fps=3.4 q=23.0 size= 3072kB time=00:00:05.15 bitrate=4882.1kbits/s speed=0.0998x 
frame= 181 fps=3.4 q=23.0 size= 3072kB time=00:00:05.24 bitrate=4795.7kbits/s speed=0.0995x 
frame= 182 fps=2.9 q=-1.0 Lsize= 3597kB time=00:00:07.16 bitrate=4115.3kbits/s speed=0.113x 
video:3543kB audio:49kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.148349%
[libx264 @ 0x7f85a5b84880] frame I:1 Avg QP: 7.45 size:1298948
[libx264 @ 0x7f85a5b84880] frame P:46 Avg QP: 8.09 size: 44457
[libx264 @ 0x7f85a5b84880] frame B:135 Avg QP:17.27 size: 2100
[libx264 @ 0x7f85a5b84880] consecutive B-frames: 1.1% 0.0% 0.0% 98.9%
[libx264 @ 0x7f85a5b84880] mb I I16..4: 24.5% 22.8% 52.6%
[libx264 @ 0x7f85a5b84880] mb P I16..4: 0.0% 0.0% 0.0% P16..4: 15.2% 0.1% 0.7% 0.0% 0.0% skip:84.0%
[libx264 @ 0x7f85a5b84880] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 11.9% 0.0% 0.0% direct: 0.0% skip:88.1% L0:69.1% L1:30.8% BI: 0.0%
[libx264 @ 0x7f85a5b84880] 8x8 transform intra:23.0% inter:72.2%
[libx264 @ 0x7f85a5b84880] coded y,uvDC,uvAC intra: 97.3% 2.2% 1.9% inter: 2.9% 0.0% 0.0%
[libx264 @ 0x7f85a5b84880] i16 v,h,dc,p: 1% 5% 83% 11%
[libx264 @ 0x7f85a5b84880] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 11% 12% 39% 7% 6% 6% 4% 6% 9%
[libx264 @ 0x7f85a5b84880] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 14% 12% 21% 10% 10% 8% 7% 7% 9%
[libx264 @ 0x7f85a5b84880] i8c dc,h,v,p: 96% 3% 0% 0%
[libx264 @ 0x7f85a5b84880] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7f85a5b84880] ref P L0: 47.1% 0.0% 52.2% 0.7%
[libx264 @ 0x7f85a5b84880] ref B L0: 55.4% 44.4% 0.2%
[libx264 @ 0x7f85a5b84880] ref B L1: 98.6% 1.4%
[libx264 @ 0x7f85a5b84880] kb/s:3986.16
[aac @ 0x7f85a5b95f40] Qavg: 2267.321


  



  2B-

  



      ffmpeg version N-92107-g4901fa1 Copyright (c) 2000-2018 the FFmpeg developers
built with gcc 4.4.7 (GCC) 20120313 (Red Hat 4.4.7-23)
configuration: --prefix=/home/centos/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/centos/ffmpeg_build/include --extra-ldflags=-L/home/centos/ffmpeg_build/lib --extra-libs=-lpthread --extra-libs=-lm --bindir=/home/centos/bin --enable-gpl --enable-libfdk_aac --enable-libfreetype --enable-libmp3lame --enable-libvpx --enable-libx264 --enable-nonfree
libavutil 56. 19.101 / 56. 19.101
libavcodec 58. 32.100 / 58. 32.100
libavformat 58. 18.104 / 58. 18.104
libavdevice 58. 4.105 / 58. 4.105
libavfilter 7. 33.100 / 7. 33.100
libswscale 5. 2.100 / 5. 2.100
libswresample 3. 2.100 / 3. 2.100
libpostproc 55. 2.100 / 55. 2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/01.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf58.18.104
Duration: 00:00:07.28, start: 0.000000, bitrate: 4047 kb/s
Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 3360x2100 [SAR 1:1 DAR 8:5], 3986 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc (default)
Metadata:
handler_name : VideoHandler
Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 73 kb/s (default)
Metadata:
handler_name : SoundHandler
Input #1, png_pipe, from '/tmp/square.png':
Duration: N/A, bitrate: N/A
Stream #1:0: Video: png, rgba(pc), 90x90 [SAR 1:1 DAR 1:1], 25 tbr, 25 tbn, 25 tbc
Stream mapping:
Stream #0:0 (h264) -> overlay:main
Stream #1:0 (png) -> overlay:overlay
overlay -> Stream #0:0 (libx264)
Stream #0:1 -> #0:1 (copy)
Press [q] to stop, [?] for help
[h264 @ 0x7f39d841d7c0] Invalid NAL unit size (2944577 > 11502).
[h264 @ 0x7f39d841d7c0] Error splitting the input into NAL units.
[h264 @ 0x7f39d8379a00] concealing 24601 DC, 24601 AC, 24601 MV errors in P frame
[h264 @ 0x7f39d839eec0] Invalid NAL unit size (1049345 > 4099).
[h264 @ 0x7f39d839eec0] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
[h264 @ 0x7f39d8379a00] Invalid NAL unit size (127861151 > 1951).
[h264 @ 0x7f39d8379a00] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
[h264 @ 0x7f39d841d7c0] Invalid NAL unit size (-1415429733 > 43938).
[h264 @ 0x7f39d841d7c0] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
Error while decoding stream #0:0: Invalid data found when processing input
[h264 @ 0x7f39d8379a00] Invalid NAL unit size (-1582612070 > 41387).
[h264 @ 0x7f39d8379a00] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
**** lots of the same NAL issue ****
Error while decoding stream #0:0: Invalid data found when processing input
[h264 @ 0x7f39d839eec0] Invalid NAL unit size (128188831 > 1956).
[h264 @ 0x7f39d839eec0] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
[h264 @ 0x7f39d8379a00] Invalid NAL unit size (13779355 > 210).
[h264 @ 0x7f39d8379a00] Error splitting the input into NAL units.
Error while decoding stream #0:0: Invalid data found when processing input
Last message repeated 1 times
[libx264 @ 0x7f39d838df00] using SAR=1/1
[libx264 @ 0x7f39d838df00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7f39d838df00] profile High, level 5.1, 4:2:0, 8-bit
[libx264 @ 0x7f39d838df00] 264 - core 157 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - 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=3 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=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to '/tmp/temp.mp4':
Metadata:
major_brand : isom
minor_version : 512
compatible_brands: isomiso2avc1mp41
encoder : Lavf58.18.104
Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p, 3360x2100 [SAR 1:1 DAR 8:5], q=-1--1, 25 fps, 12800 tbn, 25 tbc (default)
Metadata:
encoder : Lavc58.32.100 libx264
Side data:
cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 22050 Hz, mono, fltp, 73 kb/s (default)
Metadata:
handler_name : SoundHandler
frame= 5 fps=3.2 q=-1.0 Lsize= 360kB time=00:00:05.34 bitrate= 552.0kbits/s dup=3 drop=0 speed=3.44x 
video:309kB audio:49kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.543219%
[libx264 @ 0x7f39d838df00] frame I:1 Avg QP:19.79 size:301308
[libx264 @ 0x7f39d838df00] frame P:1 Avg QP:27.65 size: 4760
[libx264 @ 0x7f39d838df00] frame B:3 Avg QP:23.24 size: 3347
[libx264 @ 0x7f39d838df00] consecutive B-frames: 20.0% 0.0% 0.0% 80.0%
[libx264 @ 0x7f39d838df00] mb I I16..4: 2.3% 82.9% 14.8%
[libx264 @ 0x7f39d838df00] mb P I16..4: 0.1% 0.0% 0.0% P16..4: 5.6% 0.2% 4.5% 0.0% 0.0% skip:89.4%
[libx264 @ 0x7f39d838df00] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 16.0% 0.0% 0.0% direct: 0.0% skip:84.0% L0:13.4% L1:86.6% BI: 0.0%
[libx264 @ 0x7f39d838df00] 8x8 transform intra:82.8% inter:92.8%
[libx264 @ 0x7f39d838df00] coded y,uvDC,uvAC intra: 92.0% 1.6% 1.2% inter: 0.6% 0.0% 0.0%
[libx264 @ 0x7f39d838df00] i16 v,h,dc,p: 18% 72% 4% 6%
[libx264 @ 0x7f39d838df00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 10% 9% 49% 6% 6% 5% 4% 5% 6%
[libx264 @ 0x7f39d838df00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 17% 14% 26% 9% 10% 7% 6% 5% 5%
[libx264 @ 0x7f39d838df00] i8c dc,h,v,p: 96% 3% 1% 0%
[libx264 @ 0x7f39d838df00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7f39d838df00] ref B L0: 99.8% 0.2%
[libx264 @ 0x7f39d838df00] ref B L1: 91.7% 8.3%
[libx264 @ 0x7f39d838df00] kb/s:12644.32
Conversion failed!


  


• What is PII ? Your introduction to personally identifiable information

  15 janvier 2020, par Joselyn Khor — Analytics Tips, Privacy, Security
  Most websites you visit collect information about you via tools like Google Analytics and Matomo – sometimes collecting personally identifiable information (PII).

  When it comes to PII, people are becoming more concerned about data privacy. Identifiable information can be used for illegal purposes like identity theft and fraud. 

  So how can you protect yourself as an innocent internet browser ? In the case of website owners – how do you protect users and your company from falling prey to privacy breaches ?

  

  As one of the most trusted analytics companies, we feel our readers would benefit from being as informed as possible about data privacy issues and PII. Learn what it means, and what you can do to keep yours or others’ information safe.

  Table of Contents

  What does PII stand for ?

  PII acronym

  PII is an acronym for personally identifiable information.

  PII definition

  Personally identifiable information (PII) is a term used predominantly in the United States.

  The appendix of OMB M-10-23 (Guidance for Agency Use of Third-Party Website and Applications) gives this definition for PII :

  “The term ‘personally identifiable information’ refers to information which can be used to distinguish or trace an individual’s identity, such as their name, social security number, biometric records, etc. alone, or when combined with other personal or identifying information which is linked or linkable to a specific individual, such as date and place of birth, mother’s maiden name, etc.”

  What can be considered personally identifiable information (PII) ? Some PII examples :

  • Full name/usernames
  • Home address/mailing address
  • Email address
  • Credit card numbers
  • Date of birth
  • Phone numbers
  • Login details

  • Precise locations
  • Account numbers
  • Passwords
  • Security codes (including biometric records)
  • Personal identification numbers
  • Driver license number
  • Get a more comprehensive list here

  What’s non-PII ?

  Anonymous information, or information that can’t be traced back to an individual, can be considered non-PII.

  Who is affected by the exploitation of PII ?

  Anyone can be affected by the exploitation of personal data, where you have identity theft, account fraud and account takeovers. When websites resort to illegally selling or sharing your data and compromising your privacy, the fear is falling victim to such fraudulent activity. 

  PII can also be an issue when employees have access to the database and the data is not encrypted. For example, anyone working in a bank can access your accounts ; anyone working at Facebook may be able to read your messages. This shows how privacy breaches can easily happen when employees have access to PII.

  Website owner’s responsibility for data privacy (PII and analytics)

  To respect your website visitor’s privacy, best practice is to avoid collecting PII whenever possible. If you work in an industry which requires people to disclose personal information (e.g. healthcare, security industries, public sector), then you must ensure this data is collected and handled securely. 

  

  The US National Institute of Standards and Technology states : “The likelihood of harm caused by a breach involving PII is greatly reduced if an organisation minimises the amount of PII it uses, collects, and stores. For example, an organisation should only request PII in a new form if the PII is absolutely necessary.” 

  How you’re held accountable remains up to the privacy laws of the country you’re doing business in. Make sure you are fully aware of the privacy and data protection laws that relate specifically to you. 

  To reduce the risk of privacy breaches, try collecting as little PII as you can ; purging it as soon as you can ; and making sure your IT security is updated and protected against security threats. 

  If you’re using data collection tools like web analytics, data may be tracked through features like User ID, custom variables, and custom dimensions. Sometimes they are also harder to identify when they are present, for example, in page URLs, page titles, or referrers URLs. So make sure you’re optimising your web analytics tools’ settings to ensure you’re asking your users for consent and respecting users’ privacy.

  If you’re using a GDPR compliant tool like Matomo, learn how you can stop processing such personal data

  PII, GDPR and businesses in the US/EU

  Because PII is broad, you may run into confusion when considering PII and GDPR (which applies in the EU). The General Data Protection Regulation (GDPR) provides more safeguards for user privacy.

  GDPR grants people in the EU more rights concerning their “personal data” (more on PII vs personal data below). In the EU the GDPR restricts the collection and processing of personal data. The repercussions are severe penalties and fines for privacy infringements. Businesses are required to handle this personal data carefully. You can be fined up to 4% of their yearly revenue for data breaches or non-compliance. 

  

  Although there isn’t an overarching data protection law in the US, there are hundreds of laws on both the federal and state levels to protect the personal data of US residents. US Congress has also enacted industry-specific statutes related to data privacy, and the state of California passed the California Consumer Privacy Act. 

  To be on the safe side, if you are using analytics, follow matters relating to “personal data” in the GDPR. It’s all-encompassing when it comes to protecting user privacy. GDPR rules still apply whenever an EU citizen visits any non EU site (that processes personal data).

  Personally identifiable information (PII) vs personal data

  PII and “personal data” aren’t used interchangeably. All personal data can be PII, but not all PII can be defined as personal data.

  The definition of “personal data” according to the GDPR :

  

  This means “personal data” encompasses a greater number of identifiers which include the online sphere. Examples include : IP addresses and URL names. As well as seemingly “innocent” data like height, job position, company etc. 

  What’s considered personal data depends on the context. If a piece of information can be combined with others to establish someone’s identity then that can be considered personal data. 

  Under GDPR, when processing personal data, you need explicit consent. You need to ensure you’re compliant according to GDPR definitions of “personal data” not just what’s considered “PII”.

  How Matomo deals with PII and personal data

  Although Matomo Analytics is a web analytics software that tracks user activity on your website, we take privacy and PII very seriously – on both our Cloud and On-Premise offerings. 

  If you’re using Matomo and would like to know how you can be fully GDPR compliant and protect user privacy, read more :
  

  • Learn how to not process any personally identifiable information – Anonymise IP addresses, user IDs, and order IDs
  • Matomo protects user privacy by talking the talk and walking the walk
  • Stay ahead of the GDPR with a privacy-respecting analytics platform
  • 11 ways Matomo helps you protect your visitor’s privacy

  Disclaimer

  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to issues you may encounter when dealing with PII. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns. 

  Additional resources :

  • https://www.consumeraffairs.com/finance/identity-theft-statistics.html#identity-theft-trends-in-2019
  • https://ec.europa.eu/info/law/law-topic/data-protection_en
  • https://iclg.com/practice-areas/data-protection-laws-and-regulations/usa
  • https://www.csoonline.com/article/3215864/how-to-protect-personally-identifiable-information-pii-under-gdpr.html
  • https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/key-definitions/what-is-personal-data/