Recherche avancée

Sur d’autres sites (4484)

• Stream ffmpeg transcoding result to S3

  7 juin 2019, par mabead

  I want to transcode a large file using FFMPEG and store the result directly on AWS S3. This will be done inside of an AWS Lambda that has limited tmp space so I can’t store the transcoding result locally and then upload it to S3 in a second step. I won’t have enough tmp space. I therefore want to store the FFMPEG output directly on S3.

  I therefore created a S3 pre-signed url that allows ’PUT’ :

  var outputPath = s3Client.GetPreSignedURL(new Amazon.S3.Model.GetPreSignedUrlRequest
  
  {
  
      BucketName = "my-bucket",
  
      Expires = DateTime.UtcNow.AddMinutes(5),
  
      Key = "output.mp3",
  
      Verb = HttpVerb.PUT,
  
  });

  I then called ffmpeg with the resulting pre-signed url :

  ffmpeg -i C:\input.wav -y -vn -ar 44100 -ac 2 -ab 192k -f mp3 https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D

  FFMPEG returns an exit code of 1 with the following output :

  ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 8.2.1 (GCC) 20190212
  
    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-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-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
  
    libavutil      56. 26.100 / 56. 26.100
  
    libavcodec     58. 47.100 / 58. 47.100
  
    libavformat    58. 26.101 / 58. 26.101
  
    libavdevice    58.  6.101 / 58.  6.101
  
    libavfilter     7. 48.100 /  7. 48.100
  
    libswscale      5.  4.100 /  5.  4.100
  
    libswresample   3.  4.100 /  3.  4.100
  
    libpostproc    55.  4.100 / 55.  4.100
  
  Guessed Channel Layout for Input Stream #0.0 : stereo
  
  Input #0, wav, from 'C:\input.wav':
  
    Duration: 00:04:16.72, bitrate: 3072 kb/s
  
      Stream #0:0: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
  
  Press [q] to stop, [?] for help
  
  Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D':
  
    Metadata:
  
      TSSE            : Lavf58.26.101
  
      Stream #0:0: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, 192 kb/s
  
      Metadata:
  
        encoder         : Lavc58.47.100 libmp3lame
  
  size=     577kB time=00:00:24.58 bitrate= 192.2kbits/s speed=49.1x    
  
  size=    1109kB time=00:00:47.28 bitrate= 192.1kbits/s speed=47.2x    
  
  [tls @ 000001d73d786b00] Error in the push function.
  
  av_interleaved_write_frame(): I/O error
  
  Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D: I/O error
  
  size=    1143kB time=00:00:48.77 bitrate= 192.0kbits/s speed=  47x    
  
  video:0kB audio:1144kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
  [tls @ 000001d73d786b00] The specified session has been invalidated for some reason.
  
  [tls @ 000001d73d786b00] Error in the pull function.
  
  [https @ 000001d73d784fc0] URL read error:  -5
  
  Conversion failed!

  As you can see, I have a URL read error. This is a little surprising to me since I want to output to this url and not read it.

  Anybody know how I can store directly my FFMPEG output directly to S3 without having to store it locally first ?

  Edit 1
  I then tried to use the -method PUT parameter and use http instead of https to remove TLS from the equation. Here’s the output that I got when running ffmpeg with the -v trace option.

  ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 8.2.1 (GCC) 20190212
  
    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-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-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
  
    libavutil      56. 26.100 / 56. 26.100
  
    libavcodec     58. 47.100 / 58. 47.100
  
    libavformat    58. 26.101 / 58. 26.101
  
    libavdevice    58.  6.101 / 58.  6.101
  
    libavfilter     7. 48.100 /  7. 48.100
  
    libswscale      5.  4.100 /  5.  4.100
  
    libswresample   3.  4.100 /  3.  4.100
  
    libpostproc    55.  4.100 / 55.  4.100
  
  Splitting the commandline.
  
  Reading option '-i' ... matched as input url with argument 'C:\input.wav'.
  
  Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
  
  Reading option '-vn' ... matched as option 'vn' (disable video) with argument '1'.
  
  Reading option '-ar' ... matched as option 'ar' (set audio sampling rate (in Hz)) with argument '44100'.
  
  Reading option '-ac' ... matched as option 'ac' (set number of audio channels) with argument '2'.
  
  Reading option '-ab' ... matched as option 'ab' (audio bitrate (please use -b:a)) with argument '192k'.
  
  Reading option '-f' ... matched as option 'f' (force format) with argument 'mp3'.
  
  Reading option '-method' ... matched as AVOption 'method' with argument 'PUT'.
  
  Reading option '-v' ... matched as option 'v' (set logging level) with argument 'trace'.
  
  Reading option 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D' ... matched as output url.
  
  Finished splitting the commandline.
  
  Parsing a group of options: global .
  
  Applying option y (overwrite output files) with argument 1.
  
  Applying option v (set logging level) with argument trace.
  
  Successfully parsed a group of options.
  
  Parsing a group of options: input url C:\input.wav.
  
  Successfully parsed a group of options.
  
  Opening an input file: C:\input.wav.
  
  [NULL @ 000001fb37abb180] Opening 'C:\input.wav' for reading
  
  [file @ 000001fb37abc180] Setting default whitelist 'file,crypto'
  
  Probing wav score:99 size:2048
  
  [wav @ 000001fb37abb180] Format wav probed with size=2048 and score=99
  
  [wav @ 000001fb37abb180] Before avformat_find_stream_info() pos: 54 bytes read:65590 seeks:1 nb_streams:1
  
  [wav @ 000001fb37abb180] parser not found for codec pcm_s32le, packets or times may be invalid.
  
      Last message repeated 1 times
  
  [wav @ 000001fb37abb180] All info found
  
  [wav @ 000001fb37abb180] stream 0: start_time: -192153584101141.156 duration: 256.716
  
  [wav @ 000001fb37abb180] format: start_time: -9223372036854.775 duration: 256.716 bitrate=3072 kb/s
  
  [wav @ 000001fb37abb180] After avformat_find_stream_info() pos: 204854 bytes read:294966 seeks:1 frames:50
  
  Guessed Channel Layout for Input Stream #0.0 : stereo
  
  Input #0, wav, from 'C:\input.wav':
  
    Duration: 00:04:16.72, bitrate: 3072 kb/s
  
      Stream #0:0, 50, 1/48000: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
  
  Successfully opened the file.
  
  Parsing a group of options: output url https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
  
  Applying option vn (disable video) with argument 1.
  
  Applying option ar (set audio sampling rate (in Hz)) with argument 44100.
  
  Applying option ac (set number of audio channels) with argument 2.
  
  Applying option ab (audio bitrate (please use -b:a)) with argument 192k.
  
  Applying option f (force format) with argument mp3.
  
  Successfully parsed a group of options.
  
  Opening an output file: https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
  
  [http @ 000001fb37b15140] Setting default whitelist 'http,https,tls,rtp,tcp,udp,crypto,httpproxy'
  
  [tcp @ 000001fb37b16c80] Original list of addresses:
  
  [tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Interleaved list of addresses:
  
  [tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Starting connection attempt to 52.216.8.203 port 80
  
  [tcp @ 000001fb37b16c80] Successfully connected to 52.216.8.203 port 80
  
  [http @ 000001fb37b15140] request: PUT /output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D HTTP/1.1
  
  Transfer-Encoding: chunked
  
  User-Agent: Lavf/58.26.101
  
  Accept: */*
  
  Connection: close
  
  Host: landr-distribution-reportsdev-mb.s3.amazonaws.com
  
  Icy-MetaData: 1
  
  Successfully opened the file.
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
  
  Press [q] to stop, [?] for help
  
  cur_dts is invalid (this is harmless if it occurs once at the start per stream)
  
  detected 8 logical cores
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'time_base' to value '1/48000'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_rate' to value '48000'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_fmt' to value 's32'
  
  [graph_0_in_0_0 @ 000001fb37b21080] Setting 'channel_layout' to value '0x3'
  
  [graph_0_in_0_0 @ 000001fb37b21080] tb:1/48000 samplefmt:s32 samplerate:48000 chlayout:0x3
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_fmts' to value 's32p|fltp|s16p'
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_rates' to value '44100'
  
  [format_out_0_0 @ 000001fb37b22cc0] Setting 'channel_layouts' to value '0x3'
  
  [format_out_0_0 @ 000001fb37b22cc0] auto-inserting filter 'auto_resampler_0' between the filter 'Parsed_anull_0' and the filter 'format_out_0_0'
  
  [AVFilterGraph @ 000001fb37b0d940] query_formats: 4 queried, 6 merged, 3 already done, 0 delayed
  
  [auto_resampler_0 @ 000001fb37b251c0] picking s32p out of 3 ref:s32
  
  [auto_resampler_0 @ 000001fb37b251c0] [SWR @ 000001fb37b252c0] Using fltp internally between filters
  
  [auto_resampler_0 @ 000001fb37b251c0] ch:2 chl:stereo fmt:s32 r:48000Hz -> ch:2 chl:stereo fmt:s32p r:44100Hz
  
  Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D':
  
    Metadata:
  
      TSSE            : Lavf58.26.101
  
      Stream #0:0, 0, 1/44100: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, delay 1105, 192 kb/s
  
      Metadata:
  
        encoder         : Lavc58.47.100 libmp3lame
  
  cur_dts is invalid (this is harmless if it occurs once at the start per stream)
  
      Last message repeated 6 times
  
  size=     649kB time=00:00:27.66 bitrate= 192.2kbits/s speed=55.3x    
  
  size=    1207kB time=00:00:51.48 bitrate= 192.1kbits/s speed=51.5x    
  
  av_interleaved_write_frame(): Unknown error
  
  No more output streams to write to, finishing.
  
  [libmp3lame @ 000001fb37b147c0] Trying to remove 47 more samples than there are in the queue
  
  Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D: Error number -10054 occurred
  
  size=    1251kB time=00:00:53.39 bitrate= 192.0kbits/s speed=51.5x    
  
  video:0kB audio:1252kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
  Input file #0 (C:\input.wav):
  
    Input stream #0:0 (audio): 5014 packets read (20537344 bytes); 5014 frames decoded (2567168 samples); 
  
    Total: 5014 packets (20537344 bytes) demuxed
  
  Output file #0 (https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D):
  
    Output stream #0:0 (audio): 2047 frames encoded (2358144 samples); 2045 packets muxed (1282089 bytes); 
  
    Total: 2045 packets (1282089 bytes) muxed
  
  5014 frames successfully decoded, 0 decoding errors
  
  [AVIOContext @ 000001fb37b1f440] Statistics: 0 seeks, 2046 writeouts
  
  [http @ 000001fb37b15140] URL read error:  -10054
  
  [AVIOContext @ 000001fb37ac4400] Statistics: 20611126 bytes read, 1 seeks
  
  Conversion failed!

  So it looks like it is able to connect to my S3 pre-signed url but I still have the Error writing trailer error coupled with a URL read error.

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

• Audio out of sync, direct capture device stream (Windows 10)

  3 mai 2020, par user3459555

  Using ffplay, the video stays in sync using this command :

  



  ffplay -f dshow -rtbufsize 702000k video="Cam Link"
ffplay version git-2020-05-01-39fb1e9 Copyright (c) 2003-2020 the FFmpeg developers
  built with gcc 9.3.1 (GCC) 20200328
  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 --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. 43.100 / 56. 43.100
  libavcodec     58. 82.100 / 58. 82.100
  libavformat    58. 42.101 / 58. 42.101
  libavdevice    58.  9.103 / 58.  9.103
  libavfilter     7. 80.100 /  7. 80.100
  libswscale      5.  6.101 /  5.  6.101
  libswresample   3.  6.100 /  3.  6.100
  libpostproc    55.  6.100 / 55.  6.100
Input #0, dshow, from 'video=Cam Link':vq=    0KB sq=    0B f=0/0
  Duration: N/A, start: 141954.961000, bitrate: N/A
    Stream #0:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 1920x1080, 59.94 fps, 59.94 tbr, 10000k tbn, 10000k tbc
142992.53 M-V: -0.001 fd=   3 aq=    0KB vq=    0KB sq=    0B f=0/0


  



  Every controller button press stays in sync.

  



  The audio however :

  



  ffplay -f dshow audio="Digital Audio Interface (Cam Link)" -tune zerolatency
ffplay version git-2020-05-01-39fb1e9 Copyright (c) 2003-2020 the FFmpeg developers
  built with gcc 9.3.1 (GCC) 20200328
  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 --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. 43.100 / 56. 43.100
  libavcodec     58. 82.100 / 58. 82.100
  libavformat    58. 42.101 / 58. 42.101
  libavdevice    58.  9.103 / 58.  9.103
  libavfilter     7. 80.100 /  7. 80.100
  libswscale      5.  6.101 /  5.  6.101
  libswresample   3.  6.100 /  3.  6.100
  libpostproc    55.  6.100 / 55.  6.100
Input #0, dshow, from 'audio=Digital Audio Interface (Cam Link)':
  Duration: N/A, start: 143092.007000, bitrate: 1411 kb/s
    Stream #0:0: Audio: pcm_s16le, 44100 Hz, 2 channels, s16, 1411 kb/s
143103.21 M-A:  0.000 fd=   0 aq=    0KB vq=    0KB sq=    0B f=0/0


  



  Is always behined by about a full second.

  



  I'm not trying to record this, just trying to directly play from the Elgato Cam Link 1:1 output to my computer screen. When this is played in the Elgato Game Capture software, the video and audio are 1:1, no issues. So I know it's not the console or the capture device.