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  • 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.

  • A Guide to GDPR Sensitive Personal Data

    13 mai 2024, par Erin

    The General Data Protection Regulation (GDPR) is one of the world’s most stringent data protection laws. It provides a legal framework for collection and processing of the personal data of EU individuals.

    The GDPR distinguishes between “special categories of personal data” (also referred to as “sensitive”) and other personal data and imposes stricter requirements on collection and processing of sensitive data. Understanding these differences will help your company comply with the requirements and avoid heavy penalties.

    In this article, we’ll explain what personal data is considered “sensitive” according to the GDPR. We’ll also examine how a web analytics solution like Matomo can help you maintain compliance.

    What is sensitive personal data ?

    The following categories of data are treated as sensitive :

      1. Personal data revealing :
        • Racial or ethnic origin ;
        • Political opinions ;
        • Religious or philosophical beliefs ;
        • Trade union membership ;
      2. Genetic and biometric data ;
      3. Data concerning a person’s :
        • Health ; or
        • Sex life or sexual orientation.
    Examples of GDPR Sensitive Personal Data

    Sensitive vs. non-sensitive personal data : What’s the difference ?

    While both categories include information about an individual, sensitive data is seen as more private, or requiring a greater protection. 

    Sensitive data often carries a higher degree of risk and harm to the data subject, if the data is exposed. For example, a data breach exposing health records could lead to discrimination for the individuals involved. An insurance company could use the information to increase premiums or deny coverage. 

    In contrast, personal data like name or gender is considered less sensitive because it doesn’t carry the same degree of harm as sensitive data. 

    Unauthorised access to someone’s name alone is less likely to harm them or infringe on their fundamental rights and freedoms than an unauthorised access to their health records or biometric data. Note that financial information (e.g. credit card details) does not fall into the special categories of data.

    Table displaying different sensitive data vs non-sensitive data

    Legality of processing

    Under the GDPR, both sensitive and nonsensitive personal data are protected. However, the rules and conditions for processing sensitive data are more stringent.

    Article 6 deals with processing of non-sensitive data and it states that processing is lawful if one of the six lawful bases for processing applies. 

    In contrast, Art. 9 of the GDPR states that processing of sensitive data is prohibited as a rule, but provides ten exceptions. 

    It is important to note that the lawful bases in Art. 6 are not the same as exceptions in Art. 9. For example, while performance of a contract or legitimate interest of the controller are a lawful basis for processing non-sensitive personal data, they are not included as an exception in Art. 9. What follows is that controllers are not permitted to process sensitive data on the basis of contract or legitimate interest. 

    The exceptions where processing of sensitive personal data is permitted (subject to additional requirements) are : 

    • Explicit consent : The individual has given explicit consent to processing their sensitive personal data for specified purpose(s), except where an EU member state prohibits such consent. See below for more information about explicit consent. 
    • Employment, social security or social protection : Processing sensitive data is necessary to perform tasks under employment, social security or social protection law.
    • Vital interests : Processing sensitive data is necessary to protect the interests of a data subject or if the individual is physically or legally incapable of consenting. 
    • Non-for-profit bodies : Foundations, associations or nonprofits with a political, philosophical, religious or trade union aim may process the sensitive data of their members or those they are in regular contact with, in connection with their purposes (and no disclosure of the data is permitted outside the organisation, without the data subject’s consent).
    • Made public : In some cases, it may be permissible to process the sensitive data of a data subject if the individual has already made it public and accessible. 
    • Legal claims : Processing sensitive data is necessary to establish, exercise or defend legal claims, including legal or in court proceedings.
    • Public interest : Processing is necessary for reasons of substantial public interest, like preventing unlawful acts or protecting the public.
    • Health or social care : Processing special category data is necessary for : preventative or occupational medicine, providing health and social care, medical diagnosis or managing healthcare systems.
    • Public health : It is permissible to process sensitive data for public health reasons, like protecting against cross-border threats to health or ensuring the safety of medicinal products or medical devices. 
    • Archiving, research and statistics : You may process sensitive data if it’s done for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.

    In addition, you must adhere to all data handling requirements set by the GDPR.

    Important : Note that for any data sent that you are processing, you always need to identify a lawful basis under Art. 6. In addition, if the data sent contains sensitive data, you must comply with Art. 9.

    Explicit consent

    While consent is a valid lawful basis for processing non-sensitive personal data, controllers are permitted to process sensitive data only with an “explicit consent” of the data subject.

    The GDPR does not define “explicit” consent, but it is accepted that it must meet all Art. 7 conditions for consent, at a higher threshold. To be “explicit” a consent requires a clear statement (oral or written) of the data subject. Consent inferred from the data subject’s actions does not meet the threshold. 

    The controller must retain records of the explicit consent and provide appropriate consent withdrawal method to allow the data subject to exercise their rights.

    Examples of compliant and non-compliant sensitive data processing

    Here are examples of when you can and can’t process sensitive data :

    • When you can process sensitive data : A doctor logs sensitive data about a patient, including their name, symptoms and medicine prescribed. The hospital can process this data to provide appropriate medical care to their patients. An IoT device and software manufacturer processes their customers’ health data based on explicit consent of each customer. 
    • When you can’t process sensitive data : One example is when you don’t have explicit consent from a data subject. Another is when there’s no lawful basis for processing it or you are collecting personal data you simply do not need. For example, you don’t need your customer’s ethnic origin to fulfil an online order.

    Other implications of processing sensitive data

    If you process sensitive data, especially on a large scale, GDPR imposes additional requirements, such as having Data Privacy Impact Assessments, appointing Data Protection Officers and EU Representatives, if you are a controller based outside the EU.

    Penalties for GDPR non-compliance

    Mishandling sensitive data (or processing it when you’re not allowed to) can result in huge penalties. There are two tiers of GDPR fines :

    • €10 million or 2% of a company’s annual revenue for less severe infringements
    • €20 million or 4% of a company’s annual revenue for more severe infringements

    In the first half of 2023 alone, fines imposed in the EU due to GDPR violations exceeded €1.6 billion, up from €73 million in 2019.

    Examples of high-profile violations in the last few years include :

    • Amazon : The Luxembourg National Commission fined the retail giant with a massive $887 million fine in 2021 for not processing personal data per the GDPR. 
    • Google : The National Data Protection Commission (CNIL) fined Google €50 million for not getting proper consent to display personalised ads.
    • H&M : The Hamburg Commissioner for Data Protection and Freedom of Information hit the multinational clothing company with a €35.3 million fine in 2020 for unlawfully gathering and storing employees’ data in its service centre.

    One of the criteria that affects the severity of a fine is “data category” — the type of personal data being processed. Companies need to take extra precautions with sensitive data, or they risk receiving more severe penalties.

    What’s more, GDPR violations can negatively affect your brand’s reputation and cause you to lose business opportunities from consumers concerned about your data practices. 76% of consumers indicated they wouldn’t buy from companies they don’t trust with their personal data.

    Organisations should lay out their data practices in simple terms and make this information easily accessible so customers know how their data is being handled.

    Get started with GDPR-compliant web analytics

    The GDPR offers a framework for securing and protecting personal data. But it also distinguishes between sensitive and non-sensitive data. Understanding these differences and applying the lawful basis for processing this data type will help ensure compliance.

    Looking for a GDPR-compliant web analytics solution ?

    At Matomo, we take data privacy seriously. 

    Our platform ensures 100% data ownership, putting you in complete control of your data. Unlike other web analytics solutions, your data remains solely yours and isn’t sold or auctioned off to advertisers. 

    Additionally, with Matomo, you can be confident in the accuracy of the insights you receive, as we provide reliable, unsampled data.

    Matomo also fully complies with GDPR and other data privacy laws like CCPA, LGPD and more.

    Start your 21-day free trial today ; no credit card required. 

    Disclaimer

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

  • 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.

    What is segmentation analytics?

    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 :

    5 Types of Segmentation

    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 :

    Matomo Dashboard of Segmentation by Country

    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.

    Visitor Profile in Matomo
    Visitor profile view in Matomo with behavioural, location and technographic insights

    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.