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• A pragmatic strategy to merge multiple video files

  19 juin 2021, par saurav

  I currently am working on recording a multiparty video conference which supports up to 6 participants. I am recording the conference using a media server and storing audio/video streams individually for every participant.

  


  Next, I need to merge those individual recordings into a single video file and upload it to a cloud storage like aws s3. For this I am considering 2 options, either Gstreamer or FFMPEG. I am leaning towards FFMPEG as I have used FFMPEG previously. I currently am playing with FFMPEG things like the hstack and vstack filters etc.

  


  Here is the FFMPEG command I recently used to join 2 webm videos of 2 mins 40sec and 1min 40sec to create a mp4 video file for upload. Both the videos are 1280x720 in this case but I have included the scale part because in real life scenario different participants joining with different cameras produces video files of different resolution which is a problem for the hstack/vstack filter. Therefore, to make the video resolutions of all participant consistent, I have included the scale property.

  


  ffmpeg -i 1.webm -i 2.webm -filter_complex "[0:v]scale=1280:720,setsar=1[l];[1:v]scale=1280:720,setsar=1[r];[l][r]hstack;[0][1]amix" output-1280x720.mp4


  


  Currently I am facing 2 issues with this command.

  


  


  1. The output mp4 file is very big, in this case 140Mb (approx) for a less than 3 minutes video.

     


  


  2. How do I add delay to any video before starting to merge ?
     
Currently the videos are going out of sync if all the participants don't join at the same time which is highly unlikely to happen in a real world scenario.

     


  


  


  Any pointer in the right direction will be highly appreciated.

  


  Here is a log sample from FFmpeg (or see the full log link) :

  


  ffmpeg version 4.2.4-1ubuntu0.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.3.0-10ubuntu2)
  configuration: --prefix=/usr --extra-version=1ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Input #0, matroska,webm, from '3.webm':
  Metadata:
    title           : FFmpeg
    ENCODER         : Lavf58.29.100
  Duration: 00:01:39.63, start: 0.000000, bitrate: 707 kb/s
    Stream #0:0: Video: vp8, yuv420p(tv, bt470bg/unknown/unknown, progressive), 1280x720, SAR 1:1 DAR 16:9, 1k tbr, 1k tbn, 1k tbc (default)
    Metadata:
      DURATION        : 00:01:39.618000000
    Stream #0:1: Audio: opus, 48000 Hz, stereo, fltp (default)
    Metadata:
      DURATION        : 00:01:39.629000000
Input #1, matroska,webm, from '4.webm':
  Metadata:
    title           : FFmpeg
    ENCODER         : Lavf58.29.100
  Duration: 00:02:39.07, start: 0.000000, bitrate: 708 kb/s
    Stream #1:0: Video: vp8, yuv420p(tv, bt470bg/unknown/unknown, progressive), 1280x720, SAR 1:1 DAR 16:9, 1k tbr, 1k tbn, 1k tbc (default)
    Metadata:
      DURATION        : 00:02:39.050000000
    Stream #1:1: Audio: opus, 48000 Hz, stereo, fltp (default)
    Metadata:
      DURATION        : 00:02:39.068000000
Stream mapping:
  Stream #0:0 (vp8) -> scale
  Stream #0:1 (opus) -> amix:input0
  Stream #1:0 (vp8) -> scale
  Stream #1:1 (opus) -> amix:input1
  hstack -> Stream #0:0 (libx264)
  amix -> Stream #0:1 (aac)
Press [q] to stop, [?] for help
[libx264 @ 0x562b4842a500] using SAR=1/1
[libx264 @ 0x562b4842a500] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x562b4842a500] profile High, level 6.1
[libx264 @ 0x562b4842a500] 264 - core 155 r2917 0a84d98 - 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=18 lookahead_threads=3 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 'output-new.mp4':
  Metadata:
    title           : FFmpeg
    encoder         : Lavf58.29.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p(progressive), 2560x720 [SAR 1:1 DAR 32:9], q=-1--1, 1k fps, 16k tbn, 1k tbc (default)
    Metadata:
      encoder         : Lavc58.54.100 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), 48000 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      encoder         : Lavc58.54.100 aac

frame=  129 fps=0.0 q=33.0 size=       0kB time=00:00:00.23 bitrate=   1.6kbits/s dup=123 drop=0 speed=0.44x    
frame=  257 fps=228 q=33.0 size=       0kB time=00:00:00.51 bitrate=   0.8kbits/s dup=243 drop=0 speed=0.455x    
frame=  379 fps=224 q=33.0 size=     256kB time=00:00:00.73 bitrate=2855.1kbits/s dup=358 drop=0 speed=0.434x    
frame=  497 fps=222 q=33.0 size=     256kB time=00:00:00.86 bitrate=2431.5kbits/s dup=469 drop=0 speed=0.386x    
 
...
More than 1000 frames duplicated
...
  
frame=158751 fps=196 q=33.0 size=  134656kB time=00:02:39.00 bitrate=6937.4kbits/s dup=151385 drop=0 speed=0.196x    
frame=158851 fps=196 q=33.0 size=  134912kB time=00:02:39.00 bitrate=6950.6kbits/s dup=151482 drop=0 speed=0.196x    
frame=158983 fps=196 q=33.0 size=  134912kB time=00:02:39.00 bitrate=6950.6kbits/s dup=151610 drop=0 speed=0.196x    
frame=159081 fps=196 q=-1.0 Lsize=  137197kB time=00:02:39.07 bitrate=7065.2kbits/s dup=151706 drop=0 speed=0.196x    

video:132693kB audio:2494kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.486001%

[libx264 @ 0x562b4842a500] frame I:637   Avg QP:17.73  size:123895
[libx264 @ 0x562b4842a500] frame P:40088 Avg QP:19.73  size:  1134
[libx264 @ 0x562b4842a500] frame B:118356 Avg QP:27.54  size:    97
[libx264 @ 0x562b4842a500] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
[libx264 @ 0x562b4842a500] mb I  I16..4: 11.1% 67.3% 21.6%
[libx264 @ 0x562b4842a500] mb P  I16..4:  0.1%  0.1%  0.0%  P16..4:  2.6%  0.4%  0.3%  0.0%  0.0%    skip:96.5%
[libx264 @ 0x562b4842a500] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.7%  0.0%  0.0%  direct: 0.0%  skip:99.3%  L0:38.7% L1:61.3% BI: 0.0%
[libx264 @ 0x562b4842a500] 8x8 transform intra:66.8% inter:71.4%
[libx264 @ 0x562b4842a500] coded y,uvDC,uvAC intra: 81.8% 89.5% 72.3% inter: 0.2% 0.4% 0.0%
[libx264 @ 0x562b4842a500] i16 v,h,dc,p: 25% 21% 17% 37%
[libx264 @ 0x562b4842a500] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 33% 22% 12%  4%  5%  6%  6%  6%  6%
[libx264 @ 0x562b4842a500] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 42% 24%  6%  4%  5%  5%  6%  4%  4%
[libx264 @ 0x562b4842a500] i8c dc,h,v,p: 42% 24% 26%  9%
[libx264 @ 0x562b4842a500] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x562b4842a500] ref P L0: 82.4% 11.5%  5.3%  0.8%
[libx264 @ 0x562b4842a500] ref B L0: 83.0% 16.9%  0.1%
[libx264 @ 0x562b4842a500] ref B L1: 94.9%  5.1%
[libx264 @ 0x562b4842a500] kb/s:6833.11
[aac @ 0x562b4842b540] Qavg: 239.393


  


• 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. 

• Google Analytics 4 and GDPR : Everything You Need to Know

  17 mai 2022, par Erin

  Four years have passed since the European General Data Protection Regulation (GDPR, also known as DSGVO in German, and RGPD in French) took effect.

  That’s ample time to get compliant, especially for an organisation as big and innovative as Google. Or is it ? 

  If you are wondering how GDPR affects Google Analytics 4 and what the compliance status is at present, here’s the lowdown. 

  Is Google Analytics 4 GDPR Compliant ?

  No. As of mid-2022, Google Analytics 4 (GA4) isn’t fully GDPR compliant. Despite adding extra privacy-focused features, GA4 still has murky status with the European regulators. After the invalidation of the Privacy Shield framework in 2020, Google is yet to regulate EU-US data protection. At present, the company doesn’t sufficiently protect EU citizens’ and residents’ data against US surveillance laws. This is a direct breach of GDPR.

  Google Analytics and GDPR : a Complex Relationship 

  European regulators have scrutinised Google since GDPR came into effect in 2018.

  While the company took steps to prepare for GDPR provisions, it didn’t fully comply with important regulations around user data storage, transfer and security.

  The relationship between Google and EU regulators got more heated after the Court of Justice of the European Union (CJEU) invalidated the Privacy Shield — a leeway Google used for EU-US data transfers. After 2020, GDPR litigation against Google followed. 

  This post summarises the main milestones in this story and explains the consequences for Google Analytics users. 

  

  2018 : Google Analytics Meets GDPR 

  In 2018, the EU adopted the General Data Protection Regulation (GDPR) — a set of privacy and data security laws, covering all member states. Every business interacting with EU citizens and/or residents had to comply.

  GDPR harmonised data protection laws across member states and put down extra provisions for what constitutes sensitive personal information (or PII). Broadly, PII includes any data about the person’s :

  • Racial or ethnic origin 
  • Employment status 
  • Religious or political beliefs
  • State of health 
  • Genetic or biometric data 
  • Financial records (such as payment method data)
  • Address and phone numbers 

  Businesses were barred from collecting this information without explicit consent (and even with it in some cases). If collected, such sensitive information is also subject to strict requirements on how it should be stored, secured, transferred and used. 

  7 Main GDPR Principles Explained 

  Article 5 of the GDPR lays out seven main GDPR principles for personal data and privacy protection : 

  • Lawfulness, fairness and transparency — data must be obtained legally, collected with consent and in adherence to laws. 
  • Purpose limitation — all personal information must be collected for specified, explicit and legal purposes. 
  • Data minimisation — companies must collect only necessary and adequate data, aligned with the stated purpose. 
  • Accuracy — data accuracy must be ensured at all times. Companies must have mechanisms to erase or correct inaccurate data without delays. 
  • Storage limitation — data must be stored only for as long as the stated purpose suggests. Though there’s no upper time limit on data storage. 
  • Integrity and confidentiality (security) — companies must take measures to ensure secure data storage and prevent unlawful or unauthorised access to it. 
  • Accountability — companies must be able to demonstrate adherence to the above principles. 

  Google claimed to have taken steps to make all of their products GDPR compliant ahead of the deadline. But in practice, this wasn’t always the case.

  In March 2018, a group of publishers admonished Google for not providing them with enough tools for GDPR compliance :

  “[Y]ou refuse to provide publishers with any specific information about how you will collect, share and use the data. Placing the full burden of obtaining new consent on the publisher is untenable without providing the publisher with the specific information needed to provide sufficient transparency or to obtain the requisite specific, granular and informed consent under the GDPR.”

  The proposed Google Analytics GDPR consent form was hard to implement and lacked customisation options. In fact, Google “makes unilateral decisions” on how the collected data is stored and used. 

  Users had no way to learn about or control all intended uses of people’s data — which made compliance with the second clause impossible. 

  Unsurprisingly, Google was among the first companies to face a GDPR lawsuit (together with Facebook). 

  By 2019, French data regulator CNIL, successfully argued that Google wasn’t sufficiently disclosing its data collection across products — and hence in breach of GDPR. After a failed appeal, Google had to pay a €50 million fine and promise to do better. 

  2019 : Google Analytics 4 Announcement 

  Throughout 2019, Google rightfully attempted to resolve some of its GDPR shortcomings across all products, Google Universal Analytics (UA) included. 

  They added a more visible consent mechanism for online tracking and provided extra compliance tips for users to follow. In the background, Google also made tech changes to its data processing mechanism to get on the good side of regulations.

  Though Google addressed some of the issues, they missed others. A 2019 independent investigation found that Google real-time-bidding (RTB) ad auctions still used EU citizens’ and residents’ data without consent, thanks to a loophole called “Push Pages”. But they managed to quickly patch this up before the allegations had made it to court. 

  In November 2019, Google released a beta version of the new product version — Google Analytics 4, due to replace Universal Analytics. 

  GA4 came with a set of new privacy-focused features for ticking GDPR boxes such as :

  • Data deletion mechanism. Users can now request to surgically extract certain data from the Analytics servers via a new interface. 
  • Shorter data retention period. You can now shorten the default retention period to 2 months by default (instead of 14 months) or add a custom limit.  
  • IP Anonymisation. GA4 doesn’t log or store IP addresses by default. 

  Google Analytics also updated its data processing terms and made changes to its privacy policy. 

  Though Google made some progress, Google Analytics 4 still has many limitations — and isn’t GDPR compliant. 

  2020 : Privacy Shield Invalidation Ruling 

  As part of the 2018 GDPR preparations, Google named its Irish entity (Google Ireland Limited) as the “data controller” legally responsible for EEA and Swiss users’ information. 

  The company announcement says : 

  

  Initially, Google assumed that this legal change would help them ensure GDPR compliance as “legally speaking” a European entity was set in charge of European data. 

  Practically, however, EEA consumers’ data was still primarily transferred and processed in the US — where most Google data centres are located. Until 2020, such cross-border data transfers were considered legal thanks to the Privacy Shield framework. 

  But in July 2020, The EU Court of Justice ruled that this framework doesn’t provide adequate data protection to digitally transmitted data against US surveillance laws. Hence, companies like Google can no longer use it. The Swiss Federal Data Protection and Information Commissioner (FDPIC) reached the same conclusion in September 2020. 

  The invalidation of the Privacy Shield framework put Google in a tough position.

   Article 14. f of the GDPR explicitly states : 

  “The controller (the company) that intends to carry out a transfer of personal data to a recipient (Analytics solution) in a third country or an international organisation must provide its users with information on the place of processing and storage of its data”.

  Invalidation of the Privacy Shield framework prohibited Google from moving data to the US. At the same time, GDPR provisions mandated that they must disclose proper data location. 

  But Google Analytics (like many other products) had no a mechanism for : 

  • Guaranteeing intra-EU data storage 
  • Selecting a designated regional storage location 
  • Informing users about data storage location or data transfers outside of the EU 

  And these factors made Google Analytics in direct breach of GDPR — a territory, where they remain as of 2022.

  2020-2022 : Google GDPR Breaches and Fines 

  The 2020 ruling opened Google to GDPR lawsuits from country-specific data regulators.

  Google Analytics in particular was under a heavy cease-fire. 

  • Sweden first fined Google for violating GDPR for no not fulfilling its obligations to request data delisting in 2020. 
  • France rejected Google Analytics 4 IP address anonymisation function as a sufficient measure for protecting cross-border data transfers. Even with it, US intelligence services can still access user IPs and other PII. France declared Google Analytics illegal and pressed a €150 million fine. 
  • Austria also found Google Analytics GDPR non-compliant and proclaimed the service as “illegal”. The authority now seeks a fine too. 

  The Dutch Data Protection Authority and  Norwegian Data Protection Authority also found Google Analytics guilty of a GDPR breach and seek to limit Google Analytics usage. 

  New privacy controls in Google Analytics 4 do not resolve the underlying issue — unregulated, non-consensual EU-US data transfer. 

  Google Analytics GDPR non-compliance effectively opens any website tracking or analysing European visitors to legal persecution.

  In fact, this is already happening. noyb, a European privacy-focused NGO, has already filed over 100 lawsuits against European websites using Google Analytics.

  2022 : Privacy Shield 2.0. Negotiations

  Google isn’t the only US company affected by the Privacy Shield framework invalidation. The ruling puts thousands of digital companies at risk of non-compliance.

  To settle the matter, US and EU authorities started “peace talks” in spring 2022.

  European Commission President Ursula von der Leyen said that they are working with the Biden administration on the new agreement that will “enable predictable and trustworthy data flows between the EU and US, safeguarding the privacy and civil liberties.” 

  However, it’s just the beginning of a lengthy negotiation process. The matter is far from being settled and contentious issues remain as we discussed on Twitter (come say hi !).

  

  For one, the US isn’t eager to modify its surveillance laws and is mostly willing to make them “proportional” to those in place in the EU. These modifications may still not satisfy CJEU — which has the power to block the agreement vetting or invalidate it once again. 

  While these matters are getting hashed out, Google Analytics users, collecting data about EU citizens and/or residents, remain on slippery grounds. As long as they use GA4, they can be subject to GDPR-related lawsuits. 

  To Sum It Up 

  • Google Analytics 4 and Google Universal Analytics are not GDPR compliant because of Privacy Shield invalidation in 2020. 
  • French and Austrian data watchdogs named Google Analytics operations “illegal”. Swedish, Dutch and Norwegian authorities also claim it’s in breach of GDPR. 
  • Any website using GA for collecting data about European citizens and/or residents can be taken to court for GDPR violations (which is already happening). 
  • Privacy Shield 2.0 Framework discussions to regulate EU-US data transfers have only begun and may take years. Even if accepted, the new framework(s) may once again be invalidated by local data regulators as has already happened in the past. 

  Time to Get a GDPR Compliant Google Analytics Alternative 

  Retaining 100% data ownership is the optimal path to GDPR compliance.

  By selecting a transparent web analytics solution that offers 100% data ownership, you can rest assured that no “behind the scenes” data collection, processing or transfers take place. 

  Unlike Google Analytics 4, Matomo offers all of the features you need to be GDPR compliant : 

  • Full data anonymisation 
  • Single-purpose data usage 
  • Easy consent and an opt-out mechanism 
  • First-party cookies usage by default 
  • Simple access to collect data 
  • Fast data removals 
  • EU-based data storage for Matomo Cloud (or storage in the country of your choice with Matomo On-Premise)

  Learn about your audiences in a privacy-centred way and protect your business against unnecessary legal exposure. 

  Start your 21-day free trial (no credit card required) to see how fully GDPR-compliant website analytics works ! 

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