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• Privacy-friendly analytics : The benefits of an ethical, GDPR-compliant platform

  13 juin, par Joe

  Your visitors shouldn’t feel like you’re spying on them — even if you’re just trying to improve the user experience or track your marketing efforts. 

  While many analytics platforms make customers feel that way thanks to intrusive cookie consent banners and highly personalised ads, there is a growing movement towards ethical, privacy-friendly analytics.

  In this article, you’ll learn what privacy-friendly analytics is, why it matters, what to look for in a solution and which of the leading providers is right for you. 

  What is privacy-friendly analytics ? 

  Privacy-friendly analytics is a form of website analytics that collects and analyses data in a way that respects the user’s privacy. It’s a type of ethical web analytics.

  Privacy-friendly platforms limit personal data collection and anonymise individual user data while being transparent about collection and tracking methods. They help companies adhere to data protection laws (like GDPR, CCPA, and HIPAA) and new privacy laws (like OCPA, FDBR, and TDPSA) without configuring custom settings. 

  Why use privacy-friendly analytics ? 

  Millions of businesses choose privacy-friendly analytics platforms like Matomo. Here are a few reasons why : 

  Build trust with customers

  Research shows that the vast majority of consumers don’t trust companies with their data, believing that they prioritise profits over data protection. 

  Privacy-friendly analytics can help businesses prove they aren’t out to profit from consumer data and regain customer trust. This can ultimately boost revenue. According to Cisco’s Data Privacy Benchmark Study, organisations gain $180 for every $100 spent on privacy. 

  Comply with privacy regulations

  Data privacy regulations, such as GDPR, protect consumer privacy and establish strict rules governing how businesses can collect and use personal data.

  The cost of non-compliance is high. Under GDPR, fines can be up to €20 million, or 4% of worldwide annual revenue.

  Thanks to features like data anonymisation and the default use of first-party cookies, privacy-friendly analytics platforms can support and strengthen compliance efforts. 

  In fact, the French Data Protection Authority (CNIL) approved Matomo as one of the only web analytics tools to collect data without tracking consent.

  Minimise the impact of a breach

  According to IBM’s Cost of a Data Breach report, the average cost of a data breach is nearly $4.5 million. The more personally identifiable information (PII) is involved, the higher the fines and penalties. 

  A privacy-friendly analytics tool can reduce the potential impact of a breach by minimising the amount of personal information you hold. 

  Is Google Analytics privacy-friendly ?

  Google may be the best-known analytics platform, but it’s not the best choice for businesses that want to collect data responsibly and ethically. 

  Here are just a few of Google Analytics’s privacy issues :

  • It uses analytics data to run its advertising business.
  • It may train large language models like Gemini with analytics data.
  • It requires a specific setup to be GDPR compliant that isn’t available out of the box.

  Google Analytics’s ongoing issues with privacy laws like GDPR also raise doubt. The French and Austrian Data Protection Authorities have banned Google Analytics in the past, and there is no guarantee they won’t do so again. 

  What to look for in privacy-friendly analytics ?

  Several privacy-friendly analytics tools are available. To find the right one for your brand, look for the following features.

  Data ownership

  Choose a provider that gives you as much control over your users’ data as possible. Ideally, this will be via an on-site solution where you store data on your servers. For cloud-based options, ensure your analytics provider can’t access, use or sell it.

  With 100% data ownership, you have the power to protect your users’ privacy. You know where your customer data is stored and what’s happening to it without external influence.

  Open source

  The only genuinely privacy-friendly software is open-source software. Open-source software means anyone can review the code to ensure it does what it promises — in this case, maximising privacy. 

  Matomo is an open-source software company. Our source code is on GitHub, where everyone can see precisely how our platform tracks and stores user data. A community of developers also regularly examines and reviews our code to further strengthen security. 

  Data anonymisation 

  Privacy-friendly analytics should allow marketers to completely anonymise the data they collect. They achieve this through several techniques like IP anonymisation and pseudonymised user IDs that modify or remove personally identifiable data so it can’t be linked to individuals.

  

  Matomo’s data anonymisation settings 

  In Matomo, for example, you can anonymise the following things in the platform’s Privacy settings :

  • IP address
  • Location
  • User ID

  IP address anonymisation is enabled by default in Matomo.

  No data sampling 

  Data sampling involves extrapolating analytics reports from an incomplete data set. Google Analytics uses this practice and relies on estimates, leading to incomplete and potentially inaccurate results.

  Privacy-friendly analytics should provide 100% accurate insights without making assumptions about your users’ data.

  GDPR compliance

  Privacy-friendly web analytics platforms adhere to even the strictest privacy laws, including GDPR, HIPAA and CCPA, thanks to the following features :

  • Data anonymisation
  • Cookieless tracking
  • EU data storage
  • First-party cookies by default

  

  Matomo data subject access request settings
  (Image Source)

  Privacy-first platforms also make it easy for companies to fulfil data subject access requests. In Matomo, for example, a dedicated feature lets you find, download and delete all of the data you hold about specific individuals. 

  Cookieless tracking

  Cookieless tracking is a form of visitor tracking that uses methods other than cookies to identify individual users. It is more privacy-friendly because no personal data is collected, and users can withhold consent from cookie banners.

  Matomo uses the most privacy-friendly industry-leading cookieless tracking method, config_id, to anonymously track visitors without fingerprinting them. 

  Top 3 privacy-friendly analytics platforms

  We’ve shortlisted three of the leading privacy-friendly analytics platforms. Learn what they offer, what makes them different and how much they cost.

  Matomo

  Matomo is an open-source web analytics tool and privacy-focused Google Analytics alternative trusted by over one million sites in over 190 countries and over 50 languages. 

  

  Matomo dashboard

  Matomo prioritises privacy and keeping businesses compliant with global privacy regulations like GDPR, CCPA and HIPAA. The data you collect is 100% accurate and yours alone. We don’t share it or use it for other purposes. 

  Benefits

  • Matomo’s all-in-one solution offers traditional web and behavioural analytics, such as heatmaps and session recordings. It also includes a free, open-source tag manager. 
  • Matomo gives you the choice of where to store your user’s data. With Matomo Cloud, that’s in our European servers. With Matomo On-Premise, that’s on your servers.
  • Matomo is open-source. Hundreds of independent developers have reviewed our code, and you can view it yourself on GitHub.

  Pricing 

  Hosting Matomo On-Premise is free, while Matomo Cloud costs $26 per month. 

  Fathom

  Fathom Analytics is a simple, easy-to-use alternative to Google Analytics that puts a premium on privacy. 

  

  Fathom dashboard
  (Image Source)

  Fathom has made its platform as easy to use as possible. You can install Fathom on any website or CMS using a single line of code. It also means the platform won’t massively impact your site’s speed or SEO performance. 

  Benefits

  • Fathom complies with all major privacy regulations, including GDPR and CCPA.
  • Fathom doesn’t sample data. It also blocks bots and scrapers, so you only see human visitors.
  • Fathom anonymises IP addresses, so you don’t have to show cookie banners.

  Drawbacks

  • Fathom doesn’t offer many of Matomo’s advanced features like e-commerce tracking, heatmaps, and session recordings.
  • The premium version of Fathom is not open-source. 

  Pricing 

  From $15 per month.

  Plausible

  Plausible Analytics is an open-source, privacy-friendly analytics tool built and hosted in the EU.

  

  Plausible dashboard
  (Image Source)

  The platform launched in 2019 as a lightweight, easy-to-use alternative to Google Analytics. Its simplicity is a big selling point. Instead of dozens of menus, it presents the information you need on a single page.

  Benefits

  • Plausible boasts an ultra-lightweight script, which means it has a minimal impact on page loading times. 
  • Plausible is GDPR and CCPA-compliant by design, so there’s no need for cookie banners.
  • Plausible is an open-source software with the source code available on GitHub.

  Drawbacks

  • Plausible lacks advanced privacy controls like a GDPR manager.
  • It has none of Matomo’s advanced features like A/B testing, session recordings or heatmaps. 

  Pricing 

  From $9 per month

  Try Matomo for free

  Ready to try a privacy-friendly analytics solution ? Making the switch is easy with Matomo, as it’s one of the only platforms to import historical Google Analytics data. You can also try Matomo for free for 21 days — no credit card required. 

• ffmpeg muxing overhead converting avi to mp4

  8 avril 2017, par Slightz

  I have been trying to convert Avi to mp4 and Mkv to mp4. About half way the conversion stops and I get this error message.

  frame=39363 fps=133 q=-1.0 Lsize=   89951kB time=00:21:52.06 bitrate= 561.6kbits/s dup=7907 drop=0 speed=4.44x    
  
  video:67736kB audio:20837kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.556231%
  
  [libx264 @ 0x4607020] frame I:392   Avg QP:20.71  size: 34889
  
  [libx264 @ 0x4607020] frame P:12191 Avg QP:20.84  size:  3857
  
  [libx264 @ 0x4607020] frame B:26780 Avg QP:22.63  size:   324
  
  [libx264 @ 0x4607020] consecutive B-frames:  2.7% 15.5% 12.8% 69.0%
  
  [libx264 @ 0x4607020] mb I  I16..4: 23.7% 36.4% 39.9%
  
  [libx264 @ 0x4607020] mb P  I16..4:  1.5%  2.2%  0.9%  P16..4: 30.4%  4.7%  4.7%  0.0%  0.0%    skip:55.5%
  
  [libx264 @ 0x4607020] mb B  I16..4:  0.0%  0.1%  0.1%  B16..8: 11.9%  0.6%  0.2%  direct: 0.6%  skip:86.6%  L0:31.1% L1:67.6% BI: 1.2%
  
  [libx264 @ 0x4607020] 8x8 transform intra:43.0% inter:46.1%
  
  [libx264 @ 0x4607020] direct mvs  spatial:99.9% temporal:0.1%
  
  [libx264 @ 0x4607020] coded y,uvDC,uvAC intra: 37.6% 66.1% 49.3% inter: 3.0% 8.5% 2.6%
  
  [libx264 @ 0x4607020] i16 v,h,dc,p: 69% 17% 12%  2%
  
  [libx264 @ 0x4607020] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 15% 12% 44%  5%  5%  4%  5%  4%  6%
  
  [libx264 @ 0x4607020] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 16% 14%  7%  8%  7%  8%  7%  8%
  
  [libx264 @ 0x4607020] i8c dc,h,v,p: 43% 28% 23%  6%
  
  [libx264 @ 0x4607020] Weighted P-Frames: Y:0.2% UV:0.2%
  
  [libx264 @ 0x4607020] ref P L0: 73.2%  3.9% 13.1%  4.9%  4.7%  0.1%  0.0%
  
  [libx264 @ 0x4607020] ref B L0: 84.5%  9.6%  4.6%  1.3%
  
  [libx264 @ 0x4607020] ref B L1: 96.6%  3.4%
  
  [libx264 @ 0x4607020] kb/s:422.90
  
  [aac @ 0x460ab80] Qavg: 1245.471

  I have tried different commands I’ve found on google but no luck.

  ffmpeg -i american.dad.s01e01.dvdrip.xvid.repack-omicron.avi -r 30 -vcodec libx264 -vf scale=-1:480 -b:v 500k -bufsize 500K -bt 1600k -threads 0 -profile high -preset slow -acodec aac -ac 2 -ar 44100 -ab 128k converting/out2.mp4
  
  
  
  ffmpeg -i american.dad.s01e01.dvdrip.xvid.repack-omicron.avi -acodec aac -b:a 128k -vcodec libx264 -vf scale=-1:480 -threads 0 -profile high -preset slow -b:v 500k -bufsize 1000k -maxrate 500k -f mp4 converting/out1.mp4
  
  
  
  ffmpeg -re -i american.dad.s01e01.dvdrip.xvid.repack-omicron.avi -r 30 -isync -ac 2 -acodec aac -strict -2 -b:a 80k -ar 22050 -vcodec libx264 -vf scale=-1:480 -crf 18 -profile high -preset slow -b:v 500K -bufsize 500K -maxrate 500K -f mp4 converting/out3.mp4

• Where is moviepy getting the video fps from ?

  7 février 2017, par Gloin

  I am using the Python 3 moviepy module for video editing, and I have a few videos that are taken in slow motion. When imported into moviepy, they are massively sped up, and then sit on the last frame for the rest of their duration. Note that, the videos are supposed to be normal for the first couple and last couple of seconds, then slow in the middle.

  Unfortunately, I cannot provide the actual video for you to test with, but here is the relevant metadata (fetched with the command ffprobe -v quiet -print_format json -show_format -show_streams slo-mo_movie.mov)

  "r_frame_rate": "240/1",                               
  
  "avg_frame_rate": "1679400/39481",
  
  "time_base": "1/2400",

  For comparison, here is the equivalent metadata from a video taken, I think, from the same phone, but without slo-mo :

  "r_frame_rate": "30/1",
  
  "avg_frame_rate": "143160/4771",
  
  "time_base": "1/600",

  I can import the videos in to moviepy with clip = VideoFileClip("path/to/file.mp4"), and then for each run print(clip.fps). The first video prints 2400 (not a typo from me !), and the second 30.

  Here is the moviepy code (in moviepy/video/io/ffmpeg_reader.py) at line 293) that gets the fps :

  # Get the frame rate. Sometimes it's 'tbr', sometimes 'fps', sometimes
  
  # tbc, and sometimes tbc/2...
  
  # Current policy: Trust tbr first, then fps. If result is near from x*1000/1001
  
  # where x is 23,24,25,50, replace by x*1000/1001 (very common case for the fps).
  
  
  
  try:
  
      match = re.search("( [0-9]*.| )[0-9]* tbr", line)
  
      tbr = float(line[match.start():match.end()].split(' ')[1])
  
      result['video_fps'] = tbr
  
  
  
  except:
  
      match = re.search("( [0-9]*.| )[0-9]* fps", line)
  
      result['video_fps'] = float(line[match.start():match.end()].split(' ')[1])
  
  
  
  
  
  # It is known that a fps of 24 is often written as 24000/1001
  
  # but then ffmpeg nicely rounds it to 23.98, which we hate.
  
  coef = 1000.0/1001.0
  
  fps = result['video_fps']
  
  for x in [23,24,25,30,50]:
  
      if (fps!=x) and abs(fps - x*coef) < .01:
  
          result['video_fps'] = x*coef
  
  
  
  if check_duration:
  
      result['video_nframes'] = int(result['duration']*result['video_fps'])+1
  
      result['video_duration'] = result['duration']
  
  else:
  
      result['video_nframes'] = 1
  
      result['video_duration'] = None
  
  # We could have also recomputed the duration from the number
  
  # of frames, as follows:
  
  # >>> result['video_duration'] = result['video_nframes'] / result['video_fps']

  If I set the slo-mo video’s fps using moviepy to 24, it outputs it the same (very fast, then still on the last frame), but if I set the slo-mo video’s fps to 20, then it outputs it correctly.

  Obviously video players like VLC player and Quicktime can correctly work out what frame speed to play, but moviepy/ffmpeg fails. Moviepy/ffmpeg is getting the wrong fps from somewhere.

  So, how can I get moviepy to automatically output them as they are supposed to be without human trial and error like above ?