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• A Complete Guide to Metrics in Google Analytics

  11 janvier 2024, par Erin

  There’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.

  However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.

  What is a metric in Google Analytics ?

  In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app. 

  Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are : 

  • Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
  • Total Users : The cumulative count of individuals who accessed your site within a specified date range.
  • Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.

  

  Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.

  GA4 metrics vs. dimensions

  GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together : 

  • “Session duration” = metric, “device type” = dimension 
    • In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
  • “Bounce rate” = metric, “traffic source/medium” = dimension 
    • Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing. 
  • “Conversion rate” = metric, “Landing page” = dimension 
    • When the conversion rate data is segmented by landing page, you can better see the most effective landing pages. 

  You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.

  How do Google Analytics metrics work ?

  Before diving into the most important metrics you should track, let’s review how metrics in GA4 work. 

  

  1. Tracking code implementation

  The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.

  2. Data collection

  As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.

  3. Data processing algorithms

  When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.

  4. Segmentation and customisation

  As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.

  5. Report generation

  Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.

  What are the most important Google Analytics metrics to track ? 

  In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4. 

  

  1. Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in. 
  2. Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content. 
  3. Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate. 
  4. Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content. 
  5. Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches. 
  6. Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation. 
  7. Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers. 
  8. Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.

  Examples of how Matomo can elevate your web analytics

  Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.

  

  1. Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.

  

  1. Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.

  3. Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.

  

  4. Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.

     Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.

     See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.

     Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.

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  Final thoughts

  Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience. 

  Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data. 

  Start your 21-day free trial of Matomo — no credit card required.

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• Use Google Analytics and risk fines, after CJEU ruling on Privacy Shield

  27 août 2020, par Joselyn Khor — Privacy
  EU websites using Google Analytics and Facebook are being targeted by European privacy group noyb after the invalidation of the Privacy Shield. They filed a complaint against 101 websites for continuing to send data to the US. 

  “A quick analysis of the HTML source code of major EU webpages shows that many companies still use Google Analytics or Facebook Connect one month after a major judgment by the Court of Justice of the European Union (CJEU) - despite both companies clearly falling under US surveillance laws, such as FISA 702. Neither Facebook nor Google seem to have a legal basis for the data transfers.”

  

  The Privacy Shield previously allowed for EU data to be transferred to the US. However, this was invalidated by the Court of Justice of the European Union (CJEU) on July 16, 2020. The CJEU deemed it illegal for any websites to transfer the personal data of European citizens to the US. 

  They also made it clear in a press release that “data subjects can claim compensation for inadmissible data exports (marginal no. 143 of the judgment). This should in particular include non-material damage (“compensation for pain and suffering”) and must be of a deterrent amount under European law.” Which puts extra financial pressure on websites to take the new ruling seriously.

  Immediate action is required after Google Privacy Shield invalidation

  The Berlin Commissioner for Data Protection and Freedom of Information therefore calls on all those responsible under its supervision to observe the decision of the ECJ [CJEU]. Those responsible who transfer personal data to the USA - especially when using cloud services - are now required to immediately switch to service providers in the European Union or in a country with an adequate level of data protection.

  As the ruling is effective immediately, there’s a pressing need for websites using Google Analytics to act, or face getting fined.

  What does this mean for you ?

  If you’re using Google Analytics the safest bet is to stop using it immediately. 

  "Neither Google Analytics nor Facebook Connect are necessary for the operation of these websites and could therefore have been replaced or at least deactivated in the meantime."

  If you still need to use it, then you’ll need to inform your visitors via a clear consent screen. This banner needs to make clear their personal data will be sent to the US, and to educate them about any potential risk related to this. They will then need to explicitly agree to this. 

  Another downside of cookie consent screens is that you may also suffer a damaging loss of visitors. After implementing cookie consent best practices, the UK’s data regulator the Information Commissioner’s Office (ICO) found a 90% drop in traffic, “implying a ninety percent drop in opt-in rates.”

  With an acceptance rate for such consent screens being lower than 10% your analytics becomes guesswork rather than science. 

  Looking for a privacy-respecting alternative to Google Analytics ?

  Privacy compliant Matomo Analytics is one of the best Google Analytics alternatives availalble. 

  With Matomo you’re able to continue using analytics without facing the wrath of both the GDPR and the CJEU. Matomo On-Premise lets you choose where your data is stored, so you can ensure no data is processed in the US. 

  Matomo is privacy-friendly and can be tweaked to comply with all privacy laws. Including the GDPR, HIPAA, CCPA and PECR. The benefits of this include : not needing to use tracking or cookie consent screens (like with GA) ; and avoiding fines because no personal data is collected. You also get 100% accurate data and the ability to protect your user’s privacy.

  Download Matomo

  

  Is your EU business at risk of being fined for using Google Analytics ?

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• Unrecognized option 'c copy'

  18 mars 2017, par Eser Comak

  I have been working on a script as a part of both learning process and creating handy tools. I am trying to loop over a list of video files to extract a certain part of each video on the list. By looking at example scripts and ffmpeg documentation I finally came up with this :

  import os
  
  import sys
  
  import subprocess as sp
  
  
  
  from moviepy.tools import subprocess_call
  
  
  
  def ffmpeg_extract_pandomim_subclip():
  
  
  
      with open('videolist.txt') as f:
  
          lines = f.readlines()
  
      lines = [x.strip() for x in lines]
  
  
  
      for video in lines:
  
          name, ext = os.path.splitext(video)
  
          targetname = "%s-pandomim%s" % (name, ext)
  
          t1 = "00:10:00"
  
          t2 = "00:15:00"
  
          cmd = ["ffmpeg",
  
                 "-i", "%s%s" % (name, ext),
  
                 "-ss", t1,
  
                 "-to", t2, "-c copy", targetname]
  
  
  
          subprocess_call(cmd)
  
  
  
  ffmpeg_extract_pandomim_subclip()

  I know this is not the ideal way to do it : I created a videolist.txt and listed all the video file names in that txt file, line by line,(T1-1.mp4, T1-2.mp4,... ) that share the same folder with the python script "new 1.py" and the actual videos which are T1-1.mp4, T1-2.mp4,...

  The error I am getting really confuses me because when I use -c copy from cmd it works just fine.

  The full error is :

  C:\Users\çomak\AppData\Local\Programs\Python\Python35-32\python.exe "C:/ffmpeg/bin/new 1.py"
  
  
  
  [MoviePy] Running:
  
  >>> ffmpeg -i T1-1.mp4 -ss 00:10:00 -to 00:15:00 -c copy T1-1-pandomim.mp4
  
  [MoviePy] This command returned an error !Traceback (most recent call last):
  
    File "C:/ffmpeg/bin/new 1.py", line 28, in <module>
  
      ffmpeg_extract_pandomim_subclip()
  
    File "C:/ffmpeg/bin/new 1.py", line 25, in ffmpeg_extract_pandomim_subclip
  
      subprocess_call(cmd)
  
    File "C:\Users\çomak\AppData\Local\Programs\Python\Python35-32\lib\site-packages\moviepy\tools.py", line 48, in subprocess_call
  
      raise IOError(err.decode('utf8'))
  
  OSError: ffmpeg version N-83975-g6c4665d Copyright (c) 2000-2017 the FFmpeg developers
  
    built with gcc 6.3.0 (GCC)
  
    configuration: --enable-gpl --enable-version3 --enable-cuda --enable-cuvid --enable-d3d11va --enable-dxva2 --enable-libmfx --enable-nvenc --enable-avisynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enable-iconv --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libfreetype --enable-libgme --enable-libgsm --enable-libilbc --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librtmp --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-lzma --enable-zlib
  
    libavutil      55. 48.100 / 55. 48.100
  
    libavcodec     57. 83.100 / 57. 83.100
  
    libavformat    57. 66.104 / 57. 66.104
  
    libavdevice    57.  3.100 / 57.  3.100
  
    libavfilter     6. 76.100 /  6. 76.100
  
    libswscale      4.  3.101 /  4.  3.101
  
    libswresample   2.  4.100 /  2.  4.100
  
    libpostproc    54.  2.100 / 54.  2.100
  
  Unrecognized option 'c copy'.
  
  Error splitting the argument list: Option not found
  
  
  
  
  
  Process finished with exit code 1
  
  </module>

  I am using Pycharm and if I remove the -c copy part it works, but the process is slow... With -c copy, it is much faster.

  I appreciate your time and effort to help me out !