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• Ffmpeg input seeking - "Invalid NAL unit size"

  30 janvier 2024, par Dimitris

  I'm trying to use ffmpeg to get a 10-second clip from the middle of a video. The execution time of the command is important, that's why I've decided to use combined input & output seeking (as illustrated here).
The input video file is a CMAF with fragmented MP4, duration of 10 minutes.

  


  I'm testing on a Mac, Ffmpeg version is 6.1.1.

  


  This is the command that I'm using :

  


  ffmpeg -nostdin -y -ss 290 -copyts -start_at_zero -i https://devcdn.flowplayer.com/5f07362e-c358-41d0-857a-c64302a3fcc9/cmaf/17bdb16d-71d1-414c-a291-a028bd45b9ec/playlist_360.m3u8 -ss 300.0 -t 10 -vcodec libwebp -lossless 0 -quality 60 -compression_level 2 -loop 0 -an -sn output.webp


  


  Result : no output file is created.

  


  From what I understand it fails to seek position "290" in the video, probably due to "Invalid NAL unit size" errors.

  


  Here's the output :

  


  ffmpeg version N-106797-g580fb6a8c9-tessus Copyright (c) 2000-2022 the FFmpeg developersbuilt with Apple clang version 11.0.0 (clang-1100.0.33.17)configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libmysofa --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvmaf --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-version3 --pkg-config-flags=--static --disable-ffplaylibavutil      57. 24.101 / 57. 24.101libavcodec     59. 27.100 / 59. 27.100libavformat    59. 23.100 / 59. 23.100libavdevice    59.  6.100 / 59.  6.100libavfilter     8. 37.100 /  8. 37.100libswscale      6.  6.100 /  6.  6.100libswresample   4.  6.100 /  4.  6.100libpostproc    56.  5.100 / 56.  5.100Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://devcdn.flowplayer.com/5f07362e-c358-41d0-857a-c64302a3fcc9/cmaf/17bdb16d-71d1-414c-a291-a028bd45b9ec/playlist_360.cmfv':Metadata:major_brand     : isomminor_version   : 1compatible_brands: isomavc1dashcmfccreation_time   : 2024-01-30T07:41:03.000000ZDuration: 00:09:56.54, start: 0.083333, bitrate: 458 kb/sStream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 640x360 [SAR 1:1 DAR 16:9], 1 kb/s, 24 fps, 24 tbr, 90k tbn (default)Metadata:creation_time   : 2024-01-30T07:41:03.000000Zhandler_name    : ETI ISO Video Media Handlervendor_id       : [0][0][0]ffmpeg version 6.1.1-tessus  https://evermeet.cx/ffmpeg/  Copyright (c) 2000-2023 the FFmpeg developers
  built with Apple clang version 11.0.0 (clang-1100.0.33.17)
  configuration: --cc=/usr/bin/clang --prefix=/opt/ffmpeg --extra-version=tessus --enable-avisynth --enable-fontconfig --enable-gpl --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libfreetype --enable-libgsm --enable-libmodplug --enable-libmp3lame --enable-libmysofa --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvmaf --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-version3 --pkg-config-flags=--static --disable-ffplay
  libavutil      58. 29.100 / 58. 29.100
  libavcodec     60. 31.102 / 60. 31.102
  libavformat    60. 16.100 / 60. 16.100
  libavdevice    60.  3.100 / 60.  3.100
  libavfilter     9. 12.100 /  9. 12.100
  libswscale      7.  5.100 /  7.  5.100
  libswresample   4. 12.100 /  4. 12.100
  libpostproc    57.  3.100 / 57.  3.100
[hls @ 0x7fc7bb904280] Skip ('#EXT-X-VERSION:6')
[hls @ 0x7fc7bb904280] Opening 'https://devcdn.flowplayer.com/5f07362e-c358-41d0-857a-c64302a3fcc9/cmaf/17bdb16d-71d1-414c-a291-a028bd45b9ec/playlist_360.cmfv' for reading
    Last message repeated 2 times
Input #0, hls, from '[**]/playlist_360.m3u8':
  Duration: 00:09:56.46, start: 0.083333, bitrate: 0 kb/s
  Program 0 
    Metadata:
      variant_bitrate : 0
  Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 640x360 [SAR 1:1 DAR 16:9], 1 kb/s, 24 fps, 24 tbr, 90k tbn (default)
    Metadata:
      variant_bitrate : 0
      compatible_brands: isomavc1dashcmfc
      handler_name    : ETI ISO Video Media Handler
      vendor_id       : [0][0][0][0]
      encoder         : Elemental H.264
      major_brand     : isom
      minor_version   : 1
      creation_time   : 2024-01-30T07:41:03.000000Z
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> webp (libwebp))
[hls @ 0x7fc7bb904280] Opening 'https://devcdn.flowplayer.com/5f07362e-c358-41d0-857a-c64302a3fcc9/cmaf/17bdb16d-71d1-414c-a291-a028bd45b9ec/playlist_360.cmfv' for reading
    Last message repeated 2 times
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1772342253 > 1534).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1538
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1977545460 > 1481).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1485
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1694403391 > 1582).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1586
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1404850266 > 1661).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1665
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (703351242 > 1680).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1684
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-836978648 > 1751).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1755
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (752797651 > 1867).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1871
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1831058223 > 1833).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 1837
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1238958831 > 2067).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2071
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (435683248 > 2090).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2094
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (2136335178 > 2229).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2233
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1468707300 > 2203).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2207
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (482758774 > 2402).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2406
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1079612217 > 2417).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2421
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-608087491 > 2546).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2550
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-1457748625 > 2527).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2531
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1933919710 > 2734).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2738
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1004643870 > 2803).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2807
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-207765435 > 2988).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2992
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-196888537 > 2306).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2310
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1118966683 > 2620).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2624
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1325583054 > 2715).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2719
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-2003602869 > 2906).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2910
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1666330272 > 3085).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 3089
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (-742329993 > 2593).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2597
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (1326266794 > 2347).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2351
[NULL @ 0x7fc7bb804f40] Invalid NAL unit size (2459776 > 2155).
[NULL @ 0x7fc7bb804f40] missing picture in access unit with size 2159
[https @ 0x7fc7ba022a00] Opening 'https://devcdn.flowplayer.com/5f07362e-c358-41d0-857a-c64302a3fcc9/cmaf/17bdb16d-71d1-414c-a291-a028bd45b9ec/playlist_360.cmfv' for reading
[...]
[vost#0:0/libwebp @ 0x7fc7bbb05780] No filtered frames for output stream, trying to initialize anyway.
Output #0, webp, to 'output.webp':
  Metadata:
    encoder         : Lavf60.16.100
  Stream #0:0(und): Video: webp, yuv420p(progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 24 fps, 1k tbn (default)
    Metadata:
      variant_bitrate : 0
      compatible_brands: isomavc1dashcmfc
      handler_name    : ETI ISO Video Media Handler
      vendor_id       : [0][0][0][0]
      creation_time   : 2024-01-30T07:41:03.000000Z
      major_brand     : isom
      minor_version   : 1
      encoder         : Lavc60.31.102 libwebp
[out#0/webp @ 0x7fc7bbb04900] video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
[out#0/webp @ 0x7fc7bbb04900] Output file is empty, nothing was encoded(check -ss / -t / -frames parameters if used)
frame=    0 fps=0.0 q=0.0 Lsize=       0kB time=N/A bitrate=N/A speed=N/A 


  


  What I've tried so far :

  


  


  1. Downloaded the input file to a local directory and used it as input to ffmpeg - same results.

     


  


  2. Used the mp4 file from the playlist directly as an input to ffmpeg - worked but execution time is very slow

     


  


  3. Emmited the input seeking part (-ss 290 -copyts -start_at_zero) from the command - worked but also very slow in terms of execution time

     


  


  


  Any ideas on why I'm getting "Invalid NAL unit size" and how to make the command work with input seeking ?

  


• What is Funnel Analysis ? A Complete Guide for Quick Results

  25 janvier 2024, par Erin

  Your funnel is leaking.

  You’re losing visitors.

  You’re losing conversions and sales.

  But you don’t know how it’s happening, where it’s happening, or what to do about it.

  The reason ? You aren’t properly analysing your funnels.

  If you want to improve conversions and grow your business, you need to understand how to properly assess your sales funnels to set yourself up for success.

  In this guide, we’ll show you what funnel analysis is, why it’s important, and what steps you need to take to leverage it to improve conversions.

  What is funnel analysis ?

  Every business uses sales funnels, whether they know it or not.

  But most people aren’t analysing them, costing them conversions.

  

  Funnel analysis is a marketing method to analyse the events leading to specific conversion points. 

  It aims to look at the entire journey of potential customers from the moment they first touch base with your website or business to the moment they click “buy.”

  It’s assessing what your audience is doing at every step of the journey.

  By assessing what actions are taking place at scale, you can see where you’re falling short in your sales funnel.

  You’ll see :

  1. Where prospects are falling off.
  2. Where people are converting well.

  By gaining this understanding, you’ll better understand the health of your website’s sales funnels and overall marketing strategy.

  With that knowledge, you can optimise your marketing strategy to patch those leaks, improve conversions and grow your business.

  Why funnel analysis is important

  Funnel analysis is critical because your funnel is your business.

  When you analyse your funnel, you’re analysing your business.

  You’re looking at what’s working and what’s not so you can grow revenue and profit margins.

  Funnel analysis lets you monitor user behaviour to show you the motivation and intention behind their decisions.

  Here are five reasons you need to incorporate funnel analysis into your workflow.

  

  1. Gives insights into your funnel problems

  The core purpose of funnel analysis is to look at what’s going on on your website.

  What are the most effective steps to conversion ?

  Where do users drop off in the conversion process ?

  And which pages contribute the most to conversion or drop-offs ?

  Funnel analysis helps you understand what’s going on with your site visitors. Plus, it helps you see what’s wrong with your funnel.

  If you aren’t sure what’s happening with your funnel, you won’t know what to improve to grow your revenue.

  2. Improves conversions

  When you know what’s going on with your funnel, you’ll know how to improve it.

  To improve your conversion funnel, you need to close the leaks. These are areas where website visitors are falling off.

  It’s the moment the conversion is lost.

  You need to use funnel analysis to give insight into these problem areas. Once you can see where the issue is, you can patch that leak and improve the percentage of visitors who convert.

  For example, if your conversion rate on your flagship product page has plateaued and you can’t figure out how to increase conversions, implementing a funnel analysis tactic like heatmaps will show you that visitors are spending time reading your product description. Still, they’re not spending much time near your call to action.

  

  This might tell you that you need to update your description copy or adjust your button (i.e. colour, size, copy). You can increase conversions by making those changes in your funnel analysis insights.

  3. Improves the customer experience

  Funnel analysis helps you see where visitors spend their time, what elements they interact with and where they fall off.

  One of the key benefits of analysing your funnel is you’ll be able to help improve the experience your visitors have on your website.

  For example, if you have informational videos on a specific web page to educate your visitors, you might use the Media Analytics feature in your web analytics solution to find out that they’re not spending much time watching them.

  This could lead you to believe that the content itself isn’t good or relevant to them.

  But, after implementing session recordings within your funnel analysis, you see people clicking a ton near the play button. This might tell you that they’re having trouble clicking the actual button on the video player due to poor UX.

  In this scenario, you could update the UX on your web page so the videos are easy to click and watch, no matter what device someone uses.

  With more video viewers, you can provide value to your visitors instead of leaving them frustrated trying to watch your videos.

  4. Grows revenue

  This is what you’re likely after : more revenue.

  More often than not, this means you need to focus on improving your conversion rate.

  Funnel analysis helps you find those areas where visitors are exiting so you can patch those leaks up and turn more visitors into customers.

  Let’s say you have a conversion rate of 1.7%.

  You get 50,000 visitors per month.

  Your average order is $82.

  Even if you increase your conversion rate by 10% (to 1.87%) through funnel analysis, here’s the monthly difference in revenue :

  Before : $69,700
  After : $76,670

  In one year, you’ll make nearly $80,000 in additional revenue from funnel analysis alone.

  Different types of funnel analysis

  There are a few different types of funnel analysis.

  How you define success in your funnel all comes down to one of these four pillars.

  Depending on your goals, business and industry, you may want to assess the different funnel analyses at different times.

  1. Pageview funnel analysis

  Pageview funnel analysis is about understanding how well your website content is performing. 

  It helps you enhance user experience, making visitors stay longer on your site. By identifying poor performing pages (pages with high exit rates), you can pinpoint areas that need optimisation for better engagement.

  2. Conversion funnel analysis

  Next up, we’re looking at conversion funnel analysis.

  This type of funnel analysis is crucial for marketers aiming to turn website visitors into action-takers. This involves tracking and optimising conversion goals, such as signing up for newsletters, downloading ebooks, submitting forms or signing up for free trials. 

  The primary goal of conversion funnel analysis is to boost your website’s overall conversion rates.

  3. E-commerce funnel analysis

  For businesses selling products online, e-commerce funnel analysis is essential. 

  It involves measuring whether your products are being purchased and finding drop-off points in the purchasing process. 

  By optimising the e-commerce funnel, you can enhance revenue and improve the overall efficiency of your sales process.

  How to conduct funnel analysis

  Now that you understand what funnel analysis is, why it’s important, and the different types of analysis, it’s time to show you how to do it yourself.

  To get started with funnel analysis, you need to have the right web analytics solution.

  Here are the most common funnel analysis tools and methods you can use :

  1. Funnel analytics

  If you want to choose a single tool to conduct funnel analysis, it’s an all-in-one web analytics tool, like Matomo.

  

  With Matomo’s Funnel Analytics, you can dive into your whole funnel and analyse each step (and each step’s conversion rate).

  

  For instance, if you look at the example above, you can see the proceed rate at each funnel step before the conversion page.

  This means you can improve each proceed rate, to drive more traffic to your conversion page in order to increase conversion rates.

  In the above snapshot from Matomo, it shows visitors starting on the job board overview page, moving on to view specific job listings. The goal is to convert these visitors into job applicants.

  However, a significant issue arises at the job view stage, where 95% of visitors don’t proceed to job application. To increase conversions, we need to first concentrate on improving the job view page.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  2. Heatmaps

  Heatmaps is a behaviour analytics tool that lets you see different visitor activities, including :

  • Mouse movement
  • How far down visitors scroll
  • Clicks

  You can see which elements were clicked on and which weren’t and how far people scroll down your page.

  

  A heatmap lets you see which parts of a page are getting the most attention and which parts go unnoticed by your users.

  For example, if, during your funnel analysis, you see that a lot of visitors are falling off after they land on the checkout page, then you might want to add a heatmap on your checkout page to see where and why people are exiting.

  3. Session recordings

  Want to see what individual users are doing and how they’re interacting with your site ?

  Then, you’ll want to check out session recordings.

  A session recording is a video playback of a visitor’s time on your website.

  

  It’s the most effective method to observe your visitors’ interactions with your site, eliminating uncertainty when identifying areas for funnel improvement.

  Session recordings instill confidence in your optimisation efforts by providing insights into why and where visitors may be dropping off in the funnel.

  4. A/B testing

  If you want to take the guesswork out of optimising your funnel and increasing your conversions, you need to start A/B testing.

  An A/B test is where you create two versions of a web page to determine which one converts better.

  

  For example, if your heatmaps and session recordings show that your users are dropping off near your call to action, it may be time to test a new version.

  You may find that by simply testing a different colour button, you may increase conversions by 20% or more.

  5. Form analytics

  Are you trying to get more leads to fill out forms on your site ?

  Well, Form Analytics can help you understand how your website visitors interact with your signup forms.

  You can view metrics such as starter rate, conversion rate, average hesitation time and average time spent.

  This information allows you to optimise your forms effectively, ultimately maximising your success.

  Let’s look at the performance of a form using Matomo’s Form Analytics feature below.

  

  In the Matomo example, our starter rate stands at a solid 60.1%, but there’s a significant drop to a submitter rate of 29.3%, resulting in a conversion rate of 16.3%.

  Looking closer, people are hesitating for about 16.2 seconds and taking nearly 1 minute 39 seconds on average to complete our form.

  This could indicate our form is confusing and requesting too much. Simplifying it could help increase sign-ups.

  See first-hand how Concrete CMS tripled their leads using Form Analytics in Matomo.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Start optimising your funnels with Matomo today

  If you want to optimise your business, you must optimise your funnels.

  Without information on what’s working and what’s not, you’ll never know if your website changes are making a difference.

  Worse yet, you could have underperforming stages in your funnel, but you won’t know unless you start looking.

  Funnel analysis changes that.

  By analysing your funnels regularly, you’ll be able to see where visitors are leaking out of your funnel. That way, you can get more visitors to convert without generating more traffic.

  If you want to improve conversions and grow revenue today, try Matomo’s Funnel Analytics feature.

  You’ll be able to see conversion rates, drop-offs, and fine-tuned details on each step of your funnel so you can turn more potential customers into paying customers.

  Additionally, Matomo comes equipped with features like heatmaps, session recordings, A/B testing, and form analytics to optimise your funnels with confidence.

  Try Matomo free for 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

• 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.

  Try Matomo for Free

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