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  • Keeping control of your media in your hands

    13 avril 2011, par

    The vocabulary used on this site and around MediaSPIP in general, aims to avoid reference to Web 2.0 and the companies that profit from media-sharing.
    While using MediaSPIP, you are invited to avoid using words like "Brand", "Cloud" and "Market".
    MediaSPIP is designed to facilitate the sharing of creative media online, while allowing authors to retain complete control of their work.
    MediaSPIP aims to be accessible to as many people as possible and development is based on expanding the (...)

  • Participer à sa traduction

    10 avril 2011

    Vous pouvez nous aider à améliorer les locutions utilisées dans le logiciel ou à traduire celui-ci dans n’importe qu’elle nouvelle langue permettant sa diffusion à de nouvelles communautés linguistiques.
    Pour ce faire, on utilise l’interface de traduction de SPIP où l’ensemble des modules de langue de MediaSPIP sont à disposition. ll vous suffit de vous inscrire sur la liste de discussion des traducteurs pour demander plus d’informations.
    Actuellement MediaSPIP n’est disponible qu’en français et (...)

  • Gestion générale des documents

    13 mai 2011, par

    MédiaSPIP ne modifie jamais le document original mis en ligne.
    Pour chaque document mis en ligne il effectue deux opérations successives : la création d’une version supplémentaire qui peut être facilement consultée en ligne tout en laissant l’original téléchargeable dans le cas où le document original ne peut être lu dans un navigateur Internet ; la récupération des métadonnées du document original pour illustrer textuellement le fichier ;
    Les tableaux ci-dessous expliquent ce que peut faire MédiaSPIP (...)

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  • Marketing Cohort Analysis : How To Do It (With Examples)

    12 janvier 2024, par Erin

    The better you understand your customers, the more effective your marketing will become. 

    The good news is you don’t need to run expensive focus groups to learn much about how your customers behave. Instead, you can run a marketing cohort analysis using data from your website analytics.

    A marketing cohort groups your users by certain traits and allows you to drill down to discover why they take the actions on your website they do. 

    In this article, we’ll explain what a marketing cohort analysis is, show you what you can achieve with this analytical technique and provide a step-by-step guide to pulling it off. 

    What is cohort analysis in marketing ?

    A marketing cohort analysis is a form of behavioural analytics where you analyse the behavioural patterns of users who share a similar trait to better understand their actions. 

    These shared traits could be anything like the date they signed up for your product, users who bought your service through a paid ad or email subscribers from the United Kingdom.

    It’s a fantastic way to improve your marketing efforts, allowing you to better understand complex user behaviours, personalise campaigns accordingly and improve your ROI. 

    You can run marketing analysis using an analytics platform like Google Analytics or Matomo. With these platforms, you can measure how cohorts perform using traffic, engagement and conversion metrics.

    An example of marketing cohort chart

    There are two types of cohort analysis : acquisition-based cohort analysis and behavioural-based cohort analysis.

    Acquisition-based cohort analysis

    An acquisition-based cohort divides users by the date they purchased your product or service and tracks their behaviour afterward. 

    For example, one cohort could be all the users who signed up for your product in November. Another could be the users who signed up for your product in October. 

    You could then run a cohort analysis to see how the behaviour of the two cohorts differed. 

    Did the November cohort show higher engagement rates, increased frequency of visits post-acquisition or quicker conversions compared to the October cohort ? Analysing these cohorts can help with refining marketing strategies, optimising user experiences and improving retention and conversion rates.

    As you can see from the example, acquisition-based cohorts are a great way to track the initial acquisition and how user behaviour evolves post-acquisition.

    Behavioural-based cohort analysis

    A behavioural-based cohort divides users by their actions on your site. That could be their bounce rate, the number of actions they took on your site, their average time on site and more.

    View of returning visitors cohort report in Matomo dashboard

    Behavioural cohort analysis gives you a much deeper understanding of user behaviour and how they interact with your website.

    What can you achieve with a marketing cohort analysis ?

    A marketing cohort analysis is a valuable tool that can help marketers and product teams achieve the following goals :

    Understand which customers churn and why

    Acquisition and behavioural cohort analyses help marketing teams understand when and why customers leave. This is one of the most common goals of a marketing cohort analysis. 

    Learn which customers are most valuable

    Want to find out which channels create the most valuable customers or what actions customers take that increase their loyalty ? You can use a cohort analysis to do just that. 

    For example, you may find out you retain users who signed up via direct traffic better than those that signed up from an ad campaign. 

    Discover how to improve your product

    You can even use cohort analysis to identify opportunities to improve your website and track the impact of your changes. For example, you could see how visitor behaviour changes after a website refresh or whether visitors who take a certain action make more purchases. 

    Find out how to improve your marketing campaign

    A marketing cohort analysis makes it easy to find out which campaigns generate the best and most profitable customers. For example, you can run a cohort analysis to determine which channel (PPC ads, organic search, social media, etc.) generates customers with the lowest churn rate. 

    If a certain ad campaign generates the low-churn customers, you can allocate a budget accordingly. Alternatively, if customers from another ad campaign churn quickly, you can look into why that may be the case and optimise your campaigns to improve them. 

    Measure the impact of changes

    You can use a behavioural cohort analysis to understand what impact changes to your website or product have on active users. 

    If you introduced a pricing page to your website, for instance, you could analyse the behaviour of visitors who interacted with that page compared to those who didn’t, using behavioural cohort analysis to gauge the impact of these website changes on engagemen or conversions.

    The problem with cohort analysis in Google Analytics

    Google Analytics is often the first platform marketers turn to when they want to run a cohort analysis. While it’s a free solution, it’s not the most accurate or easy to use and users often encounter various issues

    For starters, Google Analytics can’t process user visitor data if they reject cookies. This can lead to an inaccurate view of traffic and compromise the reliability of your insights.

    In addition, GA is also known for sampling data, meaning it provides a subset rather than the complete dataset. Without the complete view of your website’s performance, you might make the wrong decisions, leading to less effective campaigns, missed opportunities and difficulties in reaching marketing goals.

    How to analyse cohorts with Matomo

    Luckily, there is an alternative to Google Analytics. 

    As the leading open-source web analytics solution, Matomo offers a robust option for cohort analysis. With its 100% accurate data, thanks to the absence of sampling, and its privacy-friendly tracking, users can rely on the data without resorting to guesswork. It is a premium feature included with our Matomo Cloud or available to purchase on the Matomo Marketplace for Matomo On-Premise users.

    Below, we’ll show how you can run a marketing cohort analysis using Matomo.

    Set a goal

    Setting a goal is the first step in running a cohort analysis with any platform. Define what you want to achieve from your analysis and choose the metrics you want to measure. 

    For example, you may want to improve your customer retention rate over the first 90 days. 

    Define cohorts

    Next, create cohorts by defining segmentation criteria. As we’ve discussed above, this could be acquisition-based or behavioural. 

    Matomo makes it easy to define cohorts and create charts. 

    In the sidebar menu, click Visitors > Cohorts. You’ll immediately see Matomo’s standard cohort report (something like the one below).

    Marketing cohort by bounce rate of visitors in Matomo dashboard

    In the example above, we’ve created cohorts by bounce rate. 

    You can view cohorts by weekly, monthly or yearly periods using the date selector and change the metric using the dropdown. Other metrics you can analyse cohorts by include :

    • Unique visitors
    • Return visitors
    • Conversion rates
    • Revenue
    • Actions per visit

    Change the data selection to create your desired cohort, and Matomo will automatically generate the report. 

    Try Matomo for Free

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

    No credit card required

    Analyse your cohort chart

    Cohort charts can be intimidating initially, but they are pretty easy to understand and packed with insights. 

    Here’s an example of an acquisition-based cohort chart from Matomo looking at the percentage of returning visitors :

    An Image of a marketing cohort chart in Matomo Analytics

    Cohorts run vertically. The oldest cohort (visitors between February 13 – 19) is at the top of the chart, with the newest cohort (April 17 – 23) at the bottom. 

    The period of time runs horizontally — daily in this case. The cells show the corresponding value for the metric we’re plotting (the percentage of returning visitors). 

    For example, 98.69% of visitors who landed on your site between February 13 – 19, returned two weeks later. 

    Usually, running one cohort analysis isn’t enough to identify a problem or find a solution. That’s why comparing several cohort analyses or digging deeper using segmentation is important.

    Segment your cohort chart

    Matomo lets you dig deeper by segmenting each cohort to examine their behaviour’s specifics. You can do this from the cohort report by clicking the segmented visitor log icon in the relevant row.

    Segmented visit log in Matomo cohort report
    Segmented cohort visitor log in Matomo

    Segmenting cohorts lets you understand why users behave the way they do. For example, suppose you find that users you purchased on Black Friday don’t return to your site often. In that case, you may want to rethink your offers for next year to target an audience with potentially better customer lifetime value. 

    Start using Matomo for marketing cohort analysis

    A marketing cohort analysis can teach you a lot about your customers and the health of your business. But you need the right tools to succeed. 

    Matomo provides an effective and privacy-first way to run your analysis. You can create custom customer segments based on almost anything, from demographics and geography to referral sources and user behaviour. 

    Our custom cohort analysis reports and colour-coded visualisations make it easy to analyse cohorts and spot patterns. Best of all, the data is 100% accurate. Unlike other web analytics solution or cohort analysis tools, we don’t sample data. 

    Find out how you can use Matomo to run marketing cohort analysis by trialling us free for 21 days. No credit card required.

  • FFMPEG = I tried resizing a video, but got different resolution than I wanted [closed]

    10 janvier 2024, par wakanasakai

    I downloaded a video that had some black bars (left & right), so I used the following command in FFmpeg to make various changes to it. I tested it on a 10 second clip to see what the result would look like.

    


    -ss 00:04:44 -to 00:04:54 -vf "crop=1870:20:20:0","scale=640x480:flags=lanczos","eq=gamma=1.5:saturation=1.3:contrast=1.2"

    


    The original video is an mp4, with a resolution of 1920 x 1080. Besides trying to crop it & adjust the gamma, saturation, & contrast, I also tried to resize it to 640 x 480. Instead, it's resulting resolution is 44880 x 480 ! I have a link to it for anybody who wants to examine it directly. (It's only 487 kb.)
text

    


    I've tried using FFmpeg before, & it never did anything so insane. (It cropped it, & adjusted the gamma a saturation (I didn't test the contrast until THIS time), but it did not resize it at all.)

    


    Here is FFmpeg's log file for it. Guesses as to the cause of the insane result, & advice on how to achieve the DESIRED result (in 1 pass, if possible) are requested.

    


    ffmpeg -hwaccel auto -y -i "/storage/emulated/0/bluetooth/Barbie & the Rockers=1080-Out of this world (1987).mp4" -ss 00:04:44 -to 00:04:54 -vf "crop=1870:20:20:0","scale=640x480:flags=lanczos","eq=gamma=1.5:saturation=1.3:contrast=1.2" "/storage/emulated/0/Movies/Barbie.mp4"

ffmpeg version 6.0 Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 4.9.x (GCC) 20150123 (prerelease)
  configuration: --enable-version3 --enable-gpl --enable-nonfree --disable-indev=v4l2 --enable-libmp3lame --enable-libx264 --enable-libx265 --enable-libvpx --enable-libvorbis --enable-libtheora --enable-libopus --enable-libfdk-aac --enable-libfreetype --enable-libass --enable-libfribidi --enable-fontconfig --enable-pthreads --enable-libxvid --enable-filters --enable-openssl --enable-librtmp --disable-protocol='udp,udplite' --enable-libopencore-amrwb --enable-libopencore-amrnb --enable-libvo-amrwbenc --enable-libspeex --enable-libsoxr --enable-libwebp --enable-libxml2 --enable-libopenh264 --enable-jni --prefix=/home/silentlexx/AndroidstudioProjects/ffmpeg/ffmpeg/build/arm-api18-r13b --sysroot=/home/silentlexx/Android/android-ndk-r13b/platforms/android-18/arch-arm --arch=arm --disable-shared --enable-static --enable-pic --enable-ffmpeg --disable-ffplay --disable-ffprobe --disable-ffnvcodec --disable-avdevice --disable-debug --disable-doc --disable-htmlpages --disable-manpages --disable-podpages --disable-txtpages --disable-symver --cross-prefix=/home/silentlexx/Android/android-ndk-r13b/toolchains/arm-linux-androideabi-4.9/prebuilt/linux-x86_64/bin/arm-linux-androideabi- --target-os=android --enable-cross-compile --pkg-config-flags=--static --extra-libs='-lgnustl_static -lm -lpng -l:libz.so -lpthread' --enable-asm --enable-neon --enable-small
  libavutil      58.  2.100 / 58.  2.100
  libavcodec     60.  3.100 / 60.  3.100
  libavformat    60.  3.100 / 60.  3.100
  libavfilter     9.  3.100 /  9.  3.100
  libswscale      7.  1.100 /  7.  1.100
  libswresample   4. 10.100 /  4. 10.100
  libpostproc    57.  1.100 / 57.  1.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/storage/emulated/0/bluetooth/Barbie & the Rockers=1080-Out of this world (1987).mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp41isomiso2
    creation_time   : 2024-01-04T01:46:07.000000Z
  Duration: 00:45:33.10, start: 0.000000, bitrate: 3404 kb/s
  Stream #0:0[0x1](und): Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1920x1080 [SAR 1:1 DAR 16:9], 3272 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
    Metadata:
      creation_time   : 2023-06-25T13:25:03.000000Z
      vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](eng): Audio: aac (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      creation_time   : 2023-06-25T13:25:03.000000Z
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 0xf38cd180] using SAR=561/8
[libx264 @ 0xf38cd180] using cpu capabilities: ARMv6 NEON
[libx264 @ 0xf38cd180] profile High, level 3.0, 4:2:0, 8-bit
[libx264 @ 0xf38cd180] 264 - core 158 r2984 3759fcb - H.264/MPEG-4 AVC codec - Copyleft 2003-2019 - 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=12 lookahead_threads=2 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 '/storage/emulated/0/Movies/Barbie.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp41isomiso2
    encoder         : Lavf60.3.100
  Stream #0:0(und): Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 640x480 [SAR 561:8 DAR 187:2], q=2-31, 30 fps, 15360 tbn (default)
    Metadata:
      creation_time   : 2023-06-25T13:25:03.000000Z
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.3.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1(eng): Audio: aac (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      creation_time   : 2023-06-25T13:25:03.000000Z
      vendor_id       : [0][0][0][0]
      encoder         : Lavc60.3.100 aac
frame=    0 fps=0.0 q=0.0 size=       0kB time=-577014:32:22.77 bitrate=  -0.0kbits/s speed=N/A    
frame=    0 fps=0.0 q=0.0 size=       0kB time=00:00:00.16 bitrate=   2.4kbits/s speed=0.00197x    
frame=    0 fps=0.0 q=0.0 size=       0kB time=00:00:00.71 bitrate=   0.5kbits/s speed=0.00867x    
frame=   13 fps=0.2 q=29.0 size=       0kB time=00:00:01.48 bitrate=   0.3kbits/s speed=0.0178x    
frame=   45 fps=0.5 q=29.0 size=       0kB time=00:00:02.55 bitrate=   0.2kbits/s speed=0.0304x    
frame=   78 fps=0.9 q=29.0 size=       0kB time=00:00:03.66 bitrate=   0.1kbits/s speed=0.0434x    
frame=  114 fps=1.3 q=29.0 size=       0kB time=00:00:04.85 bitrate=   0.1kbits/s speed=0.057x    
frame=  146 fps=1.7 q=29.0 size=       0kB time=00:00:05.92 bitrate=   0.1kbits/s speed=0.0692x    
frame=  178 fps=2.1 q=29.0 size=       0kB time=00:00:07.03 bitrate=   0.1kbits/s speed=0.0817x    
frame=  209 fps=2.4 q=29.0 size=     256kB time=00:00:08.03 bitrate= 261.1kbits/s speed=0.0928x    
frame=  240 fps=2.8 q=29.0 size=     256kB time=00:00:09.07 bitrate= 231.0kbits/s speed=0.104x    
frame=  300 fps=3.4 q=-1.0 Lsize=     445kB time=00:00:09.98 bitrate= 365.2kbits/s speed=0.114x    
video:275kB audio:159kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.692692%
[libx264 @ 0xf38cd180] frame I:10    Avg QP:20.34  size:  2434
[libx264 @ 0xf38cd180] frame P:129   Avg QP:21.89  size:  1292
[libx264 @ 0xf38cd180] frame B:161   Avg QP:21.69  size:   555
[libx264 @ 0xf38cd180] consecutive B-frames: 20.0% 18.7% 20.0% 41.3%
[libx264 @ 0xf38cd180] mb I  I16..4: 30.2% 66.5%  3.2%
[libx264 @ 0xf38cd180] mb P  I16..4: 14.3% 17.7%  0.2%  P16..4: 12.7%  2.7%  0.4%  0.0%  0.0%    skip:52.1%
[libx264 @ 0xf38cd180] mb B  I16..4:  2.1%  1.1%  0.0%  B16..8: 21.9%  1.7%  0.0%  direct: 1.5%  skip:71.6%  L0:46.0% L1:53.0% BI: 1.0%
[libx264 @ 0xf38cd180] 8x8 transform intra:54.9% inter:98.2%
[libx264 @ 0xf38cd180] coded y,uvDC,uvAC intra: 10.3% 14.9% 1.5% inter: 2.2% 5.4% 0.0%
[libx264 @ 0xf38cd180] i16 v,h,dc,p: 93%  2%  2%  4%
[libx264 @ 0xf38cd180] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 69%  1% 28%  0%  0%  1%  0%  0%  0%
[libx264 @ 0xf38cd180] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 76%  3% 17%  1%  1%  2%  0%  1%  0%
[libx264 @ 0xf38cd180] i8c dc,h,v,p: 45%  2% 53%  1%
[libx264 @ 0xf38cd180] Weighted P-Frames: Y:0.8% UV:0.8%
[libx264 @ 0xf38cd180] ref P L0: 57.0%  8.7% 24.0% 10.4%
[libx264 @ 0xf38cd180] ref B L0: 79.7% 17.3%  3.0%
[libx264 @ 0xf38cd180] ref B L1: 95.6%  4.4%
[libx264 @ 0xf38cd180] kb/s:224.32
[aac @ 0xf38cd880] Qavg: 457.489


    


  • Unveiling GA4 Issues : 8 Questions from a Marketer That GA4 Can’t Answer

    8 janvier 2024, par Alex

    It’s hard to believe, but Universal Analytics had a lifespan of 11 years, from its announcement in March 2012. Despite occasional criticism, this service established standards for the entire web analytics industry. Many metrics and reports became benchmarks for a whole generation of marketers. It truly was an era.

    For instance, a lot of marketers got used to starting each workday by inspecting dashboards and standard traffic reports in the Universal Analytics web interface. There were so, so many of those days. They became so accustomed to Universal Analytics that they would enter reports, manipulate numbers, and play with metrics almost on autopilot, without much thought.

    However, six months have passed since the sunset of Universal Analytics – precisely on July 1, 2023, when Google stopped processing requests for resources using the previous version of Google Analytics. The time when data about visitors and their interactions with the website were more clearly structured within the UA paradigm is now in the past. GA4 has brought a plethora of opportunities to marketers, but along with those opportunities came a series of complexities.

    GA4 issues

    Since its initial announcement in 2020, GA4 has been plagued with errors and inconsistencies. It still has poor and sometimes illogical documentation, numerous restrictions, and peculiar interface solutions. But more importantly, the barrier to entry into web analytics has significantly increased.

    If you diligently follow GA4 updates, read the documentation, and possess skills in working with data (SQL and basic statistics), you probably won’t feel any problems – you know how to set up a convenient and efficient environment for your product and marketing data. But what if you’re not that proficient ? That’s when issues arise.

    In this article, we try to address a series of straightforward questions that less experienced users – marketers, project managers, SEO specialists, and others – want answers to. They have no time to delve into the intricacies of GA4 but seek access to the fundamentals crucial for their functionality.

    Previously, in Universal Analytics, they could quickly and conveniently address their issues. Now, the situation has become, to put it mildly, more complex. We’ve identified 8 such questions for which the current version of GA4 either fails to provide answers or implies that answers would require significant enhancements. So, let’s dive into them one by one.

    Question 1 : What are the most popular traffic sources on my website ?

    Seemingly a straightforward question. What does GA4 tell us ? It responds with a question : “Which traffic source parameter are you interested in ?”

    GA4 traffic source

    Wait, what ?

    People just want to know which resources bring them the most traffic. Is that really an issue ?

    Unfortunately, yes. In GA4, there are not one, not two, but three traffic source parameters :

    1. Session source.
    2. First User Source – the source of the first session for each user.
    3. Just the source – determined at the event or conversion level.

    If you wanted to open a report and draw conclusions quickly, we have bad news for you. Before you start ranking your traffic sources by popularity, you need to do some mental work on which parameter and in what context you will look. And even when you decide, you’ll need to make a choice in the selection of standard reports : work with the User Acquisition Report or Traffic Acquisition.

    Yes, there is a difference between them : the first uses the First User Source parameter, and the second uses the session source. And you need to figure that out too.

    Question 2 : What is my conversion rate ?

    This question concerns everyone, and it should be simple, implying a straightforward answer. But no.

    GA4 conversion rate

    In GA4, there are three conversion metrics (yes, three) :

    1. Session conversion – the percentage of sessions with a conversion.
    2. User conversion – the percentage of users who completed a conversion.
    3. First-time Purchaser Conversion – the share of active users who made their first purchase.

    If the last metric doesn’t interest us much, GA4 users can still choose something from the remaining two. But what’s next ? Which parameters to use for comparison ? Session source or user source ? What if you want to see the conversion rate for a specific event ? And how do you do this in analyses rather than in standard reports ?

    In the end, instead of an answer to a simple question, marketers get a bunch of new questions.

    Question 3. Can I trust user and session metrics ?

    Unfortunately, no. This may boggle the mind of those not well-versed in the mechanics of calculating user and session metrics, but it’s the plain truth : the numbers in GA4 and those in reality may and will differ.

    GA4 confidence levels

    The reason is that GA4 uses the HyperLogLog++ statistical algorithm to count unique values. Without delving into details, it’s a mechanism for approximate estimation of a metric with a certain level of error.

    This error level is quite well-documented. For instance, for the Total Users metric, the error level is 1.63% (for a 95% confidence interval). In simple terms, this means that 100,000 users in the GA4 interface equate to 100,000 1.63% in reality.

    Furthermore – but this is no surprise to anyone – GA4 samples data. This means that with too large a sample size or when using a large number of parameters, the application will assess your metrics based on a partial sample – let’s say 5, 10, or 30% of the entire population.

    It’s a reasonable assumption, but it can (and probably will) surprise marketers – the metrics will deviate from reality. All end-users can do (excluding delving into raw data methodologies) is to take this error level into account in their conclusions.

    Question 4. How do I calculate First Click attribution ?

    You can’t. Unfortunately, as of late, GA4 offers only three attribution models available in the Attribution tab : Last Click, Last Click For Google Ads, and Data Driven. First Click attribution is essential for understanding where and when demand is generated. In the previous version of Google Analytics (and until recently, in the current one), users could quickly apply First Click and other attribution models, compare them, and gain insights. Now, this capability is gone.

    GA4 attribution model

    Certainly, you can look at the conversion distribution considering the First User Source parameter – this will be some proxy for First Click attribution. However, comparing it with others in the Model Comparison tab won’t be possible. In the context of the GA4 interface, it makes sense to forget about non-standard attribution models.

    Question 5. How do I account for intra-session traffic ?

    Intra-session traffic essentially refers to a change in traffic sources within a session. Imagine a scenario where a user comes to your site organically from Google and, within a minute, comes from an email campaign. In the previous version of Google Analytics, a new session with the traffic source “e-mail” would be created in such a case. But now, the situation has changed.

    A session now only ends in the case of a timeout – say, 30 minutes without interaction. This means a session will always have a source from which it started. If a user changes the source within a session (clicks on an ad, from email campaigns, and so on), you won’t know anything about it until they convert. This is a significant blow to intra-session traffic since their contribution to traffic remains virtually unnoticed. 

    Question 6. How can I account for users who have not consented to the use of third-party cookies ?

    You can’t. Google Consent Mode settings imply several options when a user rejects the use of 3rd party cookies. In GA4 and BigQuery, depersonalized cookieless pings will be sent. These pings do not contain specific client_id, session_id, or other custom dimensions. As a result, you won’t be able to consider them as users or link the actions of such users together.

    Question 7. How can I compare data in explorations with the previous year ?

    The maximum data retention period for a free GA4 account is 14 months. This means that if the date range is wider, you can only use standard reports. You won’t be able to compare or view cohorts or funnels for periods more than 14 months ago. This makes the product functionality less rich because various report formats in explorations are very convenient for comparing specific metrics in easily digestible reports.

    GA4 data retention

    Of course, you always have the option to connect BigQuery and store raw data without limitations, but this process usually requires the involvement of an advanced analyst. And precisely this option is unavailable to most marketers in small teams.

    Question 8. Is the data for yesterday accurate ?

    Unknown. Google declares that data processing in GA4 takes up to 48 hours. And although this process is faster, most users still have room for frustration. And they can be understood.

    Data processing time in GA4

    What does “data processing takes 24-48 hours” mean ? When will the data in reports be complete ? For yesterday ? Or the day before yesterday ? Or for all days that were more than two days ago ? Unclear. What should marketers tell their managers when they were asked if all the data is in this report ? Well, probably all of it… or maybe not… Let’s wait for 48 hours…

    Undoubtedly, computational resources and time are needed for data preprocessing and aggregation. It’s okay that data for today will not be up-to-date. And probably not for yesterday either. But people just want to know when they can trust their data. Are they asking for too much : just a note that this report contains all the data sent and processed by Google Analytics ?

    What should you do ?

    Credit should be given to the Google team – they have done a lot to enable users to answer these questions in one form or another. For example, you can use data streaming in BigQuery and work with raw data. The entry threshold for this functionality has been significantly lowered. In fact, if you are dissatisfied with the GA4 interface, you can organize your export to BigQuery and create your own reports without (almost) any restrictions.

    Another strong option is the widespread launch of GTM Server Side. This allows you to quite freely modify the event model and essentially enrich each hit with various parameters, doing this in a first-party context. This, of course, reduces the harmful impact of most of the limitations described in this text.

    But this is not a solution.

    The users in question – marketers, managers, developers – they do not want or do not have the time for a deep dive into the issue. And they want simple answers to simple (it seemed) questions. And for now, unfortunately, GA4 is more of a professional tool for analysts than a convenient instrument for generating insights for not very advanced users.

    Why is this such a serious issue ?

    The thing is – and this is crucial – over the past 10 years, Google has managed to create a sort of GA-bubble for marketers. Many of them have become so accustomed to Google Analytics that when faced with another issue, they don’t venture to explore alternative solutions but attempt to solve it on their own. And almost always, this turns out to be expensive and inconvenient.

    However, with the latest updates to GA4, it is becoming increasingly evident that this application is struggling to address even the most basic questions from users. And these questions are not fantastically complex. Much of what was described in this article is not an unsolvable mystery and is successfully addressed by other analytics services.

    Let’s try to answer some of the questions described from the perspective of Matomo.

    Question 1 : What are the most popular traffic sources ? [Solved]

    In the Acquisition panel, you will find at least three easily identifiable reports – for traffic channels (All Channels), sources (Websites), and campaigns (Campaigns). 

    Channel Type Table

    With these, you can quickly and easily answer the question about the most popular traffic sources, and if needed, delve into more detailed information, such as landing pages.

    Question 2 : What is my conversion rate ? [Solved]

    Under Goals in Matomo, you’ll easily find the overall conversion rate for your site. Below that you’ll have access to the conversion rate of each goal you’ve set in your Matomo instance.

    Question 3 : Can I trust user and session metrics ? [Solved]

    Yes. With Matomo, you’re guaranteed 100% accurate data. Matomo does not apply sampling, does not employ specific statistical algorithms, or any analogs of threshold values. Yes, it is possible, and it’s perfectly normal. If you see a metric in the visits or users field, it accurately represents reality by 100%.

    Try Matomo for Free

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

    No credit card required

    Question 4 : How do I calculate First Click attribution ? [Solved]

    You can do this in the same section where the other 5 attribution models, available in Matomo, are calculated – in the Multi Attribution section.

    Multi Attribution feature

    You can choose a specific conversion and, in a few clicks, calculate and compare up to 3 marketing attribution models. This means you don’t have to spend several days digging through documentation trying to understand how a particular model is calculated. Have a question – get an answer.

    Question 5 : How do I account for intra-session traffic ? [Solved]

    Matomo creates a new visit when a user changes a campaign. This means that you will accurately capture all relevant traffic if it is adequately tagged. No campaigns will be lost within a visit, as they will have a new utm_campaign parameter.

    This is a crucial point because when the Referrer changes, a new visit is not created, but the key lies in something else – accounting for all available traffic becomes your responsibility and depends on how you tag it.

    Try Matomo for Free

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

    No credit card required

    Question 6 : How can I account for users who have not consented to the use of third-party cookies ? [Solved]

    Google Analytics requires users to accept a cookie consent banner with “analytics_storage=granted” to track them. If users reject cookie consent banners, however, then Google Analytics can’t track these visitors at all. They simply won’t show up in your traffic reports. 

    Matomo doesn’t require cookie consent banners (apart from in the United Kingdom and Germany) and can therefore continue to track visitors even after they have rejected a cookie consent screen. This is achieved through a config_id variable (the user identifier equivalent which is updating once a day). 

    Matomo doesn't need cookie consent, so you see a complete view of your traffic

    This means that virtually all of your website traffic will be tracked regardless of whether users accept a cookie consent banner or not.

    Question 7 : How can I compare data in explorations with the previous year ? [Solved]

    There is no limitation on data retention for your aggregated reports in Matomo. The essence of Matomo experience lies in the reporting data, and consequently, retaining reports indefinitely is a viable option. So you can compare data for any timeframe. 7

    Date Comparison Selector