Recherche avancée

Sur d’autres sites (5845)

• 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
  

• 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.

  

  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.

  

  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).

  

  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.

  Start for free

  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 :

  

  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.

  

  

  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.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now