Advanced search

On other websites (4201)

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

  10 January 2024, by 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 January 2024, by 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?”

  

  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.

  

  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.

  

  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.

  

  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.

  

  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.

  

  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). 

  

  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.

  Start for free

  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.

  

  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.

  Start for free

  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). 

  

  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

  

• Cohort Analysis 101: How-To, Examples & Top Tools

  13 November 2023, by Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts: 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis: 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts: 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis:

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website? Do they have certain preferences? Why? 

  4. Visualising and interpreting data:

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving:

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price: 

  • Matomo Cloud: 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise: Free to self-host; Cohorts plugin: 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price: Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price: Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price: Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.