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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.

  

  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

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  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.

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• 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


  


• What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis

  3 janvier 2024, par Erin

  Handling your user data as a single mass of numbers is rarely conducive to figuring out meaningful patterns you can use to improve your marketing campaigns.

  A cohort report (or cohort analysis) can help you quickly break down that larger audience into sequential segments and contrast and compare based on various metrics. As such, it is a great tool for unlocking more granular trends and insights — for example, identifying patterns in engagement and conversions based on the date users first interacted with your site.

  In this guide, we explain the basics of the cohort report and the best way to set one up to get the most out of it.

  What is a cohort report ?

  In a cohort report, you divide a data set into groups based on certain criteria — typically a time-based cohort metric like first purchase date — and then analyse the data across those segments, looking for patterns.

  Date-based cohort analysis is the most common approach, often creating cohorts based on the day a user completed a particular action — signed up, purchased something or visited your website. Depending on the metric you choose to measure (like return visits), the cohort report might look something like this :

  

  Note that this is not a universal benchmark or anything of the sort. The above is a theoretical cohort analysis based on app users who downloaded the app, tracking and comparing the retention rates as the days go by. 

  The benchmarks will be drastically different depending on the metric you’re measuring and the basis for your cohorts. For example, if you’re measuring returning visitor rates among first-time visitors to your website, expect single-digit percentages even on the second day.

  Your industry will also greatly affect what you consider positive in a cohort report. For example, if you’re a subscription SaaS, you’d expect high continued usage rates over the first week. If you sell office supplies to companies, much less so.

  What is an example of a cohort ?

  As we just mentioned, a typical cohort analysis separates users or customers by the date they first interacted with your business — in this case, they downloaded your app. Within that larger analysis, the users who downloaded it on May 3 represent a single cohort.

  

  In this case, we’ve chosen behaviour and time — the app download day — to separate the user base into cohorts. That means every specific day denotes a specific cohort within the analysis.

  Diving deeper into an individual cohort may be a good idea for important holidays or promotional events like Black Friday.

  Of course, cohorts don’t have to be based on specific behaviour within certain periods. You can also create cohorts based on other dimensions :

  • Transactional data — revenue per user
  • Churn data — date of churn
  • Behavioural cohort — based on actions taken on your website, app or e-commerce store, like the number of sessions per user or specific product pages visited
  • Acquisition cohort — which channel referred the user or customer

  For more information on different cohort types, read our in-depth guide on cohort analysis.

  How to create a cohort report (and make sense of it)

  Matomo makes it easy to view and analyse different cohorts (without the privacy and legal implications of using Google Analytics).

  Here are a few different ways to set up a cohort report in Matomo, starting with our built-in cohorts report.

  Cohort reports

  With Matomo, cohort reports are automatically compiled based on the first visit date. The default metric is the percentage of returning visitors.

  

  Changing the settings allows you to create multiple variations of cohort analysis reports.

  Break down cohorts by different metrics

  The percentage of returning visits can be valuable if you’re trying to improve early engagement in a SaaS app onboarding process. But it’s far from your only option.

  You can also compare performance by conversion, revenue, bounce rate, actions per visit, average session duration or other metrics.

  

  Change the time and scope of your cohort analysis

  Splitting up cohorts by single days may be useless if you don’t have a high volume of users or visitors. If the average cohort size is only a few users, you won’t be able to identify reliable patterns. 

  Matomo lets you set any time period to create your cohort analysis report. Instead of the most recent days, you can create cohorts by week, month, year or custom date ranges. 

  

  Cohort sizes will depend on your customer base. Make sure each cohort is large enough to encapsulate all the customers in that cohort and not so small that you have insignificant cohorts of only a few customers. Choose a date range that gives you that without scaling it too far so you can’t identify any seasonal trends.

  Cohort analysis can be a great tool if you’ve recently changed your marketing, product offering or onboarding. Set the data range to weekly and look for any impact in conversions and revenue after the changes.

  Using the “compare to” feature, you can also do month-over-month, quarter-over-quarter or any custom date range comparisons. This approach can help you get a rough overview of your campaign’s long-term progress without doing any in-depth analysis.

  You can also use the same approach to compare different holiday seasons against each other.

  If you want to combine time cohorts with segmentation, you can run cohort reports for different subsets of visitors instead of all visitors. This can lead to actionable insights like adjusting weekend or specific seasonal promotions to improve conversion rates.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Easily create custom cohort reports beyond the time dimension

  If you want to split your audience into cohorts by focusing on something other than time, you will need to create a custom report and choose another dimension. In Matomo, you can choose from a wide range of cohort metrics, including referrers, e-commerce signals like viewed product or product category, form submissions and more.

  

  Then, you can create a simple table-based report with all the insights you need by choosing the metrics you want to see. For example, you could choose average visit duration, bounce rate and other usage metrics.

  

  If you want more revenue-focused insights, add metrics like conversions, add-to-cart and other e-commerce events.

  Custom reports make it easy to create cohort reports for almost any dimension. You can use any metric within demographic and behavioural analytics to create a cohort. (You can explore the complete list of our possible segmentation metrics.)

  We cover different types of custom reports (and ideas for specific marketing campaigns) in our guide on custom segmentation.

  Create your first cohort report and gain better insights into your visitors

  Cohort reports can help you identify trends and the impact of short-term marketing efforts like events and promotions.

  With Matomo cohort reports you have the power to create complex custom reports for various cohorts and segments. 

  If you’re looking for a powerful, easy-to-use web analytics solution that gives you 100% accurate data without compromising your users’ privacy, Matomo is a great fit. Get started with a 21-day free trial today. No credit card required. 

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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  Choose your analytics subdomain
  

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

  
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