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Mise à jour de la version 0.1 vers 0.2
24 juin 2013, parExplications des différents changements notables lors du passage de la version 0.1 de MediaSPIP à la version 0.3. Quelles sont les nouveautés
Au niveau des dépendances logicielles Utilisation des dernières versions de FFMpeg (>= v1.2.1) ; Installation des dépendances pour Smush ; Installation de MediaInfo et FFprobe pour la récupération des métadonnées ; On n’utilise plus ffmpeg2theora ; On n’installe plus flvtool2 au profit de flvtool++ ; On n’installe plus ffmpeg-php qui n’est plus maintenu au (...) -
Personnaliser en ajoutant son logo, sa bannière ou son image de fond
5 septembre 2013, parCertains thèmes prennent en compte trois éléments de personnalisation : l’ajout d’un logo ; l’ajout d’une bannière l’ajout d’une image de fond ;
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Ecrire une actualité
21 juin 2013, parPrésentez les changements dans votre MédiaSPIP ou les actualités de vos projets sur votre MédiaSPIP grâce à la rubrique actualités.
Dans le thème par défaut spipeo de MédiaSPIP, les actualités sont affichées en bas de la page principale sous les éditoriaux.
Vous pouvez personnaliser le formulaire de création d’une actualité.
Formulaire de création d’une actualité Dans le cas d’un document de type actualité, les champs proposés par défaut sont : Date de publication ( personnaliser la date de publication ) (...)
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What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis
3 janvier 2024, par ErinHandling 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.
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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swscale/x86/input.asm : add x86-optimized planer rgb2yuv functions
24 novembre 2021, par Mark Reidswscale/x86/input.asm : add x86-optimized planer rgb2yuv functions
sse2 only operates on 2 lanes per loop for to_y and to_uv functions, due
to the lack of pmulld instruction. Emulating pmulld with 2 pmuludq and shuffles
proved too costly and made to_uv functions slower then the c implementation.For to_y on sse2 only float functions are generated,
I was are not able outperform the c implementation on the integer pixel formats.For to_a on see4 only the float functions are generated.
sse2 and sse4 generated nearly identical performing code on integer pixel formats,
so only sse2/avx2 versions are generated.planar_gbrp_to_y_512_c : 1197.5
planar_gbrp_to_y_512_sse4 : 444.5
planar_gbrp_to_y_512_avx2 : 287.5
planar_gbrap_to_y_512_c : 1204.5
planar_gbrap_to_y_512_sse4 : 447.5
planar_gbrap_to_y_512_avx2 : 289.5
planar_gbrp9be_to_y_512_c : 1380.0
planar_gbrp9be_to_y_512_sse4 : 543.5
planar_gbrp9be_to_y_512_avx2 : 340.0
planar_gbrp9le_to_y_512_c : 1200.5
planar_gbrp9le_to_y_512_sse4 : 442.0
planar_gbrp9le_to_y_512_avx2 : 282.0
planar_gbrp10be_to_y_512_c : 1378.5
planar_gbrp10be_to_y_512_sse4 : 544.0
planar_gbrp10be_to_y_512_avx2 : 337.5
planar_gbrp10le_to_y_512_c : 1200.0
planar_gbrp10le_to_y_512_sse4 : 448.0
planar_gbrp10le_to_y_512_avx2 : 285.5
planar_gbrap10be_to_y_512_c : 1380.0
planar_gbrap10be_to_y_512_sse4 : 542.0
planar_gbrap10be_to_y_512_avx2 : 340.5
planar_gbrap10le_to_y_512_c : 1199.0
planar_gbrap10le_to_y_512_sse4 : 446.0
planar_gbrap10le_to_y_512_avx2 : 289.5
planar_gbrp12be_to_y_512_c : 10563.0
planar_gbrp12be_to_y_512_sse4 : 542.5
planar_gbrp12be_to_y_512_avx2 : 339.0
planar_gbrp12le_to_y_512_c : 1201.0
planar_gbrp12le_to_y_512_sse4 : 440.5
planar_gbrp12le_to_y_512_avx2 : 286.0
planar_gbrap12be_to_y_512_c : 1701.5
planar_gbrap12be_to_y_512_sse4 : 917.0
planar_gbrap12be_to_y_512_avx2 : 338.5
planar_gbrap12le_to_y_512_c : 1201.0
planar_gbrap12le_to_y_512_sse4 : 444.5
planar_gbrap12le_to_y_512_avx2 : 288.0
planar_gbrp14be_to_y_512_c : 1370.5
planar_gbrp14be_to_y_512_sse4 : 545.0
planar_gbrp14be_to_y_512_avx2 : 338.5
planar_gbrp14le_to_y_512_c : 1199.0
planar_gbrp14le_to_y_512_sse4 : 444.0
planar_gbrp14le_to_y_512_avx2 : 279.5
planar_gbrp16be_to_y_512_c : 1364.0
planar_gbrp16be_to_y_512_sse4 : 544.5
planar_gbrp16be_to_y_512_avx2 : 339.5
planar_gbrp16le_to_y_512_c : 1201.0
planar_gbrp16le_to_y_512_sse4 : 445.5
planar_gbrp16le_to_y_512_avx2 : 280.5
planar_gbrap16be_to_y_512_c : 1377.0
planar_gbrap16be_to_y_512_sse4 : 545.0
planar_gbrap16be_to_y_512_avx2 : 338.5
planar_gbrap16le_to_y_512_c : 1201.0
planar_gbrap16le_to_y_512_sse4 : 442.0
planar_gbrap16le_to_y_512_avx2 : 279.0
planar_gbrpf32be_to_y_512_c : 4113.0
planar_gbrpf32be_to_y_512_sse2 : 2438.0
planar_gbrpf32be_to_y_512_sse4 : 1068.0
planar_gbrpf32be_to_y_512_avx2 : 904.5
planar_gbrpf32le_to_y_512_c : 3818.5
planar_gbrpf32le_to_y_512_sse2 : 2024.5
planar_gbrpf32le_to_y_512_sse4 : 1241.5
planar_gbrpf32le_to_y_512_avx2 : 657.0
planar_gbrapf32be_to_y_512_c : 3707.0
planar_gbrapf32be_to_y_512_sse2 : 2444.0
planar_gbrapf32be_to_y_512_sse4 : 1077.0
planar_gbrapf32be_to_y_512_avx2 : 909.0
planar_gbrapf32le_to_y_512_c : 3822.0
planar_gbrapf32le_to_y_512_sse2 : 2024.5
planar_gbrapf32le_to_y_512_sse4 : 1176.0
planar_gbrapf32le_to_y_512_avx2 : 658.5planar_gbrp_to_uv_512_c : 2325.8
planar_gbrp_to_uv_512_sse2 : 1726.8
planar_gbrp_to_uv_512_sse4 : 771.8
planar_gbrp_to_uv_512_avx2 : 506.8
planar_gbrap_to_uv_512_c : 2281.8
planar_gbrap_to_uv_512_sse2 : 1726.3
planar_gbrap_to_uv_512_sse4 : 768.3
planar_gbrap_to_uv_512_avx2 : 496.3
planar_gbrp9be_to_uv_512_c : 2336.8
planar_gbrp9be_to_uv_512_sse2 : 1924.8
planar_gbrp9be_to_uv_512_sse4 : 852.3
planar_gbrp9be_to_uv_512_avx2 : 552.8
planar_gbrp9le_to_uv_512_c : 2270.3
planar_gbrp9le_to_uv_512_sse2 : 1512.3
planar_gbrp9le_to_uv_512_sse4 : 764.3
planar_gbrp9le_to_uv_512_avx2 : 491.3
planar_gbrp10be_to_uv_512_c : 2281.8
planar_gbrp10be_to_uv_512_sse2 : 1917.8
planar_gbrp10be_to_uv_512_sse4 : 855.3
planar_gbrp10be_to_uv_512_avx2 : 541.3
planar_gbrp10le_to_uv_512_c : 2269.8
planar_gbrp10le_to_uv_512_sse2 : 1515.3
planar_gbrp10le_to_uv_512_sse4 : 759.8
planar_gbrp10le_to_uv_512_avx2 : 487.8
planar_gbrap10be_to_uv_512_c : 2382.3
planar_gbrap10be_to_uv_512_sse2 : 1924.8
planar_gbrap10be_to_uv_512_sse4 : 855.3
planar_gbrap10be_to_uv_512_avx2 : 540.8
planar_gbrap10le_to_uv_512_c : 2382.3
planar_gbrap10le_to_uv_512_sse2 : 1512.3
planar_gbrap10le_to_uv_512_sse4 : 759.3
planar_gbrap10le_to_uv_512_avx2 : 484.8
planar_gbrp12be_to_uv_512_c : 2283.8
planar_gbrp12be_to_uv_512_sse2 : 1936.8
planar_gbrp12be_to_uv_512_sse4 : 858.3
planar_gbrp12be_to_uv_512_avx2 : 541.3
planar_gbrp12le_to_uv_512_c : 2278.8
planar_gbrp12le_to_uv_512_sse2 : 1507.3
planar_gbrp12le_to_uv_512_sse4 : 760.3
planar_gbrp12le_to_uv_512_avx2 : 485.8
planar_gbrap12be_to_uv_512_c : 2385.3
planar_gbrap12be_to_uv_512_sse2 : 1927.8
planar_gbrap12be_to_uv_512_sse4 : 855.3
planar_gbrap12be_to_uv_512_avx2 : 539.8
planar_gbrap12le_to_uv_512_c : 2377.3
planar_gbrap12le_to_uv_512_sse2 : 1516.3
planar_gbrap12le_to_uv_512_sse4 : 759.3
planar_gbrap12le_to_uv_512_avx2 : 484.8
planar_gbrp14be_to_uv_512_c : 2283.8
planar_gbrp14be_to_uv_512_sse2 : 1935.3
planar_gbrp14be_to_uv_512_sse4 : 852.3
planar_gbrp14be_to_uv_512_avx2 : 540.3
planar_gbrp14le_to_uv_512_c : 2276.8
planar_gbrp14le_to_uv_512_sse2 : 1514.8
planar_gbrp14le_to_uv_512_sse4 : 762.3
planar_gbrp14le_to_uv_512_avx2 : 484.8
planar_gbrp16be_to_uv_512_c : 2383.3
planar_gbrp16be_to_uv_512_sse2 : 1881.8
planar_gbrp16be_to_uv_512_sse4 : 852.3
planar_gbrp16be_to_uv_512_avx2 : 541.8
planar_gbrp16le_to_uv_512_c : 2378.3
planar_gbrp16le_to_uv_512_sse2 : 1476.8
planar_gbrp16le_to_uv_512_sse4 : 765.3
planar_gbrp16le_to_uv_512_avx2 : 485.8
planar_gbrap16be_to_uv_512_c : 2382.3
planar_gbrap16be_to_uv_512_sse2 : 1886.3
planar_gbrap16be_to_uv_512_sse4 : 853.8
planar_gbrap16be_to_uv_512_avx2 : 550.8
planar_gbrap16le_to_uv_512_c : 2381.8
planar_gbrap16le_to_uv_512_sse2 : 1488.3
planar_gbrap16le_to_uv_512_sse4 : 765.3
planar_gbrap16le_to_uv_512_avx2 : 491.8
planar_gbrpf32be_to_uv_512_c : 4863.0
planar_gbrpf32be_to_uv_512_sse2 : 3347.5
planar_gbrpf32be_to_uv_512_sse4 : 1800.0
planar_gbrpf32be_to_uv_512_avx2 : 1199.0
planar_gbrpf32le_to_uv_512_c : 4725.0
planar_gbrpf32le_to_uv_512_sse2 : 2753.0
planar_gbrpf32le_to_uv_512_sse4 : 1474.5
planar_gbrpf32le_to_uv_512_avx2 : 927.5
planar_gbrapf32be_to_uv_512_c : 4859.0
planar_gbrapf32be_to_uv_512_sse2 : 3269.0
planar_gbrapf32be_to_uv_512_sse4 : 1802.0
planar_gbrapf32be_to_uv_512_avx2 : 1201.5
planar_gbrapf32le_to_uv_512_c : 6338.0
planar_gbrapf32le_to_uv_512_sse2 : 2756.5
planar_gbrapf32le_to_uv_512_sse4 : 1476.0
planar_gbrapf32le_to_uv_512_avx2 : 908.5planar_gbrap_to_a_512_c : 383.3
planar_gbrap_to_a_512_sse2 : 66.8
planar_gbrap_to_a_512_avx2 : 43.8
planar_gbrap10be_to_a_512_c : 601.8
planar_gbrap10be_to_a_512_sse2 : 86.3
planar_gbrap10be_to_a_512_avx2 : 34.8
planar_gbrap10le_to_a_512_c : 602.3
planar_gbrap10le_to_a_512_sse2 : 48.8
planar_gbrap10le_to_a_512_avx2 : 31.3
planar_gbrap12be_to_a_512_c : 601.8
planar_gbrap12be_to_a_512_sse2 : 111.8
planar_gbrap12be_to_a_512_avx2 : 41.3
planar_gbrap12le_to_a_512_c : 385.8
planar_gbrap12le_to_a_512_sse2 : 75.3
planar_gbrap12le_to_a_512_avx2 : 39.8
planar_gbrap16be_to_a_512_c : 386.8
planar_gbrap16be_to_a_512_sse2 : 79.8
planar_gbrap16be_to_a_512_avx2 : 31.3
planar_gbrap16le_to_a_512_c : 600.3
planar_gbrap16le_to_a_512_sse2 : 40.3
planar_gbrap16le_to_a_512_avx2 : 30.3
planar_gbrapf32be_to_a_512_c : 1148.8
planar_gbrapf32be_to_a_512_sse2 : 611.3
planar_gbrapf32be_to_a_512_sse4 : 234.8
planar_gbrapf32be_to_a_512_avx2 : 183.3
planar_gbrapf32le_to_a_512_c : 851.3
planar_gbrapf32le_to_a_512_sse2 : 263.3
planar_gbrapf32le_to_a_512_sse4 : 199.3
planar_gbrapf32le_to_a_512_avx2 : 156.8Reviewed-by : Paul B Mahol <onemda@gmail.com>
Signed-off-by : James Almer <jamrial@gmail.com> -
Should H.264 bit rate be multiples of 8 ?
31 août 2016, par Dan SharpI’m working on a video platform receiving H.264 video and building an HLS stream (transmuxing the H.264 to Mpeg2 TS segments via calls to ffmpeg).
I wanted to set the bit rate to be about 2000 kbps, but I’m wondering : does it matter if it’s 2000 or 2048 ?
In other words, do things calculate better if the bit rate is multiples of 8, like 512 or 2024 or 2048 ?
I don’t know enough about how the bit rate is used, either on the sending side (camera) or on the processing side (ffmpeg).
From tests... I can’t see any noticeable difference between 2000 and 2048, but maybe one is slightly better than another for the transmuxing and segmenting ?
I welcome any thoughts/advice.
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