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La conservation du net art au musée. Les stratégies à l’œuvre
26 mai 2011
Mis à jour : Juillet 2013
Langue : français
Type : Texte
Autres articles (52)
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Gestion générale des documents
13 mai 2011, parMédiaSPIP ne modifie jamais le document original mis en ligne.
Pour chaque document mis en ligne il effectue deux opérations successives : la création d’une version supplémentaire qui peut être facilement consultée en ligne tout en laissant l’original téléchargeable dans le cas où le document original ne peut être lu dans un navigateur Internet ; la récupération des métadonnées du document original pour illustrer textuellement le fichier ;
Les tableaux ci-dessous expliquent ce que peut faire MédiaSPIP (...) -
Des sites réalisés avec MediaSPIP
2 mai 2011, parCette page présente quelques-uns des sites fonctionnant sous MediaSPIP.
Vous pouvez bien entendu ajouter le votre grâce au formulaire en bas de page. -
HTML5 audio and video support
13 avril 2011, parMediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...)
Sur d’autres sites (6412)
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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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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> -
Marketing Cohort Analysis : How To Do It (With Examples)
12 janvier 2024, par ErinThe 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.
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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21 day free trial. No credit card required.
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