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• Anomalie #3418 : Les tables des plugins ne s’installent pas

  19 avril 2015, par Franck Dalot

  SPIP 3.1.0-alpha [21978]
  J’ai également fait des tests en modifiant .ovhconfig
  J’ai fait des copies d’écran de ce qui me semble changeant dans "configure command"

  php 5.3 (test1)
  = ok (mais j’ai dû me reprendre à plusieurs reprise pour avoir la liste de tous les plugs compatibles)
  app.engine=phpcgi
  app.engine.version=5.3
  http.firewall=none
  environment=development

  php 5.3 (test2)
  = Bizarrement, cela à quand même fonctionné une fois, mais après, impossible d’avoir les tables d’agenda
  app.engine=php
  app.engine.version=5.3
  http.firewall=none
  environment=development

  php 5.4 (test3)
  = ok
  app.engine=php
  app.engine.version=5.4
  http.firewall=none
  environment=development

  php 5.4 (test4)
  = ok
  app.engine=phpcgi
  app.engine.version=5.4
  http.firewall=none
  environment=development

  php 5.6 (test5)
  = Les tables d’agenda ne s’installent pas
  app.engine=php
  app.engine.version=5.6
  http.firewall=none
  environment=development

  php 5.6 (test6)
  = ok
  app.engine=phpcgi
  app.engine.version=5.6
  http.firewall=none
  environment=development

• Playing With Emscripten and ASM.js

  1er mars 2014, par Multimedia Mike — General

  The last 5 years or so have provided a tremendous amount of hype about the capabilities of JavaScript. I think it really kicked off when Google announced their Chrome web browser in September, 2008 along with its V8 JS engine. This seemed to spark an arms race in JS engine performance along with much hyperbole that eventually all software could, would, and/or should be written in straight JavaScript for maximum portability and future-proofing, perhaps aided by Emscripten, a tool which magically transforms C and C++ code into JS. The latest round of rhetoric comes courtesy of something called asm.js which purports to narrow the gap between JS and native code performance.

  I haven’t been a believer, to express it charitably. But I wanted to be certain, so I set out to devise my own experiment to test modern JS performance.

  Up Front Summary
  I was extremely surprised that my experiment demonstrated JS performance FAR beyond my expectations. There might be something to these claims of magnficent JS speed in numerical applications. Basically, here were my thoughts during the process :

  • There’s no way that JavaScript can come anywhere close to C performance for a numerically intensive operation ; a simple experiment should demonstrate this.
  • Here’s a straightforward C program to perform a simple yet numerically intensive operation.
  • Let’s compile the C program on gcc and get some baseline performance numbers.
  • Let’s use Emscripten to convert the C program to JavaScript and run it under Chrome.
  • Ha ! Pitiful JS performance, just as I expected !
  • Try the same program under Firefox, since Firefox is supposed to have some crazy optimization for asm.js code, allegedly emitted by Emscripten.
  • LOL ! Firefox performs even worse than Chrome !
  • Wait a minute… the Emscripten documentation mentioned using optimization levels for generating higher performance JS, so try ‘-O1′.
  • Umm… wow : Chrome’s performance increased dramatically ! What about Firefox ? Not only is Firefox faster than Chrome, it’s faster than the gcc-generated code !
  • As my faith in C is suddenly shaken to its core, I remembered to compile the gcc version with an explicit optimization level. The native C version pulled ahead of Firefox again, but the Firefox code is still close.
  • Aha ! This is just desktop– but what about mobile ? One of the leading arguments for converting everything to pure JavaScript is that such programs will magically run perfectly in mobile browsers. So I wager that this is where the experiment will fall over.
  • I proceed to try the same converted program on a variety of mobile platforms.
  • The mobile platforms perform rather admirably as well.
  • I am surprised.

  The Experiment
  I wanted to run a simple yet numerically-intensive and relevant benchmark, and something I am familiar with. I settled on JPEG image decoding. Again, I wanted to keep this simple, ideally in a single file because I didn’t know how hard it might be to deal with Emscripten. I found NanoJPEG, which is a straightforward JPEG decoder contained in a single C file.
  

  I altered nanojpeg.c (to a new file called nanojpeg-static.c) such that the main() program would always load a 1920×1080 (a.k.a. 1080p) JPEG file (“bbb-1080p-title.jpg”, the Big Buck Bunny title), rather than requiring a command line argument. Then I used gettimeofday() to profile the core decoding function (njDecode()).

  Compiling with gcc and profiling execution :

  gcc -Wall nanojpeg-static.c -o nanojpeg-static
  ./nanojpeg-static
  

  Optimization levels such as -O0, -O3, or -Os can be applied to the compilation command.

  For JavaScript conversion, I installed Emscripten and converted using :

  /path/to/emscripten/emcc nanojpeg-static.c -o nanojpeg.html \
    —preload-file bbb-1080p-title.jpg -s TOTAL_MEMORY=32000000
  

  The ‘–preload-file’ option makes the file available to the program via standard C-style file I/O functions. The ‘-s TOTAL_MEMORY’ was necessary because the default of 16 MB wasn’t enough. Again, the -O optimization levels can be sent in.

  For running, the .html file is loaded (via webserver) in a web browser.

  Want To Try It Yourself ?
  I put the files here : http://multimedia.cx/emscripten/. The .c file, the JPEG file, and the Emscripten-converted files using -O0, -O1, -O2, -O3, -Os, and no optimization switch.

  Results and Charts
  Here is the spreadsheet with the raw results.

  I ran this experiment using Ubuntu Linux 12.04 on an Intel Atom N450-based netbook. For this part, I was able to compare the Chrome and Firefox browser results against the C results :

  
  
  

  These are the results for a 2nd generation Android Nexus 7 using both Chrome and Firefox :

  
  
  

  Here is the result for an iPad 2 running iOS 7 and Safari– there is no Firefox for iOS and while there is a version of Chrome for iOS, it apparently isn’t able to leverage an optimized JS engine. Chrome takes so long to complete this experiment that there’s no reason to muddy the graph with the results :
  

  
  
  

  Interesting that -O1 tends to provide better optimization than levels 2 or 3, and that -Os (optimize for size) seems to be a good all-around choice.

  Don’t Get Too Smug
  JavaScript can indeed get amazing performance in this day and age. Please be advised, however, that this isn’t the best that a C decoder implementation can possibly do. This version doesn’t leverage any SIMD extensions. According to profiling (using gprof against the C code), sample saturation in color conversion dominates followed by inverse DCT functions, common cases for SIMD ASM or intrinsics. Allegedly, there will be some support for JS SIMD optimizations some day. We’ll see.

  Implications For Development
  I’m still not especially motivated to try porting the entire Native Client game music player codebase to JavaScript. I’m still wondering about the recommended development flow. How are you supposed to develop for Emscripten and asm.js ? From what I can tell, Emscripten is not designed as a simple aide for porting C/C++ code to JS. No, it reduces the code into JS code you can’t possibly maintain. This seems to imply that the C/C++ code needs to be developed and debugged in its entirety and then converted to JS, which seems arduous.

• What is last click attribution ? A beginner’s guide

  10 mars 2024, par Erin

  Imagine you just finished a successful marketing campaign. You reached new highs in campaign revenue. Your conversion was higher than ever. And you did it without dramatically increasing your marketing budget.

  So, you start planning your next campaign with a bigger budget.

  But what do you do ? Where do you invest the extra money ?

  You used several marketing tactics and channels in the last campaign. To solve this problem, you need to track marketing attribution — where you give conversion credit to a channel (or channels) that acted as a touchpoint along the buyer’s journey.

  One of the most popular attribution models is last click attribution.

  In this article, we’ll break down what last click attribution is, its advantages and disadvantages, and examples of how you can use it to gain insights into the marketing strategies driving your growth.

  What is last click attribution ?

  Last click, or last interaction, is a marketing attribution model that seeks to give all credit for a conversion to the final touchpoint in the buyer’s journey. It assumes the customer’s last interaction with your brand (before the sale) was the most influential marketing channel for the conversion decision.

  

  Example of last click attribution

  Let’s say a woman named Jill stumbles across a fitness equipment website through an Instagram ad. She explores the website, looking at a few fitness bands and equipment, but she doesn’t buy anything.

  A few days later, Jill was doing a workout but wished she had equipment to use.

  So, she Googles the name of the company she checked out earlier to take a look at the fitness bands it offers. She’s not sure which one to get, but she signs up for a 10% discount by entering her email.

  A few days later, she sees an ad on Facebook and visits the site but exits before purchasing. 

  The next day, Jill gets an email from the store stating that her discount code is expiring. She clicks on the link, plugs in the discount code, and buys a fitness band for $49.99.

  Under the last click attribution model, the fitness company would attribute full credit for the sale to their email campaign while ignoring all other touchpoints (the Instagram ad, Jill’s organic Google search, and the Facebook ad).

  3 advantages of last click attribution

  Last click attribution is one of the most popular methods to credit a conversion. Here are the primary advantages of using it to measure your marketing efforts :

  

  1. Easiest attribution method for beginners

  If something’s too complicated, many people simply won’t touch it.

  So, when you start diving into attribution, you might want to keep it simple. Fortunately, last click attribution is a wonderful method for beginner marketers to try out. And when you first begin tracking your marketing efforts, it’s one of the easiest methods to grasp. 

  2. It can have more impact on revenue

  Attribution and conversions go hand in hand. But conversions aren’t just about making a sale or generating more revenue. We often need to track the conversions that take place before a sale.

  This could include gaining a new follower on Instagram or capturing an email subscriber with a new lead magnet.

  If you’re trying to attribute why someone converted into a follower or lead, you may want to ditch last click for something else.

  But when you’re looking strictly at revenue-generating conversions, last click can be one of the most impactful methods for giving credit to a conversion.

  3. It helps you understand bottom-of-funnel conversions

  If SEO is your focus, chances are pretty good that you aren’t looking for a direct sale right out of the gate. You likely want to build your authority, inform and educate your audience, and then maybe turn them into a lead.

  However, when your primary focus isn’t generating traffic or leads but turning your leads into customers, then you’re focused on the bottom of your sales funnel.

  Last click can be helpful to use in bottom-of-funnel (BoFu) conversions since it often means following a paid ad or sales email that allows you to convert your warm audience member.

  If you’re strictly after revenue, you may not need to pay as much attention to the person who reads your latest blog post. After they read the article, they may have seen a social media post. And then, maybe they saw your email with a discount to buy now — which converted them into a paying customer.

  3 challenges of last click attribution

  Last click attribution is a simple way to start analysing the channels that impact your conversions. But it’s not perfect.

  Here are a few challenges of last click attribution you should keep in mind :

  

  1. It ignores all other touchpoints

  Last click attribution is a single-touch attribution model. This type of model declares that a single channel gets 100% of the credit for a sale.

  But this can overlook impactful contributions from other channels.

  Multi-touch attribution seeks to give credit to multiple channels for each conversion. This is a more holistic approach.

  2. It fragments the customer journey

  Most customers need a few touchpoints before they’ll make a purchase.

  Maybe it’s reading a blog post via Google, checking out a social media post on Instagram, and receiving a nurture email.

  If you look only at the last touchpoint before a sale, then you ignore the impact of the other channels. This leads to a fragmented customer journey. 

  Imagine this : You tell your marketing leaders that Facebook ads are responsible for your success because they were the last touch for 65% of conversions. So, you pour your entire budget into Facebook ads.

  What happens ?

  Your sales drop by 60% in one month. This happens because you ignored the traffic you were generating from SEO blog posts that led to that conversion — the nurturing that took place in email marketing.

  3. Say goodbye to brand awareness marketing

  Without a brand, you can’t have a sustainable business.

  Some marketing activities, like brand awareness campaigns, are meant to fuel brand awareness to build a business that lasts for years.

  But if you’re going to use last click attribution to measure the effectiveness of your marketing efforts, then you’re going to diminish the impact of brand awareness.

  Your brand, as a whole, has the ability to generate multiples of your current revenue by simply reaching more people and creating unique brand experiences with new audiences.

  Last click attribution can’t easily measure brand awareness activities, which means their importance is often ignored.

  Last click attribution vs. other attribution models

  Last click attribution is just one type of attribution model. Here are five other common marketing attribution models you might want to consider :

  

  First interaction

  We’ve already touched on last click interaction as a marketing attribution model. But one of the most common models does the opposite.

  First interaction, or first touch, gives full credit to the first channel that brought a lead in. 

  First interaction is best used for top-of-funnel (ToFU) conversions, like user acquisition.

  Last non-direct interaction

  A similar model to last click attribution is one called last non-direct interaction. But one major difference is that it excludes all direct traffic from the calculation. Instead, it assigns full conversion credit to the channel that precedes it.

  For instance, let’s say you see someone comes to your website via a Facebook ad but doesn’t purchase. Then one week later, they go directly to your website through a bookmark they saved and they complete a purchase. Instead of giving attribution to the direct traffic touchpoint (entering your site through a saved bookmark), you attribute the conversion to the previous channel.

  In this case, the Facebook ad gets the credit.

  Last non-direct attribution is best used for BoFu conversions.

  Linear

  Another common attribution model is called linear attribution. Here, you split the credit for a conversion equally across every single touchpoint.

  This means if someone clicks on your blog post in Google, TikTok post, email, and a Facebook ad, then the credit for the conversion is equally split between each of these channels.

  This model is helpful for looking at both BoFu and ToFu activities.

  Time decay

  Time decay is an attribution model that more accurately credits conversions across different touchpoints. This means the closer a channel is to a conversion, the more weight is given to it.

  The time decay model assumes that the closer a channel is to a conversion, the greater that channel’s impact is on a sale.

  Position based

  Position-based, also called U-shaped attribution, is an interesting model that gives multiple channels credit for a conversion.

  But it doesn’t give equal credit to channels or weighted credit to the channels closest to the conversion.

  Instead, it gives the most credit to the first and last interactions.

  In other words, it emphasises the conversion of someone to a lead and, eventually, a customer.

  It gives the first and last interaction 40% of the credit for a conversion and then splits the remaining 20% across the other touchpoints in the customer journey.

  If you’re ever unsure about which attribution model to use, with Matomo, you can compare them to determine the one that best aligns with your goals and accurately reflects conversion paths. 

  

  In the above screenshot from Matomo, you can see how last-click compares to first-click and linear models to understand their respective impacts on conversions.

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  Use Matomo to track last click attribution

  If you want to improve your marketing, you need to start tracking your efforts. Without marketing attribution, you will never be certain which marketing activities are pushing your business forward.

  Last click attribution is one of the most popular ways to get started with attribution since it, very simply, gives full credit to the last interaction for a conversion.

  If you want to start tracking last click attribution (or any other previously mentioned attribution model), sign up for Matomo’s 21-day free trial today. No credit card required.

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