Recherche avancée

Sur d’autres sites (4890)

• How to Use Web Analytics to Improve SEO

  5 janvier 2022, par erin — Analytics Tips

  Everyone wants their website to rank highly in Google — and that’s exactly why the world of SEO is so competitive.

  In order to succeed in such a crowded space, it’s essential to equip yourself with the right tools and processes to ensure your website is maximally optimised for search engines.

  If you’d like to improve your website’s SEO rankings, leveraging web analytics is one of the best places to start. Web analytics provides valuable insights to help you assess performance, user behaviour and optimisation opportunities.

  In this blog, we’ll cover :

  The basics of SEO and web analytics

  Before we discuss how to use web analytics for SEO, let’s start with a quick explanation of both.

  SEO (Search Engine Optimisation) encompasses a broad set of activities aimed at increasing a website’s position in search engine results pages (SERPs). When a user enters a query (e.g. ‘marketing agencies in Dallas’) in a search engine, the websites that appear near the top of the page are optimised for search engines and therefore ranking for that particular term. 

  Web analytics refers to the monitoring/assessment of metrics that track traffic sources and user behaviour on a website. This involves the use of a web analytics tool to collect, aggregate, organise and visualise website data so that meaningful conclusions can be drawn.

  The importance of website analytics for SEO

  SEO revolves around search engine algorithms – a set of rules that dictates a website’s ranking for a given search query (i.e. keyword). The algorithm takes numerous factors into account to determine a particular site’s SERP ranking. So, to achieve strong SEO, your website needs to exhibit qualities that the algorithm deems important. That’s where web analytics comes into play.

  Web analytics allows you to track key metrics and data points that affect how the algorithm ranks your website. For example, how much time do users spend on your site ? Which external links are referring traffic to your site ? How do your site’s Core Web Vitals stack up ? 

  Understanding this data will supply you with the insights needed to make positive adjustments, ultimately improving your website’s SEO. 

  How do you analyse a website for SEO ?

  The SEO analysis of a website needs to be focused on relevant data that’s applicable to search engine rankings. When conducting your website SEO analysis, here are some notable metrics and data fields to pay attention to :

  1. Bounce rate and dwell time

  These metrics denote how much time users spend on your website. If users frequently exit your site after only a few seconds, Google may view this as a negative indicator. To reduce bounce rate and increase dwell time, you should work towards making your site’s content more captivating and ensuring that there aren’t any technical issues with your site (e.g. pages taking too long to load or not optimised for mobile).

  

  2. Broken/dead links

  Perform a technical analysis to scan your website for faulty links. If your site contains broken links that lead to 404 pages, this can detract from your website’s SEO rankings. Redirect those links to a related page or remove them.

  

  Matomo’s Crawling Errors report can give you instant access to this technical information so you can resolve it before it begins to impact your ranking.

  3. Scroll depth

  Measuring scroll depth (how far users scroll down the page) can help you gauge the quality of your content — and this goes hand-in-hand with bounce rate and dwell time. To assess scroll depth, you can use a Tag Manager to track the specific scroll percentage on your site’s pages.

  4. Transitions

  Studying how users transition from page to page within your site can help you understand their behaviour more holistically. Which pages do they tend to gravitate towards ? Are there CTAs on your blog that aren’t driving many click-throughs ? Optimising user journeys will, in turn, elevate the overall user experience on your site.

  

  5. Internal site search

  You can use site search tracking and reporting to learn what your audience is looking for. If you notice a trend (e.g. the majority of searches are for pricing because your pricing page isn’t in the navigation menu), this can inform both site architecture and content planning.

  

  Ecommerce sites in particular should be monitoring branded queries, especially in regards to brand misspellings that could be causing users to bounce off the site.

  6. Segments

  Separating your visitors into distinct segments can produce granular insights that paint a more accurate picture.

  For example, perhaps you notice that your bounce rate is far higher on mobile, or with users from the UK. In both cases, this knowledge will provide clarity on where to focus your optimisation efforts (e.g. mobile responsiveness, UK-specific content/landing pages, etc.).

  

  7. Acquisition channels

  It’s crucial to analyse where your website traffic is coming from. Among other things, reviewing your acquisition metrics will reveal which external websites are referring the most traffic to your website. 

  Links from external sites (also known as backlinks) are one of the most important ranking factors because this tells Google that your site is reputable and credible. So, you may choose to cultivate a relationship with these sites (or similar sites) by offering guest blogging and other link building initiatives.

  

  In addition to the above, you should also be monitoring your Core Web Vitals — which leads us to our next section.

  What are Core Web Vitals and why are they important ?

  Core Web Vitals are a set of 3 primary metrics that reflect the general user experience of a website. These metrics are load time, interactivity and stability. 

  1. Load time (LCP) refers to the amount of time it takes for your website’s text and images to load.
  2. Interactivity (FID) refers to the amount of time it takes for user input areas (buttons, form fields, etc.) to become functional.
  3. Stability (CLS) refers to the visual/spatial integrity of your website. If text, images, and other elements tend to suddenly shift position when a user is viewing the site, this will hurt your CLS score.

  

  So, why are these Core Web Vitals metrics important for SEO ? Generally speaking, Google prioritises user experience — and Core Web Vitals affect users’ satisfaction with a website. Furthermore, Google has confirmed that Core Web Vitals are, indeed, a ranking factor.

  Matomo enables you to track metrics for Core Web Vitals which we refer to as SEO Web Vitals.

  How to measure and track keyword performance

  We can’t talk about SEO and analytics without touching on keywords. Keywords (the words/phrases that users type in a search engine) are arguably the most cardinal component of SEO. So, outside of website performance, it’s also necessary to track the keywords your website is ranking for. 

  Recall from above that SEO is all about ranking highly on SERPs for certain search queries (i.e. keywords). To assess your Search Engine Keyword Performance, you can use an analytics tool to view Keyword reports for your website. These reports will show you which keywords your site ranks for, the average SERP position your site achieves for each keyword, the amount of traffic you receive from each keyword, and more.

  

  Digging into your keyword performance can help you identify valuable keyword opportunities and improvement goals.

  For example, upon reviewing your highest-traffic keywords, you may choose to create more blog content around those keywords to bolster your success. Or, perhaps you notice that your average position for a high-intent keyword is quite low. In that case, you could implement a targeted link building campaign to help boost your ranking for that keyword. 

  Final thoughts

  In this article, we’ve discussed the benefits of web analytics — particularly in regards to SEO. When it comes to selecting a web analytics tool, Google Analytics is by far the most popular choice. But that doesn’t make it the best.

  At Matomo, we’re committed to providing a superior alternative to Google Analytics. Matomo is a powerful, open-source web analytics platform that gives you 100% data ownership — protecting both your data and your customers’ privacy.

  Try our live demo or start a free 21-day trial now – no credit card required.

• Developing MobyCAIRO

  26 mai 2021, par Multimedia Mike — General

  I recently published a tool called MobyCAIRO. The ‘CAIRO’ part stands for Computer-Assisted Image ROtation, while the ‘Moby’ prefix refers to its role in helping process artifact image scans to submit to the MobyGames database. The tool is meant to provide an accelerated workflow for rotating and cropping image scans. It works on both Windows and Linux. Hopefully, it can solve similar workflow problems for other people.

  As of this writing, MobyCAIRO has not been tested on Mac OS X yet– I expect some issues there that should be easily solvable if someone cares to test it.

  The rest of this post describes my motivations and how I arrived at the solution.

  Background
  I have scanned well in excess of 2100 images for MobyGames and other purposes in the past 16 years or so. The workflow looks like this :

  
  

  

  Image workflow

  
  

  It should be noted that my original workflow featured me manually rotating the artifact on the scanner bed in order to ensure straightness, because I guess I thought that rotate functions in image editing programs constituted dark, unholy magic or something. So my workflow used to be even more arduous :

  
  

  

  I can’t believe I had the patience to do this for hundreds of scans

  
  

  Sometime last year, I was sitting down to perform some more scanning and found myself dreading the oncoming tedium of straightening and cropping the images. This prompted a pivotal question :

  
  Why can’t a computer do this for me ?
  

  After all, I have always been a huge proponent of making computers handle the most tedious, repetitive, mind-numbing, and error-prone tasks. So I did some web searching to find if there were any solutions that dealt with this. I also consulted with some like-minded folks who have to cope with the same tedious workflow.

  I came up empty-handed. So I endeavored to develop my own solution.

  Problem Statement and Prior Work
  
  I want to develop a workflow that can automatically rotate an image so that it is straight, and also find the most likely crop rectangle, uniformly whitening the area outside of the crop area (in the case of circles).

  As mentioned, I checked to see if any other programs can handle this, starting with my usual workhorse, Photoshop Elements. But I can’t expect the trimmed down version to do everything. I tried to find out if its big brother could handle the task, but couldn’t find a definitive answer on that. Nor could I find any other tools that seem to take an interest in optimizing this particular workflow.

  When I brought this up to some peers, I received some suggestions, including an idea that the venerable GIMP had a feature like this, but I could not find any evidence. Further, I would get responses of “Program XYZ can do image rotation and cropping.” I had to tamp down on the snark to avoid saying “Wow ! An image editor that can perform rotation AND cropping ? What a game-changer !” Rotation and cropping features are table stakes for any halfway competent image editor for the last 25 or so years at least. I am hoping to find or create a program which can lend a bit of programmatic assistance to the task.

  Why can’t other programs handle this ? The answer seems fairly obvious : Image editing tools are general tools and I want a highly customized workflow. It’s not reasonable to expect a turnkey solution to do this.

  Brainstorming An Approach
  I started with the happiest of happy cases— A disc that needed archiving (a marketing/press assets CD-ROM from a video game company, contents described here) which appeared to have some pretty clear straight lines :

  
  
  

  My idea was to try to find straight lines in the image and then rotate the image so that the image is parallel to the horizontal based on the longest single straight line detected.

  I just needed to figure out how to find a straight line inside of an image. Fortunately, I quickly learned that this is very much a solved problem thanks to something called the Hough transform. As a bonus, I read that this is also the tool I would want to use for finding circles, when I got to that part. The nice thing about knowing the formal algorithm to use is being able to find efficient, optimized libraries which already implement it.

  Early Prototype
  A little searching for how to perform a Hough transform in Python led me first to scikit. I was able to rapidly produce a prototype that did some basic image processing. However, running the Hough transform directly on the image and rotating according to the longest line segment discovered turned out not to yield expected results.

  
  
  

  It also took a very long time to chew on the 3300×3300 raw image– certainly longer than I care to wait for an accelerated workflow concept. The key, however, is that you are apparently not supposed to run the Hough transform on a raw image– you need to compute the edges first, and then attempt to determine which edges are ‘straight’. The recommended algorithm for this step is the Canny edge detector. After applying this, I get the expected rotation :

  
  
  

  The algorithm also completes in a few seconds. So this is a good early result and I was feeling pretty confident. But, again– happiest of happy cases. I should also mention at this point that I had originally envisioned a tool that I would simply run against a scanned image and it would automatically/magically make the image straight, followed by a perfect crop.

  Along came my MobyGames comrade Foxhack to disabuse me of the hope of ever developing a fully automated tool. Just try and find a usefully long straight line in this :

  
  

  Darn it, Foxhack…
  

  There are straight edges, to be sure. But my initial brainstorm of rotating according to the longest straight edge looks infeasible. Further, it’s at this point that we start brainstorming that perhaps we could match on ratings badges such as the standard ESRB badges omnipresent on U.S. video games. This gets into feature detection and complicates things.

  This Needs To Be Interactive
  At this point in the effort, I came to terms with the fact that the solution will need to have some element of interactivity. I will also need to get out of my safe Linux haven and figure out how to develop this on a Windows desktop, something I am not experienced with.

  I initially dreamed up an impressive beast of a program written in C++ that leverages Windows desktop GUI frameworks, OpenGL for display and real-time rotation, GPU acceleration for image analysis and processing tricks, and some novel input concepts. I thought GPU acceleration would be crucial since I have a fairly good GPU on my main Windows desktop and I hear that these things are pretty good at image processing.

  I created a list of prototyping tasks on a Trello board and made a decent amount of headway on prototyping all the various pieces that I would need to tie together in order to make this a reality. But it was ultimately slowgoing when you can only grab an hour or 2 here and there to try to get anything done.

  Settling On A Solution
  Recently, I was determined to get a set of old shareware discs archived. I ripped the data a year ago but I was blocked on the scanning task because I knew that would also involve tedious straightening and cropping. So I finally got all the scans done, which was reasonably quick. But I was determined to not manually post-process them.

  This was fairly recent, but I can’t quite recall how I managed to come across the OpenCV library and its Python bindings. OpenCV is an amazing library that provides a significant toolbox for performing image processing tasks. Not only that, it provides “just enough” UI primitives to be able to quickly create a basic GUI for your program, including image display via multiple windows, buttons, and keyboard/mouse input. Furthermore, OpenCV seems to be plenty fast enough to do everything I need in real time, just with (accelerated where appropriate) CPU processing.

  So I went to work porting the ideas from the simple standalone Python/scikit tool. I thought of a refinement to the straight line detector– instead of just finding the longest straight edge, it creates a histogram of 360 rotation angles, and builds a list of lines corresponding to each angle. Then it sorts the angles by cumulative line length and allows the user to iterate through this list, which will hopefully provide the most likely straightened angle up front. Further, the tool allows making fine adjustments by 1/10 of an angle via the keyboard, not the mouse. It does all this while highlighting in red the straight line segments that are parallel to the horizontal axis, per the current candidate angle.

  
  
  

  The tool draws a light-colored grid over the frame to aid the user in visually verifying the straightness of the image. Further, the program has a mode that allows the user to see the algorithm’s detected edges :

  
  
  

  For the cropping phase, the program uses the Hough circle transform in a similar manner, finding the most likely circles (if the image to be processed is supposed to be a circle) and allowing the user to cycle among them while making precise adjustments via the keyboard, again, rather than the mouse.

  
  
  

  Running the Hough circle transform is a significantly more intensive operation than the line transform. When I ran it on a full 3300×3300 image, it ran for a long time. I didn’t let it run longer than a minute before forcibly ending the program. Is this approach unworkable ? Not quite– It turns out that the transform is just as effective when shrinking the image to 400×400, and completes in under 2 seconds on my Core i5 CPU.

  For rectangular cropping, I just settled on using OpenCV’s built-in region-of-interest (ROI) facility. I tried to intelligently find the best candidate rectangle and allow fine adjustments via the keyboard, but I wasn’t having much success, so I took a path of lesser resistance.

  Packaging and Residual Weirdness
  I realized that this tool would be more useful to a broader Windows-using base of digital preservationists if they didn’t have to install Python, establish a virtual environment, and install the prerequisite dependencies. Thus, I made the effort to figure out how to wrap the entire thing up into a monolithic Windows EXE binary. It is available from the project’s Github release page (another thing I figured out for the sake of this project !).

  The binary is pretty heavy, weighing in at a bit over 50 megabytes. You might advise using compression– it IS compressed ! Before I figured out the --onefile command for pyinstaller.exe, the generated dist/ subdirectory was 150 MB. Among other things, there’s a 30 MB FORTRAN BLAS library packaged in !

  Conclusion and Future Directions
  Once I got it all working with a simple tkinter UI up front in order to select between circle and rectangle crop modes, I unleashed the tool on 60 or so scans in bulk, using the Windows forfiles command (another learning experience). I didn’t put a clock on the effort, but it felt faster. Of course, I was livid with proudness the whole time because I was using my own tool. I just wish I had thought of it sooner. But, really, with 2100+ scans under my belt, I’m just getting started– I literally have thousands more artifacts to scan for preservation.

  The tool isn’t perfect, of course. Just tonight, I threw another scan at MobyCAIRO. Just go ahead and try to find straight lines in this specimen :

  
  
  

  I eventually had to use the text left and right of center to line up against the grid with the manual keyboard adjustments. Still, I’m impressed by how these computer vision algorithms can see patterns I can’t, highlighting lines I never would have guessed at.

  I’m eager to play with OpenCV some more, particularly the video processing functions, perhaps even some GPU-accelerated versions.

  The post Developing MobyCAIRO first appeared on Breaking Eggs And Making Omelettes.

• Anomalie #4698 : Formulaire de recherche de rubrique dysfonctionnel

  22 mars 2021, par RastaPopoulos ♥

  Le plugin Sélecteur générique (qui gère des mécanismes d’autocomplete facile à appeler) surcharge les deux sélecteurs (l’ancien qui fait que rub/art, et le générique) pour appliquer un autocomplete dessus :
  https://git.spip.net/spip-contrib-extensions/selecteur_generique/src/branch/master/formulaires/selecteur

  Quand on l’active, on peut alors bien chercher par titre, que ce soit les articles, ou les rubriques.