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• Linux Media Player Survey Circa 2001

  2 septembre 2010, par Multimedia Mike — General

  Here’s a document I scavenged from my archives. It was dated September 1, 2001 and I now publish it 9 years later. It serves as sort of a time capsule for the state of media player programs at the time. Looking back on this list, I can’t understand why I couldn’t find MPlayer while I was conducting this survey, especially since MPlayer is the project I eventually started to work for a few months after writing this piece.

  For a little context, I had been studying multimedia concepts and tech for a year and was itching to get my hands dirty with practical multimedia coding. But I wanted to tackle what I perceived as unsolved problems– like playback of proprietary codecs. I didn’t want to have to build a new media playback framework just to start working on my problems. So I surveyed the players available to see which ones I could plug into and use as a testbed for implementing new decoders.

  Regarding Real Player, I wrote : “We’re trying to move away from the proprietary, closed-source “solutions”. Heh. Was I really an insufferable open source idealist back in the day ?

  Anyway, here’s the text with some Where are they now ? commentary [in brackets] :

  ---

  Towards an All-Inclusive Media Playing Solution for Linux

  I don’t feel that the media playing solutions for Linux set their sights high enough, even though they do tend to be quite ambitious.

  I want to create a media player for Linux that can open a file, figure out what type of file it is (AVI, MOV, etc.), determine the compression algorithms used to encode the audio and video chunks inside (MPEG, Cinepak, Sorenson, etc.) and replay the file using the best audio, video, and CPU facilities available on the computer.

  Video and audio playback is a solved problem on Linux ; I don’t wish to solve that problem again. The problem that isn’t solved is reliance on proprietary multimedia solutions through some kind of WINE-like layer in order to decode compressed multimedia files.

  Survey of Linux solutions for decoding proprietary multimedia
  updated 2001-09-01

  AVI Player for XMMS
  This is based on Avifile. All the same advantages and limitations apply.
  [Top Google hit is a Freshmeat page that doesn’t indicate activity since 2001-2002.]

  Avifile
  This player does a great job at taking apart AVI and ASF files and then feeding the compressed chunks of multimedia data through to the binary Win32 decoders.

  The program is written in C++ and I’m not very good at interpreting that kind of code. But I’m learning all over again. Examining the object hierarchy, it appears that the designers had the foresight to include native support for decoders that are compiled into the program from source code. However, closer examination reveals that there is support for ONE source decoder and that’s the “decoder” for uncompressed data. Still, I tried to manipulate this routine to accept and decode data from other codecs but no dice. It’s really confounding. The program always crashes when I feed non-uncompressed data through the source decoder.
  [Lives at http://avifile.sourceforge.net/ ; not updated since 2006.]

  Real Player
  There’s not much to do with this since it is closed source and proprietary. Even though there is a plugin architecture, that’s not satisfactory. We’re trying to move away from the proprietary, closed-source “solutions”.
  [Still kickin’ with version 11.]

  XAnim
  This is a well-established Unix media player. To his credit, the author does as well as he can with the resources he has. In other words, he supports the non-proprietary video codecs well, and even has support for some proprietary video codecs through binary-only decoders.

  The source code is extremely difficult to work with as the author chose to use the X coding format which I’ve never seen used anywhere else except for X header files. The infrastructure for extending the program and supporting other codecs and file formats is there, I suppose, but I would have to wrap my head around the coding style. Maybe I can learn to work past that. The other thing that bothers me about this program is the decoding approach : It seems that each video decoder includes routines to decompress the multimedia data into every conceivable RGB and YUV output format. This seems backwards to me ; it seems better to have one decoder function that decodes the data into its native format it was compressed from (e.g., YV12 for MPEG data) and then pass that data to another layer of the program that’s in charge of presenting the data and possibly converting it if necessary. This layer would encompass highly-optimized software conversion routines including special CPU-specific instructions (e.g., MMX and SSE) and eliminate the need to place those routines in lots of other routines. But I’m getting ahead of myself.
  [This one was pretty much dead before I made this survey, the most recent update being in 1999. Still, we owe it much respect as the granddaddy of Unix multimedia playback programs.]

  Xine
  This seems like a promising program. It was originally designed to play MPEGs from DVDs. It can also play MPEG files on a hard drive and utilizes the Xv extensions for hardware YUV playback. It’s also supposed to play AVI files using the same technique as Avifile but I have never, ever gotten it to work. If an AVI file has both video and sound, the binary video decoder can’t decode any frames. If the AVI file has video and no sound, the program gets confused and crashes, as far as I can tell.

  Still, it’s promising, and I’ve been trying to work around these crashes. It doesn’t yet have the type of modularization I’d like to see. Right now, it tailored to suit MPEG playback and AVI playback is an afterthought. Still, it appears to have a generalized interface for dropping in new file demultiplexers.

  I tried to extend the program for supporting source decoders by rewriting w32codec.c from scratch. I’m not having a smooth time of it so far. I’m able to perform some manipulations on the output window. However, I can’t get the program to deal with an RGB image format. It has trouble allocating an RGB surface with XvShmCreateImage(). This isn’t suprising, per my limited knowledge of X which is that Xv applies to YUV images, but it could also apply to RGB images as well. Anyway, the program should be able to fall back on regular RGB pixmaps if that Xv call fails.

  Right now, this program is looking the most promising. It will take some work to extend the underlying infrastructure, but it seems doable since I know C quite well and can understand the flow of this program, as opposed to Avifile and its C++. The C code also compiles about 10 times faster.
  [My home project for many years after a brief flirtation with MPlayer. It is still alive ; its latest release was just a month ago.]

  XMovie
  This library is a Quicktime movie player. I haven’t looked at it too extensively yet, but I do remember looking at it at one point and reading the documentation that said it doesn’t support key frames. Still, I should examine it again since they released a new version recently.
  [Heroine Virtual still puts out some software but XMovie has not been updated since 2005.]

  XMPS
  This program compiles for me, but doesn’t do much else. It can play an MP3 file. I have been able to get MPEG movies to play through it, but it refuses to show the full video frame, constricting it to a small window (obviously a bug).
  [This project is hosted on SourceForge and is listed with a registration date of 2003, well after this survey was made. So the project obviously lived elsewhere in 2001. Meanwhile, it doesn’t look like any files ever made it to SF for hosting.]

  XTheater
  I can’t even get this program to compile. It’s supposed to be an MPEG player based on SMPEG. As such, it probably doesn’t hold much promise for being easily extended into a general media player.
  [Last updated in 2002.]

  GMerlin
  I can’t get this to compile yet. I have a bug report in to the dev group.
  [Updated consistently in the last 9 years. Last update was in February of this year. I can’t find any record of my bug report, though.]

• 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.

• How to Conduct a Customer Journey Analysis (Step-by-Step)

  9 mai 2024, par Erin

  Your customers are everything.

  Treat them right, and you can generate recurring revenue for years. Treat them wrong ; you’ll be spinning your wheels and dealing with churn.

  How do you give your customers the best experience possible so they want to stick around ?

  Improve their customer experience.

  How ?

  By conducting a customer journey analysis.

  When you know how your customers experience your business, you can improve it to meet and exceed customer expectations.

  In this guide, we’ll break down how the customer journey works and give you a step-by-step guide to conduct a thorough customer journey analysis so you can grow your brand.

  What is a customer journey analysis ?

  Every customer you’ve ever served went on a journey to find you.

  From the moment they first heard of you, to the point that they became a customer. 

  Everything in between is the customer journey.

  A customer journey analysis is how you track and analyse how your customers use different channels to interact with your brand.

  

  Analysing your customer journey involves identifying the customer’s different touchpoints with your business so you can understand how it impacts their experience. 

  This means looking at every moment they interacted with your brand before, during and after a sale to help you gain actionable insights into their experience and improve it to reach your business objectives.

  Your customers go through specific customer touchpoints you can track. By analysing this customer journey from a bird’s eye view, you can get a clear picture of the entire customer experience.

  4 benefits of customer journey analysis

  Before we dive into the different steps involved in a customer journey analysis, let’s talk about why it’s vital to analyse the customer journey.

  By regularly analysing your customer journey, you’ll be able to improve the entire customer experience with practical insights, allowing you to :

  Understand your customers better

  What’s one key trait all successful businesses have ?

  They understand their customers.

  By analysing your customer journey regularly, you’ll gain new insights into their wants, needs, desires and behaviours, allowing you to serve them better. These insights will show you what led them to buy a product (or not).

  For example, through conducting a customer journey analysis, a company might find out that customers who come from LinkedIn are more likely to buy than those coming from Facebook.

  Find flaws in your customer journey

  Nobody wants to hear they have flaws. But the reality is your customer journey likely has a few flaws you could improve.

  By conducting customer journey analysis consistently, you’ll be able to pinpoint precisely where you’re losing prospects along the way. 

  For example, you may discover you’re losing customers through Facebook Ads. Or you may find your email strategy isn’t as good as it used to be.

  But it’s not just about the channel. It could be a transition between two channels. For example, you may have great engagement on Instagram but are not converting them into email subscribers. The issue may be that your transition between the two channels has a leak.

  Or you may find that prospects using certain devices (i.e., mobile, tablet, desktop) have lower conversions. This might be due to design and formatting issues across different devices.

  By looking closely at your customer journey and the different customer touchpoints, you’ll see issues preventing prospects from turning into leads or customers from returning to buy again as loyal customers.

  Gain insights into how you can improve your brand

  Your customer journey analysis won’t leave you with a list of problems. Instead, you’ll have a list of opportunities.

  Since you’ll be able to better understand your customers and where they’re falling off the sales funnel, you’ll have new insights into how you can improve the experience and grow your brand.

  For example, maybe you notice that your visitors are getting stuck at one stage of the customer journey and you’re trying to find out why.

  So, you leverage Matomo’s heatmaps, sessions recordings and scroll depth to find out more.

  In the case below, we can see that Matomo’s scroll map is showing that only 65% of the visitors are reaching the main call to action (to write a review). 

  

  To try to push for higher conversions and get more reviews, we could consider moving that button higher up on the page, ideally above the fold.

  Rather than guessing what’s preventing conversions, you can use user behaviour analytics to “step in our user’s shoes” so you can optimise faster and with confidence.

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

  Start for free

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  Grow your revenue

  By taking charge of your customer journey, you can implement different strategies that will help you increase your reach, gain more prospects, convert more prospects into customers and turn regulars into loyal customers.

  Using customer journey analysis will help you optimise those different touchpoints to maximise the ROI of your channels and get the most out of each marketing activity you implement.

  7 steps to conduct a customer journey analysis

  Now that you know the importance of conducting a customer journey analysis regularly, let’s dive into how to implement an analysis.

  Here are the seven steps you can take to analyse the customer journey to improve your customer experience :

  

  1. Map out your customer journey

  Your first step to conducting an effective customer journey analysis is to map your entire customer journey.

  Customer journey mapping means looking at several factors :

  • Buying process
  • Customer actions
  • Buying emotions
  • Buying pain points
  • Solutions

  Once you have an overview of your customer journey maps, you’ll gain insights into your customers, their interests and how they interact with your brand. 

  After this, it’s time to dive into the touchpoints.

  2. Identify all the customer touchpoints 

  To improve your customer journey, you need to know every touchpoint a customer can (and does) make with your brand.

  This means taking note of every single channel and medium they use to communicate with your brand :

  • Website
  • Social media
  • Search engines (SEO)
  • Email marketing
  • Paid advertising
  • And more

  Essentially, anywhere you communicate and interact with your customers is fair game to analyse.

  If you want to analyse your entire sales funnel, you can try Matomo, a privacy-friendly web analytics tool. 

  You should make sure to split up your touchpoints into different customer journey stages :

  • Awareness
  • Consideration
  • Conversion
  • Advocacy

  Then, it’s time to move on to how customers interact on these channels.

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  3. Measure how customers interact on each channel

  To understand the customer journey, you can’t just know where your customers interact with you. You end up learning how they’re interacting.

  This is only possible by measuring customer interactions.

  How ?

  By using a web analytics tool like Matomo.

  With Matomo, you can track every customer action on your website.

  This means anytime they :

  • Visit your website
  • View a web page
  • Click a link
  • Fill out a form
  • Purchase a product
  • View different media
  • And more

  You should analyse your engagement on your website, apps and other channels, like email and social media.

  4. Implement marketing attribution

  Now that you know where your customers are and how they interact, it’s time to analyse the effectiveness of each channel based on your conversion rates.

  Implementing marketing attribution (or multi-touch attribution) is a great way to do this.

  Attribution is how you determine which channels led to a conversion.

  While single-touch attribution models credit one channel for a conversion, marketing attribution gives credit to a few channels.

  For example, let’s say Bob is looking for a new bank. He sees an Instagram post and finds himself on HSBC’s website. After looking at a few web pages, he attends a webinar hosted by HSBC on financial planning and investment strategies. One week later, he gets an email from HSBC following up on the webinar. Then, he decides to sign up for HSBC’s online banking.

  Single touch attribution would attribute 100% of the conversion to email, which doesn’t show the whole picture. Marketing attribution would credit all channels : social media, website content, webinars and email.

  Matomo offers multiple attribution models. These models leverage different weighting factors, like time decay or linear, so that you can allocate credit to each touchpoint based on its impact.

  Matomo’s multi-touch attribution reports give you in-depth insights into how revenue is distributed across different channels. These detailed reports help you analyse each channel’s contribution to revenue generation so you can optimise the customer journey and improve business outcomes.

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

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  5. Use a funnels report to find where visitors are leaving

  Once you set up your marketing attribution, it’s time to analyse where visitors are falling off.

  You can leverage Matomo funnels to find out the conversion rate at each step of the journey on your website. Funnel reports can help you see exactly where visitors are falling through the cracks so you can increase conversions.

  6. Analyse why visitors aren’t converting

  Once you can see where visitors are leaving, you can start to understand why.

  For example, let’s say you analyse your funnels report in Matomo and see your landing page is experiencing the highest level of drop-offs.

  

  You can also use form analytics to find out why users aren’t converting on your landing pages – a crucial part of the customer journey.

  7. A/B test to improve the customer journey

  The final step to improve your customer journey is to conduct A/B tests. These are tests where you test one version of a landing page to see which one converts better, drives more traffic, or generates more revenue.

  For example, you could create two versions of a header on your website and drive 50% of your traffic to each version. Then, once you’ve got your winner, you can keep that as your new landing page.

  

  Using the data from your A/B tests, you can optimise your customer journey to help convert more prospects into customers.

  Use Matomo to improve your customer journey analysis

  Now that you understand why it’s important to conduct customer journey analysis regularly and how it works, it’s time to put this into practice.

  To improve the customer journey, you need to understand what’s happening at each stage of your funnel. 

  Matomo gives you insights into your customer journey so you can improve website performance and convert more visitors into customers.

  Used by over 1 million websites, Matomo is the leading privacy-friendly web analytics solution in the world. 

  Matomo provides you with accurate, unsampled data so you understand exactly what’s going on with your website performance.

  The best part ?

  It’s easy to use and is compliant with the strictest privacy regulations.

  Try Matomo free for 21-days and start Improving your customer journey. No credit card required.

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