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• Announcing our latest open source project : DeviceDetector

  30 juillet 2014, par Stefan Giehl — Community, Development, Meta, DeviceDetector

  This blog post is an announcement for our latest open source project release : DeviceDetector ! The Universal Device Detection library will parse any User Agent and detect the browser, operating system, device used (desktop, tablet, mobile, tv, cars, console, etc.), brand and model.

  Read on to learn more about this exciting release.

  Why did we create DeviceDetector ?

  Our previous library UserAgentParser only had the possibility to detect operating systems and browsers. But as more and more traffic is coming from mobile devices like smartphones and tablets it is getting more and more important to know which devices are used by the websites visitors.

  To ensure that the device detection within Piwik will gain the required attention, so it will be as accurate as possible, we decided to move that part of Piwik into a separate project, that we will maintain separately. As an own project we hope the DeviceDetector will gain a better visibility as well as a better support by and for the community !

  DeviceDetector is hosted on GitHub at piwik/device-detector. It is also available as composer package through Packagist.

  How DeviceDetector works

  Every client requesting data from a webserver identifies itself by sending a so-called User-Agent within the request to the server. Those User Agents might contain several information such as :

  • client name and version (clients can be browsers or other software like feed readers, media players, apps,…)
  • operating system name and version
  • device identifier, which can be used to detect the brand and model.

  For Example :

  Mozilla/5.0 (Linux; Android 4.4.2; Nexus 5 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.99 Mobile Safari/537.36

  This User Agent contains following information :

  Operating system is Android 4.4.2, client uses the browser Chrome Mobile 32.0.1700.99 and the device is a Google Nexus 5 smartphone.

  What DeviceDetector currently detects

  DeviceDetector is able to detect bots, like search engines, feed fetchers, site monitors and so on, five different client types, including around 100 browsers, 15 feed readers, some media players, personal information managers (like mail clients) and mobile apps using the AFNetworking framework, around 80 operating systems and nine different device types (smartphones, tablets, feature phones, consoles, tvs, car browsers, cameras, smart displays and desktop devices) from over 180 brands.

  Note : Piwik itself currently does not use the full feature set of DeviceDetector. Client detection is currently not implemented in Piwik (only detected browsers are reported, other clients are marked as Unknown). Client detection will be implemented into Piwik in the future, follow #5413 to stay updated.

  Performance of DeviceDetector

  Our detections are currently handled by an enormous number of regexes, that are defined in several .YML Files. As parsing these .YML files is a bit slow, DeviceDetector is able to cache the parsed .YML Files. By default DeviceDetector uses a static cache, which means that everything is cached in static variables. As that only improves speed for many detections within one process, there are also adapters to cache in files or memcache for speeding up detections across requests.

  How can users help contribute to DeviceDetector ?

  Submit your devices that are not detected yet

  If you own a device, that is currently not correctly detected by the DeviceDetector, please create a issue on GitHub
  In order to check if your device is detected correctly by the DeviceDetector go to your Piwik server, click on ‘Settings’ link, then click on ‘Device Detection’ under the Diagnostic menu. If the data does not match, please copy the displayed User Agent and use that and your device data to create a ticket.

  Submit a list of your User Agents

  In order to create new detections or improve the existing ones, it is necessary for us to have lists of User Agents. If you have a website used by mostly non desktop devices it would be useful if you send a list of the User Agents that visited your website. To do so you need access to your access logs. The following command will extract the User Agents :

  zcat ~/path/to/access/logs* | awk -F'"' '{print $6}' | sort | uniq -c | sort -rn | head -n20000 > /home/piwik/top-user-agents.txt

  If you want to help us with those data, please get in touch at devicedetector@piwik.org

  Submit improvements on GitHub

  As DeviceDetector is free/libre library, we invite you to help us improving the detections as well as the code. Please feel free to create tickets and pull requests on Github.

  What’s the next big thing for DeviceDetector ?

  Please check out the list of issues in device-detector issue tracker.

  We hope the community will answer our call for help. Together, we can build DeviceDetector as the most powerful device detection library !

  Happy Device Detection,

• Why Matomo is the top Google Analytics alternative

  17 juin, par Joe

  You probably made the switch to Google Analytics 4 (GA4) when Google stopped collecting Universal Analytics (UA) data in July 2023. Up to that point, UA had long been the default analytics platform, despite its many limitations. 

  This was mostly because everyone loved its free nature and simple setup. A Google account was all you needed — even a free legacy G-Suite account worked perfectly. Looking at the analytics for just about any website was easy.

  That all changed with GA4, which addressed many of UA’s shortcomings by introducing a completely new way to model website data. Unfortunately, this also meant you couldn’t transfer historical data from UA into GA4, leading to more criticism. 

  Then there’s the added cost. GA4 is still free, but its limited functionality encourages you to upgrade to the enterprise version, Google Analytics 360 (GA360). Sure, you get lots of great functionality, less data sampling, and longer data retention periods, but it comes at a hefty price — $50,000 per year, to be exact.

  There are other options, though, and Matomo Analytics is one of the best. It’s an open-source, privacy-centric platform that offers advanced features of GA360 and more. 

  In this article, we’ll compare GA4, GA360, and Matomo and give you what you need to make an informed decision.

  Google Analytics 4 in a nutshell

  Google Analytics 4 is a great tool to use to start learning about web analytics. But soon enough, you’ll likely find that GA4 doesn’t quite cover all of your needs. 

  For example, it can’t provide a detailed view of user experiences, and Google doesn’t offer dedicated support or onboarding. There are other shortcomings, too.

  Data sampling

  Google only processes a selected sample of website activity rather than every individual data point. Rather than looking at the whole picture, it sets a threshold and selects a [hopefully] representative sample for analysis. 

  This inevitably creates gaps in data. Google attempts to fill them in using AI and machine learning, inferring the rest from data patterns. Since the results rely on assumptions and estimates, they aren’t always precise.

  In practical terms, this means that the accuracy of GA4 analysis will likely decline as website traffic increases.

  

  (Image source)

  Data collection limits

  GA4’s 25 million monthly events limit seems like a lot, but they add up quickly. 

  All user interactions are recorded as events, including :

  • Session start : User visits the site.
  • Page view : User loads a page (tracked automatically).
  • First visit : User accesses the site for the first time.
  • User engagement : User stays on a page for a set time period.
  • Scroll : User scrolls past 90% of the page (enhanced measurement).
  • Click : User clicks on any element (links, buttons, etc.).
  • Video start/complete : User starts or completes a video (enhanced measurement).
  • File download : User downloads a file (enhanced measurement).

  For context, consider a website averaging 50 events per session per user. If every user logs on every third day, on average, you’ll need 10,000 individual visitors a month to reach that 25 million. But that’s not the problem. 

  The problem is that collection limits in GA4 affect your ability to capture, secure, and analyse customer data effectively.

  Customisation

  GA4 users also face configuration limits that restrict their customisation options. For example : 

  • Audience limits : Since only 100 audiences are allowed, it’s necessary to combine or optimise segments rather than track too many small groups. 
  • Retention limits : Data retention is limited to only 14 months, so external storage solutions may be necessary in situations where historical data needs to be preserved.
  • Conversion events : GA4 will only track up to 30 conversion events, so it’s best to focus on high-value interactions (e.g., purchases and lead form submissions). 
  • Event-scoped dimensions : Since e-commerce operations are limited to 50 event-scoped dimensions, they need to carefully consider custom dimensions and key metrics. This makes it important to be selective about which product details to track (color, size, discount code, etc.).

  Data privacy

  GA4 isn’t GDPR-compliant out of the box. In fact, Google Analytics 4 is banned in seven EU countries because they believe the way it collects and transfers data violates GDPR.

  Data privacy regulations may or may not be a big concern, depending on where your customers are. However, if some are in the UK or any of the 30 countries that make up the European Economic Area (EEA), you must comply with the General Data Protection Regulation (GDPR). 

  It tells your customers that you don’t respect their data if you don’t. It can also get very expensive.

  Limited attribution models

  Attribution models track how different marketing touchpoints lead to a conversion (such as a purchase, sign-up, or lead generation). They help businesses understand which marketing channels and strategies are most effective in driving results.

  GA4 supports only two of the six standard attribution models previously supported in Universal Analytics. Organisations wanting data-driven or last-click attribution models will find them in Google Analytics. But they’ll need to look elsewhere if they’re going to use any of these models :

  • First click attribution
  • Linear attribution
  • Time decay attribution
  • Position-based attribution (u-shaped)

  GA360 isn’t a solution either

  Fundamentally, GA360 is the same product as GA4, without the above limits and restrictions. For companies that pay $50,000 (or more) each year, the only changes involve how much data is collected, how long it stays and data sampling thresholds.

  Above all, the GDPR-compliance issue remains. That can be a real problem for organisations with operations that collect personal data in the EEA or the UK.

  And the problem could soon be much bigger than just those 31 countries. Many countries currently implementing data privacy laws are modelling their efforts on GDPR, which may rule out both GA4 and GA360.

  

  What makes Matomo the top alternative ?

  No data limits

  One way to overcome all these challenges is to switch to Matomo Analytics. 

  There’s no data sampling and no data collection limits whatsoever with on-premise implementation. Matomo also supports all six attribution models, is open source and fully customisable and complies with GDPR out of the box. 

  Imagine trying to change your business strategy or marketing campaigns if you’re not confident that your data is reliable and accurate.

  It’s no secret that data sampling can negatively affect the accuracy of the data, and inaccurate data can lead to poor decision-making.
  

  With Matomo, there are no limits. We don’t restrict the size of containers within the Tag Manager nor the number of containers or tags within each container. You have more control over your customers’ data. 

  And you get to make your decisions based on all that data. That’s important because data quality is critical for high-impact decisions. 

  Open source

  Open-source software allows anyone to inspect, audit, and improve the source code for security and efficiency. That means no hidden data collection, faster bug fixes, and no vendor lock-in. As a bonus, these things make complying with data privacy laws and regulations easier.

  Matomo can also be modified in any way, which provides unlimited customisation possibilities. There’s also a very active developer community around Matomo, so you don’t have to make changes yourself — you can hire someone who has the technical knowledge and expertise. They can : 

  • Modify tracking scripts for advanced analytics
  • Create custom attribution models, tracking methods and dashboards
  • Integrate Matomo with any system (CRM, eCommerce, CMS, etc.)

  Data ownership

  Matomo’s open-source nature also means full data ownership. No third parties can access the data, and there’s no risk of Google using that data for ads or AI training. Furthermore, Matomo follows privacy-first tracking principles, meaning that there’s :

  • No third-party data sharing
  • Full user consent control
  • Support for cookie-less tracking
  • IP Anonymisation, by default
  • Do Not Track (DNT) support

  All of that underlines the fact that Matomo collects, stores, and tracks data 100% ethically.

  On-premise and cloud-based options

  You can use the Matomo On-Premise web analytics solution if local data privacy laws require that you store data locally. Here’s a helpful tip : many of them do. However, this might not be necessary. 

  Due to GDPR, several countries recognise the EEA as an acceptable storage location for their citizens’ data. That means servers hosted in any of those 30 countries are already compliant in terms of data location. 

  Alternatively, you could embrace modernity and choose Matomo Cloud — our servers are also in Europe. While GA4 and GA360 are cloud-based, Google’s servers are in the US, and that’s a big problem for GDPR.

  

  Comprehensive analytics

  If you need a sophisticated web analytics platform that offers full control of your data and you have privacy concerns, Matomo is a solid choice. 

  It has built-in behavioural analytics features like Heatmaps, Scroll Depth and Session Recording. These tools allow you to collect and analyse data without relying on cookies or resorting to data sampling.

  Those standout features can’t be found in GA4 or GA360. Google also doesn’t offer an on-premise solution.

  The one area where Matomo can’t compete with Google Analytics is in its tight integration with the Google ecosystem : Google Ads, Gemini and Firebase. 

  Key things to consider before switching to Matomo

  There are pros and cons to switching from GA4 (or even GA360) to Matomo. That’s because no software is perfect. There are always tradeoffs somewhere. With Matomo, there are a few things to consider before switching :

  • Learning curve. Matomo is a full-featured analytics platform with many advanced features (session replay, custom event tracking, etc.). That can overwhelm new users and take time to understand well enough to maximise the benefits.
  • Technical resources. Choosing a Matomo On-Premise solution requires technical resources, such as a server and skills.
  • Third-party integration. Matomo provides pre-built integration tools for about a hundred platforms. However, it’s open source, so technical resources are required. On the plus side, it does make it possible to add to the list of APIs and connectors.

  Head-to-head : GA4 vs GA360 vs Matomo

  It’s always helpful to look at how different products stack up in terms of features and capabilities :

  	GA4	GA360	Matomo
  Data ownership			✔
  Event-based data	✔	✔	✔
  Session-based data			✔
  Unsampled data			✔
  Real-time data	✔	✔	✔
  Heatmaps			✔
  Session recordings			✔
  A/B testing			✔
  Open source			✔
  On-premise hosting			✔
  Data privacy	Subject to Google’s data policies	Subject to Google’s data policies	GDPR, CCPA compliant ; full control over data storage
  Custom dimensions	Yes (limited in free version)	Yes (higher limits)	Yes (unlimited in self-hosted)
  Attribution models	Last click, data-driven	Last click, data-driven, advanced Google Ads integration	Last click, first click, linear, time decay, position-based, custom
  Data retention	Up to 14 months (free)	Up to 50 months	Unlimited (self-hosted)
  Integrations	Google Ads, Search Console, BigQuery (limited in free version)	Advanced integrations (Google Ads, BigQuery, Salesforce, etc.)	100+ integrations (Google Ads, WordPress, Shopify, etc.)
  BigQuery export	Free (limited to 1M events/day)	Free (unlimited)	Paid add-on (via plugin)
  Custom reports	Limited customisation	Advanced customisation	Fully customisable
  Scalability	Suitable for small to medium businesses	Designed for large enterprises	Scalable without limits (self-hosted or cloud)
  Ease of use	Simple, requires onboarding	Steeper learning curve	Flexible, setup-intensive.
  Pricing	Free	Premium (starts at $50,000/year)	Free open-source (self-hosted) ; Cloud starts at $29/month

  So, is Matomo the right solution for you ?

  That’d be a ‘yes’ if you want a Google Analytics alternative that ticks all these boxes :

  • Complies natively with privacy laws and regulations
  • Offers real-time data and custom event tracking
  • Enables a deeper understanding of user behaviour
  • Allows you to fine-tune user experiences
  • Provides full control over your customers’ data
  • Offers conversion funnels, session recordings and heatmaps
  • Has session replay to trace user interactions
  • Includes plenty of readily actionable insights

  Find out why millions of websites trust Matomo

  Matomo is an easy-to-use, all-in-one web analytics tool with advanced behavioural analytics functionality.

  It’ll also help you future-proof your business because it supports compliance with global privacy laws in 162 countries. With an ethical alternative like Matomo, you don’t need to risk your business or customers’ private data.

  It’s not just about avoiding fines. It’s also about building trust with your customers. That’s why you need a privacy-focused, ethical solution like Matomo. 

  See for yourself : download Matomo On-Premise today, or start your 21-day free trial of Matomo Cloud (no credit card required).

• pnacl-clang doesn't know where ffmpeg libraries are (but Eclipse does ?)

  10 août 2014, par lavsprat

  I’m trying to make my first "hello world"-like app using ffmpeg libraries. I already got NaCl SDK and downloaded & compiled the ffmpeg port.

  This is my code :

  main.c

  #include <libavformat></libavformat>avformat.h>
  
  
  
  int main()
  
  {
  
      av_register_all();
  
      return 0;
  
  }

  Building with $ (...)/pnacl-clang main.c -o main -lavformat in terminal.

  The output :

  main.c:2:10: fatal error: 'libavformat/avformat.h' file not found
  
  #include <libavformat></libavformat>avformat.h>
  
           ^

  Now, why am I not using -L(...)\lib and -I(...)\include in the build command ? Because it should work without it. In my workplace nacl-clang somehow knows where the libs are and compiles everything successfully. Why is that not working on my personal computer ? How can I permanently let pnacl-clang know where to look for them ?