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• Presentation of Piwik’s collaborative translations platform : oTrance [Interview]

  19 avril 2013, par matt — Community, translation

  Piwik enables domain administrators, hobbyists, power users, personal website builders and everyone in between to access enormous amounts of data for website analytics. To support all those users, Piwik needs to be available in a number of different languages. From the start, we made internationalization (i18n) part of Piwik’s DNA. There are now dozens active volunteers who help make sure each language is well represented in the latest official release of Piwik. As of now, Piwik is available in 48 languages.

  Recently a new tool became available that makes the translation of Piwik much easier. The software we are using is an open source platform called oTrance. It has made our translation architecture more robust, and it allows us to expedite the timely delivery of high quality and up-to-date translations to the thousands of people who rely on Piwik every day.

  We’ve met with oTrance creator and lead developer Daniel Schlichtholz who answered a few questions for us.

  1. What is oTrance ?
  2. What sets oTrance apart from the other ways to manage translations ?
  3. Now that oTranCe 1.0 is out, what will you be working on next ?

  What is oTrance ?

  oTranCe is the short form of “Online Translation Center”. It was born because I needed a translation platform for my project MySQLDumper.

  Many languages have been added by the community and manual maintenance became more and more time consuming. I wanted to change that. So I searched for an existing platform I could use and tested a lot of approaches. To put a long story short : none of the given solutions satisfied my needs.

  From the view of a translator maintaining a language should be as easy as possible. In most cases they have to install a program on their local machine or the workflow was too difficult. A translator doesn’t want to struggle with technical things ; he just wants to translate the phrases and wants to know the progress.

  That’s the main goal we want to reach : to make the translation process as easy as possible.

  What sets oTrance apart from the other ways to manage translations ?

  Ease of use is one advantage of oTranCe compared to other solutions. Another advantage is that project administrators can install oTranCe on their own server – so nobody is dependant of a third party provider.

  We love to get feedback from other users. User feedback influences the way oTranCe is developed. We believe that this way oTranCe satisfies the requirements of the real world.

  We also have extensive user documentation, in our “Working with oTranCe” wiki. We try to document use cases in an understandable way. We don’t write down marketing buzz words, but try to explain the use from the view of the user/administrator.

  Now that oTranCe 1.0 is out, what will you be working on next ?

  The language files can be exported to version control and oTranCe can commit changes to the target repository. Currently we support export to Subversion, and we are working on a Git export adapter, which will be released soon.

  Another issue we are trying to solve is the context problem. When your project uses many different phrases the translator often doesn’t know in which context the current phrase is used. Version 1.1.0 (not released yet, but you can grab the latest developer version from GitHub) introduces the oTranCe-connector. The idea behind it : a small plug in grabs the used phrases/keys on the current page, and on click this list is submitted to oTranCe, where the translator can edit the words. This way the translator knows in which context these phrases are used. I wrote a small plug in for OXID eShop. Since it is really easy to implement, my hope is that other plug ins for other applications will be added by the community.

  Matthieu : Congratulations Daniel for having created such an awesome Translation Platform. At Piwik we are really thankful for oTranCe, which has resulted in much better translation process, and happier translators. Keep up the good work !

  If you are a Piwik user, and if you want to participate in translating Piwik, please sign up for an account on oTrance and become part of the team making Piwik available in more languages across the world.

• VLC can not play RTSP stream while mpv, ffplay can play RTSP stream video

  15 novembre 2018, par Harshil Makwana

  I developed ffserver based RTSP server which streams live video, I can able to stream and display video on mpv and ffplayer. but on VLC player I can see first image only then It wont show other frames.

  Here are logs of VLC -vvv output :

  [0x7846e8] main playlist debug: art not found for rtsp://127.0.0.1:1234/test.h264
  
  Received 92 new bytes of response data.
  
  Received a complete PLAY response:
  
  RTSP/1.0 200 OK
  
  CSeq: 5
  
  Date: Thu, 15 Nov 2018 04:13:15 GMT
  
  Session: 522d937eb678c50a
  
  
  
  
  
  [0x7f9684000e38] live555 demux debug: play start: 0.000000 stop:0.000000
  
  [0x7f9684000e38] main demux debug: using access_demux module "live555"
  
  [0x7f96840246b8] main decoder debug: looking for decoder module matching "any": 39 candidates
  
  [0x7f96840246b8] avcodec decoder debug: trying to use direct rendering
  
  [0x7f96840246b8] avcodec decoder debug: allowing 3 thread(s) for decoding
  
  [0x7f96840246b8] avcodec decoder debug: avcodec codec (H264 - MPEG-4 AVC (part 10)) started
  
  [0x7f96840246b8] avcodec decoder debug: using frame thread mode with 3 threads
  
  [0x7f96840246b8] main decoder debug: using decoder module "avcodec"
  
  [0x7f9684034f68] main packetizer debug: looking for packetizer module matching "any": 21 candidates
  
  [0x7f9684034f68] main packetizer debug: using packetizer module "packetizer_h264"
  
  [0x7f9684150308] main demux meta debug: looking for meta reader module matching "any": 2 candidates
  
  [0x7f9684150308] lua demux meta debug: Trying Lua scripts in /home/hashmak/.local/share/vlc/lua/meta/reader
  
  [0x7f9684150308] lua demux meta debug: Trying Lua scripts in /usr/lib/vlc/lua/meta/reader
  
  [0x7f9684150308] lua demux meta debug: Trying Lua playlist script /usr/lib/vlc/lua/meta/reader/filename.luac
  
  [0x7f9684150308] lua demux meta debug: Trying Lua scripts in /usr/share/vlc/lua/meta/reader
  
  [0x7f9684150308] main demux meta debug: no meta reader modules matched
  
  [0x7f968c0009b8] main input debug: `rtsp://127.0.0.1:1234/test.h264' successfully opened
  
  [0x7f9684000e38] live555 demux debug: tk->rtpSource->hasBeenSynchronizedUsingRTCP()
  
  [0x7f968c0009b8] main input error: ES_OUT_RESET_PCR called
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer warning: waiting for SPS/PPS
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9684034f68] packetizer_h264 packetizer debug: found NAL_SPS (sps_id=0)
  
  [0x7f9684034f68] packetizer_h264 packetizer debug: found NAL_PPS (pps_id=0 sps_id=0)
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9694002718] main spu text debug: looking for text renderer module matching "any": 2 candidates
  
  [0x7f9694002718] freetype spu text debug: Building font databases.
  
  [0x7f9694002718] freetype spu text debug: Took 0 microseconds
  
  Fontconfig warning: FcPattern object size does not accept value "0"
  
  Fontconfig warning: FcPattern object size does not accept value "0"
  
  [0x7f9694002718] freetype spu text debug: Using Serif Bold as font from file /usr/share/fonts/truetype/dejavu/DejaVuSans.ttf
  
  [0x7f9694002718] freetype spu text debug: using fontsize: 2
  
  [0x7f9694002718] main spu text debug: using text renderer module "freetype"
  
  [0x7f969400e408] main scale debug: looking for video filter2 module matching "any": 55 candidates
  
  [0x7f969400e408] swscale scale debug: 32x32 chroma: YUVA -> 16x16 chroma: RGBA with scaling using Bicubic (good quality)
  
  [0x7f969400e408] main scale debug: using video filter2 module "swscale"
  
  [0x7f9694025cc8] main scale debug: looking for video filter2 module matching "any": 55 candidates
  
  [0x7f9694025cc8] yuvp scale debug: YUVP to YUVA converter
  
  [0x7f9694025cc8] main scale debug: using video filter2 module "yuvp"
  
  [0x7f9694001428] main video output debug: Deinterlacing available
  
  [0x7f9694001428] main video output debug: deinterlace 0, mode blend, is_needed 0
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9694001428] main video output debug: Opening vout display wrapper
  
  [0x7f9674001248] main vout display debug: looking for vout display module matching "any": 12 candidates
  
  [0x7f9674002618] main window debug: looking for vout window xid module matching "qt4,any": 4 candidates
  
  [0x7f9674002618] qt4 window debug: requesting video window...
  
  [0x6f3208] qt4 interface debug: Video was requested 0, 0
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f9674002618] main window debug: using vout window xid module "qt4"
  
  [0x7f96740027e8] main inhibit debug: looking for inhibit module matching "any": 2 candidates
  
  [0x7f96740027e8] dbus_screensaver inhibit debug: found service org.freedesktop.ScreenSaver
  
  [0x7f96740027e8] main inhibit debug: using inhibit module "dbus_screensaver"
  
  [0x7f9674001248] xcb_glx vout display debug: connected to X11.0 server
  
  [0x7f9674001248] xcb_glx vout display debug:  vendor : The X.Org Foundation
  
  [0x7f9674001248] xcb_glx vout display debug:  version: 11702000
  
  [0x7f9674001248] xcb_glx vout display debug: using screen 0x73
  
  [0x7f9674001248] xcb_glx vout display debug: using GLX extension version 1.4
  
  [0x7f9674001248] xcb_glx vout display debug: using X11 window 05400000
  
  shader program 1: WARNING: Output of vertex shader 'TexCoord1' not read by fragment shader
  
  WARNING: Output of vertex shader 'TexCoord2' not read by fragment shader
  
  
  
  [0x7f9674001248] main vout display debug: VoutDisplayEvent 'fullscreen' 0
  
  [0x7f9674001248] main vout display debug: VoutDisplayEvent 'resize' 1215x724 window
  
  [0x7f9674001248] main vout display debug: using vout display module "xcb_glx"
  
  [0x7f9694001428] main video output debug: original format sz 640x480, of (0,0), vsz 640x480, 4cc I420, sar 1:1, msk r0x0 g0x0 b0x0
  
  [0x7f9694002718] main spu text debug: removing module "freetype"
  
  [0x7f9694002718] main spu text debug: looking for text renderer module matching "any": 2 candidates
  
  [0x7f9694002718] freetype spu text debug: Building font databases.
  
  [0x7f9694002718] freetype spu text debug: Took 0 microseconds
  
  Fontconfig warning: FcPattern object size does not accept value "0"
  
  Fontconfig warning: FcPattern object size does not accept value "0"
  
  [0x7f9694002718] freetype spu text debug: Using Serif Bold as font from file /usr/share/fonts/truetype/dejavu/DejaVuSans.ttf
  
  [0x7f9694002718] freetype spu text debug: using fontsize: 2
  
  [0x7f9694002718] main spu text debug: using text renderer module "freetype"
  
  [0x7f96840246b8] avcodec decoder debug: using direct rendering
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f96840246b8] main decoder debug: End of video preroll
  
  [0x7f96840246b8] main decoder debug: Received first picture
  
  [0x7f9674001248] xcb_glx vout display debug: display is visible
  
  [0x7f9674001248] main vout display error: Failed to resize display
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [h264 @ 0x7f9684037e40] illegal short term buffer state detected
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
  [0x7f968c0009b8] main input debug: Buffering 0%
  
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  Here you can see, VLC is always waiting on buffering, while other players can get buffers from my RTSP server, also I can see RTP packets on wireshark that server is continuously sending data to VLC.

  Can someone help me on this issue ?

  Very much thanks in advance.

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required.