Recherche avancée

Sur d’autres sites (5874)

• Open Media Developers Track at OVC 2011

  11 octobre 2011, par silvia

  The Open Video Conference that took place on 10-12 September was so overwhelming, I’ve still not been able to catch my breath ! It was a dense three days for me, even though I only focused on the technology sessions of the conference and utterly missed out on all the policy and content discussions.

  Roughly 60 people participated in the Open Media Software (OMS) developers track. This was an amazing group of people capable and willing to shape the future of video technology on the Web :

  • HTML5 video developers from Apple, Google, Opera, and Mozilla (though we missed the NZ folks),
  • codec developers from WebM, Xiph, and MPEG,
  • Web video developers from YouTube, JWPlayer, Kaltura, VideoJS, PopcornJS, etc.,
  • content publishers from Wikipedia, Internet Archive, YouTube, Netflix, etc.,
  • open source tool developers from FFmpeg, gstreamer, flumotion, VideoLAN, PiTiVi, etc,
  • and many more.

  To provide a summary of all the discussions would be impossible, so I just want to share the key take-aways that I had from the main sessions.

  WebRTC : Realtime Communications and HTML5

  Tim Terriberry (Mozilla), Serge Lachapelle (Google) and Ethan Hugg (CISCO) moderated this session together (slides). There are activities both at the W3C and at IETF – the ones at IETF are supposed to focus on protocols, while the W3C ones on HTML5 extensions.

  The current proposal of a PeerConnection API has been implemented in WebKit/Chrome as open source. It is expected that Firefox will have an add-on by Q1 next year. It enables video conferencing, including media capture, media encoding, signal processing (echo cancellation etc), secure transmission, and a data stream exchange.

  Current discussions are around the signalling protocol and whether SIP needs to be required by the standard. Further, the codec question is under discussion with a question whether to mandate VP8 and Opus, since transcoding gateways are not desirable. Another question is how to measure the quality of the connection and how to report errors so as to allow adaptation.

  What always amazes me around RTC is the sheer number of specialised protocols that seem to be required to implement this. WebRTC does not disappoint : in fact, the question was asked whether there could be a lighter alternative than to re-use dozens of years of protocol development – is it over-engineered ? Can desktop players connect to a WebRTC session ?

  We are already in a second or third revision of this part of the HTML5 specification and yet it seems the requirements are still being collected. I’m quietly confident that everything is done to make the lives of the Web developer easier, but it sure looks like a huge task.

  The Missing Link : Flash to HTML5

  Zohar Babin (Kaltura) and myself moderated this session and I must admit that this session was the biggest eye-opener for me amongst all the sessions. There was a large number of Flash developers present in the room and that was great, because sometimes we just don’t listen enough to lessons learnt in the past.

  This session gave me one of those aha-moments : it the form of the Flash appendBytes() API function.

  The appendBytes() function allows a Flash developer to take a byteArray out of a connected video resource and do something with it – such as feed it to a video for display. When I heard that Web developers want that functionality for JavaScript and the video element, too, I instinctively rejected the idea wondering why on earth would a Web developer want to touch encoded video bytes – why not leave that to the browser.

  But as it turns out, this is actually a really powerful enabler of functionality. For example, you can use it to :

  • display mid-roll video ads as part of the same video element,
  • sequence playlists of videos into the same video element,
  • implement DVR functionality (high-speed seeking),
  • do mash-ups,
  • do video editing,
  • adaptive streaming.

  This totally blew my mind and I am now completely supportive of having such a function in HTML5. Together with media fragment URIs you could even leave all the header download management for resources to the Web browser and just request time ranges from a video through an appendBytes() function. This would be easier on the Web developer than having to deal with byte ranges and making sure that appropriate decoding pipelines are set up.

  Standards for Video Accessibility

  Philip Jagenstedt (Opera) and myself moderated this session. We focused on the HTML5 track element and the WebVTT file format. Many issues were identified that will still require work.

  One particular topic was to find a standard means of rendering the UI for caption, subtitle, und description selection. For example, what icons should be used to indicate that subtitles or captions are available. While this is not part of the HTML5 specification, it’s still important to get this right across browsers since otherwise users will get confused with diverging interfaces.

  Chaptering was discussed and a particular need to allow URLs to directly point at chapters was expressed. I suggested the use of named Media Fragment URLs.

  The use of WebVTT for descriptions for the blind was also discussed. A suggestion was made to use the voice tag <v> to allow for “styling” (i.e. selection) of the screen reader voice.

  Finally, multitrack audio or video resources were also discussed and the @mediagroup attribute was explained. A question about how to identify the language used in different alternative dubs was asked. This is an issue because @srclang is not on audio or video, only on text, so it’s a missing feature for the multitrack API.

  Beyond this session, there was also a breakout session on WebVTT and the track element. As a consequence, a number of bugs were registered in the W3C bug tracker.

  WebM : Testing, Metrics and New features

  This session was moderated by John Luther and John Koleszar, both of the WebM Project. They started off with a presentation on current work on WebM, which includes quality testing and improvements, and encoder speed improvement. Then they moved on to questions about how to involve the community more.

  The community criticised that communication of what is happening around WebM is very scarce. More sharing of information was requested, including a move to using open Google+ hangouts instead of Google internal video conferences. More use of the public bug tracker can also help include the community better.

  Another pain point of the community was that code is introduced and removed without much feedback. It was requested to introduce a peer review process. Also it was requested that example code snippets are published when new features are announced so others can replicate the claims.

  This all indicates to me that the WebM project is increasingly more open, but that there is still a lot to learn.

  Standards for HTTP Adaptive Streaming

  This session was moderated by Frank Galligan and Aaron Colwell (Google), and Mark Watson (Netflix).

  Mark started off by giving us an introduction to MPEG DASH, the MPEG file format for HTTP adaptive streaming. MPEG has just finalized the format and he was able to show us some examples. DASH is XML-based and thus rather verbose. It is covering all eventualities of what parameters could be switched during transmissions, which makes it very broad. These include trick modes e.g. for fast forwarding, 3D, multi-view and multitrack content.

  MPEG have defined profiles – one for live streaming which requires chunking of the files on the server, and one for on-demand which requires keyframe alignment of the files. There are clear specifications for how to do these with MPEG. Such profiles would need to be created for WebM and Ogg Theora, too, to make DASH universally applicable.

  Further, the Web case needs a more restrictive adaptation approach, since the video element’s API is already accounting for some of the features that DASH provides for desktop applications. So, a Web-specific profile of DASH would be required.

  Then Aaron introduced us to the MediaSource API and in particular the webkitSourceAppend() extension that he has been experimenting with. It is essentially an implementation of the appendBytes() function of Flash, which the Web developers had been asking for just a few sessions earlier. This was likely the biggest announcement of OVC, alas a quiet and technically-focused one.

  Aaron explained that he had been trying to find a way to implement HTTP adaptive streaming into WebKit in a way in which it could be standardised. While doing so, he also came across other requirements around such chunked video handling, in particular around dynamic ad insertion, live streaming, DVR functionality (fast forward), constraint video editing, and mashups. While trying to sort out all these requirements, it became clear that it would be very difficult to implement strategies for stream switching, buffering and delivery of video chunks into the browser when so many different and likely contradictory requirements exist. Also, once an approach is implemented and specified for the browser, it becomes very difficult to innovate on it.

  Instead, the easiest way to solve it right now and learn about what would be necessary to implement into the browser would be to actually allow Web developers to queue up a chunk of encoded video into a video element for decoding and display. Thus, the webkitSourceAppend() function was born (specification).

  The proposed extension to the HTMLMediaElement is as follows :

  partial interface HTMLMediaElement 
    // URL passed to src attribute to enable the media source logic.
    readonly attribute [URL] DOMString webkitMediaSourceURL ;
    bool webkitSourceAppend(in Uint8Array data) ;
    // end of stream status codes.
  
    const unsigned short EOS_NO_ERROR = 0 ;
  
    const unsigned short EOS_NETWORK_ERR = 1 ;
  
    const unsigned short EOS_DECODE_ERR = 2 ;
    void webkitSourceEndOfStream(in unsigned short status) ;
    // states
  
    const unsigned short SOURCE_CLOSED = 0 ;
  
    const unsigned short SOURCE_OPEN = 1 ;
  
    const unsigned short SOURCE_ENDED = 2 ;
    readonly attribute unsigned short webkitSourceState ;
  
   ;
  

  The code is already checked into WebKit, but commented out behind a command-line compiler flag.

  Frank then stepped forward to show how webkitSourceAppend() can be used to implement HTTP adaptive streaming. His example uses WebM – there are no examples with MPEG or Ogg yet.

  The chunks that Frank’s demo used were 150 video frames long (6.25s) and 5s long audio. Stream switching only switched video, since audio data is much lower bandwidth and more important to retain at high quality. Switching was done on multiplexed files.

  Every chunk requires an XHR range request – this could be optimised if the connections were kept open per adaptation. Seeking works, too, but since decoding requires download of a whole chunk, seeking latency is determined by the time it takes to download and decode that chunk.

  Similar to DASH, when using this approach for live streaming, the server has to produce one file per chunk, since byte range requests are not possible on a continuously growing file.

  Frank did not use DASH as the manifest format for his HTTP adaptive streaming demo, but instead used a hacked-up custom XML format. It would be possible to use JSON or any other format, too.

  After this session, I was actually completely blown away by the possibilities that such a simple API extension allows. If I wasn’t sold on the idea of a appendBytes() function in the earlier session, this one completely changed my mind. While I still believe we need to standardise a HTTP adaptive streaming file format that all browsers will support for all codecs, and I still believe that a native implementation for support of such a file format is necessary, I also believe that this approach of webkitSourceAppend() is what HTML needs – and maybe it needs it faster than native HTTP adaptive streaming support.

  Standards for Browser Video Playback Metrics

  This session was moderated by Zachary Ozer and Pablo Schklowsky (JWPlayer). Their motivation for the topic was, in fact, also HTTP adaptive streaming. Once you leave the decisions about when to do stream switching to JavaScript (through a function such a wekitSourceAppend()), you have to expose stream metrics to the JS developer so they can make informed decisions. The other use cases is, of course, monitoring of the quality of video delivery for reporting to the provider, who may then decide to change their delivery environment.

  The discussion found that we really care about metrics on three different levels :

  • measuring the network performance (bandwidth)
  • measuring the decoding pipeline performance
  • measuring the display quality

  In the end, it seemed that work previously done by Steve Lacey on a proposal for video metrics was generally acceptable, except for the playbackJitter metric, which may be too aggregate to mean much.

  Device Inputs / A/V in the Browser

  I didn’t actually attend this session held by Anant Narayanan (Mozilla), but from what I heard, the discussion focused on how to manage permission of access to video camera, microphone and screen, e.g. when multiple applications (tabs) want access or when the same site wants access in a different session. This may apply to real-time communication with screen sharing, but also to photo sharing, video upload, or canvas access to devices e.g. for time lapse photography.

  Open Video Editors

  This was another session that I wasn’t able to attend, but I believe the creation of good open source video editing software and similar video creation software is really crucial to giving video a broader user appeal.

  Jeff Fortin (PiTiVi) moderated this session and I was fascinated to later see his analysis of the lifecycle of open source video editors. It is shocking to see how many people/projects have tried to create an open source video editor and how many have stopped their project. It is likely that the creation of a video editor is such a complex challenge that it requires a larger and more committed open source project – single people will just run out of steam too quickly. This may be comparable to the creation of a Web browser (see the size of the Mozilla project) or a text processing system (see the size of the OpenOffice project).

  Jeff also mentioned the need to create open video editor standards around playlist file formats etc. Possibly the Open Video Alliance could help. In any case, something has to be done in this space – maybe this would be a good topic to focus next year’s OVC on ?

  Monday’s Breakout Groups

  The conference ended officially on Sunday night, but we had a third day of discussions / hackday at the wonderful New York Lawschool venue. We had collected issues of interest during the two previous days and organised the breakout groups on the morning (Schedule).

  In the Content Protection/DRM session, Mark Watson from Netflix explained how their API works and that they believe that all we need in browsers is a secure way to exchange keys and an indicator of protection scheme is used – the actual protection scheme would not be implemented by the browser, but be provided by the underlying system (media framework/operating system). I think that until somebody actually implements something in a browser fork and shows how this can be done, we won’t have much progress. In my understanding, we may also need to disable part of the video API for encrypted content, because otherwise you can always e.g. grab frames from the video element into canvas and save them from there.

  In the Playlists and Gapless Playback session, there was massive brainstorming about what new cool things can be done with the video element in browsers if playback between snippets can be made seamless. Further discussions were about a standard playlist file formats (such as XSPF, MRSS or M3U), media fragment URIs in playlists for mashups, and the need to expose track metadata for HTML5 media elements.

  What more can I say ? It was an amazing three days and the complexity of problems that we’re dealing with is a tribute to how far HTML5 and open video has already come and exciting news for the kind of applications that will be possible (both professional and community) once we’ve solved the problems of today. It will be exciting to see what progress we will have made by next year’s conference.

  Thanks go to Google for sponsoring my trip to OVC.

  UPDATE : We actually have a mailing list for open media developers who are interested in these and similar topics – do join at http://lists.annodex.net/cgi-bin/mailman/listinfo/foms.

• 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. 

• Revision 30966 : eviter le moche ’doctype_ecrire’ lors de l’upgrade

  17 août 2009, par fil@… — Log

  eviter le moche ’doctype_ecrire’ lors de l’upgrade