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• Leading Google Analytics alternative, Matomo, parodies Christopher Nolan blockbuster ahead of the UA sunset

  4 juillet 2023, par Erin — Press Releases

  Wellington, New Zealand, 4 July 2023 : In the world of online data, Google Analytics has long reigned supreme. Its dominance has been unquestioned, leaving website owners with little choice but to rely on the tech giant for their data insights. However, a new dawn in web analytics is upon us, and Matomo, the leading alternative to Google Analytics, is seizing a unique opportunity to position itself as the go-to provider. In a bold move, Matomo has launched a parody trailer, “Googleheimer,” humorously taking a satirical swipe at Google in the style of the upcoming Oppenheimer biopic by Christopher Nolan.

  Capitalising on a time-bound decision

  With an important decision looming for marketers and web specialists who need to switch analytics providers by July 1st, Matomo has found the perfect window to capture their attention.

  The urgency of the situation, combined with the high intent to switch providers, sets the stage for Matomo to establish itself as the leading alternative analytics platform of choice.

  Matomo’s parody trailer addresses the frustrations of GA4 head-on by highlighting the issues and the uncertainties caused by the sunset of Universal Analytics in humorous satire with lines such as :

  “But we’re keeping everyone’s data, right ? Right ?? …RIGHT ?!”

  Riding on the coat tails of this summer’s anticipated blockbuster from Christopher Nolan, Matomo openly points at the downsides of GA4, and reflects many frustrated marketers pain points in an entertaining way. Beneath the comedic and satirical tone lies the message that users have choices, and no longer need to surrender to the behemoth incumbent.

  Matomo was founded to challenge the status quo and provide a solution for those who believe in privacy and in ethical analytics, and who prefer that their customer data not be concentrated in the hands of just a few corporations.

  Watch the full trailer here. 


  About Matomo
  

  Matomo is a world-leading open-source privacy-friendly ethical web analytics platform, trusted by over 1.4 million websites in 190 countries and translated into over 50 languages. Matomo helps businesses and organisations track and optimise their online presence allowing users to easily collect, analyse, and act on their website and marketing data to gain a deeper understanding of their visitors and drive conversions and revenue. Matomo’s vision is to create, as a community, the leading open digital analytics platform that gives every user complete control of their data.

  Visit matomo.org for more information.




  More on Google Analytics changes



  A new dawn in web analytics is upon us, and Matomo – the leading alternative to Google Analytics – is here for it. After 20 years, Google is blowing up Universal Analytics (or GA3) – and taking your data with it. Inspired by Christopher Nolan’s upcoming biopic about physicist J. Robert Oppenheimer and the making of his atomic bomb (also known as “The Manhattan Project”), this parody trailer openly points to Google and draws the comparison in humorous satire. GA4 comes with a new set of metrics, setups and reports that change how you analyse your data.

• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• javacv FFMPEG decode memory leak ?

  25 mars 2015, par Liquan Nie

  I’m new to JAVACV and I am using FFMPEG to play some video file as follows
  My enviroument is windows 8 with jdk7 and javacv0.10.

              String file_path ="D:\\1.mp4";
  
                  
  
                  // regist all format and codec
  
                  avformat.av_register_all();
  
                  avcodec.avcodec_register_all();
  
                  
  
                  // open file
  
                  avformat.AVFormatContext avFormatCtx = avformat.avformat_alloc_context();
  
                  if (avformat.avformat_open_input(avFormatCtx, file_path, null, null) != 0)
  
                  {
  
                          System.out.println("cann't open file\r\n");
  
                          return;
  
                  }
  
                  // find stream info
  
                  if (avformat.avformat_find_stream_info(avFormatCtx, (AVDictionary)null) < 0)
  
                  {
  
                          System.out.println("can't find stream info\r\n");
  
                          return;
  
                  }
  
  
  
                  int videoIndex = -1;
  
                  for(int i=0; i< avFormatCtx.nb_streams();i++)
  
                  {
  
                          if(avFormatCtx.streams(i).codec().codec_type() == avutil.AVMEDIA_TYPE_VIDEO)
  
                          {
  
                                  videoIndex = i;
  
                          }
  
                  }
  
                  // determ codec
  
                  avcodec.AVCodecContext avCodecCtx = avFormatCtx.streams(videoIndex).codec();
  
                  avcodec.AVCodec codec = avcodec.avcodec_find_decoder(avCodecCtx.codec_id());
  
                  if (codec == null)
  
                  {
  
                          System.out.println("codec not found");
  
                          return;
  
                  }
  
                  if(avcodec.avcodec_open2(avCodecCtx, codec, (AVDictionary)null) < 0)
  
                  {
  
                          System.out.println("cann't open avcodec\r\n");
  
                  }
  
                  avutil.AVFrame frame    = avcodec.avcodec_alloc_frame();
  
                  avutil.AVFrame frameRGB = avcodec.avcodec_alloc_frame();
  
                  int numByte = avcodec.avpicture_get_size(avutil.AV_PIX_FMT_RGB24, avCodecCtx.width(), avCodecCtx.height());
  
                  Pointer outBuffer = avutil.av_malloc(numByte);
  
                  
  
                  avcodec.avpicture_fill(new AVPicture(frameRGB), outBuffer.asByteBuffer(), avutil.AV_PIX_FMT_RGB24, avCodecCtx.width(), avCodecCtx.height());
  
                  avformat.av_dump_format(avFormatCtx, 0, file_path, 0);
  
                  System.out.println(avFormatCtx.duration());
  
                  SwsContext img_convert_ctx = swscale.sws_getContext(avCodecCtx.width(), avCodecCtx.height(), avCodecCtx.pix_fmt(), avCodecCtx.width(), avCodecCtx.height(), avutil.AV_PIX_FMT_RGB24, swscale.SWS_BICUBIC, null, null, (double[])null);
  
  
  
                  AVPacket pkt = new AVPacket();
  
                  int y_size = avCodecCtx.width()*avCodecCtx.height();
  
                  avcodec.av_new_packet(pkt, y_size);
  
                  opencv_highgui.cvNamedWindow(WINDOW_NAME);
  
                  
  
                  IplImage showImage = opencv_core.cvCreateImage(opencv_core.cvSize(avCodecCtx.width(), avCodecCtx.height()), opencv_core.IPL_DEPTH_8U, 3);
  
                  // read frames loop
  
                  int frameNumbers = avformat.av_read_frame(avFormatCtx, pkt);
  
              System.out.println("frame number is "+frameNumbers);
  
              
  
              while (avformat.av_read_frame(avFormatCtx, pkt) >= 0)
  
                  {
  
                      //System.out.println(pkt.asByteBuffer());
  
                          if (pkt.stream_index() == videoIndex)
  
                          {
  
                                  IntPointer ip = new IntPointer();
  
                                  int ret = avcodec.avcodec_decode_video2(avCodecCtx, frame, ip, pkt);
  
                                  if (ret < 0)
  
                                  {
  
                                          System.out.println("codec error\r\n");
  
                                          return;
  
                                  }
  
                                  
  
                                  if (ip.get()!= 0)
  
                                  {
  
                                          swscale.sws_scale(img_convert_ctx, frame.data(), frame.linesize(), 0, avCodecCtx.height(), frameRGB.data(), frameRGB.linesize());
  
                                          showImage.imageData(frameRGB.data(0));
  
                                          
  
                                          showImage.widthStep(frameRGB.linesize().get(0));
  
                                          opencv_highgui.cvShowImage(WINDOW_NAME, showImage);
  
                                          opencv_highgui.cvWaitKey(25);
  
                                  }
  
                          }
  
                  }
  
                  
  
                  showImage.release();
  
                  opencv_highgui.cvDestroyWindow(WINDOW_NAME);
  
                  avutil.av_free(frameRGB);
  
                  avcodec.avcodec_close(avCodecCtx);
  
                  avformat.avformat_close_input(avFormatCtx);

  but i run into get this error

  # A fatal error has been detected by the Java Runtime Environment:
  
  #
  
  #  EXCEPTION_ACCESS_VIOLATION (0xc0000005) at pc=0x00000000767c35ed, pid=11884, tid=3960
  
  #
  
  # JRE version: 7.0_13-b20
  
  # Java VM: Java HotSpot(TM) 64-Bit Server VM (23.7-b01 mixed mode windows-amd64 compressed oops)
  
  # Problematic frame:
  
  # C  [avcodec-56.dll+0x4835ed]  avcodec_decode_video2+0xbd
  
  #
  
  # Failed to write core dump. Minidumps are not enabled by default on client versions of Windows
  
  #
  
  # An error report file with more information is saved as:
  
  # E:\code\android\TestJAVACV\hs_err_pid11884.log
  
  #
  
  # If you would like to submit a bug report, please visit:
  
  #   http://bugreport.sun.com/bugreport/crash.jsp
  
  # The crash happened outside the Java Virtual Machine in native code.
  
  # See problematic frame for where to report the bug.
  
  #
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'D:\1.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      creation_time   : 1970-01-01 00:00:00
  
      encoder         : Lavf53.29.100
  
    Duration: 00:08:30.27, start: 0.000000, bitrate: 160 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 960x540 [SAR 1:1 DAR 16:9], 28 kb/s, 15 fps, 15 tbr, 15 tbn, 30 tbc (default)
  
      Metadata:
  
        creation_time   : 1970-01-01 00:00:00
  
        handler_name    : VideoHandler
  
      Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, stereo, fltp, 127 kb/s (default)
  
      Metadata:
  
        creation_time   : 1970-01-01 00:00:00
  
        handler_name    : SoundHandler

  and in the log file i found that the enden space heap in jvm has been used 98%. but I don’t know where is the issue, since the document of ffmpeg is not that enough, I feel difficult to know more about how to use it well ,any suggestions ??

  Heap
  
   PSYoungGen      total 23872K, used 20250K [0x00000000e5600000, 0x00000000e70a0000, 0x0000000100000000)
  
    eden space 20480K, 98% used [0x00000000e5600000,0x00000000e69c69f8,0x00000000e6a00000)