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• Revision 6ce718eb18 : Merge "End of orientation zero group experiment" into experimental

  22 avril 2013, par Deb Mukherjee

  Changed Paths : Modify /vp9/decoder/vp9_decodframe.c Modify /vp9/encoder/vp9_encodemb.c Modify /vp9/encoder/vp9_rdopt.c Merge "End of orientation zero group experiment" into experimental

• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• Final Rendered Video is Sped Up Compared to Animation Played in Processing

  15 janvier 2016, par Nightlife

  Recently I made an Audio Visualizer in Processing. From there I wanted to render the animation created in Processing into a mp4 file. I am on a windows computer, and am using ffmpeg to convert my TIF files produced in Processing into mp4.

  When I do this I am able to render the images into an mp4 file, but when I playback this file the animation is sped up compared to the animation when I play it on Processing. Because of this the animation does not sync with the audio when I combine the mp4 file and audio on a video editing program.

  When I set my frame rate to 25 and have the limit on the number of frames to be 250 and render it into a mp4 file it is 10 seconds long like it should be, but it contains more than 10 seconds of the animation when compared to the animation played directly in Processing.

  I have no idea why this is so any help will be much appreciated.

  My Processing code :

  import ddf.minim.*;
  
  import ddf.minim.analysis.*;
  
  
  
  Minim minim;
  
  AudioPlayer player;
  
  PImage img;
  
  FFT fft;
  
  
  
  void setup() {
  
    size(728, 546);
  
  
  
    minim = new Minim(this);
  
  
  
    // this loads mysong.wav from the data folder as a stream with a internal buffer of size 1024
  
    player = minim.loadFile("new_years_good.mp3");
  
    fft = new FFT(player.bufferSize(), player.sampleRate());
  
    player.play();
  
    img= loadImage("cat-in-shades-.jpg");
  
    frameRate(25);
  
  }
  
  
  
  void draw() {
  
  
  
  
  
    image(img, 0, 0);
  
    //tint(0, 100, 150);
  
    stroke(255);
  
  
  
    strokeWeight(4);
  
    float a = 0;
  
  
  
    float angle = (2*PI) / 200;
  
  
  
  
  
    fft.forward(player.mix);
  
  
  
  
  
    for(int i=0; i < player.bufferSize() - 1; i++) {
  
  
  
     //player.mix.get(i) is a value between [-1,1]
  
  
  
      float x = 250 + cos(a) * (20 * player.mix.get(i) + 100);
  
      float x2 = 540 + cos(a) * (20 * player.mix.get(i) + 100);    
  
  
  
      float y = 230 + sin(a) * (20 * player.mix.get(i) + 100);
  
      float y2 = 240 + sin(a) * (20 * player.mix.get(i) + 100);
  
  
  
  
  
      float xFinal = 250 + cos(a+angle) * (20 * player.mix.get(i+1) + 100);
  
      float x2Final = 540 + cos(a+angle) * (20 * player.mix.get(i+1) + 100);
  
  
  
  
  
      float yFinal = 230 + sin(a+angle) * (20 * player.mix.get(i+1) + 100);    
  
      float y2Final = 240 + sin(a+angle) * (20 * player.mix.get(i+1) + 100);    
  
  
  
  
  
      line(x,y,xFinal,yFinal);
  
      line(x2,y2,x2Final,y2Final);
  
      a += angle;  
  
  
  
  
  
  
  
    }
  
    noStroke();  
  
    fill(255, 0, 0, 128);
  
    for(int i = 0; i < 250; i++)
  
    {
  
      float b = fft.getBand(i);
  
      float yAxis = random(-b, b) + 480;
  
      float xAxis = i*3;
  
      ellipse(xAxis, yAxis, b, b);
  
    }
  
    saveFrame("frame-####.tif");
  
    if(frameCount>250)
  
      {
  
        noLoop();
  
        stop();
  
      }
  
  
  
  }
  
  
  
  void stop() {
  
    player.close();
  
    minim.stop();
  
  
  
    super.stop();
  
  }

  What I input into the command line (as one line) on the cmd :

  C:\Users\Robert\Documents\Processing\AudioVisulizer>ffmpeg -i C:\Users\Robert\Do
  
  cuments\Processing\AudioVisulizer\frame-%04d.tif -r 25 -pix_fmt yuv420p smallVid
  
  .mp4

  What it outputted :

      ffmpeg version N-77836-g62dfe1d Copyright (c) 2000-2016 the FFmpeg developers
  
        built with gcc 5.2.0 (GCC)
  
        configuration: --enable-gpl --enable-version3 --disable-w32threads --enable-av
  
      isynth --enable-bzlib --enable-fontconfig --enable-frei0r --enable-gnutls --enab
  
      le-iconv --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --
  
      enable-libdcadec --enable-libfreetype --enable-libgme --enable-libgsm --enable-l
  
      ibilbc --enable-libmodplug --enable-libmp3lame --enable-libopencore-amrnb --enab
  
      le-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-librtmp --en
  
      able-libschroedinger --enable-libsoxr --enable-libspeex --enable-libtheora --ena
  
      ble-libtwolame --enable-libvidstab --enable-libvo-aacenc --enable-libvo-amrwbenc
  
       --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enabl
  
      e-libx264 --enable-libx265 --enable-libxavs --enable-libxvid --enable-libzimg --
  
      enable-lzma --enable-decklink --enable-zlib
  
        libavutil      55. 13.100 / 55. 13.100
  
        libavcodec     57. 22.100 / 57. 22.100
  
        libavformat    57. 21.101 / 57. 21.101
  
        libavdevice    57.  0.100 / 57.  0.100
  
        libavfilter     6. 23.100 /  6. 23.100
  
        libswscale      4.  0.100 /  4.  0.100
  
        libswresample   2.  0.101 /  2.  0.101
  
        libpostproc    54.  0.100 / 54.  0.100
  
      Input #0, image2, from 'C:\Users\Robert\Documents\Processing\AudioVisulizer\fram
  
      e-%04d.tif':
  
        Duration: 00:00:10.04, start: 0.000000, bitrate: N/A
  
          Stream #0:0: Video: tiff, rgb24, 728x546, 25 fps, 25 tbr, 25 tbn, 25 tbc
  
      [libx264 @ 00000092cd5e3700] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
  
      AVX FMA3 AVX2 LZCNT BMI2
  
      [libx264 @ 00000092cd5e3700] profile High, level 3.0
  
      [libx264 @ 00000092cd5e3700] 264 - core 148 r2638 7599210 - H.264/MPEG-4 AVC cod
  
      ec - Copyleft 2003-2015 - http://www.videolan.org/x264.html - options: cabac=1 r
  
      ef=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed
  
      _ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pski
  
      p=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 deci
  
      mate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_
  
      adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=2
  
      5 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.6
  
      0 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
      Output #0, mp4, to 'smallVid.mp4':
  
        Metadata:
  
          encoder         : Lavf57.21.101
  
          Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 728x54
  
      6, q=-1--1, 25 fps, 12800 tbn, 25 tbc
  
          Metadata:
  
            encoder         : Lavc57.22.100 libx264
  
          Side data:
  
            unknown side data type 10 (24 bytes)
  
      Stream mapping:
  
        Stream #0:0 -> #0:0 (tiff (native) -> h264 (libx264))
  
      Press [q] to stop, [?] for help
  
      frame=   52 fps=0.0 q=28.0 size=      77kB time=00:00:00.00 bitrate=N/A speed=
  
      frame=   74 fps= 65 q=28.0 size=     127kB time=00:00:00.88 bitrate=1178.0kbits/
  
      frame=   93 fps= 57 q=28.0 size=     164kB time=00:00:01.64 bitrate= 820.0kbits/
  
      frame=  113 fps= 52 q=28.0 size=     201kB time=00:00:02.44 bitrate= 676.3kbits/
  
      frame=  136 fps= 51 q=28.0 size=     245kB time=00:00:03.36 bitrate= 596.3kbits/
  
      frame=  157 fps= 49 q=28.0 size=     282kB time=00:00:04.20 bitrate= 550.2kbits/
  
      frame=  178 fps= 48 q=28.0 size=     324kB time=00:00:05.04 bitrate= 527.2kbits/
  
      frame=  199 fps= 47 q=28.0 size=     362kB time=00:00:05.88 bitrate= 504.1kbits/
  
      frame=  219 fps= 46 q=28.0 size=     403kB time=00:00:06.68 bitrate= 494.2kbits/
  
      frame=  242 fps= 46 q=28.0 size=     452kB time=00:00:07.60 bitrate= 486.8kbits/
  
      frame=  251 fps= 38 q=-1.0 Lsize=     623kB time=00:00:09.96 bitrate= 512.3kbits
  
      /s speed=1.52x
  
      video:619kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing o
  
      verhead: 0.607807%
  
      [libx264 @ 00000092cd5e3700] frame I:2     Avg QP:21.74  size: 56596
  
      [libx264 @ 00000092cd5e3700] frame P:66    Avg QP:23.36  size:  2523
  
      [libx264 @ 00000092cd5e3700] frame B:183   Avg QP:31.50  size:  1932
  
      [libx264 @ 00000092cd5e3700] consecutive B-frames:  0.8%  4.0%  6.0% 89.2%
  
      [libx264 @ 00000092cd5e3700] mb I  I16..4: 10.9% 72.6% 16.5%
  
      [libx264 @ 00000092cd5e3700] mb P  I16..4:  0.0%  0.0%  0.2%  P16..4:  4.4%  2.3
  
      %  3.3%  0.0%  0.0%    skip:89.7%
  
      [libx264 @ 00000092cd5e3700] mb B  I16..4:  0.0%  0.0%  0.5%  B16..8:  3.2%  2.0
  
      %  2.3%  direct: 1.3%  skip:90.7%  L0:50.9% L1:42.2% BI: 7.0%
  
      [libx264 @ 00000092cd5e3700] 8x8 transform intra:50.2% inter:10.1%
  
      [libx264 @ 00000092cd5e3700] coded y,uvDC,uvAC intra: 83.4% 32.6% 14.2% inter: 3
  
      .5% 0.2% 0.0%
  
      [libx264 @ 00000092cd5e3700] i16 v,h,dc,p: 22%  8%  8% 62%
  
      [libx264 @ 00000092cd5e3700] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 12% 19%  6%  9%
  
       9%  9%  9%  9%
  
      [libx264 @ 00000092cd5e3700] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 14% 22%  6%  9%
  
       8%  8%  5%  6%
  
      [libx264 @ 00000092cd5e3700] i8c dc,h,v,p: 74% 12% 12%  2%
  
      [libx264 @ 00000092cd5e3700] Weighted P-Frames: Y:0.0% UV:0.0%
  
      [libx264 @ 00000092cd5e3700] ref P L0: 41.8%  3.9% 24.3% 30.0%
  
      [libx264 @ 00000092cd5e3700] ref B L0: 64.5% 25.0% 10.5%
  
      [libx264 @ 00000092cd5e3700] ref B L1: 82.2% 17.8%
  
      [libx264 @ 00000092cd5e3700] kb/s:504.56