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• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.

• avcodec/movtextenc : Simplify writing to AVBPrint

  15 octobre 2020, par Andreas Rheinhardt

  avcodec/movtextenc : Simplify writing to AVBPrint
  The mov_text encoder uses an AVBPrint to assemble the subtitles ;
  
  yet mov_text subtitles are not pure text ; they also have a binary
  
  portion that was mostly handled as follows :
      uint32_t size = /* calculation */ ;
  
      size = AV_RB32(&size) ;
  
      av_bprint_append_data(bprint, (const char*)&size, 4) ;
  Here AV_RB32() is a no-op on big-endian systems and a LE-BE swap
  
  on little-endian systems, making the output endian-independent.
  Yet this is ugly and unclean : On LE systems, the variable size from
  
  the snippet above won't contain the correct value any more. Furthermore,
  
  using this pattern leads to lots of small writes to the AVBPrint.
  This commit therefore changes this to using a temporary buffer instead :
      uint8_t buf[4] ;
  
      AV_WB32(buf, /* size calculation */) ;
  
      av_bprint_append_data(bprint, buf, 4) ;
  This method also allows to use bigger buffers holding more than one
  
  element, saving calls to av_bprint_append_data() and reducing codesize.
  Reviewed-by : Philip Langdale <philipl@overt.org>
  
  Signed-off-by : Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
  

  • [DH] libavcodec/movtextenc.c

• ffmpeg crashes in electron mac apple store build

  23 février 2021, par Martin

  UPDATE
I found the command otool -L ffmpeg-mac/ffmpeg | grep /usr/local which shows me which dynamicly linked files are in my ffmpeg build, it was point to 3 files so I moved them to my desktop and then built my mac apple store build, uploaded it to the app store, downloaded it, and tried to run it again but got a new less-descriptive error :

  


  /Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/app.asar/node_modules/execa/lib/error.js:59 Uncaught (in promise) Error: Command failed with exit code 1: /Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/ffmpeg -i /Users/martinbarker/Downloads/Felix Lebarty - Bobo/06. Special Lady.mp3 -y -filter_complex concat=n=1:v=0:a=1 -c:a libmp3lame -b:a 320k /Users/martinbarker/Downloads/Felix Lebarty - Bobo/output-887564.mp3
ffmpeg version N-101191-g51a9f487ae-ntd_20150128 Copyright (c) 2000-2021 the FFmpeg developers
  built with Apple LLVM version 10.0.1 (clang-1001.0.46.4)
  configuration: pkg_config='pkg-config --static' --pkg-config-flags=--static --libdir=/usr/local/lib --extra-version=ntd_20150128 --disable-shared --disable-lzma --enable-gpl --enable-pthreads --enable-nonfree --disable-libass --enable-libfdk-aac --enable-libmp3lame --enable-libx264 --enable-static --enable-filters --enable-runtime-cpudetect
  libavutil      56. 65.100 / 56. 65.100
  libavcodec     58.125.100 / 58.125.100
  libavformat    58. 68.100 / 58. 68.100
  libavdevice    58. 12.100 / 58. 12.100
  libavfilter     7.107.100 /  7.107.100
  libswscale      5.  8.100 /  5.  8.100
  libswresample   3.  8.100 /  3.  8.100
  libpostproc    55.  8.100 / 55.  8.100
[mp3 @ 0x7f9ba4802800] Estimating duration from bitrate, this may be inaccurate
Input #0, mp3, from '/Users/martinbarker/Downloads/Felix Lebarty - Bobo/06. Special Lady.mp3':
  Metadata:
    comment         : 
    album           : Bobo
    artist          : Felix Lebarty
    title           : Funkytown Eklablog
    genre           : Boogie
    date            : 1984
  Duration: 00:03:35.33, start: 0.000000, bitrate: 320 kb/s
  Stream #0:0: Audio: mp3, 44100 Hz, stereo, fltp, 320 kb/s
/Users/martinbarker/Downloads/Felix Lebarty - Bobo/output-887564.mp3: Operation not permitted
    at makeError (/Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/app.asar/node_modules/execa/lib/error.js:59)
    at handlePromise (/Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/app.asar/node_modules/execa/index.js:114)
    at async file:/Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/app.asar/src/js/newindex.js:1151


  


  The error says command failed with exit code 1 Operation not permitted, am I missing some entitlements for saving files on my computer in the final mac apple store build that gets uploaded to the app store ?

  


  ---

  


  I am trying to release an electron app on the Mac Apple Store (mas), my electron app uses ffmpeg to render videos. In order to release my app on the mac apple store, It needs to be sandboxed, and by default ffmpeg makes calls to external libraries so I need to statically build ffmpeg and package it with my app. I have successfully built my app, submitted it to the app store, had it approved, and downloaded/used it but my ffmpeg fails with this errir :

  


  Uncaught (in promise) Error: Command was killed with SIGABRT (Aborted): /Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/ffmpeg -i /Users/martinbarker/Downloads/Steve Leach With The Crystal Grass Orchestra – Ocean Potion/9. Get Out In The Sun.flac -i /Users/martinbarker/Downloads/Steve Leach With The Crystal Grass Orchestra – Ocean Potion/10. Golden Hues.flac -y -filter_complex concat=n=2:v=0:a=1 -c:a libmp3lame -b:a 320k /Users/martinbarker/Downloads/Steve Leach With The Crystal Grass Orchestra – Ocean Potion/output-261020.mp3
dyld: Library not loaded: /usr/local/opt/libass/lib/libass.9.dylib
  Referenced from: /Users/martinbarker/Documents/projects/digify-new/dist/mas/Digify.app/Contents/Resources/ffmpeg
  Reason: no suitable image found.  Did find:
    file system sandbox blocked open() of '/usr/local/opt/libass/lib/libass.9.dylib'
    /usr/local/opt/libass/lib/libass.9.dylib: stat() failed with errno=1
    file system sandbox blocked open() of '/usr/local/lib/libass.9.dylib'
    file system sandbox blocked open() of '/usr/local/Cellar/libass/0.15.0/lib/libass.9.dylib'
    at makeError (/Users/martinbarker/…eca/lib/error.js:59)
    at handlePromise (/Users/martinbarker/…/execa/index.js:114)
    at async file:/Users/ma…js/newindex.js:1151


  


  I think this line is important ; file system sandbox blocked open() of '/usr/local/opt/libass/lib/libass.9.dylib' but I'm not sure what I should change with my static ffmpeg build so that it works in production and so that I can avoid the above error.

  


  My code is available on the branch mas-attempt-after-redesign here : https://github.com/MartinBarker/digify/tree/mas-attempt-after-redesign

  


  Inside my package.json I have the command download-ffmpeg which clones the ffmpeg repo, runs a configure command with some flags, and then builds ffmpeg into a folder called 'ffmpeg-mac', this folder gets packaged with the app for the mac apple store build.

  


  git clone https://git.ffmpeg.org/ffmpeg.git ffmpeg-mac && cd ffmpeg-mac && ./configure pkg_config='pkg-config --static' --pkg-config-flags='--static' --libdir=/usr/local/lib --extra-version=ntd_20150128 --disable-shared --disable-lzma --enable-gpl --enable-pthreads --enable-nonfree --enable-libass --enable-libfdk-aac  --enable-libmp3lame  --enable-libx264 --enable-static --enable-filters --enable-runtime-cpudetect && make && cd ..


  


  You can see in the above ffmpeg command the flag --enable-libass, but even though I have that flag included, after I build and sign my mac apple store build by running sudo rm -rf dist/mas/ && npm run build-mas && sh mas-sign-script.sh, the production build (once approved) fails with the above included error.