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• Reading in pydub AudioSegment from url. BytesIO returning "OSError [Errno 2] No such file or directory" on heroku only ; fine on localhost

  24 octobre 2014, par Mark

  EDIT 1 for anyone with the same error : installing ffmpeg did indeed solve that BytesIO error

  EDIT 1 for anyone still willing to help : my problem is now that when I AudioSegment.export("filename.mp3", format="mp3"), the file is made, but has size 0 bytes — details below (as "EDIT 1")

  ---

  EDIT 2 : All problems now solved.

  • Files can be read in as AudioSegment using BytesIO
  • I found buildpacks to ensure ffmpeg was installed correctly on my app, with lame support for exporting proper mp3 files

  Answer below

  ---

  Original question

  I have pydub working nicely locally to crop a particular mp3 file based on parameters in the url.
  (?start_time=3.8&end_time=5.1)

  When I run foreman start it all looks good on localhost. The html renders nicely.
  The key lines from the views.py include reading in a file from a url using

  url = "https://s3.amazonaws.com/shareducate02/The_giving_tree__by_Alex_Blumberg__sponsored_by_mailchimp-short.mp3"
  
  mp3 = urllib.urlopen(url).read() # inspired by http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter11_image/06_speech.ipynb
  
  original=AudioSegment.from_mp3(BytesIO(mp3))  # AudioSegment.from_mp3 is a pydub command, see http://pydub.com
  
  section = original[start_time_ms:end_time_ms]

  That all works great... until I push to heroku (django app) and run it online.
  then when I load the same page now on the herokuapp.com, I get this error

  OSError at /path/to/page
  
  [Errno 2] No such file or directory
  
  Request Method: GET
  
  Request URL:    http://my.website.com/path/to/page?start_time=3.8&end_time=5
  
  Django Version: 1.6.5
  
  Exception Type: OSError
  
  Exception Value:    
  
  [Errno 2] No such file or directory
  
  Exception Location: /app/.heroku/python/lib/python2.7/subprocess.py in _execute_child, line 1327
  
  Python Executable:  /app/.heroku/python/bin/python
  
  Python Version: 2.7.8
  
  Python Path:    
  
  ['/app',
  
   '/app/.heroku/python/bin',
  
   '/app/.heroku/python/lib/python2.7/site-packages/setuptools-5.4.1-py2.7.egg',
  
   '/app/.heroku/python/lib/python2.7/site-packages/distribute-0.6.36-py2.7.egg',
  
   '/app/.heroku/python/lib/python2.7/site-packages/pip-1.3.1-py2.7.egg',
  
   '/app',
  
   '/app/.heroku/python/lib/python27.zip',
  
   '/app/.heroku/python/lib/python2.7',
  
   '/app/.heroku/python/lib/python2.7/plat-linux2',
  
   '/app/.heroku/python/lib/python2.7/lib-tk',
  
   '/app/.heroku/python/lib/python2.7/lib-old',
  
   '/app/.heroku/python/lib/python2.7/lib-dynload',
  
   '/app/.heroku/python/lib/python2.7/site-packages',
  
   '/app/.heroku/python/lib/python2.7/site-packages/setuptools-0.6c11-py2.7.egg-info']
  
  
  
  
  
  Traceback:
  
  File "/app/.heroku/python/lib/python2.7/site-packages/django/core/handlers/base.py" in get_response
  
    112.                     response = wrapped_callback(request, *callback_args, **callback_kwargs)
  
  File "/app/evernote/views.py" in finalize
  
    105.       original=AudioSegment.from_mp3(BytesIO(mp3))
  
  File "/app/.heroku/python/lib/python2.7/site-packages/pydub/audio_segment.py" in from_mp3
  
    318.         return cls.from_file(file, 'mp3')
  
  File "/app/.heroku/python/lib/python2.7/site-packages/pydub/audio_segment.py" in from_file
  
    302.         retcode = subprocess.call(convertion_command, stderr=open(os.devnull))
  
  File "/app/.heroku/python/lib/python2.7/subprocess.py" in call
  
    522.     return Popen(*popenargs, **kwargs).wait()
  
  File "/app/.heroku/python/lib/python2.7/subprocess.py" in __init__
  
    710.                                 errread, errwrite)
  
  File "/app/.heroku/python/lib/python2.7/subprocess.py" in _execute_child
  
    1327.                 raise child_exception

  I have commented out some of the original to convince myself that sure enough the single line original=AudioSegment.from_mp3(BytesIO(mp3)) is where the problem kicks in... but this is not a problem locally

  The full function in views.py starts like this :

  from django.shortcuts import render, get_object_or_404 
  
  from django.http import HttpResponseRedirect #, Http404, HttpResponse
  
  from django.core.urlresolvers import reverse
  
  from django.views import generic
  
  import pydub
  
  # Maybe only need: 
  
  from pydub import AudioSegment # == see below
  
  from time import gmtime, strftime
  
  
  
  import boto
  
  from boto.s3.connection import S3Connection
  
  from boto.s3.key import Key
  
  
  
  # http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter11_image/06_speech.ipynb
  
  import urllib
  
  from io import BytesIO
  
  # import numpy as np
  
  # import scipy.signal as sg
  
  # import pydub # mentioned above already
  
  # import matplotlib.pyplot as plt
  
  # from IPython.display import Audio, display
  
  # import matplotlib as mpl
  
  # %matplotlib inline
  
  
  
  import os
  
  # from settings import AWS_ACCESS_KEY, AWS_SECRET_KEY, AWS_BUCKET_NAME
  
  AWS_ACCESS_KEY = os.environ.get('AWS_ACCESS_KEY') # there must be a better way?
  
  AWS_SECRET_KEY = os.environ.get('AWS_SECRET_KEY')
  
  AWS_BUCKET_NAME = os.environ.get('S3_BUCKET_NAME')
  
  
  
  # http://stackoverflow.com/questions/415511/how-to-get-current-time-in-python
  
  
  
  boto_conn = S3Connection(AWS_ACCESS_KEY, AWS_SECRET_KEY)
  
  bucket = boto_conn.get_bucket(AWS_BUCKET_NAME)
  
  s3_url_format = 'https://s3.amazonaws.com/shareducate02/{end_path}'

  and specifically the view in views.py that’s called when I visit the page :

  def finalize(request):
  
  
  
      start_time = request.GET.get('start_time')
  
  
  
      end_time = request.GET.get('end_time')
  
  
  
      original_file = "https://s3.amazonaws.com/shareducate02/The_giving_tree__by_Alex_Blumberg__sponsored_by_mailchimp-short.mp3"
  
  
  
  
  
      if start_time:
  
  
  
        # original=AudioSegment.from_mp3(original_file)  #...that didn't work 
  
        # but this works below:
  
  
  
        # next three uncommented lines from http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter11_image/06_speech.ipynb
  
        # python 2.x
  
        url = original_file
  
        # req = urllib.Request(url, headers={'User-Agent': ''}) # Note: I commented out this because I got error that "Request" did not exist
  
        mp3 = urllib.urlopen(url).read()
  
        # That's for my 2.7
  
  
  
        # If I ever upgrade to python 3.x, would need to change it to:
  
        # req = urllib.request.Request(url, headers={'User-Agent': ''}) 
  
        # mp3 = urllib.request.urlopen(req).read()
  
        # as per instructions on http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter11_image/06_speech.ipynb
  
  
  
        original=AudioSegment.from_mp3(BytesIO(mp3))
  
        # original=AudioSegment.from_mp3("static/givingtree.mp3") # alternative that works locally (on laptop) but no use for heroku
  
  
  
        start_time_ms = int(float(start_time) * 1000)
  
        if end_time:
  
          end_time_ms = int(float(end_time) * 1000)
  
        else:
  
          end_time_ms = int(float(original.duration_seconds) * 1000)
  
        duration_ms = end_time_ms - start_time_ms
  
        # duration = end_time - start_time
  
        duration = duration_ms/1000
  
  
  
     #   section = original[start_time_ms:end_time_ms]
  
     #   section_with_fading = section.fade_in(100).fade_out(100)
  
  
  
        clip = "demo-"
  
        number = strftime("%Y-%m-%d_%H-%M-%S", gmtime())
  
        clip += number
  
        clip += ".mp3" 
  
  
  
        # DON'T BOTHER writing locally:
  
        # clip_with_path = "evernote/static/"+clip
  
        # section_with_fading.export(clip_with_path, format = "mp3")
  
  
  
     #   tempclip = section_with_fading.export(format = "mp3")
  
  
  
        # commented out while de-bugging, but was working earlier if run on localhost
  
        # c = boto.connect_s3()
  
        # b = c.get_bucket(S3_BUCKET_NAME)  # as defined above
  
        # k = Key(b)
  
        # k.key=clip
  
        # # k.set_contents_from_filename(clip_with_path)
  
        # k.set_contents_from_file(tempclip)
  
        # k.set_acl('public-read')
  
        clip_made = True
  
      else: 
  
        duration = 0.0
  
        clip_made = False
  
        clip = ""
  
      context = {'original_file':original_file, 'new_file':clip, 'start_time': start_time, 'end_time':end_time, 'duration':duration, 'clip_made':clip_made} 
  
      return render(request, 'finalize.html' , context) 

  Any suggestions ?

  Potentially related :
  I have ffmpeg installed locally

  But have been unable to install it onto heroku, due to not understanding buildpacks. I tried just a moment ago (http://stackoverflow.com/questions/14407388/how-to-install-ffmpeg-for-a-django-app-on-heroku and https://github.com/shunjikonishi/heroku-buildpack-ffmpeg) but so far ffmpeg is not working on heroku (ffmpeg is not recognised when I do "heroku run ffmpeg —version")
  ...do you think this is the reason ?

  An answer like any of these would be much appreciated as I’m going round in circles here :

  1. "I think ffmpeg is indeed your problem. Try harder to sort that out, to get it installed on heroku"
  2. "Actually, I think this is why BytesIO is not working for you : ..."
  3. "Your approach is terrible anyway... if you want to read in an audio file to process using pydub, you should just do this instead : ..." (since I’m just hacking my way through pydub for my first time... my approach may be poor)

  ---

  EDIT 1

  ffmpeg is now installed (e.g., I can output wav files)

  However, I can’t create mp3 files, still... or more correctly, I can, but the filesize is zero

  (venv-app)moriartymacbookair13:getstartapp macuser$ heroku config:add BUILDPACK_URL=https://github.com/ddollar/heroku-buildpack-multi.git 
  
  Setting config vars and restarting awe01... done, v93
  
  BUILDPACK_URL: https://github.com/ddollar/heroku-buildpack-multi.git
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ vim .buildpacks 
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ cat .buildpacks 
  
  https://github.com/shunjikonishi/heroku-buildpack-ffmpeg.git
  
  https://github.com/heroku/heroku-buildpack-python.git
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ git add --all
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ git commit -m "need multi, not just ffmpeg, so adding back in multi + shun + heroku, with trailing .git in .buildpacks file"
  
  [master cd99fef] need multi, not just ffmpeg, so adding back in multi + shun + heroku, with trailing .git in .buildpacks file
  
   1 file changed, 2 insertions(+), 2 deletions(-)
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ git push heroku master
  
  Fetching repository, done.
  
  Counting objects: 5, done.
  
  Delta compression using up to 4 threads.
  
  Compressing objects: 100% (3/3), done.
  
  Writing objects: 100% (3/3), 372 bytes | 0 bytes/s, done.
  
  Total 3 (delta 2), reused 0 (delta 0)
  
  
  
  -----> Fetching custom git buildpack... done
  
  -----> Multipack app detected
  
  =====> Downloading Buildpack: https://github.com/shunjikonishi/heroku-buildpack-ffmpeg.git
  
  =====> Detected Framework: ffmpeg
  
  -----> Install ffmpeg
  
         DOWNLOAD_URL =  http://flect.github.io/heroku-binaries/libs/ffmpeg.tar.gz
  
         exporting PATH and LIBRARY_PATH
  
  =====> Downloading Buildpack: https://github.com/heroku/heroku-buildpack-python.git
  
  =====> Detected Framework: Python
  
  -----> Installing dependencies with pip
  
         Cleaning up...
  
  
  
  -----> Preparing static assets
  
         Collectstatic configuration error. To debug, run:
  
         $ heroku run python ./example/manage.py collectstatic --noinput
  
  
  
  Using release configuration from last framework (Python).
  
  -----> Discovering process types
  
         Procfile declares types -> web
  
  
  
  -----> Compressing... done, 198.1MB
  
  -----> Launching... done, v94
  
         http://[redacted].herokuapp.com/ deployed to Heroku
  
  
  
  To git@heroku.com:awe01.git
  
     78d6b68..cd99fef  master -> master
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ heroku run ffmpeg
  
  Running `ffmpeg` attached to terminal... up, run.6408
  
  ffmpeg version git-2013-06-02-5711e4f Copyright (c) 2000-2013 the FFmpeg developers
  
    built on Jun  2 2013 07:38:40 with gcc 4.4.3 (Ubuntu 4.4.3-4ubuntu5.1)
  
    configuration: --enable-shared --disable-asm --prefix=/app/vendor/ffmpeg
  
    libavutil      52. 34.100 / 52. 34.100
  
    libavcodec     55. 13.100 / 55. 13.100
  
    libavformat    55.  8.102 / 55.  8.102
  
    libavdevice    55.  2.100 / 55.  2.100
  
    libavfilter     3. 74.101 /  3. 74.101
  
    libswscale      2.  3.100 /  2.  3.100
  
    libswresample   0. 17.102 /  0. 17.102
  
  Hyper fast Audio and Video encoder
  
  usage: ffmpeg [options] [[infile options] -i infile]... {[outfile options] outfile}...
  
  
  
  Use -h to get full help or, even better, run 'man ffmpeg'
  
  (venv-app)moriartymacbookair13:getstartapp macuser$ heroku run bash
  
  Running `bash` attached to terminal... up, run.9660
  
  ~ $ python
  
  Python 2.7.8 (default, Jul  9 2014, 20:47:08) 
  
  [GCC 4.4.3] on linux2
  
  Type "help", "copyright", "credits" or "license" for more information.
  
  >>> import pydub
  
  >>> from pydub import AudioSegment
  
  >>> exit()
  
  ~ $ which ffmpeg
  
  /app/vendor/ffmpeg/bin/ffmpeg
  
  ~ $ python 
  
  
  
  Python 2.7.8 (default, Jul  9 2014, 20:47:08) 
  
  [GCC 4.4.3] on linux2
  
  Type "help", "copyright", "credits" or "license" for more information.
  
  >>> import pydub
  
  >>> from pydub import AudioSegment
  
  >>> AudioSegment.silent(5000).export("/tmp/asdf.mp3", "mp3")
  
  <open file="file"></open>tmp/asdf.mp3', mode 'wb+' at 0x7f9a37d44780>
  
  >>> exit ()
  
  ~ $ cd /tmp/
  
  /tmp $ ls
  
  asdf.mp3
  
  /tmp $ open asdf.mp3
  
  bash: open: command not found
  
  /tmp $ ls -lah
  
  total 8.0K
  
  drwx------  2 u36483 36483 4.0K 2014-10-22 04:14 .
  
  drwxr-xr-x 14 root   root  4.0K 2014-09-26 07:08 ..
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:14 asdf.mp3

  Note the file size of 0 above for the mp3 file... when I do the same thing on my macbook, the file size is never zero

  Back to the heroku shell :

  /tmp $ python
  
  Python 2.7.8 (default, Jul  9 2014, 20:47:08) 
  
  [GCC 4.4.3] on linux2
  
  Type "help", "copyright", "credits" or "license" for more information.
  
  >>> import pydub
  
  >>> from pydub import AudioSegment
  
  >>> pydub.AudioSegment.ffmpeg = "/app/vendor/ffmpeg/bin/ffmpeg" 
  
  >>> AudioSegment.silence(1200).export("/tmp/herokuSilence.mp3", format="mp3")
  
  Traceback (most recent call last):
  
    File "<stdin>", line 1, in <module>
  
  AttributeError: type object 'AudioSegment' has no attribute 'silence'
  
  >>> AudioSegment.silent(1200).export("/tmp/herokuSilence.mp3", format="mp3")
  
  <open file="file"></open>tmp/herokuSilence.mp3', mode 'wb+' at 0x7fcc2017c780>
  
  >>> exit()
  
  /tmp $ ls
  
  asdf.mp3  herokuSilence.mp3
  
  /tmp $ ls -lah
  
  total 8.0K
  
  drwx------  2 u36483 36483 4.0K 2014-10-22 04:29 .
  
  drwxr-xr-x 14 root   root  4.0K 2014-09-26 07:08 ..
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:14 asdf.mp3
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:29 herokuSilence.mp3
  
  </module></stdin>

  I realised the first time that I had forgotten the pydub.AudioSegment.ffmpeg = "/app/vendor/ffmpeg/bin/ffmpeg" command, but as you can see above, the file is still zero size

  Out of desperation, I even tried adding the ".heroku" into the path to be as verbatim as your example, but that didn’t fix it :

  /tmp $ python
  
  Python 2.7.8 (default, Jul  9 2014, 20:47:08) 
  
  [GCC 4.4.3] on linux2
  
  Type "help", "copyright", "credits" or "license" for more information.
  
  >>> import pydub
  
  >>> from pydub import AudioSegment
  
  >>> pydub.AudioSegment.ffmpeg = "/app/.heroku/vendor/ffmpeg/bin/ffmpeg"
  
  >>> AudioSegment.silent(1200).export("/tmp/herokuSilence03.mp3", format="mp3")
  
  <open file="file"></open>tmp/herokuSilence03.mp3', mode 'wb+' at 0x7fc92aca7780>
  
  >>> exit()
  
  /tmp $ ls -lah
  
  total 8.0K
  
  drwx------  2 u36483 36483 4.0K 2014-10-22 04:31 .
  
  drwxr-xr-x 14 root   root  4.0K 2014-09-26 07:08 ..
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:14 asdf.mp3
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:31 herokuSilence03.mp3
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:29 herokuSilence.mp3

  Finally, I tried exporting a .wav file to check pydub was at least working correctly

  /tmp $ python
  
  Python 2.7.8 (default, Jul  9 2014, 20:47:08) 
  
  [GCC 4.4.3] on linux2
  
  Type "help", "copyright", "credits" or "license" for more information.
  
  >>> import pydub
  
  >>> from pydub import AudioSegment
  
  >>> pydub.AudioSegment.ffmpeg = "/app/vendor/ffmpeg/bin/ffmpeg"
  
  >>> AudioSegment.silent(1300).export("/tmp/heroku_wav_silence01.wav", format="wav")
  
  <open file="file"></open>tmp/heroku_wav_silence01.wav', mode 'wb+' at 0x7fa33cbf3780>
  
  >>> exit()
  
  /tmp $ ls
  
  asdf.mp3  herokuSilence03.mp3  herokuSilence.mp3  heroku_wav_silence01.wav
  
  /tmp $ ls -lah
  
  total 40K
  
  drwx------  2 u36483 36483 4.0K 2014-10-22 04:42 .
  
  drwxr-xr-x 14 root   root  4.0K 2014-09-26 07:08 ..
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:14 asdf.mp3
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:31 herokuSilence03.mp3
  
  -rw-------  1 u36483 36483    0 2014-10-22 04:29 herokuSilence.mp3
  
  -rw-------  1 u36483 36483  29K 2014-10-22 04:42 heroku_wav_silence01.wav
  
  /tmp $ 

  At least that filesize for .wav is non-zero, so pydub is working

  My current theory is that either I’m still not using ffmpeg correctly, or it’s insufficient... maybe I need an mp3 additional install on top of basic ffmpeg.

  Several sites mention "libavcodec-extra-53" but I’m not sure how to install that on heroku, or to check if I have it ? https://github.com/jiaaro/pydub/issues/36
  Similarly tutorials on libmp3lame seem to be geared towards laptop installation rather than installation on heroku, so I’m at a loss http://superuser.com/questions/196857/how-to-install-libmp3lame-for-ffmpeg

  In case relevant, I also have youtube-dl in my requirements.txt... this also works locally on my macbook, but fails when I run it in the heroku shell :

  ~/ytdl $ youtube-dl --restrict-filenames -x --audio-format mp3 n2anDgdUHic
  
  [youtube] Setting language
  
  [youtube] Confirming age
  
  [youtube] n2anDgdUHic: Downloading webpage
  
  [youtube] n2anDgdUHic: Downloading video info webpage
  
  [youtube] n2anDgdUHic: Extracting video information
  
  [download] Destination: Boyce_Avenue_feat._Megan_Nicole_-_Skyscraper_Patrick_Ebert_Edit-n2anDgdUHic.m4a
  
  [download] 100% of 5.92MiB in 00:00
  
  [ffmpeg] Destination: Boyce_Avenue_feat._Megan_Nicole_-_Skyscraper_Patrick_Ebert_Edit-n2anDgdUHic.mp3
  
  ERROR: audio conversion failed: Unknown encoder 'libmp3lame'
  
  ~/ytdl $ 

  The informative link is that it too specificies an mp3 failure, so perhaps they two issues are related.

  ---

  EDIT 2

  See answer, all problems solved

• trying to make OpenCV 3.2.0 work with virtualenv

  24 juillet 2017, par lollercoaster

  I’m on Ubuntu 16.04 with Python 2.7 and virtualenv & virtualenvwrapper.

  By following this guide I managed to get the following script working with my system Python2.7 which has cv2 globally installed.

  I used this script to install it :

  ######################################
  
  # INSTALL OPENCV ON UBUNTU OR DEBIAN #
  
  ######################################
  
  
  
  # |         THIS SCRIPT IS TESTED CORRECTLY ON         |
  
  # |----------------------------------------------------|
  
  # | OS             | OpenCV       | Test | Last test   |
  
  # |----------------|--------------|------|-------------|
  
  # | Ubuntu 16.04.2 | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 8.8     | OpenCV 3.2.0 | OK   | 20 May 2017 |
  
  # | Debian 9.0     | OpenCV 3.2.0 | OK   | 25 Jun 2017 |
  
  
  
  # 1. KEEP UBUNTU OR DEBIAN UP TO DATE
  
  
  
  sudo apt-get -y update
  
  sudo apt-get -y upgrade
  
  sudo apt-get -y dist-upgrade
  
  sudo apt-get -y autoremove
  
  
  
  
  
  # 2. INSTALL THE DEPENDENCIES
  
  
  
  # Build tools:
  
  sudo apt-get install -y build-essential cmake
  
  
  
  # GUI (if you want to use GTK instead of Qt, replace 'qt5-default' with 'libgtkglext1-dev' and remove '-DWITH_QT=ON' option in CMake):
  
  sudo apt-get install -y qt5-default libvtk6-dev
  
  
  
  # Media I/O:
  
  sudo apt-get install -y zlib1g-dev libjpeg-dev libwebp-dev libpng-dev libtiff5-dev libjasper-dev libopenexr-dev libgdal-dev
  
  
  
  # Video I/O:
  
  sudo apt-get install -y libdc1394-22-dev libavcodec-dev libavformat-dev libswscale-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev yasm libopencore-amrnb-dev libopencore-amrwb-dev libv4l-dev libxine2-dev
  
  
  
  # Parallelism and linear algebra libraries:
  
  sudo apt-get install -y libtbb-dev libeigen3-dev
  
  
  
  # Python:
  
  sudo apt-get install -y python-dev python-tk python-numpy python3-dev python3-tk python3-numpy
  
  
  
  # Documentation:
  
  sudo apt-get install -y doxygen
  
  
  
  # UI stuff
  
  sudo apt-get install libgtk-3-dev libatlas-base-dev gfortran
  
  
  
  
  
  # 3. INSTALL THE LIBRARY (YOU CAN CHANGE '3.2.0' FOR THE LAST STABLE VERSION)
  
  sudo apt-get install -y unzip wget
  
  
  
  # opencv contrib
  
  wget https://github.com/opencv/opencv_contrib/archive/3.2.0.zip -O opencv_contrib-3.2.0.zip
  
  unzip opencv_contrib-3.2.0.zip
  
  rm opencv_contrib-3.2.0.zip
  
  
  
  # opencv
  
  wget https://github.com/opencv/opencv/archive/3.2.0.zip
  
  unzip 3.2.0.zip
  
  rm 3.2.0.zip
  
  mv opencv-3.2.0 OpenCV-3.2.0
  
  cd OpenCV-3.2.0
  
  
  
  mkdir build
  
  cd build
  
  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig
  
  
  
  
  
  # 4. EXECUTE SOME OPENCV EXAMPLES AND COMPILE A DEMONSTRATION
  
  
  
  # To complete this step, please visit 'http://milq.github.io/install-opencv-ubuntu-debian'.

  The following script below works great with that system-wide installation :

  import cv2
  
  
  
  img = cv2.imread('some_img.jpg')

  Though this one doesn’t - even the system Python can’t read videos for some reason...

  import cv2
  
  
  
  video_capture = cv2.VideoCapture(0)
  
  ret, frame = video_capture.read()
  
  print ret  # always False

  but I want it to work with my virtualenv. So I recompiled OpenCV with :

  cmake -D WITH_QT=ON \
  
      -D WITH_OPENGL=ON \
  
      -D FORCE_VTK=ON \
  
      -D WITH_TBB=ON \
  
      -D WITH_GDAL=ON \
  
      -D WITH_XINE=ON \
  
      -D BUILD_EXAMPLES=ON \
  
      -D INSTALL_PYTHON_EXAMPLES=ON \
  
      -D ENABLE_PRECOMPILED_HEADERS=OFF \
  
      -D BUILD_NEW_PYTHON_SUPPORT=ON \
  
      -D OPENCV_EXTRA_MODULES_PATH=/home/me/code/myproject/opencv_contrib-3.2.0/modules \
  
      -D PYTHON_EXECUTABLE=~/.envs/myenv/bin/python \
  
      ..
  
  
  
  make -j4
  
  sudo make install
  
  sudo ldconfig

  Here’s the CMake log :

  -- Found VTK ver. 6.2.0 (usefile: /usr/lib/cmake/vtk-6.2/UseVTK.cmake)
  
  -- Caffe:   NO
  
  -- Protobuf:   YES
  
  -- Glog:   NO
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Module opencv_sfm disabled because the following dependencies are not found: Glog/Gflags
  
  -- freetype2:   YES
  
  -- harfbuzz:    YES
  
  -- Checking for modules 'tesseract;lept'
  
  --   No package 'tesseract' found
  
  --   No package 'lept' found
  
  -- Tesseract:   NO
  
  -- Check contents of vgg_generated_48.i ...
  
  -- Check contents of vgg_generated_64.i ...
  
  -- Check contents of vgg_generated_80.i ...
  
  -- Check contents of vgg_generated_120.i ...
  
  -- Check contents of boostdesc_bgm.i ...
  
  -- Check contents of boostdesc_bgm_bi.i ...
  
  -- Check contents of boostdesc_bgm_hd.i ...
  
  -- Check contents of boostdesc_binboost_064.i ...
  
  -- Check contents of boostdesc_binboost_128.i ...
  
  -- Check contents of boostdesc_binboost_256.i ...
  
  -- Check contents of boostdesc_lbgm.i ...
  
  -- 
  
  -- General configuration for OpenCV 3.2.0 =====================================
  
  --   Version control:               817bd7b-dirty
  
  -- 
  
  --   Extra modules:
  
  --     Location (extra):            /home/me/code/myproject/opencv_contrib-3.2.0/modules
  
  --     Version control (extra):     817bd7b-dirty
  
  -- 
  
  --   Platform:
  
  --     Timestamp:                   2017-07-20T18:25:26Z
  
  --     Host:                        Linux 4.8.0-58-generic x86_64
  
  --     CMake:                       3.5.1
  
  --     CMake generator:             Unix Makefiles
  
  --     CMake build tool:            /usr/bin/make
  
  --     Configuration:               Release
  
  -- 
  
  --   C/C++:
  
  --     Built as dynamic libs?:      YES
  
  --     C++ Compiler:                /usr/bin/c++  (ver 5.4.0)
  
  --     C++ flags (Release):         -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C++ flags (Debug):           -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     C Compiler:                  /usr/bin/cc
  
  --     C flags (Release):           -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -O3 -DNDEBUG  -DNDEBUG
  
  --     C flags (Debug):             -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -Wno-comment -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -g  -O0 -DDEBUG -D_DEBUG
  
  --     Linker flags (Release):
  
  --     Linker flags (Debug):
  
  --     ccache:                      NO
  
  --     Precompiled headers:         NO
  
  --     Extra dependencies:          Qt5::Test Qt5::Concurrent Qt5::OpenGL /usr/lib/x86_64-linux-gnu/libwebp.so /usr/lib/x86_64-linux-gnu/libjasper.so /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so /usr/lib/libgdal.so dc1394 xine avcodec-ffmpeg avformat-ffmpeg avutil-ffmpeg swscale-ffmpeg Qt5::Core Qt5::Gui Qt5::Widgets /usr/lib/x86_64-linux-gnu/hdf5/serial/lib/libhdf5.so /usr/lib/x86_64-linux-gnu/libpthread.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so vtkRenderingOpenGL vtkImagingHybrid vtkIOImage vtkCommonDataModel vtkCommonMath vtkCommonCore vtksys vtkCommonMisc vtkCommonSystem vtkCommonTransforms vtkCommonExecutionModel vtkDICOMParser vtkIOCore /usr/lib/x86_64-linux-gnu/libz.so vtkmetaio /usr/lib/x86_64-linux-gnu/libjpeg.so /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so vtkImagingCore vtkRenderingCore vtkCommonColor vtkFiltersExtraction vtkFiltersCore vtkFiltersGeneral vtkCommonComputationalGeometry vtkFiltersStatistics vtkImagingFourier vtkalglib vtkFiltersGeometry vtkFiltersSources vtkInteractionStyle vtkRenderingLOD vtkFiltersModeling vtkIOPLY vtkIOGeometry /usr/lib/x86_64-linux-gnu/libjsoncpp.so vtkFiltersTexture vtkRenderingFreeType /usr/lib/x86_64-linux-gnu/libfreetype.so vtkftgl vtkIOExport vtkRenderingAnnotation vtkImagingColor vtkRenderingContext2D vtkRenderingGL2PS vtkRenderingContextOpenGL /usr/lib/libgl2ps.so vtkRenderingLabel dl m pthread rt /usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so tbb
  
  --     3rdparty dependencies:       libprotobuf
  
  -- 
  
  --   OpenCV modules:
  
  --     To be built:                 core flann hdf imgproc ml photo reg surface_matching video viz dnn freetype fuzzy imgcodecs shape videoio highgui objdetect plot superres ts xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib cvv datasets rgbd stereo tracking videostab xfeatures2d ximgproc aruco optflow phase_unwrapping stitching structured_light java python2 python3
  
  --     Disabled:                    world contrib_world
  
  --     Disabled by dependency:      -
  
  --     Unavailable:                 cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev cnn_3dobj matlab sfm
  
  -- 
  
  --   GUI: 
  
  --     QT 5.x:                      YES (ver 5.5.1)
  
  --     QT OpenGL support:           YES (Qt5::OpenGL 5.5.1)
  
  --     OpenGL support:              YES (/usr/lib/x86_64-linux-gnu/libGLU.so /usr/lib/x86_64-linux-gnu/libGL.so)
  
  --     VTK support:                 YES (ver 6.2.0)
  
  -- 
  
  --   Media I/O: 
  
  --     ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.8)
  
  --     JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver )
  
  --     WEBP:                        /usr/lib/x86_64-linux-gnu/libwebp.so (ver encoder: 0x0202)
  
  --     PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.2.54)
  
  --     TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 - 4.0.6)
  
  --     JPEG 2000:                   /usr/lib/x86_64-linux-gnu/libjasper.so (ver 1.900.1)
  
  --     OpenEXR:                     /usr/lib/x86_64-linux-gnu/libImath.so /usr/lib/x86_64-linux-gnu/libIlmImf.so /usr/lib/x86_64-linux-gnu/libIex.so /usr/lib/x86_64-linux-gnu/libHalf.so /usr/lib/x86_64-linux-gnu/libIlmThread.so (ver 2.2.0)
  
  --     GDAL:                        /usr/lib/libgdal.so
  
  --     GDCM:                        NO
  
  -- 
  
  --   Video I/O:
  
  --     DC1394 1.x:                  NO
  
  --     DC1394 2.x:                  YES (ver 2.2.4)
  
  --     FFMPEG:                      YES
  
  --       avcodec:                   YES (ver 56.60.100)
  
  --       avformat:                  YES (ver 56.40.101)
  
  --       avutil:                    YES (ver 54.31.100)
  
  --       swscale:                   YES (ver 3.1.101)
  
  --       avresample:                NO
  
  --     GStreamer:                   NO
  
  --     OpenNI:                      NO
  
  --     OpenNI PrimeSensor Modules:  NO
  
  --     OpenNI2:                     NO
  
  --     PvAPI:                       NO
  
  --     GigEVisionSDK:               NO
  
  --     Aravis SDK:                  NO
  
  --     UniCap:                      NO
  
  --     UniCap ucil:                 NO
  
  --     V4L/V4L2:                    NO/YES
  
  --     XIMEA:                       NO
  
  --     Xine:                        YES (ver 1.2.6)
  
  --     gPhoto2:                     NO
  
  -- 
  
  --   Parallel framework:            TBB (ver 4.4 interface 9002)
  
  -- 
  
  --   Other third-party libraries:
  
  --     Use IPP:                     9.0.1 [9.0.1]
  
  --          at:                     /home/me/code/myproject/OpenCV-3.2.0/build/3rdparty/ippicv/ippicv_lnx
  
  --     Use IPP Async:               NO
  
  --     Use VA:                      NO
  
  --     Use Intel VA-API/OpenCL:     NO
  
  --     Use Lapack:                  NO
  
  --     Use Eigen:                   YES (ver 3.2.92)
  
  --     Use Cuda:                    NO
  
  --     Use OpenCL:                  YES
  
  --     Use OpenVX:                  NO
  
  --     Use custom HAL:              NO
  
  -- 
  
  --   OpenCL:                        <dynamic loading="loading" of="of" opencl="opencl" library="library">
  
  --     Include path:                /home/me/code/myproject/OpenCV-3.2.0/3rdparty/include/opencl/1.2
  
  --     Use AMDFFT:                  NO
  
  --     Use AMDBLAS:                 NO
  
  -- 
  
  --   Python 2:
  
  --     Interpreter:                 /home/me/.envs/myenv/bin/python (ver 2.7.12)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython2.7.so (ver 2.7.12)
  
  --     numpy:                       /home/me/.envs/myenv/local/lib/python2.7/site-packages/numpy/core/include (ver 1.13.1)
  
  --     packages path:               lib/python2.7/site-packages
  
  -- 
  
  --   Python 3:
  
  --     Interpreter:                 /usr/bin/python3 (ver 3.5.2)
  
  --     Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.5m.so (ver 3.5.2)
  
  --     numpy:                       /usr/lib/python3/dist-packages/numpy/core/include (ver 1.11.0)
  
  --     packages path:               lib/python3.5/dist-packages
  
  -- 
  
  --   Python (for build):            /home/me/.envs/myenv/bin/python
  
  -- 
  
  --   Java:
  
  --     ant:                         /usr/bin/ant (ver 1.9.6)
  
  --     JNI:                         /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
  
  --     Java wrappers:               YES
  
  --     Java tests:                  YES
  
  -- 
  
  --   Matlab:                        Matlab not found or implicitly disabled
  
  -- 
  
  --   Documentation:
  
  --     Doxygen:                     /usr/bin/doxygen (ver 1.8.11)
  
  -- 
  
  --   Tests and samples:
  
  --     Tests:                       YES
  
  --     Performance tests:           YES
  
  --     C/C++ Examples:              YES
  
  -- 
  
  --   Install path:                  /usr/local
  
  -- 
  
  --   cvconfig.h is in:              /home/me/code/myproject/OpenCV-3.2.0/build
  
  -- -----------------------------------------------------------------
  
  -- 
  
  </dynamic>

  Unfortunately, while this works and I can import cv2 in the shell, it cannot read video using the above script, probably due to incorrect compilation or linking of ffmpeg ? The confusing part is the system-wide installation of OpenCV works fine, even without ffmpeg installed !

  What am I doing wrong ? How can I get OpenCV working with a virtualenv ?

  ====

  EDIT : Running the C++ video writing example has this result :

  $ cd /home/me/code/myproject/OpenCV-3.2.0/build/bin
  
  $ ./cpp-tutorial-video-write ../../samples/data/vtest.avi R Y
  
  ------------------------------------------------------------------------------
  
  This program shows how to write video files.
  
  You can extract the R or G or B color channel of the input video.
  
  Usage:
  
  ./video-write  [ R | G | B] [Y | N]
  
  ------------------------------------------------------------------------------
  
  
  
  OpenCV: FFMPEG: tag 0xffffffff/'����' is not found (format 'avi / AVI (Audio Video Interleaved)')'
  
  
  
  (cpp-tutorial-video-write:19523): GStreamer-CRITICAL **: gst_element_make_from_uri: assertion 'gst_uri_is_valid (uri)' failed
  
  OpenCV Error: Unsupported format or combination of formats (Gstreamer Opencv backend does not support this codec.) in CvVideoWriter_GStreamer::open, file /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp, line 1388
  
  VIDEOIO(cvCreateVideoWriter_GStreamer(filename, fourcc, fps, frameSize, is_color)): raised OpenCV exception:
  
  
  
  /home/me/code/myproject/OpenCV-3.2.0/modules/videoio/src/cap_gstreamer.cpp:1388: error: (-210) Gstreamer Opencv backend does not support this codec. in function CvVideoWriter_GStreamer::open
  
  
  
  Could not open the output video for write: ../../samples/data/vtest.avi

  And the opencv_test_videoio unit test reports the following : https://pastebin.com/q4mf224Q

  However, running the c++ video starter example DOES work, with the following command and output, I can see the webcam working and streaming video in the highgui interface :

  $ ./cpp-example-videocapture_starter 0
  
  VIDEOIO ERROR: V4L: device 0: Unable to query number of channels
  
  (ERROR)icvOpenAVI_XINE(): Unable to initialize video driver.
  
  GStreamer: Error opening bin: no element "0"
  
  press space to save a picture. q or esc to quit
  
  init done
  
  opengl support available

• 6 Crucial Benefits of Conversion Rate Optimisation

  26 février 2024, par Erin

  Whether investing time or money in marketing, you want the best return on your investment. You want to get as many customers as possible with your budget and resources.

  That’s what conversion rate optimisation (CRO) aims to do. But how does it help you achieve this major goal ? 

  This guide explores the concrete benefits of conversion rate optimisation and how they lead to more effective marketing and ROI. We’ll also introduce specific CRO best practices to help unlock these benefits.

  What is conversion rate optimisation ?

  Conversion rate optimisation (CRO) is the process of examining your website for improvements and creating tests to increase the number of visitors who take a desired action, like purchasing a product or submitting a form.

  The conversion rate is the percentage of visitors who complete a specific goal.

  

  In order to improve your conversion rate, you need to figure out :

  • Where your customers come from
  • How potential customers navigate or interact with your website
  • Where potential customers are likely to exit your site (or abandon carts)
  • What patterns drive valuable actions like sign-ups and sales

  From there, you can gradually implement changes that will drive more visitors to convert. That’s the essence of conversion rate optimisation.

  6 top benefits of conversion rate optimisation (and best practices to unlock them)

  Conversion rate optimisation can help you get more out of your campaigns without investing more. CRO helps you in these six ways :

  1. Understand your visitors (and customers) better

  The main goal of CRO is to boost conversions, but it’s more than that. In the process of improving conversion rates, you’ll also benefit by gaining deep insights into user behaviour, preferences, and needs. 

  Using web analytics, tests and behavioural analytics, CRO helps marketers shape their website to match what users need.

  Best practices for understanding your customer :

  First, analyse how visitors act with full context (the pages they view, how long they stay and more). 

  In Matomo, you can use the Users Flow report to understand how visitors navigate through your site. This will help you visualise and identify trends in the buyer’s journey.

  

  Then, you can dive deeper by defining and analysing journeys with Funnels. This shows you how many potential customers follow through each step in your defined journey and identify where you might have a leaky funnel. 

  

  In the above Funnel Report, nearly half of our visitors, just 44%, are moving forward in the buyer’s journey after landing on our scuba diving mask promotion page. With 56% of potential customers dropping off at this page, it’s a prime opportunity for optimising conversions.

  Think of Funnels as your map, and pages with high drop-off rates as valuable opportunities for improvement.

  Once you notice patterns, you can try to identify the why. Analyse the pages, do user testing and do your best to improve them.

  2. Deliver a better user experience

  A better understanding of your customers’ needs means you can deliver a better user experience.

  

  For example, if you notice many people spend more time than expected on a particular step in the sign-up process, you can work to streamline it.

  Best practices for improving your user experience : 

  To do this, you need to come up with testable hypotheses. Start by using Heatmaps and Session Recordings to visualise the user experience and understand where visitors are hesitating, experiencing points of frustration, and exiting. 

  You need to outline what drives certain patterns in behaviour — like cart abandonment for specific products, and what you think can fix them.

  

  Let’s look at an example. In the screenshot above, we used Matomo’s Heatmap feature to analyse user behaviour on our website. 

  Only 65% of visitors scroll down far enough to encounter our main call to action to “Write a Review.” This insight suggests a potential opportunity for optimisation, where we can focus efforts on encouraging more users to engage with this key element on our site.

  Once you’ve identified an area of improvement, you need to test the results of your proposed solution to the problem. The most common way to do this is with an A/B test. 

  This is a test where you create a new version of the problematic page, trying different titles, comparing long, and short copy, adding or removing images, testing variations of call-to-action buttons and more. Then, you compare the results — the conversion rate — against the original. With Matomo’s A/B Testing feature, you can easily split traffic between the original and one or more variations.

  

  In the example above from Matomo, we can see that testing different header sizes on a page revealed that the wider header led to a higher conversion rate of 47%, compared to the original rate of 35% and the smaller header’s 36%.

  Matomo’s report also analyses the “statistical significance” of the difference in results. Essentially, this is the likelihood that the difference comes from the changes you made in the variation. With a small sample size, random patterns (like one page receiving more organic search visits) can cause the differences.

  If you see a significant change over a larger sample size, you can be fairly certain that the difference is meaningful. And that’s exactly what a high statistical significance rating indicates in Matomo. 

  Once a winner is identified, you can apply the change and start a new experiment. 

  3. Create a culture of data-driven decision-making

  Marketers can no longer afford to rely on guesswork or gamble away budgets and resources. In our digital age, you must use data to get ahead of the competition. In 2021, 65% of business leaders agreed that decisions were getting more complex.

  CRO is a great way to start a company-wide focus on data-driven decision-making. 

  Best practices to start a data-driven culture :
  

  Don’t only test “hunches” or “best practices” — look at the data. Figure out the patterns that highlight how different types of visitors interact with your site.

  Try to answer these questions :

  • How do our most valuable customers interact with our site before purchasing ?
  • How do potential customers who abandon their carts act ?
  • Where do our most valuable customers come from ?

  Moreover, it’s key to democratise insights by providing multiple team members access to information, fostering informed decision-making company-wide.

  4. Lower your acquisition costs and get higher ROI from all marketing efforts

  Once you make meaningful optimisations, CRO can help you lower customer acquisition costs (CAC). Getting new customers through advertising will be cheaper.

  As a result, you’ll get a better return on investment (ROI) on all your campaigns. Every ad and dollar invested will get you closer to a new customer than before. That’s the bottom line of CRO.

  Best practices to lower your CAC (customer acquisition costs) through CRO adjustments :

  The easiest way to lower acquisition costs is to understand where your customers come from. Use marketing attribution to track the results of your campaigns, revealing how each touchpoint contributes to conversions and revenue over time, beyond just last-click attribution.

  You can then compare the number of conversions to the marketing costs of each channel, to get a channel-specific breakdown of CAC.

  This performance overview can help you quickly prioritise the best value channels and ads, lowering your CAC. But these are only surface-level insights. 

  You can also further lower CAC by optimising the pages these campaigns send visitors to. Start with a deep dive into your landing pages using features like Matomo’s Session Recordings or Heatmaps.

  They can help you identify issues with an unengaging user experience or content. Using these insights, you can create A/B tests, where you implement a new page that replaces problematic headlines, buttons, copy, or visuals.

  

  When a test shows a statistically significant improvement in conversion rates, implement the new version. Repeat this over time, and you can increase your conversion rates significantly, getting more customers with the same spend. This will reduce your customer acquisition costs, and help your company grow faster without increasing your ad budget.

  5. Improve your average order value (AOV) and customer lifetime value (CLV)

  CRO isn’t only about increasing the number of customers you convert. If you adapt your approach, you can also use it to increase the revenue from each customer you bring in. 

  But you can’t do that by only tracking conversion rates, you also need to track exactly what your customers buy.

  If you only blindly optimise for CAC, you even risk lowering your CLV and the overall profitability of your campaigns. (For example, if you focus on Facebook Ads with a $6 CAC, but an average CLV of $50, over Google Ads with a $12 CAC, but a $100 CLV.)

  Best practices to track and improve CLV :

  First, integrate your analytics platform with your e-commerce (B2C) or your CRM (B2B). This will help you get a more holistic view of your customers. You don’t want the data to stop at “converted.” You want to be able to dive deep into the patterns of high-value customers.

  The sales report in Matomo’s ecommerce analytics makes it easy to break down average order value by channels, campaigns, and specific ads.

  

  In the report above, we can see that search engines drive customers who spend significantly more, on average, than social networks — $241 vs. $184. But social networks drive a higher volume of customers and more revenue.

  To figure out which channel to focus on, you need to see how the CAC compares to the AOV (or CLV for B2B customers). Let’s say the CAC of social networks is $50, while the search engine CAC is $65. Search engine customers are more profitable — $176 vs. $134. So you may want to adjust some more budget to that channel.

  To put it simply :

  Profit per customer = AOV (or CLV) – CAC

  Example :

  • Profit per customer for social networks = $184 – $50 = $134
  • Profit per customer for search engines = $241 – $65 = $176

  You can also try to A/B test changes that may increase the AOV, like creating a product bundle and recommending it on specific sales pages.

  An improvement in CLV will make your campaigns more profitable, and help stretch your advertising budget even further.

  6. Improve your content and SEO rankings

  A valuable side-effect of focusing on CRO metrics and analyses is that it can boost your SEO rankings. 

  How ? 

  CRO helps you improve the user experience of your website. That’s a key signal Google (and other search engines) care about when ranking webpages. 

  

  For example, Google’s algorithm considers “dwell time,” AKA how long a user stays on your page. If many users quickly return to the results page and click another result, that’s a bad sign. But if most people stay on your site for a while (or don’t return to Google at all), Google thinks your page gives the user their answer.

  As a result, Google will improve your website’s ranking in the search results.

  Best practices to make the most of CRO when it comes to SEO :

  Use A/B Testing, Heatmaps, and Session Recordings to run experiments and understand user behaviour. Test changes to headlines, page layout, imagery and more to see how it impacts the user experience. You can even experiment with completely changing the content on a page, like substituting an introduction.

  Bring your CRO-testing mindset to important pages that aren’t ranking well to improve metrics like dwell time.

  Start optimising your conversion rate today

  As you’ve seen, enjoying the benefits of CRO heavily relies on the data from a reliable web analytics solution. 

  But in an increasingly privacy-conscious world (just look at the timeline of GDPR updates and fines), you must tread carefully. One of the dilemmas that marketing managers face today is whether to prioritise data quality or privacy (and regulations).

  With Matomo, you don’t have to choose. Matomo values both data quality and privacy, adhering to stringent privacy laws like GDPR and CCPA.

  Unlike other web analytics, Matomo doesn’t sample data or use AI and machine learning to fill data gaps. Plus, you can track without annoying visitors with a cookie consent banner – so you capture 100% of traffic while respecting user privacy (excluding in Germany and UK).

  And as you’ve already seen above, you’ll still get plenty of reports and insights to drive your CRO efforts. With User Flows, Funnels, Session Recordings, Form Analytics, and Heatmaps, you can immediately find insights to improve your bottom line.

  And our built-in A/B testing feature will help you test your hypotheses and drive reliable progress. If you’re ready to reliably optimise conversion rates (with accuracy and without privacy concerns), try Matomo for free for 21 days. No credit card required.

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