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MediaSPIP 0.1 Beta version
25 avril 2011, parMediaSPIP 0.1 beta is the first version of MediaSPIP proclaimed as "usable".
The zip file provided here only contains the sources of MediaSPIP in its standalone version.
To get a working installation, you must manually install all-software dependencies on the server.
If you want to use this archive for an installation in "farm mode", you will also need to proceed to other manual (...) -
HTML5 audio and video support
13 avril 2011, parMediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...) -
ANNEXE : Les plugins utilisés spécifiquement pour la ferme
5 mars 2010, parLe site central/maître de la ferme a besoin d’utiliser plusieurs plugins supplémentaires vis à vis des canaux pour son bon fonctionnement. le plugin Gestion de la mutualisation ; le plugin inscription3 pour gérer les inscriptions et les demandes de création d’instance de mutualisation dès l’inscription des utilisateurs ; le plugin verifier qui fournit une API de vérification des champs (utilisé par inscription3) ; le plugin champs extras v2 nécessité par inscription3 (...)
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5 perfect feature combinations to use with Heatmaps and Session Recordings
28 janvier 2020, par Jake Thornton — UncategorizedGaining valuable insights by simply creating a heatmap or setting up recordings on your most important web pages is a good start, but using the Heatmaps and Session Recordings features in combination with other Matomo features is where the real magic happens.
If you’re serious about significantly increasing conversions on your website to impact your bottom line, you need to accurately answer these questions :
- Are you analysing the most important pages that influence conversions ?
- Are you segmenting heatmaps and recordings towards your most valuable visitors ?
- Are you using these features to create better customer journeys ?
- Have you identified current pain points for visitors before setting up heatmaps and recordings ?
- Are you learning what content resonates best for your audience ?
With Matomo Analytics, you have the ability to integrate heatmaps and session recordings with all the features of a powerful web analytics platform, which means you get the complete picture of your visitor’s experience of your website.
Here are five features that work with Heatmaps and Session Recordings to maximise conversions :
1. Behaviour feature with Heatmaps
Before creating heatmaps on pages you think are most important to your website, first check out Behaviour – Pages. Here you get valuable information around unique pageviews, bounce rate, average time on page and exit rates for every page on your website.
Use this data as your starting point for heatmaps. Here you’ll identify current pain points for your visitors before using heatmaps to analyse their interactions on these pages.
Here’s how to use the behaviour feature to determine which pages to setup heatmaps on :
- Make sure you know what pages are generating the most unique page views, it could be your blog rather than your homepage
- Which pages have the highest bounce rates – can you make some quick changes above-the-fold and see if this makes a difference
- When the average time on page is high, why are visitors so engaged with these pages ? What keeps them reading ? Setup a heatmap to learn more
- Reduce exit rates by moving them along to other pages on your website
- Determine some milestones you want to achieve e.g. use heatmaps as your visual guide to improve average time on page, bounce rates and exit rates. A milestone could be that the exit rate for your previous blog was 34%, work towards getting this down to 30%
2. Ecommerce feature and Custom Segments
If you run an ecommerce business, you may want to learn only about visitors who are more likely to be your customers. For example, if you find 65% of product sales come from customers based in New York, but visits to your product pages are from every state in the USA, how can you learn more specifically about visitors only from New York ?
Using Segments to target a particular audience :
- First, make sure you have created heatmaps and recordings on the popular product pages you want to learn about your visitor’s interactions
- Note : Make sure the segment you create generates enough pageviews to apply a heatmap for more accurate results. We recommend a minimum of 1,000 page views per sample size.
- Then create a custom Segment – search Ecommerce and find the Product Name and select the product. Learn how to do this here.
Click on ‘Add a new segment’ or on the ‘edit’ link next to an existing segment name to open the segment editor :
Click on any item on the left to see the list of information you can segment by. In this case search “City”, then select “Is” and in the third column search “New York” (example in the image above) :
You can also use the search box at the bottom to search through the whole list.
- This will give you insights across the Matomo platform based only on customers who purchased this product
- Then go to the Ecommerce feature – and find Sales. Here you will learn what your most popular locations are for your product sales.
- Once you know the location you want to segment, go back and update the custom Segment you just created. Click on the edit pencil icon and update it by selecting Add AND condition, and add the sub group you would like to track on the product page. In this example, select City – New York. Click Save & Apply.
Now you should have successfully created a segment for your popular product page with visitors only from New York.
Check out the heatmap or recordings you created for this page. You may be very surprised to see how this segment engaged with your website compared to all website visitors.
Note : If you run a lead generation website you can use the Goals feature instead of Ecommerce to track the success metrics you need.
3. Visitor Profiles within Session Recordings
Seeing visitor location, device, OS and browser for your recordings is very valuable, but it’s even more valuable to integrate visitor profiles with session recordings as you get to see everything that visitor has done on your website … ever !
What pages they visited before/after the recording, what actions they took, how long they spent on your website etc. All this is captured in the visitor profile of every individual session recording so you can see where exactly engaged viewers are in their journey with your business, for example :
- How has this visitor behaved on your website in the past ?
- Is this visitor already a customer ?
- Is this the visitors first time to your website and
- What other pages on your website are they interested in seeing in this session ?
Use the visitor profiles feature within session recordings to understand the users better when watching each session.
You get the full picture of what role the page you recorded played in the overall experience of your website’s visitor. And more importantly, to see if they took the desired action you wanted them to take.
4. Funnels feature (premium feature)
The Funnels feature lets you see the customer journey from the first entry page through to the conversion page.
Once you create a funnel, you can see the % of visitors who drop off between pages on their way to converting.
In our example, you may then see page one to page two has a drop-off rate of 47%. Page two to page three 95% users drop-off rate and page three to page four 97.3% users drop-off rate.
Why is the drop-off rate so high from page two to page three and why is the drop-off rate so low from page three to page four ?
So, you may need to simplify things on page one because you may unknowingly be offering your visitor an easy way out of the funnel. Maybe the visitor is stuck reading your content and not understanding the value of your offering.
Small tip for session recordings …
With session recordings especially you can see firsthand through live recordings where exactly visitors click away from the page which exits them from your conversion funnel. Take note to see if this is a recurring issue with other visitors, then take action into fixing this hole.
Whatever the case, work towards reducing drop-off rates through your conversion funnels by discovering where the problems exist, make changes and learn how these changes affect engagement through heatmaps and recordings.
5. A/B Testing feature (premium feature)
Following on from the example with the Funnels feature, once you identify there is a problem in your conversion funnel, how do you know what is preventing visitors from taking an action that pushes them to the next page in the funnel ? You need to test different variations of content to see what works best for your visitors.
A/B Testing lets you test a variety of things, including :
- different headlines
- less copy vs more copy
- different call-to-actions
- different colour schemes
- entirely different page layouts
Once you’ve created two or more variations of specific landing pages in the conversion funnel, see how visitors interacted differently between the variations of landing pages through your heatmaps and recordings.
You may see that your visitors have scrolled further down the page because more content was provided or an important CTA button was clicked more due to a colour change. Whatever the case, using A/B testing with heatmaps and session recordings is an effective combination for increasing user engagement.
The conversion rate optimization (CRO) strategy
CRO is the process of learning what the most valuable content/aspect of your website is and how to best optimize this for your visitors to increase conversion chances.
Heatmaps and session recordings play a vital role in this strategy, but it’s how you work these features in tandem with other valuable Matomo features that will give you the most actionable insights you need to grow your business.
Want to learn how to create an effective CRO strategy ?
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Data Privacy Day 2020
27 janvier 2020, par Matthieu Aubry — PrivacyIt’s January 28th which means it’s Data Privacy Day !
Today is an important day for the Matomo team as we reflect on our mission and our goals for 2020. This year I wanted to send a video message to all Matomo users, community members and customers.
Check it out (full transcript below)
A video message from Matomo founder, Matthieu Aubry
Privacy-friendly alternatives
- NoMoreGoogle :
a comprehensive list giving you options for other privacy-friendly alternatives. - Switching.Software :
Ethical alternatives to popular sites and apps. - Web browser :
Firefox
Brave
Vivaldi - Search engine :
DuckDuckGo
Qwant - File sharing :
Nextcloud - Email provider :
ProtonMail
Mailfence
Tutanota
Video transcript
Hey everyone,
Matthieu here, Founder of Matomo.
Today is one of the most significant days of the year for the Matomo team – it’s Data Privacy Day. And so I wanted to quickly reflect on our mission and the significance of this day.
In today’s busy online world where data is king, this day is an important reminder of being vigilant in protecting our personal information online.
Matomo began 12 years ago as an open-source alternative to Google Analytics – the goal was, and still is to give full control of data back to users.
In 2020, we are determined to see through this commitment. We will keep building a powerful and ethical web analytics platform that focuses on privacy protection, data ownership, and provides value to all Matomo users and customers.
And what’s fantastic is to see the rise of other quality software companies offering privacy-friendly alternatives for web browsers, search engines, file sharing, email providers, all with a similar mission. And with these products now widely available, we encourage you to take back control of all your online activities and begin this new decade with a resolution to stay safe online.
I’ll provide you with some links below the video to check out these privacy-friendly alternatives. If you have a website and want to gain valuable insights on the visitors while owning your data, join us !
Matomo Analytics On-Premise is and always will be free to download and install on your own servers and on your own terms.
Also feel free to join our active community or spread the word to your friends and network about the importance of data privacy.
Thank you all and wishing you a great 2020 !
For more information on how Matomo protects the privacy of your users, visit : https://matomo.org/privacy/
Do you have privacy concerns ?
What better day than today to speak up ! What privacy concerns have you experienced ?
- NoMoreGoogle :
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Error installing package in anaconda : Attempted to make prefix record for unknown package type
25 janvier 2020, par ashwinidsI’m getting the error below for doing any operation install, update or uninstall
ERROR conda.core.link:_execute(700) : An error occurred while
uninstalling package ’::ffmpeg-3.2.4-2’. Rolling back
transaction : doneValueError(’Attempted to make prefix record for unknown package type :
ffmpeg-3.2.4-2’,)Details about the environment :
OS : ubuntu 18.04
python version : intelpython3Conda info
active environment : base
active env location : /home/p****/intelpython3
shell level : 1
user config file : /home/p****/.condarc
populated config files : /home/p****/.condarc
conda version : 4.7.12
conda-build version : not installed
python version : 3.6.8.final.0
virtual packages : __cuda=10.1
base environment : /home/p****/intelpython3 (writable)
channel URLs : https://conda.anaconda.org/intel/linux-64
https://conda.anaconda.org/intel/noarch
https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/p****/intelpython3/pkgs
/home/p****/.conda/pkgs
envs directories : /home/p****/intelpython3/envs
/home/p****/.conda/envs
platform : linux-64
user-agent : conda/4.7.12 requests/2.20.1 CPython/3.6.8 Linux/5.0.0-36-generic ubuntu/18.04.3 glibc/2.27
UID:GID : 1000:1000
netrc file : None
offline mode : FalseConda list
# packages in environment at /home/p****/intelpython3:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu anaconda
absl-py 0.8.0 py36_0 anaconda
affine 2.3.0 pypi_0 pypi
asn1crypto 0.24.0 py36_3 intel
astor 0.8.0 py36_0 anaconda
atomicwrites 1.3.0 pypi_0 pypi
attrs 19.3.0 pypi_0 pypi
audioread 2.1.6 py36_0 <unknown>
awscli 1.16.292 pypi_0 pypi
backcall 0.1.0 py36_2 <unknown>
backports 1.0 py36_9 <unknown>
bayesian-optimization 1.0.1 pypi_0 pypi
bleach 2.1.3 py36_2 <unknown>
boto3 1.10.19 pypi_0 pypi
botocore 1.13.28 pypi_0 pypi
bzip2 1.0.6 17 intel
c-ares 1.15.0 h7b6447c_1001 anaconda
causalgraphicalmodels 0.0.4 pypi_0 pypi
certifi 2018.1.18 py36_2 intel
cffi 1.11.5 py36_3 intel
chardet 3.0.4 py36_3 intel
click 7.0 pypi_0 pypi
click-plugins 1.1.1 pypi_0 pypi
cligj 0.5.0 pypi_0 pypi
colorama 0.4.1 pypi_0 pypi
conda 4.7.12 py36_0
conda-env 2.6.0 1
conda-package-handling 1.6.0 py36h7b6447c_0 anaconda
cryptography 2.3 py36_1 intel
cudatoolkit 10.1.243 h6bb024c_0
cudnn 7.6.0 cuda10.1_0 anaconda
cupti 10.1.168 0 anaconda
cvxopt 1.2.3 pypi_0 pypi
cycler 0.10.0 py36_7 intel
cython 0.29.6 py36h7b7c402_0 intel
daal 2019.4 intel_243 <unknown>
daal4py 2019.4 py36h7b7c402_0 intel
dbus 1.13.12 h746ee38_0
decorator 4.3.0 py36_3 <unknown>
descartes 1.1.0 pypi_0 pypi
docutils 0.15.2 pypi_0 pypi
dowhy 0.1.1 pypi_0 pypi
entrypoints 0.2.3 py36_2 <unknown>
expat 2.2.6 he6710b0_0
ffmpeg 3.2.4 2 <unknown>
fiona 1.8.11 pypi_0 pypi
fontconfig 2.12.6 h49f89f6_0
freetype 2.8 hab7d2ae_1
funcsigs 1.0.2 py36_7 intel
gast 0.3.2 py_0 anaconda
geopandas 0.6.2 pypi_0 pypi
get_terminal_size 1.0.0 py36_7 <unknown>
glib 2.63.1 h5a9c865_0
google-pasta 0.1.7 py_0 anaconda
grpcio 1.14.1 py36h9ba97e2_0 anaconda
gst-plugins-base 1.12.4 h33fb286_0
gstreamer 1.12.4 hb53b477_0
h5py 2.9.0 py36h7918eee_0 anaconda
hdf5 1.10.4 hb1b8bf9_0 anaconda
html5lib 1.0.1 py36_4 <unknown>
icc_rt 2019.4 intel_243 <unknown>
icu 58.2 h9c2bf20_1
idna 2.6 py36_3 intel
imageio 2.6.1 pypi_0 pypi
imgaug 0.2.6 pypi_0 pypi
impi_rt 2019.4 intel_243 <unknown>
importlib-metadata 0.23 pypi_0 pypi
intel-openmp 2019.4 intel_243 <unknown>
intelpython 2019.4 0 intel
ipp 2019.4 intel_243 <unknown>
ipykernel 4.6.1 py36_2 <unknown>
ipython 6.3.1 py36_3 <unknown>
ipython_genutils 0.2.0 py36_2 <unknown>
jedi 0.12.0 py36_2 <unknown>
jinja2 2.10.3 pypi_0 pypi
jmespath 0.9.4 pypi_0 pypi
joblib 0.13.2 pypi_0 pypi
jpeg 9b h024ee3a_2
json5 0.8.5 pypi_0 pypi
jsonschema 3.2.0 pypi_0 pypi
jupyter 1.0.0 py36_7 intel
jupyter_client 5.1.0 py36_5 <unknown>
jupyter_console 5.1.0 py36_2 <unknown>
jupyter_core 4.4.0 py36_6 <unknown>
jupyterlab 1.2.3 pypi_0 pypi
jupyterlab-server 1.0.6 pypi_0 pypi
keras 2.3.1 pypi_0 pypi
keras-applications 1.0.8 py_0 anaconda
keras-bert 0.80.0 pypi_0 pypi
keras-embed-sim 0.7.0 pypi_0 pypi
keras-layer-normalization 0.14.0 pypi_0 pypi
keras-multi-head 0.22.0 pypi_0 pypi
keras-pos-embd 0.11.0 pypi_0 pypi
keras-position-wise-feed-forward 0.6.0 pypi_0 pypi
keras-preprocessing 1.1.0 py_1 anaconda
keras-rectified-adam 0.17.0 pypi_0 pypi
keras-self-attention 0.41.0 pypi_0 pypi
keras-transformer 0.31.0 pypi_0 pypi
kiwisolver 1.0.1 py36_2 intel
libffi 3.2.1 11 intel
libgcc-ng 9.1.0 hdf63c60_0 anaconda
libgfortran-ng 7.3.0 hdf63c60_0 anaconda
libiconv 1.14 4 <unknown>
libpng 1.6.36 2 intel
libprotobuf 3.9.2 hd408876_0 anaconda
librosa 0.6.3 pypi_0 pypi
libsodium 1.0.16 3 <unknown>
libstdcxx-ng 9.1.0 hdf63c60_0 anaconda
libtiff 4.0.9 he85c1e1_1
libxcb 1.13 h1bed415_1
libxml2 2.9.8 hf84eae3_0
lightgbm 2.3.1 pypi_0 pypi
llvmlite 0.27.1 py36_0 intel
markdown 3.1.1 py36_0 anaconda
markupsafe 1.0 py36_3 <unknown>
matplotlib 2.1.0 py36hba5de38_0
mercantile 1.1.2 pypi_0 pypi
metaflow 2.0.0 pypi_0 pypi
mistune 0.8.3 py36_2 <unknown>
mkl 2019.4 intel_243 <unknown>
mkl-service 1.0.0 py36h7b7c402_11 intel
mkl_fft 1.0.11 py36h7b7c402_2 intel
mkl_random 1.0.2 py36h7b7c402_4 intel
more-itertools 7.2.0 pypi_0 pypi
mpi4py 3.0.0 py36_3 intel
mpmath 1.1.0 pypi_0 pypi
munch 2.5.0 pypi_0 pypi
nbconvert 5.2.1 py36_2 <unknown>
nbformat 4.4.0 py36_2 <unknown>
networkx 2.4 pypi_0 pypi
ninja 1.9.0 py36hfd86e86_0
noisyopt 0.2.2 pypi_0 pypi
notebook 5.2.2 py36_1 intel
numba 0.42.1 np116py36_2 intel
numexpr 2.6.8 py36_2 intel
numpy 1.16.2 py36h7b7c402_0 intel
numpy-base 1.16.2 py36_0 intel
oauthlib 3.1.0 pypi_0 pypi
olefile 0.46 py_0
openssl 1.0.2r 2 intel
packaging 19.2 pypi_0 pypi
pandas 0.24.1 py36_3 intel
pandocfilters 1.4.1 py36_2 <unknown>
parso 0.2.0 py36_2 <unknown>
path.py 11.0.1 py36_2 <unknown>
pcre 8.43 he6710b0_0
pexpect 4.2.1 py36_4 <unknown>
pickleshare 0.7.4 py36_3 <unknown>
pillow 6.2.1 pypi_0 pypi
pip 19.3.1 pypi_0 pypi
pluggy 0.13.1 pypi_0 pypi
prompt_toolkit 1.0.15 py36_2 <unknown>
protobuf 3.9.2 py36he6710b0_0 anaconda
ptyprocess 0.5.2 py36_2 <unknown>
pulp 2.0 pypi_0 pypi
py 1.8.0 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pycosat 0.6.3 py36_3 intel
pycparser 2.18 py36_2 intel
pydot 1.4.1 pypi_0 pypi
pyeditline 2.0.0 py36_0 intel
pygments 2.2.0 py36_5 <unknown>
pygmy 0.2.0 pypi_0 pypi
pyopenssl 17.5.0 py36_2 intel
pyparsing 2.2.0 py36_2 intel
pyportfolioopt 0.4.3 pypi_0 pypi
pyproj 2.4.2.post1 pypi_0 pypi
pyqt 5.6.0 py36h22d08a2_6
pyrsistent 0.15.6 pypi_0 pypi
pysocks 1.6.7 py36_1 intel
pytesseract 0.3.0 pypi_0 pypi
pytest 4.6.6 pypi_0 pypi
python 3.6.8 7 intel
python-dateutil 2.6.0 py36_12 intel
python-graphviz 0.13 pypi_0 pypi
pytorch 1.3.1 py3.6_cuda10.1.243_cudnn7.6.3_0 pytorch
pytz 2018.4 py36_3 intel
pywavelets 1.1.1 pypi_0 pypi
pyyaml 4.1 py36_3 intel
pyzmq 16.0.2 py36_6 <unknown>
qt 5.6.2 hd25b39d_14
rasterio 1.1.1 pypi_0 pypi
regex 2019.11.1 pypi_0 pypi
requests 2.20.1 py36_1 intel
requests-oauthlib 1.3.0 pypi_0 pypi
resampy 0.2.1 pypi_0 pypi
rio-cogeo 1.1.6 pypi_0 pypi
rsa 3.4.2 pypi_0 pypi
ruamel_yaml 0.11.14 py36_4 intel
s3transfer 0.2.1 pypi_0 pypi
sacremoses 0.0.35 pypi_0 pypi
scikit-image 0.16.2 pypi_0 pypi
scikit-learn 0.20.3 py36h7b7c402_5 intel
scipy 1.3.3 pypi_0 pypi
seaborn 0.9.0 pypi_0 pypi
sentencepiece 0.1.83 pypi_0 pypi
setuptools 39.0.1 py36_0 intel
shapely 1.6.4.post2 pypi_0 pypi
simplegeneric 0.8.1 py36_7 <unknown>
sip 4.18.1 py36hf484d3e_2
six 1.11.0 py36_3 intel
smp 0.1.4 py36_0 intel
snuggs 1.4.7 pypi_0 pypi
sqlite 3.27.2 4 intel
supermercado 0.0.5 pypi_0 pypi
sympy 1.4 pypi_0 pypi
tbb 2019.6 intel_243 <unknown>
tbb4py 2019.6 py36_intel_0 <unknown>
tcl 8.6.4 24 intel
tensorboard 1.14.0 py36hf484d3e_0 anaconda
tensorflow 1.14.0 gpu_py36h3fb9ad6_0 anaconda
tensorflow-base 1.14.0 gpu_py36he45bfe2_0 anaconda
tensorflow-estimator 1.14.0 py_0 anaconda
tensorflow-gpu 1.14.0 h0d30ee6_0 anaconda
termcolor 1.1.0 py36_1 anaconda
terminado 0.8.1 py36_2 <unknown>
testpath 0.3.1 py36_2 <unknown>
tk 8.6.4 29 intel
torchvision 0.4.2 py36_cu101 pytorch
tornado 4.5.2 py36_5 <unknown>
tqdm 4.36.1 py_0 anaconda
traitlets 4.3.2 py36_3 <unknown>
transformers 2.1.1 pypi_0 pypi
tweepy 3.8.0 pypi_0 pypi
urllib3 1.22 pypi_0 pypi
wcwidth 0.1.7 py36_6 <unknown>
webencodings 0.5.1 py36_0 <unknown>
werkzeug 0.16.0 py_0 anaconda
wheel 0.31.0 py36_3 intel
widgetsnbextension 3.2.0 py36_1 <unknown>
wrapt 1.11.2 py36h7b6447c_0 anaconda
x264 20131218 0 <unknown>
xgboost 0.81 py36_0 intel
xz 5.2.3 2 intel
yaml 0.1.7 2 intel
zipp 0.6.0 pypi_0 pypi
zlib 1.2.11 5 intel
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