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• opencv ffmpeg vaapi 1080p resolution not working

  18 avril 2023, par yeo

  I want to use hardware acceleration with opencv manual build.
My gpu uses an i965 intel cpu built-in graphics card, and it is a debain11 environment.

  


  [OPENCV:FFMPEG:40] Reinit context to 1920x1088, pix_fmt: vaapi_vld


  


  If you look at some of the error messages below, it seems that the original file is 1920x1080 because it is converted to 1088 while reinit.
I've read that vaapi_vld reads 16 bits at a time.
In fact, it seems to work when the original file is changed to 1920x1072.
Is there a way to fix it without changing the original file resolution ?
Please advise seniors.
Sorry for my poor English skills
Thank you

  


  manual build CMAKE option

  


  "-DCMAKE_VERBOSE_MAKEFILE=ON -DWITH_VA_INTEL=ON -DWITH_VA=ON -DOPENCV_FFMPEG_ENABLE_LIBAVDEVICE=ON -DOPENCV_ENABLE_GLX=ON -DOPENCV_FFMPEG_SKIP_BUILD_CHECK=ON -DWITH_OPENVINO=ON -DWITH_INF_ENGINE=ON"



  


  build infomation

  


    OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
    Disabled:                    world
    Disabled by dependency:      -
    Unavailable:                 java python2 ts
    Applications:                -
    Documentation:               NO
    Non-free algorithms:         NO

  GUI:                           GTK3
    GTK+:                        YES (ver 3.24.24)
      GThread :                  YES (ver 2.66.8)
      GtkGlExt:                  NO
    VTK support:                 NO
  Media I/O: 
    ZLib:                        /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.11)
    JPEG:                        /usr/lib/x86_64-linux-gnu/libjpeg.so (ver 62)
    WEBP:                        /usr/lib/x86_64-linux-gnu/libwebp.so (ver encoder: 0x020e)
    PNG:                         /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.6.37)
    TIFF:                        /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 / 4.2.0)
    JPEG 2000:                   build (ver 2.4.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES
  Video I/O:
    DC1394:                      YES (2.2.6)
    FFMPEG:                      YES
      avcodec:                   YES (58.91.100)
      avformat:                  YES (58.45.100)
      avutil:                    YES (56.51.100)
      swscale:                   YES (5.7.100)
      avresample:                YES (4.0.0)
    GStreamer:                   YES (1.18.4)
    v4l/v4l2:                    YES (linux/videodev2.h)

  Parallel framework:            pthreads

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2020.0.0 Gold [2020.0.0]
    VA:                          YES
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)

  OpenCL:                        YES (INTELVA)
    Include path:                /home/xxx
    Link libraries:              Dynamic load

  Python 3:
    Interpreter:                 /usr/bin/python3 (ver 3.9.2)
    Libraries:                   /usr/lib/x86_64-linux-gnu/libpython3.9.so (ver 3.9.2)
    numpy:                       /home/../include (ver 1.19.3)
    install path:                python/cv2/python-3


  


  vainfo

  


  libva info: VA-API version 1.10.0
libva info: User environment variable requested driver 'i965'
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/i965_drv_video.so
libva info: Found init function __vaDriverInit_1_8
libva info: va_openDriver() returns 0
vainfo: VA-API version: 1.10 (libva 2.10.0)
vainfo: Driver version: Intel i965 driver for Intel(R) Haswell Mobile - 2.4.1
vainfo: Supported profile and entrypoints
      VAProfileMPEG2Simple            : VAEntrypointVLD
      VAProfileMPEG2Simple            : VAEntrypointEncSlice
      VAProfileMPEG2Main              : VAEntrypointVLD
      VAProfileMPEG2Main              : VAEntrypointEncSlice
      VAProfileH264ConstrainedBaseline: VAEntrypointVLD
      VAProfileH264ConstrainedBaseline: VAEntrypointEncSlice
      VAProfileH264Main               : VAEntrypointVLD
      VAProfileH264Main               : VAEntrypointEncSlice
      VAProfileH264High               : VAEntrypointVLD
      VAProfileH264High               : VAEntrypointEncSlice
      VAProfileH264MultiviewHigh      : VAEntrypointVLD
      VAProfileH264MultiviewHigh      : VAEntrypointEncSlice
      VAProfileH264StereoHigh         : VAEntrypointVLD
      VAProfileH264StereoHigh         : VAEntrypointEncSlice
      VAProfileVC1Simple              : VAEntrypointVLD
      VAProfileVC1Main                : VAEntrypointVLD
      VAProfileVC1Advanced            : VAEntrypointVLD
      VAProfileNone                   : VAEntrypointVideoProc
      VAProfileJPEGBaseline           : VAEntrypointVLD


  


  import os
import cv2

os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "hw_decoders_any;vaapi,vdpau" +

cap = cv2.VideoCapture(file_name,cv2.CAP_FFMPEG(cv2.CAP_PROP_HW_ACCELERATION,cv2.VIDEO_ACCELERATION_ANY))  



  


  error code

  


  [ INFO:0@0.187] global /home/u/opencv-python/opencv/modules/videoio/src/videoio_registry.cpp (223) VideoBackendRegistry VIDEOIO: Enabled backends(8, sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); V4L2(970); CV_IMAGES(960); CV_MJPEG(950); FIREWIRE(940); UEYE(930)
[OPENCV:FFMPEG:40] Reinit context to 1920x1088, pix_fmt: yuv420p
[OPENCV:FFMPEG:40] Trying to use DRM render node for device 0.
[OPENCV:FFMPEG:40] libva: VA-API version 1.10.0
libva: User environment variable requested driver 'i965'
libva: Trying to open /usr/lib/x86_64-linux-gnu/dri/i965_drv_video.so
libva: Found init function __vaDriverInit_1_8
libva: va_openDriver() returns 0
Initialised VAAPI connection: version 1.10
[OPENCV:FFMPEG:40] VAAPI driver: Intel i965 driver for Intel(R) Haswell Mobile - 2.4.1.
[OPENCV:FFMPEG:40] Driver not found in known nonstandard list, using standard behaviour.
[ INFO:0@0.228] global /home/u/opencv-python/opencv/modules/videoio/src/cap_ffmpeg_hw.hpp (276) hw_check_device FFMPEG: Using vaapi video acceleration on device: Intel i965 driver for Intel(R) Haswell Mobile - 2.4.1
[ INFO:0@0.228] global /home/u/opencv-python/opencv/modules/videoio/src/cap_ffmpeg_hw.hpp (566) hw_create_device FFMPEG: Created video acceleration context (av_hwdevice_ctx_create) for vaapi on device 'default'
[ INFO:0@0.228] global /home/u/opencv-python/opencv/modules/core/src/ocl.cpp (1186) haveOpenCL Initialize OpenCL runtime...
[ INFO:0@0.228] global /home/u/opencv-python/opencv/modules/core/src/ocl.cpp (1192) haveOpenCL OpenCL: found 0 platforms
File open : ./videoplayback1.mp4
[OPENCV:FFMPEG:40] Reinit context to 1920x1088, pix_fmt: vaapi_vld
[OPENCV:FFMPEG:16] Failed to read image from surface 0x4000014: 18 (invalid parameter).
[ERROR:0@0.245] global /home/u/opencv-python/opencv/modules/videoio/src/cap_ffmpeg_impl.hpp (1575) retrieveFrame Error copying data from GPU to CPU (av_hwframe_transfer_data)
Play video ... size=1920x1080, file=./videoplayback1.mp4
[OPENCV:FFMPEG:16] Failed to read image from surface 0x4000012: 18 (invalid parameter).
[ERROR:0@0.277] global /home/u/opencv-python/opencv/modules/videoio/src/cap_ffmpeg_impl.hpp (1575) retrieveFrame Error copying data from GPU to CPU (av_hwframe_transfer_data)
OpenCV(4.6.0) Error: Assertion failed (!image.empty()) in imencode, file /home/u/opencv-python/opencv/modules/imgcodecs/src/loadsave.cpp, line 976
err =  OpenCV(4.6.0) /home/u/opencv-python/opencv/modules/imgcodecs/src/loadsave.cpp:976: error: (-215:Assertion failed) !image.empty() in function 'imencode'



  


  I tried to do video capture by ffmpeg hwacceleration with opencv, but an error message occurred

  


• Why is there an audio delay on recording video stream with ffmpeg ?

  25 décembre 2023, par mqwerty

  I am trying to record video and audio stream (Line in Microphone Analog Audio) which are streaming from broadcaster computer with those parameters in the recorder computer ;

  


  ffmpeg record parameters :

  


  /usr/bin/ffmpeg -y -buffer_size max -thread_queue_size 8192 -i udp://225.0.5.11:1026 -buffer_size max -thread_queue_size 8192 -i udp://225.0.5.11:1032 -map 0:v -map 1:a -metadata title=COMPUTER-01_metadata_file -metadata creation_time="2023-12-25 13:25:29" -threads 0 -c:v copy -c:a copy -movflags +faststart -f segment -segment_time 01:00:00 -segment_atclocktime 1 -reset_timestamps 1 -strftime 1 -segment_format mp4 -t 120 test_record_video_with_audio_%Y-%m-%d_%H-%M-%S.mp4


  


  The ffmpeg started and finished successfully, but when I open the recorded video with mpv like (mpv test_record_video_with_audio.mp4), I realized that there is a 5-6 seconds delay in audio. How can I prevent the delay of audio in the recorded mp4 file without using offset ? My last option is setting offset but I think that it is not safe according to any changes in network or etc.

  


  FFMPEG version on both computer :

  


  ffmpeg version 4.2.9 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 8 (GCC)


  


  BROADCASTER COMPUTER :

  


  sysctl.conf :

  


  No added configurations.


  


  ethtool output :

  


  Supported ports: [ TP ]
Supported link modes:   100baseT/Full
                        1000baseT/Full
                        10000baseT/Full
                        2500baseT/Full
                        5000baseT/Full
Supported pause frame use: Symmetric
Supports auto-negotiation: Yes
Supported FEC modes: Not reported
Advertised link modes:  100baseT/Full
                        1000baseT/Full
                        10000baseT/Full
Advertised pause frame use: Symmetric
Advertised auto-negotiation: Yes
Advertised FEC modes: Not reported
Speed: 10000Mb/s
Duplex: Full
Auto-negotiation: on
Port: Twisted Pair
PHYAD: 0
Transceiver: internal
MDI-X: Unknown
Supports Wake-on: d
Wake-on: d
    Current message level: 0x00000007 (7)
                           drv probe link
Link detected: yes


  


  ffmpeg video stream :

  


  ffmpeg -fflags +genpts -f x11grab -framerate 30 -video_size uhd2160 -i :0 -c:v hevc_nvenc -preset fast -pix_fmt bgr0 -b:v 3M -g 25 -an -f mpegts udp://225.0.5.11:1026


  


  ffmpeg audio stream :

  


  ffmpeg -f alsa -i hw:0,0 -c:a aac -ar 48000 -b:a 1024K -ab 512k -f rtp_mpegts rtp://225.0.5.11:1032


  


  nvidia-smi :

  


  | NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA T400 4GB                Off | 00000000:5B:00.0 Off |                  N/A |
| 38%   38C    P8              N/A /  31W |    207MiB /  4096MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA RTX A4000               Off | 00000000:9E:00.0 Off |                  Off |
| 41%   59C    P2              41W / 140W |    766MiB / 16376MiB |     17%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      3227      G   /usr/libexec/Xorg                           114MiB |
|    0   N/A  N/A      3423      G   /usr/bin/gnome-shell                         87MiB |
|    1   N/A  N/A      3227      G   /usr/libexec/Xorg                           285MiB |
|    1   N/A  N/A      3423      G   /usr/bin/gnome-shell                         91MiB |
|    1   N/A  N/A      3762      C   ffmpeg                                      372MiB |
+---------------------------------------------------------------------------------------+


  


  lscpu output :

  


  

Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
BIOS Vendor ID:      Intel(R) Corporation
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
BIOS Model name:     Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
Stepping:            7
CPU MHz:             2200.000
CPU max MHz:         4000.0000
CPU min MHz:         1000.0000
BogoMIPS:            4400.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            36608K
NUMA node0 CPU(s):   0-23,48-71
NUMA node1 CPU(s):   24-47,72-95


  


  OS : CentOS Stream release 8


  


  RECORDER COMPUTER :

  


  sysctl.conf :

  


  net.core.rmem_max=16777216
net.core.wmem_max=16777216
net.ipv4.tcp_rmem= 4096 87380 16777216
net.ipv4.tcp_wmem= 4096 65536 16777216
net.ipv4.tcp_window_scaling = 1
net.ipv4.tcp_timestamps = 1
net.ipv4.tcp_sack = 1
net.ipv4.tcp_no_metrics_save = 0
net.core.netdev_max_backlog = 50000
net.core.optmem_max=25165824


  


  lscpu output :

  


  Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
BIOS Vendor ID:      Intel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Gold 5318Y CPU @ 2.10GHz
BIOS Model name:     Intel(R) Xeon(R) Gold 5318Y CPU @ 2.10GHz
Stepping:            6
CPU MHz:             3400.000
CPU max MHz:         3400.0000
CPU min MHz:         800.0000
BogoMIPS:            4200.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            36864K


  


  ethtool output :

  


  Supported ports: [ TP ]
    Supported link modes:   1000baseT/Full
                            10000baseT/Full
    Supported pause frame use: Symmetric Receive-only
    Supports auto-negotiation: Yes
    Supported FEC modes: Not reported
    Advertised link modes:  1000baseT/Full
                            10000baseT/Full
    Advertised pause frame use: Symmetric
    Advertised auto-negotiation: Yes
    Advertised FEC modes: Not reported
    Speed: 10000Mb/s
    Duplex: Full
    Auto-negotiation: on
    Port: Twisted Pair
    PHYAD: 12
    Transceiver: internal
    MDI-X: Unknown
    Supports Wake-on: d
    Wake-on: d
        Current message level: 0x00002081 (8321)
                               drv tx_err hw
    Link detected: yes


  


  No NVIDIA Graphic Driver

  


  OS : CentOS Stream release 8


  


  I tried audio encoding while recording like :

  


  "-c:a", "aac",  
"-ar", "48000", 
"-b:a", "128k",


  


  I also tried :

  


  "aresample=async=1"



  


  Unfortunately,these did not have any improvements on preventing latency in audio.

  


• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo free for 21 days. No credit card required.

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