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• Why does ffmpeg keep using more and more RAM and crash ?

  29 décembre 2022, par József Márton Kakas

  I am using the following command with ffmpeg to encode a video file using the libsvtav1 codec : ffmpeg -i hevc.mkv -map 0:v:0 -c:v:0 libsvtav1 -preset 8 -crf 22 -format matroska av1.mkv.

  


  However, ffmpeg is using up all of my available RAM and crashing. I have also tried using the libx264 codec, but the same issue occurs, although it happens more slowly. I have already allocated 12 GB of RAM to ffmpeg, but it still seems to be insufficient. How can I prevent ffmpeg from using all of my available RAM and crashing when using either the libsvtav1 or libx264 codecs ?

  


  I have tried it on another VM, but the same issue occurs. Here is the full output of the program.

  


  ffmpeg -i akira.mkv -map 0:v:0 -c:v:0 libsvtav1 -preset 8 -crf 22 -c:a copy -c:s copy -format matroska av1.mkv
ffmpeg version n5.1.2-7-ga6e26053c2-20221106 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.2.0 (crosstool-NG 1.25.0.90_cf9beb1)
  configuration: --prefix=/ffbuild/prefix --pkg-config-flags=--static --pkg-config=pkg-config --cross-prefix=x86_64-ffbuild-linux-gnu- --arch=x86_64 --target-os=linux --enable-gpl --enable-version3 --disable-debug --enable-iconv --enable-libxml2 --enable-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-lzma --enable-fontconfig --enable-libvorbis --enable-opencl --enable-libpulse --enable-libvmaf --enable-libxcb --enable-xlib --enable-amf --enable-libaom --enable-libaribb24 --enable-avisynth --disable-chromaprint --enable-libdav1d --enable-libdavs2 --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-mbedtls --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-lv2 --enable-libmfx --disable-openal --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-librav1e --enable-librubberband --disable-schannel --enable-sdl2 --enable-libsoxr --enable-libsrt --enable-libsvtav1 --enable-libtwolame --enable-libuavs3d --enable-libdrm --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-ldflags=-pthread --extra-ldexeflags=-pie --extra-libs='-ldl -lgomp' --extra-version=20221106
  libavutil      57. 28.100 / 57. 28.100
  libavcodec     59. 37.100 / 59. 37.100
  libavformat    59. 27.100 / 59. 27.100
  libavdevice    59.  7.100 / 59.  7.100
  libavfilter     8. 44.100 /  8. 44.100
  libswscale      6.  7.100 /  6.  7.100
  libswresample   4.  7.100 /  4.  7.100
  libpostproc    56.  6.100 / 56.  6.100
Input #0, matroska,webm, from 'akira.mkv':
  Metadata:
    title           : Akira 4K
    encoder         : libebml v1.3.10 + libmatroska v1.5.2
    creation_time   : 2020-05-04T19:57:18.000000Z
  Duration: 02:04:46.50, start: 0.000000, bitrate: 10945 kb/s
  Stream #0:0: Video: hevc (Main 10), yuv420p10le(tv, bt2020nc/bt2020/smpte2084), 3840x2074 [SAR 1:1 DAR 1920:1037], 23.98 fps, 23.98 tbr, 1k tbn (default)
    Metadata:
      BPS-eng         : 9531297
      DURATION-eng    : 02:04:46.479000000
      NUMBER_OF_FRAMES-eng: 179496
      NUMBER_OF_BYTES-eng: 8919482644
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:1(eng): Subtitle: subrip
    Metadata:
      BPS-eng         : 46
      DURATION-eng    : 01:59:00.755000000
      NUMBER_OF_FRAMES-eng: 1277
      NUMBER_OF_BYTES-eng: 41675
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:2(ita): Audio: ac3, 48000 Hz, 5.1(side), fltp, 448 kb/s (default)
    Metadata:
      title           : Nuovo doppiaggio
      BPS-eng         : 448000
      DURATION-eng    : 02:04:28.480000000
      NUMBER_OF_FRAMES-eng: 233390
      NUMBER_OF_BYTES-eng: 418234880
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:3(ita): Audio: ac3, 48000 Hz, stereo, fltp, 448 kb/s
    Metadata:
      title           : Doppiaggio Storico
      BPS-eng         : 448000
      DURATION-eng    : 02:04:28.480000000
      NUMBER_OF_FRAMES-eng: 233390
      NUMBER_OF_BYTES-eng: 418234880
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:4(ita): Subtitle: dvd_subtitle, 1920x1080
    Metadata:
      title           : Italiano
      BPS-eng         : 8426
      DURATION-eng    : 02:00:14.955000000
      NUMBER_OF_FRAMES-eng: 1201
      NUMBER_OF_BYTES-eng: 7599491
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:5(ita): Subtitle: subrip (default)
    Metadata:
      title           : Forced
      BPS-eng         : 0
      DURATION-eng    : 01:46:31.753000000
      NUMBER_OF_FRAMES-eng: 14
      NUMBER_OF_BYTES-eng: 462
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:6(jpn): Audio: eac3, 48000 Hz, 5.1(side), fltp, 256 kb/s
    Metadata:
      BPS-eng         : 256000
      DURATION-eng    : 02:04:46.496000000
      NUMBER_OF_FRAMES-eng: 233953
      NUMBER_OF_BYTES-eng: 239567872
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:7(eng): Audio: eac3, 48000 Hz, 5.1(side), fltp, 256 kb/s
    Metadata:
      BPS-eng         : 256000
      DURATION-eng    : 02:04:46.496000000
      NUMBER_OF_FRAMES-eng: 233953
      NUMBER_OF_BYTES-eng: 239567872
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
  Stream #0:8: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 1067x600, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : cover_land.jpg
      mimetype        : image/jpeg
  Stream #0:9: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 120x176, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : small_cover.jpg
      mimetype        : image/jpeg
  Stream #0:10: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 213x120, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : small_cover_land.jpg
      mimetype        : image/jpeg
  Stream #0:11: Video: mjpeg (Baseline), yuvj444p(pc, bt470bg/unknown/unknown), 600x882, 90k tbr, 90k tbn (attached pic)
    Metadata:
      filename        : cover.jpg
      mimetype        : image/jpeg
  Stream #0:12: Attachment: otf
    Metadata:
      filename        : StoneSansStd-Semibold.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:13: Attachment: otf
    Metadata:
      filename        : StoneSansStd-SemiboldItalic.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:14: Attachment: otf
    Metadata:
      filename        : ATPacella-Black.otf
      mimetype        : application/vnd.ms-opentype
  Stream #0:15: Attachment: ttf
    Metadata:
      filename        : ITC Franklin Gothic LT Medium Condensed.ttf
      mimetype        : application/x-truetype-font
File 'av1.mkv' already exists. Overwrite? [y/N] y
Stream mapping:
  Stream #0:0 -> #0:0 (hevc (native) -> av1 (libsvtav1))
Press [q] to stop, [?] for help
Svt[info]: -------------------------------------------
Svt[info]: SVT [version]:       SVT-AV1 Encoder Lib v1.3.0
Svt[info]: SVT [build]  :       GCC 12.2.0       64 bit
Svt[info]: LIB Build date: Nov  6 2022 00:41:54
Svt[info]: -------------------------------------------
Svt[info]: Number of logical cores available: 16
Svt[info]: Number of PPCS 71
Svt[info]: [asm level on system : up to avx]
Svt[info]: [asm level selected : up to avx]
Svt[info]: -------------------------------------------
Svt[info]: SVT [config]: main profile   tier (auto)     level (auto)
Svt[info]: SVT [config]: width / height / fps numerator / fps denominator               : 3840 / 2080 / 24000 / 1001
Svt[info]: SVT [config]: bit-depth / color format                      : 10 / YUV420
Svt[info]: SVT [config]: preset / tune / pred struct                   : 8 / PSNR / random access
Svt[info]: SVT [config]: gop size / mini-gop size / key-frame type     : 161 / 16 / key frame
Svt[info]: SVT [config]: BRC mode / rate factor                        : CRF / 22 
Svt[info]: -------------------------------------------
Output #0, matroska, to 'av1.mkv':
  Metadata:
    title           : Akira 4K
    encoder         : Lavf59.27.100
  Stream #0:0: Video: av1 (AV01 / 0x31305641), yuv420p10le(tv, bt2020nc/bt2020/smpte2084, progressive), 3840x2074 [SAR 1:1 DAR 1920:1037], q=2-31, 23.98 fps, 1k tbn (default)
    Metadata:
      BPS-eng         : 9531297
      DURATION-eng    : 02:04:46.479000000
      NUMBER_OF_FRAMES-eng: 179496
      NUMBER_OF_BYTES-eng: 8919482644
      _STATISTICS_WRITING_APP-eng: mkvmerge v43.0.0 ('The Quartermaster') 32-bit
      _STATISTICS_WRITING_DATE_UTC-eng: 2020-05-04 19:57:18
      _STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
      encoder         : Lavc59.37.100 libsvtav1
frame=    1 fps=0.0 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=    2 fps=1.3 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=   51 fps= 25 q=0.0 size=       1kB time=00:00:00.00 bitrate=N/A sframe=   73 fps= 16 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   76 fps= 14 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   81 fps= 14 q=16.0 size=       1kB time=00:00:00.00 bitrate=7480frame=   87 fps= 14 q=21.0 size=       1kB time=00:00:00.16 bitrate=  44frame=   95 fps= 14 q=21.0 size=       1kB time=00:00:00.50 bitrate=  14frame=  100 fps= 13 q=16.0 size=       1kB time=00:00:00.66 bitrate=  11frame=  110 fps= 13 q=23.0 size=       1kB time=00:00:00.96 bitrate=   7frame=  114 fps= 13 q=23.0 size=       1kB time=00:00:01.12 bitrate=   6frame=  122 fps= 13 q=23.0 size=       1kB time=00:00:01.46 bitrate=   5frame=  126 fps= 12 q=23.0 size=       1kB time=00:00:01.62 bitrate=   4frame=  133 fps= 12 q=22.0 size=       1kB time=00:00:01.92 bitrate=   3frame=  139 fps= 12 q=21.0 size=       1kB time=00:00:02.17 bitrate=   3frame=  141 fps= 11 q=22.0 size=       1kB time=00:00:02.25 bitrate=   3frame=  144 fps= 11 q=23.0 size=       1kB time=00:00:02.37 bitrate=   3frame=  151 fps= 11 q=13.0 size=       1kB time=00:00:02.67 bitrate=   2frame=  159 fps= 11 q=18.0 size=       1kB time=00:00:03.00 bitrate=   2frame=  164 fps= 11 q=22.0 size=       1kB time=00:00:03.21 bitrate=   2frame=  174 fps= 11 q=22.0 size=       1kB time=00:00:03.63 bitrate=   2frame=  179 fps= 11 q=20.0 size=       1kB time=00:00:03.83 bitrate=   1frame=  187 fps= 11 q=20.0 size=       1kB time=00:00:04.17 bitrate=   1frame=  189 fps= 10 q=21.0 size=       1kB time=00:00:04.25 bitrate=   1frame=  192 fps= 10 q=22.0 size=       1kB time=00:00:04.38 bitrate=   1frame=  195 fps=8.0 q=20.0 size=       1kB time=00:00:04.50 bitrate=   1frame=  201 fps=8.0 q=11.0 size=       1kB time=00:00:04.67 bitrate=   1Killeds/s speed=0.187x


  


• Matomo’s privacy-friendly web analytics software named best of the year 2022

  25 janvier 2023, par Erin

  W3Tech names Matomo ‘Traffic Analysis Tool of the Year 2022’ in its Web Technologies of the Year list of technologies that gained the most sites

  Matomo, a world-leading open-source web analytics platform, is proud to announce that it has received W3Tech’s award for the best web analytics software in its Web Technologies of the Year 2022. Matomo is the first independent, open-source tool named Traffic Analysis Tool of the Year – with previous winners including Google Analytics and Facebook Pixel.


  W3Tech, a trusted source for web technology research, determines winners for its annual Web Technologies of the Year list by technologies that gained the most websites. W3Tech surveys usage across millions of websites globally – comparing the number of sites using a technology on January 1st of one year with the number of sites using it the following year.

  W3Tech commenting on the Traffic Analysis Tool winners, said : “Matomo, the privacy-focused open source analytics platform, is the traffic analysis tool of the year for the first time, while Google Analytics and the other previous winners all lost a bit of market share in 2022. The Chinese Baidu Analytics ranks second this year. Snowplow, another open source tool, is an unexpected third.”


  Matomo launched in 2007 as an open-source analytics alternative to Google Analytics, keeps businesses GDPR and CCPA-compliant. Matomo is trusted by over 1.4 million websites in 220 countries and is translated into over 50 languages.


  Matomo founder Matthieu Aubry says, “As the first independent, open-source traffic analysis tool to receive this recognition, Matomo is humbled and honoured to lead the charge for change. It’s a testament to the hard work of our community, and it’s a clear sign that consumers and organisations are looking for ethical alternatives.


  “This recognition is a major win for the entire privacy movement and proves that the tide is turning against the big tech players who I believe have long prioritised profits over privacy. We are committed to continuing our work towards a more private and secure digital landscape for all.”


  In W3Tech’s Web Technologies of the Year 2022, Matomo was also judged third Tag Manager, behind Google Tag Manager and Adobe DTM.


  Matomo helps businesses and organisations track and optimise their online presence allowing users to easily collect, analyse, and act on their website and marketing data to gain a deeper understanding of their visitors and drive conversions and revenue. With 100% data ownership, customers using the company’s tools get the power to protect their website user’s privacy – and where their data is stored and what’s happening to it, without external influence. Furthermore, as the data is not sampled, it maintains data accuracy. 


  Aubry says its recent award is a positive reminder of how well this solution is performing internationally and is a testament to the exceptional quality and performance of Matomo’s powerful web analytics tools that respect a user’s privacy.


  “In 2020, the CJEU ruled US cloud servers don’t comply with GDPR. Then in 2022, the Austrian Data Protection Authority and French Data Protection Authority (CNIL) ruled that the use of Google Analytics is illegal due to data transfers to the US. With Matomo Cloud, the customer’s data is stored in Europe, and no data is transferred to the US. On the other hand, with Matomo On-Premise, the data is stored in your country of choice.


  “Matomo has also become one of the most popular open-source alternatives to Google Analytics for website owners and marketing teams because it empowers web professionals to make business decisions. Website investment, collateral, and arrangement are enriched by having the full picture and control of the data.”

  

  About Matomo

  Matomo is a world-leading open-source web analytics platform, trusted by over 1.4 million websites in 220 countries and translated into over 50 languages. Matomo helps businesses and organisations track and optimise their online presence allowing users to easily collect, analyse, and act on their website and marketing data to gain a deeper understanding of their visitors and drive conversions and revenue. Matomo’s vision is to create, as a community, the leading open digital analytics platform that gives every user complete control of their data.

  For more information/ press enquiries Press – Matomo

• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now.