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• Using FFmpeg to stitch together H.264 videos and variably-spaced JPEG pictures ; dealing with ffmpeg warnings

  19 octobre 2022, par LB2

  Context

  


  I have a process flow that may output either H.264 Annex B streams, variably-spaced JPEGs, or a mixture of two. By variably-spaced I mean where elapsed time between any two adjacent JPEGs may (and likely to be) different from any other two adjacent JPEGs. So an example of possible inputs are :

  


  

  1. stream1.h264

  


  2. {Set of JPEGs}

  


  3. stream1.h264 + stream2.h264

  


  4. stream1.h264 + {Set of JPEGs}

  


  5. stream1.h264 + {Set of JPEGs} + stream2.h264

  


  6. stream1.h264 + {Set of JPEGs} + stream2.h264 + {Set of JPEGs} + ...

  


  7. stream1.h264 + stream2.h264 + {Set of JPEGs} + ...

  


  


  The output needs to be a single stitched (i.e. concatenated) output in MPEG-4 container.

  


  Requirements : No re-encoding or transcoding of existing video compression (One time conversion of JPEG sets to video format is OKay).

  


  Solution Prototype

  


  To prototype the solution I have found that ffmpeg has concat demuxer that would let me specify an ordered sequence of inputs that ffmpeg would then concatenate together, but all inputs must be of the same format. So, to meet that requirement, I :

  


  

  1. Convert every JPEG set to an .mp4 using concat (and using delay # directive to specify time-spacing between each JPEG)

  


  2. Convert every .h264 to .mp4 using -c copy to avoid transcoding.

  


  3. Stitch all generated interim .mp4 files into the single final .mp4 using -f concat and -c copy.

  


  


  Here's the bash script, in parts, that performs the above :

  


  


  1. Ignore the curl comment ; that's from originally generating a 100 jpeg images with numbers and these are simply saved locally. What the loop does is it generates concat input file with file sequence#.jpeg directives and duration # directive where each successive JPEG delay is incremented by 0.1 seconds (0.1 between first and second, 0.2 b/w 2nd and 3rd, 0.3 b/w 3rd and 4th, and so on). Then it runs ffmpeg command to convert the set of JPEGs to .mp4 interim file.

     


     echo "ffconcat version 1.0" >ffconcat-jpeg.txt
echo >>ffconcat-jpeg.txt

for i in {1..100}
do
    echo "file $i.jpeg" >>ffconcat-jpeg.txt
    d=$(echo "$i" | awk '{printf "%f", $1 / 10}')
    # d=$(echo "scale=2; $i/10" | bc)
    echo "duration $d" >>ffconcat-jpeg.txt
    echo "" >>ffconcat-jpeg.txt
    # curl -o "$i.jpeg" "https://math.tools/equation/get_equaimages?equation=$i&fontsize=256"
done

ffmpeg \
    -hide_banner \
    -vsync vfr \
    -f concat \
    -i ffconcat-jpeg.txt \
    -r 30 \
    -video_track_timescale 90000 \
    video-jpeg.mp4


     


  


  2. Convert two streams from .h264 to .mp4 via copy (no transcoding).

     


     ffmpeg \
    -hide_banner \
    -i low-motion-video.h264 \
    -c copy \
    -vsync vfr \
    -video_track_timescale 90000 \
    low-motion-video.mp4

ffmpeg \
    -hide_banner \
    -i full-video.h264 \
    -c copy \
    -video_track_timescale 90000 \
    -vsync vfr \
    full-video.mp4


     


  


  3. Stitch all together by generating another concat directive file.

     


     echo "ffconcat version 1.0" >ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file low-motion-video.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file full-video.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt
echo "file video-jpeg.mp4" >>ffconcat-h264.txt
echo >>ffconcat-h264.txt

ffmpeg \
    -hide_banner \
    -f concat \
    -i ffconcat-h264.txt \
    -pix_fmt yuv420p \
    -c copy \
    -video_track_timescale 90000 \
    -vsync vfr \
    video-out.mp4



     


  


  


  Problem (and attempted troubleshooting)

  


  The above does produce a reasonable output — it plays first video, then plays second video with no timing/rate issues AFAICT, then plays JPEGs with time between each JPEG "frame" growing successively, as expected.

  


  But, the conversion process produces warnings that concern me (for compatibility with players ; or potentially other IRL streams that may result in some issue my prototyping content doesn't make obvious). Initial attempts generated 100s of warnings, but with some arguments added, I reduced it down to just a handful, but this handful is stubborn and nothing I tried would help.

  


  The first conversion of JPEGs to .mp4 goes fine with the following output :

  


  Input #0, concat, from 'ffconcat-jpeg.txt':
  Duration: 00:08:25.00, start: 0.000000, bitrate: 0 kb/s
  Stream #0:0: Video: png, pal8(pc), 176x341 [SAR 3780:3780 DAR 16:31], 25 fps, 25 tbr, 25 tbn, 25 tbc
Stream mapping:
  Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x7fe418008e00] using SAR=1/1
[libx264 @ 0x7fe418008e00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2
[libx264 @ 0x7fe418008e00] profile High 4:4:4 Predictive, level 1.3, 4:4:4, 8-bit
[libx264 @ 0x7fe418008e00] 264 - core 163 r3060 5db6aa6 - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=4 threads=11 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'video-jpeg.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv444p(tv, progressive), 176x341 [SAR 1:1 DAR 16:31], q=2-31, 30 fps, 90k tbn
    Metadata:
      encoder         : Lavc58.134.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
frame=  100 fps=0.0 q=-1.0 Lsize=     157kB time=00:07:55.33 bitrate=   2.7kbits/s speed=2.41e+03x    
video:155kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.800846%
[libx264 @ 0x7fe418008e00] frame I:1     Avg QP:20.88  size:   574
[libx264 @ 0x7fe418008e00] frame P:43    Avg QP:14.96  size:  2005
[libx264 @ 0x7fe418008e00] frame B:56    Avg QP:21.45  size:  1266
[libx264 @ 0x7fe418008e00] consecutive B-frames: 14.0% 24.0% 30.0% 32.0%
[libx264 @ 0x7fe418008e00] mb I  I16..4: 36.4% 55.8%  7.9%
[libx264 @ 0x7fe418008e00] mb P  I16..4:  5.1%  7.5% 11.2%  P16..4:  5.6%  8.1%  4.5%  0.0%  0.0%    skip:57.9%
[libx264 @ 0x7fe418008e00] mb B  I16..4:  2.4%  0.9%  3.9%  B16..8: 16.2%  8.8%  4.6%  direct: 1.2%  skip:62.0%  L0:56.6% L1:38.7% BI: 4.7%
[libx264 @ 0x7fe418008e00] 8x8 transform intra:28.3% inter:3.7%
[libx264 @ 0x7fe418008e00] coded y,u,v intra: 26.5% 0.0% 0.0% inter: 9.8% 0.0% 0.0%
[libx264 @ 0x7fe418008e00] i16 v,h,dc,p: 82% 13%  4%  0%
[libx264 @ 0x7fe418008e00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20%  8% 71%  1%  0%  0%  0%  0%  0%
[libx264 @ 0x7fe418008e00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 41% 11% 29%  4%  2%  3%  1%  7%  1%
[libx264 @ 0x7fe418008e00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x7fe418008e00] ref P L0: 44.1%  4.2% 28.4% 23.3%
[libx264 @ 0x7fe418008e00] ref B L0: 56.2% 32.1% 11.6%
[libx264 @ 0x7fe418008e00] ref B L1: 92.4%  7.6%
[libx264 @ 0x7fe418008e00] kb/s:2.50


  


  The conversion of individual streams from .h264 to .mp4 generates two types of warnings each. One is [mp4 @ 0x7faee3040400] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly, and the other is [mp4 @ 0x7faee3040400] pts has no value.

  


  Some posts on SO (can't find my original finds on that now) suggested that it's safe to ignore and comes from H.264 being an elementary stream that supposedly doesn't contain timestamps. It surprises me a bit since I produce that stream using NVENC API and clearly supply timing information for each frame via PIC_PARAMS structure : NV_STRUCT(PIC_PARAMS, pp); ...; pp.inputTimeStamp = _frameIndex++ * (H264_CLOCK_RATE / _params.frameRate);, where #define H264_CLOCK_RATE 9000 and _params.frameRate = 30.

  


  Input #0, h264, from 'low-motion-video.h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (High), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], 30 fps, 30 tbr, 1200k tbn, 60 tbc
Output #0, mp4, to 'low-motion-video.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 30 fps, 30 tbr, 90k tbn, 1200k tbc
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mp4 @ 0x7faee3040400] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
[mp4 @ 0x7faee3040400] pts has no value
[mp4 @ 0x7faee3040400] pts has no value0kB time=-00:00:00.03 bitrate=N/A speed=N/A    
    Last message repeated 17985 times
frame=17987 fps=0.0 q=-1.0 Lsize=   79332kB time=00:09:59.50 bitrate=1084.0kbits/s speed=1.59e+03x    
video:79250kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.103804%
Input #0, h264, from 'full-video.h264':
  Duration: N/A, bitrate: N/A
  Stream #0:0: Video: h264 (High), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], 30 fps, 30 tbr, 1200k tbn, 60 tbc
Output #0, mp4, to 'full-video.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0: Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 30 fps, 30 tbr, 90k tbn, 1200k tbc
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mp4 @ 0x7f9381864600] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
[mp4 @ 0x7f9381864600] pts has no value
[mp4 @ 0x7f9381864600] pts has no value0kB time=-00:00:00.03 bitrate=N/A speed=N/A    
    Last message repeated 17981 times
frame=17983 fps=0.0 q=-1.0 Lsize=   52976kB time=00:09:59.36 bitrate= 724.1kbits/s speed=1.33e+03x    
video:52893kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.156232%


  


  But the most worrisome error for me is from stitching together all interim .mp4 files into one :

  


  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9ff2010e00] Auto-inserting h264_mp4toannexb bitstream filter
Input #0, concat, from 'ffconcat-h264.txt':
  Duration: N/A, bitrate: 1082 kb/s
  Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1440x3040 [SAR 1:1 DAR 9:19], 1082 kb/s, 30 fps, 30 tbr, 90k tbn, 60 tbc
    Metadata:
      handler_name    : VideoHandler
      vendor_id       : [0][0][0][0]
Output #0, mp4, to 'video-out.mp4':
  Metadata:
    encoder         : Lavf58.76.100
  Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1440x3040 [SAR 1:1 DAR 9:19], q=2-31, 1082 kb/s, 30 fps, 30 tbr, 90k tbn, 90k tbc
    Metadata:
      handler_name    : VideoHandler
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
Press [q] to stop, [?] for help
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9fe1009c00] Auto-inserting h264_mp4toannexb bitstream filter
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 53954460, current: 53954460; changing to 53954461. This may result in incorrect timestamps in the output file.
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x7f9fd1008a00] Auto-inserting h264_mp4toannexb bitstream filter
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 107900521, current: 107874150; changing to 107900522. This may result in incorrect timestamps in the output file.
[mp4 @ 0x7f9ff2023400] Non-monotonous DTS in output stream 0:0; previous: 107900522, current: 107886150; changing to 107900523. This may result in incorrect timestamps in the output file.
frame=36070 fps=0.0 q=-1.0 Lsize=  132464kB time=00:27:54.26 bitrate= 648.1kbits/s speed=6.54e+03x    
video:132296kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.126409%


  


  I'm not sure how to deal with those non-monotonous DTS errors, and no matter what I try, nothing budges. I analyzed the interim .mp4 files using ffprobe -show_frames and found that the last frame of each interim .mp4 does not have DTS, while previous frames do. E.g. :

  


  ...
[FRAME]
media_type=video
stream_index=0
key_frame=0
pkt_pts=53942461
pkt_pts_time=599.360678
pkt_dts=53942461
pkt_dts_time=599.360678
best_effort_timestamp=53942461
best_effort_timestamp_time=599.360678
pkt_duration=3600
pkt_duration_time=0.040000
pkt_pos=54161377
pkt_size=1034
width=1440
height=3040
pix_fmt=yuv420p
sample_aspect_ratio=1:1
pict_type=B
coded_picture_number=17982
display_picture_number=0
interlaced_frame=0
top_field_first=0
repeat_pict=0
color_range=unknown
color_space=unknown
color_primaries=unknown
color_transfer=unknown
chroma_location=left
[/FRAME]
[FRAME]
media_type=video
stream_index=0
key_frame=0
pkt_pts=53927461
pkt_pts_time=599.194011
pkt_dts=N/A
pkt_dts_time=N/A
best_effort_timestamp=53927461
...


  


  My guess is that as concat demuxer reads in (or elsewhere in ffmpeg's conversion pipeline), for the last frame it sees no DTS set, and produces a virtual value equal to the last seen. Then further in pipeline it consumes this input, sees that DTS value is being repeated, issues a warning and offsets it with increment by one, which might be somewhat nonsensical/unrealistic timing value.

  


  I tried using -fflags +genpts as suggested in this SO answer, but that doesn't change anything.

  


  Per yet other posts suggesting issue being with incompatible tbn and tbc values and possible timebase issues, I tried adding -time_base 1:90000 and -enc_time_base 1:90000 and -copytb 1 and nothing budges. The -video_track_timescale 90000 is there b/c it helped reduce those DTS warnings from 100s down to 3, but doesn't eliminate them all.

  


  Question

  


  What is missing and how can I get ffmpeg to perform conversions without these warnings, to be sure it produces proper, well-formed output ?

  


• 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.