Recherche avancée

Sur d’autres sites (10477)

• FFMPEG issue with concat video made from single image

  12 mai 2023, par Jacob

  I am pulling my hair out right now. I am developing a video editing program. When I export two videos, concat works great. However, when I create a video from a single image (title screen) and try to concat that video with a regular video, only the title screen is exported. And no errors.

  


  Below is the code for creating the title screen :

  


  parameters = "-loop 1 -i " + '"' + video.VideoThumbPath + '"' + " -c:v libx264 -t " + video.TitleDuration.ToString() + " -pix_fmt yuv420p " + vidPath + @"\v" + outputFile.ToString() + ".MP4";


  


  Below is the code for exporting a regular non title screen video :

  


  parameters = "-y -i " + '"' + frames[0].VideoPath + '"' + " -filter_complex " + '"' + @"[0:v]trim=" + frames[0].TimeCode.TotalSeconds + @":" + frames[1].TimeCode.TotalSeconds +
                           @", setpts=PTS-STARTPTS[trimedv0];[trimedv0]yadif," + orientation + @"setsar=1/1, eq=contrast=1:brightness=" + video.Brightness + '"' + " -sn -an -c:v libx264 -preset ultrafast -profile:v baseline -level 3.0 " +
                           @"-pix_fmt yuv420p -f mpegts " + vidPath + @"\v" + outputFile.ToString() + ".MP4";


  


  Below is the code to concat both videos :

  


  parameters = "-probesize 100M -analyzeduration 100M -i concat:" + '"' + concatString + '"' + @" -c:v copy " + '"' + ExportOptions.CompleteDestinationPath + '"';


  


  Like I said, using this code for two MP4 video that were not made from a single image, it works great. If I create a MP4 from a single image with a duration of 5 seconds and concat it with a normal MP4, only the title screen is exported.

  


  Below is the output from FFMPEG. It shows the concat string with 2 videos but only concats the first one.

  


  09:59:32:366    ffmpeg version N-110043-gadb5f7b41f-20230318 Copyright (c) 2000-2023 the FFmpeg developers


  


  09:59:32:366 built with gcc 12.2.0 (crosstool-NG 1.25.0.90_cf9beb1)
09:59:32:366 configuration : —prefix=/ffbuild/prefix —pkg-config-flags=—static —pkg-config=pkg-config —cross-prefix=x86_64-w64-mingw32- —arch=x86_64 —target-os=mingw32 —enable-gpl —enable-version3 —disable-debug —disable-w32threads —enable-pthreads —enable-iconv —enable-libxml2 —enable-zlib —enable-libfreetype —enable-libfribidi —enable-gmp —enable-lzma —enable-fontconfig —enable-libvorbis —enable-opencl —disable-libpulse —enable-libvmaf —disable-libxcb —disable-xlib —enable-amf —enable-libaom —enable-libaribb24 —enable-avisynth —enable-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-librist —enable-libssh —enable-libtheora —enable-libvpx —enable-libwebp —enable-lv2 —disable-libmfx —enable-libvpl —enable-openal —enable-libopencore-amrnb —enable-libopencore-amrwb —enable-libopenh264 —enable-libopenjpeg —enable-libopenmpt —enable-librav1e —enable-librubberband —enable-schannel —enable-sdl2 —enable-libsoxr —enable-libsrt —enable-libsvtav1 —enable-libtwolame —enable-libuavs3d —disable-libdrm —disable-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= —extra-libs=-lgomp —extra-version=20230318
09:59:32:366 libavutil 58. 4.100 / 58. 4.100
09:59:32:366 libavcodec 60. 6.101 / 60. 6.101
09:59:32:366 libavformat 60. 4.100 / 60. 4.100
09:59:32:366 libavdevice 60. 2.100 / 60. 2.100
09:59:32:366 libavfilter 9. 4.100 / 9. 4.100
09:59:32:366 libswscale 7. 2.100 / 7. 2.100
09:59:32:366 libswresample 4. 11.100 / 4. 11.100
09:59:32:366 libpostproc 57. 2.100 / 57. 2.100
09:59:32:366 Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'concat:C :\Users\JDINES\AppData\Roaming\FCS\VidBin\v1000.MP4|C :\Users\JDINES\AppData\Roaming\FCS\VidBin\v1001.MP4' :
09:59:32:366 Metadata :
09:59:32:366 major_brand : isom
09:59:32:366 minor_version : 512
09:59:32:366 compatible_brands : isomiso2avc1mp41
09:59:32:366 encoder : Lavf60.4.100
09:59:32:366 Duration : 00:00:05.00, start : 0.000000, bitrate : 64102 kb/s
09:59:32:366 Stream #0:00x1 : Video : h264 (High) (avc1 / 0x31637661), yuv420p(tv, unknown/bt709/iec61966-2-1, progressive), 1920x1080 [SAR 1:1 DAR 16:9], 18 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
09:59:32:366 Metadata :
09:59:32:366 handler_name : VideoHandler
09:59:32:366 vendor_id : [0][0][0][0]
09:59:32:366 encoder : Lavc60.6.101 libx264
09:59:32:366 Output #0, mp4, to 'C :\Users\JDINES\Desktop\EXPORT TEST\qwe.MP4' :
09:59:32:366 Metadata :
09:59:32:366 major_brand : isom
09:59:32:366 minor_version : 512
09:59:32:366 compatible_brands : isomiso2avc1mp41
09:59:32:366 encoder : Lavf60.4.100
09:59:32:366 Stream #0:0(und) : Video : h264 (High) (avc1 / 0x31637661), yuv420p(tv, unknown/bt709/iec61966-2-1, progressive), 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 18 kb/s, 30 fps, 30 tbr, 15360 tbn (default)
09:59:32:366 Metadata :
09:59:32:366 handler_name : VideoHandler
09:59:32:366 vendor_id : [0][0][0][0]
09:59:32:366 encoder : Lavc60.6.101 libx264
09:59:32:366 Stream mapping :
09:59:32:366 Stream #0:0 -> #0:0 (copy)
09:59:32:366 Press [q] to stop, [?] for help
09:59:32:366 frame= 0 fps=0.0 q=-1.0 size= 0kB time=-00:00:00.06 bitrate= -0.0kbits/s speed=N/A
  
frame= 150 fps=0.0 q=-1.0 Lsize= 14kB time=00:00:04.90 bitrate= 23.4kbits/s speed=2e+03x
  
09:59:32:366 video:11kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead : 23.005581%

  


  Any help would be much appreciated !

  


  I figured this out finally !!!

  


  The below code fixed it :

  


  parameters = "-loop 1 -i " + '"' + video.VideoThumbPath + '"' + " -sn -an -c:v libx264 -preset ultrafast -profile:v baseline -level 3.0 " +
                           @"-t 5 -pix_fmt yuv420p -f mpegts " + vidPath + @"\v" + outputFile.ToString() + ".MP4";


  


• FFMpeg clip broken, generated from images [closed]

  5 février 2024, par thomassss

  I hope someone can help me out here, would really appreciate it :).

  


  I created a video out of images, and then merged it with other videos.
The problem is that the final result is kinda broken - when I want to add background music to it, it won't play the music on the parts with the video generated by images, and on mobile phones it seems even more broken.

  


  The commands I used :

  


  Extracting the images from the video :

  


  ffmpeg.exe -i E:\dev\chatgpt\temp11\peng\final\1_runway.mp4 -vsync 0 -f image2 E:\dev\chatgpt\temp9\peng\final/1_runway-%06d.png


  


  Then I edit the images, then merging them back with :

  


  ffmpeg.exe  -i  E:\dev\chatgpt\temp11\peng\final\2_runway_final-%06d.png E:\dev\chatgpt\temp9\peng\final\5_runway_final.mp4



  


  This file now seems to be broken

  


  Now when i merge this file with other mp4s via

  


  ffmpeg.exe -f concat   -i E:\dev\chatgpt\temp11\peng\\final\ENGLISH_ffmpeg_input.txt  -vcodec copy -acodec copy E:\dev\chatgpt\temp9\peng\\final\ENGLISH_output.mp4


  


  Ad then add background music via

  


  ffmpeg.exe -i "E:\dev\chatgpt\temp11\peng\final\ENGLISH_output.mp4" -i "E:\dev\chatgpt\background.mp3" -filter_complex "[1:a]volume=0.2[a1];[0:a][a1]amix=inputs=2:duration=longest[a]" -map 0:v -map "[a]" -c:v copy -c:a aac -shortest "E:\dev\chatgpt\temp10\peng\final\ENGLISH_output_background.mp4"


  


  the background music isn't played in the parts with the "video generated from images".
Even VLC sometimes have problem displaying all parts correctly - that's why I added Youtube-links - this seems to work somehow.

  


  Does anyone have a clue how I can investigate further ?

  


  I tried converting both, the final video-file and the generated clip from images into different formats with different framerates - but nothing worked

  


  Thanks in advance !

  


  Endfile with backgroundmusic
Dropbox :
https://www.dropbox.com/scl/fi/peouyis2eezakc91mzfvt/ENGLISH_output_background.mp4?rlkey=4yfcyxzvh6fa3w0qxbiaz4auf&dl=1
Youtube :
https://www.youtube.com/watch?v=9gV6wP08lWA

  


  Without backgroundmusic
https://www.dropbox.com/scl/fi/7543al9kngtkdy92rqhe3/ENGLISH_output.mp4?rlkey=og6jnxgbqwc2r6gxg2xpphfwc&dl=1
Youtube :
https://www.youtube.com/shorts/1zIRFs6bYgU

  


  Standalone file - generated from images :
https://www.dropbox.com/scl/fi/cy46ngkvoofnf7mur420i/3_runway_final.mp4?rlkey=70emr5t9dv53s5rcz4qaygwla&dl=1

  


  Edit :

  


  Download Single Images of one Video :
https://www.dropbox.com/scl/fi/817kakf1sksqfnulv1cja/singleimagesofonevideo.7z?rlkey=xkc73s7z8rfkp1js2epuzebs0&dl=1

  


  All Videos :
https://www.dropbox.com/scl/fi/817kakf1sksqfnulv1cja/singleimagesofonevideo.7z?rlkey=xkc73s7z8rfkp1js2epuzebs0&dl=1

  


  ffprobe of "normal" video :

  


  ffprobe.exe ENGLISH_0.mp4
ffprobe version 6.1.1-full_build-www.gyan.dev Copyright (c) 2007-2023 the FFmpeg developers
built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
configuration : —enable-gpl —enable-version3 —enable-static —pkg-config=pkgconf —disable-w32threads —disable-autodetect —enable-fontconfig —enable-iconv —enable-gnutls —enable-libxml2 —enable-gmp —enable-bzlib —enable-lzma —enable-libsnappy —enable-zlib —enable-librist —enable-libsrt —enable-libssh —enable-libzmq —enable-avisynth —enable-libbluray —enable-libcaca —enable-sdl2 —enable-libaribb24 —enable-libaribcaption —enable-libdav1d —enable-libdavs2 —enable-libuavs3d —enable-libzvbi —enable-librav1e —enable-libsvtav1 —enable-libwebp —enable-libx264 —enable-libx265 —enable-libxavs2 —enable-libxvid —enable-libaom —enable-libjxl —enable-libopenjpeg —enable-libvpx —enable-mediafoundation —enable-libass —enable-frei0r —enable-libfreetype —enable-libfribidi —enable-libharfbuzz —enable-liblensfun —enable-libvidstab —enable-libvmaf —enable-libzimg —enable-amf —enable-cuda-llvm —enable-cuvid —enable-ffnvcodec —enable-nvdec —enable-nvenc —enable-dxva2 —enable-d3d11va —enable-libvpl —enable-libshaderc —enable-vulkan —enable-libplacebo —enable-opencl —enable-libcdio —enable-libgme —enable-libmodplug —enable-libopenmpt —enable-libopencore-amrwb —enable-libmp3lame —enable-libshine —enable-libtheora —enable-libtwolame —enable-libvo-amrwbenc —enable-libcodec2 —enable-libilbc —enable-libgsm —enable-libopencore-amrnb —enable-libopus —enable-libspeex —enable-libvorbis —enable-ladspa —enable-libbs2b —enable-libflite —enable-libmysofa —enable-librubberband —enable-libsoxr —enable-chromaprint
libavutil 58. 29.100 / 58. 29.100
libavcodec 60. 31.102 / 60. 31.102
libavformat 60. 16.100 / 60. 16.100
libavdevice 60. 3.100 / 60. 3.100
libavfilter 9. 12.100 / 9. 12.100
libswscale 7. 5.100 / 7. 5.100
libswresample 4. 12.100 / 4. 12.100
libpostproc 57. 3.100 / 57. 3.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'ENGLISH_0.mp4' :
Metadata :
major_brand : isom
minor_version : 512
compatible_brands : isomiso2avc1mp41
encoder : Lavf60.16.100
Duration : 00:00:03.38, start : 0.000000, bitrate : 238 kb/s
Stream #0:00x1 : Video : h264 (Constrained Baseline) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 160 kb/s, 25 fps, 25 tbr, 12800 tbn (default)
Metadata :
handler_name : VideoHandler
vendor_id : [0][0][0][0]
encoder : Lavc60.31.102 libx264
Stream #0:10x2 : Audio : aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 69 kb/s (default)
Metadata :
handler_name : SoundHandler
vendor_id : [0][0][0][0]

  


• How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation

  13 mars 2023, par Erin — Analytics Tips

  If you struggle to connect the dots on your customer journeys, you are researching the correct solution. 

  Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.

  That said, each attribution model has inherent limitations, which make the selection process even harder.

  This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation. 

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

  First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.

  Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU). 

  Pros 

  • Reflects the start of the customer journey
  • Shows channels that bring in the best-qualified leads 
  • Helps track brand awareness campaigns

  Cons 

  • Ignores the impact of later interactions at the middle and bottom of the funnel 
  • Doesn’t provide a full picture of users’ decision-making process 

  Last Interaction 

  Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels. 

  If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect. 

  Pros 

  • Reports bottom-of-the-funnel events
  • Requires minimal data and configurations 
  • Helps estimate cost-per-lead or cost-per-acquisition

  Cons 

  • No visibility into assisted conversions and prior visitor interactions 
  • Overemphasise the importance of the last channel (which can often be direct traffic) 

  Last Non-Direct Interaction 

  Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product. 

  Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion. 

  Pros 

  • Improved channel visibility, compared to Last-Touch 
  • Avoids over-valuing direct visits
  • Reports on lead-generation efforts

  Cons 

  • Doesn’t work for account-based marketing (ABM) 
  • Devalues the quality over quantity of leads 

  Linear Model

  Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.

  It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.

  Pros 

  • Focuses on all touch points associated with a conversion 
  • Reflects more steps in the customer journey 
  • Helps analyse longer sales cycles

  Cons 

  • Doesn’t accurately reflect the varying roles of each touchpoint 
  • Can dilute the credit if too many touchpoints are involved 

  Time Decay Model 

  Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).

  This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns. 

  Pros 

  • Helps track longer sales cycles and reports on each touchpoint involved 
  • Allows customising the half-life of decay to improve reporting 
  • Promotes conversion optimization at BoFu stages

  Cons 

  • Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
  • Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)

  Position-Based Model 

  Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches. 

  For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels. 

  Pros 

  • Helps establish the main channels for lead generation and conversion
  • Adds extra layers of visibility, compared to first- and last-touch attribution models 
  • Promotes budget allocation toward the most strategic touchpoints

  Cons 

  • Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
  • Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints

  How to Choose the Right Multi-Touch Attribution Model For Your Business 

  If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.

  To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability. 

  Marketing Objectives 

  Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities. 

  In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase. 

  When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales. 

  Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases. 

  Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….

  Sales Cycle Length 

  As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry. 

  Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months. 

  That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution. 

  Data Availability 

  Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data. 

  Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK. 

  Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting. 

  Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature. 

  When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts. 

  How to Implement Multi-Touch Attribution

  Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking). 

  Here’s a step-by-step walkthrough to help you get started. 

  Select a Multi-Touch Attribution Tool 

  The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.

  To make the right call prioritise five factors :

  • Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models. 
  • Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options. 
  • Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users. 
  • Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software. 
  • Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis. 

  Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations. 

  Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price). 

  Set Up Proper Data Collection 

  Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up : 

  • Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page. 
  • Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints. 
  • Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc. 

  Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.

  Configure Goals and Events 

  Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours. 

  For example : If your goal is lead generation, you can track :

  • Newsletter sign ups 
  • Product demo requests 
  • Gated content downloads 
  • Free trial account registration 
  • Contact form submission 
  • On-site call bookings 

  In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action. 

  To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc). 

  Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy. 

  Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner). 

  Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.

  Test and Validated the Selected Model 

  A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases. 

  For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that. 

  That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis. 

  Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group. 

  In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great. 

  The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results. 

  Conclusion

  A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI. 

  Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types. 

  As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.

  Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.