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• How to Measure Marketing Effectiveness : A Step-by-Step Guide

  22 février 2024, par Erin

  Are you struggling to prove that your marketing efforts are having a measurable impact on your company’s performance ? We get it. 

  You would think that digital marketing would make it easier to track the effectiveness of your marketing efforts. But in many ways, it’s harder than ever. With so many channels and strategies competing against each other, it can feel impossible to pin down the campaign that caused a conversion. 

  That leaves you in a tricky spot as a marketing manager. It can be hard to know which campaigns to persevere with and harder still to prove your worth to stakeholders. 

  Thankfully, there are several strategies you can use to measure the success of your campaigns and put a value on your efforts. So, if you want to learn how you can measure the effectiveness of your marketing, improve the ROI of your efforts and prove your value as an employee, read on. 

  What is marketing effectiveness ?

  Marketing effectiveness measures how successful a marketing strategy or campaign is and the extent to which it achieves goals and business objectives.

  

  It’s a growing concern for brands, with research showing that 61.2% say measuring marketing effectiveness has become a more prominent factor in decision-making over the last three years. In other words, it’s becoming critical for marketers to know how to measure their effectiveness. 

  But it’s getting harder to do so. A combination of factors, including channel fragmentation, increasingly convoluted customer journeys, and the deprecation of third-party cookies, makes it hard for marketing teams to measure marketing performance. 

  Why you need to measure marketing effectiveness

  Imagine ploughing thousands of dollars into a campaign and not being confident that your efforts bore fruit. It’s unthinkable, right ? If you care about optimising campaigns and improving your worth as a marketer, measuring marketing effectiveness is necessary. 

  

  Optimise marketing campaigns

  Do you know how effectively each campaign generates conversions and drives revenue ? No ? Then, you need to measure marketing effectiveness.

  Doing so could also shine a light on ways to improve your campaigns. One paid ad campaign may suffer from a poor return on ad spend caused by high CPCs. Targeting less competitive keywords could dramatically reduce your costs. 

  Improve ROI

  Today, marketing budgets make up almost 10% of a company’s total revenue, up from 6.4% in 2021. With so much revenue at stake, you’ve got to deliver a return on that investment. 

  Measuring marketing effectiveness can help you identify the campaigns or strategies delivering the highest ROI so you can invest more heavily into them. On the other side of the same coin, you can use the data to strike off any campaigns that aren’t pulling their weight — increasing your ROI even further. 

  Demonstrate value

  Let’s get selfish for a second. Whether you’re an in-house marketing manager or work for an agency, the security of your paycheck depends on your ability to deliver high-ROI campaigns. 

  Measuring your marketing effectiveness lets you showcase your value to your company and clients. It helps you build stronger relationships that can lead to bigger and better opportunities in the future. 

  We should take this opportunity to point out that a good tool for measuring marketing effectiveness is equally important. You probably think Google Analytics will do the job, right ? But when you start implementing the strategies we discuss below, there’s a good chance you’ll have data quality issues. 

  That was the case for full-service marketing agency MHP/Team SI, which found Google Analytics’ data sampling severely limited the quantity and quality of insights they could collect. It was only by switching to Matomo, a platform that doesn’t use data sampling, that the agency could deliver the insights its clients needed to grow. 

  Further reading :

  • How to check website traffic as accurately as possible
  • 8 best tools to analyse website traffic

  Try Matomo for Free

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

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  How to measure marketing effectiveness

  Measuring marketing effectiveness is not always easy, especially if you have long buying cycles and a lack of good-quality data. Make things as easy as possible by following the steps below :

  Know what success looks like

  You can’t tell whether your campaigns are effective if you don’t know what you are trying to achieve. That’s why the first step in measuring marketing effectiveness is to set a clear goal. 

  So, ask yourself what success looks like for each campaign you launch. 

  Remember, a campaign doesn’t have to drive leads to be considered effective. If all you wanted to do was raise brand awareness or increase organic traffic, you could achieve both goals without recording a single conversion. 

  We’d wager that’s probably not true for most marketing managers. It’s much more likely you want to achieve something like the following :

  • Generating 100 new customers
  • Increasing revenue by 20%
  • Selling $5,000 of your new product line
  • Reducing customer churn by 50%
  • Achieving a return on ad spend of 150%

  Conventional goal-setting wisdom applies here. So, ensure your goals are measurable, timely, relevant and achievable. 

  Track conversions

  Setting up conversion tracking in your web analytics platform is vital to measuring marketing effectiveness accurately. 

  What you count as a conversion event will depend on the goals you’ve set above. It doesn’t have to be a sale, mind you. Downloading an ebook or signing up for a webinar are worthy conversion goals, especially if you know they increase the chances of a customer converting. 

  

  Whichever platform you choose, ensure it can meet your current and future needs. This is one of the reasons open-source content management system Concrete CMS opted for Matomo when choosing a new website analytics platform. The flexibility of the Matomo platform gave Concrete CMS the adaptability it needed for future growth. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  Decide on an attribution model

  Marketing attribution is a way of measuring the impact of different channels and touchpoints across the customer journey. If you can assign a value to each conversion, you can use a marketing attribution model to quantify the value of your channels and campaigns.

  While most web analytics platforms simply credit the last touchpoint, marketing attribution offers a more comprehensive view by considering all interactions along the customer journey. This distinction is important because relying solely on the last touchpoint can lead to skewed insights and misallocation of resources and budget. 

  By adopting a marketing attribution approach, you can make more informed decisions, optimizing your campaigns and maximizing your return on investment.

  

  There are several different attribution models you can use to give credit to your various campaigns. These include :

  • First interaction : Gives all the credit to the first channel in the customer journey.
  • Last interaction : Gives all the credit to the last channel in the customer journey.
  • Last non-direct attribution : Gives all credit to the final touchpoint in the customer journey, except for direct interactions. In those cases, credit is given to the touchpoint just before the direct one.
  • Linear attribution : Distributes credit equally across all touchpoints.
  • Position-based attribution : Attributes 40% credit to the first and last touchpoints and distributes the remaining 20% evenly across all other touchpoints. 

  Consider carefully which attribution model to use, as this can significantly impact your marketing effectiveness calculation by giving certain campaigns too much credit.

  Try Matomo for Free

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

  Start for free

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  Analyse KPIs

  Tracking KPIs is essential if you want to quantify the impact of your marketing campaigns. But which metrics should you track ?

  To improve brand awareness or traffic, so-called vanity metrics like sessions, returning visitors, and organic traffic may suffice as KPIs. 

  However, that’s not going to be the case for most marketers, whose performance is tied to revenue and ROI. If that’s you, put vanity metrics to one side and focus on the following conversion metrics instead :

  • Conversion rate : the percentage of users who complete a desired action. 
  • Return on ad spend : the revenue earned for every dollar spent on a campaign.
  • Return on investment : a broader calculation than ROAS, typically calculated across all your marketing efforts. 
  • Customer lifetime value : the total amount a customer will spend throughout their relationship with your company.
  • Customer acquisition cost : the cost to acquire each customer on average.

  

  Your analytics platform and advertising tools should track most of these KPIs by default. Matomo, for instance, automatically calculates your conversion rate in the Goals report. 

  How to present your marketing effectiveness

  Calculating your marketing effectiveness is one thing, but it’s important to share this information with stakeholders — whether those are executives in your company or your agency’s clients. 

  Follow the steps below to create an insightful and compelling marketing report :

  • Set the scene. There’s no guarantee that the people reading your report will know your goals. So, add context at the start of the reporting by spelling out what you are trying to achieve and why. 
  • Select the right data. You don’t want to overwhelm the reader with facts and figures, but you do need to provide hard evidence of your success. Include the KPIs you used to measure your success and show how these have changed over time. You can also support your report with audience insights such as heatmaps or customer surveys.
  • Tell a story with your presentation. Give your presentation a narrative arc with a beginning, middle, and end. Start with what you want to achieve, describe how you plan to achieve it and end with the results. Support your story with graphs and other visual aids that hold your reader’s attention. 
  • Provide a concise summary. Not everyone will read your presentation cover to cover. With that in mind, provide a summary of your report at the start or end that shows what you achieved and quantifies your marketing effectiveness. 

  How to improve marketing effectiveness

  Don’t settle for simply measuring your marketing effectiveness. Use the following strategies to make future campaigns as effective as possible. 

  Understand customer behaviour

  More effective marketing campaigns start by deeply understanding your customers, who they are, and how they behave. This allows you to take an audience-first approach to your marketing efforts and design campaigns around the unique needs of your customers. 

  Gather as much first-party data as you can. Surveys, focus groups, and other market research techniques can help you learn more about who your customers are, but don’t disregard the quantitative data you can gather from your web analytics platform. 

  Using Heatmaps, Session Recordings and behavioural analytics tools, you can learn exactly how customers behave when they land on your site, where they focus their attention and which pages they look at first. 

  

  These insights can help you turn an average campaign into an exceptional one. For example, a heatmap may highlight the need to move CTA buttons above the fold to increase conversions. A session recording could pinpoint the problems users have when filling out your website’s forms. 

  Further reading :

  • What Is Behavioural Analytics & How Can It Improve Website Performance
  • 9 Form Optimisation Tips to Convert More Visitors
  • How to Increase Conversions with Form Analysis

  Optimise landing pages

  Developing a culture of testing and experimentation is a great way to improve your marketing effectiveness. Let’s dive into A/B testing.

  By tweaking various elements of your landing pages, you can squeeze every last conversion from your campaigns.

  

  We have a guide on conversion funnel optimisation, which we recommend you check out, but I’ll briefly list some of the optimisations you could test :

  • Making your CTAs actionable and compelling
  • Integrating images and videos
  • Adding testimonials and other forms of social proof
  • Reducing form fields

  Use a different attribution model

  It might be that some campaigns, strategies or traffic sources aren’t getting the love they deserve. By changing your attribution model, you can significantly change the perceived effectiveness of certain campaigns. 

  Let’s say you use a last-touch attribution model, for instance. Only the last channel customers will get credit for each conversion, meaning top-of-the-funnel campaigns like SEO may be deemed less effective than they are. 

  It’s why you must continually test, tweak and validate your chosen model — and why changing it can be so powerful. 

  Measure your marketing effectiveness with Matomo

  Measuring your marketing effectiveness is hard work. But it’s vital to optimise campaigns, improve your ROI and demonstrate your value. 

  The good news is that Matomo makes things a lot easier thanks to its comprehensive conversion tracking, attribution modelling capabilities and behavioural insight features like Heatmaps, A/B Testing and Session Recordings. 

  Take steps today to start measuring (and improving) the effectiveness of your marketing with our 21-day free trial. No credit card required.

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• ffmpeg : fps drop when one -map udp output unreachable [closed]

  14 mai 2024, par user25041039

  I stream a video from a raspberry pi (server) to a splitscreen of 5x5 devices (clients) through an ethernet LAN.

  


  Server

  


  On the server side, I use the following ffmpeg command that :

  


  

  1. reads a video list in loop ;

  


  2. splits it into 25 different streams ;

  


  3. maps each stream to a device through udp.

  


  


  I also have several options to minimize latency and keep the different subscreens in sync.

  


  ffmpeg -loglevel repeat+level+verbose -re -copyts -start_at_zero -rtbufsize 100000k \
    -stream_loop -1 -f concat -i stream.lst -an \
    -filter_complex "\
    [0]crop=iw/5:ih/5:0*iw/5:0*ih/5[11];
    [0]crop=iw/5:ih/5:1*iw/5:0*ih/5[12];
    [...] # truncated for readability
    [0]crop=iw/5:ih/5:4*iw/5:4*ih/5[55]" \
    -map '[11]?' -flush_packets 1 -preset ultrafast -vcodec libx264 -tune zerolatency -f mpegts "udp://100.64.0.11:1234" \
    -map '[12]?' -flush_packets 1 -preset ultrafast -vcodec libx264 -tune zerolatency -f mpegts "udp://100.64.0.12:1234" \
    [...] # truncated for readability
    -map '[55]?' -flush_packets 1 -preset ultrafast -vcodec libx264 -tune zerolatency -f mpegts "udp://100.64.0.55:1234"


  


  Clients

  


  On the client side, I use mpv to read the stream and display it. There are also options for low-latency.

  


  mpv --no-cache --force-seekable=yes --profile=low-latency --untimed --no-audio --video-rotate=90 --fs --no-config --vo=gpu --hwdec=auto udp://100.64.0.1:1234/


  


  My problem

  


  When a device is unreachable through the LAN (eg : powered down), the FPS stated by ffmpeg drops after a short period ( 10 seconds), and the stream is laggy (= some frames, then pause for 1s, ...). What I expect is the stream to go on normally, just having one of the subscreens black.

  


  Here is the full log of ffmpeg when I start the stream normally then power down one of the clients after 15s.

  


  [info] ffmpeg version 5.1.4-0+rpt3+deb12u1 Copyright (c) 2000-2023 the FFmpeg developers
[info]   built with gcc 12 (Debian 12.2.0-14)
[info]   configuration: --prefix=/usr --extra-version=0+rpt3+deb12u1 --toolchain=hardened --incdir=/usr/include/aarch64-linux-gnu --enable-gpl --disable-stripping --disable-mmal --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librist --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sand --enable-sdl2 --disable-sndio --enable-libjxl --enable-neon --enable-v4l2-request --enable-libudev --enable-epoxy --libdir=/usr/lib/aarch64-linux-gnu --arch=arm64 --enable-pocketsphinx --enable-librsvg --enable-libdc1394 --enable-libdrm --enable-vout-drm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-libplacebo --enable-librav1e --enable-shared
[info]   libavutil      57. 28.100 / 57. 28.100
[info]   libavcodec     59. 37.100 / 59. 37.100
[info]   libavformat    59. 27.100 / 59. 27.100
[info]   libavdevice    59.  7.100 / 59.  7.100
[info]   libavfilter     8. 44.100 /  8. 44.100
[info]   libswscale      6.  7.100 /  6.  7.100
[info]   libswresample   4.  7.100 /  4.  7.100
[info]   libpostproc    56.  6.100 / 56.  6.100
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f140eca0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[info] Input #0, concat, from 'stream.lst':
[info]   Duration: N/A, start: 0.000000, bitrate: 47 kb/s
[info]   Stream #0:0(und): Video: h264 (Main), 1 reference frame (avc1 / 0x31637661), yuv420p(tv, bt709, progressive, left), 1920x1080 (1920x1088), 47 kb/s, 24 fps, 24 tbr, 12288 tbn
[info]     Metadata:
[info]       handler_name    : Core Media Video
[info]       vendor_id       : [0][0][0][0]
[info] Stream mapping:
[info]   Stream #0:0 (h264) -> crop:default
... truncated
[info]   Stream #0:0 (h264) -> crop:default
[info]   crop:default -> Stream #0:0 (libx264)
... truncated
[info]   crop:default -> Stream #24:0 (libx264)
[info] Press [q] to stop, [?] for help
[h264 @ 0x5555f13fe050] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[graph 0 input from stream 0:0 @ 0x5555f1dfad40] [verbose] w:1920 h:1080 pixfmt:yuv420p tb:1/12288 fr:24/1 sar:0/1
... truncated
[graph 0 input from stream 0:0 @ 0x5555f1e01a50] [verbose] w:1920 h:1080 pixfmt:yuv420p tb:1/12288 fr:24/1 sar:0/1
[Parsed_crop_24 @ 0x5555f1dfa860] [verbose] w:1920 h:1080 sar:0/1 -> w:384 h:216 sar:0/1
... truncated
[Parsed_crop_0 @ 0x5555f1df23e0] [verbose] w:1920 h:1080 sar:0/1 -> w:384 h:216 sar:0/1
[libx264 @ 0x5555f147c220] [info] using cpu capabilities: ARMv8 NEON
[libx264 @ 0x5555f147c220] [info] profile Constrained Baseline, level 1.3, 4:2:0, 8-bit
[mpegts @ 0x5555f148ab70] [verbose] service 1 using PCR in pid=256, pcr_period=83ms
[mpegts @ 0x5555f148ab70] [verbose] muxrate VBR, sdt every 500 ms, pat/pmt every 100 ms
[info] Output #0, mpegts, to 'udp://100.64.0.11:1234':
[info]   Metadata:
[info]     encoder         : Lavf59.27.100
[info]   Stream #0:0: Video: h264, 1 reference frame, yuv420p(tv, bt709, progressive, left), 384x216 (0x0), q=2-31, 24 fps, 90k tbn
[info]     Metadata:
[info]       encoder         : Lavc59.37.100 libx264
[info]     Side data:
[info]       cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[libx264 @ 0x5555f1428760] [info] using cpu capabilities: ARMv8 NEON
[libx264 @ 0x5555f1428760] [info] profile Constrained Baseline, level 1.3, 4:2:0, 8-bit
[mpegts @ 0x5555f148b080] [verbose] service 1 using PCR in pid=256, pcr_period=83ms
[mpegts @ 0x5555f148b080] [verbose] muxrate VBR, sdt every 500 ms, pat/pmt every 100 ms

... truncated
[info] Output #24, mpegts, to 'udp://100.64.0.55:1234':
[info]   Metadata:
[info]     encoder         : Lavf59.27.100
[info]   Stream #24:0: Video: h264, 1 reference frame, yuv420p(tv, bt709, progressive, left), 384x216 (0x0), q=2-31, 24 fps, 90k tbn
[info]     Metadata:
[info]       encoder         : Lavc59.37.100 libx264
[info]     Side data:
[info]       cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[info] frame=    1 fps=0.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 size=   [info] frame=    9 fps=0.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 size=   [info] frame=   22 fps= 21 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=   34 fps= 22 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=   46 fps= 22 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=   58 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 19517 bytes read, 0 seeks
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[info] frame=   70 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=   83 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=   95 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  107 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  119 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  132 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 19240 bytes read, 0 seeks
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[info] frame=  144 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  156 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  168 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  180 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  193 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=27.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  205 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 19218 bytes read, 0 seeks
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f17af150] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[concat @ 0x5555f13febf0] [warning] New audio stream 0:1 at pos:68549 and DTS:8.99977s
[info] frame=  217 fps= 24 q=14.0 q=20.0 q=14.0 q=26.0 q=25.0 q=17.0 q=19.0 q=13.0 q=25.0 q=25.0 q=22.0 q=19.0 q=14.0 q=25.0 q=22.0 q=22.0 q=19.0 q=14.0 q=22.0 q=13.0 q=23.0 q=20.0 q=13.0 q=22.0 q=14.0 size=   [info] frame=  229 fps= 24 q=14.0 q=22.0 q=14.0 q=26.0 q=24.0 q=18.0 q=21.0 q=15.0 q=25.0 q=24.0 q=20.0 q=22.0 q=15.0 q=25.0 q=21.0 q=21.0 q=22.0 q=15.0 q=23.0 q=12.0 q=22.0 q=22.0 q=12.0 q=23.0 q=14.0 size=   [info] frame=  241 fps= 24 q=16.0 q=22.0 q=15.0 q=26.0 q=25.0 q=17.0 q=22.0 q=18.0 q=25.0 q=24.0 q=20.0 q=22.0 q=17.0 q=25.0 q=22.0 q=21.0 q=23.0 q=17.0 q=23.0 q=15.0 q=21.0 q=23.0 q=17.0 q=23.0 q=15.0 size=   [info] frame=  253 fps= 24 q=18.0 q=24.0 q=19.0 q=26.0 q=25.0 q=17.0 q=23.0 q=20.0 q=26.0 q=25.0 q=21.0 q=23.0 q=19.0 q=26.0 q=23.0 q=22.0 q=22.0 q=19.0 q=23.0 q=15.0 q=23.0 q=22.0 q=19.0 q=22.0 q=16.0 size=   [info] frame=  265 fps= 24 q=20.0 q=23.0 q=19.0 q=27.0 q=26.0 q=19.0 q=25.0 q=18.0 q=27.0 q=25.0 q=23.0 q=25.0 q=18.0 q=26.0 q=22.0 q=24.0 q=23.0 q=17.0 q=22.0 q=14.0 q=16.0 q=19.0 q=14.0 q=18.0 q=14.0 size=   [info] frame=  278 fps= 24 q=12.0 q=22.0 q=25.0 q=21.0 q=15.0 q=12.0 q=24.0 q=24.0 q=25.0 q=13.0 q=12.0 q=25.0 q=23.0 q=24.0 q=12.0 q=12.0 q=25.0 q=26.0 q=24.0 q=12.0 q=12.0 q=17.0 q=22.0 q=17.0 q=12.0 size=   [info] frame=  290 fps= 24 q=13.0 q=22.0 q=22.0 q=22.0 q=16.0 q=18.0 q=22.0 q=22.0 q=22.0 q=18.0 q=19.0 q=22.0 q=22.0 q=21.0 q=18.0 q=16.0 q=22.0 q=21.0 q=21.0 q=17.0 q=12.0 q=22.0 q=21.0 q=22.0 q=14.0 size=   [info] frame=  302 fps= 24 q=18.0 q=21.0 q=22.0 q=21.0 q=19.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=19.0 q=20.0 q=21.0 q=21.0 q=22.0 q=19.0 q=17.0 q=22.0 q=21.0 q=21.0 q=17.0 size=   [info] frame=  314 fps= 24 q=19.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=20.0 q=21.0 q=20.0 q=19.0 q=21.0 q=21.0 q=21.0 q=19.0 size=   [info] frame=  326 fps= 24 q=20.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 size=   [info] frame=  339 fps= 24 q=20.0 q=20.0 q=21.0 q=20.0 q=21.0 q=21.0 q=20.0 q=19.0 q=21.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=20.0 q=21.0 q=20.0 q=20.0 q=21.0 q=20.0 q=21.0 q=20.0 q=21.0 q=20.0 size=   [info] frame=  351 fps= 24 q=21.0 q=20.0 q=21.0 q=20.0 q=20.0 q=20.0 q=20.0 q=19.0 q=21.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=21.0 q=20.0 size=   [info] frame=  363 fps= 24 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=19.0 q=20.0 q=20.0 q=21.0 q=20.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=20.0 q=21.0 q=21.0 q=21.0 q=20.0 q=21.0 q=21.0 size=   [info] frame=  375 fps= 24 q=20.0 q=20.0 q=20.0 q=19.0 q=20.0 q=20.0 q=20.0 q=19.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=19.0 q=21.0 q=21.0 q=20.0 q=20.0 q=20.0 q=21.0 q=20.0 q=21.0 q=20.0 q=21.0 q=21.0 size=   [info] frame=  387 fps= 24 q=20.0 q=20.0 q=20.0 q=19.0 q=20.0 q=20.0 q=19.0 q=19.0 q=20.0 q=20.0 q=20.0 q=20.0 q=20.0 q=19.0 q=21.0 q=21.0 q=20.0 q=19.0 q=19.0 q=20.0 q=21.0 q=21.0 q=19.0 q=21.0 q=20.0 size=   [info] frame=  400 fps= 24 q=20.0 q=20.0 q=20.0 q=19.0 q=20.0 q=20.0 q=18.0 q=18.0 q=19.0 q=20.0 q=20.0 q=19.0 q=20.0 q=19.0 q=20.0 q=20.0 q=20.0 q=19.0 q=19.0 q=20.0 q=20.0 q=21.0 q=19.0 q=19.0 q=20.0 size=   [info] frame=  412 fps= 24 q=20.0 q=19.0 q=19.0 q=19.0 q=19.0 q=20.0 q=18.0 q=18.0 q=19.0 q=19.0 q=20.0 q=19.0 q=20.0 q=19.0 q=19.0 q=20.0 q=20.0 q=19.0 q=19.0 q=19.0 q=20.0 q=21.0 q=19.0 q=19.0 q=19.0 size=   [info] frame=  424 fps= 24 q=20.0 q=19.0 q=20.0 q=20.0 q=19.0 q=20.0 q=18.0 q=19.0 q=19.0 q=19.0 q=20.0 q=19.0 q=20.0 q=19.0 q=19.0 q=20.0 q=20.0 q=19.0 q=19.0 q=19.0 q=21.0 q=21.0 q=19.0 q=19.0 q=19.0 size=   [info] frame=  436 fps= 24 q=20.0 q=18.0 q=19.0 q=20.0 q=18.0 q=20.0 q=18.0 q=18.0 q=19.0 q=18.0 q=20.0 q=19.0 q=20.0 q=19.0 q=19.0 q=20.0 q=19.0 q=19.0 q=19.0 q=18.0 q=21.0 q=21.0 q=19.0 q=19.0 q=19.0 size=   [info] frame=  448 fps= 24 q=19.0 q=18.0 q=19.0 q=20.0 q=18.0 q=19.0 q=18.0 q=18.0 q=19.0 q=18.0 q=19.0 q=19.0 q=20.0 q=19.0 q=18.0 q=20.0 q=19.0 q=19.0 q=19.0 q=18.0 q=21.0 q=21.0 q=19.0 q=19.0 q=19.0 size=   [info] frame=  460 fps= 24 q=19.0 q=18.0 q=19.0 q=20.0 q=18.0 q=19.0 q=18.0 q=18.0 q=19.0 q=18.0 q=19.0 q=19.0 q=20.0 q=19.0 q=18.0 q=20.0 q=19.0 q=19.0 q=20.0 q=18.0 q=20.0 q=21.0 q=19.0 q=19.0 q=18.0 size=   [info] frame=  472 fps= 24 q=19.0 q=18.0 q=19.0 q=20.0 q=18.0 q=19.0 q=18.0 q=18.0 q=19.0 q=18.0 q=19.0 q=19.0 q=19.0 q=19.0 q=18.0 q=19.0 q=19.0 q=19.0 q=20.0 q=18.0 q=20.0 q=21.0 q=19.0 q=19.0 q=18.0 size=   [info] frame=  484 fps= 24 q=19.0 q=18.0 q=19.0 q=20.0 q=18.0 q=19.0 q=18.0 q=18.0 q=19.0 q=18.0 q=19.0 q=19.0 q=19.0 q=19.0 q=18.0 q=19.0 q=19.0 q=19.0 q=19.0 q=18.0 q=20.0 q=21.0 q=19.0 q=20.0 q=18.0 size=   [info] frame=  496 fps= 24 q=21.0 q=20.0 q=21.0 q=21.0 q=18.0 q=20.0 q=21.0 q=21.0 q=21.0 q=18.0 q=20.0 q=21.0 q=22.0 q=21.0 q=18.0 q=21.0 q=21.0 q=21.0 q=21.0 q=18.0 q=21.0 q=21.0 q=21.0 q=20.0 q=19.0 size=   [info] frame=  509 fps= 24 q=21.0 q=20.0 q=22.0 q=21.0 q=17.0 q=21.0 q=22.0 q=22.0 q=22.0 q=18.0 q=20.0 q=22.0 q=22.0 q=22.0 q=17.0 q=21.0 q=22.0 q=22.0 q=22.0 q=18.0 q=21.0 q=22.0 q=22.0 q=20.0 q=18.0 size=   [info] frame=  521 fps= 24 q=20.0 q=21.0 q=22.0 q=21.0 q=17.0 q=21.0 q=22.0 q=23.0 q=22.0 q=17.0 q=20.0 q=22.0 q=23.0 q=22.0 q=17.0 q=21.0 q=22.0 q=22.0 q=22.0 q=17.0 q=21.0 q=22.0 q=22.0 q=21.0 q=18.0 size=   [info] frame=  533 fps= 24 q=20.0 q=20.0 q=22.0 q=21.0 q=15.0 q=20.0 q=22.0 q=23.0 q=22.0 q=16.0 q=19.0 q=22.0 q=23.0 q=22.0 q=16.0 q=21.0 q=22.0 q=22.0 q=22.0 q=16.0 q=21.0 q=22.0 q=22.0 q=21.0 q=17.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 3205712 bytes read, 2 seeks
[h264 @ 0x5555f1df2c00] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f1949030] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[info] frame=  546 fps= 24 q=16.0 q=17.0 q=18.0 q=18.0 q=13.0 q=17.0 q=18.0 q=18.0 q=18.0 q=13.0 q=16.0 q=18.0 q=19.0 q=18.0 q=14.0 q=17.0 q=19.0 q=18.0 q=18.0 q=14.0 q=18.0 q=19.0 q=18.0 q=18.0 q=14.0 size=   [info] frame=  558 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=14.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  570 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  582 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  594 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  606 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  618 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  631 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  643 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  655 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 120365 bytes read, 2 seeks
[h264 @ 0x5555f1df2c00] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f1949030] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[info] frame=  667 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  679 fps= 24 q=20.0 q=12.0 q=12.0 q=12.0 q=12.0 q=20.0 q=12.0 q=25.0 q=12.0 q=12.0 q=21.0 q=20.0 q=24.0 q=12.0 q=12.0 q=20.0 q=19.0 q=22.0 q=13.0 q=13.0 q=13.0 q=14.0 q=17.0 q=12.0 q=12.0 size=   [info] frame=  691 fps= 24 q=17.0 q=12.0 q=21.0 q=12.0 q=12.0 q=15.0 q=13.0 q=23.0 q=12.0 q=12.0 q=16.0 q=19.0 q=21.0 q=17.0 q=12.0 q=15.0 q=12.0 q=21.0 q=13.0 q=13.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 size=   [info] frame=  703 fps= 24 q=19.0 q=12.0 q=21.0 q=12.0 q=12.0 q=16.0 q=12.0 q=23.0 q=12.0 q=12.0 q=14.0 q=17.0 q=21.0 q=15.0 q=12.0 q=13.0 q=12.0 q=20.0 q=13.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  715 fps= 24 q=20.0 q=12.0 q=20.0 q=15.0 q=12.0 q=18.0 q=22.0 q=23.0 q=23.0 q=12.0 q=16.0 q=17.0 q=22.0 q=17.0 q=12.0 q=15.0 q=21.0 q=22.0 q=13.0 q=13.0 q=12.0 q=14.0 q=13.0 q=12.0 q=12.0 size=   [info] frame=  728 fps= 24 q=18.0 q=12.0 q=12.0 q=12.0 q=12.0 q=16.0 q=14.0 q=23.0 q=12.0 q=12.0 q=16.0 q=19.0 q=22.0 q=13.0 q=12.0 q=15.0 q=19.0 q=21.0 q=13.0 q=12.0 q=12.0 q=12.0 q=14.0 q=12.0 q=12.0 size=   [info] frame=  740 fps= 24 q=17.0 q=12.0 q=12.0 q=12.0 q=12.0 q=15.0 q=14.0 q=22.0 q=12.0 q=12.0 q=14.0 q=18.0 q=21.0 q=12.0 q=12.0 q=15.0 q=19.0 q=20.0 q=13.0 q=13.0 q=12.0 q=13.0 q=16.0 q=12.0 q=12.0 size=   [info] frame=  752 fps= 24 q=23.0 q=14.0 q=14.0 q=14.0 q=14.0 q=21.0 q=16.0 q=25.0 q=14.0 q=14.0 q=21.0 q=20.0 q=23.0 q=16.0 q=20.0 q=22.0 q=21.0 q=23.0 q=22.0 q=20.0 q=22.0 q=24.0 q=24.0 q=23.0 q=23.0 size=   [info] frame=  764 fps= 24 q=16.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=14.0 q=23.0 q=12.0 q=12.0 q=14.0 q=18.0 q=22.0 q=12.0 q=12.0 q=13.0 q=16.0 q=18.0 q=13.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  776 fps= 24 q=21.0 q=14.0 q=19.0 q=12.0 q=12.0 q=20.0 q=23.0 q=23.0 q=19.0 q=12.0 q=15.0 q=23.0 q=23.0 q=21.0 q=12.0 q=16.0 q=16.0 q=20.0 q=13.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  788 fps= 24 q=22.0 q=12.0 q=19.0 q=18.0 q=13.0 q=21.0 q=12.0 q=23.0 q=24.0 q=16.0 q=19.0 q=12.0 q=24.0 q=26.0 q=23.0 q=15.0 q=12.0 q=27.0 q=25.0 q=16.0 q=13.0 q=13.0 q=16.0 q=12.0 q=12.0 size=   [info] frame=  800 fps= 24 q=21.0 q=12.0 q=12.0 q=12.0 q=12.0 q=18.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=15.0 q=15.0 q=12.0 q=13.0 q=12.0 q=14.0 q=17.0 q=13.0 q=13.0 q=12.0 q=13.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 984786 bytes read, 2 seeks
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f187c0c0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[info] frame=  812 fps= 24 q=21.0 q=12.0 q=12.0 q=12.0 q=12.0 q=18.0 q=13.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=16.0 q=15.0 q=12.0 q=12.0 q=12.0 q=13.0 q=16.0 q=12.0 size=   [info] frame=  824 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  836 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  848 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  861 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=24.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  873 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  885 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=23.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  897 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  909 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=25.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  922 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  934 fps= 24 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  938 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [AVIOContext @ 0x5555f140fa70] [verbose] Statistics: 29154 bytes read, 0 seeks
[h264 @ 0x5555f1407de0] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x5555f1407720] [info] Auto-inserting h264_mp4toannexb bitstream filter
[h264 @ 0x5555f17af150] [verbose] Reinit context to 1920x1088, pix_fmt: yuv420p
[info] frame=  963 fps= 23 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=15.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  967 fps= 22 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=17.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  994 fps= 22 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=15.0 q=19.0 q=12.0 q=12.0 q=12.0 q=17.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame=  997 fps= 21 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=15.0 q=19.0 q=12.0 q=12.0 q=12.0 q=18.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame= 1023 fps= 21 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=14.0 q=20.0 q=18.0 q=15.0 q=15.0 q=14.0 q=13.0 q=13.0 q=13.0 q=13.0 q=13.0 q=13.0 size=   [info] frame= 1029 fps= 20 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=14.0 q=20.0 q=15.0 q=14.0 q=15.0 q=15.0 q=13.0 q=13.0 q=14.0 q=14.0 q=14.0 q=13.0 size=   [info] frame= 1055 fps= 21 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=15.0 q=19.0 q=15.0 q=14.0 q=15.0 q=16.0 q=12.0 q=13.0 q=13.0 q=13.0 q=13.0 q=12.0 size=   [info] frame= 1060 fps= 20 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=16.0 q=19.0 q=15.0 q=14.0 q=15.0 q=16.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=   [info] frame= 1086 fps= 20 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=16.0 q=15.0 q=14.0 q=14.0 q=12.0 q=13.0 q=13.0 q=13.0 q=13.0 q=12.0 size=   [info] frame= 1092 fps= 19 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=13.0 q=16.0 q=13.0 q=13.0 q=13.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 q=12.0 size=


  


  What I tried & My guesses

  


  My best guess is that the issue comes from one of the data buffers between the two applications (ffmpeg -> mpv). There are several buffers and I don't know exactly which ones, but there is at least a UDP buffer at the output of the server and another one at the input of the client.

  


  When a client is unreachable, the server's UDP buffer seems to fill up and thus don't continue streaming for other clients.

  


  I tried to tweak several parameters of ffmpeg concerning buffers but without success.

  


  

  • udp://100.64.0.32:1234?buffer_size=1024&connect=0&fifo_size=10&overrun_nonfatal=0

  


  • fps_mode

  


  • thread_queue_size

  


  


  Any help is welcome !

  


• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

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

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

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

  What is clickstream data ? 

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

  

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

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

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

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

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

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

  Types of clickstream data 

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

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

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

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

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

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  Clickstream data benefits and use cases 

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

  

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

  

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

  Customer journey mapping 

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

  Identifying customer trends 

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

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

  Here’s an example : 

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

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

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

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

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

  Improving marketing campaign ROI 

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

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

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

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

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  Delivering a better user experience (UX) 

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

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

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

  Collecting clickstream data : Tools and legal implications 

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

  

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

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

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

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

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

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

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

  Clickstream analytics data best practices 

  

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

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

  Define your goals 

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

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

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

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

  Analyse your data 

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

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

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

  Here are a few examples : 

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

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

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  Organise and visualise your data

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

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

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

  Collect clickstream data with Matomo 

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

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

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

  Try Matomo for Free

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