Recherche avancée

Sur d’autres sites (6792)

• Multivariate Testing vs A/B Testing (Quick-Start Guide)

  7 mars 2024, par Erin

  Traditional advertising (think Mad Men) was all about slogans, taglines and coming up with a one-liner that was meant to change the world.

  But that type of advertising was extremely challenging to test, so it was hard to know if it worked. Most of the time, nobody knew if they were being effective with their advertising.

  Enter modern marketing : the world of data-driven advertising.

  Thanks to the internet and web analytics tools like Matomo, you can quickly test almost anything and improve your site.

  The question is, should you do multivariate testing or A/B testing ?

  While both have their advantages, each has a specific use case.

  In this guide, we’ll break down the differences between multivariate and A/B testing, offer some pros and cons of each and show you some examples so you can decide which one is best for you.

  What is A/B testing ?

  A/B testing, or split testing, is testing an individual element in a medium against another version of the same element to see which produces better results.

  

  A/B tests are conducted by creating two different versions of a digital landmark : a website, landing page, email, or advertisement.

  The goal ? Figure out which version performs better.

  Let’s say, for example, you want to drive more sales on your core product page.

  You test two call-to-action buttons : “Buy Now” and “Add to Cart.”

  After running the test for two weeks, you see that “Buy Now” produced 1.2% conversions while “Add to Cart” produced 7.6%.

  In this scenario, you’ve found your winner : version B, “Add to Cart.”

  By conducting A/B tests regularly, you can optimise your site, increase engagement and convert more visitors into customers.

  Keep in mind that A/B testing isn’t perfect ; it doesn’t always produce a win.

  According to Noah Kagan, founder of AppSumo, only 1 out of 8 A/B tests his company conducts produces significant change.

  Advantages of A/B testing

  A/B testing is great when you need to get an accurate result fast on a specific element of your marketing efforts.

  Whether it’s a landing page or product page, you can get quick results without needing a lot of traffic.

  A/B testing is one of the most widely accepted and used testing methods for marketers and business owners.

  When you limit the number of tracked variables used in a test, you can quickly deliver reliable data, allowing you to iterate and pivot quickly if necessary.

  This is a great way to test your marketing methods, especially if you’re a newer business or you don’t have substantial traffic yet.

  Splitting up your traffic into a few segments (like with multivariate testing) will be very challenging to gain accurate results if you have lower daily traffic.

  One final advantage of A/B testing is that it’s a relatively easy way to introduce testing and optimising to a team, decision-maker, or stakeholder since it’s easy to implement. You can quickly demonstrate the value with a simple change and tangible evidence.

  Disadvantages of A/B testing

  So, what are the downsides to A/B testing ?

  Although A/B testing can get you quick results on small changes, it has limitations.

  A/B testing is all about measuring one element against another.

  This means you’re immediately limited in how many elements you can test. If you have to test out different variables, then A/B testing isn’t your best option since you’ll have to run test after test to get your result.

  If you need specific information on how different combinations of elements interact with one another on a web page, then multivariate is your best option.

  What is multivariate testing ?

  If you want to take your testing to the next level, you’ll want to try multivariate testing.

  Multivariate testing relies on the same foundational mechanism of A/B testing, but instead of matching up two elements against one another, it compares a higher number of variables at once.

  Multiple + variations = multivariate.

  Multivariate testing looks at how combinations of elements and variables interact.

  Like A/B testing, traffic to a page is split between different web page versions. Multivariate testing aims to measure each version’s effectiveness against the other versions.

  Ultimately, it’s about finding the winning combination.

  

  When to use multivariate testing

  The quick answer on when to use multivariate testing is if you have enough traffic.

  Just how much traffic, though ?

  While there’s no set number, you should aim to have 10,000 visitors per month or more, to ensure that each variant receives enough traffic to produce meaningful results within a reasonable time frame.

  Once you meet the traffic requirement, let’s talk about use cases.

  Let’s say you want to introduce a new email signup.

  But you want to create it from scratch and aren’t sure what will make your audience take action.

  So, you create a page with a signup form, a header, and an image.

  To run a multivariate test, you create two lengths of signup forms, four headlines, and two images.

  Next, you would create a test to split traffic between these sixteen combinations.

  Advantages of multivariate testing

  If you have enough traffic, multivariate testing can be an incredible way to speed up your A/B testing by testing dozens of combinations of your web page.

  This is handy when creating a new landing page and you want to determine if specific parts of your design are winners — which you can then use in future campaigns.

  Disadvantages of multivariate testing

  The main disadvantage of multivariate testing is that you need a lot of traffic to get started.

  If you try to do a multivariate analysis but you’re not getting much traffic, your results won’t be accurate (and it will take a long time to see accurate data).

  Additionally, multivariate tests are more complicated. They’re best suited for advanced marketers since more moving parts are at play.

  Key differences between multivariate and A/B testing

  Now that we’ve covered what A/B and multivariate tests are, let’s look at some key differences to help clarify which is best for you.

  

  1. Variation of combinations

  The major difference between A/B and multivariate testing is the number of combinations involved.

  With A/B testing, you only look at one element (no combinations). You simply take one part of your page (i.e., your headline copy) and make two versions.

  With multivariate testing, you’re looking at combinations of different elements (i.e., headline copy, form length, images).

  2. Number of pages to test

  The next difference lies in how many pages you will test.

  With an A/B test, you are splitting traffic on your website to two different pages : A and B.

  However, with multivariate testing, you will likely have 4-16 different test pages.

  This is because dozens of combinations can be created when you start testing a handful of elements at once.

  For example, if you want to test two headlines, two form buttons and two images on a signup form, then you have several combinations :

  • Headline A, Button A, Image A
  • Headline A, Button A, Image B
  • Headline A, Button B, Image A
  • Headline A, Button B, Image B
  • Headline B, Button A, Image A
  • Headline B, Button A, Image B
  • Headline B, Button B, Image A
  • Headline B, Button B, Image B

  In this scenario, you must create eight pages to send traffic to.

  3. Traffic requirements

  The next major difference between the two testing types is the traffic requirements.

  With A/B testing, you don’t need much traffic at all.

  Since you’re only testing two pages, you can split your traffic in half between the two types.

  However, if you plan on implementing a multivariate test, you will likely be splitting your traffic at least four or more ways.

  This means you need to have significantly more traffic coming in to get accurate data from your test. If you try to do this when your traffic is too low, you won’t have a large enough sample size.

  4. Time requirements

  Next up, just like traffic, there’s also a time requirement.

  A/B testing only tests two versions of a page against each other (while testing a single element). This means you’ll get accurate results faster than a multivariate test — usually within days.

  However, for a multivariate test, you might need to wait weeks. This is because you’re splitting your traffic by 4, 8, 12, or more web page variations. This could take months since you need a large enough sample size for accuracy.

  5. Big vs. small changes

  Another difference between A/B testing and multivariate testing is the magnitude of changes.

  With an A/B test, you’re looking at one element of a page, which means changing that element to the winning version isn’t a major overhaul of your design.

  But, with multivariate testing, you may find that the winning combination is drastically different than your control page, which could lead to a significant design change.

  6. Accuracy of results

  A/B tests are easier to decipher than multivariate testing since you only look at two versions of a single element on a page.

  You have a clear winner if one headline yields a 5% conversion rate and another yields a 1.2% conversion rate.

  But multivariate testing looks at so many combinations of a page that it can be a bit trickier to decipher what’s moving the needle.

  Pros and cons : Multivariate vs. A/B testing

  Before picking your testing method of choice, let’s look at some quick pros and cons.

  

  A/B testing pros and cons

  Here are the pros and cons of A/B testing :

  Pros

  • Get results quickly
  • Results are easier to interpret
  • Lower traffic requirement
  • Easy to get started

  Cons

  • You need to be hyper-focused on the right testing element
  • Requires performing test after test to optimise a web page

  Multivariate testing pros and cons

  Here are the pros and cons of multivariate testing :

  Pros

  • Handy when redesigning an entire web page
  • You can test multiple variables at once
  • Significant results (since traffic is higher)
  • Gather multiple data insights at once

  Cons

  • Requires substantial traffic
  • Harder to accurately decipher results
  • Not as easy to get started (more advanced)

  Use Matomo to start testing and improving your site

  

  You need to optimise your website if you want to get more leads, land more conversions and grow your business.

  A/B testing and multivariate testing are proven testing methods you can lean on to improve your website and create a better user experience.

  You may prefer one testing method now over the other, and that’s okay.

  The main thing is you’re starting to test. The best marketers and analysts in the world find what works through testing and double down on their winning tactics.

  If you want to start improving your website with testing today, get started with Matomo for free.

  With Matomo, you can conduct A/B tests and multivariate tests easily, accurately, and ethically. Unlike other web analytics tools, Matomo prioritises privacy, providing
  100% accurate data without sampling, and eliminates the need for cookie consent
  banners (except in the UK and Germany).

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

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now

  

• How can I remove silence of a video ? [closed]

  23 septembre 2024, par Alex

  I wanted to automatically remove the silence from some of my videos.

  


  I found the following command by searching :

  


  ffmpeg -i C:\Users\`*`myusername`*`\Desktop\videowithsielence.mp4 -af silenceremove=1:0:-50dB C:\Users\`*`myusername`*`\Desktop\newvideo.mp4


  


  There isn't any error and this is the following output :

  


  ffmpeg version N-117094-g2bf588f273-20240921 Copyright (c) 2000-2024 the FFmpeg developers
  built with gcc 14.2.0 (crosstool-NG 1.26.0.106_ed12fa6)
  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-zlib --enable-libfreetype --enable-libfribidi --enable-gmp --enable-libxml2 --enable-lzma --enable-fontconfig --enable-libharfbuzz --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 --enable-libdvdread --enable-libdvdnav --disable-libfdk-aac --enable-ffnvcodec --enable-cuda-llvm --enable-frei0r --enable-libgme --enable-libkvazaar --enable-libaribcaption --enable-libass --enable-libbluray --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librist --enable-libssh --enable-libtheora --enable-libvpx --enable-libwebp --enable-libzmq --enable-lv2 --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 --enable-vaapi --enable-libvidstab --enable-vulkan --enable-libshaderc --enable-libplacebo --enable-libvvenc --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libzimg --enable-libzvbi --extra-cflags=-DLIBTWOLAME_STATIC --extra-cxxflags= --extra-libs=-lgomp --extra-ldflags=-pthread --extra-ldexeflags= --cc=x86_64-w64-mingw32-gcc --cxx=x86_64-w64-mingw32-g++ --ar=x86_64-w64-mingw32-gcc-ar --ranlib=x86_64-w64-mingw32-gcc-ranlib --nm=x86_64-w64-mingw32-gcc-nm --extra-version=20240921
  libavutil      59. 37.100 / 59. 37.100
  libavcodec     61. 17.100 / 61. 17.100
  libavformat    61.  6.100 / 61.  6.100
  libavdevice    61.  2.101 / 61.  2.101
  libavfilter    10.  3.100 / 10.  3.100
  libswscale      8.  2.100 /  8.  2.100
  libswresample   5.  2.100 /  5.  2.100
  libpostproc    58.  2.100 / 58.  2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'C:\Users\*myusername*\Desktop\stille.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf61.1.100
  Duration: 00:00:17.60, start: 0.000000, bitrate: 14994 kb/s
  Stream #0:0[0x1](und): Video: h264 (Constrained Baseline) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1920x1080 [SAR 1:1 DAR 16:9], 14915 kb/s, 60 fps, 60 tbr, 15360 tbn (default)
      Metadata:
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 83 kb/s (default)
      Metadata:
        handler_name    : SoundHandler
        vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
  Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 000002b596997d00] using SAR=1/1
[libx264 @ 000002b596997d00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 000002b596997d00] profile High, level 4.2, 4:2:0, 8-bit
[libx264 @ 000002b596997d00] 264 - core 164 - H.264/MPEG-4 AVC codec - Copyleft 2003-2024 - 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=-2 threads=18 lookahead_threads=3 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 'C:\Users\*myusername*\Desktop\Aufzeich.mp4':
  Metadata:
    major_brand     : isom
    minor_version   : 512
    compatible_brands: isomiso2avc1mp41
    encoder         : Lavf61.6.100
  Stream #0:0(und): Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1920x1080 [SAR 1:1 DAR 16:9], q=2-31, 60 fps, 15360 tbn (default)
      Metadata:
        handler_name    : VideoHandler
        vendor_id       : [0][0][0][0]
        encoder         : Lavc61.17.100 libx264
      Side data:
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
  Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
      Metadata:
        handler_name    : SoundHandler
        vendor_id       : [0][0][0][0]
        encoder         : Lavc61.17.100 aac
[out#0/mp4 @ 000002b594b08800] video:2969KiB audio:180KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: 0.825545%
frame= 1055 fps=236 q=-1.0 Lsize=    3174KiB time=00:00:16.54 bitrate=1571.3kbits/s speed= 3.7x
[libx264 @ 000002b596997d00] frame I:5     Avg QP:14.66  size:136185
[libx264 @ 000002b596997d00] frame P:351   Avg QP:18.48  size:  6338
[libx264 @ 000002b596997d00] frame B:699   Avg QP:25.92  size:   191
[libx264 @ 000002b596997d00] consecutive B-frames:  1.5%  6.1% 73.1% 19.3%
[libx264 @ 000002b596997d00] mb I  I16..4: 61.0% 21.9% 17.2%
[libx264 @ 000002b596997d00] mb P  I16..4:  0.2%  0.1%  0.0%  P16..4:  4.8%  0.8%  0.8%  0.0%  0.0%    skip:93.4%
[libx264 @ 000002b596997d00] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  2.0%  0.0%  0.0%  direct: 0.0%  skip:98.0%  L0:75.0% L1:22.4% BI: 2.5%
[libx264 @ 000002b596997d00] 8x8 transform intra:22.8% inter:38.2%
[libx264 @ 000002b596997d00] coded y,uvDC,uvAC intra: 57.3% 73.0% 56.4% inter: 1.0% 1.0% 0.1%
[libx264 @ 000002b596997d00] i16 v,h,dc,p: 31% 25% 32% 12%
[libx264 @ 000002b596997d00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 24% 21% 42%  2%  2%  2%  2%  3%  3%
[libx264 @ 000002b596997d00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 33% 31% 22%  2%  2%  2%  2%  2%  3%
[libx264 @ 000002b596997d00] i8c dc,h,v,p: 41% 32% 13% 13%
[libx264 @ 000002b596997d00] Weighted P-Frames: Y:0.3% UV:0.0%
[libx264 @ 000002b596997d00] ref P L0: 68.2%  7.2% 20.4%  4.2%  0.0%
[libx264 @ 000002b596997d00] ref B L0: 79.0% 18.8%  2.2%
[libx264 @ 000002b596997d00] ref B L1: 98.4%  1.6%
[libx264 @ 000002b596997d00] kb/s:1382.74
[aac @ 000002b596a4f680] Qavg: 21440.516


  


  But the new video is exactly the same as the first one. I also tried changing the 50dB number but that didn't work either... My question is "Why ?" and "How can I fix this ?"...

  


• I created a Python code to capture live video using FFmpeg, but the output screen only shows noise

  16 octobre 2024, par chun3 hyun

  The code below is Python code that made my computer screen video capture in real time via ffmpeg.

  


  When I run the code below, it goes well until a new window named 'Captured Frame' is created. But this 'Captured Frame' window doesn't show the full screen of my computer, and the gray screen is generating a lot of noise.

  


  import cv2
import numpy as np
import subprocess

def frame_capture():
    # Set FFmpeg command (capture desired window or area)
    ffmpeg_command = [
        'ffmpeg',
        '-f', 'gdigrab',  # Windows screen capture (using gdigrab)
        '-framerate', '30',  # Setting the Frame Speed
        '-i', 'desktop',  # What to capture (for example, full screen)
        '-pix_fmt', 'bgr0',
        '-vcodec', 'rawvideo',  # Video codec settings
        '-tune', 'zerolatency',
        '-an',  # Disable audio
        '-sn',  # Disable Caption
        '-f', 'rawvideo', '-'
    ]

    # Running the FFmpeg process
    process = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, bufsize=10**8)

    while True:
        # Read Frame from FFmpeg (Resolution Example: 1920x1080)
        raw_frame = process.stdout.read(1920 * 1080 * 3)  # 1920x1080 resolution, BGR format
        if not raw_frame:
            break  # Shut down the loop when you can no longer receive frames

        # Converting frame data to a numpy array
        frame = np.frombuffer(raw_frame, np.uint8).reshape((1080, 1920, 3))

        # Add frame processing code here
        # Example: Showing a frame on the screen
        cv2.imshow('Captured Frame', frame)

        # Press the 'q' key to end
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # End of process and release of resources
    process.stdout.close()
    process.wait()
    cv2.destroyAllWindows()
frame_capture()


  


  What could I have done wrong ? When I directly input the FFmpeg command in the Windows command prompt(knows as 'cmd') as shown below to save the video (in .mp4 format), I can see that the screen is output normally in the saved file. It seems that FFmpeg itself is installed correctly, but I don't know what the cause is.

  


  hwnd=132554 -pix_fmt yuv420p -vf "scale=iw-mod(iw\,2):ih-mod(ih\,2)" -draw_mouse 1 -t 10 output.mp4


  


  The handle number written above was the handle of the active Chrome window on my computer.

  


  My ffmpeg version is 2024-10-10-git-0f5592cfc7-full_build-www.gyan.dev My Python version is 3.12.4
My Windows version and build are as specified below.
:Windows 11 Home, 10.0.22631

  


  Capturing the computer screen with FFmpeg. I tried it, but the output screen shows only noise.