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• Problems video playing in some android devices (version 4+) but not in others

  5 juin 2014, par anquegi

  following the settings for video in android developers, http://developer.android.com/guide/appendix/media-formats.html I encoded a video using ffmpeg like this :

  ffmpeg -i gravityTrailer.mp4 -y -f mp4 -vcodec libx264 -profile:v baseline -acodec aac    -strict -2  -profile:a aac_low -b:v 500k -ar 44100 -b:a 128k -ac 2 -r 30 -s 480x360  output_hq.mp4

  which corresponds on sd highquality in table 2 from android developers (link above).

  Then i put this video in a server and use the class android.media.MediaPlayer to reproduce it from that URL like this

  https://cloudapp.somedomain.com/bmftvideos/output_hq.mp4

  The problem is that the video is played on android version 4+

  you can see the video on :
  LG G2,
  Sony xperia S

  you cannot see the video on :
  Samsung galaxy S4,
  BQ (spanish) aquaris 4.5

  And I don not know why

  this is the ffmpeg output :

  ffmpeg version 1.2.6 Copyright (c) 2000-2014 the FFmpeg developers
  
    built on Mar  5 2014 08:21:01 with gcc 4.8.2 (GCC) 20131212 (Red Hat 4.8.2-7)
  
    configuration: --prefix=/usr --bindir=/usr/bin --datadir=/usr/share/ffmpeg --incdir=/usr/include/ffmpeg --libdir=/usr/lib64 --mandir=/usr/share/man --arch=x86_64 --optflags='-O2 -g -pipe -Wall -Wp,-D_FORTIFY_SOURCE=2 -fexceptions -fstack-protector --param=ssp-buffer-size=4 -grecord-gcc-switches -m64 -mtune=generic' --enable-bzlib --disable-crystalhd --enable-frei0r --enable-gnutls --enable-libass --enable-libcelt --enable-libdc1394 --disable-indev=jack --enable-libfreetype --enable-libgsm --enable-libmp3lame --enable-openal --enable-libopencv --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-libschroedinger --enable-libspeex --enable-libtheora --enable-libvorbis --enable-libv4l2 --enable-libvpx --enable-libx264 --enable-libxvid --enable-x11grab --enable-avfilter --enable-avresample --enable-postproc --enable-pthreads --disable-static --enable-shared --enable-gpl --disable-debug --disable-stripping --shlibdir=/usr/lib64 --enable-runtime-cpudetect
  
    libavutil      52. 18.100 / 52. 18.100
  
    libavcodec     54. 92.100 / 54. 92.100
  
    libavformat    54. 63.104 / 54. 63.104
  
    libavdevice    54.  3.103 / 54.  3.103
  
    libavfilter     3. 42.103 /  3. 42.103
  
    libswscale      2.  2.100 /  2.  2.100
  
    libswresample   0. 17.102 /  0. 17.102
  
    libpostproc    52.  2.100 / 52.  2.100
  
  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x14e5ba0] stream 0, timescale not set
  
  [mov,mp4,m4a,3gp,3g2,mj2 @ 0x14e5ba0] max_analyze_duration 5000000 reached at 5013333 microseconds
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'gravityTrailer.mp4':
  
    Metadata:
  
      major_brand     : mp42
  
      minor_version   : 0
  
      compatible_brands: mp42isomavc1
  
      creation_time   : 2013-12-01 03:59:56
  
      genre           : Trailer
  
      artist          : Warner Bros.
  
      title           : Gravity - 2K Trailer
  
      encoder         : HandBrake 0.9.9 2013051800
  
      date            : 2013
  
    Duration: 00:02:27.07, start: 0.000000, bitrate: 20296 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 2048x858, 20149 kb/s, 23.98 fps, 23.98 tbr, 90k tbn, 47.95 tbc
  
      Metadata:
  
        creation_time   : 2013-12-01 03:59:56
  
      Stream #0:1(eng): Audio: aac (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 153 kb/s
  
      Metadata:
  
        creation_time   : 2013-12-01 03:59:56
  
      Stream #0:2: Video: mjpeg, yuvj420p, 102x150 [SAR 72:72 DAR 17:25], 90k tbr, 90k tbn, 90k tbc
  
  [libx264 @ 0x1667680] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
  
  [libx264 @ 0x1667680] profile Constrained Baseline, level 3.0
  
  [libx264 @ 0x1667680] 264 - core 130 r2282 1db4621 - H.264/MPEG-4 AVC codec - Copyleft 2003-2013 - http://www.videolan.org/x264.html - options: cabac=0 ref=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=500 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
  
  Output #0, mp4, to 'output_hq.mp4':
  
    Metadata:
  
      major_brand     : mp42
  
      minor_version   : 0
  
      compatible_brands: mp42isomavc1
  
      date            : 2013
  
      genre           : Trailer
  
      artist          : Warner Bros.
  
      title           : Gravity - 2K Trailer
  
      encoder         : Lavf54.63.104
  
      Stream #0:0(und): Video: h264 ([33][0][0][0] / 0x0021), yuv420p, 480x360, q=-1--1, 500 kb/s, 15360 tbn, 30 tbc
  
      Metadata:
  
        creation_time   : 2013-12-01 03:59:56
  
      Stream #0:1(eng): Audio: aac ([64][0][0][0] / 0x0040), 44100 Hz, stereo, fltp, 128 kb/s
  
      Metadata:
  
        creation_time   : 2013-12-01 03:59:56
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (h264 -> libx264)
  
    Stream #0:1 -> #0:1 (aac -> aac)
  
  Press [q] to stop, [?] for help
  
  frame= 4410 fps= 65 q=-1.0 Lsize=   11154kB time=00:02:27.07 bitrate= 621.3kbits/s dup=885 drop=0    
  
  video:9062kB audio:1969kB subtitle:0 global headers:0kB muxing overhead 1.115392%
  
  [libx264 @ 0x1667680] frame I:186   Avg QP:28.41  size:  4446
  
  [libx264 @ 0x1667680] frame P:4224  Avg QP:29.20  size:  2001
  
  [libx264 @ 0x1667680] mb I  I16..4: 75.5%  0.0% 24.5%
  
  [libx264 @ 0x1667680] mb P  I16..4:  7.5%  0.0%  3.6%  P16..4: 23.1%  6.2%  1.8%  0.0%  0.0%    skip:57.7%
  
  [libx264 @ 0x1667680] final ratefactor: 27.96
  
  [libx264 @ 0x1667680] coded y,uvDC,uvAC intra: 24.0% 31.9% 8.6% inter: 7.4% 7.6% 0.6%
  
  [libx264 @ 0x1667680] i16 v,h,dc,p: 58% 15% 13% 14%
  
  [libx264 @ 0x1667680] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 28%  9% 27%  7%  6%  8%  5%  7%  3%
  
  [libx264 @ 0x1667680] i8c dc,h,v,p: 80%  7% 12%  2%
  
  [libx264 @ 0x1667680] ref P L0: 84.7%  9.4%  5.9%
  
  [libx264 @ 0x1667680] kb/s:504.98

  ---

• FFmpeg / libmp3lame crash while converting from .wav to .mp3 with vibrato

  1er juillet 2020, par Chitrang

  I have integrated mobile-ffmpeg-full-gpl:4.3.1.LTS library in my android app. And trying to convert .wav file to .mp3 format with vibrato option and libmp3lame encoder.

  


  ffmpegCommand = "-i input.wav " +
                "-af vibrato=f=4 " +
                "-c:a libmp3lame " +
                "-b:a 96k " +
                "-ac 1 " +
                "-ar 44100 " +
                "-y output.mp3"


  


  FFmpeg Logs :

  


  a.b.com I/mobile-ffmpeg: Loading mobile-ffmpeg.
a.b.com I/mobile-ffmpeg: Loaded mobile-ffmpeg-full-gpl-arm64-v8a-4.3.1-lts-20200125.
a.b.com D/mobile-ffmpeg: Callback thread started.
a.b.com I/mobile-ffmpeg: ffmpeg version git-2020-01-25-fd11dd500
a.b.com I/mobile-ffmpeg:  Copyright (c) 2000-2020 the FFmpeg developers
a.b.com I/mobile-ffmpeg:   built with Android (5220042 based on r346389c) clang version 8.0.7 (https://android.googlesource.com/toolchain/clang b55f2d4ebfd35bf643d27dbca1bb228957008617) (https://android.googlesource.com/toolchain/llvm 3c393fe7a7e13b0fba4ac75a01aa683d7a5b11cd) (based on LLVM 8.0.7svn)
a.b.com I/mobile-ffmpeg:   configuration: --cross-prefix=aarch64-linux-android- --sysroot=/files/android-sdk/ndk-bundle/toolchains/llvm/prebuilt/linux-x86_64/sysroot --prefix=/home/taner/Projects/mobile-ffmpeg/prebuilt/android-arm64/ffmpeg --pkg-config=/usr/bin/pkg-config --enable-version3 --arch=aarch64 --cpu=armv8-a --cc=aarch64-linux-android21-clang --cxx=aarch64-linux-android21-clang++ --target-os=android --enable-neon --enable-asm --enable-inline-asm --enable-cross-compile --enable-pic --enable-jni --enable-optimizations --enable-swscale --enable-shared --disable-v4l2-m2m --disable-outdev=v4l2 --disable-outdev=fbdev --disable-indev=v4l2 --disable-indev=fbdev --enable-small --disable-openssl --disable-xmm-clobber-test --disable-debug --enable-lto --disable-neon-clobber-test --disable-programs --disable-postproc --disable-doc --disable-htmlpages --disable-manpages --disable-podpages --disable-txtpages --disable-static --disable-sndio --disable-schannel --disable-securetransport --disable-xlib --disable-cuda --disable-cuvid --disa
a.b.com I/mobile-ffmpeg:   libavutil      56. 38.100 / 56. 38.100
a.b.com I/mobile-ffmpeg:   libavcodec     58. 65.102 / 58. 65.102
a.b.com I/mobile-ffmpeg:   libavformat    58. 35.101 / 58. 35.101
a.b.com I/mobile-ffmpeg:   libavdevice    58.  9.103 / 58.  9.103
a.b.com I/mobile-ffmpeg:   libavfilter     7. 70.101 /  7. 70.101
a.b.com I/mobile-ffmpeg:   libswscale      5.  6.100 /  5.  6.100
a.b.com I/mobile-ffmpeg:   libswresample   3.  6.100 /  3.  6.100
a.b.com W/mobile-ffmpeg: [wav @ 0x7294a86600] Estimating duration from bitrate, this may be inaccurate
a.b.com W/mobile-ffmpeg: Guessed Channel Layout for Input Stream #0.0 : mono
a.b.com I/mobile-ffmpeg: Input #0, wav, from '/data/user/0/a.b.com/cache/creation/input.wav':
a.b.com I/mobile-ffmpeg:   Duration: 
a.b.com I/mobile-ffmpeg: 00:00:07.15
a.b.com I/mobile-ffmpeg: , bitrate: 
a.b.com I/mobile-ffmpeg: 705 kb/s
a.b.com I/mobile-ffmpeg:     Stream #0:0
a.b.com I/mobile-ffmpeg: : Audio: pcm_s16le ([1][0][0][0] / 0x0001), 44100 Hz, mono, s16, 705 kb/s
a.b.com I/mobile-ffmpeg: Stream mapping:
a.b.com I/mobile-ffmpeg:   Stream #0:0 -> #0:0
a.b.com I/mobile-ffmpeg:  (pcm_s16le (native) -> mp3 (libmp3lame))
a.b.com I/mobile-ffmpeg: Press [q] to stop, [?] for help
a.b.com I/mobile-ffmpeg: Output #0, mp3, to '/data/user/0/a.b.com/cache/creation/output.mp3':
a.b.com I/mobile-ffmpeg:   Metadata:
a.b.com I/mobile-ffmpeg:     TSSE            : 
a.b.com I/mobile-ffmpeg: Lavf58.35.101
a.b.com I/mobile-ffmpeg:     Stream #0:0
a.b.com I/mobile-ffmpeg: : Audio: mp3 (libmp3lame), 44100 Hz, mono, fltp, 96 kb/s
a.b.com I/mobile-ffmpeg:     Metadata:
a.b.com I/mobile-ffmpeg:       encoder         : 
a.b.com I/mobile-ffmpeg: Lavc58.65.102 libmp3lame
a.b.com I/mobile-ffmpeg: --------- beginning of crash
a.b.com A/libc: psymodel.c:576: void calc_energy(const PsyConst_CB2SB_t *, const FLOAT *, FLOAT *, FLOAT *, FLOAT *): assertion "el >= 0" failed
a.b.com A/libc: Fatal signal 6 (SIGABRT), code -6 (SI_TKILL) in tid 25800 (a.b.com), pid 25800 (a.b.com)


  


  Crash :

  


  --------- beginning of crash
 A/libc: psymodel.c:576: void calc_energy(const PsyConst_CB2SB_t *, const FLOAT *, FLOAT *, FLOAT *, FLOAT *): assertion "el >= 0" failed

? A/DEBUG: *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** ***
? A/DEBUG: Build fingerprint: 'samsung/star2qltecs/star2qltecs:10/QP1A.190711.020/G965WVLS7DTE1:user/release-keys'
? A/DEBUG: Revision: '14'
? A/DEBUG: ABI: 'arm64'
? A/DEBUG: Timestamp: 2020-06-29 15:13:17-0400
? A/DEBUG: pid: 1849, tid: 1849, name: a.b.com  >>> a.b.com <<<
? A/DEBUG: uid: 12171
? A/DEBUG: signal 6 (SIGABRT), code -6 (SI_TKILL), fault addr --------
? A/DEBUG: Abort message: 'psymodel.c:576: void calc_energy(const PsyConst_CB2SB_t *, const FLOAT *, FLOAT *, FLOAT *, FLOAT *): assertion "el >= 0" failed'
? A/DEBUG:     x0  0000000000000000  x1  0000000000000739  x2  0000000000000006  x3  0000007fd65f7bb0
? A/DEBUG:     x4  0000000000000000  x5  0000000000000000  x6  0000000000000000  x7  0000000000000008
? A/DEBUG:     x8  00000000000000f0  x9  7f96d7a39856d151  x10 0000000000000001  x11 0000000000000000
? A/DEBUG:     x12 fffffff0fffffbdf  x13 000000005efa3d4c  x14 001c23c1a79207f5  x15 000079d970d48db2
? A/DEBUG:     x16 00000073e009e8c0  x17 00000073e007afe0  x18 00000073e492c000  x19 0000000000000739
? A/DEBUG:     x20 0000000000000739  x21 00000000ffffffff  x22 0000007fd65fc44c  x23 0000007fd65f8640
? A/DEBUG:     x24 0000007fd65fd120  x25 0000007fd65fd3a8  x26 0000007fd65f8240  x27 0000007fd65f9e40
? A/DEBUG:     x28 00000071f9f60900  x29 0000007fd65f7c50
? A/DEBUG:     sp  0000007fd65f7b90  lr  00000073e002c27c  pc  00000073e002c2a8
? A/DEBUG: backtrace:
? A/DEBUG:       #00 pc 00000000000832a8  /apex/com.android.runtime/lib64/bionic/libc.so (abort+160) (BuildId: 55ce0a7d78144b0290f9746ed1615719)
? A/DEBUG:       #01 pc 00000000000839e8  /apex/com.android.runtime/lib64/bionic/libc.so (__assert2+36) (BuildId: 55ce0a7d78144b0290f9746ed1615719)
? A/DEBUG:       #02 pc 0000000000969c60  /data/app/a.b.com-jXqE8oxytEkfSsn6pcdloQ==/lib/arm64/libavcodec.so


  


  I referred link1, link2 to understand the problem but could not find a solution.

  


• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.