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• Ffmpeg - Reading header information takes too long

  13 mars 2023, par Md Yeamin

  I am using ffmpeg-kit to encode videos on android devices. For some files ffmpeg takes too long to read the header information. This issue happens very randomly. Sometime the execution completes within 1 or 2 seconds, sometime it takes longer than 10 seconds to complete, for the file linked below.

  


  I have build ffmpeg to log some additional info to figure out the root cause. There is a 10s gap between the log output at 15:17:02.276 and 15:17:12.909 (added a separator for quick find). After completion of the mov_read_ftyp there is a long delay before the mov_read_dref method starts the execution. Is there any other method that executed in between and could take this much long time to complete the execution ? What could be the reason behind the delay ?

  


  Here is detailed log about the issue.

  


  Log :

  


  2023-03-12 15:17:02.096 :: ffmpeg-kit-debug: execute:
2023-03-12 15:17:02.116 :: ffmpeg-kit-debug: LogCallback: setjmp
2023-03-12 15:17:02.120 :: ffmpeg-kit-debug: LogCallback: setjmp done
2023-03-12 15:17:02.124 :: ffmpeg-kit-debug: LogCallback: ffmpeg_var_cleanup
2023-03-12 15:17:02.127 :: ffmpeg-kit-debug: LogCallback: ffmpeg_var_cleanup done
2023-03-12 15:17:02.132 :: ffmpeg-kit-debug: LogCallback: init_dynload
2023-03-12 15:17:02.135 :: ffmpeg-kit-debug: LogCallback: init_dynload done
2023-03-12 15:17:02.137 :: ffmpeg-kit-debug: LogCallback: register_exit
2023-03-12 15:17:02.141 :: ffmpeg-kit-debug: LogCallback: register_exit done
2023-03-12 15:17:02.144 :: ffmpeg-kit-debug: LogCallback: avdevice_register_all
2023-03-12 15:17:02.151 :: ffmpeg-kit-debug: LogCallback: avdevice_register_all done
2023-03-12 15:17:02.155 :: ffmpeg-kit-debug: LogCallback: avformat_network_init
2023-03-12 15:17:02.159 :: ffmpeg-kit-debug: LogCallback: avformat_network_init done
2023-03-12 15:17:02.163 :: ffmpeg-kit-debug: LogCallback: show_banner
2023-03-12 15:17:02.165 :: ffmpeg-kit-debug: LogCallback: show_banner done
2023-03-12 15:17:02.169 :: ffmpeg-kit-debug: LogCallback: ffmpeg_parse_options
2023-03-12 15:17:02.172 :: ffmpeg-kit-debug: LogCallback: allocating memory
2023-03-12 15:17:02.176 :: ffmpeg-kit-debug: LogCallback: split_commandline
2023-03-12 15:17:02.179 :: ffmpeg-kit-debug: LogCallback: Splitting the commandline.

2023-03-12 15:17:02.182 :: ffmpeg-kit-debug: LogCallback: Reading option '-hide_banner' ...
2023-03-12 15:17:02.184 :: ffmpeg-kit-debug: LogCallback:  matched as option 'hide_banner' (do not show program banner) with argument '1'.

2023-03-12 15:17:02.187 :: ffmpeg-kit-debug: LogCallback: Reading option '-y' ...
2023-03-12 15:17:02.189 :: ffmpeg-kit-debug: LogCallback:  matched as option 'y' (overwrite output files) with argument '1'.

2023-03-12 15:17:02.193 :: ffmpeg-kit-debug: LogCallback: Reading option '-i' ...
2023-03-12 15:17:02.197 :: ffmpeg-kit-debug: LogCallback:  matched as input url with argument 'saf:6.MP4'.

2023-03-12 15:17:02.199 :: ffmpeg-kit-debug: LogCallback: Finished splitting the commandline.

2023-03-12 15:17:02.202 :: ffmpeg-kit-debug: LogCallback: split_commandline done
2023-03-12 15:17:02.203 :: ffmpeg-kit-debug: LogCallback: parse_optgroup
2023-03-12 15:17:02.205 :: ffmpeg-kit-debug: LogCallback: Parsing a group of options: global .

2023-03-12 15:17:02.207 :: ffmpeg-kit-debug: LogCallback: Applying option hide_banner (do not show program banner) with argument 1.

2023-03-12 15:17:02.210 :: ffmpeg-kit-debug: LogCallback: Applying option y (overwrite output files) with argument 1.

2023-03-12 15:17:02.212 :: ffmpeg-kit-debug: LogCallback: Successfully parsed a group of options.

2023-03-12 15:17:02.217 :: ffmpeg-kit-debug: LogCallback: parse_optgroup done
2023-03-12 15:17:02.220 :: ffmpeg-kit-debug: LogCallback: term_init
2023-03-12 15:17:02.224 :: ffmpeg-kit-debug: LogCallback: term_init done
2023-03-12 15:17:02.226 :: ffmpeg-kit-debug: LogCallback: open_files INPUT
2023-03-12 15:17:02.228 :: ffmpeg-kit-debug: LogCallback: Parsing a group of options: input url saf:6.MP4.

2023-03-12 15:17:02.232 :: ffmpeg-kit-debug: LogCallback: Successfully parsed a group of options.

2023-03-12 15:17:02.236 :: ffmpeg-kit-debug: LogCallback: Opening an input file: saf:6.MP4.

2023-03-12 15:17:02.239 :: ffmpeg-kit-debug: LogCallback: [NULL @ 0xb40000730dd9dbf0] Opening 'saf:6.MP4' for reading

2023-03-12 15:17:02.243 :: ffmpeg-kit-debug: LogCallback: [saf @ 0xb40000728de0ca10] Setting default whitelist 'saf,crypto,data'

2023-03-12 15:17:02.245 :: ffmpeg-kit-debug: LogCallback: fd_open start
2023-03-12 15:17:02.248 :: ffmpeg-kit-debug: LogCallback: fd_open opening
2023-03-12 15:17:02.252 :: ffmpeg-kit-debug: LogCallback: fd_open opened
2023-03-12 15:17:02.255 :: ffmpeg-kit-debug: LogCallback: mov_probe
2023-03-12 15:17:02.257 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Format mov,mp4,m4a,3gp,3g2,mj2 probed with size=2048 and score=100

2023-03-12 15:17:02.260 :: ffmpeg-kit-debug: LogCallback: mov_read_header
2023-03-12 15:17:02.264 :: ffmpeg-kit-debug: LogCallback: mov_read_header seeking
2023-03-12 15:17:02.268 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] ISO: File Type Major Brand: mp41

2023-03-12 15:17:02.270 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp dict set
2023-03-12 15:17:02.273 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp mov_aaxc_crypto before
2023-03-12 15:17:02.276 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp mov_aaxc_crypto done
-----------------------------------------------------------------------------------------------
2023-03-12 15:17:12.909 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.911 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.915 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.918 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.920 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.923 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Processing st: 0, edit list 0 - media time: 0, duration: 39436397

2023-03-12 15:17:12.925 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.931 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.935 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.938 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.945 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.951 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.958 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.965 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.971 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.975 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.978 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.985 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.988 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.990 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.992 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.994 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.996 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.997 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.999 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:13.002 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:13.003 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] All samples in data stream index:id [4:5] have zero duration, stream set to be discarded by default. Override using AVStream->discard or -discard for ffmpeg command.

2023-03-12 15:17:13.006 :: ffmpeg-kit-debug: LogCallback: mov_read_header seek done
2023-03-12 15:17:13.008 :: ffmpeg-kit-debug: LogCallback: mov_read_header parse done
2023-03-12 15:17:13.010 :: ffmpeg-kit-debug: LogCallback: mov_read_header trex data read done
2023-03-12 15:17:13.011 :: ffmpeg-kit-debug: LogCallback: mov_read_header bitrate calculation code
2023-03-12 15:17:13.013 :: ffmpeg-kit-debug: LogCallback: mov_read_header fps calculation done
2023-03-12 15:17:13.015 :: ffmpeg-kit-debug: LogCallback: mov_read_header read side data done
2023-03-12 15:17:13.017 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Before avformat_find_stream_info() pos: 3720541874 bytes read:3720541874 seeks:0 nb_streams:5

2023-03-12 15:17:13.019 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 7(SPS), nal_ref_idc: 1

2023-03-12 15:17:13.022 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 8(PPS), nal_ref_idc: 1

2023-03-12 15:17:13.024 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.028 :: ffmpeg-kit-debug: LogCallback: fd_seek seeking
2023-03-12 15:17:13.030 :: ffmpeg-kit-debug: LogCallback: fd_seek seek done
2023-03-12 15:17:13.033 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 7(SPS), nal_ref_idc: 1

2023-03-12 15:17:13.035 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 8(PPS), nal_ref_idc: 1

2023-03-12 15:17:13.038 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 9(AUD), nal_ref_idc: 0

2023-03-12 15:17:13.042 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 5(IDR), nal_ref_idc: 1

2023-03-12 15:17:13.047 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] Format yuvj420p chosen by get_format().

2023-03-12 15:17:13.059 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] Reinit context to 1920x1088, pix_fmt: yuvj420p

2023-03-12 15:17:13.066 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] All info found

2023-03-12 15:17:13.071 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.075 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.077 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.081 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.087 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.090 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.093 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] After avformat_find_stream_info() pos: 323745 bytes read:3720930284 seeks:1 frames:3

2023-03-12 15:17:13.096 :: ffmpeg-kit-debug: LogCallback: Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'saf:6.MP4':

2023-03-12 15:17:13.099 :: ffmpeg-kit-debug: LogCallback:   Metadata:

2023-03-12 15:17:13.102 :: ffmpeg-kit-debug: LogCallback:     major_brand     : 
2023-03-12 15:17:13.108 :: ffmpeg-kit-debug: LogCallback: mp41
2023-03-12 15:17:13.110 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.113 :: ffmpeg-kit-debug: LogCallback:     minor_version   : 
2023-03-12 15:17:13.115 :: ffmpeg-kit-debug: LogCallback: 538120216
2023-03-12 15:17:13.119 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.121 :: ffmpeg-kit-debug: LogCallback:     compatible_brands: 
2023-03-12 15:17:13.124 :: ffmpeg-kit-debug: LogCallback: mp41
2023-03-12 15:17:13.127 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.129 :: ffmpeg-kit-debug: LogCallback:     creation_time   : 
2023-03-12 15:17:13.132 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.136 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.138 :: ffmpeg-kit-debug: LogCallback:     firmware        : 
2023-03-12 15:17:13.140 :: ffmpeg-kit-debug: LogCallback: HD9.01.01.60.00
2023-03-12 15:17:13.142 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.144 :: ffmpeg-kit-debug: LogCallback:   Duration: 
2023-03-12 15:17:13.146 :: ffmpeg-kit-debug: LogCallback: 00:10:57.27
2023-03-12 15:17:13.149 :: ffmpeg-kit-debug: LogCallback: , start: 
2023-03-12 15:17:13.152 :: ffmpeg-kit-debug: LogCallback: 0.000000
2023-03-12 15:17:13.154 :: ffmpeg-kit-debug: LogCallback: , bitrate: 
2023-03-12 15:17:13.157 :: ffmpeg-kit-debug: LogCallback: 45284 kb/s
2023-03-12 15:17:13.159 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.161 :: ffmpeg-kit-debug: LogCallback:   Chapters:

2023-03-12 15:17:13.164 :: ffmpeg-kit-debug: LogCallback:     Chapter #0:0: 
2023-03-12 15:17:13.166 :: ffmpeg-kit-debug: LogCallback: start 619.735000, 
2023-03-12 15:17:13.169 :: ffmpeg-kit-debug: LogCallback: end 657.273000

2023-03-12 15:17:13.171 :: ffmpeg-kit-debug: LogCallback:   Stream #0:0
2023-03-12 15:17:13.174 :: ffmpeg-kit-debug: LogCallback: [0x1]
2023-03-12 15:17:13.175 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.177 :: ffmpeg-kit-debug: LogCallback: , 1, 1/60000
2023-03-12 15:17:13.182 :: ffmpeg-kit-debug: LogCallback: : Video: h264, 1 reference frame (avc1 / 0x31637661), yuvj420p(pc, bt709, progressive, left), 1920x1080 (1920x1088) [SAR 1:1 DAR 16:9], 0/1, 45005 kb/s
2023-03-12 15:17:13.185 :: ffmpeg-kit-debug: LogCallback: , 
2023-03-12 15:17:13.189 :: ffmpeg-kit-debug: LogCallback: 59.94 fps, 
2023-03-12 15:17:13.192 :: ffmpeg-kit-debug: LogCallback: 59.94 tbr, 
2023-03-12 15:17:13.194 :: ffmpeg-kit-debug: LogCallback: 60k tbn
2023-03-12 15:17:13.197 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.199 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.200 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.203 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.204 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.206 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.209 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.211 :: ffmpeg-kit-debug: LogCallback: GoPro AVC  
2023-03-12 15:17:13.213 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.216 :: ffmpeg-kit-debug: LogCallback:       vendor_id       : 
2023-03-12 15:17:13.219 :: ffmpeg-kit-debug: LogCallback: [0][0][0][0]
2023-03-12 15:17:13.220 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.223 :: ffmpeg-kit-debug: LogCallback:       encoder         : 
2023-03-12 15:17:13.225 :: ffmpeg-kit-debug: LogCallback: GoPro AVC encoder
2023-03-12 15:17:13.227 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.229 :: ffmpeg-kit-debug: LogCallback:   Stream #0:1
2023-03-12 15:17:13.231 :: ffmpeg-kit-debug: LogCallback: [0x2]
2023-03-12 15:17:13.233 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.236 :: ffmpeg-kit-debug: LogCallback: , 1, 1/48000
2023-03-12 15:17:13.238 :: ffmpeg-kit-debug: LogCallback: : Audio: aac (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 189 kb/s
2023-03-12 15:17:13.240 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.243 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.244 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.247 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.252 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.255 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.257 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.259 :: ffmpeg-kit-debug: LogCallback: GoPro AAC  
2023-03-12 15:17:13.262 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.264 :: ffmpeg-kit-debug: LogCallback:       vendor_id       : 
2023-03-12 15:17:13.268 :: ffmpeg-kit-debug: LogCallback: [0][0][0][0]
2023-03-12 15:17:13.271 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.274 :: ffmpeg-kit-debug: LogCallback:   Stream #0:2
2023-03-12 15:17:13.276 :: ffmpeg-kit-debug: LogCallback: [0x3]
2023-03-12 15:17:13.278 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.280 :: ffmpeg-kit-debug: LogCallback: , 1, 1/60000
2023-03-12 15:17:13.281 :: ffmpeg-kit-debug: LogCallback: : Data: none (tmcd / 0x64636D74), 0/1
2023-03-12 15:17:13.284 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.288 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.290 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.291 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.292 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.294 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.296 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.299 :: ffmpeg-kit-debug: LogCallback: GoPro TCD  
2023-03-12 15:17:13.302 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.306 :: ffmpeg-kit-debug: LogCallback:   Stream #0:3
2023-03-12 15:17:13.309 :: ffmpeg-kit-debug: LogCallback: [0x4]
2023-03-12 15:17:13.310 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.313 :: ffmpeg-kit-debug: LogCallback: , 0, 1/1000
2023-03-12 15:17:13.316 :: ffmpeg-kit-debug: LogCallback: : Data: bin_data (gpmd / 0x646D7067), 0/1, 61 kb/s
2023-03-12 15:17:13.318 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.320 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.322 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.325 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.327 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.329 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.331 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.335 :: ffmpeg-kit-debug: LogCallback: GoPro MET  
2023-03-12 15:17:13.337 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.339 :: ffmpeg-kit-debug: LogCallback:   Stream #0:4
2023-03-12 15:17:13.342 :: ffmpeg-kit-debug: LogCallback: [0x5]
2023-03-12 15:17:13.344 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.345 :: ffmpeg-kit-debug: LogCallback: , 0, 1/60000
2023-03-12 15:17:13.347 :: ffmpeg-kit-debug: LogCallback: : Data: none (fdsc / 0x63736466), 0/1, 13 kb/s
2023-03-12 15:17:13.350 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.352 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.354 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.356 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.359 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.361 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.365 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.369 :: ffmpeg-kit-debug: LogCallback: GoPro SOS  
2023-03-12 15:17:13.372 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.374 :: ffmpeg-kit-debug: LogCallback: Successfully opened the file.

2023-03-12 15:17:13.376 :: ffmpeg-kit-debug: LogCallback: open_files INPUT done
2023-03-12 15:17:13.378 :: ffmpeg-kit-debug: LogCallback: apply_sync_offsets
2023-03-12 15:17:13.382 :: ffmpeg-kit-debug: LogCallback: apply_sync_offsets done
2023-03-12 15:17:13.386 :: ffmpeg-kit-debug: LogCallback: init_complex_filters
2023-03-12 15:17:13.389 :: ffmpeg-kit-debug: LogCallback: init_complex_filters done
2023-03-12 15:17:13.391 :: ffmpeg-kit-debug: LogCallback: open_files OUTPUT
2023-03-12 15:17:13.392 :: ffmpeg-kit-debug: LogCallback: open_files OUTPUT done
2023-03-12 15:17:13.395 :: ffmpeg-kit-debug: LogCallback: check_filter_outputs
2023-03-12 15:17:13.397 :: ffmpeg-kit-debug: LogCallback: check_filter_outputs done
2023-03-12 15:17:13.399 :: ffmpeg-kit-debug: LogCallback: ffmpeg_parse_options done
2023-03-12 15:17:13.402 :: ffmpeg-kit-debug: LogCallback: At least one output file must be specified

2023-03-12 15:17:13.405 :: ffmpeg-kit-debug: LogCallback: [AVIOContext @ 0xb4000072dddd6510] Statistics: 3720930284 bytes read, 1 seeks

2023-03-12 15:17:13.407 :: ffmpeg-kit-debug: LogCallback: fd_close start
2023-03-12 15:17:13.410 :: ffmpeg-kit-debug: LogCallback: fd_close done
2023-03-12 15:17:13.412 :: ffmpeg-kit-debug: LogCallback: setjmp done


  


  Note : I have built ffmpeg-kit with the following script :

  


  #!/bin/bash

export ENCODERS="libvorbis,libvpx_vp8,libvpx_vp9,libx264,libx265,mpeg1video,mpeg2video,mpeg4,flv,wmv1,wmv2,msmpeg4v3,libaom_av1,\
h261,h263,theora,libtheora,png,aac,ac3,alac,libopencore_amrnb,libvo_amrwbenc,eac3,flac,mp2,libtwolame,libmp3lame,libopus,libspeex,wavpack,wmav1,wmav2,pcm_s16le,\
ssa,ass,dvbsub,dvdsub,movtext,srt,subrip,text,ttml,webvtt,xsub"

export SETTINGS="--disable-indevs \
  --enable-pthreads \
  --enable-indev=lavfi \
  --disable-outdevs \
  --disable-protocols \
  --enable-protocol=file,fd,saf,async \
  --disable-encoders \
  --enable-encoder=${ENCODERS}"

export CUSTOM_CONFIG=${SETTINGS}

./android.sh --disable-arm-v7a --disable-arm-v7a-neon --disable-x86 --disable-x86-64 --enable-android-media-codec --enable-android-zlib --enable-chromaprint --enable-dav1d --enable-fontconfig --enable-freetype --enable-fribidi --enable-gmp --enable-gnutls --enable-kvazaar --enable-lame --enable-libaom --enable-libass --enable-libiconv --enable-libilbc --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libxml2 --enable-opencore-amr --enable-openh264 --enable-opus --enable-sdl --enable-shine --enable-snappy --enable-soxr --enable-speex --enable-tesseract --enable-twolame --enable-vo-amrwbenc --enable-zimg --enable-x264 --enable-x265 --enable-gpl -l


  


  Sample file : https://drive.google.com/file/d/1lvCiOBQqBEnUECn_HJi8qUoaCPCBnkgO/view?usp=share_link

  


  Ffmpeg version : 5.1.

  


• 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.

• 5 Top Google Optimize Alternatives to Consider

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular conversion rate optimization (CRO) tool from Alphabet (parent company of Google). With it, you can run A/B, multivariate, and redirect tests to figure out which web page designs perform best. 

  Google Optimize seamlessly integrates with Google Analytics (GA). It also has a free tier. So many marketers chose it as their default A/B testing tool…until recently. 

  Google will sunset Google Optimize by 30 September 2023. 

  Starting from this date, Google will no longer support Optimize and Optimize 360 (premium edition). All experiments, active after this date, will be paused automatically and you’ll no longer have access to your historical records (unless these are exported in advance).

  The better news is that you still have time to find a Google Optimize alternative — and this post will help you with that. 

  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. 

  Best Google Optimize Alternatives 

  Google Optimize was among the first free A/B testing apps. But as with any product, it has some disadvantages. 

  Data updates happen every 24 hours, not in real-time. A free account has caps on the number of experiments. You cannot run more than 5 experiments at a time or implement over 16 combinations for multivariate testing (MVT). A premium version (Optimize 365) has fewer usage constraints, but it costs north of $150K per year. 

  Google Optimize has native integration with GA (of course), so you can review all the CRO data without switching apps. But Optimize doesn’t work well with Google Analytics alternatives, which many choose to use for privacy-friendly user tracking, higher data accuracy and GDPR compliance. 

  At the same time, many other conversion rate optimization (CRO) tools have emerged, often boasting better accuracy and more competitive features than Google Optimize.

  Here are 5 alternative A/B testing apps worth considering.

  Adobe Target 

  

  Adobe Target is an advanced personalization platform for optimising user and marketing experiences on digital properties. It uses machine learning algorithms to deliver dynamic content, personalised promotions and custom browsing experiences to visitors based on their behaviour and demographic data. 

  Adobe Target also provides A/B testing and multivariate testing (MVT) capabilities to help marketers test and refine their digital experiences.

  Key features : 

  • Visual experience builder for A/B tests setup and replication 
  • Full factorial multivariate tests and multi-armed bandit testing
  • Omnichannel personalisation across web properties 
  • Multiple audience segmentation and targeting options 
  • Personalised content, media and product recommendations 
  • Advanced customer intelligence (in conjunction with other Adobe products)

  Pros

  • Convenient A/B test design tool 
  • Acucate MVT and MAB results 
  • Powerful segmentation capabilities 
  • Access to extra behavioural analytics 
  • One-click personalisation activation 
  • Supports rules-based, location-based and contextual personalisation
  • Robust omnichannel analytics in conjunction with other Adobe products 

  Cons 

  • Requires an Adobe Marketing Cloud subscription 
  • No free trial or freemium tier 
  • More complex product setup and configuration 
  • Steep learning curve for new users 

  Price : On-demand. 

  Adobe Target is sold as part of Adobe Marketing Cloud. Licence costs vary, based on selected subscriptions and the number of users, but are typically above $10K.

  Google Optimize vs Adobe Target : The Verdict 

  Google Optimize comes with a free tier, unlike Adobe Target. It provides you with a basic builder for A/B and MVT tests, but none of the personalisation tools Adobe has. Because of ease-of-use and low price, other Google Optimize alternatives are better suited for small to medium-sized businesses, doing baseline CRO for funnel optimisation. 

  Adobe Target pulls you into the vast Adobe marketing ecosystem, offering omnipotent customer behaviour analytics, machine-learning-driven website optimisation, dynamic content recommendations, product personalisation and extensive reporting. The app is better suited for larger enterprises with a significant investment in digital marketing.

  Matomo A/B Testing

  

  Matomo A/B Testing is a CRO tool, integrated into Matomo. All Matomo Cloud users get instant access to it, while On-Premise (free) Matomo users can purchase A/B testing as a plugin. 

  With Matomo A/B Testing, you can create multiple variations of a web or mobile page and test them with different segments of their audience. Matomo also doesn’t have any strict experiment caps, unlike Google Optimize. 

  You can split-test multiple creative variants for on-site assets such as buttons, slogans, titles, call-to-actions, image positions and more. You can even benchmark the performance of two (or more !) completely different homepage designs, for instance. 

  With us, you can compliantly and ethically collect historical user data about any visitor, who’s entered any of the active tests — and monitor their entire customer journey. You can also leverage Matomo A/B Testing data as part of multi-touch attribution modelling to determine which channels bring the best leads and which assets drive them towards conversion. 

   

  Since Matomo A/B Testing is part of our analytics platform, it works well with other features such as goal tracking, heatmaps, user session recordings and more. 

  Key features

  • Run experiments for web, mobile, email and digital campaigns 
  • Convenient A/B test design interface 
  • One-click experiment scheduling 
  • Integration with historic visitor profiles
  • Near real-time conversion tracking 
  • Apply segmentation to Matomo reports 
  • Easy creative variation sharing via a URL 

  Pros

  • High data accuracy with no reporting gaps 
  • Monitor the evolution of your success metrics for each variation
  • Embed experiments across multiple digital channels 
  • Set a custom confidence threshold for winning variations 
  • No compromises on user privacy 
  • Free 21-day trial available (for Matomo Cloud) and free 30-day plugin trial (for Matomo On-Premise)

  Cons

  • No on-site personalisation tools available 
  • Configuration requires some coding experience 

  Price : Matomo A/B Testing is included in the monthly Cloud plan (starting at €19 per month). On-Premise users can buy this functionality as a plugin (starting at €199/year). 

  Google Optimize vs Matomo A/B Testing : The Verdict 

  Matomo offers the same types of A/B testing features as Google Optimize (and some extras !), but without any usage caps. Unlike Matomo, Google Optimize doesn’t support A/B tests for mobile apps. You can access some content testing features for Android Apps via Firebase, but this requires another subscription. 

  Matomo lets you run A/B experiments across the web and mobile properties, plus desktop apps, email campaigns and digital ads. Also, Matomo has higher conversion data accuracy, thanks to our privacy-focused method for collecting website analytics. 

  When using Matomo in most EU markets, you’re legally exempt from showing a cookie consent banner. Meaning you can collect richer insights for each experiment and make data-driven decisions. Nearly 40% of global consumers reject cookie consent banners. With most other tools, you won’t be getting the full picture of your traffic. 

  Optimizely 

  

  Optimizely is a conversion optimization platform that offers several competitive products for a separate subscription. These include a flexible content management system (CMS), a content marketing platform, a web A/B testing app, a mobile featuring testing product and two eCommerce-specific website management products.

  The Web Experimentation app allows you to optimise every customer touchpoint by scheduling unlimited split or multi-variant tests and conversions across all your projects from the same app. Apart from websites, this subscription also supports experiments for single-page applications. But if you want more advanced mobile app testing features, you’ll have to purchase another product — Feature Experimentation. 

  Key features :

  • Intuitive experiment design tool 
  • Cross-browser testing and experiment preview 
  • Multi-page funnel tests design 
  • Behavioural and geo-targeting 
  • Exit/bounce rate tracking
  • Custom audience builder for experiments
  • Comprehensive reporting 

  Pros

  • Unlimited number of concurrent experiments 
  • Upload your audience data for test optimisation 
  • Dynamic content personalisation available on a higher tier 
  • Pre-made integrations with popular heatmap and analytics tools 
  • Supports segmentation by device, campaign type, traffic sources or referrer 

  Cons

  • You need a separate subscription for mobile CRO 
  • Free trial not available, pricing on-demand 
  • Multiple licences and subscriptions may be required 
  • Doesn’t support A/B tests for emails 

  Price : Available on-demand. 

  Web Experimentation tool has three subscription tiers — Grow, Accelerate, and Scale with different features included. 

  Google Optimize vs Optimizely : The Verdict 

  Optimizely is a strong contender for Google Optimize alternative as it offers more advanced audience targeting and segmentation options. You can target users by IP address, cookies, traffic sources, device type, browser, language, location or a custom utm_campaign parameter.

  Similar to Matomo A/B testing, Optimizely doesn’t limit the number of projects or concurrent experiments you can do. But you have to immediately sign an annual contract (no monthly plans are available). Pricing also varies based on the number of processed impressions (more experiments = a higher annual bill). An annual licence can cost $63,700 for 10 million impressions on average, according to an independent estimate. 

  Visual Website Optimizer (VWO) 

  

  VWO is another popular experimentation platform, supporting web, mobile and server-side A/B testing and personalisation campaigns.

  Similar to others, VWO offers a drag-and-drop visual editor for creating campaign variants. You don’t need design or coding knowledge to create tests. Once you’re all set, the app will benchmark your experiment performance against expected conversion rates, report on differences in conversion rate and point towards the best-performing creative. 

  Similar to Optimizely, VWO also offers web/mobile app optimisation as a separate subscription. Apart from testing visual page elements, you can also run in-app experiments throughout the product stack to locate new revenue opportunities. For example, you can test in-app subscription flows, search algorithms or navigation flows to improve product UX. 

  Key features :

  • Multivariate and multi-arm bandit tests 
  • Multi-step (funnel) split tests 
  • Collaborative experiment tracking dashboard 
  • Target users by different attributes (URL, device, geo-data) 
  • Personal library of creative elements 
  • Funnel analytics, session records, and heatmaps available 

  Pros

  • Free starter plan is available (similar to Google Optimize)
  • Simple tracking code installation and easy code editor
  • Offers online reporting dashboards and report downloads 
  • Slice-and-dice reports by different audience dimensions
  • No impact on website/app loading speed and performance 

  Cons

  • Multivariate testing is only available on a higher-tier plan 
  • Annual contract required, despite monthly billing 
  • Mobile app A/B split tests require another licence 
  • Requires ongoing user training 

  Price : Free limited plan available. 

  Then from $356/month, billed annually. 

  Google Optimize vs VWO : The Verdict 

  The free plan on VWO is very similar to Google Optimize. You get access to A/B testing and split URL testing features for websites only. The visual editing tool is relatively simple — and you can use URL or device targeting. 

  Free VWO reports, however, lack the advertised depth in terms of behavioural or funnel-based reporting. In-depth insights are available only to premium users. Extra advertised features like heatmaps, form analytics and session recordings require yet another subscription. With Matomo Cloud, you get all three of these together with A/B testing. 

  ConvertFlow 

  

  ConvertFlow markets itself as a funnel optimisation app for eCommerce and SaaS companies. It meshes lead generation tools with some CRO workflows. 

  With ConvertFlow, you can effortlessly design opt-in forms, pop-ups, quizzes and even entire landing pages using pre-made web elements and a visual builder. Afterwards, you can put all of these assets to a “field test” via the ConvertFlow CRO platform. Select among pre-made templates or create custom variants for split or multivariate testing. You can customise tests based on URLs, cookie data and user geolocation among other factors. 

  Similar to Adobe Target, ConvertFlow also allows you to run tests targeted at specific customer segments in your CRM. The app has native integrations with HubSpot and Salesforce, so this feature is easy to enable. ConvertFlow also offers advanced targeting and segmentation options, based on user on-site behaviour, demographics data or known interests.

  Key features :

  • Create and test landing pages, surveys, quizzes, pop-ups, surveys and other lead-gen assets. 
  • All-in-one funnel builder for creating demand-generation campaigns 
  • Campaign personalisation, based on on-site activity 
  • Re-usable dynamic visitor segments for targeting 
  • Multi-step funnel design and customisation 
  • Embedded forms for split testing CTAs on existing pages 

  Pros

  • Allows controlling the traffic split for each variant to get objective results 
  • Pre-made integration with Google Analytics and Google Tag Manager 
  • Conversion and funnel reports, available for each variant 
  • Access to a library with 300+ conversion campaign templates
  • Apply progressive visitor profiling to dynamically adjust user experiences 

  Cons

  • Each plan covers only $10K views. Each extra 10k costs another $20/mo 
  • Only one website allowed per account (except for Teams plan) 
  • Doesn’t support experiments in mobile app 
  • Not all CRO features are available on a Pro plan. 

  Price : Access to CRO features costs from $300/month on a Pro plan. Subscription costs also increase, based on the total number of monthly views. 

  Google Optimize vs CovertFlow : The Verdict 

  ConvertFlow is equally convenient to use in conjunction with Google Analytics as Google Optimize is. But the similarities end up here since ConvertFlow combines funnel design features with CRO tools. 

  With ConvertFlow, you can run more advanced experiments and apply more targeting criteria than with Google Optimize. You can observe user behaviour and conversion rates across multi-step CTA forms and page funnels, plus benefit from first-touch attribution reporting without switching apps. 

  Though CovertFlow has a free plan, it doesn’t include access to CRO features. Meaning it’s not a free alternative to Google Optimize.

  Comparison of the Top 5 Google Optimize Alternatives

  Feature	Google Optimize	Adobe Target	Matomo A/B test	Optimizely	VWO	ConvertFlow
  Supported channels	Web	Web, mobile, social media, email	Web, mobile, email, digital campaigns	Websites & mobile apps	Websites, web and mobile apps	Websites and mobile apps
  A/B testing
  Easy GA integration
  Integrations with other web analytics apps
  Audience segmentation	Basic	Advanced	Advanced	Advanced	Advanced	Advanced
  Geo-targeting
  Behavioural targeting	Basic	Advanced	Advanced	Advanced	Advanced	Advanced
  Heatmaps			No extra cost with Matomo Cloud	〰️ *via integrations	〰️ *requires another subscription
  Session recordings			No extra cost with Matomo Cloud		〰️ *requires another subscription
  Multivariate testing (MVT)
  Dynamic personalisation					〰️ *only on higher account tiers	〰️ *only on the highest account tiers
  Product recommendations				〰️ *requires another subscription	〰️ *requires another subscription
  Support	Self-help desk on a free tier	Email, live-chat, phone support	Email, self-help guides and user forum	Knowledge base, online tickets, user community	Self-help guides, email, phone	Knowledge base, email, and live chat support
  Price	Freemium	On-demand	From €19 for Cloud subscription From €199/year as plugin for On-Premise	On-demand	Freemium From $365/mo	From $300/month

  Conclusion 

  Google Optimize has served marketers well for over five years. But as the company decided to move on — so should you. 

  Oher A/B testing tools like Matomo, Optimizely or VWO offer better funnel analytics and split testing capabilities without any usage caps. Also, tools like Adobe Target, Optimizely, and VWO offer advanced content personalisation, based on aggregate analytics. However, they also come with much higher subscription costs.

  Matomo is a robust, compliant and cost-effective alternative to Google Optimize. Our tool allows you to schedule campaigns across all digital mediums (and even desktop apps !) without a