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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.

  


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

  13 mars 2023, par Erin — Analytics Tips

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

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

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

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

  Pros and Cons of Different Attribution Models 

  

  First Interaction 

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

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

  Pros 

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

  Cons 

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

  Last Interaction 

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

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

  Pros 

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

  Cons 

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

  Last Non-Direct Interaction 

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

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

  Pros 

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

  Cons 

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

  Linear Model

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

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

  Pros 

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

  Cons 

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

  Time Decay Model 

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

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

  Pros 

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

  Cons 

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

  Position-Based Model 

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

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

  Pros 

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

  Cons 

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

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

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

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

  Marketing Objectives 

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

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

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

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

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

  Sales Cycle Length 

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

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

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

  Data Availability 

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

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

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

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

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

  How to Implement Multi-Touch Attribution

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

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

  Select a Multi-Touch Attribution Tool 

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

  To make the right call prioritise five factors :

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

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

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

  Set Up Proper Data Collection 

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

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

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

  Configure Goals and Events 

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

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

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

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

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

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

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

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

  Test and Validated the Selected Model 

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

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

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

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

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

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

  Conclusion

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

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

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

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

• Benefits and Shortcomings of Multi-Touch Attribution

  13 mars 2023, par Erin — Analytics Tips

  Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer. 

  Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales. 

  Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates. 

  The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process. 

  If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it. 

  What Are the Benefits of Multi-Touch Attribution ?

  Remember an old parable of blind men and an elephant ?

  Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

  Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too. 

  Better Understanding of Customer Journeys 

  On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages : 

  • Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel). 
  • Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel). 
  • Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel). 

  You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel. 

  For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion. 

  This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that. 

  Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.

  

  Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion. 

  For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion. 

  A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines. 

  The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

  Improved Budget Allocation 

  Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

  First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions. 

  For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

  

  By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types). 

  Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

  “Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.

  More Accurate Measurements 

  The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance. 

  In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking. 

  Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

  • How many touchpoints are involved in the conversions ? 
  • How long does it take for a lead to convert on average ? 
  • When and where do different audience groups convert ? 
  • What is your average win rate for different types of campaigns ?

  Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect. 

  At the highest level, you need to collect two data points :

  • Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals. 
  • Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events. 

  Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them. 

  The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used. 

  Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo). 

  Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

  Learn more about selecting the optimal multi-channel attribution model for your business.

  What Are the Limitations of Multi-Touch Attribution ?

  Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry. 

  Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email. 

  In addition, you should keep in mind several other limitations of multi-touch attribution software.

  Limited Marketing Mix Analysis 

  Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

  Multi-touch attribution tools cannot evaluate the impact of :

  • Dark social channels 
  • Word-of-mouth 
  • Offline promotional events
  • TV or out-of-home ad campaigns 

  If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.

  Time-Based Constraints 

  Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles. 

  

  Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel. 

  At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc. 

  Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ? 

  The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time. 

  

  Limited Access to Raw Data 

  In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied. 

  Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues. 

  In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making. 

  With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data. 

  AI Application 

  On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies. 

  To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

  Difficult Technical Implementation 

  Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

  

  Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc. 

  Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams. 

  Conclusion 

  Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations. 

  That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool. 

  Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool ! 

  Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried.