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extract subtitle from video ffmpeg. subs.srt : Invalid argument
3 juillet 2019, par evgeni fotialet filename_ext = file.path.split('/').pop()
let filename = filename_ext.split('.').slice(0, filename_ext.split('.').length-1).join('.')
var result = ffmpeg({
MEMFS: [{name: filename_ext, data: buffer}],
arguments: ["-i", filename_ext, "-map", "0:s:0", "subs.srt"],
// Ignore stdin read requests
stdin: function() {},
});
// Write out.webm to disk.
var out = result.MEMFS[0];
fs.outputFile(pathname + '/' + out.name, Buffer(out.data), 'binary');I get the following
ffmpeg version n3.1.2 Copyright (c) 2000-2016 the FFmpeg developers
built with emcc (Emscripten gcc/clang-like replacement) 1.36.7 ()
configuration: --cc=emcc --enable-cross-compile --target-os=none --arch=x86 --disable-runtime-cpudetect --disable-asm --disable-fast-unaligned --disable-pthreads --disable-w32threads --disable-os2threads --disable-debug --disable-stripping --disable-all --enable-ffmpeg --enable-avcodec --enable-avformat --enable-avutil --enable-swresample --enable-swscale --enable-avfilter --disable-network --disable-d3d11va --disable-dxva2 --disable-vaapi --disable-vda --disable-vdpau --enable-decoder=vp8 --enable-decoder=vp9 --enable-decoder=theora --enable-decoder=mpeg2video --enable-decoder=mpeg4 --enable-decoder=h264 --enable-decoder=hevc --enable-decoder=png --enable-decoder=mjpeg --enable-decoder=vorbis --enable-decoder=opus --enable-decoder=mp3 --enable-decoder=ac3 --enable-decoder=aac --enable-decoder=ass --enable-decoder=ssa --enable-decoder=srt --enable-decoder=webvtt --enable-demuxer=matroska --enable-demuxer=ogg --enable-demuxer=avi --enable-demuxer=mov --enable-demuxer=flv --enable-demuxer=mpegps --enable-demuxer=image2 --enable-demuxer=mp3 --enable-demuxer=concat --enable-protocol=file --enable-filter=aresample --enable-filter=scale --enable-filter=crop --enable-filter=overlay --disable-bzlib --disable-iconv --disable-libxcb --disable-lzma --disable-sdl --disable-securetransport --disable-xlib --disable-zlib --enable-encoder=libvpx_vp8 --enable-encoder=libopus --enable-encoder=mjpeg --enable-muxer=webm --enable-muxer=ogg --enable-muxer=null --enable-muxer=image2 --enable-filter=subtitles --enable-libass --enable-libopus --enable-libvpx --extra-cflags=-I../libvpx/dist/include --extra-ldflags=-L../libvpx/dist/lib
libavutil 55. 28.100 / 55. 28.100
libavcodec 57. 48.101 / 57. 48.101
libavformat 57. 41.100 / 57. 41.100
libavfilter 6. 47.100 / 6. 47.100
libswscale 4. 1.100 / 4. 1.100
libswresample 2. 1.100 / 2. 1.100
[h264 @ 0x7d7510] Warning: not compiled with thread support, using thread emulation
[aac @ 0x7d81c0] Warning: not compiled with thread support, using thread emulation
[ssa @ 0x7d8e30] Warning: not compiled with thread support, using thread emulation
Input #0, matroska,webm, from 'censored filename.mkv':
Metadata:
encoder : no_variable_data
creation_time : 1970-01-01 00:00:00
Duration: 00:23:40.13, start: 0.000000, bitrate: 2789 kb/s
Stream #0:0: Video: h264 (High), yuv420p, 1280x720 [SAR 1:1 DAR 16:9], 23.98 fps, 23.98 tbr, 1k tbn, 47.95 tbc (default)
Metadata:
BPS : 2658044
BPS-eng : 2658044
DURATION : 00:23:40.045000000
DURATION-eng : 00:23:40.045000000
NUMBER_OF_FRAMES: 34047
NUMBER_OF_FRAMES-eng: 34047
NUMBER_OF_BYTES : 471817808
NUMBER_OF_BYTES-eng: 471817808
_STATISTICS_WRITING_APP: no_variable_data
_STATISTICS_WRITING_APP-eng: no_variable_data
_STATISTICS_WRITING_DATE_UTC: 1970-01-01 00:00:00
_STATISTICS_WRITING_DATE_UTC-eng: 1970-01-01 00:00:00
_STATISTICS_TAGS: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
_STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
Stream #0:1(jpn): Audio: aac (LC), 44100 Hz, stereo, fltp (default)
Metadata:
BPS : 128000
BPS-eng : 128000
DURATION : 00:23:40.109000000
DURATION-eng : 00:23:40.109000000
NUMBER_OF_FRAMES: 61159
NUMBER_OF_FRAMES-eng: 61159
NUMBER_OF_BYTES : 22721748
NUMBER_OF_BYTES-eng: 22721748
_STATISTICS_WRITING_APP: no_variable_data
_STATISTICS_WRITING_APP-eng: no_variable_data
_STATISTICS_WRITING_DATE_UTC: 1970-01-01 00:00:00
_STATISTICS_WRITING_DATE_UTC-eng: 1970-01-01 00:00:00
_STATISTICS_TAGS: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
_STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
Stream #0:2(eng): Subtitle: ass (default)
Metadata:
BPS : 110
BPS-eng : 110
DURATION : 00:23:25.280000000
DURATION-eng : 00:23:25.280000000
NUMBER_OF_FRAMES: 298
NUMBER_OF_FRAMES-eng: 298
NUMBER_OF_BYTES : 19407
NUMBER_OF_BYTES-eng: 19407
_STATISTICS_WRITING_APP: no_variable_data
_STATISTICS_WRITING_APP-eng: no_variable_data
_STATISTICS_WRITING_DATE_UTC: 1970-01-01 00:00:00
_STATISTICS_WRITING_DATE_UTC-eng: 1970-01-01 00:00:00
_STATISTICS_TAGS: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
_STATISTICS_TAGS-eng: BPS DURATION NUMBER_OF_FRAMES NUMBER_OF_BYTES
Stream #0:3: Attachment: ttf
Metadata:
filename : OpenSans-Semibold.ttf
mimetype : application/x-truetype-font
[NULL @ 0x9eac90] Unable to find a suitable output format for 'subs.srt'
subs.srt: Invalid argumentthe file is a mkv video file
Other info
Codecs:
D..... = Decoding supported
.E.... = Encoding supported
..V... = Video codec
..A... = Audio codec
..S... = Subtitle codec
...I.. = Intra frame-only codec
....L. = Lossy compression
.....S = Lossless compression
-------
..VI.. 012v Uncompressed 4:2:2 10-bit
..V.L. 4xm 4X Movie
..VI.S 8bps QuickTime 8BPS video
..VIL. a64_multi Multicolor charset for Commodore 64
..VIL. a64_multi5 Multicolor charset for Commodore 64, extended with 5th color (colram)
..V..S aasc Autodesk RLE
..VIL. aic Apple Intermediate Codec
..VI.S alias_pix Alias/Wavefront PIX image
..VIL. amv AMV Video
..V.L. anm Deluxe Paint Animation
..V.L. ansi ASCII/ANSI art
..V..S apng APNG (Animated Portable Network Graphics) image
..VIL. asv1 ASUS V1
..VIL. asv2 ASUS V2
..VIL. aura Auravision AURA
..VIL. aura2 Auravision Aura 2
..V... avrn Avid AVI Codec
..VI.. avrp Avid 1:1 10-bit RGB Packer
..V.L. avs AVS (Audio Video Standard) video
..VI.. avui Avid Meridien Uncompressed
..VI.. ayuv Uncompressed packed MS 4:4:4:4
..V.L. bethsoftvid Bethesda VID video
..V.L. bfi Brute Force & Ignorance
..V.L. binkvideo Bink video
..VI.. bintext Binary text
..VI.S bmp BMP (Windows and OS/2 bitmap)
..V..S bmv_video Discworld II BMV video
..VI.S brender_pix BRender PIX image
..V.L. c93 Interplay C93
..V.L. cavs Chinese AVS (Audio Video Standard) (AVS1-P2, JiZhun profile)
..V.L. cdgraphics CD Graphics video
..VIL. cdxl Commodore CDXL video
..V.L. cfhd Cineform HD
..V.L. cinepak Cinepak
..VIL. cljr Cirrus Logic AccuPak
..VI.S cllc Canopus Lossless Codec
..V.L. cmv Electronic Arts CMV video
..V... cpia CPiA video format
..V..S cscd CamStudio
..VIL. cyuv Creative YUV (CYUV)
..V.LS daala Daala
..VILS dds DirectDraw Surface image decoder
..V.L. dfa Chronomaster DFA
..V.LS dirac Dirac
..VIL. dnxhd VC3/DNxHD
..VI.S dpx DPX (Digital Picture Exchange) image
..V.L. dsicinvideo Delphine Software International CIN video
..VIL. dvvideo DV (Digital Video)
..V..S dxa Feeble Files/ScummVM DXA
..VI.S dxtory Dxtory
..VIL. dxv Resolume DXV
..V.L. escape124 Escape 124
..V.L. escape130 Escape 130
..VILS exr OpenEXR image
..V..S ffv1 FFmpeg video codec #1
..VI.S ffvhuff Huffyuv FFmpeg variant
..V.L. fic Mirillis FIC
..V..S flashsv Flash Screen Video v1
..V.L. flashsv2 Flash Screen Video v2
..V..S flic Autodesk Animator Flic video
..V.L. flv1 FLV / Sorenson Spark / Sorenson H.263 (Flash Video)
..V..S fraps Fraps
..VI.S frwu Forward Uncompressed
..V.L. g2m Go2Meeting
..V..S gif GIF (Graphics Interchange Format)
..V.L. h261 H.261
D.V.L. h263 H.263 / H.263-1996, H.263+ / H.263-1998 / H.263 version 2
..V.L. h263i Intel H.263
..V.L. h263p H.263+ / H.263-1998 / H.263 version 2
D.V.LS h264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10
..VIL. hap Vidvox Hap decoder
D.V.L. hevc H.265 / HEVC (High Efficiency Video Coding)
..V.L. hnm4video HNM 4 video
..VIL. hq_hqa Canopus HQ/HQA
..VIL. hqx Canopus HQX
..VI.S huffyuv HuffYUV
..V.L. idcin id Quake II CIN video
..VI.. idf iCEDraw text
..V.L. iff_ilbm IFF ACBM/ANIM/DEEP/ILBM/PBM/RGB8/RGBN
..V.L. indeo2 Intel Indeo 2
..V.L. indeo3 Intel Indeo 3
..V.L. indeo4 Intel Indeo Video Interactive 4
..V.L. indeo5 Intel Indeo Video Interactive 5
..V.L. interplayvideo Interplay MVE video
..VILS jpeg2000 JPEG 2000
..VILS jpegls JPEG-LS
..VIL. jv Bitmap Brothers JV video
..V.L. kgv1 Kega Game Video
..V.L. kmvc Karl Morton's video codec
..VI.S lagarith Lagarith lossless
..VI.S ljpeg Lossless JPEG
..VI.S loco LOCO
..VI.S m101 Matrox Uncompressed SD
..V.L. mad Electronic Arts Madcow Video
..VI.S magicyuv MagicYUV Lossless Video
..VIL. mdec Sony PlayStation MDEC (Motion DECoder)
..V.L. mimic Mimic
DEVIL. mjpeg Motion JPEG
..VIL. mjpegb Apple MJPEG-B
..V.L. mmvideo American Laser Games MM Video
..V.L. motionpixels Motion Pixels video
..V.L. mpeg1video MPEG-1 video
D.V.L. mpeg2video MPEG-2 video
D.V.L. mpeg4 MPEG-4 part 2
..V.L. mpegvideo_xvmc MPEG-1/2 video XvMC (X-Video Motion Compensation)
..V.L. msa1 MS ATC Screen
..V.L. msmpeg4v1 MPEG-4 part 2 Microsoft variant version 1
..V.L. msmpeg4v2 MPEG-4 part 2 Microsoft variant version 2
..V.L. msmpeg4v3 MPEG-4 part 2 Microsoft variant version 3
..V..S msrle Microsoft RLE
..V.L. mss1 MS Screen 1
..VIL. mss2 MS Windows Media Video V9 Screen
..V.L. msvideo1 Microsoft Video 1
..VI.S mszh LCL (LossLess Codec Library) MSZH
..V.L. mts2 MS Expression Encoder Screen
..VIL. mvc1 Silicon Graphics Motion Video Compressor 1
..VIL. mvc2 Silicon Graphics Motion Video Compressor 2
..V.L. mxpeg Mobotix MxPEG video
..V.L. nuv NuppelVideo/RTJPEG
..V.L. paf_video Amazing Studio Packed Animation File Video
..VI.S pam PAM (Portable AnyMap) image
..VI.S pbm PBM (Portable BitMap) image
..VI.S pcx PC Paintbrush PCX image
..VI.S pgm PGM (Portable GrayMap) image
..VI.S pgmyuv PGMYUV (Portable GrayMap YUV) image
..VIL. pictor Pictor/PC Paint
..V..S png PNG (Portable Network Graphics) image
..VI.S ppm PPM (Portable PixelMap) image
..VIL. prores Apple ProRes (iCodec Pro)
..VIL. ptx V.Flash PTX image
..VI.S qdraw Apple QuickDraw
..V.L. qpeg Q-team QPEG
..V..S qtrle QuickTime Animation (RLE) video
..VI.S r10k AJA Kona 10-bit RGB Codec
..VI.S r210 Uncompressed RGB 10-bit
..VI.S rawvideo raw video
..VIL. rl2 RL2 video
..V.L. roq id RoQ video
..V.L. rpza QuickTime video (RPZA)
..V..S rscc innoHeim/Rsupport Screen Capture Codec
..V.L. rv10 RealVideo 1.0
..V.L. rv20 RealVideo 2.0
..V.L. rv30 RealVideo 3.0
..V.L. rv40 RealVideo 4.0
..V.L. sanm LucasArts SANM/SMUSH video
..V..S screenpresso Screenpresso
..VI.S sgi SGI image
..VI.S sgirle SGI RLE 8-bit
..VI.S sheervideo BitJazz SheerVideo
..V.L. smackvideo Smacker video
..V.L. smc QuickTime Graphics (SMC)
..V... smvjpeg Sigmatel Motion Video
..V.LS snow Snow
..VIL. sp5x Sunplus JPEG (SP5X)
..VI.S sunrast Sun Rasterfile image
..V.L. svq1 Sorenson Vector Quantizer 1 / Sorenson Video 1 / SVQ1
..V.L. svq3 Sorenson Vector Quantizer 3 / Sorenson Video 3 / SVQ3
..VI.S targa Truevision Targa image
..VI.. targa_y216 Pinnacle TARGA CineWave YUV16
..V.L. tdsc TDSC
..V.L. tgq Electronic Arts TGQ video
..V.L. tgv Electronic Arts TGV video
D.V.L. theora Theora
..VIL. thp Nintendo Gamecube THP video
..V.L. tiertexseqvideo Tiertex Limited SEQ video
..VI.S tiff TIFF image
..VIL. tmv 8088flex TMV
..V.L. tqi Electronic Arts TQI video
..V.L. truemotion1 Duck TrueMotion 1.0
..V.L. truemotion2 Duck TrueMotion 2.0
..V.L. truemotion2rt Duck TrueMotion 2.0 Real Time
..V..S tscc TechSmith Screen Capture Codec
..V.L. tscc2 TechSmith Screen Codec 2
..VIL. txd Renderware TXD (TeXture Dictionary) image
..V.L. ulti IBM UltiMotion
..VI.S utvideo Ut Video
..VI.S v210 Uncompressed 4:2:2 10-bit
..VI.S v210x Uncompressed 4:2:2 10-bit
..VI.. v308 Uncompressed packed 4:4:4
..VI.. v408 Uncompressed packed QT 4:4:4:4
..VI.S v410 Uncompressed 4:4:4 10-bit
..V.L. vb Beam Software VB
..VI.S vble VBLE Lossless Codec
..V.L. vc1 SMPTE VC-1
..V.L. vc1image Windows Media Video 9 Image v2
..VIL. vcr1 ATI VCR1
..VIL. vixl Miro VideoXL
..V.L. vmdvideo Sierra VMD video
..V..S vmnc VMware Screen Codec / VMware Video
D.V.L. vp3 On2 VP3
..V.L. vp5 On2 VP5
..V.L. vp6 On2 VP6
..V.L. vp6a On2 VP6 (Flash version, with alpha channel)
..V.L. vp6f On2 VP6 (Flash version)
..V.L. vp7 On2 VP7
DEV.L. vp8 On2 VP8 (encoders: libvpx )
D.V.L. vp9 Google VP9
..VILS webp WebP
..V.L. wmv1 Windows Media Video 7
..V.L. wmv2 Windows Media Video 8
..V.L. wmv3 Windows Media Video 9
..V.L. wmv3image Windows Media Video 9 Image
..VIL. wnv1 Winnov WNV1
..V..S wrapped_avframe AVFrame to AVPacket passthrough
..V.L. ws_vqa Westwood Studios VQA (Vector Quantized Animation) video
..V.L. xan_wc3 Wing Commander III / Xan
..V.L. xan_wc4 Wing Commander IV / Xxan
..VI.. xbin eXtended BINary text
..VI.S xbm XBM (X BitMap) image
..VIL. xface X-face image
..VI.S xwd XWD (X Window Dump) image
..VI.. y41p Uncompressed YUV 4:1:1 12-bit
..VI.S ylc YUY2 Lossless Codec
..V.L. yop Psygnosis YOP Video
..VI.. yuv4 Uncompressed packed 4:2:0
..V..S zerocodec ZeroCodec Lossless Video
..VI.S zlib LCL (LossLess Codec Library) ZLIB
..V..S zmbv Zip Motion Blocks Video
..A.L. 4gv 4GV (Fourth Generation Vocoder)
..A.L. 8svx_exp 8SVX exponential
..A.L. 8svx_fib 8SVX fibonacci
D.A.L. aac AAC (Advanced Audio Coding)
..A.L. aac_latm AAC LATM (Advanced Audio Coding LATM syntax)
D.A.L. ac3 ATSC A/52A (AC-3)
..A.L. adpcm_4xm ADPCM 4X Movie
..A.L. adpcm_adx SEGA CRI ADX ADPCM
..A.L. adpcm_afc ADPCM Nintendo Gamecube AFC
..A.L. adpcm_aica ADPCM Yamaha AICA
..A.L. adpcm_ct ADPCM Creative Technology
..A.L. adpcm_dtk ADPCM Nintendo Gamecube DTK
..A.L. adpcm_ea ADPCM Electronic Arts
..A.L. adpcm_ea_maxis_xa ADPCM Electronic Arts Maxis CDROM XA
..A.L. adpcm_ea_r1 ADPCM Electronic Arts R1
..A.L. adpcm_ea_r2 ADPCM Electronic Arts R2
..A.L. adpcm_ea_r3 ADPCM Electronic Arts R3
..A.L. adpcm_ea_xas ADPCM Electronic Arts XAS
..A.L. adpcm_g722 G.722 ADPCM
..A.L. adpcm_g726 G.726 ADPCM
..A.L. adpcm_g726le G.726 ADPCM little-endian
..A.L. adpcm_ima_amv ADPCM IMA AMV
..A.L. adpcm_ima_apc ADPCM IMA CRYO APC
..A.L. adpcm_ima_dat4 ADPCM IMA Eurocom DAT4
..A.L. adpcm_ima_dk3 ADPCM IMA Duck DK3
..A.L. adpcm_ima_dk4 ADPCM IMA Duck DK4
..A.L. adpcm_ima_ea_eacs ADPCM IMA Electronic Arts EACS
..A.L. adpcm_ima_ea_sead ADPCM IMA Electronic Arts SEAD
..A.L. adpcm_ima_iss ADPCM IMA Funcom ISS
..A.L. adpcm_ima_oki ADPCM IMA Dialogic OKI
..A.L. adpcm_ima_qt ADPCM IMA QuickTime
..A.L. adpcm_ima_rad ADPCM IMA Radical
..A.L. adpcm_ima_smjpeg ADPCM IMA Loki SDL MJPEG
..A.L. adpcm_ima_wav ADPCM IMA WAV
..A.L. adpcm_ima_ws ADPCM IMA Westwood
..A.L. adpcm_ms ADPCM Microsoft
..A.L. adpcm_mtaf ADPCM MTAF
..A.L. adpcm_psx ADPCM Playstation
..A.L. adpcm_sbpro_2 ADPCM Sound Blaster Pro 2-bit
..A.L. adpcm_sbpro_3 ADPCM Sound Blaster Pro 2.6-bit
..A.L. adpcm_sbpro_4 ADPCM Sound Blaster Pro 4-bit
..A.L. adpcm_swf ADPCM Shockwave Flash
..A.L. adpcm_thp ADPCM Nintendo THP
..A.L. adpcm_thp_le ADPCM Nintendo THP (Little-Endian)
..A.L. adpcm_vima LucasArts VIMA audio
..A.L. adpcm_xa ADPCM CDROM XA
..A.L. adpcm_yamaha ADPCM Yamaha
..A..S alac ALAC (Apple Lossless Audio Codec)
..A.L. amr_nb AMR-NB (Adaptive Multi-Rate NarrowBand)
..A.L. amr_wb AMR-WB (Adaptive Multi-Rate WideBand)
..A..S ape Monkey's Audio
..A.L. atrac1 ATRAC1 (Adaptive TRansform Acoustic Coding)
..A.L. atrac3 ATRAC3 (Adaptive TRansform Acoustic Coding 3)
..A.L. atrac3p ATRAC3+ (Adaptive TRansform Acoustic Coding 3+)
..A.L. avc On2 Audio for Video Codec
..A.L. binkaudio_dct Bink Audio (DCT)
..A.L. binkaudio_rdft Bink Audio (RDFT)
..A.L. bmv_audio Discworld II BMV audio
..A.L. celt Constrained Energy Lapped Transform (CELT)
..A.L. comfortnoise RFC 3389 Comfort Noise
..A.L. cook Cook / Cooker / Gecko (RealAudio G2)
..A.L. dsd_lsbf DSD (Direct Stream Digital), least significant bit first
..A.L. dsd_lsbf_planar DSD (Direct Stream Digital), least significant bit first, planar
..A.L. dsd_msbf DSD (Direct Stream Digital), most significant bit first
..A.L. dsd_msbf_planar DSD (Direct Stream Digital), most significant bit first, planar
..A.L. dsicinaudio Delphine Software International CIN audio
..A.L. dss_sp Digital Speech Standard - Standard Play mode (DSS SP)
..A..S dst DST (Direct Stream Transfer)
..A.LS dts DCA (DTS Coherent Acoustics)
..A.L. dvaudio DV audio
..A.L. eac3 ATSC A/52B (AC-3, E-AC-3)
..A.L. evrc EVRC (Enhanced Variable Rate Codec)
..A..S flac FLAC (Free Lossless Audio Codec)
..A.L. g723_1 G.723.1
..A.L. g729 G.729
..A.L. gsm GSM
..A.L. gsm_ms GSM Microsoft variant
..A.L. iac IAC (Indeo Audio Coder)
..A.L. ilbc iLBC (Internet Low Bitrate Codec)
..A.L. imc IMC (Intel Music Coder)
..A.L. interplay_dpcm DPCM Interplay
..A.L. interplayacm Interplay ACM
..A.L. mace3 MACE (Macintosh Audio Compression/Expansion) 3:1
..A.L. mace6 MACE (Macintosh Audio Compression/Expansion) 6:1
..A.L. metasound Voxware MetaSound
..A..S mlp MLP (Meridian Lossless Packing)
..A.L. mp1 MP1 (MPEG audio layer 1)
..A.L. mp2 MP2 (MPEG audio layer 2)
D.A.L. mp3 MP3 (MPEG audio layer 3)
..A.L. mp3adu ADU (Application Data Unit) MP3 (MPEG audio layer 3)
..A.L. mp3on4 MP3onMP4
..A..S mp4als MPEG-4 Audio Lossless Coding (ALS)
..A.L. musepack7 Musepack SV7
..A.L. musepack8 Musepack SV8
..A.L. nellymoser Nellymoser Asao
DEA.L. opus Opus (Opus Interactive Audio Codec) (encoders: libopus )
..A.L. paf_audio Amazing Studio Packed Animation File Audio
..A.L. pcm_alaw PCM A-law / G.711 A-law
..A..S pcm_bluray PCM signed 16|20|24-bit big-endian for Blu-ray media
..A..S pcm_dvd PCM signed 20|24-bit big-endian
..A..S pcm_f32be PCM 32-bit floating point big-endian
..A..S pcm_f32le PCM 32-bit floating point little-endian
..A..S pcm_f64be PCM 64-bit floating point big-endian
..A..S pcm_f64le PCM 64-bit floating point little-endian
..A..S pcm_lxf PCM signed 20-bit little-endian planar
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What Is Incrementality & Why Is It Important in Marketing ?
26 mars 2024, par ErinImagine this : you just launched your latest campaign and it was a major success.
You blew last month’s results out of the water.
You combined a variety of tactics, channels and ad creatives to make it work.
Now, it’s time to build the next campaign.
The only issue ?
You don’t know what made it successful or how much your recent efforts impacted the results.
You’ve been building your brand for years. You’ve built up a variety of marketing pillars that are working for you. So, how do you know how much of your campaign is from years of effort or a new tactic you just implemented ?
The key is incrementality.
This is a way to properly attribute the right weight to your marketing tactics.
In this article, we break down what incrementality is in marketing, how it differs from traditional attribution and how you can calculate and track it to grow your business.
What is incrementality in marketing ?
Incrementality in marketing is growth that can be directly credited to a marketing effort above and beyond the success of the branding.
It looks at how much a specific tactic positively impacted a campaign on top of overall branding and marketing strategies.
For example, this could be how much a specific tactic, campaign or channel helped increase conversions, email sign-ups or organic traffic.
The primary purpose of incrementally in marketing is to more accurately determine the impact a single marketing variable had on the success of a project.
It removes every other factor and isolates the specific method to help marketers double down on that strategy or move on to new tactics.
With Matomo, you can track conversions simply. With our last non-direct channel attribution system, you’ll be able to quickly see what channels are converting (and which aren’t) so you can gain insights into incrementality.
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How incrementality differs from attribution
In marketing and advertising, it’s crucial to understand what tactics and activities drive growth.
Incrementality and attribution help marketers and business owners understand what efforts impact their results.
But they’re not the same.
Here’s how they differ :
Incrementality explained
Incrementality measures how much a specific marketing campaign or activity drives additional sales or growth.
Simply put, it’s analysing the difference between having never implemented the campaign (or tactic or channel) in the first place versus the impact of the activity.
In other words, how much revenue would you have generated this month without campaign A ?
And how much additional revenue did you generate directly due to campaign A ?
The reality is that dozens of factors impact revenue and growth.
You aren’t just pouring your marketing into one specific channel or campaign at a time.
Chances are, you’ve got your hands on several marketing initiatives like SEO, PPC, organic social media, paid search, email marketing and more.
Beyond that, you’ve built a brand with a not-so-tangible impact on your recurring revenue.
So, the question is, if you took away your new campaign, would you still be generating the same amount of revenue ?
And, if you add in that campaign, how much additional revenue and growth did it directly create ?
That is incrementality. It’s how much a campaign went above and beyond to add new revenue that wouldn’t have been there otherwise.
So, how does attribution play into all of this ?
Attribution explained
Attribution is simply the process of assigning credit for a conversion to a particular marketing touchpoint.
While incrementality is about narrowing down the overall revenue impact from a particular campaign, attribution seeks to point to a specific channel to attribute a sale.
For example, in any given marketing campaign, you have a few marketing tactics.
Let’s say you’re launching a limited-time product.
You might have :
- Paid ads via Facebook and Instagram
- A blog post sharing how the product works
- Organic social media posts on Instagram and TikTok
- Email waitlist campaign building excitement around the upcoming product
- SMS campaigns to share a limited-time discount
So, when the time comes for the sale launch, and you generate $30,000 in revenue, what channel gets the credit ?
Do you give credit to the paid ads on Facebook ? What about Instagram ? They got people to follow you and got them on the email waitlist.
Do you give credit to email for reminding people of the upcoming sale ? What about your social media posts that reminded people there ?
Or do you credit your SMS campaign that shared a limited-time discount ?
Which channel is responsible for the sale ?
This is what attribution is all about.
It’s about giving credit where credit is due.
The reason you want to attribute credit ? So you know what’s working and can double down your efforts on the high-impact marketing activities and channels.
Leveraging incrementality and attribution together
Incrementality and attribution aren’t competing methods of analysing what’s working.
They’re complementary to one another and go hand in hand.
You can (and should) use attribution and incrementality in your marketing to help understand what activities, campaigns and channels are making the biggest incremental impact on your business growth.
Why it’s important to measure incrementality
Incrementality is crucial to measure if you want to pour your time, money and effort into the right marketing channels and tactics.
Here are a few reasons why you need to measure incrementality if you want to be successful with your marketing and grow your business :
1. Accurate data
If you want to be an effective marketer, you need to be accurate.
You can’t blindly start marketing campaigns in hopes that you will sell many products or services.
That’s not how it works.
Sure, you’ll probably make some sales here and there. But to truly be effective with your work, you must measure your activities and channels correctly.
Incrementality helps you see how each channel, tactic or campaign made a difference in your marketing.
Matomo gives you 100% accurate data on your website activities. Unlike Google Analytics, we don’t use data sampling which limits how much data is analysed.
2. Helps you to best determine the right tactics for success
How can you plan your marketing strategy if you don’t know what’s working ?
Think about it.
You’ll be blindly sailing the seas without a compass telling you where to go.
Measuring incrementality in your marketing tactics and channels helps you understand the best tactics.
It shows you what’s moving the needle (and what’s not).
Once you can see the most impactful tactics and channels, you can forge future campaigns that you know will work.
3. Allows you to get the most out of your marketing budget
Since incrementality sheds light on what’s moving your business forward, you can confidently implement your efforts on the right tactics and channels.
Guess what happens when you start doubling down on the most impactful activities ?
You start increasing revenue, decreasing ad spend and getting a higher return on investment.
The result is that you will get more out of your marketing budget.
Not only will you boost revenue, but you’ll also be able to boost profit margins since you’re not wasting money on ineffective tactics.
4. Increase traffic
When you see what’s truly working in your business, you can figure out what channels and tactics you should be working.
Incrementality helps you understand not only what your best revenue tactics are but also what channels and campaigns are bringing in the most traffic.
When you can increase traffic, you can increase your overall marketing impact.
5. Increase revenue
Finally, with increased traffic, the inevitable result is more conversions.
More conversions mean more revenue.
Incrementality gives you a vision of the tactics and channels that are converting the best.
If you can see that your SMS campaigns are driving the best ROI, then you know that you’ll grow your revenue by pouring more into acquiring SMS leads.
By calculating incrementality regularly, you can rest assured that you’re only investing time and money into the most impactful activities in terms of revenue generation.
How to calculate and test incrementality in marketing
Now that you understand how incrementality works and why it’s important to calculate, the question is :
How do you calculate and conduct incrementality tests ?
Given the ever-changing marketing landscape, it’s crucial to understand how to calculate and test incrementally in your business.
If you’re not sure how incrementality testing works, then follow these simple steps :
Your first step to get an incrementality measurement is to conduct what’s referred to as a “holdout test.”
It’s not a robust test, but it’s an easy way to get the ball rolling with incrementality.
Here’s how it works :
- Choose your target audience.
With Matomo’s segmentation feature, you can get pretty specific with your target audience, such as :
- Visitors from the UK
- Returning visitors
- Mobile users
- Visitors who clicked on a specific ad
- Split your audience into two groups :
- Control group (60% of the segment)
- Test group (40% of the segment)
- Target the control group with your marketing tactic (the simpler the tactic, the better).
- Target the test group with a different marketing tactic.
- Analyse the results. The difference between the control and test groups is the incremental lift in results. The new marketing tactic is either more effective or not.
- Repeat the test with a new control group (with an updated tactic) and a new test group (with a new tactic).
Matomo can help you analyse the results of your campaigns in our Goals feature. Set up business objectives so you can easily track different goals like conversions.
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Here’s an example of how this incrementality testing could look in real life.
Imagine a fitness retailer wants to start showing Facebook ads in their marketing mix.
The marketing manager decided to conduct a holdout test. If we match our example below with the steps above, this is how the holdout test might look.
- They choose people who’ve purchased free weights in the past as their target audience (see how that segmentation works ?).
- They split this segment into a control group and a test group.
- For this test, they direct their regular marketing campaign to the control group (60% of the segment). The campaign includes promoting a 20% off sale on organic social media posts, email marketing, and SMS.
- They direct their regular marketing campaign plus Facebook ads to the test group (40% of the segment).
- They ran the campaign for three weeks with the goal for sale conversions and noticed :
- The control group had a 1.5% conversion rate.
- The test group (with Facebook ads) had a 2.1% conversion rate.
- In this scenario, they could see the group who saw the Facebook ads convert better.
- They created the following formula to measure the incremental lift of the Facebook ads :
- Here’s how the calculation works out : (2.1% – 1.5%) / 1.5% = 40%
The Facebook ads had a positive 40% incremental lift in conversions during the sale.
Incrementality testing isn’t a one-and-done process, though.
While this first test is a great sign for the marketing manager, it doesn’t mean they should immediately throw all their money into Facebook ads.
They should continue conducting tests to verify the initial test.
Use Matomo to track incrementality today
Incrementality can give you insights into exactly what’s working in your marketing (and what’s not) so you can design proven strategies to grow your business.
If you want more help tracking your marketing efforts, try Matomo today.
Our web analytics and behaviour analytics platform gives you firsthand data on your website visitors you can use to craft effective marketing strategies.
Matomo provides 100% accurate data. Unlike other major web analytics platforms, we don’t do data sampling. What you see is what’s really going on in your website. That way, you can make more informed decisions for better results.
At Matomo, we take privacy very seriously and include several advanced privacy protections to ensure you are in full control.
As a fully compliant web analytics solution, we’re fully compliant with some of the world’s strictest privacy regulations like GDPR. With Matomo, you get peace of mind knowing you can make data-driven decisions while also being compliant.
If you’re ready to launch a data-driven marketing strategy today and grow your business, get started with our 21-day free trial now. No credit card required.
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What is Multi-Touch Attribution ? (And How To Get Started)
2 février 2023, par Erin — Analytics TipsGood marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event).
An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion.
Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture.
That’s when multi-touch attribution analysis comes to the fore.
What is Multi-Touch Attribution ?
Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion.
Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.
To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model.
Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial.
- A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial.
- A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.
In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome.
Types of Attribution Models
As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability.
First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).
First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert.
Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.
The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion.
Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later.
This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.
Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.
For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.
This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between.
Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last.
This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.
Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).
This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage.
Why Use Multi-Touch Attribution Modelling
Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign.
Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.
Issues multi-touch attribution solves
- Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not.
- Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI).
- Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly.
- Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
- Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.
How To Get Started With Multi-Touch Attribution
To make multi-touch attribution part of your analytics setup, follow the next steps :
1. Define Your Marketing Objectives
Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.
Touchpoints include :
- On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
- Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)
Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion).
Depending on your business model, you can count any of the following as a conversion :
- Purchase
- Account registration
- Free trial request
- Contact form submission
- Online reservation
- Demo call request
- Newsletter subscription
So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives.
Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them.
2. Select an Attribution Model
Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.
To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ?
Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles.
3. Collect and Organise Data From All Touchpoints
Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling.
Types of data you should collect :
- General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
- Goals (Conversions) : Reports on successful conversions across different types of assets.
- Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.
You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy.
Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc).
Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings.
Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy.
Marketing analytics has an accuracy problem, mainly for two reasons :
- Cookie consent banner rejection
- Data sampling application
Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.
If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners.
For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo.
Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations.
Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.
4. Evaluate and Select an Attribution Tool
Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.
At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year.
Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us.
Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys.
Conclusion
Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts.
The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either.
Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now.
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