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  • Error transcoding with FFmpeg : Error : Output format hls is not available

    6 mai 2024, par asif mohmd

    I am using FFmpeg library to transcode a video file into multiple resolutions and create an HLS (HTTP Live Streaming) master playlist.

    


    It takes a video file as input but its does give me the output with HLS playlist.I got a error called "Output format hls is not available". Only the Output directory is creating

    


    I am using FFMpeg 7.0 full build version and also tried older versions and ffmpeg essentials and also tried chocolatey.

    


    if i remove the implementation of HLS from this code.it will create 4 different resolution videos in my output.

    


    Note:I just tried this same code on my friend MAC Book by only changing the setffmpegPath : "ffmpeg.setFfmpegPath("C :\ffmpeg\bin\ffmpeg.exe") ;" to his ffmpeg directory.
Its working perfectly in his mac book

    


    import "dotenv/config";&#xA;import * as fs from "fs";&#xA;import * as path from "path";&#xA;import ffmpeg from "fluent-ffmpeg";&#xA;import crypto from "crypto";&#xA;&#xA;ffmpeg.setFfmpegPath("C:\\ffmpeg\\bin\\ffmpeg.exe");&#xA;&#xA;export const FFmpegTranscoder = async (file: any): Promise<any> => {&#xA;  try {&#xA;    console.log("Starting script");&#xA;    console.time("req_time");&#xA;&#xA;    const randomName = (bytes = 32) =>&#xA;      crypto.randomBytes(bytes).toString("hex");&#xA;    const fileName = randomName();&#xA;    const directoryPath = path.join(__dirname, "..", "..", "input");&#xA;    const filePath = path.join(directoryPath, `${fileName}.mp4`);&#xA;&#xA;    if (!fs.existsSync(directoryPath)) {&#xA;      fs.mkdirSync(directoryPath, { recursive: true });&#xA;    }&#xA;&#xA;    const paths = await new Promise<any>((resolve, reject) => {&#xA;      fs.writeFile(filePath, file, async (err) => {&#xA;        if (err) {&#xA;          console.error("Error saving file:", err);&#xA;          throw err;&#xA;        }&#xA;        console.log("File saved successfully:", filePath);&#xA;&#xA;        try {&#xA;          const outputDirectoryPath = await transcodeWithFFmpeg(&#xA;            fileName,&#xA;            filePath&#xA;          );&#xA;          resolve({ directoryPath, filePath, fileName, outputDirectoryPath });&#xA;        } catch (error) {&#xA;          console.error("Error transcoding with FFmpeg:", error);&#xA;        }&#xA;      });&#xA;    });&#xA;    return paths;&#xA;  } catch (e: any) {&#xA;    console.log(e);&#xA;  }&#xA;};&#xA;&#xA;const transcodeWithFFmpeg = async (fileName: string, filePath: string) => {&#xA;  const directoryPath = path.join(&#xA;    __dirname,&#xA;    "..",&#xA;    "..",&#xA;    `output/hls/${fileName}`&#xA;  );&#xA;&#xA;  if (!fs.existsSync(directoryPath)) {&#xA;    fs.mkdirSync(directoryPath, { recursive: true });&#xA;  }&#xA;&#xA;  const resolutions = [&#xA;    {&#xA;      resolution: "256x144",&#xA;      videoBitrate: "200k",&#xA;      audioBitrate: "64k",&#xA;    },&#xA;    {&#xA;      resolution: "640x360",&#xA;      videoBitrate: "800k",&#xA;      audioBitrate: "128k",&#xA;    },&#xA;    {&#xA;      resolution: "1280x720",&#xA;      videoBitrate: "2500k",&#xA;      audioBitrate: "192k",&#xA;    },&#xA;    {&#xA;      resolution: "1920x1080",&#xA;      videoBitrate: "5000k",&#xA;      audioBitrate: "256k",&#xA;    },&#xA;  ];&#xA;&#xA;  const variantPlaylists: { resolution: string; outputFileName: string }[] = [];&#xA;&#xA;  for (const { resolution, videoBitrate, audioBitrate } of resolutions) {&#xA;    console.log(`HLS conversion starting for ${resolution}`);&#xA;    const outputFileName = `${fileName}_${resolution}.m3u8`;&#xA;    const segmentFileName = `${fileName}_${resolution}_%03d.ts`;&#xA;&#xA;    await new Promise<void>((resolve, reject) => {&#xA;      ffmpeg(filePath)&#xA;        .outputOptions([&#xA;          `-c:v h264`,&#xA;          `-b:v ${videoBitrate}`,&#xA;          `-c:a aac`,&#xA;          `-b:a ${audioBitrate}`,&#xA;          `-vf scale=${resolution}`,&#xA;          `-f hls`,&#xA;          `-hls_time 10`,&#xA;          `-hls_list_size 0`,&#xA;          `-hls_segment_filename ${directoryPath}/${segmentFileName}`,&#xA;        ])&#xA;        .output(`${directoryPath}/${outputFileName}`)&#xA;        .on("end", () => resolve())&#xA;        .on("error", (err) => reject(err))&#xA;        .run();&#xA;    });&#xA;    const variantPlaylist = {&#xA;      resolution,&#xA;      outputFileName,&#xA;    };&#xA;    variantPlaylists.push(variantPlaylist);&#xA;    console.log(`HLS conversion done for ${resolution}`);&#xA;  }&#xA;  console.log(`HLS master m3u8 playlist generating`);&#xA;&#xA;  let masterPlaylist = variantPlaylists&#xA;    .map((variantPlaylist) => {&#xA;      const { resolution, outputFileName } = variantPlaylist;&#xA;      const bandwidth =&#xA;        resolution === "256x144"&#xA;          ? 264000&#xA;          : resolution === "640x360"&#xA;          ? 1024000&#xA;          : resolution === "1280x720"&#xA;          ? 3072000&#xA;          : 5500000;&#xA;      ``;&#xA;      return `#EXT-X-STREAM-INF:BANDWIDTH=${bandwidth},RESOLUTION=${resolution}\n${outputFileName}`;&#xA;    })&#xA;    .join("\n");&#xA;  masterPlaylist = `#EXTM3U\n` &#x2B; masterPlaylist;&#xA;&#xA;  const masterPlaylistFileName = `${fileName}_master.m3u8`;&#xA;&#xA;  const masterPlaylistPath = `${directoryPath}/${masterPlaylistFileName}`;&#xA;  fs.writeFileSync(masterPlaylistPath, masterPlaylist);&#xA;  console.log(`HLS master m3u8 playlist generated`);&#xA;  return directoryPath;&#xA;};&#xA;</void></any></any>

    &#xA;

    My console.log is :

    &#xA;

        Starting script&#xA;    HLS conversion starting for 256x144&#xA;    Error transcoding with FFmpeg: Error: Output format hls is not available&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\capabilities.js:589:21&#xA;        at nextTask (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:5791:13)&#xA;        at next (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:5799:13)&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\async\dist\async.js:329:20&#xA;        at C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\capabilities.js:549:7&#xA;        at handleExit (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\processor.js:170:11)&#xA;        at ChildProcess.<anonymous> (C:\Users\asifa\Desktop\Genius Grid\Transcode-service\node_modules\fluent-ffmpeg\lib\processor.js:184:11)&#xA;        at ChildProcess.emit (node:events:518:28)&#xA;        at ChildProcess.emit (node:domain:488:12)&#xA;        at Process.ChildProcess._handle.onexit (node:internal/child_process:294:12) &#xA;</anonymous>

    &#xA;

    I am using Windows 11 and FFMpeg version 7.0. I repeatedly checked, using CMD commands, that my FFMpeg was installed correctly and confirmed the environment variables path, experimented with various FFMpeg versions, and tried with FFMpeg full build Chocolatey package.

    &#xA;

    In Command Line its working perfectly :

    &#xA;

    PS C:\Users\asifa\Desktop\test fmmpeg> ffmpeg -hide_banner -y -i .\SampleVideo_1280x720_30mb.mp4 -vf scale=w=640:h=360:force_original_aspect_ratio=decrease -c:a aac -b:v 800k -c:v h264 -b:a 128k -f hls -hls_time 14 -hls_list_size 0 -hls_segment_filename beach/480p_%03d.ts beach/480p.m3u8&#xA;Input #0, mov,mp4,m4a,3gp,3g2,mj2, from &#x27;.\SampleVideo_1280x720_30mb.mp4&#x27;:&#xA;  Metadata:&#xA;    major_brand     : isom&#xA;    minor_version   : 512&#xA;    compatible_brands: isomiso2avc1mp41&#xA;    creation_time   : 1970-01-01T00:00:00.000000Z&#xA;    encoder         : Lavf53.24.2&#xA;  Duration: 00:02:50.86, start: 0.000000, bitrate: 1474 kb/s&#xA;  Stream #0:0[0x1](und): Video: h264 (Main) (avc1 / 0x31637661), yuv420p(progressive), 1280x720 [SAR 1:1 DAR 16:9], 1086 kb/s, 25 fps, 25 tbr, 12800 tbn (default)&#xA;      Metadata:&#xA;        creation_time   : 1970-01-01T00:00:00.000000Z&#xA;        handler_name    : VideoHandler&#xA;        vendor_id       : [0][0][0][0]&#xA;  Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 383 kb/s (default)&#xA;      Metadata:&#xA;        creation_time   : 1970-01-01T00:00:00.000000Z&#xA;        handler_name    : SoundHandler&#xA;        vendor_id       : [0][0][0][0]&#xA;Stream mapping:&#xA;  Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))&#xA;  Stream #0:1 -> #0:1 (aac (native) -> aac (native))&#xA;Press [q] to stop, [?] for help&#xA;[libx264 @ 000001ef1288ec00] using SAR=1/1&#xA;[libx264 @ 000001ef1288ec00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2&#xA;[libx264 @ 000001ef1288ec00] profile High, level 3.0, 4:2:0, 8-bit&#xA;[libx264 @ 000001ef1288ec00] 264 - core 164 r3190 7ed753b - H.264/MPEG-4 AVC codec - Copyleft 2003-2024 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=11 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=abr mbtree=1 bitrate=800 ratetol=1.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00&#xA;Output #0, hls, to &#x27;beach/480p.m3u8&#x27;:&#xA;  Metadata:&#xA;    major_brand     : isom&#xA;    minor_version   : 512&#xA;    compatible_brands: isomiso2avc1mp41&#xA;    encoder         : Lavf61.1.100&#xA;  Stream #0:0(und): Video: h264, yuv420p(progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 800 kb/s, 25 fps, 90k tbn (default)&#xA;      Metadata:&#xA;        creation_time   : 1970-01-01T00:00:00.000000Z&#xA;        handler_name    : VideoHandler&#xA;        vendor_id       : [0][0][0][0]&#xA;        encoder         : Lavc61.3.100 libx264&#xA;      Side data:&#xA;        cpb: bitrate max/min/avg: 0/0/800000 buffer size: 0 vbv_delay: N/A&#xA;  Stream #0:1(und): Audio: aac (LC), 48000 Hz, 5.1, fltp, 128 kb/s (default)&#xA;      Metadata:&#xA;        creation_time   : 1970-01-01T00:00:00.000000Z&#xA;        handler_name    : SoundHandler&#xA;        vendor_id       : [0][0][0][0]&#xA;        encoder         : Lavc61.3.100 aac&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_000.ts&#x27; for writing speed=15.5x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_001.ts&#x27; for writing speed=17.9x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_002.ts&#x27; for writing speed=17.3x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_003.ts&#x27; for writing speed=19.4x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_004.ts&#x27; for writing speed=19.3x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_005.ts&#x27; for writing speed=19.2x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_006.ts&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_007.ts&#x27; for writing speed=19.4x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_008.ts&#x27; for writing speed=19.5x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_009.ts&#x27; for writing speed=19.5x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_010.ts&#x27; for writing speed=19.4x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p_011.ts&#x27; for writing/A    =19.4x&#xA;[hls @ 000001ef12482040] Opening &#x27;beach/480p.m3u8.tmp&#x27; for writing&#xA;[out#0/hls @ 000001ef11d4e880] video:17094KiB audio:2680KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown&#xA;frame= 4271 fps=485 q=-1.0 Lsize=N/A time=00:02:50.76 bitrate=N/A speed=19.4x&#xA;[libx264 @ 000001ef1288ec00] frame I:45    Avg QP:10.29  size: 60418&#xA;[libx264 @ 000001ef1288ec00] frame P:1914  Avg QP:14.53  size:  5582&#xA;[libx264 @ 000001ef1288ec00] frame B:2312  Avg QP:20.63  size:  1774&#xA;[libx264 @ 000001ef1288ec00] consecutive B-frames: 22.9% 11.9%  8.6% 56.6%&#xA;[libx264 @ 000001ef1288ec00] mb I  I16..4: 15.6% 32.1% 52.2%&#xA;[libx264 @ 000001ef1288ec00] mb P  I16..4:  0.3%  3.4%  1.2%  P16..4: 20.3% 10.0% 13.1%  0.0%  0.0%    skip:51.8%&#xA;[libx264 @ 000001ef1288ec00] mb B  I16..4:  0.1%  0.9%  0.4%  B16..8: 17.2%  5.6%  2.8%  direct: 2.0%  skip:71.0%  L0:41.5% L1:44.1% BI:14.4%&#xA;[libx264 @ 000001ef1288ec00] final ratefactor: 16.13&#xA;[libx264 @ 000001ef1288ec00] 8x8 transform intra:58.4% inter:51.7%&#xA;[libx264 @ 000001ef1288ec00] coded y,uvDC,uvAC intra: 86.7% 94.3% 78.8% inter: 12.6% 15.0% 4.5%&#xA;[libx264 @ 000001ef1288ec00] i16 v,h,dc,p: 17% 42% 14% 28%&#xA;[libx264 @ 000001ef1288ec00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 19% 11%  6%  7%  8%  8%  9%  9%&#xA;[libx264 @ 000001ef1288ec00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 18% 12%  6%  9%  9%  8%  8%  7%&#xA;[libx264 @ 000001ef1288ec00] i8c dc,h,v,p: 44% 24% 20% 12%&#xA;[libx264 @ 000001ef1288ec00] Weighted P-Frames: Y:0.0% UV:0.0%&#xA;[libx264 @ 000001ef1288ec00] ref P L0: 78.3%  9.7%  8.8%  3.2%&#xA;[libx264 @ 000001ef1288ec00] ref B L0: 92.5%  6.0%  1.5%&#xA;[libx264 @ 000001ef1288ec00] ref B L1: 97.1%  2.9%&#xA;[libx264 @ 000001ef1288ec00] kb/s:819.63&#xA;[aac @ 000001ef128f7c80] Qavg: 452.137&#xA;

    &#xA;

    When I use the .on(&#x27;start&#x27;, (cmdline) => console.log(cmdline))} code with the -f hls command, the error "Output format hls is not available" appears, as previously mentioned. But my Console.log looks like this if I run my code without using -f hls command :

    &#xA;

    Without -f hls command

    &#xA;

    await new Promise<void>((resolve, reject) => {&#xA;  ffmpeg(filePath)&#xA;    .outputOptions([&#xA;      `-c:v h264`,&#xA;      `-b:v ${videoBitrate}`,&#xA;      `-c:a aac`,&#xA;      `-b:a ${audioBitrate}`,&#xA;      `-vf scale=${resolution}`,&#xA; &#xA;      `-hls_time 10`,&#xA;      `-hls_list_size 0`,&#xA;      `-hls_segment_filename ${directoryPath}/${segmentFileName}`,&#xA;    ])&#xA;    .output(`${directoryPath}/${outputFileName}`)&#xA;    .on(&#x27;start&#x27;, (cmdline) => console.log(cmdline)) &#xA;    .on("end", () => resolve())&#xA;    .on("error", (err) => reject(err))&#xA;    .run();&#xA;});&#xA;</void>

    &#xA;

    Console.log is :

    &#xA;

    `Starting script&#xA;File saved successfully: C:\Users\asifa\Desktop\Genius Grid\Transcode-service\input\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1.mp4&#xA;HLS conversion starting for 256x144&#xA;ffmpeg -i C:\Users\asifa\Desktop\Genius Grid\Transcode-service\input\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1.mp4 -y -c:v h264 -b:v 200k -c:a aac -b:a 64k -vf scale=256x144 -hls_time 10 -hls_list_size 0 -hls_segment_filename C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144_%03d.ts C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144.m3u8&#xA;Error transcoding with FFmpeg: Error: ffmpeg exited with code 2880417800: Unrecognized option &#x27;hls_segment_filename C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144_%03d.ts&#x27;.&#xA;Error splitting the argument list: Option not found`&#xA;

    &#xA;

  • How to Implement Cross-Channel Analytics : A Guide for Marketers

    17 avril 2024, par Erin

    Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

    You need a cross-channel analytics solution if you answered no to either of these questions. 

    In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

    What is cross-channel analytics ? 

    Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

    The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

    Cross-channel analytics lets you track every channel you use to convert customers, including :

    • Your website
    • Social media profiles
    • Email
    • Paid search
    • E-commerce
    • Retargeting campaigns

    Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

    Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

    That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

    The difference between cross-channel analytics and multi-channel analytics

    Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

    What are the benefits of cross-channel analytics 

    Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

    Get a complete view of the customer journey

    Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

    Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint

    A diagram showing how complex customer journeys are

    The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

    In short, you get to see why customers convert so you can learn how to convert more of them.

    Personalise the customer experience

    According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

    When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

    Identify your most effective marketing channels

    Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

    Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

    A Matomo screenshot showing channel attribution

    This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

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    Attribute conversions accurately 

    An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

    These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

    Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

    How to set up cross-channel analytics

    Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

    1. Determine your objectives

    Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

    If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

    1. Set relevant KPIs

    You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics

    These include :

    • Conversion rate
    • Average visit duration
    • Bounce rate
    1. Implement tracking and analytics tools

    Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

    The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

    1. Set up a multi-touch attribution model

    Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

    There are several attribution models to choose from, including :

    Image of six different attribution models

    Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

    For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

    1. Turn data into insights with reports

    One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

    When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

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    1. Take action

    There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

    But where should you start ?

    Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

    You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

    Cross-channel analytics best practices

    If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

    Use a centralised solution to track everything

    Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

    Think about consumer privacy 

    If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

    You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

    It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

    Monitor data in real time

    So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

    Great, now go further by monitoring the impact of your marketing efforts in real time.

    A screenshot of Matomo's real-time visitor log

    With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

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    Reallocate marketing budgets based on performance

    When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

    Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

    Cross-channel analytics platforms to get started with 

    The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

    So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

    Matomo

    Screenshot example of the Matomo dashboard

    Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

    Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

    Google Analytics

    A screenshot of Google Analytics 4 UI

    Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

    Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

    Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

    It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

    With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

    Improvado

    A screenshot of Improvado's homepage

    Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

    While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

    Make the most of cross-channel analytics with Matomo

    Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

    Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

    Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

    Try it free for 21 days. No credit card required.

  • Clickstream Data : Definition, Use Cases, and More

    15 avril 2024, par Erin

    Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

    In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

    This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

    What is clickstream data ? 

    As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

    Illustration of collecting and analysing data

    Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

    With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

    Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

    Depending on the specific events you’re tracking, clickstream data can reveal the following : 

    • How visitors reach your website 
    • The terms they type into the search engine
    • The first page they land on
    • The most popular pages and sections of your website
    • The amount of time they spend on a page 
    • Which elements of the page they interact with, and in what sequence
    • The click path they take 
    • When they convert, cancel, or abandon their cart
    • Where the user goes once they leave your website

    As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

    Types of clickstream data 

    While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

    • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
    • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

    One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

    On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

    • Web navigation data : referring URL, visited pages, click path, and exit page
    • User interaction data : mouse movements, click rate, scroll depth, and button clicks
    • Conversion data : form submissions, sign-ups, and transactions 
    • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
    • Session data : duration, start, and end times and number of pages viewed per session
    • Error data : 404 errors and network or server response issues 

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    Clickstream data benefits and use cases 

    Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

    Example of using clickstream data for marketing ROI

    According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

    The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

    Customer journey mapping 

    Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

    Identifying customer trends 

    Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

    Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

    Here’s an example : 

    It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

    Preventing site abandonment 

    Cart abandonment remains a serious issue for online retailers : 

    According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

    That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

    In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

    Improving marketing campaign ROI 

    As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness

    Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

    You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

    When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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    Delivering a better user experience (UX) 

    Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

    It’s clear how this would be beneficial to your business : 

    Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

    Collecting clickstream data : Tools and legal implications 

    Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

    Illustration of user’s data protection and online security

    Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

    Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

    That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

    While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

    Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

    It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

    The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

    Clickstream analytics data best practices 

    Illustration of collecting, analysing and presenting data

    As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

    Here are some best practices to keep in mind when it comes to clickstream analysis : 

    Define your goals 

    It’s essential to take the time to define your goals and objectives. 

    Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

    Here are a few examples of goals and objectives you can set for clickstream analysis : 

    • Understanding and predicting users’ behavioural patterns 
    • Optimising marketing campaigns and ROI 
    • Attributing conversions to specific marketing touchpoints and channels

    Analyse your data 

    Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

    In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

    Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

    Here are a few examples : 

    • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
    • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis
    • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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    Organise and visualise your data

    As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

    Here are a few examples of easily digestible formats that facilitate quick decision-making : 

    • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
    • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
    • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

    Collect clickstream data with Matomo 

    Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

    Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

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