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Bug de détection d’ogg
22 mars 2013, par
Mis à jour : Avril 2013
Langue : français
Type : Video
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Node 18 or Node 20 break ffmpeg (in google cloud functions -> ffprobe was killed with signal SIGSEGV)
10 janvier 2024, par user20206929Please see below, the code is working on node js 16, but not when upgrading to node 18 or 20.


const ffmpeg = require("fluent-ffmpeg");

// Following is inside a .https.onRequest Google Cloud function with enough memory

try {
 const duration = new Promise((resolve, reject) => {
 ffmpeg.ffprobe(videoUrl, async (err, metadata) => {
 if (err) {
 if (res.headersSent) {
 console.error("Response already sent");
 return;
 } else {
 console.log("Metadata:", metadata);
 console.log("err: " + err);
 res.status(400).send("Error getting video metadata");
 return;
 }
 }
 const duration = metadata.format.duration;
 console.log("video duration in second: " + duration);
 resolve(duration);
 });
});
 videoDuration = await duration;
} catch (err) {
 console.log(err);
 throw err;
}


When upgrading to node 18/20 (No other change than upgrading node), the error "ffprobe not found" appears.


But setting the path manually using ffmpeg.setFfprobePath(ffprobePath) ;
trigger the error : Error : ffprobe was killed with signal SIGSEGV


So it seem its a permissions issue.


However, I tried a lot of different solutions, none of them made this work.
For instance i tried to download manually the ffprobe from the official website https://ffbinaries.com/downloads. Then manually add it to the code.


I tried to use https://www.npmjs.com/package/@ffprobe-installer/ffprobe or others package like https://www.npmjs.com/package/ffprobe-static


I also tried to download the ffprobe file to the temporary folder of google cloud, and change the permission of this folder.


All of those was doing the same error.


None of what i could think of made any difference.


Please help because i need to update node 16 to 18 or 20 before google remove node 16 on january 31 2024 and for now i don't see a solution.


I also looked for other solution to get this duration from a video file url, but using ffmpeg seem to be the only one that should work out of the box. As it is working on node 16.


Thank you,


UPDATE - 11/26/2023


GCP Functions NodeJS 16 runtime uses Ubuntu 18.04 with FFMpeg installed.
NodeJS 18/20 use Ubuntu 22.04, and Google decided not to include FFMpeg.


https://cloud.google.com/functions/docs/runtime-support#node.js
https://cloud.google.com/functions/docs/reference/system-packages


No workaround or solutions found as of now


UPDATE - 01/10/2024


Google added back ffmpeg to latest version, this is working as before now.


-
Error transcoding with FFmpeg : Error : Output format hls is not available
6 mai 2024, par asif mohmdI 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";
import * as fs from "fs";
import * as path from "path";
import ffmpeg from "fluent-ffmpeg";
import crypto from "crypto";

ffmpeg.setFfmpegPath("C:\\ffmpeg\\bin\\ffmpeg.exe");

export const FFmpegTranscoder = async (file: any): Promise<any> => {
 try {
 console.log("Starting script");
 console.time("req_time");

 const randomName = (bytes = 32) =>
 crypto.randomBytes(bytes).toString("hex");
 const fileName = randomName();
 const directoryPath = path.join(__dirname, "..", "..", "input");
 const filePath = path.join(directoryPath, `${fileName}.mp4`);

 if (!fs.existsSync(directoryPath)) {
 fs.mkdirSync(directoryPath, { recursive: true });
 }

 const paths = await new Promise<any>((resolve, reject) => {
 fs.writeFile(filePath, file, async (err) => {
 if (err) {
 console.error("Error saving file:", err);
 throw err;
 }
 console.log("File saved successfully:", filePath);

 try {
 const outputDirectoryPath = await transcodeWithFFmpeg(
 fileName,
 filePath
 );
 resolve({ directoryPath, filePath, fileName, outputDirectoryPath });
 } catch (error) {
 console.error("Error transcoding with FFmpeg:", error);
 }
 });
 });
 return paths;
 } catch (e: any) {
 console.log(e);
 }
};

const transcodeWithFFmpeg = async (fileName: string, filePath: string) => {
 const directoryPath = path.join(
 __dirname,
 "..",
 "..",
 `output/hls/${fileName}`
 );

 if (!fs.existsSync(directoryPath)) {
 fs.mkdirSync(directoryPath, { recursive: true });
 }

 const resolutions = [
 {
 resolution: "256x144",
 videoBitrate: "200k",
 audioBitrate: "64k",
 },
 {
 resolution: "640x360",
 videoBitrate: "800k",
 audioBitrate: "128k",
 },
 {
 resolution: "1280x720",
 videoBitrate: "2500k",
 audioBitrate: "192k",
 },
 {
 resolution: "1920x1080",
 videoBitrate: "5000k",
 audioBitrate: "256k",
 },
 ];

 const variantPlaylists: { resolution: string; outputFileName: string }[] = [];

 for (const { resolution, videoBitrate, audioBitrate } of resolutions) {
 console.log(`HLS conversion starting for ${resolution}`);
 const outputFileName = `${fileName}_${resolution}.m3u8`;
 const segmentFileName = `${fileName}_${resolution}_%03d.ts`;

 await new Promise<void>((resolve, reject) => {
 ffmpeg(filePath)
 .outputOptions([
 `-c:v h264`,
 `-b:v ${videoBitrate}`,
 `-c:a aac`,
 `-b:a ${audioBitrate}`,
 `-vf scale=${resolution}`,
 `-f hls`,
 `-hls_time 10`,
 `-hls_list_size 0`,
 `-hls_segment_filename ${directoryPath}/${segmentFileName}`,
 ])
 .output(`${directoryPath}/${outputFileName}`)
 .on("end", () => resolve())
 .on("error", (err) => reject(err))
 .run();
 });
 const variantPlaylist = {
 resolution,
 outputFileName,
 };
 variantPlaylists.push(variantPlaylist);
 console.log(`HLS conversion done for ${resolution}`);
 }
 console.log(`HLS master m3u8 playlist generating`);

 let masterPlaylist = variantPlaylists
 .map((variantPlaylist) => {
 const { resolution, outputFileName } = variantPlaylist;
 const bandwidth =
 resolution === "256x144"
 ? 264000
 : resolution === "640x360"
 ? 1024000
 : resolution === "1280x720"
 ? 3072000
 : 5500000;
 ``;
 return `#EXT-X-STREAM-INF:BANDWIDTH=${bandwidth},RESOLUTION=${resolution}\n${outputFileName}`;
 })
 .join("\n");
 masterPlaylist = `#EXTM3U\n` + masterPlaylist;

 const masterPlaylistFileName = `${fileName}_master.m3u8`;

 const masterPlaylistPath = `${directoryPath}/${masterPlaylistFileName}`;
 fs.writeFileSync(masterPlaylistPath, masterPlaylist);
 console.log(`HLS master m3u8 playlist generated`);
 return directoryPath;
};
</void></any></any>

My console.log is :


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

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.


In Command Line its working perfectly :


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
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '.\SampleVideo_1280x720_30mb.mp4':
 Metadata:
 major_brand : isom
 minor_version : 512
 compatible_brands: isomiso2avc1mp41
 creation_time : 1970-01-01T00:00:00.000000Z
 encoder : Lavf53.24.2
 Duration: 00:02:50.86, start: 0.000000, bitrate: 1474 kb/s
 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)
 Metadata:
 creation_time : 1970-01-01T00:00:00.000000Z
 handler_name : VideoHandler
 vendor_id : [0][0][0][0]
 Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, 5.1, fltp, 383 kb/s (default)
 Metadata:
 creation_time : 1970-01-01T00:00:00.000000Z
 handler_name : SoundHandler
 vendor_id : [0][0][0][0]
Stream mapping:
 Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
 Stream #0:1 -> #0:1 (aac (native) -> aac (native))
Press [q] to stop, [?] for help
[libx264 @ 000001ef1288ec00] using SAR=1/1
[libx264 @ 000001ef1288ec00] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 000001ef1288ec00] profile High, level 3.0, 4:2:0, 8-bit
[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
Output #0, hls, to 'beach/480p.m3u8':
 Metadata:
 major_brand : isom
 minor_version : 512
 compatible_brands: isomiso2avc1mp41
 encoder : Lavf61.1.100
 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)
 Metadata:
 creation_time : 1970-01-01T00:00:00.000000Z
 handler_name : VideoHandler
 vendor_id : [0][0][0][0]
 encoder : Lavc61.3.100 libx264
 Side data:
 cpb: bitrate max/min/avg: 0/0/800000 buffer size: 0 vbv_delay: N/A
 Stream #0:1(und): Audio: aac (LC), 48000 Hz, 5.1, fltp, 128 kb/s (default)
 Metadata:
 creation_time : 1970-01-01T00:00:00.000000Z
 handler_name : SoundHandler
 vendor_id : [0][0][0][0]
 encoder : Lavc61.3.100 aac
[hls @ 000001ef12482040] Opening 'beach/480p_000.ts' for writing speed=15.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_001.ts' for writing speed=17.9x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_002.ts' for writing speed=17.3x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_003.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_004.ts' for writing speed=19.3x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_005.ts' for writing speed=19.2x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_006.ts' for writing
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_007.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_008.ts' for writing speed=19.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_009.ts' for writing speed=19.5x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_010.ts' for writing speed=19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[hls @ 000001ef12482040] Opening 'beach/480p_011.ts' for writing/A =19.4x
[hls @ 000001ef12482040] Opening 'beach/480p.m3u8.tmp' for writing
[out#0/hls @ 000001ef11d4e880] video:17094KiB audio:2680KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
frame= 4271 fps=485 q=-1.0 Lsize=N/A time=00:02:50.76 bitrate=N/A speed=19.4x
[libx264 @ 000001ef1288ec00] frame I:45 Avg QP:10.29 size: 60418
[libx264 @ 000001ef1288ec00] frame P:1914 Avg QP:14.53 size: 5582
[libx264 @ 000001ef1288ec00] frame B:2312 Avg QP:20.63 size: 1774
[libx264 @ 000001ef1288ec00] consecutive B-frames: 22.9% 11.9% 8.6% 56.6%
[libx264 @ 000001ef1288ec00] mb I I16..4: 15.6% 32.1% 52.2%
[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%
[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%
[libx264 @ 000001ef1288ec00] final ratefactor: 16.13
[libx264 @ 000001ef1288ec00] 8x8 transform intra:58.4% inter:51.7%
[libx264 @ 000001ef1288ec00] coded y,uvDC,uvAC intra: 86.7% 94.3% 78.8% inter: 12.6% 15.0% 4.5%
[libx264 @ 000001ef1288ec00] i16 v,h,dc,p: 17% 42% 14% 28%
[libx264 @ 000001ef1288ec00] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 19% 11% 6% 7% 8% 8% 9% 9%
[libx264 @ 000001ef1288ec00] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 23% 18% 12% 6% 9% 9% 8% 8% 7%
[libx264 @ 000001ef1288ec00] i8c dc,h,v,p: 44% 24% 20% 12%
[libx264 @ 000001ef1288ec00] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 000001ef1288ec00] ref P L0: 78.3% 9.7% 8.8% 3.2%
[libx264 @ 000001ef1288ec00] ref B L0: 92.5% 6.0% 1.5%
[libx264 @ 000001ef1288ec00] ref B L1: 97.1% 2.9%
[libx264 @ 000001ef1288ec00] kb/s:819.63
[aac @ 000001ef128f7c80] Qavg: 452.137


When I use the
.on('start', (cmdline) => console.log(cmdline))}code with the-f hlscommand, 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 hlscommand :

Without -f hls command


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

Console.log is :


`Starting script
File saved successfully: C:\Users\asifa\Desktop\Genius Grid\Transcode-service\input\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1.mp4
HLS conversion starting for 256x144
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
Error transcoding with FFmpeg: Error: ffmpeg exited with code 2880417800: Unrecognized option 'hls_segment_filename C:\Users\asifa\Desktop\Genius Grid\Transcode-service\output\hls\c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1/c9fcf43726e617a295b203d5acb7b81658b5f05f80eafc74cee21b053422fef1_256x144_%03d.ts'.
Error splitting the argument list: Option not found`


-
How to Implement Cross-Channel Analytics : A Guide for Marketers
17 avril 2024, par ErinEvery 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
- 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.
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.
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.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
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.
-
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.
-
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
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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.
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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 :
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.
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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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Get the web insights you need, without compromising data accuracy.
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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.
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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Get the web insights you need, without compromising data accuracy.
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
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
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
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.
Try Matomo for Free
21 day free trial. No credit card required.
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