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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
-
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.
-
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.
-
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.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
-
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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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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A Beginner’s Guide to Omnichannel Analytics
14 avril 2024, par ErinLinear customer journeys are as obsolete as dial-up internet and floppy disks. As a marketing manager, you know better than anyone that customers interact with your brand hundreds of times across dozens of channels before purchasing. That can make tracking them a nightmare unless you build an omnichannel analytics solution.
Alas, if only it were that simple.
Unfortunately, it’s not enough to collect data on your customers’ complex journeys just by buying an omnichannel platform. You need to generate actionable insights by using marketing attribution to tie channels to conversions.
This article will explain how to build a useful omnichannel analytics solution that lets you understand and improve the customer journey.
What is omnichannel analytics ?
Omnichannel analytics collects and analyses customer data from every touchpoint and device. The goal is to collect all this omnichannel data in one place, creating a single, real-time, unified view of your customer’s journey.
Unfortunately, most businesses haven’t achieved this yet. As Karen Lellouche Tordjman and Marco Bertini say :
“Despite all the buzz around the concept of omnichannel, most companies still view customer journeys as a linear sequence of standardised touchpoints within a given channel. But the future of customer engagement transforms touchpoints from nodes along a predefined distribution path to full-blown portals that can serve as points of sale or pathways to many other digital and virtual interactions. They link to chatbots, kiosks, robo-advisors, and other tools that customers — especially younger ones — want to engage with.”
However, doing so is more important than ever — especially when consumers have over 300 digital touchpoints, and the average number of touchpoints in the B2B buyer journey is 27.
Not only that, but customers expect personalised experiences across every platform — that’s the kind you can only create when you have access to omnichannel data.
What might omnichannel analytics look like in practice for an e-commerce store ?
An online store would integrate data from channels like its website, mobile app, social media accounts, Google Ads and customer service records. This would show how customers find its brand, how they use each channel to interact with it and which channels convert the most customers.
This would allow the e-commerce store to tailor marketing channels to customers’ needs. For instance, they could focus social media use on product discovery and customer support. Google Ads campaigns could target the best-converting products. While all this is happening, the store could also ensure every channel looks the same and delivers the same experience.
What are the benefits of omnichannel analytics ?
Why go to all the trouble of creating a comprehensive view of the customer’s experience ? Because you stand to gain some pretty significant benefits when implementing omnichannel analytics.
Understand the customer journey
You want to understand how your customers behave, right ? No other method will allow you to fully understand your customer journey the way omnichannel analytics does.
It doesn’t matter how customers engage with your brand — whether that’s your website, app, social media profiles or physical stores — omnichannel analytics capture every interaction.
With this 360-degree view of your customers, it’s easy to understand how they move between channels, where they encounter issues and what bottlenecks prevent them from converting.
Deliver better personalisation
We don’t have to tell you that personalisation matters. But do you know just how important it is ? Since 56% of customers will become repeat buyers after a personalised experience, delivering them as often as possible is critical.
Omnichannel analytics helps in your quest for personalisation by highlighting the individual preferences of customer segments. For example, e-commerce stores can use omnichannel analytics to understand how shoppers behave across different devices and tailor their offers accordingly.
Upgrade the customer experience
Omnichannel analytics gives you the insights to improve every aspect of the customer experience.
For starters, you can ensure a consistent brand experience across all your top channels by making sure they look and behave the same.
Then, you can use omnichannel insights to tailor each channel to your customers’ requirements. For example, most people interacting with your brand on social media may seek support. Knowing that you can create dedicated support accounts to assist users.
Improve marketing campaigns
Which marketing campaigns or traffic sources convert the most customers ? How can you improve these campaigns ? Omnichannel analytics has the answers.
When you implement omnichannel analytics you automatically track the performance of every marketing channel by attributing each conversion to one or more traffic sources. This lets you see whether Google Ads bring in more customers than your SEO efforts. Or whether social media ads are the most profitable acquisition channel.
Armed with this information, you can improve your marketing efforts — either by focusing on your profitable channels or rectifying problems that stop less profitable channels from converting.
What are the challenges of omnichannel analytics ?
There are three challenges when implementing an omnichannel analytics solution :
- Complex customer journeys : Customer journeys aren’t linear and can be incredibly difficult to track.
- Regulatory and privacy issues : When you start gathering customer data, you quickly come up against consumer privacy laws.
- No underlying goal : There has to be a reason to go to all this effort, but brands don’t always have goals in mind before they start.
You can’t do anything about the first challenge.
After all, your customer journey will almost never be linear. And isn’t the point of implementing an omnichannel solution to understand these complex journeys in the first place ? Once you set up omnichannel analytics, these journeys will be much easier to decipher.
As for the other two :
Using the right software that respects user privacy and complies with all major privacy laws will avoid regulatory issues. Take Matomo, for instance. Our software was designed with privacy in mind and is configured to follow the strictest privacy laws, such as GDPR.
Tying omnichannel analytics to marketing attribution will solve the final challenge by giving your omnichannel efforts a goal. When you tie omnichannel analytics to your marketing efforts, you aren’t just getting a 360-degree view of your customer journey for the sake of it. You are getting that view to improve your marketing efforts and increase sales.
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Get the web insights you need, without compromising data accuracy.
How to set up an omnichannel analytics solution
Want to set up a seamless analytical environment that incorporates data from every possible source ? Follow these five steps :
Choose one or more analytics providers
You can use several tools to build an omnichannel analytics solution. These include web and app analytics tools, customer data platforms that centralise first-party data and business intelligence tools (typically used for visualisation).
Which tools you use will depend on your goals and your budget — the loftier your ambitions and the higher your budget, the more tools you can use.
Ideally, you should use as few tools as possible to capture your data. Most teams won’t need business intelligence platforms, for example. However, you may or may not need both an analytics platform and a customer data platform. Your decision will depend on how many channels your customers use and how well your analytics tool tracks everything.
If it can capture web and app usage while integrating with third-party platforms like your back-end e-commerce platform, then it’s probably enough.
Collect accurate data at every touchpoint
Your omnichannel analytics efforts hinge on the quantity and quality of data you can collect. You want to gather data from every touchpoint possible and store that data in as few places as possible. That’s why choosing as few tools as possible in the step above is so important.
So, where should you start ? Common data sources include :
- Your website
- Apps (iOS and Android)
- Social media profiles
- ERPs
- PoS systems
At the same time, make sure you’re tracking all relevant metrics. Revenue, customer engagement and conversion-focused metrics like conversion rate, dwell time, cart abandonment rate and churn rate are particularly important.
Set up marketing attribution
Setting up marketing attribution (also known as multi-touch attribution) is essential to tie omnichannel data to business goals. It’s the only way to know exactly how valuable each marketing channel is and where each customer comes from.
You’ll want to use multi-touch attribution, given you have data from across the customer journey.
Multi-touch attribution models can include (but are not limited to) :
- Linear : where each touchpoint is given equal weighting
- Time decay : where touchpoints are more valuable the nearer they are to conversion
- Position-based : where the first and last touch points are more valuable than all the others.
You don’t have to use just one of the models above, however. One of the benefits of using a web analytics tool like Matomo is that you can choose between different attribution models and compare them.
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Get the web insights you need, without compromising data accuracy.
Create reports that help you visualise data
Dashboards are your friend here. They’ll let you see KPIs at a glance, allowing you to keep track of day-to-day changes in your customer journey. Ideally, you’ll want a platform that lets you customise dashboard widgets so only relevant KPIs are shown.
Setting up standard and custom reports is also important. Custom reports allow you to choose metrics and dimensions that align with your goals. They will also allow you to present your data most meaningfully to your team, increasing the likelihood they act upon insights.
Analyse data and take action
Now that you have customer journey data at your fingertips, it’s time to analyse it. After all, there’s no point in implementing an omnichannel analytics solution if you aren’t going to take action.
If you’re unsure where to start, re-read the benefits we listed at the start of this article. You could use your omnichannel insights to improve your marketing campaigns by doubling down on the channels that bring in the best customers.
Or you could identify (and fix) bottlenecks in the customer journey so customers are less likely to fall out of your funnel between certain channels.
Just make sure you take action based on your data alone.
Make the most of omnichannel analytics with Matomo
A comprehensive web and app analytics platform is vital to any omnichannel analytics strategy.
But not just any solution will do. When privacy regulations impede an omnichannel analytics solution, you need a platform to capture accurate data without breaking privacy laws or your users’ trust.
That’s where Matomo comes in. Our privacy-friendly web analytics platform ensures accurate tracking of web traffic while keeping you compliant with even the strictest regulations. Moreover, our range of APIs and SDKs makes it easy to track interactions from all your digital products (website, apps, e-commerce back-ends, etc.) in one place.
Try Matomo for free for 21 days. No credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
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