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• How to Stream RTP (IP camera) Into React App setup

  10 novembre 2024, par sharon2469

  I am trying to transfer a live broadcast from an IP camera or any other broadcast coming from an RTP/RTSP source to my REACT application. BUT MUST BE LIVE

  


  My setup at the moment is :

  


  IP Camera -> (RTP) -> FFmpeg -> (udp) -> Server(nodeJs) -> (WebRTC) -> React app

  


  In the current situation, There is almost no delay, but there are some things here that I can't avoid and I can't understand why, and here is my question :

  


  1) First, is the SETUP even correct and this is the only way to Stream RTP video in Web app ?

  


  2) Is it possible to avoid re-encode the stream , RTP transmission necessarily comes in H.264, hence I don't really need to execute the following command :

  


      return spawn('ffmpeg', [
    '-re',                              // Read input at its native frame rate Important for live-streaming
    '-probesize', '32',                 // Set probing size to 32 bytes (32 is minimum)
    '-analyzeduration', '1000000',      // An input duration of 1 second
    '-c:v', 'h264',                     // Video codec of input video
    '-i', 'rtp://238.0.0.2:48888',      // Input stream URL
    '-map', '0:v?',                     // Select video from input stream
    '-c:v', 'libx264',                  // Video codec of output stream
    '-preset', 'ultrafast',             // Faster encoding for lower latency
    '-tune', 'zerolatency',             // Optimize for zero latency
    // '-s', '768x480',                    // Adjust the resolution (experiment with values)
    '-f', 'rtp', `rtp://127.0.0.1:${udpPort}` // Output stream URL
]);


  


  As you can se in this command I re-encode to libx264, But if I set FFMPEG a parameter '-c:v' :'copy' instead of '-c:v', 'libx264' then FFMPEG throw an error says : that it doesn't know how to encode h264 and only knows what is libx264-> Basically, I want to stop the re-encode because there is really no need for it, because the stream is already encoded to H264. Are there certain recommendations that can be made ?

  


  3) I thought about giving up the FFMPEG completely, but the RTP packets arrive at a size of 1200+ BYTES when WEBRTC is limited to up to 1280 BYTE. Is there a way to manage these sabotages without damaging the video and is it to enter this world ? I guess there is the whole story with the JITTER BUFFER here

  


  This is my server side code (THIS IS JUST A TEST CODE)

  


  import {
    MediaStreamTrack,
    randomPort,
    RTCPeerConnection,
    RTCRtpCodecParameters,
    RtpPacket,
} from 'werift'
import {Server} from "ws";
import {createSocket} from "dgram";
import {spawn} from "child_process";
import LoggerFactory from "./logger/loggerFactory";

//

const log = LoggerFactory.getLogger('ServerMedia')

// Websocket server -> WebRTC
const serverPort = 8888
const server = new Server({port: serverPort});
log.info(`Server Media start om port: ${serverPort}`);

// UDP server -> ffmpeg
const udpPort = 48888
const udp = createSocket("udp4");
// udp.bind(udpPort, () => {
//     udp.addMembership("238.0.0.2");
// })
udp.bind(udpPort)
log.info(`UDP port: ${udpPort}`)


const createFFmpegProcess = () => {
    log.info(`Start ffmpeg process`)
    return spawn('ffmpeg', [
        '-re',                              // Read input at its native frame rate Important for live-streaming
        '-probesize', '32',                 // Set probing size to 32 bytes (32 is minimum)
        '-analyzeduration', '1000000',      // An input duration of 1 second
        '-c:v', 'h264',                     // Video codec of input video
        '-i', 'rtp://238.0.0.2:48888',      // Input stream URL
        '-map', '0:v?',                     // Select video from input stream
        '-c:v', 'libx264',                  // Video codec of output stream
        '-preset', 'ultrafast',             // Faster encoding for lower latency
        '-tune', 'zerolatency',             // Optimize for zero latency
        // '-s', '768x480',                    // Adjust the resolution (experiment with values)
        '-f', 'rtp', `rtp://127.0.0.1:${udpPort}` // Output stream URL
    ]);

}

let ffmpegProcess = createFFmpegProcess();


const attachFFmpegListeners = () => {
    // Capture standard output and print it
    ffmpegProcess.stdout.on('data', (data) => {
        log.info(`FFMPEG process stdout: ${data}`);
    });

    // Capture standard error and print it
    ffmpegProcess.stderr.on('data', (data) => {
        console.error(`ffmpeg stderr: ${data}`);
    });

    // Listen for the exit event
    ffmpegProcess.on('exit', (code, signal) => {
        if (code !== null) {
            log.info(`ffmpeg process exited with code ${code}`);
        } else if (signal !== null) {
            log.info(`ffmpeg process killed with signal ${signal}`);
        }
    });
};


attachFFmpegListeners();


server.on("connection", async (socket) => {
    const payloadType = 96; // It is a numerical value that is assigned to each codec in the SDP offer/answer exchange -> for H264
    // Create a peer connection with the codec parameters set in advance.
    const pc = new RTCPeerConnection({
        codecs: {
            audio: [],
            video: [
                new RTCRtpCodecParameters({
                    mimeType: "video/H264",
                    clockRate: 90000, // 90000 is the default value for H264
                    payloadType: payloadType,
                }),
            ],
        },
    });

    const track = new MediaStreamTrack({kind: "video"});


    udp.on("message", (data) => {
        console.log(data)
        const rtp = RtpPacket.deSerialize(data);
        rtp.header.payloadType = payloadType;
        track.writeRtp(rtp);
    });

    udp.on("error", (err) => {
        console.log(err)

    });

    udp.on("close", () => {
        console.log("close")
    });

    pc.addTransceiver(track, {direction: "sendonly"});

    await pc.setLocalDescription(await pc.createOffer());
    const sdp = JSON.stringify(pc.localDescription);
    socket.send(sdp);

    socket.on("message", (data: any) => {
        if (data.toString() === 'resetFFMPEG') {
            ffmpegProcess.kill('SIGINT');
            log.info(`FFMPEG process killed`)
            setTimeout(() => {
                ffmpegProcess = createFFmpegProcess();
                attachFFmpegListeners();
            }, 5000)
        } else {
            pc.setRemoteDescription(JSON.parse(data));
        }
    });
});


  


  And this fronted :

  


  &#xA;&#xA;&#xA;    &#xA;    &#xA;    <code class="echappe-js">&lt;script&amp;#xA;            crossorigin&amp;#xA;            src=&quot;https://unpkg.com/react@16/umd/react.development.js&quot;&amp;#xA;    &gt;&lt;/script&gt;&#xA;    &lt;script&amp;#xA;            crossorigin&amp;#xA;            src=&quot;https://unpkg.com/react-dom@16/umd/react-dom.development.js&quot;&amp;#xA;    &gt;&lt;/script&gt;&#xA;    &lt;script&amp;#xA;            crossorigin&amp;#xA;            src=&quot;https://cdnjs.cloudflare.com/ajax/libs/babel-core/5.8.34/browser.min.js&quot;&amp;#xA;    &gt;&lt;/script&gt;&#xA;    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/babel-regenerator-runtime@6.5.0/runtime.min.js&quot;&gt;&lt;/script&gt;&#xA;&#xA;&#xA;
  &#xA;    
  
  &#xA;
  
  &#xA;&lt;script type=&quot;text/babel&quot;&gt;&amp;#xA;    let rtc;&amp;#xA;&amp;#xA;    const App = () =&gt; {&amp;#xA;        const [log, setLog] = React.useState([]);&amp;#xA;        const videoRef = React.useRef();&amp;#xA;        const socket = new WebSocket(&quot;ws://localhost:8888&quot;);&amp;#xA;        const [peer, setPeer] = React.useState(null); // Add state to keep track of the peer connection&amp;#xA;&amp;#xA;        React.useEffect(() =&gt; {&amp;#xA;            (async () =&gt; {&amp;#xA;                await new Promise((r) =&gt; (socket.onopen = r));&amp;#xA;                console.log(&quot;open websocket&quot;);&amp;#xA;&amp;#xA;                const handleOffer = async (offer) =&gt; {&amp;#xA;                    console.log(&quot;new offer&quot;, offer.sdp);&amp;#xA;&amp;#xA;                    const updatedPeer = new RTCPeerConnection({&amp;#xA;                        iceServers: [],&amp;#xA;                        sdpSemantics: &quot;unified-plan&quot;,&amp;#xA;                    });&amp;#xA;&amp;#xA;                    updatedPeer.onicecandidate = ({ candidate }) =&gt; {&amp;#xA;                        if (!candidate) {&amp;#xA;                            const sdp = JSON.stringify(updatedPeer.localDescription);&amp;#xA;                            console.log(sdp);&amp;#xA;                            socket.send(sdp);&amp;#xA;                        }&amp;#xA;                    };&amp;#xA;&amp;#xA;                    updatedPeer.oniceconnectionstatechange = () =&gt; {&amp;#xA;                        console.log(&amp;#xA;                            &quot;oniceconnectionstatechange&quot;,&amp;#xA;                            updatedPeer.iceConnectionState&amp;#xA;                        );&amp;#xA;                    };&amp;#xA;&amp;#xA;                    updatedPeer.ontrack = (e) =&gt; {&amp;#xA;                        console.log(&quot;ontrack&quot;, e);&amp;#xA;                        videoRef.current.srcObject = e.streams[0];&amp;#xA;                    };&amp;#xA;&amp;#xA;                    await updatedPeer.setRemoteDescription(offer);&amp;#xA;                    const answer = await updatedPeer.createAnswer();&amp;#xA;                    await updatedPeer.setLocalDescription(answer);&amp;#xA;&amp;#xA;                    setPeer(updatedPeer);&amp;#xA;                };&amp;#xA;&amp;#xA;                socket.onmessage = (ev) =&gt; {&amp;#xA;                    const data = JSON.parse(ev.data);&amp;#xA;                    if (data.type === &quot;offer&quot;) {&amp;#xA;                        handleOffer(data);&amp;#xA;                    } else if (data.type === &quot;resetFFMPEG&quot;) {&amp;#xA;                        // Handle the resetFFMPEG message&amp;#xA;                        console.log(&quot;FFmpeg reset requested&quot;);&amp;#xA;                    }&amp;#xA;                };&amp;#xA;            })();&amp;#xA;        }, []); // Added socket as a dependency to the useEffect hook&amp;#xA;&amp;#xA;        const sendRequestToResetFFmpeg = () =&gt; {&amp;#xA;            socket.send(&quot;resetFFMPEG&quot;);&amp;#xA;        };&amp;#xA;&amp;#xA;        return (&amp;#xA;            &lt;div&gt;&amp;#xA;                Video: &amp;#xA;                &lt;video ref={videoRef} autoPlay muted /&gt;&amp;#xA;                &lt;button onClick={() =&gt; sendRequestToResetFFmpeg()}&gt;Reset FFMPEG&lt;/button&gt;&amp;#xA;            &lt;/div&gt;&amp;#xA;        );&amp;#xA;    };&amp;#xA;&amp;#xA;    ReactDOM.render(&lt;App /&gt;, document.getElementById(&quot;app1&quot;));&amp;#xA;&lt;/script&gt;&#xA;&#xA;&#xA;
  

  


• Conversion Rate Optimisation Statistics for 2024 and Beyond

  21 novembre 2023, par Erin — Analytics Tips

  Driving traffic to your website is only half the battle. The real challenge — once you’ve used a web analytics solution to understand how users behave — is turning more of those visitors into customers.

  That doesn’t happen by accident. You need to employ conversion rate optimisation strategies and tools to see even a small lift in conversion rates. The good news is that it doesn’t take much to see massive results. Raising your conversion rate from 1% to 3% can triple your revenue. 

  In even better news, you don’t have to guess at the best ways to improve your conversion rate. We’ve done the hard work and collected the most recent and relevant conversion rate optimisation statistics to help you. 

  General conversion rate optimisation statistics

  It appears the popularity of conversion rate optimisation is soaring. According to data collected by Google Trends, there were more people searching for the term “conversion rate optimization” in September 2023 than ever before. 

  As you can see from the chart below, the term’s popularity is on a clear upward trajectory, meaning even more people could be searching for it in the near future. (Source)

  

  Do you want to know what the average landing page conversion rate is ? According to research by WordStream, the average website conversion rate across all industries is 2.35%. 

  That doesn’t paint the whole picture, however. Better-performing websites have significantly higher conversion rates. The top 25% of websites across all industries convert at a rate of 5.31% or higher. (Source)

  Let’s break things down by industry now. The Unbounce Conversion Benchmark Report offers a detailed analysis of how landing pages convert across various industries.

  First, we have the Finance and Insurance industry, which boasts a conversion rate of 15.6%. 

  On the other end, agencies appears to be one of the worst-performing. Agencies’ landing pages convert at a rate of 8.8%. (Source)

  

  What about the size of the conversion rate optimisation industry ? Given the growth in popularity of the term in Google, surely the industry is experiencing growth, right ?

  You’d be correct in that assumption. The conversion rate optimisation software market was valued at $771.2 million in 2018 and is projected to reach $1.932 billion by 2026 — a compound annual growth rate (CAGR) of 9.6%.

  Statistics on the importance of conversion rate optimisation

  If you’re reading this article, you probably think conversion rate optimisation is pretty important. But do you know its importance and where it ranks in your competitors’ priorities ? Read on to find out. 

  Bounce rate — the number of people who leave your website without visiting another page or taking action — is the scourge of conversion rate optimisation efforts. Every time someone bounces from your site, you lose the chance to convert them.

  The questions, then, are : how often do people bounce on average and how does your bounce rate compare ? 

  Siege Media analysed over 1.3 billion sessions from a range of traffic sources, including 700 million bounces, to calculate an average bounce rate of 50.9%. (Source)

  

  Bounce rates vary massively from website to website and industry to industry, however. Siege Media’s study unveils an array of average bounce rates across industries :

  • Travel – 82.58%
  • B2B – 65.17%
  • Lifestyle – 64.26%
  • Business and Finance – 63.51%
  • Healthcare – 59.50%
  • eCommerce – 54.54%
  • Insurance – 45.96%
  • Real Estate – 40.78%

  It won’t come as much of a surprise to learn that marketers are determined to reduce bounce rates and improve lead conversion. Today’s marketers are highly performance-based. When asked about their priorities for the coming year, 79% of marketers said their priority was generating quality qualified leads — the most popular answer in the survey. (Source)

  Just because it is a priority for marketers doesn’t mean that everyone has their stuff together. If you have a conversion rate optimisation process in place, you’re in the minority. According to research by HubSpot, less than one in five marketers (17%) use landing page A/B tests to improve their conversion rates. (Source)

  When it comes to personalisation strategies – a common and effective tool to increase conversion rates — the picture isn’t any rosier. Research by Salesforce found just over one-quarter of markets are confident their organisation has a successful strategy for personalisation. (Source)

  Conversion rate optimisation tactics statistics

  There are hundreds of ways to improve your website’s conversion rates. From changing the color of buttons to the structure of your landing page to your entire conversion funnel, in this section, we’ll look at the most important statistics you need to know when choosing tactics and building your own CRO experiments. 

  If you are looking for the best method to convert visitors, then email lead generation forms are the way to go, according to HubSpot. This inoffensive and low-barrier data collection method boasts a 15% conversion rate, according to the marketing automation company’s research. (Source)

  Where possible, make your call-to-actions personalised. Marketing personalisation, whether through behavioral segmentation or another strategy, is an incredibly powerful way of showing users that you care about their specific needs. It’s no great surprise, then, that HubSpot found personalised calls-to-actions perform a whopping 202% better than basic CTAs. (Source)

  If you want to boost conversion rates, then it’s just as important to focus on quantity as well as quality. Yes, a great-looking, well-written landing page will go a long way to improving your conversion rate, but having a dozen of these pages will do even more. 

  Research by HubSpot found companies see a 55% increase in leads when they increase the number of landing pages from 10 to 15. What’s more, companies with over 40 landing pages increase conversion by more than 500%. (Source)

  

  User-generated content (UGC) should also be high on your priority list to boost conversion rates. Several statistics show how powerful, impactful and persuasive social proof like user reviews can be. 

  Research shows that visitors who scroll to the point where they encounter user-generated content increase the likelihood they convert by a staggering 102.4%. (Source)

  Other trust signs can be just as impactful. Research by Trustpilot found that the following four trust signals make consumers more likely to make a purchase when shown on a product page :

  • Positive star rating and reviews (85% more likely to make a purchase)
  • Positive star rating (78%)
  • Positive customer testimonials (82%)
  • Approved or authorised seller badge (76%)

  (Source)

  Showing ratings and reviews has also increased conversion rates by 38% on home appliances and electronics stores. (Source)

  And no wonder, given that consumers are more likely to buy from brands they trust than brands they love, according to the 2021 Edelman Trust Barometer Special Report. (Source) 

  A lack of trust is also one of the top four reasons consumers abandon their shopping cart at checkout. (Source) 

  Traffic source conversion rate statistics

  What type of traffic works the best when it comes to conversions, or how often you should be signing up users to your mailing list ? Let’s look at the stats to find out. 

  Email opt-ins are one of the most popular methods for collecting customer information — and an area where digital marketers spend a lot of time and effort when it comes to conversion rate optimisation. So, what is the average conversion rate of an email opt-in box ?

  According to research by Sumo — based on 3.2 billion users who have seen their opt-in boxes — the average email opt-in rate is 1.95%. (Source)

  Search advertising is an effective way of driving website traffic, but how often do those users click on these ads ?

  WordStream’s research puts the average conversion of search advertising for all industries at 6.11%. (Source)

  The arts and entertainment industry enjoys the highest clickthrough rates (11.78%), followed by sports and recreation (10.53%) and travel (10.03%). Legal services and the home improvement industry have the lowest clickthrough rates at 4.76% and 4.8%, respectively.

  

  If you’re spending money on Google ads, then you’d better hope a significant amount of users convert after clicking them. 

  Unfortunately, conversion rates from Google ads decreased year-on-year for most industries in 2023, according to research by WordStream — in some cases, those decreases were significant. The only two industries that didn’t see a decrease in conversion rates were beauty and personal care and education and instruction. (Source)

  The average conversion rate for search ads across all industries is 7.04%. The animal and pet niche has the highest conversion rate (13.41%), while apparel, fashion and jewelry have the lowest conversion rate (1.57%). (Source)

  What about other forms of traffic ? Well, there’s good reason to try running interstitial ads on smartphone apps if you aren’t already. Ads on the iOS app see a 14.3 percent conversion rate on average. (Source)

  E-commerce conversion rate optimisation statistics (400 words)

  Conversion rate optimisation can be the difference between a store that sets new annual sales records and one struggling to get by. 

  The good news is that the conversion rate among US shoppers was the highest it’s ever been in 2021, with users converting at 2.6%. (Source)

  If you have a Shopify store, then you may find conversion rates a little lower. A survey by Littledata found the average conversion rate for Shopify was 1.4% in September 2022. (Source)

  What about specific e-commerce categories ? According to data provided by Dynamic Yield, the consumer goods category converted at the highest rate in September 2023 (4.22%), a spike of 0.34% from August. 

  Generally, the food and beverage niche boasts the highest conversion rate (4.87%), and the home and furniture niche has the lowest conversion rate (1.44%). (Source)

  If you’re serious about driving sales, don’t focus on mobile devices at the expense of consumers who shop on desktop devices. The conversion rate among US shoppers tends to be higher for desktop users than for mobile users. 

  

  In the second quarter of 2022, for instance, desktop shoppers converted at a rate of 3% on average compared to smartphone users who converted at an average rate of 2%. (Source)

  Increase your conversions with Matomo

  Conversion rate optimisation can help you grow your subscriber list, build your customer base and increase your revenue. Now, it’s time to put what you’ve learned into practice.

  Use the advice above to guide your experiments and track everything with Matomo. Achieve unparalleled data accuracy while harnessing an all-in-one solution packed with essential conversion optimisation features, including Heatmaps, Session Recordings and A/B Testing. Matomo makes it easier than ever to analyse conversion-focused experiments.

  Get more from your conversion rate optimisations by trying Matomo free for 21 days. No credit card required.

• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.