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• 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.

• OCPA, FDBR and TDPSA – What you need to know about the US’s new privacy laws

  22 juillet 2024, par Daniel Crough

  On July 1, 2024, new privacy laws took effect in Florida, Oregon, and Texas. People in these states now have more control over their personal data, signaling a shift in privacy policy in the United States. Here’s what you need to know about these laws and how privacy-focused analytics can help your business stay compliant.

  Consumer rights are front and centre across all three laws

  The Florida Digital Bill of Rights (FDBR), Oregon Consumer Privacy Act (OCPA), and Texas Data Privacy and Security Act (TDPSA) grant consumers similar rights.

  Access : Consumers can access their personal data held by businesses.

  Correction : Consumers can correct inaccurate data.

  Deletion : Consumers may request data deletion.

  Opt-Out : Consumers can opt-out of the sale of their personal data and targeted advertising.

  Oregon Consumer Privacy Act (OCPA)

  The Oregon Consumer Privacy Act (OCPA), signed into law on June 23, 2023, and effective as of July 1, 2024, grants Oregonians new rights regarding their personal data and imposes obligations on businesses. Starting July 1, 2025, authorities will enforce provisions that require data protection assessments, and businesses must recognize universal opt-out mechanisms by January 1, 2026. In Oregon, the OCPA applies to business that :

  • Either conduct business in Oregon or offer products and services to Oregon residents

  • Control or process the personal data of 100,000 consumers or more, or

  • Control or process the data of 25,000 or more consumers while receiving over 25% of their gross revenues from selling personal data.

  Exemptions include public bodies like state and local governments, financial institutions, and insurers that operate under specific financial regulations. The law also excludes protected health information covered by HIPAA and other specific federal regulations.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for high-risk processing activities, such as those involving sensitive data or targeting children.

  Consent for Sensitive Data : Businesses must secure explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Universal Opt-out : Starting January 1, 2025, businesses must acknowledge universal opt-out mechanisms, like the Global Privacy Control, that allow consumers to opt out of data collection and processing activities.

  Enforcement

  The Oregon Attorney General can issue fines up to $7,500 per violation. There is no private right of action.

  Unique characteristics of the OCPA

  The OCPA differs from other state privacy laws by requiring affirmative opt-in consent for processing sensitive and children’s data, and by including nonprofit organisations under its scope. It also requires global browser opt-out mechanisms starting in 2026.

  Florida Digital Bill of Rights (FDBR)

  The Florida Digital Bill of Rights (FDBR) became law on June 6, 2023, and it came into effect on July 1, 2024. This law targets businesses with substantial operations or revenues tied to digital activities and seeks to protect the personal data of Florida residents by granting them greater control over their information and imposing stricter obligations on businesses. It applies to entities that :

  • Conduct business in Florida or provide products or services targeting Florida residents,

  • Have annual global gross revenues exceeding $1 billion,

  • Receive 50% or more of their revenues from digital advertising or operate significant digital platforms such as app stores or smart speakers with virtual assistants.

  Exemptions include governmental entities, nonprofits, financial institutions covered by the Gramm-Leach-Bliley Act, and entities covered by HIPAA.

  Business obligations

  Data Security Measures : Companies are required to implement reasonable data security measures to protect personal data from unauthorised access and breaches.

  Handling Sensitive Data : Explicit consent is required for processing sensitive data, which includes information like racial or ethnic origin, religious beliefs, and biometric data.

  Non-Discrimination : Entities must ensure they do not discriminate against consumers who exercise their privacy rights.

  Data Minimisation : Businesses must collect only necessary data.

  Vendor Management : Businesses must ensure that their processors and vendors also comply with the FDBR, regarding the secure handling and processing of personal data.

  Enforcement

  The Florida Attorney General can impose fines of up to $50,000 per violation, with higher penalties for intentional breaches.

  Unique characteristics of the FDBR

  Unlike broader privacy laws such as the California Consumer Privacy Act (CCPA), which apply to a wider range of businesses based on lower revenue thresholds and the volume of data processed, the FDBR distinguishes itself by targeting large-scale businesses with substantial revenues from digital advertising. The FDBR also emphasises specific consumer rights related to modern digital interactions, reflecting the evolving landscape of online privacy concerns.

  Texas Data Privacy and Security Act (TDPSA)

  The Texas Data Privacy and Security Act (TDPSA), signed into law on June 16, 2023, and effective as of July 1, 2024, enhances data protection for Texas residents. The TDPSA applies to entities that :

  • Conduct business in Texas or offer products or services to Texas residents.

  • Engage in processing or selling personal data.

  • Do not fall under the classification of small businesses according to the U.S. Small Business Administration’s criteria, which usually involve employee numbers or average annual receipts. 

  The law excludes state agencies, political subdivisions, financial institutions compliant with the Gramm-Leach-Bliley Act, and entities compliant with HIPAA.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for processing activities that pose a heightened risk of harm to consumers, such as processing for targeted advertising, selling personal data, or profiling.

  Consent for Sensitive Data : Businesses must get explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Companies must have adequate data security practices based on the personal information they handle.

  Data Subject Access Requests (DSARs) : Businesses must respond to consumer requests regarding their personal data (e.g., access, correction, deletion) without undue delay, but no later than 45 days after receipt of the request.

  Sale of Data : If businesses sell personal data, they must disclose these practices to consumers and provide them with an option to opt out.

  Universal Opt-Out Compliance : Starting January 1, 2025, businesses must recognise universal opt-out mechanisms like the Global Privacy Control, enabling consumers to opt out of data collection and processing activities.

  Enforcement

  The Texas Attorney General can impose fines up to $25,000 per violation. There is no private right of action.

  Unique characteristics of the TDPSA

  The TDPSA stands out for its small business carve-out, lack of specific thresholds based on revenue or data volume, and requirements for recognising universal opt-out mechanisms starting in 2025. It also mandates consent for processing sensitive data and includes specific measures for data protection assessments and privacy notices.

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  Privacy notices across Florida, Oregon, and Texas

  All three laws include a mandate for privacy notices, though there are subtle variations in their specific requirements. Here’s a breakdown of these differences :

  FDBR privacy notice requirements

  Clarity : Privacy notices must clearly explain the collection and use of personal data.

  Disclosure : Notices must inform consumers about their rights, including the right to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  Specificity : Businesses must disclose if they sell personal data or use it for targeted advertising.

  Security Practices : The notice should describe the data security measures in place.

  OCPA privacy notice requirements

  Comprehensive Information : Notices must provide information about the personal data collected, the purposes for processing, and any third parties that can access it.

  Consumer Rights : Must plainly outline consumers’ rights to access, correct, delete their data, and opt-out of data sales, targeted advertising, and profiling.

  Sensitive Data : To process sensitive data, businesses or entities must get explicit consent and communicate it.

  Universal Opt-Out : Starting January 1, 2026, businesses must recognise and honour universal opt-out mechanisms.

  TDPSA privacy notice requirements

  Detailed Notices : Must provide clear and detailed information about data collection practices, including the data collected and the purposes for its use.

  Consumer Rights : Must inform consumers of their rights to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  High-Risk Processing : Notices should include information about any high-risk processing activities and the safeguards in place.

  Sensitive Data : To process sensitive data, entities and businesses must get explicit consent.

  What these laws mean for your businesses

  Businesses operating in Florida, Oregon, and Texas must now comply with these new data privacy laws. Here’s what you can do to avoid fines :

  1. Understand the Laws : Familiarise yourself with the specific requirements of the FDBR, OCPA, and TDPSA, including consumer rights and business obligations.

  2. Implement Data Protection Measures : Ensure you have robust data security measures in place. This includes conducting regular data protection assessments, especially for high-risk processing activities.

  3. Update Privacy Policies : Provide clear and comprehensive privacy notices that inform consumers about their rights and how their data is processed.

  4. Obtain Explicit Consent : For sensitive data, make sure you get explicit consent from consumers. This includes information like health, race, sexual orientation, and more.

  5. Manage Requests Efficiently : Be prepared to handle requests from consumers to access, correct, delete their data, and opt-out of data sales and targeted advertising within the stipulated timeframes.

  6. Recognise Opt-Out Mechanisms : For Oregon, businesses must be ready to implement and recognise universal opt-out mechanisms by January 1, 2026. In Texas, opt-out enforcement begins in 2026. In Florida, the specific opt-out provisions began on July 1, 2024.

  7. Stay Updated : Keep abreast of any changes or updates to these laws to ensure ongoing compliance. Keep an eye on the Matomo blog or sign up for our newsletter to stay in the know.

  Are we headed towards a more privacy-focused future in the United States ?

  Florida, Oregon, and Texas are joining states like California, Virginia, Colorado, Connecticut, Utah, Iowa, Indiana, Tennessee, and Montana in strengthening consumer privacy protections. This trend could signify a shift in US policy towards a more privacy-focused internet, underlining the importance of consumer data rights and transparent business practices. Even if these laws do not apply to your business, considering updates to your data and privacy policies is wise. Fortunately, there are tools and solutions designed for privacy and compliance to help you navigate these changes.

  Avoid fines and get better data with Matomo

  Most analytics tools don’t prioritize safeguarding user data. At Matomo, we believe everyone has the right to data sovereignty, privacy and amazing analytics. Matomo offers a solution that meets privacy regulations while delivering incredible insights. With Matomo, you get :

  100% Data Ownership : Keep full control over your data, ensuring it is used according to your privacy policies.

  Privacy Protection : Built with privacy in mind, Matomo helps businesses comply with privacy laws.

  Powerful Features : Gain insights with tools like heatmaps, session recordings, and A/B testing.

  Open Source : Matomo’s is open-source and committed to transparency and customisation.

  Flexibility : Choose to host Matomo on your servers or in the cloud for added security.

  No Data Sampling : Ensure accurate and complete insights without data sampling.

  Privacy Compliance : Easily meet GDPR and other requirements, with data stored securely and never sold or shared.

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  Disclaimer : This content is provided for informational purposes only and is not intended as legal advice. While we strive to ensure the accuracy and timeliness of the information provided, the laws and regulations surrounding privacy are complex and subject to change. We recommend consulting with a qualified legal professional to address specific legal issues related to your circumstances. 

• (C_UDP Socket Programming) How can I convert binary file to video format ?

  30 avril 2024, par user24723398

  I am practicing UDP socket programming. My code's functions are below.

  


  

  1. Connect Server-Client and send "hello" message each other (it is working).

  


  2. Then Server is sending video file to client (problem).

  


  


  Transfer video file to client is working. But it is written in binary so I can't open the video.

  


  So I try to use ffmpeg to convert the video, but it doesn't work.

  


  Is there something wrong in my code ? How can I transfer a received file to a video file ?

  


  My environment is MacOs.

  


  Server.c (Server Code) :

  


  #include &#xA;#include &#xA;#include &#xA;#include <arpa></arpa>inet.h>&#xA;#include &#xA;#include <sys></sys>socket.h>&#xA;&#xA;#define PORT 8888&#xA;#define BUF_SIZE 256&#xA;&#xA;int main(){&#xA;    int serv_sock;&#xA;    char message[BUF_SIZE];&#xA;    char buf[BUF_SIZE];&#xA;    int str_len;&#xA;    socklen_t clnt_adr_sz;&#xA;&#xA;    struct sockaddr_in serv_adr, clnt_adr;&#xA;    &#xA;    //create socket&#xA;    serv_sock=socket(PF_INET, SOCK_DGRAM, 0);&#xA;    if(serv_sock == -1){&#xA;        perror("socket() error");&#xA;        exit(1);&#xA;    }&#xA;    &#xA;    //socket address&#xA;    memset(&amp;serv_adr, 0, sizeof(serv_adr));&#xA;    serv_adr.sin_family=AF_INET;&#xA;    serv_adr.sin_addr.s_addr=htonl(INADDR_ANY);&#xA;    serv_adr.sin_port=htons(PORT);&#xA;    //binding socket&#xA;    if(bind(serv_sock, (struct sockaddr*)&amp;serv_adr, sizeof(serv_adr)) == -1){&#xA;        perror("bind() error");&#xA;        exit(1);&#xA;    }&#xA;    &#xA;    while(1){&#xA;        clnt_adr_sz=sizeof(clnt_adr);&#xA;        str_len=recvfrom(serv_sock, message, BUF_SIZE, 0, (struct sockaddr *)&amp;clnt_adr, &amp;clnt_adr_sz);&#xA;         if (str_len &lt; 0) {&#xA;            perror("recvfrom error");&#xA;            exit(1);&#xA;        }&#xA;    &#xA;        char hello_message[] = "hello i am server";&#xA;        if (sendto(serv_sock, hello_message, strlen(hello_message), 0, (struct sockaddr *)&amp;clnt_adr, clnt_adr_sz) &lt; 0) {&#xA;            perror("sendto error");&#xA;            exit(1);&#xA;        }&#xA;        &#xA;        //print message&#xA;        message[str_len] = &#x27;\0&#x27;;&#xA;        printf("client say: %s\n", message);&#xA;        &#xA;        char buf[BUF_SIZE];&#xA;        ssize_t bytes_read;&#xA;        // sending viedo file&#xA;        printf("sending video file...\n");&#xA;        size_t fsize;&#xA;    &#xA;        //video file&#xA;        FILE *file;&#xA;        char *filename = "video.mp4";&#xA;        // open video file&#xA;        file = fopen(filename, "rb");&#xA;        if (file == NULL) {&#xA;            perror("File opening failed");&#xA;            exit(EXIT_FAILURE);&#xA;        }&#xA;        //calculate video file memory&#xA;        fseek(file, 0, SEEK_END);&#xA;        fsize = ftell(file);&#xA;        fseek(file,0,SEEK_SET);&#xA;    &#xA;        size_t size = htonl(fsize);&#xA;        int nsize =0;&#xA;        &#xA;        while(nsize!=fsize){&#xA;            int fpsize = fread(buf,1, BUF_SIZE, file);&#xA;            nsize &#x2B;= fpsize;&#xA;            if (sendto(serv_sock, &amp;size, sizeof(size), 0, (struct sockaddr *)&amp;clnt_adr, clnt_adr_sz) &lt; 0) {&#xA;                perror("sendto");&#xA;                exit(EXIT_FAILURE);    &#xA;            }&#xA;            fclose(file);&#xA;            /*&#xA;            while ((bytes_read = fread(buf, 1, BUF_SIZE, file)) > 0) {&#xA;                if (sendto(serv_sock, buf, bytes_read, 0,&#xA;                       (struct sockaddr *)&amp;clnt_adr, clnt_adr_sz) &lt; 0) {&#xA;                    perror("sendto");&#xA;                    exit(EXIT_FAILURE);&#xA;                }       &#xA;            }&#xA;            */&#xA;        }        &#xA;    }&#xA;    close(serv_sock);&#xA;    return 0;&#xA;}&#xA;

  


  Client.c (Client code)

  


  #include &#xA;#include &#xA;#include &#xA;#include <arpa></arpa>inet.h>&#xA;#include &#xA;#include <sys></sys>socket.h>&#xA;&#xA;#define BUFSIZE 256&#xA;#define PORT 8888&#xA;&#xA;int main(){&#xA;    int sock;&#xA;    char message[BUFSIZE];&#xA;    int str_len;&#xA;    socklen_t adr_sz;&#xA;&#xA;    struct sockaddr_in serv_addr, client_addr;   &#xA;    &#xA;    sock = socket(PF_INET, SOCK_DGRAM, 0);&#xA;    if(sock == -1){&#xA;        printf("socket() error\n");&#xA;        exit(1);&#xA;    }&#xA;&#xA;    memset(&amp;serv_addr, 0, sizeof(serv_addr));&#xA;    serv_addr.sin_family = AF_INET;&#xA;    serv_addr.sin_addr.s_addr = inet_addr("127.0.0.1");&#xA;    serv_addr.sin_port = htons(PORT);&#xA;&#xA;    char hello_message[] = "hello i am client";&#xA;    sendto(sock, hello_message, strlen(hello_message), 0, (struct sockaddr*)&amp;serv_addr, sizeof(serv_addr));&#xA;    adr_sz = sizeof(client_addr);&#xA;    str_len=recvfrom(sock,message,BUFSIZE,0,(struct sockaddr*)&amp;client_addr,&amp;adr_sz);&#xA;   &#xA;    message[str_len] = &#x27;\0&#x27;;&#xA;    printf("client say: %s\n", message);&#xA;    &#xA;    /*&#xA;    char buf[BUFSIZE];&#xA;    ssize_t bytes_received;&#xA;    socklen_t serv_len = sizeof(serv_addr);&#xA;    while ((bytes_received = recvfrom(sock, buf, BUFSIZE, 0,&#xA;                                      (struct sockaddr *)&amp;serv_addr, &amp;serv_len)) > 0) {&#xA;        fwrite(buf, 1, bytes_received, file);&#xA;    }&#xA;    */&#xA;     &#xA;    FILE *file = fopen("received_test.mp4", "wb");&#xA;&#xA;    int nbyte = BUFSIZE;&#xA;    while(nbyte>= BUFSIZE){&#xA;        nbyte = recvfrom(sock, message, BUFSIZE, 0, (struct sockaddr*)&amp;serv_addr, &amp;adr_sz);&#xA;        fwrite(message, sizeof(char), nbyte, file);&#xA;    }&#xA;&#xA;    if (file == NULL) {&#xA;        perror("File opening failed");&#xA;        exit(EXIT_FAILURE);&#xA;    }&#xA;&#xA;    fclose(file);&#xA;    close(sock);&#xA;    printf("File received successfully\n");&#xA;    &#xA;    return 0;&#xA;}&#xA;

  


  I try to convert the binary file to an .mp4 file using ffmpeg
but it doesn't work :

  


  ffmpeg -i received_test.mp4 output.mp4
ffmpeg version 7.0 Copyright (c) 2000-2024 the FFmpeg developers
  built with Apple clang version 15.0.0 (clang-1500.3.9.4)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/7.0 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags='-Wl,-ld_classic' --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libaribb24 --enable-libbluray --enable-libdav1d --enable-libharfbuzz --enable-libjxl --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopenvino --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-audiotoolbox --enable-neon
  libavutil      59.  8.100 / 59.  8.100
  libavcodec     61.  3.100 / 61.  3.100
  libavformat    61.  1.100 / 61.  1.100
  libavdevice    61.  1.100 / 61.  1.100
  libavfilter    10.  1.100 / 10.  1.100
  libswscale      8.  1.100 /  8.  1.100
  libswresample   5.  1.100 /  5.  1.100
  libpostproc    58.  1.100 / 58.  1.100
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x12a62bdb0] Format mov,mp4,m4a,3gp,3g2,mj2 detected only with low score of 1, misdetection possible!
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x12a62bdb0] moov atom not found
[in#0 @ 0x12b0043c0] Error opening input: Invalid data found when processing input
Error opening input file received_test.mp4.
Error opening input files: Invalid data found when processing input