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• Consent Mode v2 : Everything You Need to Know

  7 mai 2024, par Alex — Analytics Tips

  Confused about Consent Mode v2 and its impact on your website analytics ? You’re not the only one. 

  Google’s latest update has left many scratching their heads about data privacy and tracking. 

  In this blog, we’re getting straight to the point. We’ll break down what Consent Mode v2 is, how it works, and the impact it has.

  What is Consent Mode ?

  What exaclty is Google Consent Mode and why is there so much buzz surrounding it ? This question has been frustrating analysts and marketers worldwide since the beginning of this year. 

  Consent Mode is the solution from Google designed to manage data collection on websites in accordance with user privacy requirements.

  This mode enables website owners to customise how Google tags respond to users’ consent status for cookie usage. At its core, Consent Mode adheres to privacy regulations such as GDPR in Europe and CCPA in California, without significant loss of analytical data.

  

  How does Consent Mode work ?

  Consent Mode operates by adjusting the behaviour of tags on a website depending on whether consent for cookie usage is provided or not. If a user does not consent to the use of analytical or advertising cookies, Google tags automatically switch to collecting a limited amount of data, ensuring privacy compliance.

  This approach allows for continued valuable insights into website traffic and user behavior, even if users opt out of most tracking cookies.

  What types of consent are available in Consent Mode ?

  As of 6 March 2024, Consent Mode v2 has become the current standard (and in terms of utilising Google Advertising Services, practically mandatory), indicating the incorporation of four consent types :

  1. ad_storage : allows for the collection and storage of data necessary for delivering personalised ads based on user actions.
  2. ad_user_data : pertains to the collection and usage of data that can be associated with the user for ad customisation and optimisation.
  3. ad_personalization : permits the use of user data for ad personalisation and providing more relevant content.
  4. analytics_storage : relates to the collection and storage of data for analytics, enabling websites to analyse user behaviour and enhance user experience.

  Additionally, in Consent Mode v2, there are two modes :

  1. Basic Consent Mode : in which Google tags are not used for personalised advertising and measurements if consent is not obtained.
  2. Advanced Consent Mode : allows Google tags to utilise anonymised data for personalised advertising campaigns and measurements, even if consent is not obtained.

  What is Consent Mode v2 ? (And how does it differ from Consent Mode v1 ?)

  Consent Mode v2 is an improved version of the original Consent Mode, offering enhanced customisation capabilities and better compliance with privacy requirements. 

  The new version introduces additional consent configuration parameters, allowing for even more precise control over which data is collected and how it’s used. The key difference between Consent Mode v2 and Consent Mode v1 lies in more granular consent management, making this tool even more flexible and powerful in safeguarding personal data.

  In Consent Mode v2, the existing markers (ad_storage and analytics_storage) are accompanied by two new markers :

  1. ad_user_data – does the user agree to their personal data being utilized for advertising purposes ?
  2. ad_personalization – does the user agree to their data being employed for remarketing ?

  In contrast to ad_storage and analytics_storage, these markers don’t directly affect how the tags operate on the site itself. 

  They serve as additional directives sent alongside the pings to Google services, indicating how user data can be utilised for advertising purposes.

  While ad_storage and analytics_storage serve as upstream qualifiers for data (determining which identifiers are sent with the pings), ad_user_data and ad_personalization serve as downstream instructions for Google services regarding data processing.

  How is the implementation of Consent Mode v2 going ?

  The implementation of Consent Mode v2 is encountering some issues and bugs (as expected). The most important thing to understand :

  1. Advanced Consent Mode v2 is essential if you have traffic and campaigns with Google Ads in the European Union.
  2. If you don’t have substantially large traffic, enabling Advanced Consent Mode v2 will likely result in a traffic drop in GA4 – because this version of consent mode (unlike the basic one) applies behavioural modelling to users who haven’t accepted the use of cookies. And modelling the behaviour requires time.

  The aspect of behavioural modelling in Consent Mode v2 implies the following : the data of users who have declined tracking options begin to be modelled using machine learning. 

  However, training the model requires a suitable data volume. As the Google’s documentation states :

  The property should collect at least 1,000 events per day with analytics_storage=’denied’ for at least 7 days. The property should have at least 1,000 daily users submitting events with analytics_storage=’granted’ for at least 7 of the previous 28 days.

  Largely due to this, the market’s response to the Consent Mode v2 implementation was mixed : many reported a significant drop in traffic in their GA4 and Google Ads reports upon enabling the Advanced mode. Essentially, a portion of the data was lost because Google’s models lacked enough data for training. 

  And from the very beginning of implementation, users regularly report about a few examples of that scenario. If your website doesn’t have enough traffic for behaviour modelling, after Consent Mode v2 switching you will face significant drop in your traffic in Google Ads and GA4 reports. There are a lot of cases of observing 90-95% drop in metrics of users and sessions.

  In a nutshell, you should be prepared for significant data losses if you are planning to switch to Google Consent Mode v2.

  How does Consent Mode v2 impact web analytics ? 

  The transition to Consent Mode v2 alters the methods of user data collection and processing. The main concerns arise from the potential loss of accuracy and completeness of analytical data due to restrictions on the use of cookies and other identifiers when user consent is absent. 

  With Google Consent Mode v2, the data of visitors who have not agreed to tracking will be modelled and may not accurately reflect your actual visitors’ behaviours and actions. So as an analyst or marketer, you will not have true insights into these visitors and the data acquired will be more generalised and less accurate.

  Google Consent Mode v2 appears to be a kind of compromise band-aid solution. 

  It tries to solve these issues by using data modelling and anonymised data collection. However, it’s critical to note that there are specific limitations inherent to the modelling mechanism.

  This complicates the analysis of visitor behavior, advertising campaigns, and website optimisation, ultimately impacting decision-making and resulting in poor website performance and marketing outcomes.

  Wrap up

  Consent Mode v2 is a mechanism of managing Google tag operations based on user consent settings. 

  It’s mandatory if you’re using Google’s advertising services, and optional (at least for Advanced mode) if you don’t advertise on Google Ads. 

  There are particular indications that this technology is unreliable from a GDPR perspective. 

  Using Google Consent Mode will inevitably lead to data losses and inaccuracies in its analysis. 

  In other words, it in some sense jeopardises your business.

• C# server problem with FFmpeg Visual Studio 2022

  16 mai 2024, par seanofdead

  Hello everyone I've spent countless hours on my server I'm trying to create that will share video from one stream to another client computer. That is the goal anyway. I'm using Visual Studio 2022 most recent update. Currently this is my code

  


  using System;
using System.IO;
using System.Net;
using System.Net.Sockets;
using System.Threading.Tasks;
using FFmpeg.AutoGen;
using NLog;

public class VideoStreamServer
{
    private const int Port = 5000;
    private TcpListener server;
    private FFmpegVideoEncoder encoder;
    private static readonly Logger logger = LogManager.GetCurrentClassLogger();

    public VideoStreamServer()
    {
        // Initialize FFmpeg network components
        Console.WriteLine("Initializing FFmpeg network components...");
        int result = ffmpeg.avformat_network_init();
        if (result != 0)
        {
            throw new ApplicationException($"Failed to initialize FFmpeg network components: {result}");
        }

        // Initialize encoder
        encoder = new FFmpegVideoEncoder(1920, 1080, AVCodecID.AV_CODEC_ID_H264); // Screen size & codec
    }

    public static void Main()
    {
        // Initialize NLog
        var logger = LogManager.GetCurrentClassLogger();
        logger.Info("Application started");

        var server = new VideoStreamServer();
        server.Start().Wait();
    }

    public async Task Start()
    {
        server = new TcpListener(IPAddress.Any, Port);
        server.Start();
        logger.Info($"Server started on port {Port}...");

        try
        {
            while (true)
            {
                TcpClient client = await server.AcceptTcpClientAsync();
                logger.Info("Client connected...");
                _ = HandleClientAsync(client);
            }
        }
        catch (Exception ex)
        {
            logger.Error(ex, "An error occurred");
        }
    }

    private async Task HandleClientAsync(TcpClient client)
    {
        using (client)
        using (NetworkStream netStream = client.GetStream())
        {
            try
            {
                await encoder.StreamEncodedVideoAsync(netStream);
            }
            catch (IOException ex)
            {
                logger.Error(ex, $"Network error: {ex.Message}");
                // Additional logging and recovery actions
            }
            catch (Exception ex)
            {
                logger.Error(ex, $"Unexpected error: {ex.Message}");
                // Log and handle other exceptions
            }
            finally
            {
                // Ensure all resources are cleaned up properly
                client.Close();
                logger.Info("Client connection closed properly.");
            }
        }
    }
}

public class FFmpegVideoEncoder
{
    private readonly int width;
    private readonly int height;
    private readonly AVCodecID codecId;
    private static readonly Logger logger = LogManager.GetCurrentClassLogger();

    public FFmpegVideoEncoder(int width, int height, AVCodecID codecId)
    {
        this.width = width;
        this.height = height;
        this.codecId = codecId;
        InitializeEncoder();
    }

    private void InitializeEncoder()
    {
        // Initialize FFmpeg encoder here
        logger.Debug("FFmpeg encoder initialized");
    }

    public async Task StreamEncodedVideoAsync(NetworkStream netStream)
    {
        // Initialize reusable buffers for efficiency
        byte[] buffer = new byte[4096];

        // Capture and encode video frames
        while (true)
        {
            try
            {
                // Simulate capture and encoding
                byte[] videoData = new byte[1024]; // This would come from the encoder

                // Simulate streaming
                for (int i = 0; i < videoData.Length; i += buffer.Length)
                {
                    int chunkSize = Math.Min(buffer.Length, videoData.Length - i);
                    Buffer.BlockCopy(videoData, i, buffer, 0, chunkSize);
                    await netStream.WriteAsync(buffer, 0, chunkSize);
                }
            }
            catch (Exception ex)
            {
                logger.Error(ex, "Error streaming video data");
                throw; // Rethrow to handle in the calling function
            }

            await Task.Delay(1000 / 30); // For 30 FPS
        }
    }
}


  


  When I build no issues but when i run it in debug mode I get this error

  


  System.DllNotFoundException
  HResult=0x80131524
  Message=Unable to load DLL 'avformat.59 under C:\Users\phlfo\Downloads\cfolder\server\ConsoleApp\bin\Debug\': The specified module could not be found.
  Source=FFmpeg.AutoGen
  StackTrace:
   at FFmpeg.AutoGen.ffmpeg.LoadLibrary(String libraryName, Boolean throwException)
   at FFmpeg.AutoGen.ffmpeg.<>c.<.cctor>b__7_0(String libraryName)
   at FFmpeg.AutoGen.ffmpeg.<>c.<.cctor>b__7_242()
   at FFmpeg.AutoGen.ffmpeg.avformat_network_init()
   at VideoStreamServer..ctor() in C:\Users\phlfo\Downloads\cfolder\server\ConsoleApp\Program.cs:line 20
   at VideoStreamServer.Main() in C:\Users\phlfo\Downloads\cfolder\server\ConsoleApp\Program.cs:line 36


  


  I have FFmpeg.AutoGen package installed through VS i originally started with 7.0 but then switched to 5.0 to see if an older version might work (it did not). I also have the files in the same directory as the .exe for testing purposes as seen in the screenshot. Can someone please help me figure out this error.


  


• aarch64 : vvc : Fix compilation of alf.S with MSVC 2022 17.7 and older

  23 juillet 2024, par Martin Storsjö

  aarch64 : vvc : Fix compilation of alf.S with MSVC 2022 17.7 and older
  Use the "ldur" instruction explicitly, instead of having the
  
  assembler implicitly convert "ldr" instructions to "ldur".
  This fixes build errors like these :
  libavcodec\aarch64\vvc\alf.o.asm(1023) : error A2518 : operand 2 : Memory offset must be aligned
  
          ldr             q22, [x3, #24]
  
  libavcodec\aarch64\vvc\alf.o.asm(1024) : error A2518 : operand 2 : Memory offset must be aligned
  
          ldr             q24, [x2, #24]
  
  libavcodec\aarch64\vvc\alf.o.asm(1393) : error A2518 : operand 2 : Memory offset must be aligned
  
          ldr             q22, [x3, #24]
  
  libavcodec\aarch64\vvc\alf.o.asm(1394) : error A2518 : operand 2 : Memory offset must be aligned
  
          ldr             q24, [x2, #24]
  Signed-off-by : Martin Storsjö <martin@martin.st>
  

  • [DH] libavcodec/aarch64/vvc/alf.S