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• Livestream playback on Hololens2

  20 avril 2023, par Денис Черный

  I have encountered the following problem
My task is as follows, I need to play streaming video (raw h264 video over UDP protocol) on a 3d object. At the moment I'm using FFmpegInteropX to set a MediaSource to a Windows object.Media.Playback.MediaPlayer.
Media Player works in frame server mode, then I subscribe to the videoFrameAvailable event and transmit the resulting frame to Unity

  


  The problem is that the performance on Hololens2 (UWP) is quite low, I can't get enough smoothness and low latency if I use texture sizes greater than 720x720. At the same time, if I run the application on a PC, I can play everything up to 4096x4096 smoothly and without delay.
Perhaps someone has some ideas on how to improve performance on Hololens2 ?

  


  
private SoftwareBitmap frameServerDest = new SoftwareBitmap(BitmapPixelFormat.Rgba8, 1024,1024,BitmapAlphaMode.Premultiplied );
private UnityEngine.Texture2D tex = new UnityEngine.Texture2D(frameServerDest.PixelWidth, frameServerDest.PixelHeight, UnityEngine.TextureFormat.RGBA32, false);

private async void InitializeMediaPlayer(){
FFmpegInteropLogging.SetDefaultLogProvider();
                FFmpegInteropConfig configuration = new FFmpegInteropConfig()
                {
                    MaxVideoThreads = 8,
                    SkipErrors = uint.MaxValue,
                    DefaultBufferTime = TimeSpan.Zero,
                    FastSeek = true,
                    VideoDecoderMode = VideoDecoderMode.ForceFFmpegSoftwareDecoder,
                };
                configuration.FFmpegOptions.Add("tune", "zerolatency");
                configuration.FFmpegOptions.Add("flags", "low_delay");
                configuration.FFmpegOptions.Add("fflags", "discardcorrupt+shortest+sortdts+ignidx+nobuffer");
                decoder = await FFmpegInteropMSS.CreateFromUriAsync("udp://127.0.0.1:9005",configuration)

                var mediaStreamSource = decoder.GetMediaStreamSource();
                mediaStreamSource.BufferTime = TimeSpan.FromSeconds(0);
                Debug.WriteLine($"{decoder.CurrentVideoStream.CodecName} {decoder.CurrentVideoStream.DecoderEngine} {decoder.CurrentVideoStream.HardwareDecoderStatus}  {decoder.CurrentVideoStream.PixelWidth} x {decoder.CurrentVideoStream.PixelHeight}");
              var FrameServer = new Windows.Media.Playback.MediaPlayer() { IsVideoFrameServerEnabled = true };
                FrameServer.Source = MediaSource.CreateFromMediaStreamSource(mediaStreamSource);
                FrameServer.RealTimePlayback = true;
                FrameServer.VideoFrameAvailable += MediaPlayer_VideoFrameAvailable;
                FrameServer.Play();
}

//FrameAvailable:
  private void MediaPlayer_VideoFrameAvailable(Windows.Media.Playback.MediaPlayer sender, object args)
        {
           CanvasDevice canvasDevice = CanvasDevice.GetSharedDevice();
            using (CanvasBitmap canvasBitmap = CanvasBitmap.CreateFromSoftwareBitmap(canvasDevice, frameServerDest))
            {
               
                sender.CopyFrameToVideoSurface(canvasBitmap);
                byte[] bytes = canvasBitmap.GetPixelBytes();

                if (AppCallbacks.Instance.IsInitialized())
                {
                    AppCallbacks.Instance.InvokeOnAppThread(() =>
                    {

                        tex.LoadRawTextureData(bytes);
                        tex.Apply();
                        Display.GetComponent().texture = tex;
                    }, false);
                }
                GC.Collect();
            }
         }


  


  My FFmpeg output setting
ffmpeg -r 60   -f gdigrab   -i desktop  -f h264 -framerate 60  -vcodec libx264 -preset ultrafast -tune zerolatency -threads 8 -thread_type slice  udp://127.0.0.1:9005

  


  UPDATE :
Hello, I did some work.
What I've done :

  


  

  1. I have established a direct connection via usb-s - etnernet

  


  2. I begin to look towards using directx surface

  


  


  I found the following way to get d3d11 device using by Unity
For this I had to use the library SharpDX, and similar thread https://forum.unity.com/threads/d3d11-texture2d-blitting-framerate.562552

  


  But there are problems that I can't solve yet :
1 FFmpeg works only in the VideoDecoderMode = VideoDecoderMode.Automatic or VideoDecoderMode.ForceFFmpegSoftwareDecoder mode ;
2 In the event handler (videoframe_available), there is still a very large load on the Garbage Collector, and apparently this causes performance problems. Moreover, the performance suffers only on Hololens.
In other VideoDecoderMode`s, the stream parameters are determined, but the videoframe_available event never fires. Latency is approx to zero, but perfomance still not very good

  


  Perhaps there are ideas how to solve the problem with GarbageColletor ?

  


  
private SoftwareBitmap frameServerDist = new SoftwareBitmap(BitmapPixelFormat.Rgba8, 780, 780, 
     BitmapAlphaMode.Premultiplied);
        private FFmpegInteropMSS decoder;

        private UnityEngine.GameObject Display;
        private UnityEngine.Texture2D targetTexture;
        private UnityEngine.GameObject MainCamera;
        private SharpDX.Direct3D11.Device dstDevice;
        private SharpDX.Direct3D11.DeviceContext dstContenxt;
        private SharpDX.Direct3D11.Texture2D m_DstTexture;
        private SharpDX.Direct3D11.Device srcDevice;
        private SharpDX.Direct3D11.DeviceContext srcContext;
        private static DataRectangle _rect;

        private SharpDX.Direct3D11.Texture2DDescription Texture2DDescription = new SharpDX.Direct3D11.Texture2DDescription()
        {

            ArraySize = 1,
            BindFlags = SharpDX.Direct3D11.BindFlags.ShaderResource,
            Usage = SharpDX.Direct3D11.ResourceUsage.Immutable, //GPU Only
            CpuAccessFlags = SharpDX.Direct3D11.CpuAccessFlags.None,
            Format = SharpDX.DXGI.Format.R8G8B8A8_UNorm,
            MipLevels = 1,
            OptionFlags = SharpDX.Direct3D11.ResourceOptionFlags.None,
            SampleDescription = new SharpDX.DXGI.SampleDescription()
            {
                Count = 1,
                Quality = 0
            }
        };




  


  //This event occurs when UnityEngine Initialized 
  private void AppCallbacks_Initialized()
        {

            srcDevice = new SharpDX.Direct3D11.Device(SharpDX.Direct3D.DriverType.Hardware);
            srcContext = srcDevice.ImmediateContext;

            UnityEngine.WSA.Application.InvokeOnAppThread(() =>
            {
                Display = UnityEngine.GameObject.Find("Display");
                targetTexture = null;
                //Create texture for get Device and Device context 
                UnityEngine.Texture2D deviceTexture = new UnityEngine.Texture2D(frameServerDist.PixelWidth, frameServerDist.PixelHeight, UnityEngine.TextureFormat.RGBA32, false);
                IntPtr txPtr = deviceTexture.GetNativeTexturePtr();
                SharpDX.Direct3D11.Texture2D dstTextureX = new SharpDX.Direct3D11.Texture2D(txPtr);
                dstDevice = dstTextureX.Device;
                dstContenxt = dstDevice.ImmediateContext;
                //Create sharedResource
                SharpDX.Direct3D11.Texture2DDescription sharedTextureDesc = dstTextureX.Description;
                sharedTextureDesc.OptionFlags = SharpDX.Direct3D11.ResourceOptionFlags.Shared;
                m_DstTexture = new SharpDX.Direct3D11.Texture2D(dstDevice, sharedTextureDesc);

                SharpDX.Direct3D11.ShaderResourceViewDescription rvdesc = new SharpDX.Direct3D11.ShaderResourceViewDescription
                {
                    Format = sharedTextureDesc.Format,
                    Dimension = SharpDX.Direct3D.ShaderResourceViewDimension.Texture2D
                };
                rvdesc.Texture2D.MostDetailedMip = 0; rvdesc.Texture2D.MipLevels = 1;
                SharpDX.Direct3D11.ShaderResourceView rvptr = new SharpDX.Direct3D11.ShaderResourceView(
                       dstDevice,
                       m_DstTexture, rvdesc);

                targetTexture = UnityEngine.Texture2D.CreateExternalTexture(sharedTextureDesc.Width, sharedTextureDesc.Height, UnityEngine.TextureFormat.BGRA32, false, false, rvptr.NativePointer);
                MainCamera = UnityEngine.GameObject.Find("Main Camera");
                Display.GetComponent().texture = targetTexture;
                InitializeMediaPlayer();
            }, false);




  


     private void MediaPlayer_VideoFrameAvailable(Windows.Media.Playback.MediaPlayer sender, object args)
        {
                canvasBitmap = CanvasBitmap.CreateFromSoftwareBitmap(canvasDevice, frameServerDist);
                sender.CopyFrameToVideoSurface(canvasBitmap);
                var sharedResourceDst = m_DstTexture.QueryInterface();
                var sharedTexDst = srcDevice.OpenSharedResource(sharedResourceDst.SharedHandle);
                using (var _stream = DataStream.Create(canvasBitmap.GetPixelBytes(), true, false))
                {
                    _rect.DataPointer = _stream.DataPointer;
                    _rect.Pitch = Texture2DDescription.Width * 4;
                    var srcTexture = new SharpDX.Direct3D11.Texture2D(srcDevice, Texture2DDescription, _rect);
                    srcContext.CopyResource(srcTexture, sharedTexDst);
                    srcContext.Flush();
                    sharedResourceDst.Dispose();
                    sharedTexDst.Dispose();
                    srcTexture.Dispose();
                }
        }


  


• Parallelize Youtube video frame download using yt-dlp and cv2

  4 mars 2023, par zulle99

  My task is to download multiple sequences of successive low resolution frames of Youtube videos.

  


  I summarize the main parts of the process :

  


  

  • Each bag of shots have a dimension of half a second (depending on the current fps)

  


  • In order to grab useful frames I've decided to remove the initial and final 10% of each video since it is common to have an intro and outro. Moreover

  


  • I've made an array of pair of initial and final frame to distribute the load on multiple processes using ProcessPoolExecutor(max_workers=multiprocessing.cpu_count())

  


  • In case of failure/exception I completly remove the relative directory

  


  


  The point is that it do not scale up, since while running I noticesd that all CPUs had always a load lower that the 20% more or less. In addition since with these shots I have to run multiple CNNs, to prevent overfitting it is suggested to have a big dataset and not a bounch of shots.

  


  Here it is the code :

  


  import yt_dlp
import os
from tqdm import tqdm
import cv2
import shutil
import time
import random
from concurrent.futures import ProcessPoolExecutor
import multiprocessing
import pandas as pd
import numpy as np
from pathlib import Path
import zipfile


# PARAMETERS
percentage_train_test = 50
percentage_bag_shots = 20
percentage_to_ignore = 10

zip_f_name = f'VideoClassificationDataset_{percentage_train_test}_{percentage_bag_shots}_{percentage_to_ignore}'
dataset_path = Path('/content/VideoClassificationDataset')

# DOWNOAD ZIP FILES
!wget --no-verbose https://github.com/gtoderici/sports-1m-dataset/archive/refs/heads/master.zip

# EXTRACT AND DELETE THEM
!unzip -qq -o '/content/master.zip' 
!rm '/content/master.zip'

DATA = {'train_partition.txt': {},
        'test_partition.txt': {}}

LABELS = []

train_dict = {}
test_dict = {}

path = '/content/sports-1m-dataset-master/original'

for f in os.listdir(path):
  with open(path + '/' + f) as f_txt:
    lines = f_txt.readlines()
    for line in lines:
      splitted_line = line.split(' ')
      label_indices = splitted_line[1].rstrip('\n').split(',') 
      DATA[f][splitted_line[0]] = list(map(int, label_indices))

with open('/content/sports-1m-dataset-master/labels.txt') as f_labels:
  LABELS = f_labels.read().splitlines()


TRAIN = DATA['train_partition.txt']
TEST = DATA['test_partition.txt']
print('Original Train Test length: ', len(TRAIN), len(TEST))

# sample a subset percentage_train_test
TRAIN = dict(random.sample(TRAIN.items(), (len(TRAIN)*percentage_train_test)//100))
TEST = dict(random.sample(TEST.items(), (len(TEST)*percentage_train_test)//100))

print(f'Sampled {percentage_train_test} Percentage  Train Test length: ', len(TRAIN), len(TEST))


if not os.path.exists(dataset_path): os.makedirs(dataset_path)
if not os.path.exists(f'{dataset_path}/train'): os.makedirs(f'{dataset_path}/train')
if not os.path.exists(f'{dataset_path}/test'): os.makedirs(f'{dataset_path}/test')


  


  Function to extract a sequence of continuous frames :

  


  def extract_frames(directory, url, idx_bag, start_frame, end_frame):
  capture = cv2.VideoCapture(url)
  count = start_frame

  capture.set(cv2.CAP_PROP_POS_FRAMES, count)
  os.makedirs(f'{directory}/bag_of_shots{str(idx_bag)}')

  while count < end_frame:

    ret, frame = capture.read()

    if not ret: 
      shutil.rmtree(f'{directory}/bag_of_shots{str(idx_bag)}')
      return False

    filename = f'{directory}/bag_of_shots{str(idx_bag)}/shot{str(count - start_frame)}.png'

    cv2.imwrite(filename, frame)
    count += 1

  capture.release()
  return True


  


  Function to spread the load along multiple processors :

  


  def video_to_frames(video_url, labels_list, directory, dic, percentage_of_bags):
  url_id = video_url.split('=')[1]
  path_until_url_id = f'{dataset_path}/{directory}/{url_id}'
  try:   

    ydl_opts = {
        'ignoreerrors': True,
        'quiet': True,
        'nowarnings': True,
        'simulate': True,
        'ignorenoformatserror': True,
        'verbose':False,
        'cookies': '/content/all_cookies.txt',
        #https://stackoverflow.com/questions/63329412/how-can-i-solve-this-youtube-dl-429
    }
    ydl = yt_dlp.YoutubeDL(ydl_opts)
    info_dict = ydl.extract_info(video_url, download=False)

    if(info_dict is not None and  info_dict['fps'] >= 20):
      # I must have a least 20 frames per seconds since I take half of second bag of shots for every video

      formats = info_dict.get('formats', None)

      # excluding the initial and final 10% of each video to avoid noise
      video_length = info_dict['duration'] * info_dict['fps']

      shots = info_dict['fps'] // 2

      to_ignore = (video_length * percentage_to_ignore) // 100
      new_len = video_length - (to_ignore * 2)
      tot_stored_bags = ((new_len // shots) * percentage_of_bags) // 100   # ((total_possbile_bags // shots) * percentage_of_bags) // 100
      if tot_stored_bags == 0: tot_stored_bags = 1 # minimum 1 bag of shots

      skip_rate_between_bags = (new_len - (tot_stored_bags * shots)) // (tot_stored_bags-1) if tot_stored_bags > 1 else 0

      chunks = [[to_ignore+(bag*(skip_rate_between_bags+shots)), to_ignore+(bag*(skip_rate_between_bags+shots))+shots] for bag in range(tot_stored_bags)]
      # sequence of [[start_frame, end_frame], [start_frame, end_frame], [start_frame, end_frame], ...]


      # ----------- For the moment I download only shots form video that has 144p resolution -----------

      res = {
          '160': '144p',
          '133': '240p',
          '134': '360p',
          '135': '360p',
          '136': '720p'
      }

      format_id = {}
      for f in formats: format_id[f['format_id']] = f
      #for res in resolution_id:
      if list(res.keys())[0] in list(format_id.keys()):
          video = format_id[list(res.keys())[0]]
          url = video.get('url', None)
          if(video.get('url', None) != video.get('manifest_url', None)):

            if not os.path.exists(path_until_url_id): os.makedirs(path_until_url_id)

            with ProcessPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
              for idx_bag, f in enumerate(chunks): 
                res = executor.submit(
                  extract_frames, directory = path_until_url_id, url = url, idx_bag = idx_bag, start_frame = f[0], end_frame = f[1])
                
                if res.result() is True: 
                  l = np.zeros(len(LABELS), dtype=int) 
                  for label in labels_list: l[label] = 1
                  l = np.append(l, [shots]) # appending the number of shots taken in the list before adding it on the dictionary

                  dic[f'{directory}/{url_id}/bag_of_shots{str(idx_bag)}'] = l.tolist()


  except Exception as e:
    shutil.rmtree(path_until_url_id)
    pass


  


  Download of TRAIN bag of shots :

  


  start_time = time.time()
pbar = tqdm(enumerate(TRAIN.items()), total = len(TRAIN.items()), leave=False)

for _, (url, labels_list) in pbar: video_to_frames(
  video_url = url, labels_list = labels_list, directory = 'train', dic = train_dict, percentage_of_bags = percentage_bag_shots)

print("--- %s seconds ---" % (time.time() - start_time))


  


  Download of TEST bag of shots :

  


  start_time = time.time()
pbar = tqdm(enumerate(TEST.items()), total = len(TEST.items()), leave=False)

for _, (url, labels_list) in pbar: video_to_frames(
  video_url = url, labels_list = labels_list, directory = 'test', dic = test_dict, percentage_of_bags = percentage_bag_shots)

print("--- %s seconds ---" % (time.time() - start_time))


  


  Save the .csv files

  


  train_df = pd.DataFrame.from_dict(train_dict, orient='index', dtype=int).reset_index(level=0)
train_df = train_df.rename(columns={train_df.columns[-1]: 'shots'})
train_df.to_csv('/content/VideoClassificationDataset/train.csv', index=True)

test_df = pd.DataFrame.from_dict(test_dict, orient='index', dtype=int).reset_index(level=0)
test_df = test_df.rename(columns={test_df.columns[-1]: 'shots'})
test_df.to_csv('/content/VideoClassificationDataset/test.csv', index=True)


  


• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now.