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• How to broadcast a livestream from YouTube to Telegram using vlc from the command-line ?

  22 février 2024, par shackra

  I want to re-transmit a live stream from YouTube (with streamlink) and broadcast it on a Telegram channel using VLC on the command-line. I think VLC is my best option for getting data from the source and sending it to the new destination.

  


  The thing is, I don't know how to configure the output correctly for the Telegram channel, nor how to put the transmission key.

  


  I was reading this answer https://stackoverflow.com/a/40461349/2020214 and using it as a guide unsuccessfully (Telegram does not detect any livestream)

  


• 7 Best Marketing Attribution Software in 2024

  22 février 2024, par Erin

  It can be hard to accurately track the impact of your marketing efforts across marketing channels and campaigns. That’s where marketing attribution software comes in. 

  It goes beyond basic web analytics solutions that just look at the final click. Instead, it shows how different channels, content, and ads are performing at every step of the buyer’s journey, which gives a more accurate picture than just focusing on the last click.

  In this guide, we’ll cover the basics of marketing attribution, list the top marketing attribution software and explain how the issue of privacy is transforming the web analytics industry.

  What is marketing attribution ?

  Marketing attribution is the process of assigning credit to each touchpoint in a buyer’s journey that leads to a desired action (such as a conversion or sale) in order to understand the effectiveness of various marketing channels and campaigns in influencing the customer’s decision-making process.

  Marketers use software tools like website analytics to to track and analyse customer interactions across different touchpoints, allowing them to attribute conversions or sales to specific marketing efforts and optimise their strategies and budgets accordingly.

  Why is marketing attribution so important ?

  If you don’t track your campaigns correctly, it’s easy to spend thousands (or even millions) in an ineffective way. A 2022 survey by Australian marketing agency Next&Co revealed their clients wasted AU$5.46 billion in ineffective ad spend.

  

  That’s 41% of all the ad spend tracked by Next&Co in 2022. A wasted marketing spend percentage this high isn’t exactly a recipe for a high marketing return on investment (ROI). And yet, it’s the average.

  Why is that ? 

  Most companies don’t actively track the results of their marketing campaigns actively enough.

  By improving your marketing attribution, you can determine which channels, ads, and campaigns work and which don’t. Then, you can move the budget from ineffective channels to effective ones.

  Even if you can only identify half of your wastage, this could be 20% or more of your total spend. Just imagine what your bottom line would look like if your marketing budget were 20% more effective.

  That’s the power that marketing attribution, when done right, brings to the table. It’s the road to a higher marketing ROI.

  Common marketing attribution models and how they’re different 

  The default model for attributing completed goals in most analytics tools is either the last interaction or the last non-direct interaction.

  However, some multi-touch models can help you get a more holistic view of the impact of your marketing efforts.

  

  • Last interaction model : attributes the conversion to the final interaction or referring source (campaign or ad).
  • Last non-direct interaction model : attributes the conversion to the final touchpoint that was not a direct visit to your website. (For example, if a search ad took them to a product page, the user bookmarked it and returned directly the next day to finish the purchase. The credit would go to the search ad as it’s the last non-direct touchpoint.)
  • First interaction model : attributes the conversion to the first referring event alone.
  • Linear model : gives equal value to every touchpoint throughout the customer journey. 
  • Time decay model : gives more value to touchpoints the closer they were to the actual sale.
  • Position-based model : gives more value to the first and last touchpoints — often 40% each, while splitting 20% among the rest.

  You can read our guide dedicated to marketing attribution models for more details on these models.

  Types of marketing attribution software and the impact of privacy regulations

  Until recently, digital advertising was the “scientific” advertisers’ utopia. Everything could be measured, with cookies from giants like Google and Facebook stalking every user across the web.

  But with the advent of regulations like GDPR and the CCPA, you can no longer blindly trust Google Analytics or the Meta Pixel without consequences.

  Multi-channel attribution tools with third-party cookies and GDPR

  Google, Meta, and other companies used to track and combine user data from their own platforms and websites across the web that installed their tags. These third-party cookies have long been under fire and have caused several GDPR fines.

  

  The alternative : analytics platforms with first-party cookies

  In a post-GDPR digital marketing landscape, a compliant-by-default web analytics platform like Matomo is a more reliable and accurate alternative.

  Plus, with a platform like Matomo, you don’t need to rely on data from digital advertising platforms like Facebook Ads and Google Ads. You can accurately track referral sources using our campaign tracking parameters.

  7 best marketing attribution software in 2024

  Below is the list of our favourite marketing attribution tools in 2024. If you find and use one that suits your needs correctly, you can quickly boost your marketing performance.

  1. Matomo — Accurate and easiest to set up for marketing attribution

  Matomo is a privacy-friendly web analytics suite that empowers you to accurately attribute marketing efforts and gain valuable insights while prioritising user privacy and compliance.

  Matomo integrates with e-commerce platforms like WooCommerce and Magenta. That makes it easy for B2C marketing teams to track the revenue impact of their campaigns.

  

  You can also compare a variety of attribution models against each other. B2B teams can use our API to integrate Matomo with their CRM.

  Pros :

  • Relies on first-party cookies for tracking, ensuring accurate data collection and attribution of user actions
  • Includes additional features like Heatmaps, Session Recordings, Form Analytics, A/B Testing, and more
  • Easy to set up and use
  • Features most common multi-touch attribution models

  Cons :

  • Limited to owned channels (website and e-commerce store) due to first-party cookies and data (but you can integrate other data sources through a CRM)

  Pricing

  The self-hosted version is free. The cloud hosted version starts at $19 per month and includes a 21-day free trial. No credit card requierd. 

  Try Matomo for Free

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  2. WhatConverts — Great option for leads-based businesses with high ad spend

  WhatConverts is a marketing attribution tool with a focus on lead tracking. With most web analytics setups, it adds call and text tracking to the typical form-only tracking.

  

  Pros :

  • Reliable call and text tracking
  • Revenue attribution to specific leads (and, by extension, campaigns and ads)

  Cons :

  • Focused exclusively on leads — little utility for e-commerce companies 

  Pricing

  The cheapest plan starts at $30/month but does not include analytics integrations or form tracking. To access this and advanced flow tracking and attribution features, you need the Elite plan, which starts at $160/month.

  3. HubSpot Marketing Hub — Ideal CRM for larger B2B companies

  HubSpot is a marketing CRM with attribution features for tracking and analysis.

  

  The platform is very broad — encompassing CRM, email automation and other tools — which makes it challenging to use effectively. The price tag is also quite steep for smaller companies and marketing teams.

  Pros :

  • Concretely tracks revenue to multiple different touchpoints and marketing channels
  • Includes several different multi-touch attribution models
  • Allows offline conversion tracking

  Cons :

  • The price point is too high for smaller teams
  • Cam be difficult to set up effectively

  Pricing

  Since marketing attribution is only included in HubSpot Marketing Hub’s Professional and Enterprise plans, pricing starts at $800/month (paid annually). If you commit for a year but pay monthly, the price is $890/month for the professional plan. This goes up with additional add-ons and as your contacts increase as well. 

  4. ActiveCampaign — Good CRM option for small B2B companies

  ActiveCampaign is a CRM and marketing automation platform that can help you trace leads and revenue back to their source.

  

  Although it has a similar scope of features to HubSpot, it is more affordable and slightly easier to use for beginners.

  Pros :

  • Tracks sales revenue back to specific marketing touchpoints
  • Powerful marketing automation features

  Cons :

  • B2B companies may need to purchase two plans, one ActiveCampaign marketing and one CRM.

  Pricing

  Unlike HubSpot, ActiveCampaign offers a much more affordable plan, starting at $29/month billed annually (for up to 1,000 contacts). The marketing and sales CRM bundle starts at $93/month with up to five users.

  5. Salesforce Data Cloud for Marketing — Ideal CRM for enterprises

  Salesforce is a robust and feature-rich CRM that many enterprises rely on for their sales teams.

  

  That makes Salesforce’s marketing attribution platform a logical choice for existing Salesforce users.

  Pros :

  • Uses prospect and sales data from CRM to attribute revenue
  • Revenue prediction analytics
  • Lead scoring to help your sales team focus on high-value leads

  Cons :

  • Difficult to set up and use
  • Clunky and aged user interface
  • Relatively high price point

  Pricing

  The limited Marketing Cloud Account Engagement Growth plan starts at $1,250/month, billed annually. To access advanced cross-channel journeys, you need the Pro plan, which starts at $2,750 monthly.

  6. Terminus — Great for account-based marketing

  If your marketing team uses an account-based marketing (ABM) approach, Terminus might be the right option for you.

  

  It offers ABM tools like target account event tracking and revenue attribution tools for your marketing campaigns.

  Pros :

  • Advanced multi-channel revenue attribution tools with a wide range of reports
  • Track intent touchpoints back to target accounts
  • Reliable revenue predictions help you focus your marketing activities

  Cons :

  • Complex and difficult to set up, understand and use effectively
  • Lacks native integrations with many common advertising platforms and analytics tools

  Pricing

  Terminus offers no standard pricing plans. You must contact their sales team for a custom quote based on your needs.

  7. Adobe Analytics — An analytics for enterprises

  Adobe Analytics is part of the Adobe Experience Cloud, with plenty of big data analysis tools for enterprises. Although the platform is quite powerful, it is equally complex and difficult to use. The price point is also prohibitive for many smaller companies.

  

  Pros :

  • Very extensive reporting tools
  • Predictive analytics give you solid leading indicator for future campaign performance
  • Track multiple digital touchpoints across the entire customer journey

  Cons :

  • Like Google Analytics, Adobe Analytics aggregates your visitor data by default, making compliant “consent-free tracking” — tracking user actions without asking for consent — impossible according to GDPR. (See more differences in Matomo’s comparison against Adobe Analytics and Google Analytics.)
  • Prohibitively expensive for most smaller companies
  • Very steep learning curve for setting up and using it correctly

  Pricing

  Adobe Analytics uses usage-based pricing — which means they adjust the pricing based on the traffic volume to your website. Still, their lower price points aren’t exactly SMB-friendly — multiple sources put Adobe’s lowest starting price point at $2,000–2,500 per month.

  Get accurate marketing attribution with Matomo (without privacy concerns)

  Matomo allows you to do marketing attribution effectively and accurately without compromising your users’ privacy. By default, we only use first-party cookies and offer consent-free tracking – meaning no more annoying cookie consent banners (excluding in Germany and the UK).

  If you want to boost your marketing performance without disregarding your users’ privacy, get started with our 21-day free trial. No credit card required. It’s time to make more informed decisions about your marketing campaigns.

  Try Matomo for Free

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• Python ImageIO : Too many open files

  23 avril 2016, par orbv12

  I am using imageio in python in order to open all video files in a directory and convert them to numpy arrays.

  Here is the script I am using :

    1 from __future__ import print_function
  
    2 from avi_to_numpy import *
  
    3 from os import listdir
  
    4 import numpy as np
  
    5 import imageio 
  
    6       
  
    7 class_path = '../Diving/'
  
    8 max_frames = 16
  
    9 stride = 8
  
   10 videos = [vid for vid in listdir(class_path)]
  
   11 train = []
  
   12 
  
   13 for vid in videos:
  
   14     print(str.format('Loading {}...', vid), end="")
  
   15     filename = class_path + vid
  
   16     reader = imageio.get_reader(filename, 'ffmpeg')
  
   17     frames = []
  
   18     
  
   19     for i, im in enumerate(reader):
  
   20         if len(frames) == max_frames:
  
   21             break
  
   22         
  
   23         if i % stride == 0:
  
   24             frames.append(im)
  
   25     
  
   26     reader.close()
  
   27     train.append(np.array(frames))
  
   28     print('done')        
  
   29 
  
   30 
  
   31 print(len(train))

  Eventually this script crashes with the following error output :

  Traceback (most recent call last):
  
    File "load_class_test.py", line 16, in <module>
  
      reader = imageio.get_reader(filename, 'ffmpeg')
  
    File "/usr/local/lib/python2.7/site-packages/imageio/core/functions.py", line 111, in get_reader
  
      return format.get_reader(request)
  
    File "/usr/local/lib/python2.7/site-packages/imageio/core/format.py", line 158, in get_reader
  
      return self.Reader(self, request)
  
    File "/usr/local/lib/python2.7/site-packages/imageio/core/format.py", line 207, in __init__
  
      self._open(**self.request.kwargs.copy())
  
    File "/usr/local/lib/python2.7/site-packages/imageio/plugins/ffmpeg.py", line 260, in _open
  
      self._initialize()
  
    File "/usr/local/lib/python2.7/site-packages/imageio/plugins/ffmpeg.py", line 326, in _initialize
  
      stdout=sp.PIPE, stderr=sp.PIPE)
  
    File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.py", line 710, in __init__
  
      errread, errwrite)
  
    File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.py", line 1223, in _execute_child
  
      errpipe_read, errpipe_write = self.pipe_cloexec()
  
    File "/usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.py", line 1175, in pipe_cloexec
  
      r, w = os.pipe()
  
  OSError: [Errno 24] Too many open files
  
  </module>

  I am closing the Reader object from imageio. It seems as if the files opened by ffmpeg are not being closed properly.

  Is there an obvious step I am missing here ? Am I closing the files properly ?

  EDIT : Found temporary solution. Opened a new issue on github.

  I was able to resolve the issue by uncommenting the following lines of code from ’imageio/plugins/ffmpeg.py’ :

  381         def _close_streams(self):
  
  382             for std in (self._proc.stdin,
  
  383                         self._proc.stdout,
  
  384                         self._proc.stderr):
  
  385                 try:
  
  386                     std.close()
  
  387                 except Exception:  # pragma: no cover
  
  388                     pass

  I then added a call to the above function in _close(self) :

  271         def _close(self):
  
  272             self._terminate(0.05)  # Short timeout
  
  273             self._close_streams()
  
  274             self._proc = None

  I am not sure what the side effects of doing this are, but it provides a solution for me.

  Here is the link to the issue : https://github.com/imageio/imageio/issues/145