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• fate : Add test for APV 422-10 profile

  27 avril, par Mark Thompson

  fate : Add test for APV 422-10 profile
  Bitstream generated using the reference encoder, then edited to fix the
  
  colour description and an extra metadata block added.  FFmpeg decoder
  
  output is identical to the reference decoder output.
  The content used is the first three frames of "Waterfall" from the SVT
  
  Open Content Video Test Suite 2022.  This is copyright Sveriges
  
  Television AB and is used under the Creative Commons Attribution 4.0
  
  International License.
  

  • [DH] tests/Makefile
  • [DH] tests/fate/apv.mak
  • [DH] tests/ref/fate/apv-422-10

• Linear Attribution Model : What Is It and How Does It Work ?

  16 février 2024, par Erin

  Want a more in-depth way to understand the effectiveness of your marketing campaigns ? Then, the linear attribution model could be the answer.

  Although you can choose from several different attribution models, a linear model is ideal for giving value to every touchpoint along the customer journey. It can help you identify your most effective marketing channels and optimise your campaigns. 

  So, without further ado, let’s explore what a linear attribution model is, when you should use it and how you can get started. 

  What is a linear attribution model ?

  A linear attribution model is a multi-touch method of marketing attribution where equal credit is given to each touchpoint. Every marketing channel used across the entire customer journey gets credit, and each is considered equally important. 

  So, if a potential customer has four interactions before converting, each channel gets 25% of the credit.

  

  Let’s look at how linear attribution works in practice using a hypothetical example of a marketing manager, Sally, who is looking for an alternative to Google Analytics. 

  Sally starts her conversion path by reading a Matomo article comparing Matomo to Google Analytics she finds when searching on Google. A few days later she signs up for a webinar she saw on Matomo’s LinkedIn page. Two weeks later, Sally gets a sign-off from her boss and decides to go ahead with Matomo. She visits the website and starts a free trial by clicking on one of the paid Google Ads. 

  Using a linear attribution model, we credit each of the channels Sally uses (organic traffic, organic social, and paid ads), ensuring no channel is overlooked in our marketing analysis. 

  Are there other types of attribution models ?

  Absolutely. There are several common types of attribution models marketing managers can use to measure the impact of channels in different ways. 

  

  • First interaction : Also called a first-touch attribution model, this method gives all the credit to the first channel in the customer journey. This model is great for optimising the top of your sales funnel.
  • Last interaction : Also called a last-touch attribution model, this approach gives all the credit to the last channel the customer interacts with. It’s a great model for optimising the bottom of your marketing funnel. 
  • Last non-direct interaction : This attribution model excludes direct traffic and credits the previous touchpoint. This is a fantastic alternative to a last-touch attribution model, especially if most customers visit your website before converting. 
  • Time decay attribution model : This model adjusts credit according to the order of the touchpoints. Those nearest the conversion get weighted the highest. 
  • Position-based attribution model : This model allocates 40% of the credit to the first and last touchpoints and splits the remaining 20% evenly between every other interaction.

  Why use a linear attribution model ?

  Marketing attribution is vital if you want to understand which parts of your marketing strategy are working. All of the attribution models described above can help you achieve this to some degree, but there are several reasons to choose a linear attribution model in particular. 

  It uses multi-touch attribution

  Unlike single-touch attribution models like first and last interaction, linear attribution is a multi-touch attribution model that considers every touchpoint. This is vital to get a complete picture of the modern customer journey, where customers interact with companies between 20 and 500 times. 

  Single-touch attribution models can be misleading by giving conversion credit to a single channel, especially if it was the customer’s last use. In our example above, Sally’s last interaction with our brand was through a paid ad, but it was hardly the most important. 

  It’s easy to understand

  Attribution models can be complicated, but linear attribution is easy to understand. Every touchpoint gets the same credit, allowing you to see how your entire marketing function works. This simplicity also makes it easy for marketers to take action. 

  It’s great for identifying effective marketing channels

  Because linear attribution is one of the few models that provides a complete view of the customer journey, it’s easy to identify your most common and influential touchpoints. 

  It accounts for the top and bottom of your funnel, so you can also categorise your marketing channels more effectively and make more informed decisions. For example, PPC ads may be a more common bottom-of-the-full touchpoint and should, therefore, not be used to target broad, top-of-funnel search terms.

  Are there any reasons not to use linear attribution ?

  Linear attribution isn’t perfect. Like all attribution models, it has its weaknesses. Specifically, linear attribution can be too simple, dilute conversion credit and unsuitable for long sales cycles.

  

  It can be too simple

  Linear attribution lacks nuance. It only considers touchpoints while ignoring other factors like brand image and your competitors. This is true for most attribution models, but it’s still important to point it out. 

  It can dilute conversion credit

  In reality, not every touchpoint impacts conversions to the same extent. In the example above, the social media post promoting the webinar may have been the most effective touchpoint, but we have no way of measuring this. 

  The risk with using a linear model is that credit can be underestimated and overestimated — especially if you have a long sales cycle. 

  It’s unsuitable for very long sales cycles

  Speaking of long sales cycles, linear attribution models won’t add much value if your customer journey contains dozens of different touchpoints. Credit will get diluted to the point where analysis becomes impossible, and the model will also struggle to measure the precise ways certain touchpoints impact conversions. 

  Should you use a linear attribution model ?

  A linear attribution model is a great choice for any company with shorter sales cycles or a reasonably straightforward customer journey that uses multiple marketing channels. In these cases, it helps you understand the contribution of each touchpoint and find your best channels. 

  It’s also a practical choice for small businesses and startups that don’t have a team of data scientists on staff or the budget to hire outside help. Because it’s so easy to set up and understand, anyone can start generating insights using this model. 

  How to set up a linear attribution model

  Are you sold on the idea of using a linear attribution model ? Then follow the steps below to get started :

  

  Choose a marketing attribution tool

  Given the market is worth $3.1 billion, you won’t be surprised to learn there are plenty of tools to choose from. But choose carefully. The tool you pick can significantly impact your success with attribution modelling. 

  Take Google Analytics, for instance. While GA4 offers several marketing attribution models for free, including linear attribution, it lacks accuracy due to cookie consent rejection and data sampling. 

  Accurate marketing attribution is included as a feature in Matomo Cloud and is available as a plugin for Matomo On-Premise users. We support a full range of attribution models that use 100% accurate data because we don’t use data sampling, and cookie consent isn’t an issue (with the exception of Germany and the UK). That means you can trust our insights.

  Matomo’s marketing attribution is available out of the box, and we also provide access to raw data, allowing you to develop your custom attribution model. 

  Collect data

  The quality of your marketing attribution also depends on the quality and quantity of your data. It’s why you need to avoid a platform that uses data sampling. 

  This should include :

  • General data from your analytics platform, like pages visited and forms filled
  • Goals and conversions, which we’ll discuss in more detail in the next step
  • Campaign tracking data so you can monitor the behaviour of traffic from different referral channels
  • Behavioural data from features like Heatmaps or Session Recordings

  Set up goals and conversions

  You can’t assign conversion values to customer journey touchpoints if you don’t have conversion goals in place. That’s why the next step of the process is to set up conversion tracking in your web analytics platform. 

  Depending on your type of business and the product you sell, conversions could take one of the following forms :

  • A product purchase
  • Signing up for a webinar
  • Downloading an ebook
  • Filling in a form
  • Starting a free trial

  Setting up these kinds of goals is easy if you use Matomo. 

  Just head to the Goals section of the dashboard, click Manage Goals and then click the green Add A New Goal button. 

  Fill in the screen below, and add a Goal Revenue at the bottom of the page. Doing so will mean Matomo can automatically calculate the value of each touchpoint when using your attribution model. 

  

  If your analytics platform allows it, make sure you also set up Event Tracking, which will allow you to analyse how many users start to take a desired action (like filling in a form) but never complete the task. 

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  Test and validate

  As we’ve explained, linear attribution is a great model in some scenarios, but it can fall short if you have a long or complex sales funnel. Even if you’re sure it’s the right model for your company, testing and validating is important. 

  Ideally, your chosen attribution tool should make this process pretty straightforward. For example, Matomo’s Marketing Attribution feature makes comparing and contrasting three different attribution models easy. 

  Here we compare the performance of three attribution models—linear, first-touch, and last-non-direct—in Matomo’s Marketing Attribution dashboard, providing straightforward analysis.

  

  If you think linear attribution accurately reflects the value of your channels, you can start to analyse the insights it generates. If not, then consider using another attribution model.

  Don’t forget to take action from your marketing efforts, either. Linear attribution helps you spot the channels that contribute most to conversions, so allocate more resources to those channels and see if you can improve your conversion rate or boost your ROI. 

  Make the most of marketing attribution with Matomo

  A linear attribution model lets you measure every touchpoint in your customer journey. It’s an easy attribution model to start with and lets you identify and optimise your most effective marketing channels. 

  However, accurate data is essential if you want to benefit the most from marketing attribution data. If your web analytics solution doesn’t play nicely with cookies or uses sampled data, then your linear model isn’t going to tell you the whole story. 

  That’s why over 1 million sites trust Matomo’s privacy-focused web analytics, ensuring accurate data for a comprehensive understanding of customer journeys.

  Now you know what linear attribution modelling is, start employing the model today by signing up for a free 21-day trial, no credit card required. 

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• FFmpeg streaming to Akamai like FMLE does

  21 juin 2013, par xdev.developer

  I am trying to stream video from my webcam to AkamaiHD service using ffmpeg (like it is implemented in Flash Media Live Encoder)

      ffmpeg -f dshow -i video="Webcam C110" -s 640x360 -aspect 16:9 -profile:v baseline -    pix_fmt yuv420p -vcodec libx264 -f flv "rtmp://..."
  
  ...
  
     Input #0, dshow, from 'video=Webcam C110':
  
    Duration: N/A, start: 31296.194000, bitrate: N/A
  
      Stream #0:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 640x480, 30 tbr, 10000k tbn, 30 tbc
  
  Output #0
  
    Metadata:
  
      encoder         : Lavf55.8.102
  
      Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p, 640x360 [SAR 1:1 DAR 16:9], q=-1--1, 1k tbn, 30 tbc
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (rawvideo -> libx264)
  
  ....

  The video is sreamed, but when I try to view it at http://mediapm.edgesuite.net/edgeflash/public/zeri/debug/Main.html?url=myplayback_url/manifest.f4m
  it is not displayed.

  I've found out that if the video is recorded using FMLE and restreamed to akamai, then HLS stream is played.

  ffmpeg -re -i sample.f4v -c copy -f flv "rtmp://..."
  
  ....
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'sample.f4v':
  
    Metadata:
  
      major_brand     : f4v
  
      minor_version   : 0
  
      compatible_brands: isommp42m4v
  
      creation_time   : 2018-10-06 09:23:33
  
    Duration: 00:01:01.77, start: 0.460000, bitrate: 718 kb/s
  
      Stream #0:0(eng): Video: h264 (Baseline) (avc1 / 0x31637661), yuv420p, 640x360 [SAR 1:1 DAR 16:9], 624 kb/s, 30 tbr, 1k tbn, 48 tbc
  
      Metadata:
  
        creation_time   : 2018-10-06 09:23:33
  
        handler_name    : MainConcept
  
  
  
  Output #0
  
    Metadata:
  
      major_brand     : f4v
  
      minor_version   : 0
  
      compatible_brands: isommp42m4v
  
      encoder         : Lavf55.8.102
  
      Stream #0:0(eng): Video: h264 ([7][0][0][0] / 0x0007), yuv420p, 640x360 [SAR 1:1 DAR 16:9], q=2-31, 624 kb/s, 1k tbn, 1k tbc
  
      Metadata:
  
        creation_time   : 2018-10-06 09:23:33
  
        handler_name    : MainConcept
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (copy)
  
  ....

  It seems that the problem is with h.264 codec configuration, but I have found no solution yet.

  Could you please advice, how can I implement streaming like FMLE using ffmpeg ?