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• Clickstream Data : Definition, Use Cases, and More

  15 avril 2024, par Erin

  Gaining a deeper understanding of user behaviour — customers’ different paths, digital footprints, and engagement patterns — is crucial for providing a personalised experience and making informed marketing decisions. 

  In that sense, clickstream data, or a comprehensive record of a user’s online activities, is one of the most valuable sources of actionable insights into users’ behavioural patterns. 

  This article will cover everything marketing teams need to know about clickstream data, from the basic definition and examples to benefits, use cases, and best practices. 

  What is clickstream data ? 

  As a form of web analytics, clickstream data focuses on tracking and analysing a user’s online activity. These digital breadcrumbs offer insights into the websites the user has visited, the pages they viewed, how much time they spent on a page, and where they went next.

  

  Your clickstream pipeline can be viewed as a “roadmap” that can help you recognise consistent patterns in how users navigate your website. 

  With that said, you won’t be able to learn much by analysing clickstream data collected from one user’s session. However, a proper analysis of large clickstream datasets can provide a wealth of information about consumers’ online behaviours and trends — which marketing teams can use to make informed decisions and optimise their digital marketing strategy. 

  Clickstream data collection can serve numerous purposes, but the main goal remains the same — gaining valuable insights into visitors’ behaviours and online activities to deliver a better user experience and improve conversion likelihood. 

  Depending on the specific events you’re tracking, clickstream data can reveal the following : 

  • How visitors reach your website 
  • The terms they type into the search engine
  • The first page they land on
  • The most popular pages and sections of your website
  • The amount of time they spend on a page 
  • Which elements of the page they interact with, and in what sequence
  • The click path they take 
  • When they convert, cancel, or abandon their cart
  • Where the user goes once they leave your website

  As you can tell, once you start collecting this type of data, you’ll learn quite a bit about the user’s online journey and the different ways they engage with your website — all without including any personal details about your visitors.

  Types of clickstream data 

  While all clickstream data keeps a record of the interactions that occur while the user is navigating a website or a mobile application — or any other digital platform — it can be divided into two types : 

  • Aggregated (web traffic) data provides comprehensive insights into the total number of visits and user interactions on a digital platform — such as your website — within a given timeframe 
  • Unaggregated data is broken up into smaller segments, focusing on an individual user’s online behaviour and website interactions 

  One thing to remember is that to gain valuable insights into user behaviour and uncover sequential patterns, you need a powerful tool and access to full clickstream datasets. Matomo’s Event Tracking can provide a comprehensive view of user interactions on your website or mobile app — everything from clicking a button and completing a form to adding (or removing) products from their cart. 

  On that note, based on the specific events you’re tracking when a user visits your website, clickstream data can include : 

  • Web navigation data : referring URL, visited pages, click path, and exit page
  • User interaction data : mouse movements, click rate, scroll depth, and button clicks
  • Conversion data : form submissions, sign-ups, and transactions 
  • Temporal data : page load time, timestamps, and the date and time of day of the user’s last login 
  • Session data : duration, start, and end times and number of pages viewed per session
  • Error data : 404 errors and network or server response issues 

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  Clickstream data benefits and use cases 

  Given the actionable insights that clickstream data collection provides, it can serve a wide range of use cases — from identifying behavioural patterns and trends and examining competitors’ performance to helping marketing teams map out customer journeys and improve ROI.

  

  According to the global Clickstream Analytics Market Report 2024, some key applications of clickstream analytics include click-path optimisation, website and app optimisation, customer analysis, basket analysis, personalisation, and traffic analysis. 

  

  The behavioural patterns and user preferences revealed by clickstream analytics data can have many applications — we’ve outlined the prominent use cases below. 

  Customer journey mapping 

  Clickstream data allows you to analyse the e-commerce customer’s online journey and provides insights into how they navigate your website. With such a comprehensive view of their click path, it becomes easier to understand user behaviour at each stage — from initial awareness to conversion — identify the most effective touchpoints and fine-tune that journey to improve their conversion likelihood. 

  Identifying customer trends 

  Clickstream data analytics can also help you identify trends and behavioural patterns — the most common sequences and similarities in how users reached your website and interacted with it — especially when you can access data from many website visitors. 

  Think about it — there are many ways in which you can use these insights into the sequence of clicks and interactions and recurring patterns to your team’s advantage. 

  Here’s an example : 

  It can reveal that some pieces of content and CTAs are performing well in encouraging visitors to take action — which shows how you should optimise other pages and what you should strive to create in the future, too. 

  Preventing site abandonment 

  Cart abandonment remains a serious issue for online retailers : 

  According to a recent report, the global cart abandonment rate in the fourth quarter of 2023 was at 83%. 

  That means that roughly eight out of ten e-commerce customers will abandon their shopping carts — most commonly due to additional costs, slow website loading times and the requirement to create an account before purchasing. 

  In addition to cart abandonment predictions, clickstream data analytics can reveal the pages where most visitors tend to leave your website. These drop-off points are clear indicators that something’s not working as it should — and once you can pinpoint them, you’ll be able to address the issue and increase conversion likelihood.

  Improving marketing campaign ROI 

  As previously mentioned, clickstream data analysis provides insights into the customer journey. Still, you may not realise that you can also use this data to keep track of your marketing effectiveness. 

  Global digital ad spending continues to grow — and is expected to reach $836 billion by 2026. It’s easy to see why relying on accurate data is crucial when deciding which marketing channels to invest in. 

  You want to ensure you’re allocating your digital marketing and advertising budget to the channels — be it SEO, pay-per-click (PPC) ads, or social media campaigns — that impact driving conversions. 

  When you combine clickstream e-commerce data with conversion rates, you’ll find the latter in Matomo’s goal reports and have a solid, data-driven foundation for making better marketing decisions.

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  Delivering a better user experience (UX) 

  Clickstream data analysis allows you to identify specific “pain points” — areas of the website that are difficult to use and may cause customer frustration. 

  It’s clear how this would be beneficial to your business : 

  Once you’ve identified these pain points, you can make the necessary changes to your website’s layout and address any technical issues that users might face, improving usability and delivering a smoother experience to potential customers. 

  Collecting clickstream data : Tools and legal implications 

  Your team will need a powerful tool capable of handling clickstream analytics to reap the benefits we’ve discussed previously. But at the same time, you need to respect users’ online privacy throughout clickstream data collection.

  

  Generally speaking, there are two ways to collect data about users’ online activity — web analytics tools and server log files.

  Web analytics tools are the more commonly used solution. Specifically designed to collect and analyse website data, these tools rely on JavaScript tags that run in the browser, providing actionable insights about user behaviour. Server log files can be a gold mine of data, too — but that data is raw and unfiltered, making it much more challenging to interpret and analyse. 

  That brings us to one of the major clickstream challenges to keep in mind as you move forward — compliance.

  While Google remains a dominant player in the web analytics market, there’s one area where Matomo has a significant advantage — user privacy. 

  Matomo operates according to privacy laws — including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), making it an ethical alternative to Google Analytics. 

  It should go without saying, but compliance with data privacy laws — the most talked-about one being the GDPR framework introduced by the EU — isn’t something you can afford to overlook. 

  The GDPR was first implemented in the EU in 2018. Since then, several fines have been issued for non-compliance — including the record fine of €1.2 billion that Meta Platforms, Inc. received in 2023 for transferring personal data of EU-based users to the US.

  Clickstream analytics data best practices 

  

  As valuable as it might be, processing large amounts of clickstream analytics data can be a complex — and, at times, overwhelming — process. 

  Here are some best practices to keep in mind when it comes to clickstream analysis : 

  Define your goals 

  It’s essential to take the time to define your goals and objectives. 

  Once you have a clear idea of what you want to learn from a given clickstream dataset and the outcomes you hope to see, it’ll be easier to narrow down your scope — rather than trying to tackle everything at once — before moving further down the clickstream pipeline. 

  Here are a few examples of goals and objectives you can set for clickstream analysis : 

  • Understanding and predicting users’ behavioural patterns 
  • Optimising marketing campaigns and ROI 
  • Attributing conversions to specific marketing touchpoints and channels

  Analyse your data 

  Collecting clickstream analytics data is only part of the equation ; what you do with raw data and how you analyse it matters. You can have the most comprehensive dataset at your disposal — but it’ll be practically worthless if you don’t have the skill set to analyse and interpret it. 

  In short, this is the stage of your clickstream pipeline where you uncover common sequences and consistent patterns in user behaviour. 

  Clickstream data analytics can extract actionable insights from large datasets using various approaches, models, and techniques. 

  Here are a few examples : 

  • If you’re working with clickstream e-commerce data, you should perform funnel or conversion analyses to track conversion rates as users move through your sales funnel. 
  • If you want to group and analyse users based on shared characteristics, you can use Matomo for cohort analysis. 
  • If your goal is to predict future trends and outcomes — conversion and cart abandonment prediction, for example — based on available data, prioritise predictive analytics.

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  Get the web insights you need, without compromising data accuracy.

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  Organise and visualise your data

  As you reach the end of your clickstream pipeline, you need to start thinking about how you will present and communicate your data. And what better way to do that than to transform that data into easy-to-understand visualisations ? 

  Here are a few examples of easily digestible formats that facilitate quick decision-making : 

  • User journey maps, which illustrate the exact sequence of interactions and user flow through your website 
  • Heatmaps, which serve as graphical — and typically colour-coded — representations of a website visitor’s activity 
  • Funnel analysis, which are broader at the top but get increasingly narrower towards the bottom as users flow through and drop off at different stages of the pipeline 

  Collect clickstream data with Matomo 

  Clickstream data is hard to beat when tracking the website visitor’s journey — from first to last interaction — and understanding user behaviour. By providing real-time insights, your clickstream pipeline can help you see the big picture, stay ahead of the curve and make informed decisions about your marketing efforts. 

  Matomo accurate data and compliance with GDPR and other data privacy regulations — it’s an all-in-one, ethical platform that can meet all your web analytics needs. That’s why over 1 million websites use Matomo for their web analytics.

  Try Matomo free for 21 days. No credit card required.

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• ffmpeg mp4 to mp3 conversion generates 1 second audio files only [closed]

  24 janvier 2024, par doesnotcompile

  I am having the following problem : I am having trouble converting an mp4 to an mp3.

  


  I have mp4 audio recordings that I need to convert in to mp3 audio files. The mp4 files have been recorded on iOS.

  


  I am currently doing this by using ffmpeg in python like so :

  


  subprocess.run(
            [
                "ffmpeg",
                "-loglevel", "error",
                "-i", ,
                "-q:a", "0",
                "-map", "a",
                
            ]
        )


  


  This runs with no error outputs.

  


  However, the resulting mp3 is only 1 second long, even though the input mp4 is around 5 seconds long.

  


  The mp4 is playable in Quicktime and Apple Music.

  


  ffprobe output :

  


  > ffprobe -v error -show_format input.mp4
[FORMAT]
filename=input.mp4
nb_streams=1
nb_programs=0
format_name=mov,mp4,m4a,3gp,3g2,mj2
format_long_name=QuickTime / MOV
start_time=0.000000
duration=5.120000
size=119636
bit_rate=186931
probe_score=100
TAG:major_brand=iso5
TAG:minor_version=1
TAG:compatible_brands=isomiso5hlsf
TAG:creation_time=2024-01-24T12:47:24.000000Z
[/FORMAT]


  


  No errors shown, and clearly 5 seconds long audio.

  


  Has anyone else experienced similar issues before ?

  


  Happy about any pointers !

  


• avformat/rtsp : Send mode=record instead of mode=receive in Transport header

  15 janvier 2024, par Paul Orlyk

  avformat/rtsp : Send mode=record instead of mode=receive in Transport header
  Fixes server compatibility issues with rtspclientsink GStreamer plugin.
  >From specification :
  
  RFC 7826 "Real-Time Streaming Protocol Version 2.0" (https://datatracker.ietf.org/doc/html/rfc7826), section 18.54 :
  
     mode : The mode parameter indicates the methods to be supported for
  
           this session.  The currently defined valid value is "PLAY".  If
  
           not provided, the default is "PLAY".  The "RECORD" value was
  
           defined in RFC 2326 ; in this specification, it is unspecified
  
           but reserved.  RECORD and other values may be specified in the
  
           future.
  
  RFC 2326 "Real Time Streaming Protocol (RTSP)" (https://datatracker.ietf.org/doc/html/rfc2326), section 12.39 :
  
     mode :
  
            The mode parameter indicates the methods to be supported for
  
            this session. Valid values are PLAY and RECORD. If not
  
            provided, the default is PLAY.
  mode=receive was always like this, from the initial commit 'a8ad6ffa rtsp : Add listen mode'.
  For comparison, Wowza was used to push RTSP stream to. Both GStreamer and FFmpeg had no issues.
  
  Here is the capture of Wowza responding to SETUP request :
  
  200 OK
  
  CSeq : 3
  
  Server : Wowza Streaming Engine 4.8.26+4 build20231212155517
  
  Cache-Control : no-cache
  
  Expires : Mon, 15 Jan 2024 19:40:31 GMT
  
  Transport : RTP/AVP/UDP ;unicast ;client_port=11640-11641 ;mode=record ;source=172.17.0.2 ;server_port=6976-6977
  
  Date : Mon, 15 Jan 2024 19:40:31 GMT
  
  Session : 1401457689 ;timeout=60
  Test setup :
  
      Server : ffmpeg -loglevel trace -y -rtsp_flags listen -i rtsp ://0.0.0.0:30800/live.stream t.mp4
  
      FFmpeg client : ffmpeg -re -i "Big Buck Bunny - FULL HD 30FPS.mp4" -c:v libx264 -f rtsp rtsp ://127.0.0.1:30800/live.stream
  
      GStreamer client : gst-launch-1.0 videotestsrc is-live=true pattern=smpte ! queue ! videorate ! videoscale ! video/x-raw,width=640,height=360,framerate=60/1 ! timeoverlay font-desc="Sans, 84" halignment=center valignment=center ! queue ! videoconvert ! tee name=t t. ! x264enc bitrate=9000 pass=cbr speed-preset=ultrafast byte-stream=false key-int-max=15 threads=1 ! video/x-h264,profile=baseline ! queue ! rsink. audiotestsrc ! voaacenc ! queue ! rsink. t. ! queue ! autovideosink rtspclientsink name=rsink location=rtsp ://localhost:30800/live.stream
  Test results :
  
  modified FFmpeg client -> stock server : ok
  
  stock FFmpeg client    -> modified server : ok
  
  modified FFmpeg client -> modified server : ok
  
  GStreamer client       -> modified server : ok
  Signed-off-by : Paul Orlyk <paul.orlyk@gmail.com>
  
  Signed-off-by : Michael Niedermayer <michael@niedermayer.cc>
  

  • [DH] libavformat/rtspdec.c