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• A Guide to App Analytics Tools that Drive Growth

  7 mars, par Daniel Crough — App Analytics

  Mobile apps are big business, generating £438 billion in global revenue between in-app purchases (38%) and ad revenue (60%). And with 96% of apps relying on in-app monetisation, the competition is fierce.

  To succeed, app developers and marketers need strong app analytics tools to understand their customers’ experiences and the effectiveness of their development efforts.

  This article discusses app analytics, how it works, the importance and benefits of mobile app analytics tools, key metrics to track, and explores five of the best app analytics tools on the market.

  What are app analytics tools ?

  Mobile app analytics tools are software solutions that provide insights into how users interact with mobile applications. They track user behaviour, engagement and in-app events to reveal what’s working well and what needs improvement.

  Insights gained from mobile app analytics help companies make more informed decisions about app development, marketing campaigns and monetisation strategies.

  What do app analytics tools do ?

  App analytics tools embed a piece of code, called a software development kit (SDK), into an app. These SDKs provide the essential infrastructure for the following functions :

  • Data collection : The SDK collects data within your app and records user actions and events, like screen views, button clicks, and in-app purchases.
  • Data filtering : SDKs often include mechanisms to filter data, ensuring that only relevant information is collected.
  • Data transmission : Once collected and filtered, the SDK securely transmits the data to an analytics server. The SDK provider can host this server (like Firebase or Amplitude), or you can host it on-premise.
  • Data processing and analysis : Servers capture, process and analyse large stores of data and turn it into useful information.
  • Visualisation and reporting : Dashboards, charts and graphs present processed data in a user-friendly format.

  

  Six ways mobile app analytics tools fuel marketing success and drive product growth

  Mobile app analytics tools are vital in driving product development, enhancing user experiences, and achieving business objectives.

  #1. Improving user understanding

  The better a business understands its customers, the more likely it is to succeed. For mobile apps, that means understanding how and why people use them.

  Mobile analytics tools provide detailed insights into user behaviours and preferences regarding apps. This knowledge helps marketing teams create more targeted messaging, detailed customer journey maps and improve user experiences.

  It also helps product teams understand the user experience and make improvements based on those insights.

  For example, ecommerce companies might discover that users in a particular area are more likely to buy certain products. This allows the company to tailor its offers and promotions to target the audience segments most likely to convert.

  #2 Optimising monetisation strategies for increased revenue and user retention

  In-app purchases and advertising make up 38% and 60% of mobile app revenue worldwide, respectively. App analytics tools provide insights companies need to optimise app monetisation by :

  • Analysing purchase patterns to identify popular products and understand pricing sensitivities.
  • Tracking in-app behaviour to identify opportunities for enhancing user engagement.

  App analytics can track key metrics like visit duration, user flow, and engagement patterns. These metrics provide critical information about user experiences and can help identify areas for improvement.

  How meaningful are the impacts ?

  Duolingo, the popular language learning app, reported revenue growth of 45% and an increase in daily active users (DAU) of 65% in its Q4 2023 financial report. The company attributed this success to its in-house app analytics platform.

  

  #3. Understanding user experiences

  Mobile app analytics tools track the performance of user interactions within your app, such as :

  • Screen views : Which screens users visit most frequently
  • User flow : How users navigate through your app
  • Session duration : How long users spend in your app
  • Interaction events : Which buttons, features, and functions users engage with most

  Knowing how users interact with your app can help refine your approach, optimise your efforts, and drive more conversions.

  #4. Personalising user experiences

  A recent McKinsey survey showed that 71% of users expect personalised app experiences. Product managers must stay on top of this since 76% of users get frustrated if they don’t receive the personalisation they expect.

  Personalisation on mobile platforms requires data capture and analysis. Mobile analytics platforms can provide the data to personalise the user onboarding process, deliver targeted messages and recommend relevant content or offers.

  Spotify is a prime example of personalisation done right. A recent case study by Pragmatic Institute attributed the company’s growth to over 500 million active daily users to its ability to capture, analyse and act on :

  • Search behaviour
  • Individual music preferences
  • Playlist data
  • Device usage
  • Geographical location

  The streaming service uses its mobile app analytics software to turn this data into personalised music recommendations for its users. Spotify also has an in-house analytics tool called Spotify Premium Analytics, which helps artists and creators better understand their audience.

  #5. Enhancing app performance

  App analytics tools can help identify performance issues that might be affecting user experience. By monitoring metrics like load time and app performance, developers can pinpoint areas that need improvement.

  Performance optimisation is crucial for user retention. According to Google research, 53% of mobile site visits are abandoned if pages take longer than three seconds to load. While this statistic refers to websites, similar principles apply to apps—users expect fast, responsive experiences.

  Analytics data can help developers prioritise performance improvements by showing which screens or features users interact with most frequently, allowing teams to focus their optimisation efforts where they’ll have the greatest impact.

  #6. Identifying growth opportunities

  App analytics tools can reveal untapped opportunities for growth by highlighting :

  • Features users engage with most
  • Underutilised app sections that might benefit from redesign
  • Common user paths that could be optimised
  • Moments where users tend to drop off

  This intelligence helps product teams make data-informed decisions about future development priorities, feature enhancements, and potential new offerings.

  For example, a streaming service might discover through analytics that users who create playlists have significantly higher retention rates. This insight could lead to development of enhanced playlist functionality to encourage more users to create them, ultimately boosting overall retention.

  Key app metrics to track

  Using mobile analytics tools, you can track dozens of key performance indicators (KPIs) that measure everything from customer engagement to app performance. This section focuses on the most important KPIs for app analytics, classified into three categories :

  • App performance KPIs
  • User engagement KPIs
  • Business impact KPIs

  While the exact metrics to track will vary based on your specific goals, these fundamental KPIs form the foundation of effective app analytics.

  

  App performance KPIs

  App performance metrics tell you whether an app is reliable and operating properly. They help product managers identify and address technical issues that may negatively impact user experiences.

  Some key metrics to assess performance include :

  • Screen load time : How quickly screens load within your app
  • App stability : How often your app crashes or experiences errors
  • Response time : How quickly your app responds to user interactions
  • Network performance : How efficiently your app handles data transfers

  User engagement KPIs

  Engagement KPIs provide insights into how users interact with an app. These metrics help you understand user behaviour and make UX improvements.

  Important engagement metrics include :

  • Returning visitors : A measure of how often users return to an app
  • Visit duration : How long users spend in your app per session
  • User flow : Visualisation of the paths users take through your app, offering insights into navigation patterns
  • Event tracking : Specific interactions users have with app elements
  • Screen views : Which screens are viewed most frequently

  Business impact KPIs

  Business impact KPIs connect app analytics to business outcomes, helping demonstrate the app’s value to the organisation.

  Key business impact metrics include :

  • Conversion events : Completion of desired actions within your app
  • Goal completions : Tracking when users complete specific objectives
  • In-app purchases : Monitoring revenue from within the app
  • Return on investment : Measuring the business value generated relative to development costs

  Privacy and app analytics : A delicate balance

  While app analytics tools can be a rich source of user data, they must be used responsibly. Tracking user in-app behaviour and collecting user data, especially without consent, can raise privacy concerns and erode user trust. It can also violate data privacy laws like the GDPR in Europe or the OCPA, FDBR and TDPSA in the US.

  With that in mind, it’s wise to choose user-tracking tools that prioritise user privacy while still collecting enough data for reliable analysis.

  Matomo is a privacy-focused web and app analytics solution that allows you to collect and analyse user data while respecting user privacy and following data protection rules like GDPR.

  The five best app analytics tools to prove marketing value

  In this section, we’ll review the five best app analytics tools based on their features, pricing and suitability for different use cases.

  Matomo — Best for privacy-compliant app analytics

  Matomo app analytics is a powerful, open-source platform that prioritises data privacy and compliance.

  

  It offers a suite of features for tracking user engagement and conversions across websites, mobile apps and intranets.

  Key features

  • Complete data ownership : Full control over your analytics data with no third-party access
  • User flow analysis : Track user journeys across different screens in your app
  • Custom event tracking : Monitor specific user interactions with customisable events
  • Ecommerce tracking : Measure purchases and product interactions
  • Goal conversion monitoring : Track completion of important user actions
  • Unified analytics : View web and app analytics in one platform for a complete digital picture

  Benefits

  • Eliminate compliance risks without sacrificing insights
  • Get accurate data with no sampling or data manipulation
  • Choose between self-hosting or cloud deployment
  • Deploy one analytics solution across your digital properties (web and app) for a single source of truth

  Pricing

  Plan	Price
  Cloud	Starts at £19/month
  On-Premise	Free

  Matomo is a smart choice for businesses that value data privacy and want complete control over their analytics data. It’s particularly well-suited for organisations in highly regulated industries, like banking.

  While Matomo’s app analytics features focus on core analytics capabilities, its privacy-first approach offers unique advantages. For organisations already using Matomo for web analytics, extending to mobile creates a unified analytics ecosystem with consistent privacy standards across all digital touchpoints, giving organisations a complete picture of the customer journey.

  Firebase — Best for Google services integration

  Firebase is the mobile app version of Google Analytics. It’s the most popular app analytics tool on the market, with over 99% of Android apps and 77% of iOS apps using Firebase.

  

  Firebase is popular because it works well with other Google services. It also has many features, like crash reporting, A/B testing and user segmentation.

  Pricing

  Plan	Price
  Spark	Free
  Blaze	Pay-as-you-go based on usage
  Custom	Bespoke pricing for high-volume enterprise users

  Adobe Analytics — Best for enterprise app analytics

  Adobe Analytics is an enterprise-grade analytics solution that provides valuable insights into user behaviour and app performance.

  

  It’s part of the Adobe Marketing Cloud and integrates easily with other Adobe products. Adobe Analytics is particularly well-suited for large organisations with complex analytics needs.

  Pricing

  Plan	Price
  Select	Pricing on quote
  Prime	Pricing on quote
  Ultimate	Pricing on quote

  While you must request a quote for pricing, Scandiweb puts Adobe Analytics at £2,000/mo–£2,500/mo for most companies, making it an expensive option.

  Apple App Analytics — Best for iOS app analysis

  Apple App Analytics is a free, built-in analytics tool for iOS app developers.

  

  This analytics platform provides basic insights into user engagement, app performance and marketing campaigns. It has fewer features than other tools on this list, but it’s a good place for iOS developers who want to learn how their apps work.

  Pricing

  Apple Analytics is free.

  Amplitude — Best for product analytics

  Amplitude is a product analytics platform that helps businesses understand user behaviour and build better products.

  

  It excels at tracking user journeys, identifying user segments and measuring the impact of product changes. Amplitude is a good choice for product managers and data analysts who want to make informed decisions about product development.

  Pricing

  Plan	Price
  Starter	Free
  Plus	From £49/mo
  Growth	Pricing on quote

  Choose Matomo’s app analytics to unlock growth

  App analytics tools help marketers and product development teams understand user experiences, improve app performance and enhance products. Some of the best app analytics tools available for 2025 include Matomo, Firebase and Amplitude.

  However, as you evaluate your options, consider taking a privacy-first approach to app data collection and analysis, especially if you’re in a highly regulated industry like banking or fintech. Matomo Analytics offers a powerful and ethical solution that allows you to gain valuable insights while respecting user privacy.

  Ready to take control of your app analytics ? Start your 21-day free trial.

• 10 Matomo Features You Possibly Didn’t Know About

  28 octobre 2022, par Erin

  Most users know Matomo as the privacy-focussed web analytics tool with data accuracy, superior to Google Analytics. 

  And we’re thrilled to be that — and more ! 

  At Matomo, our underlying product vision is to provide a full stack of accurate, user-friendly and privacy-mindful online marketing tools. 

  Over the years, we’ve expanded beyond baseline website statistics. Matomo Cloud users also get to benefit from additional powerful tools for audience segmentation, conversion optimisation, advanced event tracking and more. 

  Here are the top 10 advanced Matomo features you wish you knew about earlier (but won’t stop using now !). 

  Funnels

  At first glance, most customer journeys look sporadic. But every marketer will tell you that there is a method to almost every users’ madness. Or more precisely — there’s a method you can use to guide users towards conversions. 

  That’s called a customer journey — a schematic set of steps and actions people complete from developing awareness and interest in your solution to consideration and finally conversion.

  On average, 8 touchpoints are required to turn a prospect into a customer. Though the number can be significantly bigger in B2B sales and smaller for B2C Ecommerce websites. 

  With the Funnels feature, you can first map all the on-site touchpoints (desired actions) for different types of customers. Then examine the results you’re getting as prospects move through these checkbox steps.

  Funnel reports provide :

  • High-level metrics such as “Funnel conversion rate”, “Number of funnel conversions”, “Number of funnel entries”. 
  • Drilled-down reports for each funnel and each tracked action within it. This way you can track the success rates of each step and estimate their contribution to the cumulative effect.

  Segmented funnel reports for specific user cohorts (with Matomo Segmentation enabled).

  

  What makes funnels so fun (pun intended) ? The variety of use cases and configurations ! 

  You can build funnels to track conversion rates for :

  • Newsletter subscriptions
  • Job board applications 
  • Checkout or payment 
  • Product landing pages
  • Seasonal promo campaigns

  …. And pretty much any other page where users must complete a meaningful action. So go test this out. 

  Form Analytics

  On-site forms are a 101 tactic for lead generation. For most service businesses, a “contact request” or a “booking inquiry” submission means a new lead in your pipeline. 

  That said : the average on-site form conversion rates across industries stand at below 50% : 

  • Property – 37% 
  • Telecoms – 40%
  • Software — 46.83%

  That’s not bad, but it could be better. If only you could figure out why people abandon your forms….

  Oh wait, Matomo Form Analytics can supply you with answers. Form Analytics provide real-time information on key form metrics — total views, starter rate, submitter rate, conversions and more.

  

  Separately the average form hesitation time is also provided (in other words, the time a user contemplates if filling in a form is worth the effort). Plus, Matomo also tracks the time spent on form submission.

  You can review : 

  • Top drop-off fields – to understand where you are losing prospects. These fields should either be removed or simplified (e.g., with a dropdown menu) to increase conversions.
  • Most corrected-field – this will provide a clear indication of where your prospects are struggling with a form. Providing help text can simplify the process and increase conversions. 
  • Unesserary fields – with this metric, you’ll know which optional fields your leads aren’t interested in filling in and can remove them to help drive conversions. 

  With Form Analytics, you’ll be able to boost conversions and create a better on-site experience with accurate user data. 

  A/B testing

  Marketing is both an art and a science. A/B testing (or split testing) helps you statistically verify which creative ideas perform better. 

  A good conversion rate optimisation (CRO) practice is to test different elements and to do so often to find your top contenders.

  What can you split test ? Loads of things :

  • Page slogans and call-to-actions 
  • Button or submission form placements
  • Different landing page designs and layouts
  • Seasonal promo offers and banners
  • Pricing information 
  • Customer testimonial placements 

  More times than not, those small changes in page design or copy can lead to a double-digit lift in conversion rates. Accounting software Sage saw a 30% traffic boost after changing the homepage layout, copy and CTAs based on split test data. Depositphotos, in turn, got a 9.32% increase in account registration rate (CR) after testing a timed pop-up registration form. 

  The wrinkle ? A/B testing software isn’t exactly affordable, with tools averaging $119 – $1,995 per month. Plus, you then have to integrate a third-party tool with your website analytics for proper attribution — and this can get messy.

  Matomo saves you the hassle in both cases. An A/B testing tool is part of your Cloud subscription and plays nicely with other features — goal tracking, heatmaps, historic visitor profiles and more. 

  You can run split tests with Matomo on your websites or mobile apps — and find out if version A, B, C or D is the top performer. 

  

  Advertising Conversion Exports

  A well-executed search marketing or banner remarketing campaign can drive heaps of traffic to your website. But the big question is : How much of it will convert ?

  The AdTech industry has a major problem with proper attribution and, because of it, with ad fraud. 

  Globally, digital ad fraud will cost advertisers a hefty $8 billion by the end of 2022. That’s when another $74 million in ad budgets get wasted per quarter. 

  The reasons for ad budget waste may vary, but they often have a common denominator : lack of reliable conversion tracking data.

  Matomo helps you get a better sense of how you spend your cents with Advertising Conversion Reports. Unlike other MarTech analytics tools, you don’t need to embed any third-party advertising network trackers into your website or compromise user privacy.

  Instead, you can easily export accurate conversion data from Matomo (either manually via a CSV file or automated with an HTTPS link) into your Google Ads, Microsoft Advertising or Yandex Ads for cross-validation. This way you can get an objective view of the performance of different campaigns and optimise your budget allocations accordingly. 

  Find out more about tracking ad campaigns with Matomo.

  Matomo Tag Manager

  The marketing technology landscape is close to crossing 10,000 different solutions. Cross-platform advertising trackers and all sorts of customer data management tools comprise the bulk of that growing stack. 

  Remember : Each new tool embed adds extra “weight” to your web page. More tracking scripts equal slower page loading speed — and more frustration for your users. Likewise, extra embeds often means dialling up the developer (which takes time). Or tinkering with the site code yourself (which can result in errors and still raise the need to call a developer). 

  With Tag Manager, you can easily generate tags for :

  • Custom analytics reports 
  • Newsletter signups
  • Affiliates 
  • Form submission tracking 
  • Exit popups and surveys
  • Ads and more

  With Matomo Tag Manager, you can monitor, update or delete everything from one convenient interface. Finally, you can programme custom triggers — conditions when the tag gets activated — and specify data points (variables) it should collect. The latter is a great choice for staying privacy-focused and excluding any sensitive user information from processing. 

  With our tag management system (TMS), no rogue tags will mess up your analytics or conversion tracking. 

  Session recordings

  User experience (UX) plays a pivotal role in your conversion rates. 

  A five-year McKinsey study of 300 publicly listed companies found that companies with strong design practices have 32 percentage points higher revenue growth than their peers. 

  But what makes up a great website design and browsing experience ? Veteran UX designers name seven qualities :

  

  To figure out if your website meets all these criteria, you can use Session Recording — a tool for recording how users interact with your website. 

  By observing clicks, mouse moves, scrolls and form interactions you can determine problematic website design areas such as poor header navigation, subpar button placements or “boring” blocks of text. 

  Such observational studies are a huge part of the UX research process because they provide unbiased data on interaction. Or as Nielsen Norman Group puts it :

  “The way to get user data boils down to the basic rules of usability :

  • Watch what people actually do.
  • Do not believe what people say they do.
  • Definitely don’t believe what people predict they may do in the future.” 

  Most user behaviour analytics tools sell such functionality for a fee. With Matomo Cloud, this feature is included in your subscription. 

  Heatmaps

  While Session Replays provide qualitative insights, Heatmaps supply you with first-hand qualitative insights. Instead of individual user browsing sessions, you get consolidated data on where they click and how they scroll through your website. 

  

  Heatmaps are another favourite among UX designers and their CRO peers because you can :

  • Validate earlier design decisions around information architecture, page layout, button placements and so on. 
  • Develop new design hypotheses based on stats and then translate them into website design improvements. 
  • Identify distractive no-click elements that confuse users and remove them to improve conversions. 
  • Locate problematic user interface (UI) areas on specific devices or operating systems and improve them for a seamless experience.

  To get even more granular results, you can apply up to 100 Matomo segments to drill down on specific user groups, geographies or devices. 

  This way you can make data-based decisions for A/B testing, updating or redesigning your website pages. 

  Custom Alerts

  When it comes to your website, you don’t want to miss anything big — be it your biggest sales day or a sudden nosedive in traffic. 

  That’s when Custom Alerts come in handy. 

  

  With a few clicks, you can set up email or text-based alerts about important website metrics. Once you hit that metric, Matomo will send a ping. 

  You can also set different types of Custom Alerts for your teams. For example, your website administrator can get alerted about critical technical performance issues such as a sudden spike in traffic. It can indicate a DDoS attack (in the worst case) — and timely resolution is crucial here. Or suggest that your website is going viral and you might need to provision extra computing resources to ensure optimal site performance.

  Your sales team, in turn, can get alerted about new form submissions, so that they can quickly move on to lead scoring and subsequent follow-ups. 

  Use cases are plentiful with this feature. 

  Custom Dashboards and Reports

  Did you know you can get a personalised view of the main Matomo dashboards ? 

  By design, we made different website stats available as separate widgets. Hence, you can cherry-pick which stats get a prominent spot. Moreover, you can create and embed custom widgets into your Matomo dashboard to display third-party insights (e.g., POS data).

  Set up custom dashboard views for different teams, business stakeholders or clients to keep them in the loop on relevant website metrics. 

  

  Custom Reports feature, in turn, lets you slice and dice your traffic analytics the way you please. You can combine up to three different data dimensions per report and then add any number of supported metrics to get a personalised analytics report.

  For example, to zoom in on your website performance in a specific target market you can apply “location” (e.g., Germany) and “action type” (e.g., app downloads) dimensions and then get segmented data on metrics such as total visits, conversion rates, revenue and more. 

  Get to know even more ways to customise Matomo deployment.

  Roll Up Report

  Need to get aggregated traffic analytics from multiple web properties, but not ready to pay $150K per year for Google Analytics 360 for that ?

  We’ve got you with Roll-Up Reporting. You can get a 360-degree view into important KPIs like global revenue, conversion rates or form performance across multiple websites, online stores, mobile apps and even Intranet properties.

  

  Setting up this feature takes minutes, but saves you hours on manually exporting and cross-mapping data from different web analytics tools. 

  Channel all those saved hours into more productive things like increasing your conversion rates or boosting user engagement. 

  Avoid Marketing Tool Sprawl with Matomo 

  With Matomo as your website analytics and conversion optimisation app, you don’t need to switch between different systems, interfaces or have multiple tracking codes embedded on your site.

  And you don’t need to cultivate a disparate (and expensive !) MarTech tool stack — and then figure out if each of your tools is compliant with global privacy laws.

  All the tools you need are conveniently housed under one roof. 

  Want to learn more about Matomo features ? Check out product training videos next ! 

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• RTMP server with OpenCV (python)

  12 février 2024, par Overnout

  I'm trying to process an RTMP stream in Python, using OpenCV2 but I'm not able to get OpenCV to capture it (i.e. act as RTMP server).

  


  I can run FFmpeg/FFplay from the command line and receive the stream successfully.
What could cause OpenCV to fail opening the stream in listening mode ?

  


  Here is my code :

  


  import cv2

cap = cv2.VideoCapture("rtmp://0.0.0.0:8000/live", cv2.CAP_FFMPEG)

if not cap.isOpened():
    print("Cannot open video source")
    exit()


  


  And the output :

  


  [tcp @ 00000192c490d640] Connection to tcp://0.0.0.0:8000 failed: Error number -138 occurred
[rtmp @ 00000192c490d580] Cannot open connection tcp://0.0.0.0:8000 
Cannot open video source


  


  edit2 : Output with debug logging turned on :

  


  output of the python script with debug logging on:
[DEBUG:0@0.017] global videoio_registry.cpp:218 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Builtin backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[DEBUG:0@0.026] global videoio_registry.cpp:242 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Available backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ INFO:0@0.031] global videoio_registry.cpp:244 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Enabled backends(9, sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ WARN:0@0.037] global cap.cpp:132 cv::VideoCapture::open VIDEOIO(FFMPEG): trying capture filename='rtmp://192.168.254.101:8000/live' ...
[ INFO:0@0.040] global backend_plugin.cpp:383 cv::impl::getPluginCandidates Found 2 plugin(s) for FFMPEG
[ INFO:0@0.043] global plugin_loader.impl.hpp:67 cv::plugin::impl::DynamicLib::libraryLoad load C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll => OK
[ INFO:0@0.047] global backend_plugin.cpp:50 cv::impl::PluginBackend::initCaptureAPI Found entry: 'opencv_videoio_capture_plugin_init_v1'
[ INFO:0@0.049] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Capture plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.055] global backend_plugin.cpp:69 cv::impl::PluginBackend::initCaptureAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Capture plugin'
[ INFO:0@0.058] global backend_plugin.cpp:84 cv::impl::PluginBackend::initWriterAPI Found entry: 'opencv_videoio_writer_plugin_init_v1'
[ INFO:0@0.061] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Writer plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.065] global backend_plugin.cpp:103 cv::impl::PluginBackend::initWriterAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Writer plugin'
[tcp @ 00000266b2f0d0c0] Connection to tcp://192.168.254.101:8000 failed: Error number -138 occurred
[rtmp @ 00000266b2f0cfc0] Cannot open connection tcp://192.168.254.101:8000
[ WARN:0@5.630] global cap.cpp:155 cv::VideoCapture::open VIDEOIO(FFMPEG): can't create capture
[DEBUG:0@5.632] global cap.cpp:225 cv::VideoCapture::open VIDEOIO: choosen backend does not work or wrong. Please make sure that your computer support chosen backend and OpenCV built with right flags.
Cannot open video source
[ INFO:1@5.661] global plugin_loader.impl.hpp:74 cv::plugin::impl::DynamicLib::libraryRelease unload C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll


  


  Here is the output of cv2.getBuildInformation()

  


  General configuration for OpenCV 4.9.0 =====================================
  Version control:               4.9.0

  Platform:
    Timestamp:                   2023-12-31T11:21:12Z
    Host:                        Windows 10.0.17763 AMD64
    CMake:                       3.24.2
    CMake generator:             Visual Studio 14 2015
    CMake build tool:            MSBuild.exe
    MSVC:                        1900
    Configuration:               Debug Release

  CPU/HW features:
    Baseline:                    SSE SSE2 SSE3
      requested:                 SSE3
    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2
      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
      SSE4_1 (16 files):         + SSSE3 SSE4_1
      SSE4_2 (1 files):          + SSSE3 SSE4_1 POPCNT SSE4_2
      FP16 (0 files):            + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
      AVX (8 files):             + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
      AVX2 (36 files):           + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2

  C/C++:
    Built as dynamic libs?:      NO
    C++ standard:                11
    C++ Compiler:                C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe  (ver 19.0.24247.2)
    C++ flags (Release):         /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /O2 /Ob2 /DNDEBUG 
    C++ flags (Debug):           /DWIN32 /D_WINDOWS /W4 /GR  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP  /Zi /Ob0 /Od /RTC1 
    C Compiler:                  C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe
    C flags (Release):           /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP   /O2 /Ob2 /DNDEBUG 
    C flags (Debug):             /DWIN32 /D_WINDOWS /W3  /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi  /fp:precise     /MP /Zi /Ob0 /Od /RTC1 
    Linker flags (Release):      /machine:x64  /NODEFAULTLIB:atlthunk.lib /INCREMENTAL:NO  /NODEFAULTLIB:libcmtd.lib /NODEFAULTLIB:libcpmtd.lib /NODEFAULTLIB:msvcrtd.lib
    Linker flags (Debug):        /machine:x64  /NODEFAULTLIB:atlthunk.lib /debug /INCREMENTAL  /NODEFAULTLIB:libcmt.lib /NODEFAULTLIB:libcpmt.lib /NODEFAULTLIB:msvcrt.lib
    ccache:                      NO
    Precompiled headers:         YES
    Extra dependencies:          wsock32 comctl32 gdi32 ole32 setupapi ws2_32
    3rdparty dependencies:       libprotobuf ade ittnotify libjpeg-turbo libwebp libpng libtiff libopenjp2 IlmImf zlib ippiw ippicv

  OpenCV modules:
    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
    Disabled:                    java world
    Disabled by dependency:      -
    Unavailable:                 python2 ts
    Applications:                -
    Documentation:               NO
    Non-free algorithms:         NO

  Windows RT support:            NO

  GUI:                           WIN32UI
    Win32 UI:                    YES
    VTK support:                 NO

  Media I/O: 
    ZLib:                        build (ver 1.3)
    JPEG:                        build-libjpeg-turbo (ver 2.1.3-62)
      SIMD Support Request:      YES
      SIMD Support:              NO
    WEBP:                        build (ver encoder: 0x020f)
    PNG:                         build (ver 1.6.37)
    TIFF:                        build (ver 42 - 4.2.0)
    JPEG 2000:                   build (ver 2.5.0)
    OpenEXR:                     build (ver 2.3.0)
    HDR:                         YES
    SUNRASTER:                   YES
    PXM:                         YES
    PFM:                         YES

  Video I/O:
    DC1394:                      NO
    FFMPEG:                      YES (prebuilt binaries)
      avcodec:                   YES (58.134.100)
      avformat:                  YES (58.76.100)
      avutil:                    YES (56.70.100)
      swscale:                   YES (5.9.100)
      avresample:                YES (4.0.0)
    GStreamer:                   NO
    DirectShow:                  YES
    Media Foundation:            YES
      DXVA:                      YES

  Parallel framework:            Concurrency

  Trace:                         YES (with Intel ITT)

  Other third-party libraries:
    Intel IPP:                   2021.11.0 [2021.11.0]
           at:                   D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/icv
    Intel IPP IW:                sources (2021.11.0)
              at:                D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/iw
    Lapack:                      NO
    Eigen:                       NO
    Custom HAL:                  NO
    Protobuf:                    build (3.19.1)
    Flatbuffers:                 builtin/3rdparty (23.5.9)

  OpenCL:                        YES (NVD3D11)
    Include path:                D:/a/opencv-python/opencv-python/opencv/3rdparty/include/opencl/1.2
    Link libraries:              Dynamic load

  Python 3:
    Interpreter:                 C:/hostedtoolcache/windows/Python/3.7.9/x64/python.exe (ver 3.7.9)
    Libraries:                   C:/hostedtoolcache/windows/Python/3.7.9/x64/libs/python37.lib (ver 3.7.9)
    numpy:                       C:/hostedtoolcache/windows/Python/3.7.9/x64/lib/site-packages/numpy/core/include (ver 1.17.0)
    install path:                python/cv2/python-3

  Python (for build):            C:\hostedtoolcache\windows\Python\3.7.9\x64\python.exe

  Java:                          
    ant:                         NO
    Java:                        YES (ver 1.8.0.392)
    JNI:                         C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include/win32 C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include
    Java wrappers:               NO
    Java tests:                  NO

  Install to:                    D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-install
-----------------------------------------------------------------


  


  edit : Receiving the stream with ffplay from command line :

  


  >ffplay.exe -i "rtmp://0.0.0.0:8000/live"  -listen 1 -f flv
ffplay version 2024-02-04-git-7375a6ca7b-full_build-www.gyan.dev Copyright (c) 2003-2024 the FFmpeg developers
  built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
  configuration: --enable-gpl --enable-version3 --enable-static --pkg-config=pkgconf --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-dxva2 --enable-d3d11va --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
  libavutil      58. 36.101 / 58. 36.101
  libavcodec     60. 38.100 / 60. 38.100
  libavformat    60. 20.100 / 60. 20.100
  libavdevice    60.  4.100 / 60.  4.100
  libavfilter     9. 17.100 /  9. 17.100
  libswscale      7.  6.100 /  7.  6.100
  libswresample   4. 13.100 /  4. 13.100
  libpostproc    57.  4.100 / 57.  4.100
[rtmp @ 0000018a564ed340] Unexpected stream , expecting livef=0/0
    Last message repeated 1 times
Input #0, flv, from 'rtmp://0.0.0.0:8000/live':KB sq=    0B f=0/0
  Metadata:
    fileSize        : 0
    audiochannels   : 2
    2.1             : false
    3.1             : false
    4.0             : false
    4.1             : false
    5.1             : false
    7.1             : false
    encoder         : obs-output module (libobs version 30.0.2)
  Duration: 00:00:00.00, start: 0.000000, bitrate: N/A
  Stream #0:0: Audio: aac (LC), 48000 Hz, stereo, fltp, 163 kb/s
  Stream #0:1: Video: h264 (Constrained Baseline), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 2560 kb/s, 30 fps, 30 tbr, 1k tbn
   7.54 A-V: -0.024 fd=  18 aq=   24KB vq=  498KB sq=    0B f=0/0