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• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.

• 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.

• ffmpeg video slideshow only takes first image

  14 décembre 2018, par Erhan

  I wanted to create an .mp4 video with a framerate of 1 fps out of 10 images. I followed https://trac.ffmpeg.org/wiki/Slideshow and got to (my images are in the folder I am running the command from and are name img000.png, img001.png, etc.)

  path_to_ffmpeg\ffmpeg.exe -framerate 1 -i img%03d.png output.mp4

  However only the first image is taken to the video and it only shows one image !

  After trying multiple permutations of -framerate and -r options and positions of the options, I did not recieve any better results.

  Does anyone know what might have gone wrong even in this simple case ? Thanks.

  Full log :

  C:\Users\foo\Documents\path>C:\bar\ffmpeg-4.0.2-win64-static\bin\ffmpeg.ex
  
  e -framerate 1 -i img%03d.png output.mp4    
  
  ffmpeg version 4.0.2 Copyright (c) 2000-2018 the FFmpeg developers    
  
    built with gcc 7.3.1 (GCC) 20180722    
  
    configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-bzlib --e    
  
  nable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libblur    
  
  ay --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-    
  
  libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enab    
  
  le-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-li    
  
  bvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --en    
  
  able-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-    
  
  libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enabl    
  
  e-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enabl    
  
  e-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enab    
  
  le-dxva2 --enable-avisynth    
  
    libavutil      56. 14.100 / 56. 14.100    
  
    libavcodec     58. 18.100 / 58. 18.100    
  
    libavformat    58. 12.100 / 58. 12.100    
  
    libavdevice    58.  3.100 / 58.  3.100    
  
    libavfilter     7. 16.100 /  7. 16.100    
  
    libswscale      5.  1.100 /  5.  1.100    
  
    libswresample   3.  1.100 /  3.  1.100    
  
    libpostproc    55.  1.100 / 55.  1.100    
  
  Input #0, image2, from 'img%03d.png':    
  
    Duration: 00:00:10.00, start: 0.000000, bitrate: N/A    
  
      Stream #0:0: Video: png, rgba(pc), 959x550 [SAR 2834:2834 DAR 959:550], 1 fp    
  
  s, 1 tbr, 1 tbn, 1 tbc    
  
  File 'output.mp4' already exists. Overwrite ? [y/N] y    
  
  Stream mapping:    
  
    Stream #0:0 -> #0:0 (png (native) -> h264 (libx264))    
  
  Press [q] to stop, [?] for help    
  
  [libx264 @ 0000000000611040] using SAR=1/1    
  
  [libx264 @ 0000000000611040] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2    
  
  AVX FMA3 BMI2 AVX2    
  
  [libx264 @ 0000000000611040] profile High 4:4:4 Predictive, level 3.1, 4:4:4 8-b    
  
  it    
  
  [libx264 @ 0000000000611040] 264 - core 155 r2901 7d0ff22 - H.264/MPEG-4 AVC cod    
  
  ec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 r    
  
  ef=3 deblock=1:0:0 analyse=0x1:0x111 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed    
  
  _ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=0 cqm=0 deadzone=21,11 fast_pski    
  
  p=1 chroma_qp_offset=4 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 deci    
  
  mate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_    
  
  adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=1    
  
  scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60    
  
  qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00    
  
  Output #0, mp4, to 'output.mp4':    
  
    Metadata:    
  
      encoder         : Lavf58.12.100    
  
      Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv444p, 959x550 [SA    
  
  R 1:1 DAR 959:550], q=-1--1, 1 fps, 16384 tbn, 1 tbc    
  
      Metadata:    
  
        encoder         : Lavc58.18.100 libx264    
  
      Side data:    
  
        cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1    
  
  frame=   10 fps=0.0 q=-1.0 Lsize=      20kB time=00:00:07.00 bitrate=  23.1kbits    
  
  /s speed=68.6x    
  
  video:19kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing ov    
  
  erhead: 5.046419%    
  
  [libx264 @ 0000000000611040] frame I:1     Avg QP: 4.41  size: 14084    
  
  [libx264 @ 0000000000611040] frame P:3     Avg QP:12.31  size:   632    
  
  [libx264 @ 0000000000611040] frame B:6     Avg QP:19.11  size:   436    
  
  [libx264 @ 0000000000611040] consecutive B-frames: 20.0%  0.0%  0.0% 80.0%    
  
  [libx264 @ 0000000000611040] mb I  I16..4: 89.7%  0.0% 10.3%    
  
  [libx264 @ 0000000000611040] mb P  I16..4:  0.2%  0.0%  0.1%  P16..4:  0.5% 0.1    
  
  %  0.1%  0.0%  0.0%    skip:98.9%    
  
  [libx264 @ 0000000000611040] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.6%  0.1    
  
  %  0.1%  direct: 0.0%  skip:99.1%  L0:22.0% L1:73.7% BI: 4.3%    
  
  [libx264 @ 0000000000611040] coded y,u,v intra: 6.1% 2.5% 2.6% inter: 0.2% 0.1%    
  
  0.1%    
  
  [libx264 @ 0000000000611040] i16 v,h,dc,p: 72% 25%  3%  0%    
  
  [libx264 @ 0000000000611040] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 35% 31% 20%  2%  2%    
  
  2%  3%  5%  2%    
  
  [libx264 @ 0000000000611040] Weighted P-Frames: Y:0.0% UV:0.0%    
  
  [libx264 @ 0000000000611040] ref P L0: 65.8%  1.5% 26.5%  6.1%    
  
  [libx264 @ 0000000000611040] ref B L0: 77.0% 12.7% 10.3%    
  
  [libx264 @ 0000000000611040] ref B L1: 88.6% 11.4%
  
  [libx264 @ 0000000000611040] kb/s:14.87