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• Quicktime cannot play recorded mp4 video file

  17 septembre 2014, par Dude

  I have an mp4 related issue. I’m not very good in video processing related stuff. We have a problem with an image file that is recorded via HTTP Live Streaming (HLS) from a live stream. The HLS chunks are just put together and saved on the file system as .mp4 file. This file cannot be played with Apple QuickTime. VLC can play it. But it is a requirement that it can be played with QuickTime.

  However, I found a solution that works. When I use the ffmpeg command -reset_timestamps, the video file can be played with QuickTime. I also see which parameter changes. It’s the start value.

  Before use of the command :

  ffmpeg -i recorded_file.mp4 
  
  
  
  ffmpeg version 1.2.4 Copyright (c) 2000-2013 the FFmpeg developers
  
    built on Jan  9 2014 09:06:57 with Apple LLVM version 5.0 (clang-500.2.79) (based on LLVM 3.3svn)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/1.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-nonfree --enable-hardcoded-tables --enable-avresample --enable-vda --cc=clang --host-cflags= --host-ldflags= --enable-libx264 --enable-libfaac --enable-libmp3lame --enable-libxvid
  
    libavutil      52. 18.100 / 52. 18.100
  
    libavcodec     54. 92.100 / 54. 92.100
  
    libavformat    54. 63.104 / 54. 63.104
  
    libavdevice    54.  3.103 / 54.  3.103
  
    libavfilter     3. 42.103 /  3. 42.103
  
    libswscale      2.  2.100 /  2.  2.100
  
    libswresample   0. 17.102 /  0. 17.102
  
    libpostproc    52.  2.100 / 52.  2.100
  
  [h264 @ 0x7fcb7b015a00] non-existing SPS 2 referenced in buffering period
  
  [h264 @ 0x7fcb7b015a00] non-existing PPS referenced
  
  [h264 @ 0x7fcb7b015a00] non-existing SPS 2 referenced in buffering period
  
  [h264 @ 0x7fcb7b015a00] non-existing PPS 0 referenced
  
  [h264 @ 0x7fcb7b015a00] decode_slice_header error
  
  [h264 @ 0x7fcb7b015a00] no frame!
  
  [mpegts @ 0x7fcb7b004a00] max_analyze_duration 5000000 reached at 5013333 microseconds
  
  [mpegts @ 0x7fcb7b004a00] Could not find codec parameters for stream 2 (Unknown: none ([21][0][0][0] / 0x0015)): unknown codec
  
  Consider increasing the value for the 'analyzeduration' and 'probesize' options
  
  [NULL @ 0x7fcb7b016a00] start time is not set in estimate_timings_from_pts
  
  Input #0, mpegts, from 'recorded_file.mp4':
  
    Duration: 00:25:00.01, start: 68428.580933, bitrate: 1499 kb/s
  
    Program 1 
  
      Stream #0:0[0x100]: Video: h264 (High) ([27][0][0][0] / 0x001B), yuv420p, 1024x576 [SAR 1:1 DAR 16:9], 25 fps, 25 tbr, 90k tbn, 50 tbc
  
      Stream #0:1[0x101]: Audio: aac ([15][0][0][0] / 0x000F), 48000 Hz, stereo, fltp, 124 kb/s
  
      Stream #0:2[0x102]: Unknown: none ([21][0][0][0] / 0x0015)

  Use of the command :

  ffmpeg -i recorded_file.mp4 output.mp4 -reset_timestamps 0

  File after use of the command :

  ffmpeg -i output.mp4 
  
  
  
  ffmpeg version 1.2.4 Copyright (c) 2000-2013 the FFmpeg developers
  
    built on Jan  9 2014 09:06:57 with Apple LLVM version 5.0 (clang-500.2.79) (based on LLVM 3.3svn)
  
    configuration: --prefix=/usr/local/Cellar/ffmpeg/1.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-nonfree --enable-hardcoded-tables --enable-avresample --enable-vda --cc=clang --host-cflags= --host-ldflags= --enable-libx264 --enable-libfaac --enable-libmp3lame --enable-libxvid
  
    libavutil      52. 18.100 / 52. 18.100
  
    libavcodec     54. 92.100 / 54. 92.100
  
    libavformat    54. 63.104 / 54. 63.104
  
    libavdevice    54.  3.103 / 54.  3.103
  
    libavfilter     3. 42.103 /  3. 42.103
  
    libswscale      2.  2.100 /  2.  2.100
  
    libswresample   0. 17.102 /  0. 17.102
  
    libpostproc    52.  2.100 / 52.  2.100
  
  Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'output.mp4':
  
    Metadata:
  
      major_brand     : isom
  
      minor_version   : 512
  
      compatible_brands: isomiso2avc1mp41
  
      encoder         : Lavf54.63.104
  
    Duration: 00:25:00.04, start: 0.021333, bitrate: 1358 kb/s
  
      Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p, 1024x576 [SAR 1:1 DAR 16:9], 1224 kb/s, 25 fps, 25 tbr, 12800 tbn, 50 tbc
  
      Metadata:
  
        handler_name    : VideoHandler
  
      Stream #0:1(und): Audio: aac (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 127 kb/s
  
      Metadata:
  
        handler_name    : SoundHandler

  A lot of the text output changes but the line with the duration, start and bitrate is interesting, as the start value is set to near zero.

  Duration: 00:25:00.04, start: 0.021333, bitrate: 1358 kb/s

  So to use this solution would be enough. The problem is, I cannot use ffmpeg in the solution that downloads the file (it’s for Android). So I try to find a way to do this in Java. However, as I have not much experience in Video processing, I can’t figure out, what this ffmpeg command does and therefore don’t know in which direction to look for a Java solution.

  Is there a library that may do the same operation in Java or is there an easy way to do it myself in Java (like adjusting the file timestamp) ?

  Thanks for you help !

  EDIT : The whole console output is now shown, instead of just a part of it.

• What is Audience Segmentation ? The 5 Main Types & Examples

  16 novembre 2023, par Erin — Analytics Tips

  The days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.

  They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.

  In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  

  Audience segmentation inherently makes a lot of sense. Consider this : an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. They would run a golf club ad in a golf magazine, not in the national newspaper.

  How narrow you can make your audience segments by leveraging multiple data points has changed.

  Why audience segmentation matters

  In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.

  

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.

  If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask : “What ? Why would you think I need that ?” Then they’d promptly opt out of your email marketing campaigns.

  To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.

  5 key types of audience segmentation

  To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation : demographic, behavioural, psychographic, technographic and transactional.

  

  Demographic segmentation 

  Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.

  The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.

  Potential data points :

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation :

  A clothing brand targeting diverse locations needs to account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, in warmer climates, promoting lightweight or summer attire could be more effective. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :

  

  Each collection is featured differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.

  Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.

  This is a great way to segment ethically and without the need of data-mining companies.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with your website or app.

  You use various data points to segment your target audience based on their actions.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Goal completion (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency :

  One effective method involves using a web analytics tool such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions. 

  

  For instance, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation is when you segment audiences based on your interpretation of their personality or preferences.

  Potential data points :

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation :

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. But to rugby athletes, the ad is very relevant.

  

  If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.

  Technographic segmentation

  Technographic segmentation is when you single out specific parts of your audience based on which hardware or software they use.

  Potential data points :

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience :

  Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.

  When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • X days since the last purchase of a consumable product

  Example of effective transactional segmentation :

  A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.

  If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.

  B2B-specific : Firmographic segmentation

  Beyond the five main segmentation types, B2B marketers often use “firmographic” factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points :

  • Company size
  • Number of employees
  • Company industry
  • Geographic location (office)

  Example of effective firmographic segmentation :

  Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).

  Start segmenting and analysing your audience more deeply with Matomo

  Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.

  But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.

  Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.

• Revision 28930 : on bouge

  31 mai 2009, par ben.spip@… — Log

  on bouge