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• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

  The deeper you go with your customer analytics, the better your insights will be.

  The result ? Your marketing performance soars to new heights.

  Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

  By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

  In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

  What is customer segmentation ?

  Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general. 

  Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

  These groups could be based on demographics like age or location or behaviours like recent purchases or website visits. 

  By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

  Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

  But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

  What is customer segmentation analytics ?

  Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

  

  With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

  One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

  In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country. 

  For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

  The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

  Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

  By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

  5 types of segmentation

  There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

  While you can segment your customers in almost endless ways, five common types the majority fall under are :

  

  Geographic

  Another way to segment is by geography.

  This is important because you could have drastically different interests, pain points and desires based on where you live.

  If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

  For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

  Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

  Geographic segmentation typically includes the following subtypes :

  • Cities (i.e., Austin, Paris, Berlin, etc.)
  • State (i.e., Massachusetts)
  • Country (i.e., Thailand)

  Psychographic

  Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

  Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

  While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

  Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons. 

  Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

  By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

  Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

  While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

  Technographic segmentation typically includes the following subtypes :

  • Smartphone type
  • Device type : smartphone, desktop, tablet
  • Apps
  • Video games

  Demographic

  The most common approach to segmentation is to split your customers up by demographics. 

  Demographic segmentation typically includes subtypes like language, job title, age or education.

  This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

  However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

  How to build website segmentation analytics

  With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

  • The device they used while visiting your site
  • What channel they entered your site from
  • What country they are located
  • Whether or not they visited a key page of your website
  • And more

  While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

  For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :

  

  The criteria you use to define these segments are based on the data collected within your web analytics platform.

  Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

  • Do returning visitors show different traits than first-time visitors ?
  • Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

  This information can inform your content strategy, user interface design and marketing efforts.

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

  This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

  • Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
  • Device type (i.e., smartphone, tablet, desktop)

  This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

  • Events (i.e., when someone clicks a specific URL on your website)
  • Goals (i.e., when someone stays on your site for a certain period)

  Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

  • Visitors arriving from specific traffic sources
  • Visitors arriving from specific advertising campaigns

  With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

  • Visitors who purchased vs. those who didn’t
  • Visitors who purchased a specific product

  This allows you to refine your website and marketing strategy for increased conversions and revenue.

  Leverage Matomo for your segmentation analytics

  By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

  If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

  Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.

• JavaCV Create Compatible MP4

  15 juin 2017, par Hamish258

  Trying to modify JavaCV 3.2.0 sample https://github.com/bytedeco/javacv/blob/master/samples/WebcamAndMicrophoneCapture.java, to produce an mp4 that quicktime on macOS 10.12.5 can use. Output mp4 works fine with VLC but for the software I’m producing I’d like to minimise users having to install additional products.

  The sample code produces the following output

  [libx264 @ 0x7f927794e200] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7f927794e200] profile Constrained Baseline, level 3.1
[libx264 @ 0x7f927794e200] 264 - core 148 - H.264/MPEG-4 AVC codec - Copyleft 2003-2016 - http://www.videolan.org/x264.html - options: cabac=0 ref=1 deblock=0:0:0 analyse=0:0 me=dia subme=0 psy=1 psy_rd=1.00:0.00 mixed_ref=0 me_range=16 chroma_me=1 trellis=0 8x8dct=0 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=0 threads=4 lookahead_threads=4 sliced_threads=1 slices=4 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=60 keyint_min=6 scenecut=0 intra_refresh=0 rc=crf mbtree=0 crf=28.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=0
Output #0, mp4, to 'alatest.mp4':
 Metadata:
   encoder         : Lavf57.56.100
   Stream #0:0: Video: h264 (Constrained Baseline) ([33][0][0][0] / 0x0021), yuv420p, 1280x720, q=2-31, 2000 kb/s, 15360 tbn
   Stream #0:1: Audio: aac (LC) ([64][0][0][0] / 0x0040), 44100 Hz, stereo fltp, 192 kb/s

  The constrained baseline to 3.1 should produce usable mp4 according to https://trac.ffmpeg.org/wiki/Encode/H.264

  If I use the command line ffmpeg -i alatest.mp4 -pix_fmt yuv420p alantest.mp4 it produces a usable mo4, even though as you can see above yuv420p is the pixel format in use so there’s something else I don’t understand, the code snippet from the sample link is :

     FFmpegFrameRecorder recorder = new FFmpegFrameRecorder("alatest.mp4",1280, 720, 2);
   recorder.setInterleaved(true);
   recorder.setVideoOption("tune", "zerolatency");
   recorder.setVideoOption("preset", "ultrafast");
   recorder.setVideoOption("crf", "28");
   recorder.setVideoBitrate(2000000);
   recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
   recorder.setPixelFormat(avutil.AV_PIX_FMT_YUV420P);
   recorder.setFormat("flv");
   recorder.setFrameRate(30);
   recorder.setGopSize(60);

  The command line ffmpeg is displaying

  [libx264 @ 0x7ffca4019400] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX
[libx264 @ 0x7ffca4019400] profile High, level 3.1
[libx264 @ 0x7ffca4019400] 264 - core 148 - H.264/MPEG-4 AVC codec - Copyleft 2003-2016 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=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=25 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 'alantest.mp4':
 Metadata:
   encoder         : Lavf57.71.100
   Stream #0:0: Video: h264 (libx264) ([33][0][0][0] / 0x0021), yuv420p, 1280x720, q=-1--1, 30 fps, 15360 tbn, 30 tbc
   Metadata:
     encoder         : Lavc57.89.100 libx264
   Side data:
     cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
   Stream #0:1: Audio: aac (LC) ([64][0][0][0] / 0x0040), 44100 Hz, stereo, fltp, 128 kb/s
   Metadata:
     encoder         : Lavc57.89.100 aac
frame=  179 fps= 54 q=-1.0 Lsize=     570kB time=00:00:05.94 bitrate= 786.2kbits/s dup=93 drop=0 speed= 1.8x
video:470kB audio:93kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.341890%

  This is using a High profile and I see q being set differently and tbc. I can’t figure out how to mimic this behaviour in JavaCV, all help greatly appreciated !

• Introducing WebM, an open web media project

  19 mai 2010, par noreply@blogger.com (christosap)

  A key factor in the web’s success is that its core technologies such as HTML, HTTP, TCP/IP, etc. are open and freely implementable. Though video is also now core to the web experience, there is unfortunately no open and free video format that is on par with the leading commercial choices. To that end, we are excited to introduce WebM, a broadly-backed community effort to develop a world-class media format for the open web.

  WebM includes :
  

  • VP8, a high-quality video codec we are releasing today under a BSD-style, royalty-free license
  • Vorbis, an already open source and broadly implemented audio codec
  • a container format based on a subset of the Matroska media container

  The team that created VP8 have been pioneers in video codec development for over a decade. VP8 delivers high quality video while efficiently adapting to the varying processing and bandwidth conditions found on today’s broad range of web-connected devices. VP8’s efficient bandwidth usage will mean lower serving costs for content publishers and high quality video for end-users. The codec’s relative simplicity makes it easy to integrate into existing environments and requires less manual tuning to produce high quality results. These existing attributes and the rapid innovation we expect through the open-development process make VP8 well suited for the unique requirements of video on the web.

  A developer preview of WebM and VP8, including source code, specs, and encoding tools is available today at www.webmproject.org.

  We want to thank the many industry leaders and web community members who are collaborating on the development of WebM and integrating it into their products. Check out what Mozilla, Opera, Google Chrome, Adobe, and many others below have to say about the importance of WebM to the future of web video.

  
  

  AMD

  ARM

  Brightcove

  Broadcom

  Collabora

  Digital Rapids

  Encoding.com

  Grab Networks

  iLinc

  INLET

  Kaltura

  Logitech

  MIPS

  Nvidia

  Ooyala

  Qualcomm

  Skype

  Sorenson

  Telestream
  

  Texas Instruments

  Verisilicon

  ViewCast

  Wildform

  
  

  Posted by Jeremy Doig, Engineering Director of video and Mike Jazayeri, Group Product Manager, Google