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• 10 Customer Segments Examples and Their Benefits

  9 mai 2024, par Erin

  Now that companies can segment buyers, the days of mass marketing are behind us. Customer segmentation offers various benefits for marketing, content creation, sales, analytics teams and more. Without customer segmentation, your personalised marketing efforts may fall flat. 

  According to the Twilio 2023 state of personalisation report, 69% of business leaders have increased their investment in personalisation. There’s a key reason for this — customer retention and loyalty directly benefit from personalisation. In fact, 62% of businesses have cited improved customer retention due to personalisation efforts. The numbers don’t lie. 

  Keep reading to learn how customer segments can help you fine-tune your personalised marketing campaigns. This article will give you a better understanding of customer segmentation and real-world customer segment examples. You’ll leave with the knowledge to empower your marketing strategies with effective customer segmentation. 

  What are customer segments ?

  Customer segments are distinct groups of people or organisations with similar characteristics, needs and behaviours. Like different species of plants in a garden, each customer segment has specific needs and care requirements. Customer segments are useful for tailoring personalised marketing campaigns for specific groups.

  Personalised marketing has been shown to have significant benefits — with 56% of consumers saying that a personalised experience would make them become repeat buyers. 

  Successful marketing teams typically focus on these types of customer segmentation :

  

  1. Geographic segmentation : groups buyers based on their physical location — country, city, region or climate — and language.
  2. Purchase history segmentation : categorises buyers based on their purchasing habits — how often they make purchases — and allows brands to distinguish between frequent, occasional and one-time buyers. 
  3. Product-based segmentation : groups buyers according to the products they prefer or end up purchasing. 
  4. Customer lifecycle segmentation : segments buyers based on where they are in the customer journey. Examples include new, repeat and lapsed buyers. This segmentation category is also useful for understanding the behaviour of loyal buyers and those at risk of churning. 
  5. Technographic segmentation : focuses on buyers’ technology preferences, including device type, browser type, and operating system. 
  6. Channel preference segmentation : helps us understand why buyers prefer to purchase via specific channels — whether online channels, physical stores or a combination of both. 
  7. Value-based segmentation : categorises buyers based on their average purchase value and sensitivity to pricing, for example. This type of segmentation can provide insights into the behaviours of price-conscious buyers and those willing to pay premium prices. 

  Customer segmentation vs. market segmentation

  Customer segmentation and market segmentation are related concepts, but they refer to different aspects of the segmentation process in marketing. 

  Market segmentation is the broader process of dividing the overall market into homogeneous groups. Market segmentation helps marketers identify different groups based on their characteristics or needs. These market segments make it easier for businesses to connect with new buyers by offering relevant products or new features. 

  On the other hand, customer segmentation is used to help you dig deep into the behaviour and preferences of your current customer base. Marketers use customer segmentation insights to create buyer personas. Buyer personas are essential for ensuring your personalised marketing efforts are relevant to the target audience. 

  10 customer segments examples

  Now that you better understand different customer segmentation categories, we’ll provide real-world examples of how customer segmentation can be applied. You’ll be able to draw a direct connection between the segmentation category or categories each example falls under.

  One thing to note is that you’ll want to consider privacy and compliance when you are considering collecting and analysing types of data such as gender, age, income level, profession or personal interests. Instead, you can focus on these privacy-friendly, ethical customer segmentation types :

  1. Geographic location (category : geographic segmentation)

  The North Face is an outdoor apparel and equipment company that relies on geographic segmentation to tailor its products toward buyers in specific regions and climates. 

  For instance, they’ll send targeted advertisements for insulated jackets and snow gear to buyers in colder climates. For folks in seasonal climates, The North Face may send personalised ads for snow gear in winter and ads for hiking or swimming gear in summer. 

  The North Face could also use geographic segmentation to determine buyers’ needs based on location. They can use this information to send targeted ads to specific customer segments during peak ski months to maximise profits.

  2. Preferred language (category : geographic segmentation)

  Your marketing approach will likely differ based on where your customers are and the language they speak. So, with that in mind, language may be another crucial variable you can introduce when identifying your target customers. 

  Language-based segmentation becomes even more important when one of your main business objectives is to expand into new markets and target international customers — especially now that global reach is made possible through digital channels. 

  Coca-Cola’s “Share a Coke” is a multi-national campaign with personalised cans and bottles featuring popular names from countries around the globe. It’s just one example of targeting customers based on language.

  3. Repeat users and loyal customers (category : customer lifecycle segmentation)

  Sephora, a large beauty supply company, is well-known for its Beauty Insider loyalty program. 

  It segments customers based on their purchase history and preferences and rewards their loyalty with gifts, discounts, exclusive offers and free samples. And since customers receive personalised product recommendations and other perks, it incentivises them to remain members of the Beauty Insider program — adding a boost to customer loyalty.

  By creating a memorable customer experience for this segment of their customer base, staying on top of beauty trends and listening to feedback, Sephora is able to keep buyers coming back.

  

  4. New customers (category : customer lifecycle segmentation)

  Subscription services use customer lifecycle segmentation to offer special promotions and trials for new customers. 

  HBO Max is a great example of a real company that excels at this strategy : 

  They offer 40% savings on an annual ad-free plan, which targets new customers who may be apprehensive about the added monthly cost of a recurring subscription.

  This marketing strategy prioritises fostering long-term customer relationships with new buyers to avoid high churn rates. 

  5. Cart abandonment (category : purchase history segmentation)

  With a rate of 85% among US-based mobile users, cart abandonment is a huge issue for ecommerce businesses. One way to deal with this is to segment inactive customers and cart abandoners — those who showed interest by adding products to their cart but haven’t converted yet — and send targeted emails to remind them about their abandoned carts.

  E-commerce companies like Ipsy, for example, track users who have added items to their cart but haven’t followed through on the purchase. The company’s messaging often contains incentives — like free shipping or a limited-time discount — to encourage passive users to return to their carts. 

  Research has found that cart abandonment emails with a coupon code have a high 44.37% average open rate. 

  6. Website activity (category : technographic segmentation)

  It’s also possible to segment customers based on website activity. Now, keep in mind that this is a relatively broad approach ; it covers every interaction that may occur while the customer is browsing your website. As such, it leaves room for many different types of segmentation. 

  For instance, you can segment your audience based on the pages they visited, the elements they interacted with — like CTAs and forms — how long they stayed on each page and whether they added products to their cart. 

  Matomo’s Event Tracking can provide additional context to each website visit and tell you more about the specific interactions that occur, making it particularly useful for segmenting customers based on how they spend their time on your website. 

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  Amazon segments its customers based on browsing behaviour — recently viewed products and categories, among other things — which, in turn, allows them to improve the customer’s experience and drive sales.

  7. Traffic source (category : channel segmentation) 

  You can also segment your audience based on traffic sources. For example, you can determine if your website visitors arrived through Google and other search engines, email newsletters, social media platforms or referrals. 

  In other words, you’ll create specific audience segments based on the original source. Matomo’s Acquisition feature can provide insights into five different types of traffic sources — search engines, social media, external websites, direct traffic and campaigns — to help you understand how users enter your website.

  You may find that most visitors arrive at your website through social media ads or predominantly discover your brand through search engines. Either way, by learning where they’re coming from, you’ll be able to determine which conversion paths you should prioritise and optimise further. 

  8. Device type (category : technographic segmentation)

  Device type is customer segmentation based on the devices that potential customers may use to access your website and view your content. 

  It’s worth noting that, on a global level, most people (96%) use mobile devices — primarily smartphones — for internet access. So, there’s a high chance that most of your website visitors are coming from mobile devices, too. 

  However, it’s best not to assume anything. Matomo can detect the operating system and the type of device — desktop, mobile device, tablet, console or TV, for example. 

  By introducing the device type variable into your customer segmentation efforts, you’ll be able to determine if there’s a preference for mobile or desktop devices. In return, you’ll have a better idea of how to optimise your website — and whether you should consider developing an app to meet the needs of mobile users.

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  9. Browser type (category : technographic segmentation)

  Besides devices, another type of segmentation that belongs to the technographic category and can provide valuable insights is browser-related. In this case, you’re tracking the internet browser your customers use. 

  Many browser types are available — including Google Chrome, Microsoft Edge, Safari, Firefox and Brave — and each may display your website and other content differently. 

  So, keeping track of your customers’ preferred choices is important. Otherwise, you won’t be able to fully understand their online experience — or ensure that these browsers are displaying your content properly. 

  

  10. Ecommerce activity (category : purchase history, value based, channel or product based segmentation) 

  Similar to website activity, looking at ecommerce activity can tell your sales teams more about which pages the customer has seen and how they have interacted with them. 

  With Matomo’s Ecommerce Tracking, you’ll be able to keep an eye on customers’ on-site behaviours, conversion rates, cart abandonment, purchased products and transaction data — including total revenue and average order value.

  Considering that the focus is on sales channels — such as your online store — this approach to customer segmentation can help you improve the sales experience and increase profitability. 

  Start implementing these customer segments examples

  With ever-evolving demographics and rapid technological advancements, customer segmentation is increasingly complex. The tips and real-world examples in this article break down and simplify customer segmentation so that you can adapt to your customer base. 

  Customer segmentation lays the groundwork for your personalised marketing campaigns to take off. By understanding your users better, you can effectively tailor each campaign to different segments. 

  If you’re ready to see how Matomo can elevate your personalised marketing campaigns, try it for free for 21 days. No credit card required.

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• FFmpeg "Non-monotonous DTS in output stream" error when processing video from Safari's MediaRecorder

  17 juillet 2024, par Hackermon

  I'm recording a video stream in Safari with MediaRecorder, then sending it to a remote server which then uses ffmpeg to reencode the video. When reencoding with FFmpeg, I get a lot of warnings and the final video is broken, frame are glitching and out of sync but the audio sounds fine.

  


  Here's my MediaRecorder script :

  


  const camera = await navigator.mediaDevices.getUserMedia({ audio: true, video: true });
const recorder = new MediaRecorder(camera, {
        mimeType: 'video/mp4', // Safari only supports MP4
        bitsPerSecond: 1_000_000,
});

recorder.ondataavailable = async ({ data: blob }) => {
        // open contents in new tab
        var fileURL = URL.createObjectURL(file);
         window.open(fileURL, '_blank');
};

recorder.start();
setTimeout(() => recorder.stop(), 5000);


  


  I download the video blob from Safari and use this command to reencode it :

  


  ffmpeg -i ./blob.mp4 -preset ultrafast -strict -2 -threads 10 -c copy ./output.mp4


  


  Logs :

  


  ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
[mov,mp4,m4a,3gp,3g2,mj2 @ 0x559826616000] DTS 29 < 313 out of order
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from './chunk1.mp4':
  Metadata:
    major_brand     : iso5
    minor_version   : 1
    compatible_brands: isomiso5hlsf
    creation_time   : 2024-07-17T14:30:47.000000Z
  Duration: 00:00:01.00, start: 0.000000, bitrate: 3937 kb/s
    Stream #0:0(und): Video: h264 (Baseline) (avc1 / 0x31637661), yuv420p(progressive), 640x480 [SAR 1:1 DAR 4:3], 6218 kb/s, 33.36 fps, 600 tbr, 600 tbn, 1200 tbc (default)
    Metadata:
      creation_time   : 2024-07-17T14:30:47.000000Z
      handler_name    : Core Media Video
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 420 kb/s (default)
    Metadata:
      creation_time   : 2024-07-17T14:30:47.000000Z
      handler_name    : Core Media Audio
File './safari3.mp4' already exists. Overwrite ? [y/N] Output #0, mp4, to './safari3.mp4':
  Metadata:
    major_brand     : iso5
    minor_version   : 1
    compatible_brands: isomiso5hlsf
    encoder         : Lavf58.29.100
    Stream #0:0(und): Video: h264 (Baseline) (avc1 / 0x31637661), yuv420p(progressive), 640x480 [SAR 1:1 DAR 4:3], q=2-31, 6218 kb/s, 33.36 fps, 600 tbr, 19200 tbn, 600 tbc (default)
    Metadata:
      creation_time   : 2024-07-17T14:30:47.000000Z
      handler_name    : Core Media Video
    Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 44100 Hz, mono, fltp, 420 kb/s (default)
    Metadata:
      creation_time   : 2024-07-17T14:30:47.000000Z
      handler_name    : Core Media Audio
Stream mapping:
  Stream #0:0 -> #0:0 (copy)
  Stream #0:1 -> #0:1 (copy)
Press [q] to stop, [?] for help
[mp4 @ 0x559826644340] Non-monotonous DTS in output stream 0:0; previous: 10016, current: 928; changing to 10017. This may result in incorrect timestamps in the output file.
[mp4 @ 0x559826644340] Non-monotonous DTS in output stream 0:0; previous: 10017, current: 1568; changing to 10018. This may result in incorrect timestamps in the output file.
[mp4 @ 0x559826644340] Non-monotonous DTS in output stream 0:0; previous: 10018, current: 2208; changing to 10019. This may result in incorrect timestamps in the output file.
...x100
frame=  126 fps=0.0 q=-1.0 Lsize=     479kB time=00:00:00.97 bitrate=4026.2kbits/s speed= 130x    
video:425kB audio:51kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.652696%


  


  Not sure what's happening or how to fix it. This issue only happens in Safari, videos from Chrome are perfectly fine.

  


  I've tried various flags :

  


  -fflags +igndts
-bsf:a aac_adtstoasc
-c:v libvpx-vp9 -c:a libopus
etc


  


  None of them seem to fix the issue.

  


• python subprocess ffmpeg return code = 69

  13 juin 2023, par Tim Chen

  I try to call ffmpeg through the subprocess.run(['ffmpeg', '-i', file_name, output_file_name], capture_output=True, text=True) command in python to convert the audio file incoming from the front end to wav format file. The backend code is as follows, using python+fastapi :

  


  @app.post("/api/upload/convert")
async def convert_upload_file(request: Request, file: UploadFile = File(...)):
    token = uuid.uuid4().hex
    tmpFileName = os.path.join(os.path.dirname(__file__), token)
    with open(tmpFileName, "wb") as buffer:
        buffer.write(await file.read())
    await file.seek(0)
    output_path = tmpFileName + '-output.wav'
    command = ['ffmpeg', '-i', tmpFileName, output_path]
    result = subprocess.run(command, capture_output=True, text=True)


  


  This code usually works, but there are some scenarios where it doesn't work. The audio file is recorded by js code (specifically navigator.mediaDevices.getUserMedia({audio: true})).
The code of the audio recorded in windows chrome can run normally and get the converted wav file, but the audio recorded from ios15 safari for more than 3 seconds cannot be converted, prompting returncode=69. The error message is as follows :

  


  CompletedProcess(args=['ffmpeg', '-i', '5cfb52c503a646bda0f422b517c8014a', '5cfb52c503a646bda0f422b517c8014a-output.wav'], returncode=69, stdout='', stderr="
ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers
built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)
configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
libavutil      56. 70.100 / 56. 70.100
libavcodec     58.134.100 / 58.134.100
libavformat    58. 76.100 / 58. 76.100
libavdevice    58. 13.100 / 58. 13.100
libavfilter     7.110.100 /  7.110.100
libswscale      5.  9.100 /  5.  9.100
libswresample   3.  9.100 /  3.  9.100
libpostproc    55.  9.100 / 55.  9.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '5cfb52c503a646bda0f422b517c8014a':
  Metadata:
    major_brand     : iso5
    minor_version   : 1
    compatible_brands: isomiso5hlsf
    creation_time   : 2023-06-11T16:36:53.000000Z
  Duration: 00:00:07.06, start: 0.000000, bitrate: 187 kb/s
  Stream #0:0(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, mono, fltp, 184 kb/s (default)
    Metadata:
      creation_time   : 2023-06-11T16:36:53.000000Z
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]
Stream mapping:
  Stream #0:0 -> #0:0 (aac (native) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '5cfb52c503a646bda0f422b517c8014a-output.wav':
  Metadata:
    major_brand     : iso5
    minor_version   : 1
    compatible_brands: isomiso5hlsf
    ISFT            : Lavf58.76.100
  Stream #0:0(und): Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s (default)
    Metadata:
      creation_time   : 2023-06-11T16:36:53.000000Z
      handler_name    : Core Media Audio
      vendor_id       : [0][0][0][0]
      encoder         : Lavc58.134.100 pcm_s16le
size=       2kB time=00:00:00.00 bitrate=N/A speed=N/A    
[aac @ 0x55f1f8f19fc0] Sample rate index in program config element does not match the sample rate index configured by the container.
[aac @ 0x55f1f8f19fc0] Too large remapped id is not implemented. Update your FFmpeg version to the newest one from Git. If the problem still occurs, it means that your file has a feature which has not been implemented.
[aac @ 0x55f1f8f19fc0] If you want to help, upload a sample of this file to https://streams.videolan.org/upload/ and contact the ffmpeg-devel mailing list. (ffmpeg-devel@ffmpeg.org)
Error while decoding stream #0:0: Not yet implemented in FFmpeg, patches welcome
[aac @ 0x55f1f8f19fc0] Multiple frames in a packet.
[aac @ 0x55f1f8f19fc0] Reserved bit set.
[aac @ 0x55f1f8f19fc0] Number of bands (18) exceeds limit (13).
Error while decoding stream #0:0: Invalid data found when processing input
[aac @ 0x55f1f8f19fc0] Reserved bit set.
[aac @ 0x55f1f8f19fc0] Prediction is not allowed in AAC-LC.
Error while decoding stream #0:0: Invalid data found when processing input
[aac @ 0x55f1f8f19fc0] Reserved bit set.


  


  For the abnormal code, I tried to execute ffmpeg -i input output.wav after fastapi handle request on the command line and subprocess.run(['ffmpeg', '-i', file_name, output_path], capture_output =True, text=True), all succeeded, which means that the final file must be normal, otherwise the subsequent verification work will get the same error.

  


  This confuses me, is there some information I'm missing ?