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• Raspberry Pi 4 live streaming with ffmpeg [closed]

  12 décembre 2019, par Berri

  So speedify created a blog post and youtube video about making an IRL streaming backpack using the Elgato Cam Link 4k, Raspberry Pi 4, and ffmpeg.

  They gave pretty detailed instructions, and included downloads to prebuilt scripts/commands to get it all running once put together.
  Blog post :
  https://speedify.com/blog/how-to/build-irl-streaming-backpack-complete-guide/

  ffmpeg command from post :

  ffmpeg_command = “/home/pi/bin/ffmpeg -nostdin -re -f v4l2 -s ‘1280×720’ -framerate 24 -i /dev/video0 -f alsa -ac 2 -i hw:CARD=Link,DEV=0 -vcodec libx264 -framerate 24 -rtbufsize 1500k -s 1280×720 -preset ultrafast -pix_fmt yuv420p -crf 17 -force_key_frames ‘expr:gte(t,n_forced*2)’ -minrate 850k -maxrate 1000k -b:v 1000k -bufsize 1000k -acodec libmp3lame -rtbufsize 1500k -b 96k -ar 44100 -f flv – | ffmpeg -f flv -i – -c copy -f flv -drop_pkts_on_overflow 1 -attempt_recovery 1 -recovery_wait_time 1 rtmp://live.twitch.tv/app/live_” + streamKey + “‘\n” 

  I replaced -i hw:card=link,dev=0 in that command with -i hw:2,0 because -i hw:card=link,dev=0 gave me "file does not exist" errors in the log. "streamkey" is filled with the appropriate key for my twitch.

  Github Resources + Instructions used :
  https://github.com/speedify/rpi-streaming-experiment

  I’m using all the exact same hardware as outlined in the post, and have gotten everything installed correctly as far as I can tell.
  But when I go to run the ffmpeg command, it seems like nothing actually gets sent over to twitch correctly.

  The log after trying to run it looks like this.
  If anybody has any insight as to what may be going wrong, it would be greatly appreciated.

  Starting ffmpeg
  
  ffmpeg version N-95970-gd5274f8 Copyright (c) 2000-2019 the FFmpeg developers
  
  built with gcc 8 (Raspbian 8.3.0-6+rpi1)  
  
  configuration: --prefix=/home/pi/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/pi/ffmpeg_build/include --extra-ldflags=-L/home/pi/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/pi/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-nonfree  
  
  libavutil 56. 36.101 / 56. 36.101  
  
  libavcodec 58. 64.101 / 58. 64.101  
  
  ffmpeg version N-95970-gd5274f8 libavformat 58. 35.101 / 58. 35.101  
  
  Copyright (c) 2000-2019 the FFmpeg developers libavdevice 58. 9.101 / 58. 9.101  
  
  libavfilter 7. 67.100 / 7. 67.100  
  
  built with gcc 8 (Raspbian 8.3.0-6+rpi1)  
  
  libswscale 5. 6.100 / 5. 6.100  
  
  configuration: --prefix=/home/pi/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/pi/ffmpeg_build/include --extra-ldflags=-L/home/pi/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/pi/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-nonfree  
  
  libswresample 3. 6.100 / 3. 6.100  
  
  libpostproc 55. 6.100 / 55. 6.100  
  
  libavutil 56. 36.101 / 56. 36.101  
  
  libavcodec 58. 64.101 / 58. 64.101  
  
  libavformat 58. 35.101 / 58. 35.101  
  
  libavdevice 58. 9.101 / 58. 9.101  
  
  libavfilter 7. 67.100 / 7. 67.100  
  
  libswscale 5. 6.100 / 5. 6.100  
  
  libswresample 3. 6.100 / 3. 6.100  
  
  libpostproc 55. 6.100 / 55. 6.100  
  
  [video4linux2,v4l2 @ 0x2aac5e0] The V4L2 driver changed the video from 1280x720 to 1920x1080  
  
  [video4linux2,v4l2 @ 0x2aac5e0] The driver changed the time per frame from 1/24 to 117/7013  
  
  [video4linux2,v4l2 @ 0x2aac5e0] Dequeued v4l2 buffer contains 4147200 bytes, but 3110400 were expected. Flags: 0x00012001.  
  
  Input #0, video4linux2,v4l2, from '/dev/video0':  
  
  Duration: N/A, start: 4683.201589, bitrate: 1491503 kb/s  
  
  Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 1920x1080, 1491503 kb/s, 59.94 fps, 59.94 tbr, 1000k tbn, 1000k tbc  
  
  Guessed Channel Layout for Input Stream #1.0 : stereo  
  
  Input #1, alsa, from 'hw:2,0':  
  
  Duration: N/A, start: 1576099663.557438, bitrate: 1536 kb/s  
  
  Stream #1:0: Audio: pcm_s16le, 48000 Hz, stereo, s16, 1536 kb/s  
  
  Please use -b:a or -b:v, -b is ambiguous  
  
  Stream mapping:  
  
  Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))  
  
  Stream #1:0 -> #0:1 (pcm_s16le (native) -> mp3 (libmp3lame))  
  
  [video4linux2,v4l2 @ 0x2aac5e0] Dequeued v4l2 buffer contains 4147200 bytes, but 3110400 were expected. Flags: 0x00012001.  
  
  Last message repeated 9 times
  
  [video4linux2,v4l2 @ 0x2aac5e0] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)  
  
  [video4linux2,v4l2 @ 0x2aac5e0] Dequeued v4l2 buffer contains 4147200 bytes, but 3110400 were expected. Flags: 0x00012001.  
  
  Last message repeated 28 times  
  
  terminated script  
  
  pipe:: could not find codec parameters  
  
  Exiting normally, received signal 15.  
  
  Last message repeated 15 times  
  
  [alsa @ 0x2aaf2c0] Thread message queue blocking; consider raising the thread_queue_size option (current value: 8)  
  
  Finishing stream 0:0 without any data written to it.  
  
  [libx264 @ 0x2abee40] using cpu capabilities: ARMv6 NEON  
  
  [libx264 @ 0x2abee40] profile Constrained Baseline, level 3.2, 4:2:0, 8-bit  
  
  [libx264 @ 0x2abee40] 264 - core 158 - H.264/MPEG-4 AVC codec - Copyleft 2003-2019 - 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=6 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=0 weightp=0 keyint=250 keyint_min=25 scenecut=0 intra_refresh=0 rc_lookahead=0 rc=crf mbtree=0 crf=17.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 vbv_maxrate=1000 vbv_bufsize=1000 crf_max=0.0 nal_hrd=none filler=0 ip_ratio=1.40 aq=0  
  
  Finishing stream 0:1 without any data written to it.  
  
  Output #0, flv, to 'pipe:':  
  
  Metadata:  
  
  encoder : Lavf58.35.101  
  
  Stream #0:0: Video: h264 (libx264) ([7][0][0][0] / 0x0007), yuv420p, 1280x720, q=-1--1, 96 kb/s, 59.94 fps, 1k tbn, 59.94 tbc  
  
  Metadata:  
  
  encoder : Lavc58.64.101 libx264  
  
  Side data:  
  
  cpb: bitrate max/min/avg: 1000000/0/96000 buffer size: 1000000 vbv_delay: N/A  
  
  Stream #0:1: Audio: mp3 (libmp3lame) ([2][0][0][0] / 0x0002), 44100 Hz, stereo, s16p  
  
  Metadata:  
  
  encoder : Lavc58.64.101 libmp3lame  
  
  [flv @ 0x2abda90] Failed to update header with correct duration.  
  
  [flv @ 0x2abda90] Failed to update header with correct filesize.  
  
  Error writing trailer of pipe:: Broken pipe  
  
  frame= 0 fps=0.0 q=0.0 Lsize= 0kB time=00:00:00.00 bitrate=N/A speed= 0x
  
  video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown  
  
  Exiting normally, received signal 15.         

  This message repeats until script is terminated with the Circuit Express button. For length, many instances of this line were cut out.

  [video4linux2,v4l2 @ 0x2aac5e0] Dequeued v4l2 buffer contains 4147200
  
  bytes, but 3110400 were expected. Flags: 0x00012001.
  
  Last message repeated xx times

  Output from v4l2-ctl --list-formats-ext

  ioctl: VIDIOC_ENUM_FMT
  
      Type: Video Capture
  
  
  
      [0]: 'YUYV' (YUYV 4:2:2)
  
          Size: Discrete 1920x1080
  
              Interval: Discrete 0.017s (59.940 fps)
  
      [1]: 'NV12' (Y/CbCr 4:2:0)
  
          Size: Discrete 1920x1080
  
              Interval: Discrete 0.017s (59.940 fps)
  
      [2]: 'YU12' (Planar YUV 4:2:0)
  
          Size: Discrete 1920x1080
  
              Interval: Discrete 0.017s (59.940 fps)

  Log output after ffmpeg command modification.

  Starting ffmpeg
  
  ffmpeg version N-95970-gd5274f8 Copyright (c) 2000-2019 the FFmpeg developers
  
    built with gcc 8 (Raspbian 8.3.0-6+rpi1)
  
    configuration: --prefix=/home/pi/ffmpeg_build --pkg-config-flags=--static --extra-cflags=-I/home/pi/ffmpeg_build/include --extra-ldflags=-L/home/pi/ffmpeg_build/lib --extra-libs='-lpthread -lm' --bindir=/home/pi/bin --enable-gpl --enable-libass --enable-libfdk-aac --enable-libfreetype --enable-libmp3lame --enable-libopus --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-nonfree
  
    libavutil      56. 36.101 / 56. 36.101
  
    libavcodec     58. 64.101 / 58. 64.101
  
    libavformat    58. 35.101 / 58. 35.101
  
    libavdevice    58.  9.101 / 58.  9.101
  
    libavfilter     7. 67.100 /  7. 67.100
  
    libswscale      5.  6.100 /  5.  6.100
  
    libswresample   3.  6.100 /  3.  6.100
  
    libpostproc    55.  6.100 / 55.  6.100
  
  terminated script
  
  Input #0, video4linux2,v4l2, from '/dev/video0':
  
    Duration: N/A, bitrate: 1491503 kb/s
  
      Stream #0:0: Video: rawvideo (I420 / 0x30323449), yuv420p, 1920x1080, 1491503 kb/s, 59.94 fps, 59.94 tbr, 1000k tbn, 1000k tbc
  
  Guessed Channel Layout for Input Stream #1.0 : stereo
  
  Input #1, alsa, from 'hw:1,0':
  
    Duration: N/A, bitrate: 1536 kb/s
  
      Stream #1:0: Audio: pcm_s16le, 48000 Hz, stereo, s16, 1536 kb/s
  
  [rtmp @ 0x2605cd0] Cannot open connection tcp://live.twitch.tv:1935
  
  rtmp://live.twitch.tv/app/live: Immediate exit requested
  
  Exiting normally, received signal 15.

• cv::cudacodec::VideoReader unable to Play rtsp stream

  22 février 2018, par Pawan

  System information

  • OpenCV => 3.3.0
  • Operating System / Platform => Ubuntu 16.04, x86_64
  • Compiler => gcc version 5.4.1 20160904
  • Cuda => 8.0
  • Nvidia card => GTX 1080 Ti
  • ffmpeg details
    • libavutil 55. 74.100 / 55. 74.100
    • libavcodec 57.103.100 / 57.103.100
    • libavformat 57. 77.100 / 57. 77.100
    • libavdevice 57. 7.101 / 57. 7.101
    • libavfilter 6.100.100 / 6.100.100
    • libswscale 4. 7.103 / 4. 7.103
    • libswresample 2. 8.100 / 2. 8.100

  Detailed description

  i am trying to play a rtsp stream using cudacodec::VideoReader

  Rtsp Stream Details ( from vlc )

  

  this stream plays fine in vlc and cv::VideoCapture but when i try to play it in cudacodec::VideoReader i get a error saying :

  OpenCV Error: Gpu API call (CUDA_ERROR_FILE_NOT_FOUND [Code = 301]) in CuvidVideoSource, file /home/deep/Development/libraries/opencv/opencv/modules/cudacodec/src/cuvid_video_source.cpp, line 66

  OpenCV Error: Assertion failed (init_MediaStream_FFMPEG()) in FFmpegVideoSource, file /home/deep/Development/libraries/opencv/opencv/modules/cudacodec/src/ffmpeg_video_source.cpp, line 101

  Steps to reproduce

  #include <iostream>
  
  #include "opencv2/opencv_modules.hpp"
  
  
  
  #if defined(HAVE_OPENCV_CUDACODEC)
  
  
  
  #include <opencv2></opencv2>core.hpp>
  
  #include <opencv2></opencv2>cudacodec.hpp>
  
  #include <opencv2></opencv2>highgui.hpp>
  
  
  
  int main(int argc, const char* argv[])
  
  {
  
      const std::string fname = "rtsp://admin:admin@192.168.1.13/media/video2";
  
  
  
      cv::namedWindow("GPU", cv::WINDOW_NORMAL);
  
  
  
      cv::cuda::GpuMat d_frame;
  
      cv::Ptr d_reader = cv::cudacodec::createVideoReader(fname);
  
  
  
      for (;;)
  
      {
  
  
  
          if (!d_reader->nextFrame(d_frame))
  
              break;
  
  
  
          cv::Mat frame;
  
          d_frame.download(frame);
  
          cv::imshow("GPU", frame);
  
  
  
          if (cv::waitKey(3) > 0)
  
              break;
  
      }
  
      return 0;
  
  }
  
  
  
  #else
  
  int main()
  
  {
  
      std::cout &lt;&lt; "OpenCV was built without CUDA Video decoding support\n" &lt;&lt; std::endl;
  
      return 0;
  
  }
  
  #endif
  
  </iostream>

  I tried debugging it using GDB and saw that in ffmpeg_video_source.cpp bool init_MediaStream_FFMPEG() directly returns without checking the if condition.

  GDB output

  cv::cudacodec::detail::FFmpegVideoSource::FFmpegVideoSource 
  
  (this=0x402a20 <_start>, fname=...) at /home/deep/Development/libraries/opencv/opencv/modules/cudacodec/src/ffmpeg_video_source.cpp:98
  
  98      cv::cudacodec::detail::FFmpegVideoSource::FFmpegVideoSource(const String& fname) :
  
  (gdb) n
  
  99          stream_(0)
  
  (gdb) n
  
  101         CV_Assert( init_MediaStream_FFMPEG() );
  
  (gdb) s
  
  (anonymous namespace)::init_MediaStream_FFMPEG () at /home/deep/Development/libraries/opencv/opencv/modules/cudacodec/src/ffmpeg_video_source.cpp:94
  
  94              return initialized;
  
  (gdb) display initialized
  
  4: initialized = false
  
  (gdb) s
  
  95          }

  UPDATE :

  I have solved the problem. solution link

• Top 5 Customer Segmentation Software in 2024

  12 mars 2024, par Erin

  In marketing, we all know the importance of reaching the right customer with the right message at the right time. That’s how you cut through the noise.

  For that, you need data on your customers — even though gathering the data is not enough. You can have all the data worldwide, but that raises an ethical responsibility and the need to make sense of it.

  Enter customer segmentation software — the answer to delivering personalised customer experiences at scale. 

  This article lists some of the best customer segmentation tools currently in the market. 

  We’ll also go over the benefits of using such tools and how you can choose the best one for your business.

  Let’s get started !

  What is customer segmentation software ?

  Customer segmentation software is a tool that helps businesses analyse customer data and group them based on common characteristics like age, income, and buying habits.

  The main goal of customer segmentation is to gain deeper insights into customer behaviours and preferences. This helps create targeted marketing and product strategies that fit each group and makes it easier to predict how customers will behave in the future.

  

  Benefits of a customer segmentation software

  Understanding your customers is the cornerstone of effective marketing, and customer segmentation software plays a pivotal role in this endeavour. 

  You can deliver more targeted and relevant marketing campaigns by dividing your audience into distinct groups based on shared characteristics. 

  Specifically, here are the main benefits of using customer segmentation tools :

  • Understand your audience better : The software helps businesses group customers with common traits to better understand their preferences and behaviour.
  • Make data-driven decisions : Base your business and marketing decisions on data analytics.
  • Aid product development : Insights from segmentation analytics can guide the creation of products that meet specific customer group needs.
  • Allocate your resources efficiently : Focusing on the customer segments that generate the most revenue leads to more effective and strategic use of your marketing resources.

  Best customer segmentation software in 2024 

  In this section, we go over the top customer segmentation tools in 2024. 

  We’ll look at these tools’ key features and pros and cons.

  1. Matomo

  

  Matomo is a comprehensive web analytics tool that merges traditional web analytics, such as tracking pageviews and visitor bounce rates, with more advanced web analytics features for tracking user behaviour. 

  With robust segmentation features, users can filter website traffic based on criteria such as location and device type, enabling them to analyse specific visitor groups and their behaviour. Users can create custom segments to analyse specific groups of visitors and their behaviour.

  Presenting as the ethical alternative to Google Analytics, Matomo emphasises transparency, 100% accurate data, and compliance with privacy laws.

  Key features

  • Heatmaps and Session Recordings : Matomo provides tools that allow businesses to understand website user interactions visually. This insight is crucial for optimising user experience and increasing conversions.
  • Form Analytics : This feature in Matomo tracks how users interact with website forms, helping businesses understand user behaviour in detail and improve form design and functionality.
  • User Flow Analysis : The tool tracks the journey of a website’s visitors, highlighting the paths taken and where users drop off. This is key for optimising website structure for better user experience and more conversions.
  • A/B Testing : Businesses can use Matomo to test different versions of web pages, determining which is more effective in driving conversions.
  • Conversion Funnels : This feature allows businesses to visualise and optimise the steps customers take toward conversion, identifying areas for improvement.

  Pros 

  • Affordability : With plans starting at $19 per month, Matomo is a cost-effective solution for CRO.
  • Free support : Matomo provides free email support to all Matomo Cloud users.
  • Open-source benefits : Being open-source, Matomo offers enhanced security, privacy, customisation options, and a supportive community.
  • Hosting options : Matomo is available either as a self-hosted solution or cloud-hosted.

  Cons

  • Cost for advanced features : Access to advanced features may incur additional costs for Matomo On-Premise users, although the On-Premise solution itself is free.
  • Technical knowledge required : The self-hosted version of Matomo requires technical knowledge for effective management.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  2. Google Analytics 

  

  Google Analytics 4 (GA4) comprehensively understands website and app performance. It focuses on event-based data collection, allowing businesses to understand user interactions across platforms. 

  Similarly to Matomo, GA4 provides features that allow businesses to segment their audience based on various criteria such as demographics, behaviours, events, and more.

  Key features

  • Event-based tracking : GA4’s shift to an event-based model allows for a flexible and predictive analysis of user behaviour. This includes a detailed view of user interactions on websites and apps.
  • Machine Learning and Smarter Insights : GA4 uses machine learning to automatically detect trends, estimate purchase probabilities and provide marketing insights.
  • Google Ads integration : The integration with Google Ads in GA4 enables tracking customer interactions from first ad engagement, providing a holistic view of the customer experience across various platforms.
  • Customer-centric measurements : GA4 collects data as events, covering a wide range of user interactions and offering a comprehensive view of customer behaviour.
  • Pathing reports : GA4 introduces new pathing reports, allowing detailed user flow analysis through websites and apps.
  • Audiences and filters : GA4 allows the creation of audiences based on specific criteria and the application of filters to segment and refine data analysis.

  Pros 

  • Integration with various platforms, including Google Ads, enhances cross-platform user journey analysis.
  • GA4 has a clean reporting interface, making it easier for marketers to identify key trends and data irregularities.
  • Google Analytics has an active community with an abundance of educational resources available for users.

  Cons

  • Complexity for beginners : The wide range of features and new event-based model might overwhelm users new to analytics tools.
  • Dependence on machine learning : Reliance on machine learning for insights and predictions may require trust in the tool’s data processing and large volumes of traffic for accuracy.
  • Transition from UA to GA4 : Users familiar with Universal Analytics (UA) might find the transition to GA4 challenging due to differences in features and data models.

  3. HubSpot

  

  HubSpot is a marketing and sales software that helps businesses attract visitors and turn them into paying customers. 

  It supports various business processes, from social media posts to email marketing, sales, and customer service. HubSpot organises and tracks user interactions across different channels, providing a unified and efficient approach to customer relationship management (CRM) and customer segmentation.

  Businesses can leverage HubSpot’s customer segmentation through lists, workflows, and smart content.

  Key features

  • Integration capabilities : HubSpot offers over 1,000 integrations in its ecosystem, ensuring seamless connectivity across various marketing, sales, and service tools, which helps maintain data consistency and reduces manual efforts.
  • Segmentation and personalisation : HubSpot allows businesses to deliver personalised content and interactions based on customer behaviour and preferences, using its robust CRM features and advanced automation capabilities.

  Pros 

  • Comprehensive support : HubSpot offers a range of support options, including a knowledge base, real-time chat, and more.
  • User-friendly interface : The platform is designed for ease of use, ensuring a smooth experience even for less tech-savvy users.
  • Personalisation capabilities : HubSpot provides personalised marketing, sales and service experiences, leveraging customer data effectively.

  Cons

  • High price point : HubSpot can be expensive, especially as you scale up and require more advanced features.
  • Steep learning curve : For businesses new to such comprehensive platforms, there might be an initial learning curve to utilise its features effectively.

  4. Klaviyo

  

  Klaviyo is a marketing automation software primarily focused on email and SMS messaging for e-commerce businesses. It’s designed to personalise and optimise customer communication. 

  Klaviyo integrates with e-commerce platforms like Shopify, making it a go-to solution for online stores. Its strength lies in its ability to use customer data to deliver targeted and effective marketing campaigns.

  Key features

  • Email marketing automation : Klaviyo allows users to send automated and personalised emails based on customer behaviour and preferences. This feature is crucial for e-commerce businesses in nurturing leads and maintaining customer engagement.
  • SMS marketing : It includes SMS messaging capabilities, enabling businesses to engage customers directly through text messages.
  • Segmentation and personalisation : Klaviyo offers advanced segmentation tools that enable businesses to categorise customers based on their behaviour, preferences and purchase history, facilitating highly targeted marketing efforts.
  • Integration with e-commerce platforms : Klaviyo integrates with popular e-commerce platforms like Shopify, Magento, and WooCommerce, allowing easy data synchronisation and campaign management.

  Pros 

  • Enhanced e-commerce integration : Klaviyo’s deep integration with e-commerce platforms greatly benefits online retailers regarding ease of use and campaign effectiveness.
  • Advanced segmentation and personalisation : The platform’s strong segmentation capabilities enable businesses to tailor their marketing messages more effectively.
  • Robust automation features : Klaviyo’s automation tools are powerful and user-friendly, saving time and improving marketing efficiency.

  Cons

  • Cost : Klaviyo can be more expensive than other options in this list, particularly as you scale up and add more contacts.
  • Complexity for beginners : The platform’s wide range of features and advanced capabilities might overwhelm beginners or small businesses with simpler needs.

  5. UserGuiding

  

  UserGuiding is a no-code product adoption tool that lets businesses create in-app user walkthroughs, guides, and checklists to onboard, engage, and retain users.

  UserGuiding facilitates customer segmentation by enabling businesses to create segmented onboarding flows, analyse behavioural insights, deliver personalised guidance, and collect feedback tailored to different user segments.

  Key features

  • In-app walkthroughs, guides and checklists : UserGuiding has multiple features that can promote product adoption early in the user journey.
  • In-app messaging : UserGuiding offers in-app messaging to help users learn more about the product and various ways to get value.
  • User feedback : UserGuiding allows businesses to gather qualitative feedback to streamline the adoption journey for users.

  Pros 

  • User-friendly interface
  • Customisable onboarding checklists
  • Retention analytics

  Cons

  • Need for technical expertise to maximise all features
  • Limited customisation options for less tech-savvy users

  What to look for in a customer segmentation software 

  When choosing a customer segmentation software, choosing the right one for your specific business needs is important. 

  Here are a few factors to consider when choosing your customer segmentation tool :

  1. Ease of use : Select a tool with an intuitive interface that simplifies navigation. This enhances the user experience, making complex tasks more manageable. Additionally, responsive customer support is crucial. It ensures that issues are promptly resolved, contributing to a smoother operation.
  2. Scalability and flexibility : Your chosen tool should adjust to your needs. A flexible tool like Matomo can adjust to your growing requirements, offering capabilities that evolve as your business expands.
  3. Integration capabilities : The software should seamlessly integrate with your existing systems, such as CRM, marketing, and automation platforms. 
  4. Advanced analytics and reporting : Assess the software’s capability to analyse and interpret complex data sets, without relying on machine learning to fill data gaps. A robust tool should provide accurate insights and detailed reports, enabling you to make informed decisions based on real data.
  5. Privacy and security considerations : Data security is paramount in today’s digital landscape. Look for features like data encryption, security storage, and adherence to privacy standards like GDPR and CCPA compliance. 
  6. Reviews and recommendations : Before making a decision, consider the reputation of the software providers. Look for reviews and recommendations from other users, especially those in similar industries. This can provide real-world insights into the software’s performance and reliability.

  

  Leverage Matomo’s segmentation capabilities to deliver personalised experiences

  Segmentation is the best place to start if you want to deliver personalised customer experiences. There are several customer segmentation software in the market. But they’re not all the same.

  In this article, we reviewed the top segmentation tools — based on factors like their user base, features, and ethical data privacy considerations.

  Ideally, you want a tool to support your evolving business and segmentation needs. Not to mention one that cares about your customers’ privacy and ensures you stay compliant. 

  Enter Matomo at the top of the list. You can leverage Matomo’s accurate insights and comprehensive segmentation capabilities without compromising on privacy. Try it free for 21-days. No credit card required.

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