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• Frames took with ELP camera has unknown pixel format at FHD ?

  11 novembre 2024, par Marcel Kopera

  I'm trying to take a one frame ever x seconds from my usb camera. Name of the camera is : ELP-USBFHD06H-SFV(5-50).
Code is not 100% done yet, but I'm using it this way right now ↓ (shot fn is called from main.py in a loop)

  


  
import cv2
import subprocess

from time import sleep
from collections import namedtuple

from errors import *

class Camera:
    def __init__(self, cam_index, res_width, res_height, pic_format, day_time_exposure_ms, night_time_exposure_ms):
        Resolution = namedtuple("resolution", ["width", "height"])
        self.manual_mode(True)

        self.cam_index = cam_index
        self.camera_resolution = Resolution(res_width, res_height)
        self.picture_format = pic_format
        self.day_time_exposure_ms = day_time_exposure_ms
        self.night_time_exposure_ms = night_time_exposure_ms

        self.started: bool = False
        self.night_mode = False

        self.cap = cv2.VideoCapture(self.cam_index, cv2.CAP_V4L2)
        self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.camera_resolution.width)
        self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.camera_resolution.height)
        self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*self.picture_format))

    

    def start(self):
        sleep(1)
        if not self.cap.isOpened():
            return CameraCupError()

        self.set_exposure_time(self.day_time_exposure_ms)
        self.set_brightness(0)
        sleep(0.1)
        
        self.started = True



    def shot(self, picture_name, is_night):
        if not self.started:
            return InitializationError()

        self.configure_mode(is_night)

        # Clear buffer
        for _ in range(5):
            ret, _ = self.cap.read()

        ret, frame = self.cap.read()

        sleep(0.1)

        if ret:
            print(picture_name)
            cv2.imwrite(picture_name, frame)
            return True

        else:
            print("No photo")
            return False


    
    def release(self):
        self.set_exposure_time(156)
        self.set_brightness(0)
        self.manual_mode(False)
        self.cap.release()



    def manual_mode(self, switch: bool):
        if switch:
            subprocess.run(["v4l2-ctl", "--set-ctrl=auto_exposure=1"])
        else:
            subprocess.run(["v4l2-ctl", "--set-ctrl=auto_exposure=3"])
        sleep(1)

    
    
    def configure_mode(self, is_night):
        if is_night == self.night_mode:
            return

        if is_night:
            self.night_mode = is_night
            self.set_exposure_time(self.night_time_exposure_ms)
            self.set_brightness(64)
        else:
            self.night_mode = is_night
            self.set_exposure_time(self.day_time_exposure_ms)
            self.set_brightness(0)
        sleep(0.1)



    def set_exposure_time(self, ms: int):
        ms = int(ms)
        default_val = 156

        if ms < 1 or ms > 5000:
            ms = default_val

        self.cap.set(cv2.CAP_PROP_EXPOSURE, ms)



    def set_brightness(self, value: int):
        value = int(value)
        default_val = 0

        if value < -64 or value > 64:
            value = default_val

        self.cap.set(cv2.CAP_PROP_BRIGHTNESS, value)


  


  Here are settings for the camera (yaml file)

  


  camera:
  camera_index: 0
  res_width: 1920
  res_height: 1080
  picture_format: "MJPG"
  day_time_exposure_ms: 5
  night_time_exposure_ms: 5000
  photos_format: "jpg"



  


  I do some configs like set manual mode for the camera, change exposure/brightness and saving frame.
Also the camera is probably catching the frames to the buffer (it is not saving latest frame in real time : it's more laggish), so I have to clear buffer every time. like this

  


          # Clear buffer from old frames
        for _ in range(5):
            ret, _ = self.cap.read()
        
        # Get a new frame
        ret, frame = self.cap.read()


  


  What I really don't like, but I could find a better way (tldr : setting buffer for 1 frame doesn't work on my camera).

  


  Frames saved this method looks good with 1920x1080 resolution. BUT when I try to run ffmpeg command to make a timelapse from saved jpg file like this

  


  ffmpeg -framerate 20 -pattern_type glob -i "*.jpg" -c:v libx264 output.mp4


  


  I got an error like this one

  


  [image2 @ 0x555609c45240] Could not open file : 08:59:20.jpg
[image2 @ 0x555609c45240] Could not find codec parameters for stream 0 (Video: mjpeg, none(bt470bg/unknown/unknown)): unspecified size
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, image2, from '*.jpg':
  Duration: 00:00:00.05, start: 0.000000, bitrate: N/A
  Stream #0:0: Video: mjpeg, none(bt470bg/unknown/unknown), 20 fps, 20 tbr, 20 tbn
Output #0, mp4, to 'output.mp4':
Output file #0 does not contain any stream


  


  Also when I try to copy the files from Linux to Windows I get some weird copy failing error and option to skip the picture. But even when I press the skip button, the picture is copied and can be opened. I'm not sure what is wrong with the format, but the camera is supporting MPEG at 1920x1080.

  


  >>> v4l2-ctl --all

Driver Info:
        Driver name      : uvcvideo
        Card type        : H264 USB Camera: USB Camera
        Bus info         : usb-xhci-hcd.1-1
        Driver version   : 6.6.51
        Capabilities     : 0x84a00001
                Video Capture
                Metadata Capture
                Streaming
                Extended Pix Format
                Device Capabilities
        Device Caps      : 0x04200001
                Video Capture
                Streaming
                Extended Pix Format
Media Driver Info:
        Driver name      : uvcvideo
        Model            : H264 USB Camera: USB Camera
        Serial           : 2020032801
        Bus info         : usb-xhci-hcd.1-1
        Media version    : 6.6.51
        Hardware revision: 0x00000100 (256)
        Driver version   : 6.6.51
Interface Info:
        ID               : 0x03000002
        Type             : V4L Video
Entity Info:
        ID               : 0x00000001 (1)
        Name             : H264 USB Camera: USB Camera
        Function         : V4L2 I/O
        Flags            : default
        Pad 0x0100000d   : 0: Sink
          Link 0x0200001a: from remote pad 0x1000010 of entity 'Extension 4' (Video Pixel Formatter): Data, Enabled, Immutable
Priority: 2
Video input : 0 (Camera 1: ok)
Format Video Capture:
        Width/Height      : 1920/1080
        Pixel Format      : 'MJPG' (Motion-JPEG)
        Field             : None
        Bytes per Line    : 0
        Size Image        : 4147789
        Colorspace        : sRGB
        Transfer Function : Default (maps to sRGB)
        YCbCr/HSV Encoding: Default (maps to ITU-R 601)
        Quantization      : Default (maps to Full Range)
        Flags             :
Crop Capability Video Capture:
        Bounds      : Left 0, Top 0, Width 1920, Height 1080
        Default     : Left 0, Top 0, Width 1920, Height 1080
        Pixel Aspect: 1/1
Selection Video Capture: crop_default, Left 0, Top 0, Width 1920, Height 1080, Flags:
Selection Video Capture: crop_bounds, Left 0, Top 0, Width 1920, Height 1080, Flags:
Streaming Parameters Video Capture:
        Capabilities     : timeperframe
        Frames per second: 15.000 (15/1)
        Read buffers     : 0

User Controls

                     brightness 0x00980900 (int)    : min=-64 max=64 step=1 default=0 value=64
                       contrast 0x00980901 (int)    : min=0 max=64 step=1 default=32 value=32
                     saturation 0x00980902 (int)    : min=0 max=128 step=1 default=56 value=56
                            hue 0x00980903 (int)    : min=-40 max=40 step=1 default=0 value=0
        white_balance_automatic 0x0098090c (bool)   : default=1 value=1
                          gamma 0x00980910 (int)    : min=72 max=500 step=1 default=100 value=100
                           gain 0x00980913 (int)    : min=0 max=100 step=1 default=0 value=0
           power_line_frequency 0x00980918 (menu)   : min=0 max=2 default=1 value=1 (50 Hz)
                                0: Disabled
                                1: 50 Hz
                                2: 60 Hz
      white_balance_temperature 0x0098091a (int)    : min=2800 max=6500 step=1 default=4600 value=4600 flags=inactive
                      sharpness 0x0098091b (int)    : min=0 max=6 step=1 default=3 value=3
         backlight_compensation 0x0098091c (int)    : min=0 max=2 step=1 default=1 value=1

Camera Controls

                  auto_exposure 0x009a0901 (menu)   : min=0 max=3 default=3 value=1 (Manual Mode)
                                1: Manual Mode
                                3: Aperture Priority Mode
         exposure_time_absolute 0x009a0902 (int)    : min=1 max=5000 step=1 default=156 value=5000
     exposure_dynamic_framerate 0x009a0903 (bool)   : default=0 value=0


  


  I also tried to save the picture using ffmpeg in a case something is not right with opencv like this :

  


  ffmpeg -f v4l2 -framerate 30 -video_size 1920x1080 -i /dev/video0 -c:v libx264 -preset fast -crf 23 -t 00:01:00 output.mp4



  


  It is saving the picture but also changing its format

  


  [video4linux2,v4l2 @ 0x555659ed92b0] The V4L2 driver changed the video from 1920x1080 to 800x600
[video4linux2,v4l2 @ 0x555659ed92b0] The driver changed the time per frame from 1/30 to 1/15


  


  But the format looks right when set it back to FHD using v4l2

  


  
>>> v4l2-ctl --device=/dev/video0 --set-fmt-video=width=1920,height=1080,pixelformat=MJPG
>>> v4l2-ctl --get-fmt-video

Format Video Capture:
        Width/Height      : 1920/1080
        Pixel Format      : 'MJPG' (Motion-JPEG)
        Field             : None
        Bytes per Line    : 0
        Size Image        : 4147789
        Colorspace        : sRGB
        Transfer Function : Default (maps to sRGB)
        YCbCr/HSV Encoding: Default (maps to ITU-R 601)
        Quantization      : Default (maps to Full Range)
        Flags             :


  


  I'm not sure what could be wrong with the format/camera and I don't think I have enough information to figure it out.

  


  I tried to use ffmpeg instead of opencv and also change a few settings in opencv's cup config.

  


• A Comprehensive Guide to Robust Digital Marketing Analytics

  30 octobre 2023, par Erin

  First impressions are everything. This is not only true for dating and job interviews but also for your digital marketing strategy. Like a poorly planned resume getting tossed in the “no thank you” pile, 38% of visitors to your website will stop engaging with your content if they find the layout unpleasant. Thankfully, digital marketers can access data that can be harnessed to optimise websites and turn those “no thank you’s” into “absolutely’s.”

  So, how can we transform raw data into valuable insights that pay off ? The key is web analytics tools that can help you make sense of it all while collecting data ethically. In this article, we’ll equip you with ways to take your digital marketing strategy to the next level with the power of web analytics.

  What are the different types of digital marketing analytics ?

  Digital marketing analytics are like a cipher into the complex behaviour of your buyers. Digital marketing analytics help collect, analyse and interpret data from any touchpoint you interact with your buyers online. Whether you’re trying to gauge the effectiveness of a new email marketing campaign or improve your mobile app layout, there’s a way for you to make use of the insights you gain. 

  As we go through the eight commonly known types of digital marketing analytics, please note we’ll primarily focus on what falls under the umbrella of web analytics. 

  1. Web analytics help you better understand how users interact with your website. Good web analytics tools will help you understand user behaviour while securely handling user data. 
  2. Learn more about the effectiveness of your organisation’s social media platforms with social media analytics. Social media analytics include user engagement, post reach and audience demographics. 
  3. Email marketing analytics help you see how email campaigns are being engaged with.
  4. Search engine optimisation (SEO) analytics help you understand your website’s visibility in search engine results pages (SERPs). 
  5. Pay-per-click (PPC) analytics measure the performance of paid advertising campaigns.
  6. Content marketing analytics focus on how your content is performing with your audience. 
  7. Customer analytics helps organisations identify and examine buyer behaviour to retain the biggest spenders. 
  8. Mobile app analytics track user interactions within mobile applications. 

  Choosing which digital marketing analytics tools are the best fit for your organisation is not an easy task. When making these decisions, it’s critical to remember the ethical implications of data collection. Although data insights can be invaluable to your organisation, they won’t be of much use if you lose the trust of your users. 

  Tips and best practices for developing robust digital marketing analytics 

  So, what separates top-notch, robust digital marketing analytics from the rest ? We’ve already touched on it, but a big part involves respecting user privacy and ethically handling data. Data security should be on your list of priorities, alongside conversion rate optimisation when developing a digital marketing strategy. In this section, we will examine best practices for using digital marketing analytics while retaining user trust.

  

  Clear objectives

  Before comparing digital marketing analytics tools, you should define clear and measurable goals. Try asking yourself what you need your digital marketing analytics strategy to accomplish. Do you want to improve conversion rates while remaining data compliant ? Maybe you’ve noticed users are not engaging with your platform and want to fix that. Save yourself time and energy by focusing on the most relevant pain points and areas of improvement.

  Choose the right tools for the job

  Don’t just base your decision on what other people tell you. Take the tool for a test drive — free trials allow you to test features and user interfaces and learn more about the platform before committing. When choosing digital marketing analytics tools, look for ones that ensure compliance with privacy laws like GDPR.

  Don’t overlook data compliance

  GDPR ensures organisations prioritise data protection and privacy. You could be fined up to €20 million, or 4% of the previous year’s revenue for violations. Without data compliance practices, you can say goodbye to the time and money spent on digital marketing strategies. 

  Don’t sacrifice data quality and accuracy

  Inaccurate and low-quality data can taint your analysis, making it hard to glean valuable insights from your digital marketing analytics efforts. Regularly audit and clean your data to remove inaccuracies and inconsistencies. Address data discrepancies promptly to maintain the integrity of your analytics. Data validation measures also help to filter out inaccurate data.

  Communicate your findings

  Having insights is one thing ; effectively communicating complex data findings is just as important. Customise dashboards to display key metrics aligned with your objectives. Make sure to automate reports, allowing stakeholders to stay updated without manual intervention. 

  Understand the user journey

  To optimise your conversion rates, you need to understand the user journey. Start by analysing visitors interactions with your website — this will help you identify conversion bottlenecks in your sales or lead generation processes. Implement A/B testing for landing page optimisation, refining elements like call-to-action buttons or copy, and leverage Form Analytics to make informed, data-driven improvements to your forms.

  Continuous improvement

  Learn from the data insights you gain, and iterate your marketing strategies based on the findings. Stay updated with evolving web analytics trends and technologies to leverage new growth opportunities.

  Why you need web analytics to support your digital marketing analytics toolbox

  You wouldn’t set out on a roadtrip without a map, right ? Digital marketing analytics without insights into how users interact with your website are just as useless. Used ethically, web analytics tools can be an invaluable addition to your digital marketing analytics toolbox. 

  The data collected via web analytics reveals user interactions with your website. These could include anything from how long visitors stay on your page to their actions while browsing your website. Web analytics tools help you gather and understand this data so you can better understand buyer preferences. It’s like a domino effect : the more you understand your buyers and user behaviour, the better you can assess the effectiveness of your digital content and campaigns. 

  Web analytics reveal user behaviour, highlighting navigation patterns and drop-off points. Understanding these patterns helps you refine website layout and content, improving engagement and conversions for a seamless user experience.

  

  Concrete CMS harnessed the power of web analytics, specifically Form Analytics, to uncover a crucial insight within their user onboarding process. Their data revealed a significant issue : the “address” input field was causing visitors to drop off and not complete the form, severely impacting the overall onboarding experience and conversion rate.

  Armed with these insights, Concrete CMS made targeted optimisations to the form, resulting in a substantial transformation. By addressing the specific issue identified through Form Analytics, they achieved an impressive outcome – a threefold increase in lead generation.

  This case is a great example of how web analytics can uncover customer needs and preferences and positively impact conversion rates. 

  Ethical implications of digital marketing analytics

  As we’ve touched on, digital marketing analytics are a powerful tool to help better understand online user behaviour. With great power comes great responsibility, however, and it’s a legal and ethical obligation for organisations to protect individual privacy rights. Let’s get into the benefits of practising ethical digital marketing analytics and the potential risks of not respecting user privacy : 

  • If someone uses your digital platform and then opens their email one day to find it filled with random targeted ad campaigns, they won’t be happy. Avoid losing user trust — and facing a potential lawsuit — by informing users what their data will be used for. Give them the option to consent to opt-in or opt-out of letting you use their personal information. If users are also assured you’ll safeguard personal information against unauthorised access, they’ll be more likely to trust you to handle their data securely.
  • Protecting data against breaches means investing in technology that will let you end-to-end encrypt and securely store data. Other important data-security best practices include access control, backing up data regularly and network and physical security of assets.
  • A fine line separates digital marketing analytics and misusing user data — many companies have gotten into big trouble for crossing it. (By big trouble, we mean millions of dollars in fines.) When it comes to digital marketing analytics, you should never cut corners when it comes to user privacy and data security. This balance involves understanding what data can be collected and what should be collected and respecting user boundaries and preferences.

  

  Learn more 

  We discussed a lot of facets of digital marketing analytics, namely how to develop a robust digital marketing strategy while prioritising data compliance. With Matomo, you can protect user data and respect user privacy while gaining invaluable insights into user behaviour. Save your organisation time and money by investing in a web analytics solution that gives you the best of both worlds. 

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

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• GA360 vs GA4 : Key Differences and Challenges

  20 mai 2024, par Erin

  While the standard Universal Analytics (UA) was sunset for free users in July 2023, Google Analytics 360 (GA360) users could postpone the switch to GA4 for another 12 months. But time is running out. As July is rapidly approaching, GA360 customers need to prepare for the switch to Google Analytics 4 (GA4) or another solution. 

  This comparison post will help you understand the differences between GA360 vs. GA4. We’ll dive beneath the surface, examining each solution’s privacy implications and their usability, features, new metrics and measurement methods.

  What is Google Analytics 4 (Standard) ?

  GA4 is the latest version of Google Analytics, succeeding Universal Analytics. It was designed to address privacy issues with Universal Analytics, which made compliance with privacy regulations like GDPR difficult.

  It completely replaced Universal Analytics for free users in July 2023. GA4 Standard features many differences from the original UA, including :

  • Tracking and analysis are now events-based.
  • Insights are primarily powered by machine learning. (There are fewer reports and manual analysis tools).
  • Many users find the user interface to be too complex compared to Universal Analytics.

  The new tracking, reports and metrics already make GA4 feel like a completely different web analytics platform. The user interface itself also includes notable changes in navigation and implementation. These changes make the transition hard for experienced analysts and digital marketers alike. 

  For a more in-depth look at the differences, read our comparison of Google Analytics 4 and Universal Analytics.

  What is Google Analytics 360

  Google Analytics 360 is a paid version of Google Analytics, mostly aimed at enterprises that need to analyse a large amount of data.

  It significantly increases standard limits on data collection, sampling and processing. It also improves data granularity with more custom events and dimensions.

  Transitioning from Universal Analytics 360 to GA4 360

  You may still use the Universal Analytics tag and interface if you’ve been a Google Analytics 360 customer for multiple years. However, access to Universal Analytics 360 will be discontinued on July 1, 2024. Unlike the initial UA sunset (free version), you won’t be able to access the interface or your data after that, so it will be deleted.

  That means you will have to adapt to the new GA4 user interface, reports and metrics before the sunset or find an alternative solution.

  What is the difference between GA4 360 and free GA4 ?

  The key differences between GA4 360 and free GA4 are higher data limits, enterprise support, uptime guarantees and more robust administrative controls.

  

  GA4 offers most of the same features across the paid and free versions, but there are certain limits on data sampling, data processing and integrations. With the free version, you also can’t define as detailed events using event parameters as you can with GA4 360.

  Higher data collection, accuracy, storage and processing limits

  The biggest difference that GA4 360 brings to the table is more oomph in data collection, accuracy and analysis.

  You can collect more specific data (with 100 event parameters instead of 25 for custom metrics). GA4 360 lets you divide users using more custom dimensions based on events or user characteristics. Instead of 50 per property, you get up to 125 per property.

  And with up to 400 custom audiences, 360 is better for companies that heavily segment their users. More audiences, events and metrics per property mean more detailed insights.

  Sampling limits are also of a completely different scale. The max sample size in GA4 360 is 100x the free version of GA4, with up to 1 billion events per query. This makes analysis a lot more accurate for high-volume users. A slice of 10 million events is hardly representative if you have 200 million monthly events.

  Finally, GA4 360 lets you store all of that data for longer (up to 50 months vs up to 14 months). While new privacy regulations demand that you store user data only for the shortest time possible, website analytics data is often used for year-over-year analysis.

  Enterprise-grade support and uptime guarantees

  Because GA360 users are generally enterprises, Google offers service-level agreements for uptime and technical support response times.

  • Tracking : 99.9% uptime guarantee
  • Reporting : 99% uptime guarantee
  • Data processing : within 4 hours at a 98% uptime guarantee

  The free version of GA4 includes no such guarantees and limited access to professional support in the first place.

  Integrations

  GA4 360 increases limits for BigQuery and Google Ads Manager exports.

  

  The standard limits in the free version are 1 million events per day to BigQuery. In GA4 360, this is increased to billions of events per day. You also get up to 400 audiences for Search Ads 360 instead of the 100 limit in standard GA4.

  Roll-up analytics for agencies and enterprises

  If you manage a wide range of digital properties, checking each one separately isn’t very effective. You can export the data into a tool like Looker Studio (formerly Google Data Studio), but this requires extra work.

  With GA360, you can create “roll-up properties” to analyse data from multiple properties in the same space. It’s the best way to analyse larger trends and patterns across sites and apps.

  Administration and user access controls

  Beyond roll-up reporting, the other unique “advanced features” found in GA360 are related to administration and user access controls.

  

  First, GA360 lets you create custom user roles, giving different access levels to different properties. Sub-properties and roll-up properties are also useful tools for data governance purposes. They make it easier to limit access for specific analysts to the area they’re directly working on.

  You can also design custom reports for specific roles and employees based on their access levels.

  Pricing 

  While GA4 is free, Google Analytics 360 is priced based on your traffic volume. 

  With the introduction of GA4, Google implemented a revised pricing model. For GA4 360, pricing typically begins at USD $50,000/year which covers up to 25 million events per month. Beyond this limit, costs increase based on data usage, scaling accordingly.

  What’s not different : the interface, metrics, reports and basic features

  GA4 360 is the same analytics tool as the free version of GA4, with higher usage limits and a few enterprise features. You get more advanced tracking capabilities and more accurate analysis in the same GA4 packaging.

  If you already use and love GA4 but need to process more data, that’s great news. But if you’re using UA 360 and are hesitant to switch to the new interface, not so much. 

  Making the transition from UA to GA4 isn’t easy. Transferring the data means you need to figure out how to work with the API or use Google BigQuery.

  Plus, you have to deal with new metrics, reports and a new interface. For example, you don’t get to keep your custom funnel reports. You need to use “funnel explorations.”

  Going from UA to GA4 can feel like starting from scratch in a completely new web analytics tool.

  Which version of Google Analytics 4 is right for you ?

  Standard GA4 is a cost-effective web analytics option, but it’s not without its problems :

  • If you’re used to the UA interface, it feels clunky and difficult to analyse.
  • Data sampling is prevalent in the free version, leading to inaccuracies that can negatively affect decision-making and performance.

  And that’s just scratching the surface of common GA4 issues.

  Google Analytics 4 360 is a more reliable web analytics solution for enterprises. However, it suffers from many issues that made the GA4 transition painful for many free UA users last year.

  • You need to rebuild reports and adjust to the new complex interface.
  • To transfer historical data, you must use spreadsheets, the API, or BigQuery.

  You will still lose some of the data due to changes to the metrics and reporting.

  What if neither option is right for you ? Key considerations for choosing a Google Analytics alternative

  Despite what Google would like you to think, GA4 isn’t the only option for website analytics in 2024 — far from it. For companies that are used to UA 360, the right alternative can offer unique benefits to your company.

  Privacy regulations and future-proofing your analytics and marketing

  Although less flagrant than UA, GA4 is still in murky waters regarding compliance with GDPR and other privacy regulations. 

  And the issue isn’t just that you can get fined (which is bad enough). As part of a ruling, you may be ordered to change your analytics platform and protocol, which can completely disrupt your marketing workflow.

  When most marketing teams rely on web analytics to judge the ROI of their campaigns, this can be catastrophic. You may even have to pause campaigns as your team makes the adjustments.

  Avoid this risk completely by going with a privacy-friendly alternative.

  Features beyond basic web analytics

  To understand your users, you need to look at more than just events and conversions.

  That’s why some web analytics solutions have built-in behavioural analytics tools. Features like heatmaps (a visual pattern of popular clicks, scrolling and cursor movement) can help you understand how users interact with specific pages.

  

  Matomo allows you to consolidate behavioural analytics and regular web analytics into a single platform. You don’t need separate tools and subscriptions for heatmaps, session recordings, from analytics, media analytics and A/B testing. You can do all of this with Matomo.

  With insights about visits, sales, conversions, and usability in the same place, it’s a lot easier to improve your website.

  Try Matomo for Free

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

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  Usability and familiar metrics

  The move to event tracking means new metrics, reports and tools. So, if you’re used to Universal Analytics, it can be tricky to transition to GA4. 

  But there’s no need to start from zero, learning to work with a brand-new interface. Many competing web analytics platforms offer familiar reports and metrics — ones your team has gotten used to. This will help you speed up the time to value with a shorter learning curve.

  Why Matomo is a better option than GA4 360 for UA 360 users

  Matomo offers privacy-friendly tracking, built from the ground up to comply with regulations — including IP anonymisation and DoNotTrack settings. You also get 100% ownership of the data, which means we will never use your data for our own profit (unlike Google and other data giants).

  This is a big deal, as breaking GDPR rules can lead to fines of up to 4% of your annual revenue. At the same time, you’ll also future-proof your marketing workflow by choosing a web analytics provider built with privacy regulations in mind.

  Plus, for legacy UA 360 users, the Matomo interface will also feel a lot more intuitive and familiar. Matomo also provides marketing attribution models you know, like first click, which GA4 has removed.

  Finally, you can access various behavioural analytics tools in a single platform — heatmaps, session recordings, form analytics, A/B testing and more. That means you don’t need to pay for separate solutions for conversion rate optimisation efforts.

  And the transition is smooth. Matomo lets you import Universal Analytics data and offers ready-made Google Ads integration and Looker Studio Connector.

  Join over 1 million websites that choose Matomo as their web analytics solution. Try it free for a 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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