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• How to Check Website Traffic As Accurately As Possible

  18 août 2023, par Erin — Analytics Tips

  If you want to learn about the health of your website and the success of your digital marketing initiatives, there are few better ways than checking your website traffic. 

  It’s a great way to get a quick dopamine hit when things are up, but you can also use traffic levels to identify issues, learn more about your users or benchmark your performance. That means you need a reliable and easy way to check your website traffic over time — as well as a way to check out your competitors’ traffic levels, too. 

  In this article, we’ll show you how to do just that. You’ll learn how to check website traffic for both your and your competitor’s sites and discover why some methods of checking website traffic are better than others. 

  Why check website traffic ? 

  Dopamine hits aside, it’s important to constantly monitor your website’s traffic for several reasons.

  

  Benchmark site performance

  Keeping regular tabs on your traffic levels is a great way to track your website’s performance over time. It can help you plan for the future or identify problems. 

  For instance, growing traffic levels may mean expanding your business’s offering or investing in more inventory. On the flip side, decreasing traffic levels may suggest it’s time to revamp your marketing strategies or look into issues impacting your SEO. 

  Analyse user behaviour

  Checking website traffic and user behaviour lets marketing managers understand how users interact with your website. Which pages are they visiting ? Which CTAs do they click on ? What can you do to encourage users to take the actions you want ? You can also identify issues that lead to high bounce rates and other problems. 

  The better you understand user behaviour, the easier it will be to give them what they want. For example, you may find that users spend more time on your landing pages than they do your blog pages. You could use that information to revise how you create blog posts or focus on creating more landing pages. 

  Improve the user experience

  Once you understand how users behave on your website, you can use that information to fix errors, update your content and improve the user experience for the site. 

  You can even personalise the experience for customers, leading to significant growth. Research shows companies that grow faster derive 40% more of their revenue from personalisation. 

  That could come in the form of sweeping personalisations — like rearranging your website’s navigation bar based on user behaviour — or individual personalisation that uses analytics to transform sections or entire pages of your site based on user behaviour. 

  Optimise marketing strategies

  You can use website traffic reports to understand where users are coming from and optimise your marketing plan accordingly. You may want to double down on organic traffic, for instance, or invest more in PPC advertising. Knowing current traffic estimates and how these traffic levels have trended over time can help you benchmark your campaigns and prioritise your efforts. 

  Increasing traffic levels from other countries can also help you identify new marketing opportunities. If you start seeing significant traffic levels from a neighbouring country or a large market, it could be time to take your business international and launch a cross-border campaign. 

  Filter unwanted traffic

  A not-insignificant portion of your site’s traffic may be coming from bots and other unwanted sources. These can compromise the quality of your analytics and make it harder to draw insights. You may not be able to get rid of this traffic, but you can use analytics tools to remove it from your stats. 

  How to check website traffic on Matomo

  If you want to check your website’s traffic, you’d be forgiven for heading to Google Analytics first. It’s the most popular analytics tool on the market, after all. But if you want a more reliable assessment of your website’s traffic, then we recommend using Matomo alongside Google Analytics. 

  The Matomo web analytics platform is an open-source solution that helps you collect accurate data about your website’s traffic and make more informed decisions as a result — all while enhancing the customer experience and ensuring GDPR compliance and user privacy. 

  Matomo also offers multiple ways to check website traffic :

  • Visits log report
  • Real-time visitor map
  • Visits in real-time report

  Let’s look at all of them one by one. 

  The visits log report is a unique rundown of all of the individual visitors to your site. This offers a much more granular view than other tools that just show the total number of visitors for a given period. 

  

  You can access the visits log report by clicking on the reporting menu, then clicking Visitor and Visits Log. From there, you’ll be able to scroll through every user session and see the following information :

  • The location of the user
  • The total number of actions they took
  • The length of time on site
  • How they arrived at your site
  • And the device they used to access your site 

  This may be overwhelming if your site receives thousands of visitors at a time. But it’s a great way to understand users at an individual level and appreciate the lifetime activity of specific users. 

  The Real-time visitor map is a visual display of users’ location for a given timeframe. If you have an international website, it’s a fantastic way to see exactly where in the world your traffic comes from.

  

  You can access the Real-time Visitor Map by clicking Visitor in the main navigation menu and then Real-time Map. The map itself is colour-coded. Larger orange bubbles represent recent visits, and smaller dark orange and grey bubbles represent older visits. The map will refresh every five seconds, and new users appear with a flashing effect. 

  If you run TV or radio adverts, Matomo’s Real-time Map provides an immediate read on the effectiveness of your campaign. If your map lights up in the minutes following your ad, you know it’s been effective. It can also help you identify the source of bot attacks, too. 

  Finally, the Visits in Real-time report provides a snapshot of who is browsing your website. You can access this report under Visitors > Real-time and add it to your custom dashboards as a widget. 

  Open the report, and you’ll see the real-time flow of your site’s users and counters for visits and pageviews over the last 30 minutes and 24 hours. The report refreshes every five seconds with new users added to the top of the report with a fade-in effect.

  

  The report provides a snapshot of each visitor, including :

  • Whether they are new or a returning 
  • Their country
  • Their browser
  • Their operating system
  • The number of actions they took
  • The time they spent on the site
  • The channel they came in from
  • Whether the visitor converted a goal

  3 other ways to check website traffic

  You don’t need to use Matomo to check your website traffic. Here are three other tools you can use instead. 

  How to check website traffic on Google Analytics

  Google Analytics is usually the first starting point for anyone looking to check their website traffic. It’s free to use, incredibly popular and offers a wide range of traffic reports. 

  Google Analytics lets you break down historical traffic data almost any way you wish. You can split traffic by acquisition channel (organic, social media, direct, etc.) by country, device or demographic.

  

  It also provides real-time traffic reports that give you a snapshot of users on your site right now and over the last 30 minutes. 

  

  Google Analytics may be one of the most popular ways to check website traffic, but it could be better. Google Analytics 4 is difficult to use compared to its predecessor, and it also limits the amount of data you can track in accordance with privacy laws. If users refuse your cookie consent, Google Analytics won’t record these visits. In other words, you aren’t getting a complete view of your traffic by using Google Analytics alone. 

  That’s why it’s important to use Google Analytics alongside other web analytics tools (like Matomo) that don’t suffer from the same privacy issues. That way, you can make sure you track every single user who visits your site. 

  How to check website traffic on Google Search Console

  Google Search Console is a free tool from Google that lets you analyse the search traffic that your site gets from Google. 

  The top-line report shows you how many times your website has appeared in Google Search, how many clicks it has received, the average clickthrough rate and the average position of your website in the search results. 

  

  Google Search Console is a great way to understand what you rank for and how much traffic your organic rankings generate. It will also show you which pages are indexed in Google and whether there are any crawling errors. 

  Unfortunately, Google Search Console is limited if you want to get a complete view of your traffic. While you can analyse search traffic in a huge amount of detail, it will not tell you how users who access your website directly or via social media behave. 

  How to check website traffic on Similarweb

  Similarweb is a website analysis tool that estimates the total traffic of any site on the internet. It is one of the best tools for estimating how much traffic your competitors receive. 

  What’s great about Similarweb is that it estimates total traffic, not just traffic from search engines like many SEO tools. It even breaks down traffic by different channels, allowing you to see how your website compares against your competitors. 

  

  As you can see from the image above, Similarweb provides an estimate of total visits, bounce rate, the average number of pages users view per visit and the average duration on the site. The company also has a free browser extension that lets you check website traffic estimates as you browse the web. 

  You can use Similarweb for free to a point. But to really get the most out of this tool, you’ll need to upgrade to a premium plan which starts at $125 per user per month. 

  The price isn’t the only downside of using Similarweb to check the traffic of your own and your competitor’s websites. Ultimately, Similarweb is only an estimate — even if it’s a reasonably accurate one — and it’s no match for a comprehensive analytics tool. 

  7 website traffic metrics to track

  Now that you know how to check your website’s traffic, you can start to analyse it. You can use plenty of metrics to assess the quality of your website traffic, but here are some of the most important metrics to track. 

  • New visitors : These are users who have never visited your website before. They are a great sign that your marketing efforts are working and your site is reaching more people. But it’s also important to track how they behave on the website to ensure your site caters effectively to new visitors. 
  • Returning visitors : Returning visitors are coming back to your site for a reason : either they like the content you’re creating or they want to make a purchase. Both instances are great. The more returning visitors, the better. 
  • Bounce rate : This is a measure of how many users leave your website without taking action. Different analytics tools measure this metric differently.
  • Session duration : This is the length of time users spend on your website, and it can be a great gauge of whether they find your site engaging. Especially when combined with the metric below. 
  • Pages per session : This measures how many different pages users visit on average. The more pages they visit and the longer users spend on your website, the more engaging it is. 
  • Traffic source : Traffic can come from a variety of sources (organic, direct, social media, referral, etc.) Tracking which sources generate the most traffic can help you analyse and prioritise your marketing efforts. 
  • User demographics : This broad metric tells you more about who the users are that visit your website, what device they use, what country they come from, etc. While the bulk of your website traffic will come from the countries you target, an influx of new users from other countries can open the door to new opportunities.

  Why do my traffic reports differ ?

  If you use more than one of the methods above to check your website traffic, you’ll quickly realise that every traffic report differs. In some cases, the reasons are obvious. Any tool that estimates your traffic without adding code to your website is just that : an estimate. Tools like Similarweb will never offer the accuracy of analytics platforms like Matomo and Google Analytics. 

  But what about the differences between these analytics platforms themselves ? While each platform has a different way of recording user behaviour, significant differences in website traffic reports between analytics platforms are usually a result of how each platform handles user privacy. 

  A platform like Google Analytics requires users to accept a cookie consent banner to track them. If they accept, great. Google collects all of the data that any other analytics platform does. It may even collect more. If users reject cookie consent banners, however, then Google Analytics can’t track these visitors at all. They simply won’t show up in your traffic reports. 

  That doesn’t happen with all analytics platforms, however. A privacy-focused alternative like Matomo doesn’t require cookie consent banners (apart from in the United Kingdom and Germany) and can therefore continue to track visitors even after they have rejected a cookie consent screen from Google Analytics. This means that virtually all of your website traffic will be tracked regardless of whether users accept a cookie consent banner or not. And it’s why traffic reports in Matomo are often much higher than they are in Google Analytics.

  

  Given that around half (47.32%) of adults in the European Union refuse to allow the use of personal data tracking for advertising purposes and that 95% of people will reject additional cookies when it is easy to do so, this means you could have vastly different traffic reports — and be missing out on a significant amount of user data. 

  If you’re serious about using web analytics to improve your website and optimise your marketing campaigns, then it is essential to use another analytics platform alongside Google Analytics. 

  Get more accurate traffic reports with Matomo

  There are several methods to check website traffic. Some, like Similarweb, can provide estimates on your competitors’ traffic levels. Others, like Google Analytics, are free. But data doesn’t lie. Only privacy-focused analytics solutions like Matomo can provide accurate reports that account for every visitor. 

  Join over one million organisations using Matomo to accurately check their website traffic. Try it for free alongside GA today. No credit card required. 

• Ffmpeg - Reading header information takes too long

  13 mars 2023, par Md Yeamin

  I am using ffmpeg-kit to encode videos on android devices. For some files ffmpeg takes too long to read the header information. This issue happens very randomly. Sometime the execution completes within 1 or 2 seconds, sometime it takes longer than 10 seconds to complete, for the file linked below.

  


  I have build ffmpeg to log some additional info to figure out the root cause. There is a 10s gap between the log output at 15:17:02.276 and 15:17:12.909 (added a separator for quick find). After completion of the mov_read_ftyp there is a long delay before the mov_read_dref method starts the execution. Is there any other method that executed in between and could take this much long time to complete the execution ? What could be the reason behind the delay ?

  


  Here is detailed log about the issue.

  


  Log :

  


  2023-03-12 15:17:02.096 :: ffmpeg-kit-debug: execute:
2023-03-12 15:17:02.116 :: ffmpeg-kit-debug: LogCallback: setjmp
2023-03-12 15:17:02.120 :: ffmpeg-kit-debug: LogCallback: setjmp done
2023-03-12 15:17:02.124 :: ffmpeg-kit-debug: LogCallback: ffmpeg_var_cleanup
2023-03-12 15:17:02.127 :: ffmpeg-kit-debug: LogCallback: ffmpeg_var_cleanup done
2023-03-12 15:17:02.132 :: ffmpeg-kit-debug: LogCallback: init_dynload
2023-03-12 15:17:02.135 :: ffmpeg-kit-debug: LogCallback: init_dynload done
2023-03-12 15:17:02.137 :: ffmpeg-kit-debug: LogCallback: register_exit
2023-03-12 15:17:02.141 :: ffmpeg-kit-debug: LogCallback: register_exit done
2023-03-12 15:17:02.144 :: ffmpeg-kit-debug: LogCallback: avdevice_register_all
2023-03-12 15:17:02.151 :: ffmpeg-kit-debug: LogCallback: avdevice_register_all done
2023-03-12 15:17:02.155 :: ffmpeg-kit-debug: LogCallback: avformat_network_init
2023-03-12 15:17:02.159 :: ffmpeg-kit-debug: LogCallback: avformat_network_init done
2023-03-12 15:17:02.163 :: ffmpeg-kit-debug: LogCallback: show_banner
2023-03-12 15:17:02.165 :: ffmpeg-kit-debug: LogCallback: show_banner done
2023-03-12 15:17:02.169 :: ffmpeg-kit-debug: LogCallback: ffmpeg_parse_options
2023-03-12 15:17:02.172 :: ffmpeg-kit-debug: LogCallback: allocating memory
2023-03-12 15:17:02.176 :: ffmpeg-kit-debug: LogCallback: split_commandline
2023-03-12 15:17:02.179 :: ffmpeg-kit-debug: LogCallback: Splitting the commandline.

2023-03-12 15:17:02.182 :: ffmpeg-kit-debug: LogCallback: Reading option '-hide_banner' ...
2023-03-12 15:17:02.184 :: ffmpeg-kit-debug: LogCallback:  matched as option 'hide_banner' (do not show program banner) with argument '1'.

2023-03-12 15:17:02.187 :: ffmpeg-kit-debug: LogCallback: Reading option '-y' ...
2023-03-12 15:17:02.189 :: ffmpeg-kit-debug: LogCallback:  matched as option 'y' (overwrite output files) with argument '1'.

2023-03-12 15:17:02.193 :: ffmpeg-kit-debug: LogCallback: Reading option '-i' ...
2023-03-12 15:17:02.197 :: ffmpeg-kit-debug: LogCallback:  matched as input url with argument 'saf:6.MP4'.

2023-03-12 15:17:02.199 :: ffmpeg-kit-debug: LogCallback: Finished splitting the commandline.

2023-03-12 15:17:02.202 :: ffmpeg-kit-debug: LogCallback: split_commandline done
2023-03-12 15:17:02.203 :: ffmpeg-kit-debug: LogCallback: parse_optgroup
2023-03-12 15:17:02.205 :: ffmpeg-kit-debug: LogCallback: Parsing a group of options: global .

2023-03-12 15:17:02.207 :: ffmpeg-kit-debug: LogCallback: Applying option hide_banner (do not show program banner) with argument 1.

2023-03-12 15:17:02.210 :: ffmpeg-kit-debug: LogCallback: Applying option y (overwrite output files) with argument 1.

2023-03-12 15:17:02.212 :: ffmpeg-kit-debug: LogCallback: Successfully parsed a group of options.

2023-03-12 15:17:02.217 :: ffmpeg-kit-debug: LogCallback: parse_optgroup done
2023-03-12 15:17:02.220 :: ffmpeg-kit-debug: LogCallback: term_init
2023-03-12 15:17:02.224 :: ffmpeg-kit-debug: LogCallback: term_init done
2023-03-12 15:17:02.226 :: ffmpeg-kit-debug: LogCallback: open_files INPUT
2023-03-12 15:17:02.228 :: ffmpeg-kit-debug: LogCallback: Parsing a group of options: input url saf:6.MP4.

2023-03-12 15:17:02.232 :: ffmpeg-kit-debug: LogCallback: Successfully parsed a group of options.

2023-03-12 15:17:02.236 :: ffmpeg-kit-debug: LogCallback: Opening an input file: saf:6.MP4.

2023-03-12 15:17:02.239 :: ffmpeg-kit-debug: LogCallback: [NULL @ 0xb40000730dd9dbf0] Opening 'saf:6.MP4' for reading

2023-03-12 15:17:02.243 :: ffmpeg-kit-debug: LogCallback: [saf @ 0xb40000728de0ca10] Setting default whitelist 'saf,crypto,data'

2023-03-12 15:17:02.245 :: ffmpeg-kit-debug: LogCallback: fd_open start
2023-03-12 15:17:02.248 :: ffmpeg-kit-debug: LogCallback: fd_open opening
2023-03-12 15:17:02.252 :: ffmpeg-kit-debug: LogCallback: fd_open opened
2023-03-12 15:17:02.255 :: ffmpeg-kit-debug: LogCallback: mov_probe
2023-03-12 15:17:02.257 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Format mov,mp4,m4a,3gp,3g2,mj2 probed with size=2048 and score=100

2023-03-12 15:17:02.260 :: ffmpeg-kit-debug: LogCallback: mov_read_header
2023-03-12 15:17:02.264 :: ffmpeg-kit-debug: LogCallback: mov_read_header seeking
2023-03-12 15:17:02.268 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] ISO: File Type Major Brand: mp41

2023-03-12 15:17:02.270 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp dict set
2023-03-12 15:17:02.273 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp mov_aaxc_crypto before
2023-03-12 15:17:02.276 :: ffmpeg-kit-debug: LogCallback: mov_read_ftyp mov_aaxc_crypto done
-----------------------------------------------------------------------------------------------
2023-03-12 15:17:12.909 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.911 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.915 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.918 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.920 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.923 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Processing st: 0, edit list 0 - media time: 0, duration: 39436397

2023-03-12 15:17:12.925 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.931 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.935 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.938 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.945 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.951 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.958 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.965 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.971 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.975 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.978 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.985 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.988 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.990 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:12.992 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:12.994 :: ffmpeg-kit-debug: LogCallback: mov_read_dref started
2023-03-12 15:17:12.996 :: ffmpeg-kit-debug: LogCallback: mov_read_dref check entries
2023-03-12 15:17:12.997 :: ffmpeg-kit-debug: LogCallback: mov_read_dref drefs_count 0 
2023-03-12 15:17:12.999 :: ffmpeg-kit-debug: LogCallback: mov_read_dref av_free sc->drefs
2023-03-12 15:17:13.002 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Unknown dref type 0x73696c61 size 12

2023-03-12 15:17:13.003 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] All samples in data stream index:id [4:5] have zero duration, stream set to be discarded by default. Override using AVStream->discard or -discard for ffmpeg command.

2023-03-12 15:17:13.006 :: ffmpeg-kit-debug: LogCallback: mov_read_header seek done
2023-03-12 15:17:13.008 :: ffmpeg-kit-debug: LogCallback: mov_read_header parse done
2023-03-12 15:17:13.010 :: ffmpeg-kit-debug: LogCallback: mov_read_header trex data read done
2023-03-12 15:17:13.011 :: ffmpeg-kit-debug: LogCallback: mov_read_header bitrate calculation code
2023-03-12 15:17:13.013 :: ffmpeg-kit-debug: LogCallback: mov_read_header fps calculation done
2023-03-12 15:17:13.015 :: ffmpeg-kit-debug: LogCallback: mov_read_header read side data done
2023-03-12 15:17:13.017 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] Before avformat_find_stream_info() pos: 3720541874 bytes read:3720541874 seeks:0 nb_streams:5

2023-03-12 15:17:13.019 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 7(SPS), nal_ref_idc: 1

2023-03-12 15:17:13.022 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 8(PPS), nal_ref_idc: 1

2023-03-12 15:17:13.024 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.028 :: ffmpeg-kit-debug: LogCallback: fd_seek seeking
2023-03-12 15:17:13.030 :: ffmpeg-kit-debug: LogCallback: fd_seek seek done
2023-03-12 15:17:13.033 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 7(SPS), nal_ref_idc: 1

2023-03-12 15:17:13.035 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 8(PPS), nal_ref_idc: 1

2023-03-12 15:17:13.038 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 9(AUD), nal_ref_idc: 0

2023-03-12 15:17:13.042 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] nal_unit_type: 5(IDR), nal_ref_idc: 1

2023-03-12 15:17:13.047 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] Format yuvj420p chosen by get_format().

2023-03-12 15:17:13.059 :: ffmpeg-kit-debug: LogCallback: [h264 @ 0xb40000731df00bc0] Reinit context to 1920x1088, pix_fmt: yuvj420p

2023-03-12 15:17:13.066 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] All info found

2023-03-12 15:17:13.071 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.075 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.077 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.081 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.087 :: ffmpeg-kit-debug: LogCallback: fd_seek start
2023-03-12 15:17:13.090 :: ffmpeg-kit-debug: LogCallback: fd_seek size check error
2023-03-12 15:17:13.093 :: ffmpeg-kit-debug: LogCallback: [mov,mp4,m4a,3gp,3g2,mj2 @ 0xb40000730dd9dbf0] After avformat_find_stream_info() pos: 323745 bytes read:3720930284 seeks:1 frames:3

2023-03-12 15:17:13.096 :: ffmpeg-kit-debug: LogCallback: Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'saf:6.MP4':

2023-03-12 15:17:13.099 :: ffmpeg-kit-debug: LogCallback:   Metadata:

2023-03-12 15:17:13.102 :: ffmpeg-kit-debug: LogCallback:     major_brand     : 
2023-03-12 15:17:13.108 :: ffmpeg-kit-debug: LogCallback: mp41
2023-03-12 15:17:13.110 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.113 :: ffmpeg-kit-debug: LogCallback:     minor_version   : 
2023-03-12 15:17:13.115 :: ffmpeg-kit-debug: LogCallback: 538120216
2023-03-12 15:17:13.119 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.121 :: ffmpeg-kit-debug: LogCallback:     compatible_brands: 
2023-03-12 15:17:13.124 :: ffmpeg-kit-debug: LogCallback: mp41
2023-03-12 15:17:13.127 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.129 :: ffmpeg-kit-debug: LogCallback:     creation_time   : 
2023-03-12 15:17:13.132 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.136 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.138 :: ffmpeg-kit-debug: LogCallback:     firmware        : 
2023-03-12 15:17:13.140 :: ffmpeg-kit-debug: LogCallback: HD9.01.01.60.00
2023-03-12 15:17:13.142 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.144 :: ffmpeg-kit-debug: LogCallback:   Duration: 
2023-03-12 15:17:13.146 :: ffmpeg-kit-debug: LogCallback: 00:10:57.27
2023-03-12 15:17:13.149 :: ffmpeg-kit-debug: LogCallback: , start: 
2023-03-12 15:17:13.152 :: ffmpeg-kit-debug: LogCallback: 0.000000
2023-03-12 15:17:13.154 :: ffmpeg-kit-debug: LogCallback: , bitrate: 
2023-03-12 15:17:13.157 :: ffmpeg-kit-debug: LogCallback: 45284 kb/s
2023-03-12 15:17:13.159 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.161 :: ffmpeg-kit-debug: LogCallback:   Chapters:

2023-03-12 15:17:13.164 :: ffmpeg-kit-debug: LogCallback:     Chapter #0:0: 
2023-03-12 15:17:13.166 :: ffmpeg-kit-debug: LogCallback: start 619.735000, 
2023-03-12 15:17:13.169 :: ffmpeg-kit-debug: LogCallback: end 657.273000

2023-03-12 15:17:13.171 :: ffmpeg-kit-debug: LogCallback:   Stream #0:0
2023-03-12 15:17:13.174 :: ffmpeg-kit-debug: LogCallback: [0x1]
2023-03-12 15:17:13.175 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.177 :: ffmpeg-kit-debug: LogCallback: , 1, 1/60000
2023-03-12 15:17:13.182 :: ffmpeg-kit-debug: LogCallback: : Video: h264, 1 reference frame (avc1 / 0x31637661), yuvj420p(pc, bt709, progressive, left), 1920x1080 (1920x1088) [SAR 1:1 DAR 16:9], 0/1, 45005 kb/s
2023-03-12 15:17:13.185 :: ffmpeg-kit-debug: LogCallback: , 
2023-03-12 15:17:13.189 :: ffmpeg-kit-debug: LogCallback: 59.94 fps, 
2023-03-12 15:17:13.192 :: ffmpeg-kit-debug: LogCallback: 59.94 tbr, 
2023-03-12 15:17:13.194 :: ffmpeg-kit-debug: LogCallback: 60k tbn
2023-03-12 15:17:13.197 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.199 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.200 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.203 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.204 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.206 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.209 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.211 :: ffmpeg-kit-debug: LogCallback: GoPro AVC  
2023-03-12 15:17:13.213 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.216 :: ffmpeg-kit-debug: LogCallback:       vendor_id       : 
2023-03-12 15:17:13.219 :: ffmpeg-kit-debug: LogCallback: [0][0][0][0]
2023-03-12 15:17:13.220 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.223 :: ffmpeg-kit-debug: LogCallback:       encoder         : 
2023-03-12 15:17:13.225 :: ffmpeg-kit-debug: LogCallback: GoPro AVC encoder
2023-03-12 15:17:13.227 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.229 :: ffmpeg-kit-debug: LogCallback:   Stream #0:1
2023-03-12 15:17:13.231 :: ffmpeg-kit-debug: LogCallback: [0x2]
2023-03-12 15:17:13.233 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.236 :: ffmpeg-kit-debug: LogCallback: , 1, 1/48000
2023-03-12 15:17:13.238 :: ffmpeg-kit-debug: LogCallback: : Audio: aac (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 189 kb/s
2023-03-12 15:17:13.240 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.243 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.244 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.247 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.252 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.255 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.257 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.259 :: ffmpeg-kit-debug: LogCallback: GoPro AAC  
2023-03-12 15:17:13.262 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.264 :: ffmpeg-kit-debug: LogCallback:       vendor_id       : 
2023-03-12 15:17:13.268 :: ffmpeg-kit-debug: LogCallback: [0][0][0][0]
2023-03-12 15:17:13.271 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.274 :: ffmpeg-kit-debug: LogCallback:   Stream #0:2
2023-03-12 15:17:13.276 :: ffmpeg-kit-debug: LogCallback: [0x3]
2023-03-12 15:17:13.278 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.280 :: ffmpeg-kit-debug: LogCallback: , 1, 1/60000
2023-03-12 15:17:13.281 :: ffmpeg-kit-debug: LogCallback: : Data: none (tmcd / 0x64636D74), 0/1
2023-03-12 15:17:13.284 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.288 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.290 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.291 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.292 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.294 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.296 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.299 :: ffmpeg-kit-debug: LogCallback: GoPro TCD  
2023-03-12 15:17:13.302 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.306 :: ffmpeg-kit-debug: LogCallback:   Stream #0:3
2023-03-12 15:17:13.309 :: ffmpeg-kit-debug: LogCallback: [0x4]
2023-03-12 15:17:13.310 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.313 :: ffmpeg-kit-debug: LogCallback: , 0, 1/1000
2023-03-12 15:17:13.316 :: ffmpeg-kit-debug: LogCallback: : Data: bin_data (gpmd / 0x646D7067), 0/1, 61 kb/s
2023-03-12 15:17:13.318 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.320 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.322 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.325 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.327 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.329 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.331 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.335 :: ffmpeg-kit-debug: LogCallback: GoPro MET  
2023-03-12 15:17:13.337 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.339 :: ffmpeg-kit-debug: LogCallback:   Stream #0:4
2023-03-12 15:17:13.342 :: ffmpeg-kit-debug: LogCallback: [0x5]
2023-03-12 15:17:13.344 :: ffmpeg-kit-debug: LogCallback: (eng)
2023-03-12 15:17:13.345 :: ffmpeg-kit-debug: LogCallback: , 0, 1/60000
2023-03-12 15:17:13.347 :: ffmpeg-kit-debug: LogCallback: : Data: none (fdsc / 0x63736466), 0/1, 13 kb/s
2023-03-12 15:17:13.350 :: ffmpeg-kit-debug: LogCallback:  (default)
2023-03-12 15:17:13.352 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.354 :: ffmpeg-kit-debug: LogCallback:     Metadata:

2023-03-12 15:17:13.356 :: ffmpeg-kit-debug: LogCallback:       creation_time   : 
2023-03-12 15:17:13.359 :: ffmpeg-kit-debug: LogCallback: 2022-02-06T13:53:53.000000Z
2023-03-12 15:17:13.361 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.365 :: ffmpeg-kit-debug: LogCallback:       handler_name    : 
2023-03-12 15:17:13.369 :: ffmpeg-kit-debug: LogCallback: GoPro SOS  
2023-03-12 15:17:13.372 :: ffmpeg-kit-debug: LogCallback: 

2023-03-12 15:17:13.374 :: ffmpeg-kit-debug: LogCallback: Successfully opened the file.

2023-03-12 15:17:13.376 :: ffmpeg-kit-debug: LogCallback: open_files INPUT done
2023-03-12 15:17:13.378 :: ffmpeg-kit-debug: LogCallback: apply_sync_offsets
2023-03-12 15:17:13.382 :: ffmpeg-kit-debug: LogCallback: apply_sync_offsets done
2023-03-12 15:17:13.386 :: ffmpeg-kit-debug: LogCallback: init_complex_filters
2023-03-12 15:17:13.389 :: ffmpeg-kit-debug: LogCallback: init_complex_filters done
2023-03-12 15:17:13.391 :: ffmpeg-kit-debug: LogCallback: open_files OUTPUT
2023-03-12 15:17:13.392 :: ffmpeg-kit-debug: LogCallback: open_files OUTPUT done
2023-03-12 15:17:13.395 :: ffmpeg-kit-debug: LogCallback: check_filter_outputs
2023-03-12 15:17:13.397 :: ffmpeg-kit-debug: LogCallback: check_filter_outputs done
2023-03-12 15:17:13.399 :: ffmpeg-kit-debug: LogCallback: ffmpeg_parse_options done
2023-03-12 15:17:13.402 :: ffmpeg-kit-debug: LogCallback: At least one output file must be specified

2023-03-12 15:17:13.405 :: ffmpeg-kit-debug: LogCallback: [AVIOContext @ 0xb4000072dddd6510] Statistics: 3720930284 bytes read, 1 seeks

2023-03-12 15:17:13.407 :: ffmpeg-kit-debug: LogCallback: fd_close start
2023-03-12 15:17:13.410 :: ffmpeg-kit-debug: LogCallback: fd_close done
2023-03-12 15:17:13.412 :: ffmpeg-kit-debug: LogCallback: setjmp done


  


  Note : I have built ffmpeg-kit with the following script :

  


  #!/bin/bash

export ENCODERS="libvorbis,libvpx_vp8,libvpx_vp9,libx264,libx265,mpeg1video,mpeg2video,mpeg4,flv,wmv1,wmv2,msmpeg4v3,libaom_av1,\
h261,h263,theora,libtheora,png,aac,ac3,alac,libopencore_amrnb,libvo_amrwbenc,eac3,flac,mp2,libtwolame,libmp3lame,libopus,libspeex,wavpack,wmav1,wmav2,pcm_s16le,\
ssa,ass,dvbsub,dvdsub,movtext,srt,subrip,text,ttml,webvtt,xsub"

export SETTINGS="--disable-indevs \
  --enable-pthreads \
  --enable-indev=lavfi \
  --disable-outdevs \
  --disable-protocols \
  --enable-protocol=file,fd,saf,async \
  --disable-encoders \
  --enable-encoder=${ENCODERS}"

export CUSTOM_CONFIG=${SETTINGS}

./android.sh --disable-arm-v7a --disable-arm-v7a-neon --disable-x86 --disable-x86-64 --enable-android-media-codec --enable-android-zlib --enable-chromaprint --enable-dav1d --enable-fontconfig --enable-freetype --enable-fribidi --enable-gmp --enable-gnutls --enable-kvazaar --enable-lame --enable-libaom --enable-libass --enable-libiconv --enable-libilbc --enable-libtheora --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libxml2 --enable-opencore-amr --enable-openh264 --enable-opus --enable-sdl --enable-shine --enable-snappy --enable-soxr --enable-speex --enable-tesseract --enable-twolame --enable-vo-amrwbenc --enable-zimg --enable-x264 --enable-x265 --enable-gpl -l


  


  Sample file : https://drive.google.com/file/d/1lvCiOBQqBEnUECn_HJi8qUoaCPCBnkgO/view?usp=share_link

  


  Ffmpeg version : 5.1.

  


• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.