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• GA360 Sunset : Is Now the Time to Switch ?

  20 mai 2024, par Erin

  Google pushed the sunset date of Universal Analytics 360 to July 2024, giving enterprise users more time to transition to Google Analytics 4. This extension is also seen by some as time to find a suitable alternative. 

  While Google positions GA4 as an upgrade to Universal Analytics, the new platform has faced its fair share of backlash. 

  So before you rush to meet the new sunset deadline, ask yourself this question : Is now the time to switch to a Google Analytics alternative ?

  In this article, we’ll explain what the new GA360 sunset date means and show you what you could gain by choosing a privacy-friendly alternative. 

  What’s happening with the final GA360 sunset ?

  Google has given Universal Analytics 360 properties with a current 360 licence a one-time extension, which will end on 1 July 2024.

  Why did Google extend the sunset ?

  In a blog post on Google, Russell Ketchum, Director of Product Management at Google Analytics, provided more details about the final GA360 sunset. 

  In short, the tech giant realised it would take large enterprise accounts (which typically have complex analytics setups) much longer to transition smoothly. The extension gives them time to migrate to GA4 and check everything is tracking correctly. 

  What’s more, Google is also focused on improving the GA4 experience before more GA360 users migrate :

  “We’re focusing our efforts and investments on Google Analytics 4 to deliver a solution built to adapt to a changing ecosystem. Because of this, throughout 2023 we’ll be shifting support away from Universal Analytics 360 and will move our full focus to Google Analytics 4 in 2024. As a result, performance will likely degrade in Universal Analytics 360 until the new sunset date.”

  Despite the extension, the July sunset is definitive. 

  Starting the week of 1 July 2024, you won’t be able to access any Universal Analytics properties or the API (not even with read-only access), and all data will be deleted.

  In other words, it’s not just data collection that will cease at the start of July. You won’t be able to access the platform, and all your data will be deleted. 

  What GA360 features is Google deprecating, and when ?

  If you’re wondering which GA360 features are being deprecated and when, here is the timeline for Google’s final GA360 sunset :

  • 1 January 2024 : From the beginning of the year, Google doesn’t guarantee all features and functionalities in UA 360 will continue to work as expected. 
  • 29 January 2024 : Google began deprecating a string of advertising and measurement features as it shifts resources to focus on GA4. These features include :
    • Realtime reports
    • Lifetime Value report
    • Model Explorer
    • Cohort Analysis
    • Conversion Probability report
    • GDN Impression Beta
  • Early March 2024 : Google began deprecating more advertising and measurement features. Deprecated advertising features include Demographic and Interest reports, Publisher reporting, Phone Analytics, Event and Salesforce Data Import, and Realtime BigQuery Export. Deprecated measurement features include Universal Analytics property creation, App Views, Unsampled reports, Custom Tables and annotations.
  • Late March 2024 : This is the last recommended date for migration to GA4 to give users three months to validate data and settings. By this date, Google recommends that you migrate your UA’s Google Ads links to GA4, create new Google Ad conversions based on GA4 events, and add GA4 audiences to campaigns and ad groups for retargeting. 
  • 1 July 2024 : From 1 July 2024, you won’t be able to access any UA properties, and all data will be deleted.

  What’s different about GA4 360 ? 

  GA4 comes with a new set of metrics, setups and reports that change how you analyse your data. We highlight the key differences between Universal Analytics and GA4 below. 

  

  New dashboard

  The layout of GA4 is completely different from Universal Analytics, so much so that the UX can be very complex for first-time and experienced GA users alike. Reports or metrics that used to be available in a couple of clicks in UA now take five or more to find. While you can do more in theory with GA4, it takes much more work. 

  New measurements

  The biggest difference between GA4 and UA is how Google measures data. GA4 tracks events — and everything counts as an event. That includes pageviews, scrolls, clicks, file downloads and contact form submissions. 

  The idea is to anonymise data while letting you track complex buyer journeys across multiple devices. However, it can be very confusing, even for experienced marketers and analysts. 

  New metrics

  You won’t be able to track the same metrics in GA4 as in Universal Analytics. Rather than bounce rate, for example, you are forced to track engagement rate, which is the percentage of engaged sessions. These sessions last at least ten seconds, at least two pageviews or at least one conversion event. 

  Confused ? You’re not alone. 

  New reports

  Most reports you’ll be familiar with in Universal Analytics have been replaced in GA4. The new platform also has a completely different reporting interface, with every report grouped under the following five headings : realtime, audience, acquisition, behaviour and conversions. It can be hard for experienced marketers, let alone beginners, to find their way around these new reports. 

  AI insights

  GA4 has machine learning (ML) capabilities that allow you to generate AI insights from your data. Specifically, GA4 has predictive analytics features that let you track three trends : 

  • Purchase probability : the likelihood that a consumer will make a purchase in a given timeframe.
  • Churn probability : the likelihood a customer will churn in a given period.
  • Predictive revenue : the amount of revenue a user is likely to generate over a given period. 

  Google generates these insights using historical data and machine learning algorithms. 

  Cross-platform capabilities

  GA4 also offers cross-platform capabilities, meaning it can track user interactions across websites and mobile apps, giving businesses a holistic view of customer behaviour. This allows for better decision-making throughout the customer journey.

  Does GA4 360 come with other risks ?

  Aside from the poor usability, complexity and steep learning curve, upgrading your GA360 property to GA4 comes with several other risks.

  GA4 has a rocky relationship with privacy regulations, and while you can use it in a GDPR-compliant way at the moment, there’s no guarantee you’ll be able to do so in the future. 

  This presents the prospect of fines for non-compliance. A worse risk, however, is regulators forcing you to change web analytics platforms in the future—something that’s already happened in the EU. Migrating to a new application can be incredibly painful and time-consuming, especially when you can choose a privacy-friendly alternative that avoids the possibility of this scenario. 

  If all this wasn’t bad enough, switching to GA4 risks your historical Universal Analytics data. That’s because you can’t import Universal Analytics data into GA4, even if you migrate ahead of the sunset deadline.

  Why you should consider a GA4 360 alternative instead

  With the GA360 sunset on the horizon, what are your options if you don’t want to deal with GA4’s problems ? 

  The easiest solution is to migrate to a GA4 360 alternative instead. And there are plenty of reasons to migrate from Google Analytics to a privacy-friendly alternative like Matomo. 

  Keep historical data

  As we’ve explained, Google isn’t letting users import their Universal Analytics data from GA360 to GA4. The easiest way to keep it is by switching to a Google Analytics alternative like Matomo that lets you import your historical data. 

  Any business using Google Analytics, whether a GA360 user or otherwise, can import data into Matomo using our Google Analytics Importer plugin. It’s the best way to avoid disruption or losing data when moving on from Universal Analytics.

  Collect 100% accurate data

  Google Analytics implements data sampling and machine learning to fill gaps in your data and generate the kind of predictive insights we mentioned earlier. For standard GA4 users, data sampling starts at 10 million events. For GA4 360 users, data sampling starts at one billion events. Nevertheless, Google Analytics data may not accurately reflect your web traffic. 

  You can fix this using a Google Analytics alternative like Matomo that doesn’t use data sampling. That way, you can be confident that your data-driven decisions are being made with 100% accurate user data. 

  Try Matomo for Free

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

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  No credit card required

  Guarantee user privacy first

  Google has a stormy relationship with the EU-US Data Privacy Framework—being banned and added back to the framework in recent years.

  Currently, organisations governed by GDPR can use Google Analytics to collect data about EU residents, but there’s no guarantee of their ability to do so in the future. Nor does the Framework prevent Google from using EU customer data for ulterior purposes such as marketing and training large language models. 

  By switching to a privacy-focused alternative like Matomo, you don’t have to worry about your user’s data ending up in the wrong hands.

  Upgrade to an all-in-one analytics tool

  Switching from Google Analytics can actually give organisations access to more features. That’s because some GA4 alternatives, like Matomo, offer advanced conversion optimisation features like heatmaps, session recordings, A/B testing, form analytics and more right out of the box. 

  

  This makes Matomo a great choice for marketing teams that want to minimise their tech stack and use one tool for both web and behavioural analytics. 

  Get real-time reports

  GA4 isn’t the best tool for analysing website visitors in real time. That’s because it can take up to 4 hours to process new reports in GA360.

  However, Google Analytics alternatives like Matomo have a range of real-time reports you can leverage.

  

  In Matomo, the Real Time Visitor World Map and other reports are processed every 15 minutes. There is also a Visits in Real-time report, which refreshes every five seconds and shows a wealth of data for each visitor. 

  Matomo makes migration easy

  Whether it’s the poor usability, steep learning curve, inaccurate data or privacy issues, there’s every reason to think twice about migrating your UA360 account to GA4. 

  So why not migrate to a Google Analytics alternative like Matomo instead ? One that doesn’t sample data, guarantees your customers’ privacy, offers all the features GA4 doesn’t and is already used by over 1 million sites worldwide.

  Making the switch is easy. Matomo is one of the few web analytics tools that lets you import historical Google Analytics data. In doing so, you can continue to access your historical data and develop more meaningful insights by not having to start from scratch.

  If you’re ready to start a Google Analytics migration, you can try Matomo free for 21 days — no credit card required. 

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• Make better marketing decisions with attribution modeling

  19 décembre 2017, par InnoCraft

  Do you suspect some traffic sources are not getting the rewards they deserve ? Do you want to know how much credit each of your marketing channel actually gets ?

  When you look at which referrers contribute the most to your goal conversions or purchases, Matomo (Piwik) shows you only the referrer of the last visit. However, in reality, a visitor often visits a website multiple times from different referrers before they convert a goal. Giving all credit to the referrer of the last visit ignores all other referrers that contributed to a conversion as well.

  You can now push your marketing analysis to the next level with attribution modeling and finally discover the true value of all your marketing channels. As a result, you will be able to shift your marketing efforts and spending accordingly to maximize your success and stop wasting resources. In marketing, studying this data is called attribution modeling.

  Get the true value of your referrers

  Attribution is a premium feature that you can easily purchase from the Matomo (Piwik) marketplace.

  Once installed, you will be able to :

  • identify valuable referrers that you did not see before
  • invest in potential new partners
  • attribute a new level of conversion
  • make this work very easily by filling just a couple of form information

  Identify valuable referrers that you did not see before

  You probably have hundreds or even thousands of different sources listed within the referrer reports. We also guess that you have the feeling that it is always the same referrers which are credited of conversions.
  Guess what, those data are probably biased or at least are not telling you the whole story.
  Why ? Because by default, Matomo (Piwik) only attributes all credit to the last referrer.

  

  It is likely that many non credited sources played a role in the conversion process as well as people often visit your website several times before converting and they may come from different referrers.

  This is exactly where attribution modeling comes into play. With attribution modeling, you can decide which touchpoint you want to study. For example, you can choose to give credit to all the referrers a single visitor came from each time the user visits your website, and not only look at the last one. Without this feature, chances are, that you have spent too much money and / or efforts on the wrong referrer channels in the past because many referrers that contributed to conversions were ignored. Based on the insights you get by applying different attribution models, you can make better decisions on where to shift your marketing spending and efforts.

  Invest in potential new partners

  Once you apply different attribution models, you will find out that you need to consider a new list of referrers which you before either over- or under-estimated in terms of how much they contributed to your conversions. You probably did not identify those sources before because Matomo (Piwik) shows only the last referrer before a conversion. But you can now also look at what these newly discovered referrers are saying about your company, looking for any advertising programs they may offer, getting in contact with the owner of the website, and more.

  Apply up to 6 different attribution models

  By default, Matomo (Piwik) is attributing the conversion to the last referrer only. With attribution modeling you can analyze 6 different models :

  • Last Interaction : the conversion is attributed to the last referrer, even if it is a direct access.
  • Last Non-Direct : the conversion is attributed to the last referrer, but not in the case of a direct access.
  • First Interaction : the conversion is attributed to the first referrer which brought you the visit.
  • Linear : whatever the number of referrers which brought you the conversion, they will all get the same value.
  • Position Based : first and last referrer will be attributed 40% each the conversion value, the remaining 60% is divided between the rest of the referrers.
  • Time Decay : this attribution model means that the closer to the date of the conversion is, the more your last referrers will get credit.

  Those attribution models will enable you to analyze all your referrers deeply and increase your conversions.

  Let’s look at an example where we are comparing two models : “last interaction” and “first interaction”. Our goal is to identify whether some referrers that we are currently considering as less important, are finally playing a serious role in the total amount of conversions :

  

  Comparing Last Interaction model to First Interaction model

  Here it is interesting to observe that the website www.hongkiat.com is bringing almost 90% conversion more with the first interaction model rather than the last one.

  As a result we can look at this website and take the following actions :

  • have a look at the message on this website
  • look at opportunities to change the message
  • look at opportunities to display extra marketing messages
  • get in contact with the owner to identify any other communication opportunities

  The Multi Channel Attribution report

  Attribution modeling in Matomo (Piwik) does not require you to add any tracking code. The only thing you need is to install the plugin and let the magic happen.
  Simple as pie is the word you should keep in mind for this feature. Once installed, you will find the report within the goal section, just above the goals you created :

  

  The Multi Attribution menu

  There you can select the attribution model you would like to apply or compare.

  Attribution modeling is not just about playing with a new report. It is above all an opportunity to increase the number of conversions by identifying referrers that you may have not recognized as valuable in the past. To grow your business, it is crucial to identify the most (and least) successful channels correctly so you can spend your time and money wisely.

  Learn more about Multi Channel Conversion Attribution

  The post Make better marketing decisions with attribution modeling appeared first on Analytics Platform - Matomo.

• Java shelling out to FFMPEG not running nor giving error

  4 janvier, par Todd

  I'm writing a Podcast downloader where I want to be able to download one or more podcasts then run them through FFMPEG.

  


  It does run when I run the program in Windows.
  
It does not run when I run the program in Linux - Meaning FFMPEG may or may not have been called. I have no way of knowing. I get a -1 back from process.waitfor() but no error in my Java logs and no entries at all in the ffmpeg.log file. And there is no file in the processed file directory that FFMPEG would have created.
  
It does run in Linux if I run the same command that fails in Java from the command line.

  


  FFMPEG has the permissions : rwxr-xr-x , so it doesn't seem as if it'd be a permission error. The Java code looks like :

  


      ProcessBuilder processBuilder = new ProcessBuilder( commands );
    processBuilder.redirectErrorStream( true );
    //  Added the next line to see if I could log an error from ffmpeg
    processBuilder.redirectOutput( ProcessBuilder.Redirect.appendTo( new File( "./ffmpeg.log" ) ) );

    Process process = processBuilder.start();
    BufferedReader bufferedOutputReader = new BufferedReader( new InputStreamReader( process.getInputStream() ) );
    do {
        outputString = bufferedOutputReader.readLine();
        if ( outputString != null ) {
            LOGGER.trace( outputString );
        }
    } while ( outputString != null );
    results = process.waitFor();
    LOGGER.debug( "Exit value: " + results );


  


  The commands are :

  


  2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: /bin/ffmpeg 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: -y 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: -i 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: "/home/todd/aggregator/incoming/Downrange Radio/Downrange_Radio_2023-02-15_The_Avidity_PD-10_Delivers_.mp3" 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: -ac 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: 1 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: -af 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: "atempo=1.4,volume=1.4" 
2023-10-25 16:21:52,452 224257 [pool-1-thread-4] DEBUG org.sperbolink.utils.CodecUtils - command string: "/home/todd/aggregator/processed/Downrange Radio/Downrange_Radio_2023-02-15_The_Avidity_PD-10_Delivers_.mp3"


  


  And the manually constructed and run command line that DOES work is :

  


  /bin/ffmpeg -y -i "/home/todd/aggregator/incoming/Downrange Radio/Downrange_Radio_2023-02-15_The_Avidity_PD-10_Delivers_.mp3" -ac 1 -af "atempo=1.4,volume=1.4" "/home/todd/aggregator/processed/Downrange Radio/Downrange_Radio_2023-02-15_The_Avidity_PD-10_Delivers_.mp3"


  


  The ffmpeg.log file is empty, so I'm doubting whether FFMPEG is ever being reached.

  


  What am I doing wrong ?

  


  EDIT :
I changed the commands to be

  


  "/bin/ffmpeg 2> foo.txt" 


  


  and the execution code to :

  


      ProcessBuilder processBuilder = new ProcessBuilder( commands );
//        processBuilder.redirectErrorStream( true );
    //  Added the next line to see if I could log an error from ffmpeg
//        processBuilder.redirectOutput( ProcessBuilder.Redirect.appendTo( new File( "./ffmpeg.log" ) ) );
        Process process = processBuilder.start();
        results = process.waitFor();


  


  Running that command from the command line displays a list of MMPEG commands that can be run within foo.txt. Upon running from within the Java app, the foo.txt file is empty, so it doesn't appear that FFMPEG ever gets reached. But, I'm clueless why not.

  


  EDIT 2 :
I converted the /bin/ffmpeg path to uppercase to see if that would return an error and it did.

  


  ERROR org.mrpc.utilities.ShellUtils - Cannot run program "/BIN/FFMPEG": error=2, No such file or directory


  


  This seems to confirm that the Java Process object can locate ffmpeg successfully when it is properly cased (i.e. my original code) as I don't see an error message when the case is incorrect. Still not sure why nothing happens when it is called.

  


  EDIT 3 :
I punted on this a year ago, but recently had to revisit. This time I found the answer. It is because I included double quotes around some of the command parameters. That caused the files to not be found. This link states that the Process class handles embedded spaces without any intervention required by you : ProcessBuilder adds extra quotes to command line