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• How to Implement Cross-Channel Analytics : A Guide for Marketers

  17 avril 2024, par Erin

  Every modern marketer knows they have to connect with consumers across several channels. But do you know how well Instagram works alongside organic traffic or your email list ? Are you even tracking the impacts of these channels in one place ?

  You need a cross-channel analytics solution if you answered no to either of these questions. 

  In this article, we’ll explain cross-channel analytics, why your company probably needs it and how to set up a cross-channel analytics solution as quickly and easily as possible.

  What is cross-channel analytics ? 

  Cross-channel analytics is a form of marketing analytics that collects and analyses data from every channel and campaign you use.

  The result is a comprehensive view of your customer’s journey and each channel’s role in converting customers. 

  Cross-channel analytics lets you track every channel you use to convert customers, including :

  • Your website
  • Social media profiles
  • Email
  • Paid search
  • E-commerce
  • Retargeting campaigns

  Cross-channel analytics solves one of the most significant issues of cross-channel or multi-channel marketing efforts : measurement. 

  Research shows that only 16% of marketing tech stacks allow for accurate measurement of multi-channel initiatives across channels. 

  That’s a problem, given the staggering number of touchpoints in a typical buyer’s conversion path. However, it can be fixed using a cross-channel analytics approach that lets you measure the performance of every channel and assign a dollar value to its role in every conversion. 

  The difference between cross-channel analytics and multi-channel analytics

  Cross-channel analytics and multi-channel analytics sound very similar, but there’s one key difference you need to know. Multi-channel analytics measures the performance of several channels, but not necessarily all of them, nor the extent to which they work together to drive conversions. Conversely, cross-channel analytics measures the performance of all your marketing channels and how they work together. 

  What are the benefits of cross-channel analytics 

  Cross-channel analytics offers a lot of marketing and business benefits. Here are the ones marketing managers love most.

  Get a complete view of the customer journey

  Implementing a cross-channel analytics solution is the only way to get a complete view of your customer journey. 

  Cross-channel marketing analytics lets you see your customer journey in high definition, allowing you to build comprehensive customer profiles using data from multiple sources across every touchpoint. 

  

  The result ? You get to understand how every customer behaves at every point of the customer journey, why they convert or leave your funnel, and which channels play the biggest role. 

  In short, you get to see why customers convert so you can learn how to convert more of them.

  Personalise the customer experience

  According to a McKinsey study, customers demand personalisation, and brands that excel at it generate 40% more revenue. Deliver the personalisation they desire and reap the benefits with cross-channel analytics. 

  When you understand the customer journey in detail, it becomes much easier to personalise your website and marketing efforts to their preferences and behaviours.

  Identify your most effective marketing channels

  Cross-channel marketing helps you understand your marketing efforts to see how every channel impacts conversions. 

  Take a look at the screenshot from Matomo below. Cross-channel analytics lets you get incredibly granular — we can see the number of conversions of organic search drives and the performance of individual search engines. 

  

  This makes it easy to identify your most effective marketing channels and allocate your resources appropriately. It also allows you to ask (and answer) which channels are the most effective.

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  Attribute conversions accurately 

  An attribution model decides how you assign credit for each customer conversion to different touchpoints on the customer journey. Without a cross-channel analytics solution, you’re stuck using a standard attribution model like first or last click. 

  These models will show you how customers first found your brand or which channel finally convinced them to convert, but it doesn’t help you understand the role all your channels played in the conversion. 

  Cross-channel analytics solves this attribution problem. Rather than attributing a conversion to the touchpoint that directly led to the sale, cross-channel data gives you the real picture and allows you to use multi-touch attribution to understand which touchpoints generate the most revenue.

  How to set up cross-channel analytics

  Now that you know what cross-channel analytics is and why you should use it, here’s how to set up your solution. 

  1. Determine your objectives

  Defining your marketing goals will help you build a more relevant and actionable cross-channel analytics solution. 

  If you want to improve marketing attribution, for example, you can choose a platform with that feature built-in. If you care about personalisation, you could choose a platform with A/B testing capabilities to measure the impact of your personalisation efforts. 

  1. Set relevant KPIs

  You’ll want to track relevant KPIs to measure the marketing effectiveness of each channel. Put top-of-the-funnel metrics aside and focus on conversion metrics. 

  These include :

  • Conversion rate
  • Average visit duration
  • Bounce rate

  1. Implement tracking and analytics tools

  Gathering customer data from every channel and centralising it in a single location is one of the biggest challenges of cross-channel analytics. Still, it’s made easier with the right tracking tool or analytics platform. 

  The trick is to choose a platform that lets you measure as many of your channels as possible in a single platform. With Matomo, for example, you can track search, paid search, social and email campaigns and your website analytics.

  1. Set up a multi-touch attribution model

  Now that you have all of your data in one place, you can set up a multi-touch attribution model that lets you understand the extent to which each marketing channel contributes to your overall success. 

  There are several attribution models to choose from, including :

  

  Each model has benefits and drawbacks, so choosing the right model for your organisation can be tricky. Rather than take a wild guess, evaluate each model against your marketing objectives, sales length cycle and data availability.

  For example, if you want to focus on optimising customer acquisition costs, a model that prioritises earlier touchpoints will be better. If you care about conversions, you might try a time decay model. 

  1. Turn data into insights with reports

  One of the big benefits of choosing a tool like Matomo, which consolidates data in one place, is that it significantly speeds up and simplifies reporting.

  When all the data is stored in one platform, you don’t need to spend hours combing through your social media platforms and copying and pasting analytics data into a spreadsheet. It’s all there and ready for you to run reports.

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

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  1. Take action

  There’s no point implementing a cross-channel analytics system if you aren’t going to take action. 

  But where should you start ?

  Optimising your budgets and prioritising marketing spend is a great starting point. Use your cross-channel insights to find your most effective marketing channels (they’re the ones that convert the most customers or have the highest ROI) and allocate more of your budget to them. 

  You can also optimise the channels that aren’t pulling their weight if social media is letting you down ; for example, experiment with tactics like social commerce that could drive more conversions. Alternatively, you could choose to stop investing entirely in these channels.

  Cross-channel analytics best practices

  If you already have a cross-channel analytics solution, take things to the next level with the following best practices. 

  Use a centralised solution to track everything

  Centralising your data in one analytics tool can streamline your marketing efforts and help you stay on top of your data. It won’t just save you from tabbing between different browsers or copying and pasting everything into a spreadsheet, but it can also make it easier to create reports. 

  Think about consumer privacy 

  If you are looking at a new cross-channel analytics tool, consider how it accounts for data privacy regulations in your area. 

  You’re going to be collecting a lot of data, so it’s important to respect their privacy wishes. 

  It’s best to choose a platform like Matomo that complies with the strictest privacy laws (CCPA, GDPR, etc.).

  Monitor data in real time

  So, you’ve got a holistic view of your marketing efforts by integrating all your channels into a single tool ?

  Great, now go further by monitoring the impact of your marketing efforts in real time.

  

  With a web analytics platform like Matomo, you can see who visits your site, what they do, and where they come from through features like the visits log report, which even lets you view individual user sessions. This lets you measure the impact of posting on a particular social channel or launching a new offer. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  Reallocate marketing budgets based on performance

  When you track every channel, you can use a multi-touch attribution model like position-based or time-decay to give every channel the credit it deserves. But don’t just credit each channel ; turn your valuable insights into action. 

  Use cross-channel attribution analytics data to reallocate your marketing budget to the most profitable channels or spend time optimising the channels that aren’t pulling their weight. 

  Cross-channel analytics platforms to get started with 

  The marketing analytics market is huge. Mordor Intelligence valued it at $6.31 billion in 2024 and expects it to reach $11.54 billion by 2029. Many of these platforms offer cross-channel analytics, but few can track the impact of multiple marketing channels in one place. 

  So, rather than force you to trawl through confusing product pages, we’ve shortlisted three of the best cross-channel analytics solutions. 

  Matomo

  

  Matomo is a web analytics platform that lets you collect and centralise your marketing data while giving you 100% accurate data. That includes search, social, e-commerce, campaign tracking data and comprehensive website analytics.

  Better still, you get the necessary tools to turn those insights into action. Custom reporting lets you track and visualise the metrics that matter, while conversion optimisation tools like built-in A/B testing, heatmaps, session recordings and more let you test your theories. 

  Google Analytics

  

  Google Analytics is the most popular and widely used tool on the market. The level of analysis and customisation you can do with it is impressive for a free tool. That includes tracking just about any event and creating reports from scratch. 

  Google Analytics provides some cross-channel marketing features and lets you track the impact of various channels, such as social and search, but there are a couple of drawbacks. 

  Privacy can be a concern because Google Analytics collects data from your customers for its own remarketing purposes. 

  It also uses data sampling to generate wider insights from a small subset of your data. This lack of accurate data reporting can cause you to generate false insights.

  With Google Analytics, you’ll also need to subscribe to additional tools to gain advanced insights into the user experience. So, consider that while this tool is free, you’ll need to pay for heatmaps, session recording and A/B testing tools to optimise effectively.

  Improvado

  

  Improvado is an analytics tool for sales and marketing teams that extracts thousands of metrics from hundreds of sources. It centralises data in data warehouses, from which you can create a range of marketing dashboards.

  While Improvado does have analytics capabilities, it is primarily an ETL (extraction, transform, load) tool for organisations that want to centralise all their data. That means marketers who aren’t familiar with data transformations may struggle to get their heads around the complexity of the platform.

  Make the most of cross-channel analytics with Matomo

  Cross-channel analytics is the only way to get a comprehensive view of your customer journey and understand how your channels work together to drive conversions.

  Then you’re dealing with so many channels and data ; keeping things as simple as possible is the key to success. That’s why over 1 million websites choose Matomo. 

  Our all-in-one analytics solution measures traditional web analytics, behavioural analytics, attribution and SEO, so you have 100% accurate data in one place. 

  Try it free for 21 days. No credit card required.

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• FFmpeg Concatenation Command Fails in Flutter App

  18 février 2024, par Petro

  I'm developing a Flutter application where I need to concatenate several images into a single video file using FFmpeg. Despite following the recommended practices and trying multiple variations of the FFmpeg command, all my attempts result in failure with an exit code of 1.

  


  FFMPEG Version : ffmpeg_kit_flutter: ^6.0.3-LTS

  


  All of the files are present when this happens...


  


  Environment :
Flutter app targeting Android
Using ffmpeg-kit-flutter for FFmpeg operations

  


  Objective :
To concatenate multiple images stored in the app's file system into a video.

  


  Code Snippet :
I'm generating a list of image paths, writing them to a file (ffmpeg_list.txt), and using that file with FFmpeg's concat demuxer. Here's a simplified version of my code :

  


  Future<void> _createVideoFromImages() async {&#xA;  final Directory appDir = await getApplicationDocumentsDirectory();&#xA;  final Uuid uuid = Uuid();&#xA;  final String videoFileName = uuid.v4();&#xA;  final String outputPath = &#x27;${appDir.path}/$videoFileName.mp4&#x27;;&#xA;  &#xA;  final Directory tempImageDir = await Directory(&#x27;${appDir.path}/tempImages&#x27;).create();&#xA;  final StringBuffer ffmpegInput = StringBuffer();&#xA;  int index = 0;&#xA;&#xA;  for (var image in _images) {&#xA;    String newFileName = &#x27;img${index&#x2B;&#x2B;}${Path.extension(image.path)}&#x27;.replaceAll(&#x27; &#x27;, &#x27;_&#x27;);&#xA;    final String newPath = &#x27;${tempImageDir.path}/$newFileName&#x27;;&#xA;    await image.copy(newPath);&#xA;    ffmpegInput.writeln("file &#x27;$newPath&#x27;");&#xA;  }&#xA;&#xA;  final String listFilePath = &#x27;${appDir.path}/ffmpeg_list.txt&#x27;;&#xA;  await File(listFilePath).writeAsString(ffmpegInput.toString());&#xA;&#xA;  if(await File(listFilePath).exists()) {&#xA;    String ffmpegCommand = "-v verbose -f concat -safe 0 -i $listFilePath -vsync vfr -pix_fmt yuv420p -c:v libx264 -r 30 $outputPath";&#xA;    // Additional commands tried here...&#xA;    await FFmpegKit.execute(ffmpegCommand).then((session) async {&#xA;      // Error handling code...&#xA;    });&#xA;  }&#xA;}&#xA;&#xA;Result Logs:&#xA;I/flutter: file exists at /data/user/0/com.example.app/app_flutter/ffmpeg_list.txt&#xA;I/flutter: FFmpeg command: -v verbose -f concat -safe 0 -i /data/user/0/com.example.app/app_flutter/ffmpeg_list.txt -vsync vfr -pix_fmt yuv420p -c:v libx264 -r 30 /data/user/0/com.example.app/app_flutter/58fdf92b-47b0-49d1-be93-d9c95870c733.mp4&#xA;I/flutter: Failed to create video&#xA;I/flutter: FFmpeg process exited with:1&#xA;I/flutter: FFmpeg command failed with logs: ffmpeg version n6.0 Copyright (c) 2000-2023 the FFmpeg developers...&#xA;</void>

  


  Attempts :
-Simplified the FFmpeg command by removing -vsync vfr, -pix_fmt yuv420p, and adjusting -r 30 parameters.
-Tried using the -c copy option to avoid re-encoding.
-Tested with a single image to ensure basic functionality works.
-Checked file permissions and ensured all images and the list file are accessible.

  


  Despite these attempts, the command fails without providing specific error messages related to the command's execution. The verbose logs do not offer insights beyond the FFmpeg version and configuration.

  


  Questions :
Are there known issues with FFmpeg's concat that might lead to such failures ?
Are there alternative approaches ?

  


  I appreciate any insights or suggestions the community might have. Thank you !

  


  Full code :

  


  Future<void> _createVideoFromImages() async {&#xA;    final Directory appDir = await getApplicationDocumentsDirectory();&#xA;    final Uuid uuid = Uuid();&#xA;    final String videoFileName = uuid.v4();&#xA;    final String outputPath = &#x27;${appDir.path}/$videoFileName.mp4&#x27;;&#xA;    final String singleImagePath = _images[0]!.path;&#xA;&#xA;// Create a directory to store renamed images to avoid any naming conflict&#xA;    final Directory tempImageDir = await Directory(&#x27;${appDir.path}/tempImages&#x27;).create();&#xA;&#xA;    final StringBuffer ffmpegInput = StringBuffer();&#xA;    int index = 0; // To ensure unique filenames&#xA;&#xA;    for (var image in _images) {&#xA;      // Generate a new filename by replacing spaces with underscores and adding an index&#xA;      String newFileName = &#x27;img${index&#x2B;&#x2B;}${p.extension(image!.path)}&#x27;.replaceAll(&#x27; &#x27;, &#x27;_&#x27;);&#xA;      final String newPath = &#x27;${tempImageDir.path}/$newFileName&#x27;;&#xA;&#xA;      // Copy and rename the original file to the new path&#xA;      await image!.copy(newPath);&#xA;&#xA;      // Add the new, safely named file path to the ffmpegInput&#xA;      ffmpegInput.writeln("file &#x27;$newPath&#x27;");&#xA;    }&#xA;&#xA;// Write the paths to a temporary text file for FFmpeg&#x27;s concat demuxer&#xA;    final String listFilePath = &#x27;${appDir.path}/ffmpeg_list.txt&#x27;;&#xA;    await File(listFilePath).writeAsString(ffmpegInput.toString());&#xA;&#xA;    //check if file exists&#xA;    if(await File(listFilePath).exists()) {&#xA;      print("file exists at $listFilePath");&#xA;&#xA;// Use the generated list file in the concat command&#xA;      String ffmpegCommand = "-v verbose -f concat -safe 0 -i $listFilePath -vsync vfr -pix_fmt yuv420p -c:v libx264 -r 30 $outputPath";&#xA;      String ffmpegCommand2 = "-v verbose -f concat -safe 0 -i $listFilePath -c:v libx264 $outputPath";&#xA;      String ffmpegCommand3 = "-v verbose -i $singleImagePath -frames:v 1 $outputPath";&#xA;      //print command&#xA;      print("FFmpeg command: $ffmpegCommand");&#xA;&#xA;&#xA;      await FFmpegKit.execute(ffmpegCommand).then((session) async {&#xA;        // Check the session for success or failure&#xA;        final returnCode = await session.getReturnCode();&#xA;        if (returnCode!.isValueSuccess()) {&#xA;          print("Video created successfully: $outputPath");&#xA;          //okay all set, now set the video to be this:&#xA;          _actual_video_file_ready_to_upload = File(outputPath);&#xA;          print ("video path is: ${outputPath}");&#xA;        } else {&#xA;          print("Failed to create video");&#xA;          print("FFmpeg process exited with:" &#x2B; returnCode.toString());&#xA;          // Command failed; capture and log error details&#xA;          await session.getLogsAsString().then((logs) {&#xA;            print("FFmpeg command failed with logs: $logs");&#xA;          });&#xA;          // Handle failure, e.g., by showing an error message&#xA;          showSnackBarHelperERRORWrapLongString(context, "Failed to create video");&#xA;        }&#xA;      });&#xA;&#xA;      //try command 2&#xA;      if(_actual_video_file_ready_to_upload == null) {&#xA;        await FFmpegKit.execute(ffmpegCommand2).then((session) async {&#xA;          // Check the session for success or failure&#xA;          final returnCode = await session.getReturnCode();&#xA;          if (returnCode!.isValueSuccess()) {&#xA;            print("Video created successfully: $outputPath");&#xA;            //okay all set, now set the video to be this:&#xA;            _actual_video_file_ready_to_upload = File(outputPath);&#xA;            print ("video path is: ${outputPath}");&#xA;          } else {&#xA;            print("Failed to create video");&#xA;            print("FFmpeg process exited with:" &#x2B; returnCode.toString());&#xA;            // Command failed; capture and log error details&#xA;            await session.getLogsAsString().then((logs) {&#xA;              print("FFmpeg command failed with logs: $logs");&#xA;            });&#xA;            // Handle failure, e.g., by showing an error message&#xA;            showSnackBarHelperERRORWrapLongString(context, "Failed to create video");&#xA;          }&#xA;        });&#xA;      }&#xA;      //try command 3&#xA;      if(_actual_video_file_ready_to_upload == null) {&#xA;        await FFmpegKit.execute(ffmpegCommand3).then((session) async {&#xA;          // Check the session for success or failure&#xA;          final returnCode = await session.getReturnCode();&#xA;          if (returnCode!.isValueSuccess()) {&#xA;            print("Video created successfully: $outputPath");&#xA;            //okay all set, now set the video to be this:&#xA;            _actual_video_file_ready_to_upload = File(outputPath);&#xA;            print ("video path is: ${outputPath}");&#xA;          } else {&#xA;            print("Failed to create video");&#xA;            print("FFmpeg process exited with:" &#x2B; returnCode.toString());&#xA;            // Command failed; capture and log error details&#xA;            await session.getLogsAsString().then((logs) {&#xA;              print("FFmpeg command failed with logs: $logs");&#xA;            });&#xA;            // Handle failure, e.g., by showing an error message&#xA;            showSnackBarHelperERRORWrapLongString(context, "Failed to create video");&#xA;          }&#xA;        });&#xA;      }&#xA;    }else{&#xA;      print("file does not exist at $listFilePath");&#xA;    }&#xA;  }&#xA;</void>

  


• Privacy-enhancing technologies : Balancing data utility and security

  18 juillet, par Joe

  In the third quarter of 2024, data breaches exposed 422.61 million records, affecting millions of people around the world. This highlights the need for organisations to prioritise user privacy. 

  Privacy-enhancing technologies can help achieve this by protecting sensitive information and enabling safe data sharing. 

  This post explores privacy-enhancing technologies, including their types, benefits, and how our website analytics platform, Matomo, supports them by providing privacy-focused features.

  What are privacy-enhancing technologies ? 

  Privacy Enhancing Technologies (PETs) are tools that protect personal data while allowing organisations to process information responsibly. 

  In industries like healthcare, finance and marketing, businesses often need detailed analytics to improve operations and target audiences effectively. However, collecting and processing personal data can lead to privacy concerns, regulatory challenges, and reputational risks.

  PETs minimise the collection of sensitive information, enhance security and allow users to control how companies use their data. 

  Global privacy laws like the following are making PETs essential for compliance :

  • General Data Protection Regulation (GDPR) in the European Union
  • California Consumer Privacy Act (CCPA) in California
  • Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada
  • Lei Geral de Proteção de Dados (LGPD) in Brazil

  Non-compliance can lead to severe penalties, including hefty fines and reputational damage. For example, under GDPR, organisations may face fines of up to €20 million or 4% of their global annual revenue for serious violations. 

  Types of PETs 

  What are the different types of technologies available for privacy protection ? Let’s take a look at some of them. 

  Homomorphic encryption

  Homomorphic encryption is a cryptographic technique in which users can perform calculations on cipher text without decrypting it first. When the results are decrypted, they match those of the same calculation on plain text. 

  This technique keeps data safe during processing, and users can analyse data without exposing private or personal data. It is most useful in financial services, where analysts need to protect sensitive customer data and secure transactions. 

  Despite these advantages, homomorphic encryption can be complex to compute and take longer than other traditional methods. 

  Secure Multi-Party Computation (SMPC)

  SMPC enables joint computations on private data without revealing the raw data. 

  In 2021, the European Data Protection Board (EDPB) issued technical guidance supporting SMPC as a technology that protects privacy requirements. This highlights the importance of SMPC in healthcare and cybersecurity, where data sharing is necessary but sensitive information must be kept safe. 

  For example, several hospitals can collaborate on research without sharing patient records. They use SMPC to analyse combined data while keeping individual records confidential. 

  Synthetic data

  Synthetic data is artificially generated to mimic real datasets without revealing actual information. It is useful for training models without compromising privacy. 

  Imagine a hospital wants to train an AI model to predict patient outcomes based on medical records. Sharing real patient data, however, poses privacy challenges, so that can be changed with synthetic data. 

  Synthetic data may fail to capture subtle nuances or anomalies in real-world datasets, leading to inaccuracies in AI model predictions.

  Pseudonymisation

  Pseudonymisation replaces personal details with fake names or codes, making it hard to determine who the information belongs to. This helps keep people’s personal information safe. Even if someone gets hold of the data, it’s not easy to connect it back to real individuals. 

  

  Pseudonymisation works differently from synthetic data, though both help protect individual privacy. 

  When we pseudonymise, we take factual information and replace the bits that could identify someone with made-up labels. Synthetic data takes an entirely different approach. It creates new, artificial information that looks and behaves like real data but doesn’t contain any details about real people.

  Differential privacy

  Differential privacy adds random noise to datasets. This noise helps protect individual entries while still allowing for overall analysis of the data. 

  It’s useful in statistical studies where trends need to be understood without accessing personal details.

  For example, imagine a survey about how many hours people watch TV each week. 

  Differential privacy would add random variation to each person’s answer, so users couldn’t tell exactly how long John or Jane watched TV. 

  However, they could still see the average number of hours everyone in the group watched, which helps researchers understand viewing habits without invading anyone’s privacy.

  Zero-Knowledge Proofs (ZKP)

  Zero-knowledge proofs help verify the truth without exposing sensitive details. This cryptographic approach lets someone prove they know something or meet certain conditions without revealing the actual information behind that proof.

  Take ZCash as a real-world example. While Bitcoin publicly displays every financial transaction detail, ZCash offers privacy through specialised proofs called Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). These mathematical proofs confirm that a transaction follows all the rules without broadcasting who sent money, who received it, or how much changed hands.

  The technology comes with trade-offs, though. 

  Creating and checking these proofs demands substantial computing power, which slows down transactions and drives up costs. Implementing these systems requires deep expertise in advanced cryptography, which keeps many organisations from adopting them despite their benefits.

  Trusted Execution Environment (TEE)

  TEEs create special protected zones inside computer processors where sensitive code runs safely. These secure areas process valuable data while keeping it away from anyone who shouldn’t see it.

  TEEs are widely used in high-security applications, such as mobile payments, digital rights management (DRM), and cloud computing.

  Consider how companies use TEEs in the cloud : A business can run encrypted datasets within a protected area on Microsoft Azure or AWS Nitro Enclaves. Due to this setup, even the cloud provider can’t access the private data or see how the business uses it. 

  TEEs do face limitations. Their isolated design makes them struggle with large or spread-out computing tasks, so they don’t work well for complex calculations across multiple systems.

  Different TEE implementations often lack standardisation, so there can be compatibility issues and dependence on specific vendors. If the vendor stops the product or someone discovers a security flaw, switching to a new solution often proves expensive and complicated.

  Obfuscation (Data masking)

  Data masking involves replacing or obscuring sensitive data to prevent unauthorised access. 

  It replaces sensitive data with fictitious but realistic values. For example, a customer’s credit card number might be masked as “1234-XXXX-XXXX-5678.” 

  The original data is permanently altered or hidden, and the masked data can’t be reversed to reveal the original values.

  Federated learning

  Federated learning is a machine learning approach that trains algorithms across multiple devices without centralising the data. This method allows organisations to leverage insights from distributed data sources while maintaining user privacy.

  For example, NVIDIA’s Clara platform uses federated learning to train AI models for medical imaging (e.g., detecting tumours in MRI scans). 

  Hospitals worldwide contribute model updates from their local datasets to build a global model without sharing patient scans. This approach may be used to classify stroke types and improve cancer diagnosis accuracy.

  Now that we have explored the various types of PETs, it’s essential to understand the principles that guide their development and use. 

  Key principles of PET (+ How to enable them with Matomo) 

  PETs are based on several core principles that aim to balance data utility with privacy protection. These principles include :

  Data minimisation

  Data minimisation is a core PET principle focusing on collecting and retaining only essential data.

  Matomo, an open-source web analytics platform, helps organisations to gather insights about their website traffic and user behaviour while prioritising privacy and data protection. 

  Recognising the importance of data minimisation, Matomo offers several features that actively support this principle :

  • Cookieless tracking : Eliminates reliance on cookies, reducing unnecessary data collection.
  • IP anonymisation : Automatically anonymises IP addresses, preventing identification of individual users.

  

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  • Custom data retention policies : Allows organisations to define how long user data is stored before automatic deletion.

  7Assets, a fintech company, was using Google Analytics and Plausible as their web analytics tools. 

  However, with Google Analytics, they faced a problem of unnecessary data tracking, which created legal work overhead. Plausible didn’t have the features for the kind of analysis they wanted. 

  They switched to Matomo to enjoy the balance of privacy yet detailed analytics. With Matomo, they had full control over their data collection while also aligning with privacy and compliance requirements.

  Transparency and User Control

  Transparency and user control are important for trust and compliance. 

  Matomo enables these principles through :

  • Consent management : Offers integration with Consent Mangers (CMPs), like Cookiebot and Osano, for collecting and managing user consent.
  • Respect for DoNotTrack settings : Honours browser-based privacy preferences by default, empowering users with control over their data.

  

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  • Opt-out mechanisms : These include iframe features that allow visitors to opt out of tracking. 

  Security and Confidentiality

  Security and confidentiality protect sensitive data against inappropriate access. 

  Matomo achieves this through :

  • On-premise hosting : Gives organisations the ability to host analytics data on-site for complete data control.
  • Data security : Protects stored information through access controls, audit logs, two-factor authentication and SSL encryption.
  • Open source code : Enables community reviews for better security and transparency.

  Purpose Limitation

  Purpose limitation means organisations use data solely for the intended purpose and don’t share or sell it to third parties. 

  Matomo adheres to this principle by using first-party cookies by default, so there’s no third-party involvement. Matomo offers 100% data ownership, meaning all the data organisations get from our web analytics is of the organisation, and we don’t sell it to any external parties. 

  Compliance with Privacy Regulations

  Matomo aligns with global privacy laws such as GDPR, CCPA, HIPAA, LGPD and PECR. Its compliance features include :

  • Configurable data protection : Matomo can be configured to avoid tracking personally identifiable information (PII).
  • Data subject request tools : These provide mechanisms for handling requests like data deletion or access in accordance with legal frameworks.
  • GDPR manager : Matomo provides a GDPR Manager that helps businesses manage compliance by offering features like visitor log deletion and audit trails to support accountability.

  

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  Mandarine Academy is a French-based e-learning company. It found that complying with GDPR regulations was difficult with Google Analytics and thought GA4 was hard to use. Therefore, it was searching for a web analytics solution that could help it get detailed feedback on its site’s strengths and friction points while respecting privacy and GDPR compliance. With Matomo, it checked all the boxes.

  Data collaboration : A key use case of PETs

  One specific area where PETs are quite useful is data collaboration. Data collaboration is important for organisations for research and innovation. However, data privacy is at stake. 

  This is where tools like data clean rooms and walled gardens play a significant role. These use one or more types of PETs (they aren’t PETs themselves) to allow for secure data analysis. 

  Walled gardens restrict data access but allow analysis within their platforms. Data clean rooms provide a secure space for data analysis without sharing raw data, often using PETs like encryption. 

  Tackling privacy issues with PETs 

  Amidst data breaches and privacy concerns, organisations must find ways to protect sensitive information while still getting useful insights from their data. Using PETs is a key step in solving these problems as they help protect data and build customer trust. 

  Tools like Matomo help organisations comply with privacy laws while keeping data secure. They also allow individuals to have more control over their personal information, which is why 1 million websites use Matomo.

  In addition to all the nice features, switching to Matomo is easy :

  “We just followed the help guides, and the setup was simple,” said Rob Jones. “When we needed help improving our reporting, the support team responded quickly and solved everything in one step.” 

  To experience Matomo, sign up for our 21-day free trial, no credit card details needed.