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• Unlocking the power of web analytics dashboards

  22 juillet, par Joe — Analytics Tips, App Analytics

  In the web analytics world, we have no shortage of data — clicks, views, scrolls, bounce rates — yet still struggle to extract valuable, actionable insights. There are facts and figures about any action anybody takes (or doesn’t take) when they visit your website, place an order or abandon their shopping cart. But all that data is often without context.

  That’s where dashboards come in : More than visual summaries, the right dashboards give context, reduce noise, and help us focus on what matters most — whether it’s boosting conversions, optimising campaigns, or monitoring data quality and compliance efforts.

  In this article, we’ll focus on :

  • The importance of data quality in web analytics dashboards
  • Different types of dashboards to use depending on your goals 
  • How to work with built-in dashboards in Matomo
  • How to customise them for your organisation’s needs

  Whether you’re building your first dashboard or refining a mature analytics strategy, this guide will help you get more out of your data.

  What is a web analytics dashboard ?

  A web analytics dashboard is an interactive interface that displays key website metrics and data visualisations in an easy-to-grasp format. It presents key data clearly and highlights potential problems, helping users quickly spot trends, patterns, and areas for improvement.

  Dashboards present data in charts, graphs and tables that are easier to understand and act upon. Users can usually drill down on individual elements for more detail, import other relevant data or adjust the time scale to get daily, weekly, monthly or seasonal views.

  Types of web analytics dashboards

  Web analytics dashboards may vary in the type of information they present and the website KPIs (key performance indicators) they track. However, sometimes the information can be the same or similar, but the context is what changes.

  Overview dashboard

  This offers a comprehensive overview of key metrics and KPIs. For example, it might show :

  • Traffic metrics, such as the total number of sessions, visits to the website, distinct users, total pages viewed and/or the average number of pages viewed per visit.
  • Engagement metrics, like average session duration, the bounce rate and/ or the exit rate by specific pages.
  • Audience metrics, including new vs. returning visitors, or visitor demographics such as age, gender or location. It might also show details of the specific device types used to access the website : desktop, mobile, or tablet.

  An overview dashboard might also include snapshots of some of the examples below.

  Acquisition dashboard

  This reveals how users arrive at a website. Although an overview dashboard can provide a snapshot of these metrics, a focused acquisition dashboard can break down website traffic even further. 

  They can reveal the percentages of traffic coming from organic search engines, social platforms, or users typing the URL directly. They can also show referrals from other websites and visitors clicking through from paid advertising sources. 

  An acquisition dashboard can also help measure campaign performance and reveal which marketing efforts are working and where to focus efforts for better results.

  Behavioural dashboard

  This dashboard shows how users interact with a website, including which pages get the most traffic and how long visitors stay before they leave. It also reveals which pages get the least traffic, highlighting where SEO optimisation or greater use of internal links may be needed.

  Behavioural dashboards can show a range of metrics, such as user engagement, navigation, page flow analysis, scroll depth, click patterns, form completion rates, event tracking, etc. 

  This behavioural data lets companies identify engaging vs. underperforming content, fix usability issues and optimise pages for better conversions. It may even show the data in heat maps, click maps or user path diagrams.

  Goals and ecommerce dashboard

  Dashboards of this type are mostly used by e-commerce websites. They’re useful because they track things like sales goal completions and revenue targets, as well as conversions, revenue, and user actions that deliver business results. 

  

  The typical metrics seen here are :

  • Goal tracking (aka conversions) in terms of completed user actions (form submissions, sign-ups, downloads, etc.) will provide funnel analysis and conversion rates. It’ll also give details about which traffic sources offer the most conversions.
  • Revenue tracking is provided via a combination of metrics. These include sales and revenue figures, average order value, top-selling items, revenue per product, and refund rates. It can also reveal how promotions, discounts and coupons affect total sales.
  • Shopping behaviour analysis tracks how users move from browsing to cart abandonment or purchase.

  These metrics help marketing teams measure campaign ROI. They also help identify high-value products and audiences and provide pointers for website refinement. For example, checkout flow optimisation might reduce abandonment.

  Technical performance dashboard

  This monitors a website’s technical health and performance metrics. It focuses on how a website’s infrastructure and backend health affect user experiences. It’ll track a lot of things, including :

  • Page load time
  • Server response time
  • DNS lookup time
  • Error rates
  • Mobile optimisation scores
  • Browser usage
  • Operating system distribution
  • Network performance
  • API response times
  • Core web vitals
  • Mobile usability issues

  This information helps organisations quickly fix issues that hurt SEO and conversions. It also helps to reduce errors that frustrate users, like checkout failures. Critically, it also helps to improve reliability and avoid downtime that can cost revenue.

  Geographic dashboard

  When an organisation wants to analyse user behaviour based on geographic location, this is the one to use. It reveals where website visitors are physically located and how their location influences their behaviour. Here’s what it tracks :

  • City, country/region 
  • Granular hotspots
  • Language preferences
  • Conversion rates by location
  • Bounce rates/engagement by location
  • Device type : Mobile vs. tablet vs desktop
  • Campaign performance by location
  • Paid ads effectiveness by location
  • Social media referrals by location
  • Load times by location

  Geographic dashboards allow companies to target marketing efforts at high-value regions. They also inform content localisation in terms of language, currency, or offers. And they help identify and address regional issues such as speed, payment methods, or cultural relevance.

  Custom segments dashboard

  This kind of dashboard allows specific subsets of an audience to be analysed based on specific criteria. For example, these subsets might include :

  • VIP customers
  • Mobile users
  • New vs. returning visitors
  • Logged-in users
  • Campaign responders
  • Product category enthusiasts. 

  What this dashboard reveals depends very much on what questions the user is trying to answer. It can provide actionable insight into why specific subsets of visitors or customers drop off at certain points. It allows specific metrics (bounce rate, conversions, etc.) to be compared across segments. 

  It can also track the performance of marketing campaigns across different audience segments, allowing marketing efforts to be tailored to serve high-potential segments. Its custom reports can also assist in problem-solving and testing hypotheses.

  

  Content performance dashboard

  This is useful for understanding how a website’s content engages users and drives business goals. Here’s what it tracks and why it matters :

  • Top-performing content
    • Most viewed pages
    • Highest time-on-page content
    • Most shared/linked content
  • Engagement metrics
    • Scroll depth (how far users read)
    • Video plays/podcast listens
    • PDF/downloads of gated content
  • Which content pieces lead to
    • Newsletter sign-ups
    • Demo requests
    • Product purchases
  • SEO health
    • Organic traffic per page
    • Keyword rankings for specific content
    • Pages with high exit rates
  • Content journey analysis
    • Entry pages that start user sessions
    • Common click paths through a site
    • Pages that often appear before conversions

  All this data helps improve website effectiveness. It lets organisations double down on what works, identify and replicate top-performing content and fix underperforming content. It can also identify content gaps, author performance and seasonal trends. The data then informs content strategy and optimisation efforts.

  The importance of data quality

  The fundamental reason we look at data is to make decisions that are informed by facts. So, it stands to reason that the quality of the underlying data is critical because it governs the quality of the information in the dashboard.

  And the data source for web analytics dashboards is often Google Analytics 4 (GA4), since it’s free and frequently installed by default on new websites. But this can be a problem because the free version of Google Analytics is limited and resorts to data sampling beyond a certain point. Let’s dig into that.

  Google Analytics 4 (GA4)

  It’s the default option for most organisations because it’s free, but GA4 has notable limitations that affect data accuracy and functionality. The big one is data sampling, which kicks in for large datasets (500,000+ events). This can skew reporting because the analysis is of subsets rather than complete data. 

  In addition, user privacy tools like ad blockers, tracking opt-outs, and disabled JavaScript can cause underreporting by 10-30%. GA4 also restricts data retention to 2-14 months and offers limited filtering and reduced control over data collection thresholds. Cross-domain tracking requires manual setup and lacks seamless integration. 

  One solution is to upgrade to Google Analytics 360 GA360, but it’s expensive. Pricing starts at $12,500/month (annual contract) plus $150,000 minimum yearly spend. The costs also scale with data volume, typically requiring $150,000−500,000 annually.

  

  Matomo’s built-in dashboards

  Matomo is a better solution for organisations needing unsampled data, longer data retention, and advanced attribution. It also provides functionality for enterprises to export their data and import it into Google BigQuery if that’s what they already use for analysis.

  Matomo Analytics takes a different approach to data quality. By focusing on privacy and data ownership, we ensure that businesses have full control over all of their data. Matomo also includes a range of built-in dashboards designed to meet the needs of different users. 

  The default options provide a starting point for tracking key metrics and gaining insight into their performance. They’re accessible by simply navigating to the reports section and selecting the relevant dashboard. These dashboards draw on raw data to provide more detailed and accurate analysis than is possible with GA4. And at a fraction of the price of GA360. 

  You can get Matomo completely free of charge as a self-hosted solution or via Matomo Cloud for a mere $29/month — vs. GA360’s $150k+/year. It also has other benefits :

  • 100% data ownership and no data sampling
  • Privacy compliance by design :
    • GDPR/CCPA-ready
    • No ad-blocker distortion
    • Cookieless tracking options
  • No data limits or retention caps
  • Advanced features without restriction :
    • Cross-domain tracking
    • Custom dimensions/metrics
    • Heatmaps/session recordings

  Customisation options

  Although Matomo’s default dashboards are powerful, the real value lies in the customisation options. These extensive and easy-to-use options empower users to tailor custom dashboards to their precise needs.

  Unlike GA4’s rigid layouts, Matomo offers drag-and-drop widgets to create, rearrange or resize reports effortlessly. You can :

  • Add 50+ pre-built widgets (e.g., traffic trends, conversion funnels, goal tracking) or create custom SQL/PHP widgets for unique metrics.
  • Segment data dynamically with filters (by country, device, campaign) and compare date ranges side-by-side.
  • Create white-label dashboards for client reporting, with custom logos, colours and CSS overrides.
  • Schedule automated PDF/email reports with personalised insights.
  • Build role-based dashboards (e.g., marketing vs. executive views) and restrict access to sensitive data.

  For developers, Matomo’s open API enables deep integrations (CRM, ERP, etc.) and custom visualisations via JavaScript. Self-hosted users can even modify the core user interface.

  Matomo : A fully adaptable analytics hub

  Web analytics dashboards can be powerful tools for visualising data, generating actionable insights and making better business decisions. But that’s only true as long as the underlying data is unrestricted and the analytics platform delivers high-quality data for analysis. 

  Matomo’s commitment to data quality and privacy sets it apart as a reliable source of accurate data to inform accurate and detailed insights. And the range of reporting options will meet just about any business need, often without any customisation.

  To see Matomo in action, watch this two-minute video. Then, when you’re ready to build your own, download Matomo On-Premise for free or start your 21-day free trial of Matomo Cloud — no credit card required.

• 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. 

• Choosing the best self-hosted open-source analytics platform

  16 juillet, par Joe

  Google Analytics (GA) is the most widely used analytics platform, with 50.3% of the top 1 million active websites using it today. You’re probably using it right now. 

  But despite being a free tool, Google Analytics is proprietary software, which means you’re handing over your browsing data, metadata and search history to a third party.

  Do you trust them ? We sure don’t.

  This lack of control can lead to potential privacy risks and compliance issues. These issues have so far resulted in fines under the EU’s General Data Protection Regulation (GDPR) of an average of €2.5 million each, for a total of almost €6.6 billion since 2018.

  Open-source analytics platforms offer a solution. They’re a safer and more transparent alternative that lets you retain full control over how you collect and store your customers’ data. But what are these tools ? Where do you find them ? And, most importantly, how do you choose the best one for your needs ?

  This guide explores the benefits and features of open-source analytics platforms and compares popular options, including Matomo, a leading self-hosted, open-source Google Analytics alternative.

  What is an open-source analytics platform ?

  An analytics platform is software that collects, processes and analyses data to gain insights, identify trends, and make informed decisions. It helps users understand past performance, monitor current activities and predict future outcomes.

  An open-source analytics platform is a type of analytics suite in which anyone can view, modify and distribute the underlying source code.

  In contrast to proprietary analytics platforms, where a single entity owns and controls the code, open-source analytics platforms adhere to the principles of free and open-source software (FOSS). This allows everyone to use, study, share, and customise the software to meet their needs, fostering collaboration and transparency.

  Open-source analytics and the Free Software Foundation

  The concept of FOSS is rooted in the idea of software freedom. According to the Free Software Foundation (FSF), this idea is defined by four fundamental freedoms granted to the user the freedom to :

  • Use or run the program as they wish, for any purpose.
  • Study how the program works and change it as they wish.
  • Redistribute copies to help others.
  • Improve the code and distribute copies of their improved versions to others.

  Open access to the source code is a precondition for guaranteeing these freedoms.

  The importance of FOSS licensing

  The FSF has been instrumental in the free software movement, which serves as the foundation for open-source analytics platforms. Among other things, it created the GNU General Public Licence (GPL), which guarantees that all software distributions include the source code and are distributed under the same licence.

  However, other licences, including several copyleft and permissive licences, have been developed to address certain legal issues and loopholes in the GPL. Analytics platforms distributed under any of these licences are considered open-source since they are FSF-compliant.

  Benefits and drawbacks of open-source analytics platforms

  Open-source analytics platforms offer a compelling alternative to their proprietary counterparts, but they also have a few challenges.

  

  Benefits of open-source analytics

  • Full data ownership : Many open-source solutions let you host the analytics platform yourself. This gives you complete control over your customers’ data, ensuring privacy and security.
  • Customisable solution : With access to the source code, you can tailor the platform to your specific needs.
  • Full transparency : You can inspect the code to see exactly how data is collected, processed and stored, helping you ensure compliance with privacy regulations.
  • Community-driven development : Open-source projects benefit from the contributions of a global community of developers. This leads to faster innovation, quicker bug fixes and, in some cases, a wider range of features.
  • No predefined limits : Self-hosted open-source analytics platforms don’t impose arbitrary limits on data storage or processing. You’re only limited by your own server resources.

  Cons of open-source analytics

  • Technical expertise required : Setting up and maintaining a self-hosted open-source platform often requires technical knowledge.
  • No live/dedicated support team : While many projects have active communities, dedicated support might be limited compared to commercial offerings.
  • Integration challenges : Integrating with other tools in your stack might require custom development, especially if pre-built integrations aren’t available.
  • Feature gaps : Depending on the specific platform, there might be gaps in functionality compared to mature proprietary solutions.

  Why open-source is better than proprietary analytics

  Proprietary analytics platforms, like Google Analytics, have long been the go-to choice for many businesses. However, growing concerns around data privacy, vendor lock-in and limited customisation are driving a shift towards open-source alternatives.

  No vendor lock-in

  Proprietary platforms lock you into their ecosystem, controlling terms, pricing and future development. Migrating data can be costly, and you’re dependent on the vendor for updates. 

  Open-source platforms allow users to switch providers, modify software and contribute to development. Contributors can also create dedicated migration tools to import data from GA and other proprietary platforms.

  Data privacy concerns

  Proprietary analytics platforms can heighten the risk of data privacy violations and subsequent fines under regulations like the GDPR and the California Consumer Privacy Act (CCPA). This is because their opaque ‘black box’ design often obscures how they collect, process and use data. 

  Businesses often have limited visibility and even less control over a vendor’s data handling. They don’t know whether these vendors are using it for their own benefit or sharing it more widely, which can lead to privacy breaches and other data protection violations.

  These fines can reach into the millions and even billions. For example, Zoom was fined $85 million in 2021 for CCPA violations, while the largest fine in history has been the €1.2 billion fine imposed on Meta by the Irish Data Protection Act (DPA) under the EU GDPR.

  Customisation

  Proprietary platforms often offer a one-size-fits-all approach. While they might have some customisation options, you’re ultimately limited by what the vendor provides. Open-source platforms, on the other hand, offer unparalleled flexibility.

  Unlimited data processing

  Proprietary analytics platforms often restrict the amount of data you can collect and process, especially on free plans. Going over these limits usually requires upgrading to a paid plan, which can be a problem for high-traffic websites or businesses with large datasets. 

  Self-hosted tools only limit data processing based on your server resources, allowing you to collect and analyse as much data as you need at no extra cost.

  No black box effect

  Since proprietary tools are closed-source, they often lack transparency in their data processing methods. It’s difficult to understand and validate how their algorithms work or how they calculate specific metrics. This “black box” effect can lead to trust issues and make it challenging to validate your data’s accuracy.

  11 Key features to look for in an open-source analytics platform

  Choosing the right open-source analytics platform is crucial for unlocking actionable insights from your customers’ data. Here are 11 key features to consider :

  

  #1. Extensive support documentation and resource libraries

  Even with technical expertise, you might encounter challenges or have questions about the platform. A strong support system is essential. Look for platforms with comprehensive documentation, active community forums and the option for professional support for mission-critical deployments.

  #2. Live analytics

  Having access to live data and reports is crucial for making timely and informed decisions. A live analytics feature allows you to :

  • Monitor website traffic as it happens.
  • Optimise campaign performance tracking.
  • Identify and respond to issues like traffic spikes, drops or errors quickly, allowing for rapid troubleshooting.

  For example, Matomo updates tracking data every 10 seconds, which is more than enough to give you a live view of your website performance.

  #3. Personal data tracking

  Understanding user behaviour is at the heart of effective analytics. Look for a platform that allows you to track personal data while respecting privacy. This might include features like :

  • Creating detailed profiles of individual users and tracking their interactions across multiple sessions.
  • Track user-specific attributes like demographics, interests or purchase history.
  • Track user ID across different devices and platforms to understand user experience.

  #4. Conversion tracking

  Ultimately, you want to measure how effective your website is in achieving your business goals. Conversion tracking allows you to :

  • Define and track key performance indicators (KPIs) like purchases, sign-ups or downloads.
  • Identify bottlenecks in the user journey that prevent conversions.
  • Measure the ROI of your marketing campaigns.

  #5. Session recordings

  Session recordings give your development team a qualitative understanding of user behaviour by letting you watch replays of individual user sessions. This can help you :

  • Identify usability issues.
  • Understand how users navigate your site and interact with different elements.
  • Uncover bugs or errors.

  #6. A/B testing

  Experimentation is key to optimising your website and improving conversion rates. Look for an integrated A/B testing feature that allows you to :

  • Test different variations of your website in terms of headlines, images, calls to action or page layouts.
  • Measure the impact on key metrics.
  • Implement changes based on statistically significant differences in user behaviour patterns, rather than guesswork.

  #7. Custom reporting and dashboards

  Every business has unique reporting needs. Look for a flexible platform that allows you to :

  • Build custom reports that focus on the metrics that matter most to you.
  • Create personalised dashboards that provide a quick overview of those KPIs.
  • Automate report generation to save your team valuable time.

  #8. No data sampling

  Data sampling can save time and processing power, but it can also lead to inaccurate insights if the sample isn’t representative of the entire dataset. The solution is to avoid data sampling entirely.

  Processing 100% of your customers’ data ensures that your reports are accurate and unbiased, providing a true picture of customer behaviour.

  #9. Google Analytics migration tools

  If you’re migrating from Google Analytics, a data export/import tool can save you time and effort. Some open-source analytics projects offer dedicated data importers to transfer historical data from GA into the new platform, preserving valuable insights. These tools help maintain data continuity and simplify the transition, reducing the manual effort involved in setting up a new analytics platform.

  #10 A broad customer base

  The breadth and diversity of an analytics platform’s customer base can be a strong indicator of its trustworthiness and capabilities. Consider the following :

  • Verticals served
  • The size of the companies that use it
  • Whether it’s trusted in highly-regulated industries

  If a platform is trusted by a large entity with stringent security and privacy requirements, such as governments or military branches, it speaks volumes about its security and data protection capabilities.

  #11 Self-hosting

  Self-hosting offers unparalleled control over your customers’ data and infrastructure.

  Unlike cloud-based solutions, where your customers’ data resides on third-party servers, self-hosting means you manage your own servers and databases. This approach ensures that your customers’ data remains within your own infrastructure, enhancing privacy and security.

  There are other features, like analytics for mobile apps, but these 11 will help shortlist your options to find the ideal tool.

  Choosing your self-hosted open-source analytics platform : A step-by-step guide

  The right self-hosted open-source analytics platform can significantly impact your data strategy. Follow these steps to make the best choice :

  

  Step #1. Define your needs and objectives

  Begin by clearly outlining what you want to achieve with your analytics platform :

  • Identify relevant KPIs.
  • Determine what type of reports to generate, their frequency and distribution.
  • Consider your privacy and compliance needs, like GDPR and CCPA.

  Step #2. Define your budget

  While self-hosted open-source platforms are usually free to use, there are still costs associated with self-hosting, including :

  • Server hardware and infrastructure.
  • Ongoing maintenance, updates and potential support fees.
  • Development resources if you plan to customise the platform.

  Step #3. Consider scalability and performance

  Scaling your analytics can be an issue with self-hosted platforms since it means scaling your server infrastructure as well. Before choosing a platform, you must think about :

  • Current traffic volume and projected growth.
  • Your current capacity to handle traffic.
  • The platform’s scalability options.

  Step #4. Research and evaluate potential solutions

  Shortlist a few different open-source analytics platforms that align with your requirements. In addition to the features outlined above, also consider factors like :

  • Ease of use.
  • Community and support.
  • Comprehensive documentation.
  • The platform’s security track record.

  Step #5. Sign up for a free trial and conduct thorough testing

  Many platforms offer free trials or demos. Take advantage of these opportunities to test the platform’s features, evaluate the user interface and more.

  You can embed multiple independent tracking codes on your website, which means you can test multiple analytics platforms simultaneously. Doing so helps you compare and validate results based on the same data, making comparisons more objective and reliable.

  Step #6. Plan for implementation and ongoing management

  After choosing a platform, follow the documentation to install and configure the software. Plan how you’ll migrate existing data if you’re switching from another platform.

  Ensure your team is trained on the platform, and establish a plan for updates, security patches and backups. Then, you’ll be ready to migrate to the new platform while minimising downtime.

  Top self-hosted open-source analytics tools

  Let’s examine three prominent self-hosted open-source analytics tools.

  Matomo

  Main Features	Analytics updated every 10 seconds, custom reports, dashboards, user segmentation, goal tracking, e-commerce tracking, funnels, heatmaps, session recordings, A/B testing, SEO tools and more advanced features.
  Best for	Businesses of all sizes and from all verticals. Advanced users
  Licencing	GPLv3 (core platform).Various commercial licences for plugins.
  Pricing	Self-hosted : Free (excluding paid plugins).Cloud version : Starts at $21.67/mo for 50K website hits when paid annually.

  

  Matomo Analytics dashboard

  Matomo is a powerful web analytics platform that prioritises data privacy and user control. It offers a comprehensive suite of features, including live analytics updated every 10 seconds, custom reporting, e-commerce tracking and more. You can choose between a full-featured open-source, self-hosted platform free of charge or a cloud-based, fully managed paid analytics service.

  Matomo also offers 100% data ownership and has a user base of over 1 million websites, including heavyweights like NASA, the European Commission, ahrefs and the United Nations.

  Plausible Analytics

  Main Features	Basic website analytics (page views, visitors, referrers, etc.), custom events, goal tracking and some campaign tracking features.
  Best for	Website owners, bloggers and small businesses.Non-technical users.
  Licencing	AGPLv3.
  Pricing	Self-hosted : FreeCloud version : Starts at $7.50/mo for 10K website hits when paid annually.

  

  Plausible Analytics 
  (Image source)

  Plausible Analytics is a lightweight, privacy-focused analytics tool designed to be simple and easy to use. It provides essential website traffic data without complex configurations or intrusive tracking.

  Fathom Lite & Fathom Analytics

  Main features	Basic website analytics (page views, visitors, referrers, etc.), custom events and goal tracking.
  Best for	Website owners and small businesses.Non-technical users.
  Licencing	Fathom Lite : MIT Licence (self-hosted).Fathom Analytics : Proprietary.
  Pricing	Fathom Lite : Free but currently unsupported.Cloud version : Starts at $12.50/month for up to 50 sites when paid annually.

  

  Fathom Analytics 
  (Image source)

  Fathom started as an open-source platform in 2018. But after the founders released V1.0.1, they switched to a closed-source, paid, proprietary model called Fathom Analytics. Since then, it has always been closed-source.

  However, the open-source version, Fathom Lite, is still available. It has very limited functionality, uses cookies and is currently unsupported by the company. No new features are under development and uptime isn’t guaranteed.

  Matomo vs. Plausible vs. Fathom

  Matomo, Plausible, and Fathom are all open-source, privacy-focused alternatives to Google Analytics. They offer features like no data sampling, data ownership, and EU-based cloud hosting.

  Here’s a head-to-head comparison of the three :

  	Matomo	Plausible	Fathom
  Focus	Comprehensive, feature-rich, customizable	Simple, lightweight, beginner-friendly	Simple, lightweight, privacy-focused
  Target User	Businesses, marketers and analysts seeking depth	Beginners, bloggers, and small businesses	Website owners and users prioritising simplicity
  Open Source	Fully open-source	Fully open-source	Limited open-source version
  Advanced analytics	Extensive	Very limited	Very limited
  Integrations	100+	Limited	Fewer than 15
  Customisation	High	Low	Low
  Data management	Granular control, raw data access, complex queries	Simplified, no raw data access	Simplified, no raw data access
  GDPR features	Compliant by design, plus GDPR Manager	Guides only	Compliant by design
  Pricing	Generally higher	Generally lower	Intermediate
  Learning curve	Steeper	Gentle	Gentle

  The open-core dilemma

  Open-source platforms are beneficial and trustworthy, leading some companies to falsely market themselves as such.

  Some were once open-source but later became commercial, criticised as “bait-and-switch.” Others offer a limited open-source “core” with proprietary features, called the “open core” model. While this dual licensing can be ethical and sustainable, some abuse it by offering a low-value open-source version and hiding valuable features behind a paywall.

  However, other companies have embraced the dual-licensing model in a more ethical way, providing a valuable solution with a wide range of tools under the open-source license and only leaving premium, non-essential add-ons as paid features.

  Matomo is a prime example of this practice, championing the principles of open-source analytics while developing a sustainable business model for its users’ benefit.

  Choose Matomo as your open-source data analytics tool

  Open-source analytics platforms offer compelling advantages over proprietary solutions like Google Analytics. They provide greater transparency, data ownership and customisation. Choosing an open-source analytics platform over a proprietary one gives you more control over your customers’ data and supports compliance with user privacy regulations.

  With its comprehensive features, powerful tools, commitment to privacy and active community, Matomo stands out as a leading choice. Make the switch to Matomo for ethical, user-focused analytics.

  Try Matomo for free.