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• 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.

• 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.

  

  (Image Source)

  • 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.

  

  (Image Source)

  • 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.

  

  (Image Source)

  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. 

• 7 Mixpanel alternatives to consider for better web and product analytics

  1er août, par Joe

  Mixpanel is a web and mobile analytics platform that brings together product and marketing data so teams can see the impact of their actions and understand the customer journey. 

  It’s a well-rounded tool with features that help product teams understand how customers navigate their website or app. It’s also straightforward to set up, GDPR compliant, and easy for non-technical folks to use, thanks to an intuitive UI and drag-and-drop reports. 

  However, Mixpanel is just one of many product and web analytics platforms. Some are cheaper, others are more secure, and a few have more advanced or specialist features.

  This article will explore the leading Mixpanel alternatives for product teams and marketers. We’ll cover their key features, what users love about them, and why they may (or may not) be the right pick for you. 

  Mixpanel : an overview

  Let’s start by giving Mixpanel its dues. The platform does a great job of arming product teams with an arsenal of tools to track the impact of their updates, find ways to boost engagement and track which features users love. 

  Marketing teams use the platform to track customers through the sales funnel, attribute marketing campaigns and find ways to optimise spend. 

  There’s plenty to like about Mixpanel, including : 

  • Easy setup and maintenance : Mixpanel’s onboarding flow allows you to build a tracking plan and choose the specific events to measure. When Mixpanel collects data, you’ll see an introductory “starter board.” 
  • Generous free plan : Mixpanel doesn’t limit freemium users like some platforms. Collect data on 20 million monthly events, use pre-built templates and access its Slack community. There are also no limits on collaborators or integrations.
  • Extensive privacy configurations : Mixpanel provides strong consent management configurations. Clients can let their users opt out of tracking, disable geolocation and anonymise their data. It also automatically deletes user data after five years and offers an EU Data Residency Program that can help customers meet GDPR regulations. 
  • Comprehensive features : Mixpanel gives marketers and product teams the tools and features they need to understand the customer, improve the product and increase conversions. 
  • Easy-to-use UI : The platform prioritises self-service data, meaning users don’t need to be technically minded to use Mixpanel. Drag-and-drop dashboards democratise access to data and let anyone on your team find answers to their questions.

  You wouldn’t be reading this page if Mixpanel offered everything, though. No platform is perfect, and there are several reasons people may want to look for a Mixpanel alternative :
  

  • No self-hosted option : You’ll never have complete control over your data with Mixpanel due to the lack of a self-hosted option. Data will always live on Mixpanel’s servers, meaning compliance with data regulations like GDPR isn’t a given.
  • Lack of customisation : Mixpanel doesn’t offer much flexibility when it comes to visualising data. While the platform’s in-built reports are accessible to everyone, you’ll need a developer to build custom reports. 
  • Not open source : Mixpanel’s proprietary software doesn’t provide the transparency, security and community that comes with using open-source software like Matomo. Proprietary software isn’t inherently wrong, but it could mean your analytics solution isn’t future-proof. 
  • Steep learning curve : The learning curve can be steep unless you’re a developer. While setting up the software is straightforward, Mixpanel’s reliance on manual tracking means teams must spend a lot of time creating and structuring events to collect the data they need.

  If any of those struck a chord, see if one of the following seven Mixpanel alternatives might better fulfil your needs. 

  The top 7 Mixpanel alternatives

  Now, let’s look at the alternatives.

  We’ll explain exactly how each platform differs from Mixpanel, its standout features, strengths, common community critiques, and when it may be (or may not be) the right choice. 

  1. Matomo

  Matomo is a privacy-focused, open-source web and mobile analytics platform. As a proponent of an ethical web, Matomo prioritises data ownership and privacy protection. 

  It’s a great Mixpanel alternative for those who care about data privacy. You own 100% of your data and will always comply with data regulations like GDPR when using the platform. 

  

  Main dashboard with visits log, visits over time, visitor map, combined keywords, and traffic sources
  (Image Source)

  Matomo isn’t short on features, either. Product teams and marketers can evaluate the entire user journey, capture detailed visitor profiles, combine web, mobile and app reports, and use custom reporting to generate the specific insides they need.

  Key features :

  • Complete app and web analytics : Matomo tracks performance metrics and KPIs across web, app and mobile. Understand which pages users visit, how long they stay and how they move between devices.
  • Marketing attribution : Built-in marketing attribution capabilities make it easy for marketers to pinpoint their most profitable campaigns and channels. 
  • User behaviour tracking : Generate in-depth user behaviour data thanks to heatmaps, form analytics and session recordings.

  Strengths

  • On-premise and cloud versions : Use Matomo for free on your servers or subscribe to Matomo Cloud for hosting and additional support. Either way, you remain in control of your data.
  • Exceptional customer support : On-premise and Matomo Cloud users get free access to the forum. Cloud customers get dedicated support, which is available at an additional cost for on-premise customers. 
  • Consent-free tracking : Matomo doesn’t ruin the user’s experience with cookie banners. 
  • Open-source software : Matomo’s software is free to use, modify, and distribute. Users get a more secure, reliable and transparent solution thanks to the community of developers and contributors working on the project. Matomo will never become proprietary software, so there’s no risk of vendor lock-in. You will always have access to the source code, raw data and APIs. 

  Common community critiques :

  • On-premise setup : The on-premise version requires some technical knowledge and a server.
  • App tracking features : Some features, like heatmaps, available on web analytics aren’t available in-app analytics. Features may also differ between Android SDK and iOS SDK.

  Price : 

  Matomo has three plans :

  • Free : on-premise analytics is free to use
  • Cloud : Hosted business plans start at €22 per month
  • Enterprise : custom-priced, cloud-hosted enterprise plan tailored to meet a business’s specific requirements.

  There’s a free 21-day trial for Matomo Cloud and a 30-day plugin trial for Matomo On-Premise.

  2. Adobe Analytics

  Adobe Analytics is an enterprise analytics platform part of the Adobe Experience Cloud. This makes it a great Mixpanel alternative for those already using other Adobe products. But, getting the most from the platform is challenging without the rest of the Adobe ecosystem. 

  

  Adobe Analytics Analysis Workspace training tutorial
  (Image Source)

  Adobe Analytics offers many marketing tools, but product teams may find their offer lacking. Small or inexperienced teams may also need help using this feature-heavy platform. 

  Key features :

  • Detailed web and marketing analytics : Adobe lets marketers draw in data from almost any source to get a comprehensive view of the customer journey. 
  • Marketing attribution : There’s a great deal of flexibility when crediting conversions. There are unlimited attribution models, too, including both paid and organic media channels.
  • Live Stream : This feature lets brands access raw data in near real time (with a 30- to 90-second delay) to assess the impact of marketing campaigns as soon as they launch. 

  Strengths :

  • Enterprise focus : Adobe Analytics’s wide range of advanced features makes It attractive to large companies with one or more high-traffic websites or apps. 
  • Integrations : Adobe Analytics integrates neatly with other Adobe products like Campaign and Experience Cloud). Access marketing, analytics and content management tools in one place. 
  • Customisation : The platform makes it easy for users to tailor reports and dashboards to their specific needs.

  Common community critiques :

  • Few product analytics features : While marketers will likely love Adobe, product teams may find it lacking. For example, the heatmap tool isn’t well developed. You’ll need to use Adobe Target to run A/B tests.
  • Complexity : The sheer number of advanced features can make Adobe Analytics a confusing experience for inexperienced or non-technically minded users. While a wealth of support documentation is available, it will take longer to generate value. 
  • Price : Adobe Analytics costs several thousand dollars monthly, making it suitable only for enterprise clients.

  Price : 

  Adobe offers three tiers : Select, Prime and Ultimate. Pricing is only available on request.

  3. Amplitude

  Amplitude is a product analytics and event-tracking platform. It is arguably the most like-for-like platform on this list, and there is a lot of overlap between Amploitduce’s and Mixpanel’s capabilities. 

  

  The Ask Amplitude™ feature helps build and analyse conversion funnel charts.
  (Image Source)

  The platform is an excellent choice for marketers who want to create a unified view of the customer by tracking them across different devices. This is possible with several other analytics platforms on this list (Matomo included), but Mixpanel doesn’t centralise data from web and app users in a signal report. 

  Amplitude also has advanced features Mixpanel doesn’t have, like feature management and AI, as well as better customisation. 

  Key features :

  • Product analytics : Amplitude comes packed with features product teams will use regularly, including customer journey analysis, session replays and heatmaps. 
  • AI : Amplitude AI can clean up data, generate insights and detect anomalies.
  • Feature management : Amplitude provides near-real-time feedback on feature usage and adoption rates so that product teams can analyse the impact of their work. Developers can also use the platform to manage progressive rollouts. 

  Strengths :

  • Self-serve reporting : The platform’s self-serve nature means employees of all levels and abilities can get the insights they need. That includes data teams that want to run detailed and complex analyses. 
  • Integrated web experimentation. Product teams or marketers don’t need a third-party tool to run A/B tests because Amplitude has a comprehensive feature that lets users set up tests, collect data and create reports. 
  • Extensive customer support : Amplitude records webinars, holds out-of-office sessions and runs a Slack community to help customers extract as much value as possible.

  Common community critiques :

  • Off-site tracking : While Amplitude has many features for tracking customer interaction across your product, it lacks ways to track customers once they are off-site. This is not great for marketing attribution, for example, or growing search traffic. 
  • Too complex : The sheer number of things Amplitude tracks can overwhelm inexperienced users who must spend time learning how to use the platform. 
  • Few templates : Few stock templates make getting started with Amplitude even harder. Users have to create reports from scratch rather than customise a stock graph. 

  Price : 

  • Starter : Free to track up to 50,000 users per month. 
  • Plus : $49 per month to track up to 300,000 users.
  • Growth : Custom pricing for no tracking limits
  • Enterprise : Custom pricing for dedicated account managers and predictive analytics
    

  4. Google Analytics

  Google Analytics is the most popular web analytics platform. It’s completely free to use and easy to install. Although there’s no customer support, the thousands of online how-to videos and articles go some way to making up for it. 

  

  GA dashboard showing acquisition, conversion and behaviour data across all channels 
  (Image Source)

  Most people are familiar with Google’s web analytics data, which makes it a great Mixpanel alternative for marketers. However, product teams may struggle to get the qualitative data they need.

  Key features :

  • User and conversion tracking : People don’t just use Google Analytics because it’s free. The platform boasts a competitive user engagement and conversion tracking offering, which lets businesses of any size understand how consumers navigate their sites and make purchases. 
  • Audience segmentation : Segment audiences based on time and event parameters.
  • Google Ads integration : Track users from the moment they interact with one of your ads. 

  Strengths :

  • It’s free : Web and product analytics platforms can cost hundreds of dollars monthly and put a sizable dent in a small business marketing budget. Google provides the basic tools most marketers need for free.
  • Cross-platform tracking : GA4 lets teams track mobile and web analytics in one place, which wasn’t possible in Universal Analytics.
  • A wealth of third-party support : There’s no shortage of Google Analytics tutorials on YouTube to help you set up and use the platform. 

  Common community critiques :

  • Data privacy concerns : There are concerns about Google’s lack of compliance with regulations like GDPR. The workaround is asking people for permission to collect their data, but that requires a consent pop-up that can disrupt the user experience. 
  • No CRO features : Google Analytics lacks the conversion optimisation features of other tools in this list, including Matomo. It can’t record sessions, track user interactions via a heatmap or run A/B tests. 
  • AI data sampling : Google generates insights using AI-powered data sampling rather than analysing your actual data, which may make your data inaccurate. 

  Price : 

  Google Analytics is free to use. Google also offers a premium version, GA 360, which starts at $50,000 per year. 

  5. Heap

  Heap is a digital insights and product analytics platform. It gives product managers and marketers the quantitative and qualitative data they need to improve conversion rates, improve product features, and reduce churn. 

  

  Heap marketing KPI dashboard
  (Image Source)

  The platform offers everything you’d expect from a product analytics perspective, including session replays, heatmaps and user journey analysis. It even has an AI tool that can answer your questions. 

  Key features :

  • Auto-capture : Unlike other analytics tools (Mixpanel and Google Analytics, for instance), you don’t need to manually code events. Heap’s auto-capture feature automatically collects every user interaction, allowing for retroactive analysis. 
  • Segmentation : Create distinct customer cohorts based on behaviour. Integrate other platforms like Marketo to use that information to personalise marketing campaigns. 
  • AI CoPilot : Heap has a generative AI tool, CoPilot, that answers questions like “How many people visited the About page last week ?” It can also handle follow-up questions and suggest what to search next. 

  Strengths :

  • Integrations : Heap’s integrations allow teams to centralise data from dozens of third-party applications. Popular integrations include Shopify and Salesforce. Heap can also connect to your data warehouse. 
  • Near real-time tracking : Heap has a live data feed that lets teams track user behaviour in near real-time (there’s a 15-second delay).
  • Collaboration : Heap facilitates cross-department collaboration via shared spaces and shared reports. You can also share session replays across teams.

  Common community critiques :

  • Struggles at scale : Heap’s auto-capture functionality can be more of a pain than a perk when working at scale. Sites with a million or more weekly visitors may need to limit data capture.
  • Data overload : Heap tracks so much data it can be hard to find the specific events you want to measure.
  • Poor-quality graphics : Heap’s visualisations are basic and may not appeal to non-technically minded users.

  Price : 

  Heap offers four plans with pricing available on request.

  • Free
  • Growth
  • Pro
  • Premier
    

  6. Hotjar

  Hotjar is a product experience insight tool that analyses why users behave as they do. The platform collects behavioural data using heatmaps, surveys and session recordings. 

  It’s a suitable alternative for product teams and marketers who care about collecting qualitative rather than quantitative data. 

  

  New heatmap feature in hotjar
  (Image Source)

  It’s not your typical analytics platform, however. Hotjar doesn’t track site visits or conversions, so teams use it alongside a web analytics platform like Google Analytics or Matomo.

  Key features :

  • Surveys : Product teams can place surveys on specific pages to capture quantitative and qualitative data. 
  • Heatmaps : Hotjar provides several heatmaps — click, scroll and interaction — that show how users behave when browsing your site. 
  • Session recordings : Support quantitative analytics data with videos of genuine user behaviour. It’s like watching someone browsing your site over their shoulder. 

  Strengths :

  • User-friendly interface : The tool is easy to navigate and accessible to all employees. Anyone can start using it quickly. 
  • Funnel analysis : Use Hotjar’s range of tools to analyse your entire funnel, identifying friction points and opportunities to improve the customer experience. 
  • Cross-platform tracking : Hotjar compares user behaviour across desktop, mobile and app. 

  Common community critiques :

  • Limited web analytics : While Hotjar is great for understanding customer behaviour, it doesn’t collect standard web analytics data. 
  • Data retention : Hotjar only retains data for one month to a year on some plans.
  • Impacts page speed : The tool’s code impacts your site’s performance, leading to slower load times. 

  Price : 

  • Free : Up to five thousand monthly sessions, including screen recordings and heatmaps
  • Growth : $49 per month for 7,000 to 10,000 monthly sessions
  • Pro : Custom pricing for up to 500 million monthly sessions
  • Enterprise : Custom pricing for up to 6 billion monthly sessions. 
    

  7. Kissmetrics

  Kissmetrics is a web and mobile analytics platform that aims to help teams generate more revenue and acquire more users through product-led growth. 

  As such, the platform offers more to marketers than product teams — particularly online store owners and SaaS businesses. 

  

  Kissmetrics funnel report 
  (Image Source)

  Kissmetrics provides a suite of behavioural analytics tools that analyse how customers move through your funnel, where they drop off and why. That’s great for marketers, but product teams will struggle to understand how customers actually use their product once they’ve converted.

  Key features :

  • User journey mapping : Follow individual customer journeys to learn how each customer finds and engages with your brand. 
  • Funnel analysis : Funnel reports help marketers track cart abandonments and other drop-offs along the customer journey. 
  • A/B testing : Kissmetrics’s A/B testing tool measures how customers respond to different page layouts

  Strengths :

  • Detailed revenue metrics : Kissmetrics makes measuring customer lifetime value, churn rate, and other revenue-focused KPIs easy. 
  • Stellar onboarding experience : Kissmetrics gives new users a detailed walkthrough and tutorial, which helps non-technical users get up to speed. 
  • Integrations : Integrate data from dozens of platforms and tools, such as Facebook, Instagram, Shopify, and Woocommerce, so all your data is in one place. 

  Common community critiques :

  • Predominantly web-based : Kissmetrics focuses on web-based traffic over app- or cross-platform tracking. It may be fine for some teams, but product managers or marketers who track users across apps and smartphones may want to look elsewhere. 
  • Slow to load large data sources : The platform can be slow to load, react to, and analyse large volumes of data, which could be an issue for enterprise clients. 
  • Price : Kissmetrics is significantly more expensive than Mixpanel. There is no freemium tier, meaning you’ll need to pay at least $199 monthly. 

  Price : 

  • Silver : $199 per month for up to 2 million monthly events
  • Gold : $499 per month for up to five million monthly events
  • Platinum : Custom pricing

  Switch from Mixpanel to Matomo

  When it comes to extracting deep insights from user data while balancing compliance and privacy protection, Mixpanel delivers mixed results. If you want a more straightforward alternative, more websites chose Matomo over Mixpanel for their analytics because of its :

  • Accurate web analytics collected in an ethical, GDPR-compliant manner
  • Behavioural analytics (like heatmaps and session recordings) to understand how users engage with your site
  • Rolled-up cross-platform reporting for mobile and apps
  • Flexibility and customisation with 250+ settings, plentiful plugins and integrations, APIs, raw data access
  • Open-source code to create plugins to fit your specific business needs
  • 100% data ownership with Matomo On-Premise and Matomo Cloud

  Over one million websites in 190+ countries use Matomo’s powerful web analytics platform. Join them today by starting a free 21-day trial — no credit card required.