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• Consent management platforms : Keys to compliance and user trust

  14 juin, par Joe

  Today’s marketing managers and data analysts face a tricky balancing act : gaining meaningful customer insights while respecting user privacy. Finding ways to navigate the maze of complex privacy regulations while managing consent at scale can be daunting. 

  Consent management platforms (CMPs) offer a solution. They allow companies to collect data ethically, manage user consent efficiently, and comply with privacy regulations like Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

  This guide explains everything you need to know about CMPs : how they function, why they’re essential for data governance, and how they work hand-in-hand with analytics platforms to promote transparency and build trust with users.

  What is a consent management platform (CMP) and what is it for ?

  A consent management platform (CMP) helps organisations collect, organise, and store user consent for personal data processing purposes. In short, it’s a tool that ensures data collection respects user privacy and complies with regulations like the GDPR and CCPA.

  Without a CMP, businesses could face hefty fines and penalties for violating data privacy laws in different parts of the world. This shows how vital these tools are to all modern businesses.

  How do consent management platforms work ?

  CMPs give users a clear and straightforward way to provide explicit consent for data collection. These platforms manage both the technical aspects of consent storage and the user experience on your site or app.

  Here’s a simplified breakdown :

  • Cookie consent banners : The CMP displays a banner whenever a user visits your website. This banner explains the types of personal information collected and for what purpose.
  • User choice : The user can accept or reject cookies and trackers. They can often customise their preferences to choose which specific data types they’re willing to share.
  • Preference storage : The CMP stores the user’s choices. This information helps ensure that you only collect and process the permitted data.
  • Integration with other systems : CMPs integrate with other systems, such as analytics platforms and advertising networks, to ensure that data collection and processing comply with the user’s choices throughout the customer experience.

  

  A key feature of CMPs is their role in shaping privacy policy design. This design encompasses the layout, visual elements, and cues employed to seek user consent.

  A recent study by Karlstad University in Sweden showed that privacy policy design significantly influences user comprehension and willingness to disclose information. In other words, it affects consent rates considerably and is key to enhancing data collection.

  Importance of consent management for compliance

  As the world becomes increasingly interconnected, consent management is taking centre stage. Although it applies to all technologies and systems that gather or handle personal data, few instances are as relevant as smart homes.

  Smart home devices have unique access to our personal spaces and private lives. They represent a unique challenge to consent management since one person is potentially granting access to personal data from themselves and other people who may be inside or around the house.

  A 2023 study by the University College London and the University of Oxford pointed out that clear design principles and granular, contextual permission structures are essential in these situations.

  However, consent management isn’t just best practice. It’s a widespread legal requirement. Not meeting these requirements can result in hefty penalties and reputational damage to your organisation.

  Consent management under GDPR

  The European Union’s GDPR is a data protection law applicable to organisations that process the personal data of individuals residing in the European Economic Area (EEA). It’s based on the principle of opting in.

  The GDPR is one of the strongest data privacy laws globally. For non-compliance, fines can be up to €20 million or 4% of the company’s total global turnover (whichever is higher).

  It’s also one of the most heavily enforced privacy laws. According to enforcementtracker.com, Meta was fined €1.2 billion in 2023, with GDPR fines reaching over €2 billion that year alone. In the UK, the largest GDPR fine is €22.05 million, according to Statista. It pays to comply.

  The GDPR has specific rules around consent, including that it must be :

  • Freely given : Users must not be pressured or coerced.
  • Specific : Must be given for specific data processing purposes.
  • Informed : Users must be provided with clear and concise information.
  • Unambiguous : Permission must be granted through clear and affirmative action, such as checking a box or tapping a button.

  CMPs help you meet these requirements by providing a transparent and user-friendly way to obtain and manage consent.

  Consent management under CCPA

  The CCPA is another privacy protection law for businesses collecting personal information from California residents. It grants Californians the right to know what data is being collected about them, to prevent it from being sold, and to request its deletion.

  CMPs support CCPA compliance by enabling users to exercise their rights and ensuring transparent data collection practices.

  Managing consent under other regulatory frameworks

  In addition to the GDPR and CCPA, numerous other privacy regulations can impact your organisation. These regulations include :

  • The COPPA in the US
  • Brazil’s LGPD
  • Japan’s APPI
  • Canada’s PIPEDA.
  • Australia’s Privacy Act 1988 

  A CMP will help streamline the process by providing a clear, practical framework to ensure you meet all applicable requirements.

  Key features to look for in a CMP

  Choosing the right CMP is crucial for global business.

  Here are some key features to consider :

  Custom banners

  Consent banners are often among users’ first digital interactions with your brand. It should be clear, concise and visually appealing. Look for a CMP that allows you to :

  • Customise the banner’s design to match your website’s branding and aesthetics.
  • Control the banner’s positioning for optimal visibility.

  End-user management tools

  The CMP should also offer a user-friendly interface allowing visitors to grant, manage and withdraw consent.

  This includes customisable banners, granular permissions, and a preference centre. The latter is a dedicated space where users can manage their preferences anytime.

  Integration capabilities with existing systems

  The CMP should integrate with your existing technology stack, including your analytics platform, marketing automation tools and CRM. This integration ensures a smooth workflow and prevents data silos.

  How to select the right CMP for your organisation

  To find the perfect CMP, focus on your specific needs and priorities. Here’s a step-by-step guide to help you make an informed decision :

  Assessing organisational needs and goals

  Start by clearly defining your organisation’s requirements. Consider the following :

  • Types of data collected : What personal data do you collect (for example, cookies, IP addresses, location data) ?
  • Compliance requirements : Which privacy regulations must you comply with (GDPR, CCPA, COPPA) ?
  • Website or app complexity : How complex is your website or app in terms of user interactions and data collection points ?
  • Budget : How much are you willing to invest in a CMP ?

  Comparing features and pricing

  Once you thoroughly understand your needs, you can compare the features and pricing of various CMPs. Look for key features like :

  • Customisable banners
  • Granular options
  • Preference centre
  • Integration with existing systems
  • Analytics and reporting

  Once you’ve shortlisted a few options, compare the pricing and choose the best value for your budget. Take advantage of free trials before committing to a paid plan.

  Checking verified user reviews

  Read user reviews on platforms like G2 or Trustpilot to get an idea of the strengths and weaknesses of different CMPs. Look for reviews from similar organisations regarding size, industry and compliance requirements.

  Integration with a privacy-focused analytics platform

  A consent management platform acts as the bridge between your users and your analytics and marketing teams. It ensures user preferences are communicated to your analytics setup, so data collection and analysis align with their choices and comply with privacy regulations. 

  Finding a consent manager integration that works with your analytics setup is essential for businesses.

  Top five consent management platforms

  The CMP market is pretty competitive, with many players providing excellent solutions. According to Emergen Research, it was valued at $320.9 million in 2021 and is growing at 21.2%.

  Here are five of our top choices 

  1. usercentrics

  usercentrics is a comprehensive CMP with customisable banners, granular consent options and a preference centre.

  

  usercentrics geolocation rulesets page (Source : Usercentrics)

  This Google-certified CMP allows you to create global and regional consent rules to ensure compliance with local regulations like GDPR, CCPA and LGPD. For a smooth implementation, usercentrics provides access to a knowledgeable support team and a dedicated customer success executive.

  It’s worth noting that Usercentrics is the CMP we use here at Matomo. It helps us in our mission to collect and analyse data ethically and with a privacy-first mindset.

  • Key features : Customisable banners, granular permissions, cross-domain and cross-device capabilities, automatic website scans, reporting and analytics.
  • Pricing : Usercentrics offers a free plan and four paid subscription plans from €7 to €50 per month.

  2. Osano

  Osano is a user-friendly CMP focusing on transparency and ease of use.

  

  Osano’s main dashboard (Source : Osano)

  Osano can scan websites for tracking technologies without impacting the user experience.

  • Key features : Customisable banners, multi-language support, granular consent options, a preference centre and access to a knowledgeable team of compliance specialists.
  • Pricing : Osano offers a self-service free plan and a paid plan at $199 per month.

  3. Cookiebot

  Cookiebot is another popular CMP with numerous integration options, including Matomo and other analytics tools. 

  

  Cookiebot consent banner options (Source : Cookiebot)

  • Key features : A cookie scanner, a privacy trigger or button allowing users to change their consent settings, a consent management API and advanced analytics.
  • Pricing : Cookiebot offers a free plan and paid plans ranging from €7 to €50 per month.

  4. CookieYes

  CookieYes is well-suited for small businesses and websites with basic privacy needs. 

  

  CookieYes cookie banner options (Source : CookieYes)

  It offers various features, including multilingual support, geo-targeting, privacy policy generation, and a preference centre. CookieYes also integrates with analytics and CMS tools, making it easy to implement as part of your stack.

  • Key features : Customisable consent banners, granular consent options, preference centre, integration with Matomo, reporting and analytics.
  • Pricing : You can use CookieYes for free or subscribe to one of their three paid plans, which range from $10 to $55 per month.

  5. Tarte au Citron

  Tarte au Citron is an open-source, lightweight, and customisable CMP developed in France.

  

  (Source : Tarte au Citron)

  Its focus is on transparency and user experience. It provides many features free of charge, but many do require some technical knowledge to deploy. There’s also a paid subscription with ongoing support and faster implementation.

  Tarte au Citron integrates with Matomo, which is also open-source. If you’re building an open-source stack for your analytics, Matomo and Tarte au Citron make an excellent pair.

  • Key features : Open-source, customisable consent banners, integration with Matomo, works with over 220 services.
  • Pricing : You can deploy the open-source core for free, but Tarte au Citron offers three paid licenses starting at €190 for one year and reaching €690 for a lifetime license.

  How to implement cookie consent the right way

  Implementing cookie consent requires precision, time and effort. But doing it wrong can result in significant legal penalties and severe reputational damage, eroding user trust and impacting your brand’s standing. Here are the key dos and don’ts of consent :

  

  Provide clear and concise information

  Use plain language that is easy for anyone to understand. Avoid using technical terms or legal jargon that may confuse users.

  Prioritise transparency

  Be upfront about your data collection practices. Clearly state what data you collect, how you use it and who you share it with. Provide links to your privacy and cookie policies for users who want to learn more.

  Offer granular control

  Give users detailed control over as many of their cookie preferences as possible. Allow them to choose which categories of tracking cookies they consent to, such as strictly necessary, performance and marketing cookies.

  Implement user-friendly banners

  Ensure banners are prominently displayed, easy to understand, and use clear and concise language. Also, make sure they’re accessible to all users, including those with disabilities.

  Respect “do not track” settings

  It’s essential to honour users’ choices when they enable their “do not track” browser setting.

  Document consent

  Maintain a record of user consent. This will help you demonstrate compliance with data privacy regulations and provide evidence of user consent in case of an audit or investigation.

  Regularly review and update consent policies

  Review and update your customer consent policies regularly to ensure they comply with evolving data privacy regulations and reflect your current data collection practices.

  Cookie consent pitfalls to avoid

  Here are some common pitfalls to avoid that may lead to legal penalties, loss of user trust or inaccurate analytics :

  • Avoid lengthy and complicated explanations. Overwhelming users with dense legal jargon or overly technical details can lead to consent fatigue and reduce the likelihood of informed consent.
  • Don’t force users to accept all cookies or none. Blanket consent options violate user autonomy and fail to comply with regulations like the GDPR.
  • Don’t make information about your data collection practices hard to find. Hidden or buried privacy policies breed suspicion and erode trust.
  • Avoid pre-checking all cookie consents. Pre-checked boxes imply consent without explicit user action, which is not compliant with GDPR and similar regulations. Users must actively opt in, not out.

  Emerging consent management trends 

  Consent management is constantly evolving and driven by new technologies, regulations, and user expectations. Here are some emerging trends to watch out for in the short term :

  • Increased automation : AI and machine learning are helping automate consent management processes, making them more efficient and effective.
  • Enhanced user experience : CMPs are becoming more user-friendly, focusing on providing an intuitive experience.
  • Privacy-preserving analytics : CMPs are being integrated with privacy-preserving analytics platforms, such as Matomo, to enable organisations to gain insights into user behaviour without compromising privacy.
  • Google Consent Mode : In 2024, Google rolled out Consent Mode v2 to align with the Digital Markets Act. Due to upcoming privacy regulations, more versions may be coming soon.

  The Privacy Governance Report 2024 also highlights the increasing complexity of managing data privacy, with more than four in five privacy professionals taking on additional responsibilities in their existing roles. This trend will likely continue in the coming years as more privacy laws are enacted.

  Addressing upcoming privacy regulations

  Data privacy and user consent requirements continue to emerge and evolve. Businesses must stay informed and adapt their practices accordingly.

  

  In 2025, several new privacy regulations are going into effect, including :

  • New state-level privacy laws in eight US states :
    • Delaware (1 January 2025)
    • Iowa (1 January 2025)
    • Nebraska (1 January 2025)
    • New Hampshire (1 January 2025)
    • New Jersey (15 January 2025)
    • Tennessee (1 July 2025)
    • Minnesota (31 July 2025)
    • Maryland (1 October 2025)
  • The EU’s Artificial Intelligence Act (which will be implemented from 1 August 2024 through 2 August 2026) and other AI-focused regulations.
  • The UK Adequacy Decision Review has a deadline of 27 December 2025.

  Organisations that collect, process or otherwise handle data from Europe and the above-named US states should proactively prepare for these changes by :

  • Conducting regular privacy impact assessments
  • Reviewing consent mechanisms regularly
  • Implementing data minimisation strategies
  • Providing user-friendly privacy controls

  Future-proofing your consent management strategy

  CMPs are essential for managing consent preferences, protecting user privacy, and earning customers’ trust through transparency and ethical data practices.

  When choosing a CMP, you should consider key features such as integration capabilities, customisation options and user-friendly interfaces.

  Integrating a CMP with a privacy-first analytics solution like Matomo allows you to collect and analyse data in a way that’s compliant and respectful of user preferences. This combination helps maintain data integrity while demonstrating a strong commitment to privacy. 

  Start your 21-day free trial today.

• What is audience segmentation ? The 8 main types and examples

  8 juillet, par Joe

  Marketers must reach the right person at the right time with the most relevant messaging. Customers now expect personalised experiences, which means generic campaigns won’t work. Audience segmentation is the key to doing this. 

  This isn’t an easy process because there are many types of audience segmentation. The wrong approach or poor data management can lead to irrelevant messaging or lost customer trust.

  This article breaks down the most common types of audience segmentation with examples highlighting their usefulness and information on segmenting campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation involves dividing a customer base into distinct, smaller groups with similar traits or common characteristics. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  Consider this : an urban office worker and a rural farmer have vastly different needs. Targeted marketing efforts aimed at agriculture workers in rural areas can stir up interest in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. For example, they would run a golf club ad in a golf magazine, not the national newspaper.

  Now that businesses have more customer data, audience segments can be narrower and more specific.

  Why audience segmentation matters

  Hyken’s latest Customer Service and CX Research Study revealed that 81% of customers expect a personalised experience.

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect relevant product recommendations — like a shoe polishing kit after buying nice leather loafers.

  Without audience segmentation, customers can get frustrated with post-sale activities. For example, the same follow-up email won’t make sense for all customers because each is at a different stage of the user journey. 

  Some more benefits that audience segmentation offers : 

  • Personalised targeting is a major advantage. Tailored messaging makes customers feel valued and understood, enhancing their loyalty to the brand. 
  • Businesses can understand users’ unique needs, which helps in better product development. For example, a fitness brand might develop separate offerings for casual exercisers and professional athletes.
  • Marketers can allocate more resources to the most promising segments. For example, a luxury skincare brand might target affluent customers with premium ads and use broader campaigns for entry-level products.

  8 types of audience segmentation

  There are eight types of audience segmentation : demographic, behavioural, psychographic, technographic, transactional, contextual, lifecycle and predictive segmentation.

  

  Let’s take an in-depth look at each of them.

  Demographic segmentation 

  Demographic segmentation divides a larger audience based on data points like location, age or other factors.

  The most basic segmentation factor is location, which is critical in marketing campaigns. Geographic segmentation can use IP addresses to separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle, especially in Europe, where the GDPR makes advanced demographics a more tentative subject. 

  It’s also possible to use age, education level, and occupation to target marketing campaigns. It’s essential to navigate this terrain thoughtfully, responsibly, and strictly adhere to privacy regulations.

  Potential data points :

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation :

  A clothing brand targeting diverse locations must account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, promoting lightweight or summer attire would be more effective in warmer climates. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :

  

  (Image Source)

  Each collection features differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — just single out the factors when building a campaign. And it’s unnecessary to rely on data mining to get information for segmentation. 

  Consider incorporating a short survey into email sign-up forms so people can self-select their interests and preferences. This is a great way to segment ethically and without the need for data-mining companies. Responses can offer valuable insights into audience preferences while enhancing engagement, decreasing bounce rates, and improving conversion rates.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with a website or an app.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Conversions (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency :

  One effective method involves using a web analytics tool like Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, identify what can significantly enhance visitor conversions. 

  

  For example, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, set up a conditional CTA within the video player. Make it pop up after the user finishes watching the video. Use a specific form and assign it to a particular segment for each case study. This way, you can get the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation involves segmenting audiences based on interpretations of their personality or preferences.

  Potential data points :

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation :

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. However, for rugby athletes, the ad is very relevant.

  

  (Image Source)

  Brands that want to avoid social platforms can use surveys about hobbies and interests to segment their target audience ethically.

  Technographic segmentation

  Technographic segmentation separates customers based on the hardware or software they use. 

  Potential data points :

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience :

  After noticing a serious influx of tablet users accessing their platform, a leading news outlet optimised their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better tablet-readability. These changes gave users a more enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation uses customers’ past purchases to match marketing messages with user needs.

  Consumers often relate personalisation with their actual transactions rather than their social media profiles. 

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • Most recent purchase date

  Example of effective transactional segmentation :

  Relevant product recommendations and coupons are among the best uses of transactional segmentation. These individualised marketing emails can strengthen brand loyalty and increase revenue.

  A pet supply store identifies a segment of customers who consistently purchase cat food but not other pet products. To encourage repeat purchases within this segment, the store creates targeted email campaigns offering discounts or loyalty rewards for cat-related items.

  Contextual segmentation 

  Contextual segmentation helps marketers connect with audiences based on real-time factors like time of day, weather or location. It’s like offering someone exactly what they need when they need it the most.

  Potential data points :

  • GPS location
  • Browsing activity
  • Device type

  Examples of contextual segmentation :

  A ride-hailing app might promote discounted rides during rush hour in busy cities or suggest carpooling options on rainy days. Similarly, an outdoor gear retailer could target users in snowy regions with ads for winter jackets or snow boots.

  The key is relevance. Messages that align with what someone needs at that moment feel helpful rather than intrusive. Businesses need tools like geolocation tracking and real-time analytics to make this work. 

  Also, keep it subtle and respectful. While personalisation is powerful, being overly intrusive can backfire. For example, instead of bombarding someone with notifications every time they pass a store, focus on moments when an offer truly adds value — like during bad weather or peak commute times.

  Lifecycle segmentation 

  Lifecycle segmentation is about crafting interactions based on where customers are in their journey with a brand.

  

  Lifecycle segmentation isn’t just about selling ; it’s about building relationships. After a big purchase like furniture, sending care tips instead of another sales pitch shows customers that the brand cares about their experience beyond just the sale.

  This approach helps brands avoid generic messaging that might alienate customers. By understanding the customer’s lifecycle stage, businesses can tailor their communications to meet specific needs, whether nurturing new relationships or rewarding long-term loyalty.

  Potential data points :

  • Purchase history
  • Sign-up dates

  Examples of effective lifecycle segmentation :

  An online clothing store might send first-time buyers a discount code to encourage repeat purchases. On the other hand, if someone hasn’t shopped in months, they might get an email with “We miss you” messaging and a special deal to bring them back.

  Predictive segmentation 

  Predictive segmentation uses past behaviour and preferences to understand or predict what customers might want next. Its real power lies in its ability to make customers feel understood without them having to ask for anything.

  Potential data points :

  • Purchase patterns
  • Browsing history
  • Interaction frequency

  Examples of effective predictive segmentation :

  Streaming platforms are great examples — they analyse what shows and genres users watch to recommend related content they might enjoy. Similarly, grocery delivery apps can analyse past orders to suggest when to reorder essentials like milk or bread.

  B2B-specific : Firmographic segmentation

  Beyond the eight main segmentation types, B2B marketers often use firmographic factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points :

  • Annual revenue
  • Number of employees
  • Industry
  • Geographic location (main office)

  Example of effective firmographic segmentation :

  Startups and well-established companies will not need the same solution, so segmenting leads by size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns involve more manual research. With an account-based marketing approach, you start by researching potential customers. Then, you separate the target audience into smaller segments (or even a one-to-one campaign).

  Audience segmentation challenges (+ how to overcome them) 

  Below, we explore audience segmentation challenges organisations can face and practical ways to overcome them.

  Data privacy 

  Regulations like GDPR and CCPA require businesses to handle customer data responsibly. Ignoring these rules can lead to hefty fines and harm a brand’s reputation. Customers are also more aware of and sensitive to how their data is used, making transparency essential.

  Businesses should adopt clear data policies and provide opt-out options to build trust and demonstrate respect for user preferences. 

  

  (Image Source) 

  Privacy-focused analytics tools can help businesses handle these requirements effectively. For example, Matomo allows businesses to anonymise user data and offers features that give users control over their tracking preferences.

  Data quality

  Inconsistent, outdated or duplicate data can result in irrelevant messaging that frustrates customers instead of engaging them.

  This is why businesses should regularly audit their data sources for accuracy and completeness.

  Integrate multiple data sources into a unified platform for a more in-depth customer view. Implement data cleansing processes to remove duplicates, outdated records, and errors. 

  Segment management 

  Managing too many segments can become overwhelming, especially for businesses with limited resources. Creating and maintaining numerous audience groups requires significant time and effort, which may not always be feasible.

  Automated tools and analytics platforms can help. Matomo Segments can analyse reports on specific audience groups based on criteria such as visit patterns, interactions, campaign sources, ecommerce behaviour, demographics and technology usage for more targeted analysis.

  Detailed reporting of each segment’s characteristics can further simplify the process. By prioritising high-impact segments — those that offer the best potential return on investment — businesses can focus their efforts where they matter most.

  Behaviour shifts 

  Customer behaviour constantly evolves due to changing trends, new technology and shifting social and economic conditions. 

  Segmentation strategies that worked in the past can quickly become outdated. 

  Businesses need to monitor market trends and adjust their strategies accordingly. Flexibility is key here — segmentation should never be static.

  For example, if a sudden spike in mobile traffic is detected, campaigns can be optimised for mobile-first users.

  Tools and technologies that help 

  Here are some key segmentation tools to support your efforts : 

  • Analytics platforms : Get insights into audience behaviour with Matomo. Track user interactions, such as website visits, clicks and time spent on pages, to identify patterns and segment users based on their online activity.
  • CRM systems : Utilize customer records to create meaningful segments based on characteristics like purchase history or engagement levels.
  • Marketing automation platforms : Streamline personalised messages by automating emails, social media posts or SMS campaigns for specific audience segments.
  • Consent management tools : Collect and manage user consent, implement transparent data tracking and provide users with opt-out options. 
  • Survey tools : Gather first-party data directly from customers. 
  • Social listening solutions : Monitor conversations and brand mentions across social media to gauge audience sentiment.

  Start segmenting and analysing audiences more deeply with Matomo

  Modern consumers expect to get relevant content, and segmentation can make this possible.

  But doing so in a privacy-sensitive way is not always easy. Organisations need to adopt an approach that doesn’t break regulations while still allowing them to segment their audiences. 

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. It provides features for privacy control, enables cookieless configurations, and supports compliance with GDPR and other regulations — all without compromising user privacy. 

  Take advantage of Matomo’s 21-day free trial to explore its capabilities firsthand — 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.