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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.

• 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.

• Server-side tracking vs client-side tracking : What you need to know

  3 juillet, par Joe

  Server-side tracking vs client-side tracking : What you need to know

  Today, consumers are more aware of their online privacy rights, leading to an extensive use of ad blockers and stricter cookie policies. Organisations are facing some noteworthy challenges with this trend, including :

  • Limited data collection, which makes it harder to understand user behaviour and deliver personalised ads that resonate with customers
  • Rising compliance costs as businesses adapt to new regulations, straining resources and budgets.
  • Growing customer scepticism in data practices, affecting brand reputation.
  • Maintaining transparency and fostering trust with customers through clear communication about data practices.

  Server-side tracking can help resolve these problems. This article will cover server-side tracking, how it works, implementation methods and its benefits.

  What is server-side tracking ? 

  Server-side tracking refers to a method where user data is collected directly by a server rather than through a user’s browser.

  The key advantage of server-side tracking is that data collection, processing, and storage occur directly on the website’s server.

  For example, when a visitor interacts with any website, the server captures that activity through the backend system, allowing for greater data control and security. 

  Client-side tracking vs. server-side tracking 

  There are two methods to collect user data : client-side and server-side. 

  Let’s understand their differences. 

  Client-side tracking : Convenience with caveats

  Client-side tracking embeds JavaScript tags, pixels or other scripts directly into a website’s code. When a user interacts with the site, these tags fire, collecting data from their browser. This information might include page views, button clicks, form submissions and other user actions. 

  The collected data is then sent directly to third-party analytics platforms like Google Analytics or Adobe Analytics, or internal teams can also analyse it.

  This method is relatively easy to implement. That’s because marketers can often deploy these tags without needing extensive developer support, enabling quick adjustments and A/B testing. 

  However, there are some challenges. 

  Ad blockers and browser privacy settings, such as Intelligent Tracking Prevention (ITP), restrict the ability of third-party tags to collect data. 

  This results in data gaps and inaccuracies skewing analytics reports and potentially leading to misguided business decisions. 

  Reliance on numerous JavaScript tags can also negatively impact website performance, slowing down page load times and affecting user experience. This is especially true on mobile devices where processing power and network speeds are often limited.

  

  Now, let’s see how server-side tracking changes this.

  Server-side tracking : Control and reliability

  Server-side tracking shifts the burden of data collection from the user’s browser to a server controlled by the business. 

  Instead of relying on JavaScript tags firing directly from the user’s device, user interactions are first sent to the business’s own server. Here, the data can be processed, enriched, and analysed. 

  This method provides numerous advantages, including enhanced control over data integrity, improved privacy, and more, which we discuss in the next section.

  Benefits of server-side tracking 

  Server-side tracking offers a compelling alternative to traditional client-side methods, providing numerous business advantages. Let’s take a look at them.

  Improved data accuracy

  This method reduces inaccuracies caused by ad blockers or cookie restrictions by bypassing browser limitations. As a result, the data collected is more reliable, leading to better analytics and marketing attribution.

  Data minimisation

  Data minimisation is a fundamental principle in data protection. It emphasises that organisations should collect only data that is strictly needed for a specific purpose. 

  In server-side tracking, this translates into collecting just the essential data points and discarding anything extra before the data is sent to analytics platforms. It helps organisations avoid accumulating excessive personal information, reducing the risk of data breaches and misuse.

  For example, consider a scenario where a user purchases a product on an e-commerce website. 

  With client-side tracking scripts, the system might inadvertently collect a range of data, including the user’s IP address, browser type, operating system and even details about other websites they have visited. 

  However, for conversions, the organisation only needs to know the purchase amount, product IDs, user IDS, and timestamps. 

  Server-side tracking filters unnecessary information. This reduces the privacy impact and simplifies data analysis and storage.

  Cross-device tracking capabilities

  Server-side tracking provides a unified view of customer behaviour regardless of the device they use, allowing for more personalised and targeted marketing campaigns. 

  In-depth event tracking

  Server-side tracking helps businesses track events that occur outside their websites, such as payment confirmations. Companies gain insights into the entire customer journey, from initial interaction to final purchase, optimising every touchpoint. 

  Enhanced privacy compliance

  With increasing regulations like GDPR and CCPA, businesses can better manage user consent and data handling practices through server-side solutions. 

  Server-side setups make honouring user consent easier. If a user opts out, server-side logic can exclude their data from all outgoing analytics calls in one central place. 

  

  Server-side methods reassure users and regulators that data is collected and secured with minimal risk. 

  In sectors like government and banking, this level of control is often a non-negotiable part of their duty of care. 

  Extended cookie lifetime

  Traditional website tracking faces growing obstacles as modern browsers prioritise user privacy. Initiatives like Safari’s ITP block third-party cookies and also constrain the use of first-party cookies. 

  Other browsers, such as Firefox and Brave, are implementing similar methods, while Chrome is beginning to phase out third-party cookies. Retargeting and cross-site analytics, which rely on these cookies, encounter significant challenges.

  Server-side tracking overcomes this by allowing businesses to collect data over a longer duration. 

  When a website’s server directly sets a cookie, that cookie often lasts longer than cookies created by JavaScript code running inside the browser. This lets websites get around some of the limits browsers put on tracking and allows them to remember a visitor when they return to the site later, which gives better customer insights. Plus, server-side tracking typically classifies cookies as first-party data, which is less susceptible to blocking by browsers and ad blockers.

  Server-side tracking : Responsibilities and considerations

  While server-side tracking delivers powerful capabilities, remember that it also brings increased responsibility. Companies must remain vigilant in upholding privacy regulations and user consent. It’s up to the organisation to make sure the server follows user consent, for example, not sending data if someone has opted out.

  Server-side setups introduce technical complexity, which can potentially lead to data errors that are more difficult to identify and resolve. Therefore, monitoring processes and quality assurance practices are essential for data integrity. 

  How does server-side tracking work ? 

  When a user interacts with a website (e.g., clicking a button), this action triggers an event. The event could be anything from a page view to a form submission.

  The backend system captures relevant details such as the event type, user ID and timestamp. This information helps in understanding user behaviour and creating meaningful analytics.

  The captured data is processed directly on the organisation’s server, allowing for immediate validation. For example, organisations can add additional context or filter out irrelevant information.

  Instead of sending data to third-party endpoints, the organisation stores everything in its own database or data warehouse. This ensures full control over data privacy and security.

  Organisations can perform their own analysis using tools like SQL or Python. To visualise data, custom dashboards and reports can be created using self-hosted analytics tools. This way, businesses can present complex data in a clear and actionable manner.

  How to implement server-side tracking ?

  Server-side tracking can work in four common ways, each offering a different blend of control, flexibility and complexity.

  1. Server-side tag management

  In this method, organisations use platforms like Google Tag Manager Server-Side to manage tracking tags on the server, often using containers to isolate and manage different tagging environments. 

  

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  This approach offers a balance between control and ease of use. It allows for the deployment and management of tags without modifying the application code, which is particularly useful for marketers who want to adjust tracking configurations quickly.

  2. Direct server-to-server tracking via APIs

  This method involves sharing information between two servers without affecting the user’s browser or device. 

  A unique identifier is generated and stored on a server when a user interacts with an ad or webpage. 

  If a user takes some action, like making a purchase, the unique identifier is sent from the advertiser’s server directly to the platform’s server (Google or Facebook) via an API. 

  It requires more development effort but is ideal for organisations needing fine-grained data control.

  3. Using analytics platforms with built-in server SDKs

  Another way is to employ analytics platforms like Matomo that provide SDKs for various programming languages to instrument the server-side code. 

  This eases integration with the platform’s analytics features and is a good choice for organisations primarily using a single analytics platform and want to use its server-side capabilities.

  4. Hybrid approaches

  Finally, organisations can also combine client- and server-side tracking to capture different data types and maximise accuracy. 

  This method involves client-side scripts for specific interactions (like UI events) and server-side tracking for more sensitive or critical data (like transactions). 

  While these are general approaches, dedicated analytics platforms can also be helpful. Matomo, for example, facilitates server-side tracking through two specific methods.

  Using server logs

  Matomo can import existing web server logs, such as Apache or Nginx, that capture each request. Every page view or resource load becomes a data point. 

  Matomo’s log processing script reads log files, importing millions of hits. This removes the need to add code to the site, making it suitable for basic page analytics (like the URL) without client-side scripts, particularly on security-sensitive sites.

  Using the Matomo tracking API (Server-side SDKs)

  This method integrates application code with calls to Matomo’s API. For example, when a user performs a specific action, the server sends a request to Matomo.php, the tracking endpoint, which includes details like the user ID and action. 

  Matomo offers SDKs in PHP, Java C#, and community SDKs to simplify these calls. These allow tracking of not just page views but custom events such as downloads and transactions from the backend, functioning similarly to Google’s Measurement Protocol but sending data to the Matomo instance. 

  Data privacy, regulations and Matomo

  As privacy concerns grow and regulations like GDPR and CCPA become more stringent, businesses must adopt data collection methods that respect user consent and data protection rights. 

  Server-side tracking allows organisations to collect first-party data directly from their servers, which is generally considered more compliant with privacy regulations.

  Matomo is a popular open-source web analytics platform that is committed to privacy. It gives organisations 100% data ownership and control, and no data is sent to third parties by default.

  

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  Matomo is a full-featured analytics platform with dashboards and segmentation comparable to Google Analytics. It can self-host and provides DoNotTrack settings and the ability to anonymise IP addresses.

  Governments and organisations requiring data sovereignty, such as the EU Commission and the Swiss government, choose Matomo for web analytics due to its strong compliance posture.

  Balancing data collection and user privacy

  Ad blockers and other restrictions prevent data from being accurate. Server-side tracking helps get data on the server and makes it more reliable while respecting user privacy. Matomo supports server-side tracking, and over one million websites use Matomo to optimise their data strategies. 

  Get started today by trying Matomo for free for 21 days, no credit card required.