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• Lean Analytics in a Privacy-First Environment – Bootcamp with Timo Dechau

  29 novembre 2024, par Daniel Crough — Banking and Financial Services, GDPR, Marketing, Privacy, Videos, Featured Banking Content

  In a recent bootcamp, Timo Dechau walked attendees through his approach to data and measurement in privacy-focused analytics environments. He demonstrates how to shift from a chaotic, ‘track-it-all’ mentality to a focused method that prioritizes quality over quantity. This post will summarize some of his key privacy-first analytics ideas, but be sure to check out the on-demand video for more detail.

  Watch the bootcamp on demand

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  Unrestrained data collection leads to data bloat

  Marketing and the business world are experiencing a data problem. Analysts and business intelligence teams grapple with large amounts of data that aren’t always useful and are often incomplete. The idea that “more data is better” became a guiding principle in the early 2000s, encouraging companies to gather everything possible using all available data collection methods. This unrestrained pursuit often led to an unexpected problem : data bloat. Too much data, too little clarity. Digital marketers, analysts, and business leaders now try to navigate vast amounts of information that create more confusion than insight, especially when the data is incomplete due to privacy regulations.

  

  Cutting through the noise, focusing on what matters

  The “more data is better” mindset emerged when digital marketers were beginning to understand data’s potential. It seemed logical : more data should mean more opportunities to optimise, personalise, and drive results. But in practice, gathering every possible piece of data often leads to a cluttered, confusing pile of metrics that can mislead more than guide.

  This approach carries hidden costs. Excessive data collection burns resources, increases privacy concerns, and leaves teams unfocused. It’s easy to get lost trying to make sense of endless dashboards, metrics, and reports. More data doesn’t necessarily lead to better decisions ; it often just leads to more noise, hindering effective data management.

  Rethinking data management : From data overload to data mindfulness

  Data management has often prioritised comprehensive data gathering without considering the specific value of each data point. This approach has created more information, but not necessarily better insights.

  Data mindfulness is about taking a deliberate, focused approach to data collection and analysis. Instead of trying to collect everything, it emphasises gathering only what truly adds value. It’s about ensuring the data you collect serves a purpose and directly contributes to better insights and data-driven decision-making.

  Think of it like applying a “lean” methodology to data—trimming away the unnecessary and keeping only what is essential. Or consider embracing data minimalism to declutter your data warehouse, keeping only what truly sparks insight.

  

  Mindful data is ethical data

  Adopting a mindful approach to data can pay off in several ways :

  • Reduces overwhelm : When you reduce the clutter, you’re left with fewer, clearer metrics that lead to stronger decisions and actionable data insights.

  • Mitigates compliance risks : By collecting less, companies align better with privacy regulations and build trust with their customers. Privacy-first analytics and privacy-compliant analytics practices mean there’s no need for invasive tracking if it doesn’t add value—and customers will appreciate that.

  • Enhances data ethics : Focusing on the quality rather than the quantity of data collected ensures ethical data collection and management. Companies use data responsibly, respect user privacy, and minimise unnecessary data handling, strengthening customer relationships and brand integrity.

  • Improves data efficiency : Focused analytics means better use of resources. You’re spending less time managing meaningless metrics and more time working on meaningful insights. Many companies have found success by switching to a leaner, quality-first data approach, reporting sharper, more impactful results.

  Shifting towards simplicity and lean analytics

  If data mindfulness sounds appealing, here’s how you can get started :

  1. Ask the right questions. Before collecting any data, ask yourself : Why are we collecting this ? How will it drive value ? If you can’t answer these questions clearly, that data probably isn’t worth collecting. This is a key step in smart data management.

  2. Simplify metrics. Focus on the KPIs that truly matter for your business. Choose a handful of key metrics that reflect your goals rather than a sprawling list of nice-to-haves. Embracing data simplicity helps in targeting data collection effectively.

  3. Audit your current data. Review your existing data collection processes. Which metrics are you actively using to make decisions ? Eliminate any redundant or low-value metrics that create noise. Use ethical data management practices to ensure data efficiency and compliance. Understanding what is data management in this context is crucial.

  4. Implement lean analytics practices. Shift towards lean analytics by cutting down on unnecessary tracking. This can involve reducing reliance on multiple tracking scripts, simplifying your reporting, and setting up a streamlined dashboard focused on key outcomes. Embrace data reduction strategies to eliminate waste and boost effectiveness.

  Who should watch this bootcamp

  This bootcamp is perfect for data analysts, product managers, digital marketers and business leaders who are seeking a more streamlined approach to data measurement. If you’re interested in moving away from a chaotic “track-it-all” mentality and towards a focused, lean, and privacy-first analytics strategy, this workshop is for you.

  What you’ll discover

  • Practical steps : Learn actionable strategies to reduce data bloat and implement lean, privacy-first analytics in your organisation.

  • Real-life examples : Explore case studies of companies that have successfully adopted focused and privacy-first analytics.

  • Deep insights : Gain a deeper understanding of how to prioritise quality over quantity without sacrificing valuable insights.

  Watch the bootcamp on-demand

  For a comprehensive dive into these topics, watch the full workshop video or download the detailed transcript. Equip yourself with the knowledge and tools to transform your data management approach today.

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• A Guide to GDPR Sensitive Personal Data

  13 mai 2024, par Erin

  The General Data Protection Regulation (GDPR) is one of the world’s most stringent data protection laws. It provides a legal framework for collection and processing of the personal data of EU individuals.

  The GDPR distinguishes between “special categories of personal data” (also referred to as “sensitive”) and other personal data and imposes stricter requirements on collection and processing of sensitive data. Understanding these differences will help your company comply with the requirements and avoid heavy penalties.

  In this article, we’ll explain what personal data is considered “sensitive” according to the GDPR. We’ll also examine how a web analytics solution like Matomo can help you maintain compliance.

  What is sensitive personal data ?

  The following categories of data are treated as sensitive :

  1. 1. Personal data revealing :
        • Racial or ethnic origin ;
        • Political opinions ;
        • Religious or philosophical beliefs ;
        • Trade union membership ;
     2. Genetic and biometric data ;
     3. Data concerning a person’s :
        • Health ; or
        • Sex life or sexual orientation.

  

  Sensitive vs. non-sensitive personal data : What’s the difference ?

  While both categories include information about an individual, sensitive data is seen as more private, or requiring a greater protection. 

  Sensitive data often carries a higher degree of risk and harm to the data subject, if the data is exposed. For example, a data breach exposing health records could lead to discrimination for the individuals involved. An insurance company could use the information to increase premiums or deny coverage. 

  In contrast, personal data like name or gender is considered less sensitive because it doesn’t carry the same degree of harm as sensitive data. 

  Unauthorised access to someone’s name alone is less likely to harm them or infringe on their fundamental rights and freedoms than an unauthorised access to their health records or biometric data. Note that financial information (e.g. credit card details) does not fall into the special categories of data.

  

  Legality of processing

  Under the GDPR, both sensitive and nonsensitive personal data are protected. However, the rules and conditions for processing sensitive data are more stringent.

  Article 6 deals with processing of non-sensitive data and it states that processing is lawful if one of the six lawful bases for processing applies. 

  In contrast, Art. 9 of the GDPR states that processing of sensitive data is prohibited as a rule, but provides ten exceptions. 

  It is important to note that the lawful bases in Art. 6 are not the same as exceptions in Art. 9. For example, while performance of a contract or legitimate interest of the controller are a lawful basis for processing non-sensitive personal data, they are not included as an exception in Art. 9. What follows is that controllers are not permitted to process sensitive data on the basis of contract or legitimate interest. 

  The exceptions where processing of sensitive personal data is permitted (subject to additional requirements) are : 

  • Explicit consent : The individual has given explicit consent to processing their sensitive personal data for specified purpose(s), except where an EU member state prohibits such consent. See below for more information about explicit consent. 
  • Employment, social security or social protection : Processing sensitive data is necessary to perform tasks under employment, social security or social protection law.
  • Vital interests : Processing sensitive data is necessary to protect the interests of a data subject or if the individual is physically or legally incapable of consenting. 
  • Non-for-profit bodies : Foundations, associations or nonprofits with a political, philosophical, religious or trade union aim may process the sensitive data of their members or those they are in regular contact with, in connection with their purposes (and no disclosure of the data is permitted outside the organisation, without the data subject’s consent).
  • Made public : In some cases, it may be permissible to process the sensitive data of a data subject if the individual has already made it public and accessible. 
  • Legal claims : Processing sensitive data is necessary to establish, exercise or defend legal claims, including legal or in court proceedings.
  • Public interest : Processing is necessary for reasons of substantial public interest, like preventing unlawful acts or protecting the public.
  • Health or social care : Processing special category data is necessary for : preventative or occupational medicine, providing health and social care, medical diagnosis or managing healthcare systems.
  • Public health : It is permissible to process sensitive data for public health reasons, like protecting against cross-border threats to health or ensuring the safety of medicinal products or medical devices. 
  • Archiving, research and statistics : You may process sensitive data if it’s done for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.

  In addition, you must adhere to all data handling requirements set by the GDPR.

  Important : Note that for any data sent that you are processing, you always need to identify a lawful basis under Art. 6. In addition, if the data sent contains sensitive data, you must comply with Art. 9.

  Explicit consent

  While consent is a valid lawful basis for processing non-sensitive personal data, controllers are permitted to process sensitive data only with an “explicit consent” of the data subject.

  The GDPR does not define “explicit” consent, but it is accepted that it must meet all Art. 7 conditions for consent, at a higher threshold. To be “explicit” a consent requires a clear statement (oral or written) of the data subject. Consent inferred from the data subject’s actions does not meet the threshold. 

  The controller must retain records of the explicit consent and provide appropriate consent withdrawal method to allow the data subject to exercise their rights.

  Examples of compliant and non-compliant sensitive data processing

  Here are examples of when you can and can’t process sensitive data :

  • When you can process sensitive data : A doctor logs sensitive data about a patient, including their name, symptoms and medicine prescribed. The hospital can process this data to provide appropriate medical care to their patients. An IoT device and software manufacturer processes their customers’ health data based on explicit consent of each customer. 
  • When you can’t process sensitive data : One example is when you don’t have explicit consent from a data subject. Another is when there’s no lawful basis for processing it or you are collecting personal data you simply do not need. For example, you don’t need your customer’s ethnic origin to fulfil an online order.

  Other implications of processing sensitive data

  If you process sensitive data, especially on a large scale, GDPR imposes additional requirements, such as having Data Privacy Impact Assessments, appointing Data Protection Officers and EU Representatives, if you are a controller based outside the EU.

  Penalties for GDPR non-compliance

  Mishandling sensitive data (or processing it when you’re not allowed to) can result in huge penalties. There are two tiers of GDPR fines :

  • €10 million or 2% of a company’s annual revenue for less severe infringements
  • €20 million or 4% of a company’s annual revenue for more severe infringements

  In the first half of 2023 alone, fines imposed in the EU due to GDPR violations exceeded €1.6 billion, up from €73 million in 2019.

  Examples of high-profile violations in the last few years include :

  • Amazon : The Luxembourg National Commission fined the retail giant with a massive $887 million fine in 2021 for not processing personal data per the GDPR. 
  • Google : The National Data Protection Commission (CNIL) fined Google €50 million for not getting proper consent to display personalised ads.
  • H&M : The Hamburg Commissioner for Data Protection and Freedom of Information hit the multinational clothing company with a €35.3 million fine in 2020 for unlawfully gathering and storing employees’ data in its service centre.

  One of the criteria that affects the severity of a fine is “data category” — the type of personal data being processed. Companies need to take extra precautions with sensitive data, or they risk receiving more severe penalties.

  What’s more, GDPR violations can negatively affect your brand’s reputation and cause you to lose business opportunities from consumers concerned about your data practices. 76% of consumers indicated they wouldn’t buy from companies they don’t trust with their personal data.

  Organisations should lay out their data practices in simple terms and make this information easily accessible so customers know how their data is being handled.

  Get started with GDPR-compliant web analytics

  The GDPR offers a framework for securing and protecting personal data. But it also distinguishes between sensitive and non-sensitive data. Understanding these differences and applying the lawful basis for processing this data type will help ensure compliance.

  Looking for a GDPR-compliant web analytics solution ?

  At Matomo, we take data privacy seriously. 

  Our platform ensures 100% data ownership, putting you in complete control of your data. Unlike other web analytics solutions, your data remains solely yours and isn’t sold or auctioned off to advertisers. 

  Additionally, with Matomo, you can be confident in the accuracy of the insights you receive, as we provide reliable, unsampled data.

  Matomo also fully complies with GDPR and other data privacy laws like CCPA, LGPD and more.

  Start your 21-day free trial today ; no credit card required. 

  Disclaimer

  We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to GDPR. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns.

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• Web Analytics : The Quick Start Guide

  25 janvier 2024, par Erin

  You’ve spent ages carefully designing your website, crafting copy to encourage as many users as possible to purchase your product. 

  But they aren’t. And you don’t know why. 

  The good news is you don’t have to remain in the dark. Collecting and analysing web analytics lets you understand how your users behave on your site and why they aren’t converting. 

  But before you can do that, you need to know what those metrics and KPIs mean. That’s why this article is taking things back to basics. Below, we’ll show you which metrics to track, what they mean and how to choose the best web analytics platform. 

  What is web analytics ?

  Web analytics is the process of collecting, analysing and reporting website data to understand how users behave on your website. Web analytics platforms like Matomo collect this data by adding a code line to every site page. 

  Why is it important to track web analytics ?

  There are plenty of reasons you should start tracking web analytics, including the following :

  

  Analyse user behaviour

  Being able to analyse user behaviour is the most important reason to track website analytics. After all, you can’t improve your website’s conversion rate if you don’t know what users do on your site.

  A web analytics platform can show you how users move around your site, the links they click on and the forms they fill in. 

  Improve site experience

  Web analytics is a fantastic way to identify issues and find areas where your site could improve. You could look at your site’s exit pages, for example, and see why so many users leave your site when viewing one of these pages and what you can do to fix it.

  It can also teach you about your user’s preferences so you can improve the user experience in the future. Maybe they always click a certain type of button or prefer one page’s design over another. Whatever the case, you can use the data to make your site more user-friendly and increase conversions.

  Boost marketing efforts

  Web analytics is one of the best ways to understand your marketing efforts and learn how to improve them.

  A good platform can collect valuable data about your marketing campaigns, including :

  • Where users came from
  • What actions these users take on your site
  • Which traffic sources create the most conversions

  This information can help you decide which marketing campaigns send the best users to your site and generate the highest ROI. 

  Make informed decisions

  Ultimately, web analytics simplifies decision-making for your website and marketing efforts by relying on concrete data instead of guesswork.

  Rather than wonder why users aren’t adding products to their shopping cart or signing up for your newsletter, you can analyse how they behave and use that information to hypothesise how you can improve conversions. Web analytics will even give you the data to confirm whether you were right or wrong. 

  What are the key metrics you should track ?

  Getting your head around web analytics means knowing the most important metrics to track. Below are seven key metrics and how to track them using Matomo. 

  Traffic

  Traffic is the number of people visiting your website over a period of time. It is the lifeblood of your website since the more visits your site receives, the more revenue it stands to generate.

  However, simply having a high volume of visitors does not guarantee substantial revenue. To maximise your success, focus on attracting your ideal customers and generating quality traffic from those who are most likely to engage with your offerings.

  Ideally, you should be seeing an upward trend in traffic over time though. The longer your website has been published and the more quality and targeted content you create, the more traffic you should receive. 

  Matomo offers multiple ways to check your website’s traffic :

  • Visits log report
  • Real-time visitor map
  • Visits in real-time report

  The visits log report in Matomo is perfect if you want a granular view of your visitors.

  

  It shows you each user session and get a detailed picture of each user, including :

  • Their geographic location
  • The number of actions they took
  • How they found your site
  • The length of time they stayed
  • Their device type
  • What browser they are using
  • The keyword they used to find your site

  Traffic sources

  Traffic sources show how users access your website. They can enter via a range of traffic sources, including search engines, email and direct visits, for instance.

  Matomo has five default traffic source types :

  • Search engine – visitors from search platforms (like Google, Bing, etc.)
  • Direct traffic – individuals who directly type your website’s URL into their browser or have it bookmarked, bypassing search engines or external links
  • Websites – visits from other external sites
  • Campaigns – traffic resulting from specific marketing initiatives (like a newsletter or ad campaign, for instance)
  • Social networks  – visitors who access your website through various social media platforms (such as Facebook, LinkedIn, Instagram. etc.)

  But each of these can be broken into more granular sources. Take organic traffic from search engines, for example :

  

  Matomo tracks visits from each search engine, showing you how many visits you had in total, how many actions those visitors took, and the average amount of time those visitors spent on your site. 

  You can even integrate Google, Bing and Yahoo search consoles to monitor keyword performance and enhance your search engine optimisation efforts.

  Pageviews

  Whenever a browser loads a page, your web analytics tool records a pageview. This term, pageview, represents the count of unique times a page on your website is loaded.

  You can track pageviews in Matomo by opening the Pages tab in the Behaviour section of the main navigation. 

  

  You can quickly see your site’s most visited pages in this report in Matomo. 

  Be careful of deriving too much meaning from pageviews. Just because a page has lots of views, doesn’t necessarily mean it’s quality or valuable. There are a couple of reasons for this. First, the page might be confusing, so users have to keep revisiting it to understand the content. Second, it could be the default page most visitors land on when they enter your site, like the homepage. 

  While pageviews offer insights, it’s important to dig deeper into user behaviour and other metrics to truly gauge a page’s importance and impact.

  Average time on page

  Time on page is the amount of time users spend on the page on average. You can see average time on page in Matomo’s page analytics report.

  A low time on page score isn’t necessarily a bad thing. Users will naturally spend less time on gateway pages and checkout pages. A short time spent on checkout pages, especially if users are successfully completing their transactions, indicates that the checkout process is easy and seamless.

  Conversely, a longer time on blog posts is a positive indicator. It suggests that readers are genuinely engaged with the content.

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  Returning visitors

  Returning visitors measures the number of people who visit your site more than once. It can be expressed as a number or a percentage. 

  While some analytics tools only show returning visitors as a percentage, Matomo lets you learn more about each of them in the Visitor profile report. 

  

  This report offers a full summary of a user’s previous actions, including :

  • How many times they’ve visited your site
  • The pages they viewed on each visit
  • Where they visited from
  • The devices they used
  • How quickly pages loaded

  When people keep coming back to a website, it’s usually a positive sign and means they like the service, content or products. But, it depends on the type of website. If it’s the kind of site where people make one-off purchases, the focus might not be on getting visitors to return. For a site like this, a high number of returning visitors could indicate that the website is confusing or difficult to use. 

  It’s all about the context – different websites have different goals, and it’s important to keep this in mind when analysing your site.

  Conversions

  A conversion is when a user takes a desired action on your website. This could be :

  • Making a purchase
  • Subscribing to your newsletter
  • Signing up for a webinar

  You can track virtually any action as a conversion in Matomo by setting goals and analysing the goals report.

  

  As you can see in the screenshot above, Matomo shows your conversions plotted over time. You can also see your conversion rate to get a complete picture and assign a value to each conversion to calculate how much revenue each conversion generates. 

  Bounce rate

  A visitor bounces when they leave your website without taking an action or visiting another page. 

  Typically, you want bounce rate to be low because it means people are engaged with your site and more likely to convert. However, in some cases, a high bounce rate isn’t necessarily bad. It might mean that visitors found what they needed on the first page and didn’t feel the need to look further. 

  The impact of bounce rate depends on your website’s purpose and goals.

  You can view your website’s bounce rate using Matomo’s page analytics report — the same report that shows pageviews.

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  Web analytics best practices

  You should follow several best practices to get the most from website analytics data. 

  Choose metrics that align with your goals

  Only some metrics your analytics platform tracks will be relevant to your business. So don’t waste time analysing all of them.

  Instead, focus on the ones that matter most to your business. A marketer for an e-commerce store, for example, might focus on conversion-related metrics like conversion rate and total number of transactions. They might also want to look at campaign-related metrics, like traffic sources and bounce rates, so they can optimise paid ad campaigns accordingly. 

  A marketer looking to improve their site’s SEO, on the other hand, will want to track SEO web analytics like bounce rate and broken links.

  Add context to your data

  Don’t take your data at face value. There could be dozens of factors that impact how visitors access and use your site — many of which are outside your control. 

  For example, you may think an update to your site has sent your conversions crashing when, in reality, a Google algorithm update has negatively impacted your search traffic.

  Adding annotations within Matomo can provide invaluable context to your data. These annotations can be used to highlight specific events, changes or external factors that might influence your website metrics.

  

  By documenting significant occurrences, such as website updates, marketing campaigns or algorithm changes, you create a timeline that helps explain fluctuations in your data.

  Go further with advanced web analytics features

  It’s clear that a web analytics platform is a necessary tool to understand your website’s performance.

  However, if you want greater confidence in decision-making, quicker insights and better use of budget and resources, you need an advanced solution with behavioural analytics features like heatmaps, A/B testing and session recordings. 

  Most web analytics solutions don’t offer these advanced features, but Matomo does, so we’ll be showcasing Matomo’s behavioural analytics features.

  Now, if you don’t have a Matomo account, you can try it free for 21-days to see if it’s the right tool for you.

  

  A heatmap, like the example above, makes it easy to discover where your users pay attention, which part of your site they have problems with, and how they convert. It adds a layer of qualitative data to the facts offered by your web analytics tool.

  Similarly, session recordings will offer you real-time playbacks of user interactions, helping you understand their navigation patterns, identify pain points and gain insights into the user experience.

  Then you can run experiments bu using A/B testing to compare different versions of your website or specific elements, allowing you to make informed decisions based on actual user preferences and behaviour. For instance, you can compare different headlines, images, page layouts or call-to-action buttons to see which resonates better with your audience. 

  Together, these advanced features will give you the confidence to optimise your website, improve user satisfaction and make data-driven decisions that positively impact your business.

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  How to choose a web analytics tool

  A web analytics tool is the best way to track the above metrics. Choose the best one for your company by following the steps below. 

  Look for the right features

  Most popular web analytics platforms, like Google Analytics, will offer the same core features like tracking website traffic, monitoring conversions and generating reports. 

  But it’s the added features that set great tools apart. Do you need specific tools to measure the performance of your e-commerce store, for example ? What about paid ad performance, A/B testing or form analytics ?

  By understanding exactly what you need from an analytics platform, you can make an informed choice. 

  Think about data accuracy

  Data accuracy is one of the biggest issues with analytics tools. Many users block cookies or opt out of tracking, making it difficult to get a clear picture of user behaviour — and meaning that you have to think about how your user data will be collected with your chosen platform.

  Google Analytics, for instance, uses data sampling to make assumptions about traffic levels rather than relying on accurate data. This can lead to inaccurate reports and false conclusions. 

  It’s why Matomo doesn’t use data sampling and provides 100% accurate data. 

  Understand how you’ll deal with data privacy

  Data privacy is another big concern for analytics users. Several major analytics platforms aren’t compatible with regional data privacy laws like GDPR, which can impact your ability to collect data in these regions. 

  It’s why many companies trust privacy-focused analytics tools that abide by regulations without impacting your ability to collect data. Matomo is a market leader in this respect and is one of the few web analytics tools that the Centre for Data Privacy Protection in France has said is exempt from tracking consent requirements.

  Many government agencies across Europe, Asia, Africa and North America, including organisations like the United Nations and European Commission, rely on Matomo for web analytics.

  Conclusion

  Web analytics is a powerful tool that helps you better understand your users, improve your site’s performance and boost your marketing efforts. 

  If you want a platform that offers advanced features, 100% accurate data and protects your users’ privacy, then look no further than Matomo. 

  Try Matomo free for 21 days, no credit card required. 

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