Recherche avancée

Sur d’autres sites (11099)

• Your guide to cookies, web analytics, and GDPR compliance

  25 février 2020, par Joselyn Khor — Analytics Tips, Privacy, Security
  It’s been almost two years since the GDPR came into effect and turned the online world on its head. Confusion around cookies/cookie consent/cookie compliance remains till today. So we’d like to take this chance to talk more about the supposed “big bad” of the latest century. 

  Online cookies seem to have a bad reputation, but are they as bad as they seem ?

  To start, what are cookies on the internet ?

  An internet cookie a.k.a. an HTTP cookie, is a small piece of data sent from websites that is stored on your computer or mobile when you visit that site.

  Are all cookies bad ?

  No. Cookies themselves are usually harmless as they can’t infect computers with malware. 

  They can also be helpful for both websites who use them and individuals visiting those websites. For example, when online shopping, cookies on ecommerce sites keep track of what you’re shopping for. If you didn’t have that tracking, your cart would be empty every time you moved away from that site.

  For businesses/websites, cookies can be used for authentication (logins) and tracking website user experience. For example, tracking multiple visits to the same site in order to provide better experiences to customers visiting their website.

  

  The not-so-sweet types of cookies :

  Cookies that contain personal data

  Another example of a bad cookie is when cookies contain personal data directly in the cookie itself. For example, when websites store demographics or your name in a cookie ; or when a website stores survey results in a cookie. Use of cookies in these ways is considered bad practice nowadays.

  Third-party cookies
  

  They can be used by websites to learn about your visit and activity across multiple websites. Cookies can enter harmful territory when employed for “big brother” types of tracking i.e. when they’re used to build a virtual fingerprint of individuals after their activity is tracked from website to website. For example most advertising networks create third party cookies in your browser when you view an ad, which lets these advertisers track users across these websites and let companies buy more targeted ads.

  Why does Matomo use cookies ?

  

  For accurate reporting of new and returning visitors. Matomo uses cookies to store some information about visitors between visits. We also use cookies to remember if someone gave consent to tracking, or opted out of tracking. 

  Types of cookies Matomo uses :

  • Matomo by default uses first-party cookies, set on the domain of your site.
  • Cookies created by Matomo start with : _pk_ref, _pk_cvar, _pk_id, _pk_ses. See a list of all Matomo cookies : https://matomo.org/faq/general/faq_146/

  Cookie-less tracking - disable cookies and ensure cookie compliance :

  It’s possible to disable tracking cookies in Matomo by adding a line on the javascript code. When cookies are disabled, Matomo data will become slightly less accurate. Also, when cookies are disabled, there may still be a few cookies created in specific cases.

  If you disable cookies, Matomo tries to detect unique visitors by a fingerprint based on a few browser attributes : operating system, browser, browser plugins, IP address and browser language.

  By disabling tracking cookies, you may also use Matomo without needing to display a cookie consent screen. You can also keep tracking when they reject cookie consent by keeping cookies disabled.

  Cookies and the GDPR

  In some countries and according to the GDPR, websites need to provide a way for users to opt-out of all tracking, in particular tracking cookies.

  The GDPR regulates the use of cookies when they compromise an individual’s privacy. When cookies can identify an individual, it is considered personal data.

  

  Cookie compliance and the GDPR

  To be GDPR compliant you must :

  • Receive user consent before using any cookies (except strictly necessary cookies). Read more on cookies that are “clearly exempt from consent”.
  • Provide accurate and specific information about the data each cookie tracks and its purpose in plain language before consent is received.
  • Document and store consent received from users.
  • Allow users to access your service even if they refuse to allow the use of certain cookies
  • Make it as easy for users to withdraw their consent as it was for them to give their consent in the first place.

  Source : https://gdpr.eu/cookies/

  When does GDPR require cookie consent ?

  The purpose of the GDPR is to give individuals control over their personal data. As such this regulation has provisions and requirements which regulate the processing of personal data to protect the privacy of individuals. 

  This means in order to use cookies, you will sometimes need explicit consent from those individuals.

  When does GDPR not require cookie consent ?

  Then there are many cookies that generally do NOT require consent (Source : https://wikis.ec.europa.eu/display/WEBGUIDE/04.+Cookies). 

  These are :

  • user input cookies, for the duration of a session
  • authentication cookies, for the duration of a session
  • user-centric security cookies, used to detect authentication abuses and linked to the functionality explicitly requested by the user, for a limited persistent duration
  • multimedia content player session cookies, such as flash player cookies, for the duration of a session
  • load balancing session cookies and other technical cookies, for the duration of session
  • user interface customisation cookies, for a browser session or a few hours, when additional information in a prominent location is provided (e.g. “uses cookies” written next to the customisation feature)

  Tracking cookies and consent vs legitimate interest

  

  User consent is not always required :

  We understand that whenever you collect and process personal data, you need – almost always – to ask for their consent. However, there are instances where you have to process data under “legitimate interests”. The GDPR states that processing of personal data is lawful “if processing is necessary for the purposes of the legitimate interests”. This means if you have “legitimate interests” you can avoid asking for consent for collecting and processing personal information. Learn more : https://cookieinformation.com/resources/blog/what-is-legitimate-interest-under-the-gdpr 

  A lawful basis for processing personal data (proceeding with caution) :

  We’ve also written about having a lawful basis for processing personal data under GDPR with Matomo. The caveat here is you need to have a strong argument for legitimate interests. If you are processing personal data which may represent a risk to the final user, then getting consent is, for us, still the right lawful basis. If you are not sure, at the time of writing ICO is providing a tool in order to help you make this decision.

  How is Matomo Analytics GDPR compliant ?

  Matomo can be configured to automatically anonymise data so you don’t process any personal data. This allows you to completely avoid GDPR. If you decide to process personal data, Matomo provides you with 12 steps to easily comply with the GDPR guidelines.

  New developments on cookies and the GDPR

  In the early days of the GDPR, a spate of cookie management platforms (CMPs) popped up to help websites and people comply with GDPR rules around cookies.

  These have become problematic in recent years. Europe’s highest court ruled pre-checked box for cookie boxes does not give enough consent. 

  As well as that, new research suggests most cookie consent pop-ups in the EU fall short of GDPR. A new study called, ‘Dark Patterns after the GDPR’ from MIT, UCL and Aarhus University found that a vast majority of websites aren’t following GDPR rules around cookies. The study found most cookie consent pop-ups in the EU to be undermining the GDPR by finding sneaky ways to convince website visitors to click ‘accept’.

  Disclaimer

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

  Additional resources :

  • How do the cookie rules relate to the GDPR ? 
  • Cookies, the GDPR, and the ePrivacy Directive

• What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis

  3 janvier 2024, par Erin

  Handling your user data as a single mass of numbers is rarely conducive to figuring out meaningful patterns you can use to improve your marketing campaigns.

  A cohort report (or cohort analysis) can help you quickly break down that larger audience into sequential segments and contrast and compare based on various metrics. As such, it is a great tool for unlocking more granular trends and insights — for example, identifying patterns in engagement and conversions based on the date users first interacted with your site.

  In this guide, we explain the basics of the cohort report and the best way to set one up to get the most out of it.

  What is a cohort report ?

  In a cohort report, you divide a data set into groups based on certain criteria — typically a time-based cohort metric like first purchase date — and then analyse the data across those segments, looking for patterns.

  Date-based cohort analysis is the most common approach, often creating cohorts based on the day a user completed a particular action — signed up, purchased something or visited your website. Depending on the metric you choose to measure (like return visits), the cohort report might look something like this :

  

  Note that this is not a universal benchmark or anything of the sort. The above is a theoretical cohort analysis based on app users who downloaded the app, tracking and comparing the retention rates as the days go by. 

  The benchmarks will be drastically different depending on the metric you’re measuring and the basis for your cohorts. For example, if you’re measuring returning visitor rates among first-time visitors to your website, expect single-digit percentages even on the second day.

  Your industry will also greatly affect what you consider positive in a cohort report. For example, if you’re a subscription SaaS, you’d expect high continued usage rates over the first week. If you sell office supplies to companies, much less so.

  What is an example of a cohort ?

  As we just mentioned, a typical cohort analysis separates users or customers by the date they first interacted with your business — in this case, they downloaded your app. Within that larger analysis, the users who downloaded it on May 3 represent a single cohort.

  

  In this case, we’ve chosen behaviour and time — the app download day — to separate the user base into cohorts. That means every specific day denotes a specific cohort within the analysis.

  Diving deeper into an individual cohort may be a good idea for important holidays or promotional events like Black Friday.

  Of course, cohorts don’t have to be based on specific behaviour within certain periods. You can also create cohorts based on other dimensions :

  • Transactional data — revenue per user
  • Churn data — date of churn
  • Behavioural cohort — based on actions taken on your website, app or e-commerce store, like the number of sessions per user or specific product pages visited
  • Acquisition cohort — which channel referred the user or customer

  For more information on different cohort types, read our in-depth guide on cohort analysis.

  How to create a cohort report (and make sense of it)

  Matomo makes it easy to view and analyse different cohorts (without the privacy and legal implications of using Google Analytics).

  Here are a few different ways to set up a cohort report in Matomo, starting with our built-in cohorts report.

  Cohort reports

  With Matomo, cohort reports are automatically compiled based on the first visit date. The default metric is the percentage of returning visitors.

  

  Changing the settings allows you to create multiple variations of cohort analysis reports.

  Break down cohorts by different metrics

  The percentage of returning visits can be valuable if you’re trying to improve early engagement in a SaaS app onboarding process. But it’s far from your only option.

  You can also compare performance by conversion, revenue, bounce rate, actions per visit, average session duration or other metrics.

  

  Change the time and scope of your cohort analysis

  Splitting up cohorts by single days may be useless if you don’t have a high volume of users or visitors. If the average cohort size is only a few users, you won’t be able to identify reliable patterns. 

  Matomo lets you set any time period to create your cohort analysis report. Instead of the most recent days, you can create cohorts by week, month, year or custom date ranges. 

  

  Cohort sizes will depend on your customer base. Make sure each cohort is large enough to encapsulate all the customers in that cohort and not so small that you have insignificant cohorts of only a few customers. Choose a date range that gives you that without scaling it too far so you can’t identify any seasonal trends.

  Cohort analysis can be a great tool if you’ve recently changed your marketing, product offering or onboarding. Set the data range to weekly and look for any impact in conversions and revenue after the changes.

  Using the “compare to” feature, you can also do month-over-month, quarter-over-quarter or any custom date range comparisons. This approach can help you get a rough overview of your campaign’s long-term progress without doing any in-depth analysis.

  You can also use the same approach to compare different holiday seasons against each other.

  If you want to combine time cohorts with segmentation, you can run cohort reports for different subsets of visitors instead of all visitors. This can lead to actionable insights like adjusting weekend or specific seasonal promotions to improve conversion rates.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Easily create custom cohort reports beyond the time dimension

  If you want to split your audience into cohorts by focusing on something other than time, you will need to create a custom report and choose another dimension. In Matomo, you can choose from a wide range of cohort metrics, including referrers, e-commerce signals like viewed product or product category, form submissions and more.

  

  Then, you can create a simple table-based report with all the insights you need by choosing the metrics you want to see. For example, you could choose average visit duration, bounce rate and other usage metrics.

  

  If you want more revenue-focused insights, add metrics like conversions, add-to-cart and other e-commerce events.

  Custom reports make it easy to create cohort reports for almost any dimension. You can use any metric within demographic and behavioural analytics to create a cohort. (You can explore the complete list of our possible segmentation metrics.)

  We cover different types of custom reports (and ideas for specific marketing campaigns) in our guide on custom segmentation.

  Create your first cohort report and gain better insights into your visitors

  Cohort reports can help you identify trends and the impact of short-term marketing efforts like events and promotions.

  With Matomo cohort reports you have the power to create complex custom reports for various cohorts and segments. 

  If you’re looking for a powerful, easy-to-use web analytics solution that gives you 100% accurate data without compromising your users’ privacy, Matomo is a great fit. Get started with a 21-day free trial today. No credit card required. 

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now

  

• Unlocking the power of web analytics dashboards

  22 juillet, par Joe — Analytics Tips, App Analytics

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

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

  In this article, we’ll focus on :

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

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

  What is a web analytics dashboard ?

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

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

  Types of web analytics dashboards

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

  Overview dashboard

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

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

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

  Acquisition dashboard

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

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

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

  Behavioural dashboard

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

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

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

  Goals and ecommerce dashboard

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

  

  The typical metrics seen here are :

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

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

  Technical performance dashboard

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

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

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

  Geographic dashboard

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

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

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

  Custom segments dashboard

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

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

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

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

  

  Content performance dashboard

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

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

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

  The importance of data quality

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

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

  Google Analytics 4 (GA4)

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

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

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

  

  Matomo’s built-in dashboards

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

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

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

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

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

  Customisation options

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

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

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

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

  Matomo : A fully adaptable analytics hub

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

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

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