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• 9 Ways to Customise Your Matomo Like a Pro

  5 octobre 2022, par Erin

  Matomo is a feature-rich web analytics platform. As such, it has many layers of depth — core features, extra plug-ins, custom dimensions, reports, extensions and integrations. 

  Most of the product elements you see can be personalised and customised to your needs with minimal restrictions. However, this breadth of choice can be overlooked by new users. 

  In this post, we explain how to get the most out of Matomo with custom reports, dashboards, dimensions and even app design. 

  How to customise your Matomo web analytics

  To make major changes to Matomo (e.g., create custom dashboards or install new plugins), you’ll have to be a Matomo Super User (a.k.a. The Admin). Super Users can also grant administrator permissions to others so that more people could customise your Matomo deployment. 

  Most feature-related customisations (e.g. configuring a custom report, adding custom goal tracking, etc.) can be done by all users. 

  With the above in mind, here’s how you can tweak Matomo to better serve your website analytics needs : 

  1. Custom dashboards

  

  Dashboards provide a panorama view of all the collected website statistics. We display different categories of stats and KPIs as separate widgets — a standalone module you can also customise. 

  On your dashboard, you can change the type, position and number of widgets on display. This is an easy way to create separate dashboard views for different projects, clients or team members. Rather than a one-size-fits-all dashboard, a custom dashboard designed for a specific role or business unit will increase data-driven decision-making and efficiency across the business.

  You can create a new dashboard view in a few clicks. Then select a preferred layout — a split-page view or multi columns. Next, populate the new dashboard area with preferred widgets showing :

  • SEO website rankings 
  • Visits, conversions and revenue 
  • Custom reports 

  Or code a custom widget area to pull specific website stats or other reporting data you need. Once you are done, arrange everything with our drag-and-drop functionality. 

  

  Popular feature use cases

  • Personalised website statistics layout for convenient viewing 
  • Simplified analytics dashboards for the line of business leaders/stakeholders 
  • Project- or client-specific dashboards for easy report sharing 

  Read more about customising Matomo dashboards and widget areas. 

  2. Custom reports

  

  As the name implies, Custom Reports widget allows you to mesh any of the dimensions and metrics collected by Matomo into a custom website traffic analysis. Custom reports save users time by providing specific data needed in one view so there is no need to jump back and forth between multiple reports or toggle through a report to find data.

  For each custom report, you can select up to three dimensions and then apply additional quantitative measurements (metrics) to drill down into the data.

  For example, if you want to closely observe mobile conversion rates in one market, you can create the following custom report :

  • Dimensions : User Type (registered), Device type (mobile), Location (France)
  • Metrics : Visits, Conversion Rate, Revenue, Avg. Generation Time.

  Custom Report widget is available within Matomo Cloud and as a plugin for Matomo On-Premise.

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  Popular feature use cases

  • Campaign-specific reporting to better understand the impact of different promo strategies 
  • Advanced event tracking for conversion optimization 
  • Market segmentation reports to analyse different audience cohorts 

  Read more about creating and analysing Custom Reports.

  3. Custom widgets

  

  We realise that our users have different degrees of analytics knowledge. Some love in-depth reporting dimensions and multi-row reporting tables. Others just want to see essential stats. 

  To delight both the pros and the novice users, we’ve created widgets — reporting sub-modules you can add, delete or rearrange in a few clicks. Essentially, a widget is a slice of a dashboard area you can populate with extra information. 

  You can add pre-made custom widgets to Matomo or develop your own widget to display custom reports or even external data (e.g., offline sales volume). At the same time, you can also embed Matomo widgets into other applications (e.g., a website CMS or corporate portal).

  Popular feature use cases

  • Display main goals (e.g., new trial sign-ups) on the main dashboard for greater visibility 
  • Highlight cost-per-conversion reporting by combining goals and conversion data to keep your budgets in check 
  • Run omnichannel eCommerce analytics (with embedded offline sales data) to get a 360-degree view into your operations 

  Read more about creating widgets in Matomo (beginner’s guide). 

  4. Custom dimensions 

  

  Dimensions describe the characteristics of reported data. Think of them as “filters” — a means to organise website analytics data by a certain parameter such as “Browser”, “Country”, “Device Type”, “User Type” and many more. 

  Custom Dimensions come in handy for all sorts of segmentation reports. For example, comparing conversion rates between registered and guest users. Or tracking revenue by device type and location. 

  For convenience, we’ve grouped Custom Dimensions in two categories :

  Visit dimensions. These associate metadata about a user with Visitor profiles — a summary of different knowledge you have about your audience. Reports for Visit scoped custom dimensions are available in the Visitors section of your dashboard. 

  Action dimensions. These segment users by specific actions tracked by Matomo such as pageviews, events completion, downloads, form clicks, etc. When configuring Custom Dimensions, you can select among pre-defined action types or code extra action dimensions. Action scoped custom dimensions are available in the Behaviours section of Matomo. 

  Depending on your Matomo version, you can apply 5 – 15 custom dimensions to reports. 

  Important : Since you can’t delete dimensions (only deactivate them), think about your use case first. Custom Dimensions each have their own dedicated reports page on your Matomo dashboard. 

  Popular custom dimension use cases among users :

  • Segmenting reports by users’ screen resolution size to understand how your website performs on different devices
  • Monitor conversion rates for different page types to determine your best-performing assets 

  Read more about creating, tracking and managing Custom Dimensions. 

  5. Custom scheduled reports

  Manually sending reports can be time consuming, especially if you have multiple clients or provide reports to numerous stakeholders. Custom scheduled reports remove this manual process to improve efficiency and ensure timely distribution of data to relevant users.

  Any report in Matomo (default or custom) can be shared with others by email as a PDF file, HTML content or as an attached CSV document. 

  You can customise which data you want to send to different people — your colleagues, upper management, clients or other company divisions. Then set up the frequency of email dispatches and Matomo will do the rest. 

  Auto-scheduling an email report is easy. Name your report, select a Segment (aka custom or standard report), pick time, file format and sender. 

  

  You can also share links to Matomo reports as text messages, if you are using ASPSMS or Clockwork SMS. 

  Popular feature use cases

  • Convenient stakeholder reporting on key website KPIs 
  • Automated client updates to keep clients informed and reduce workload 
  • Easy data downloads for doing custom analysis with business intelligence tools 

  Read more about email reporting features in Matomo. 

  6. Custom alerts

  

  Custom Alerts is a Matomo plugin for keeping you updated on the most important analytics events. Unlike Custom Reports, which provide a complete or segmented analytics snapshot, alerts are better suited for tracking individual events. For example, significant traffic increases from a specific channel, new 404 pages or major goal achievement (e.g., hitting 1,000 sales in a week). 

  Custom Alerts are a convenient way to keep your finger on the pulse of your site so you can quickly remedy an issue or get updated on reaching a crucial KPI promptly. You can receive custom alerts via email or text message in a matter of minutes.

  To avoid flooding your inbox with alerts, we recommend reserving Custom Alerts for a select few use cases (3 to 5) and schedule custom Email Reports to receive general web page analytics. 

  Popular custom alerts use cases among users :

  • Monitor sudden drops in revenue to investigate the cause behind them and solve any issues promptly 
  • Get notified of traffic spikes or sudden dips to better manage your website’s technical performance 

  Read more about creating and managing Custom Alerts. 

  7. Goals

  

  Goals feature helps you better understand how your website performs on certain business objectives such as lead generation, online sales or content discovery. A goal is a quantifiable action you want to measure (e.g., a specific page visit, form submission or a file download). 

  When combined together, Goals make up your sales funnel — a series of specific actions you expect users to complete in order to convert. 

  Goals-setting and Funnel Analytics are a powerful, customisable combo for understanding how people navigate your website ; what makes them take action or, on the contrary, lose interest and bounce off. 

  On Matomo, you can simultaneously track multiple goals, monitor multiple conversions per one visit (e.g., when one user requests two content downloads) and assign revenue targets to specific goals.

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  Separately, Matomo Cloud users also get access to a premium Funnels feature and Multi Channel Conversion Attribution. On-Premises Matomo users can get both as paid plugins via our Marketplace.

  Popular goal tracking use cases among users :

  • Tracking newsletter subscription to maximise subscriber growth 
  • Conversion tracking for gated content (e.g., eBooks) to understand how each asset performs 
  • Analysing the volume of job applications per post to better interpret your HR marketing performance 

  Read more about creating and managing Goals in Matomo.

  8. Themes

  

  Want to give your Matomo app a distinctive visual flair ? Pick a new free theme for your On-Premises installation. Minimalistic, dark or classic — our community created six different looks that other Matomo users can download and install in a few clicks. 

  If you have some HTML/CSS/JS knowledge, you can also design your own Matomo theme. Since Matomo is an open-source project, we don’t restrict interface customisation and always welcome creativity from our users.

  Read more about designing your own Matomo theme (developer documentation).

  9. White labelling

  

  Matomo is one of the few website analytics tools to support white labelling. White labelling means that you can distribute our product to others under your brand. 

  For example, as a web design agency, you can delight customers with pre-installed GDPR-friendly website analytics. Marketing services providers, in turn, can present their clients with embedded reporting widgets, robust funnel analytics and 100% unsampled data. 

  Apart from selecting a custom theme, you can also align Matomo with your brand by :

  • Customising product name
  • Using custom header/font colours 
  • Change your tracking endpoint
  • Remove links to Matomo.org

  To streamline Matomo customisation and set-up, we developed a White Label plug-in. It provides a convenient set of controls for changing your Matomo deployment and distributing access rights to other users or sharing embedded Matomo widgets). 

  Read more about white labelling Matomo. 

  Learning more about Matomo 

  Matomo has an ever-growing list of features, ranging from standard website tracking controls to unique conversion rate optimisation tools (heatmaps, media analytics, user cohorts and more).

  To learn more about Matomo features you can check our free video web analytics training series where we cover the basics. For feature-specific tips, tricks and configurations, browse our video content or written guides. 

  21 day free trial. No credit card required.

• How HSBC and ING are transforming banking with AI

  9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

  We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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  Meet the expert panel

  Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

   

  Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

   

  Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

   

  Key findings from the webinar

  The discussion highlighted four essential elements for advancing analytics capabilities :

  1. Strong data foundations matter most

  “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

  Johri explained. Banks need robust data governance that works across different regulatory environments.

  2. Transform rather than tweak

  Li Mandri emphasised the need to reconsider entire processes :

  “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

  3. Bridge technical and business understanding

  Both leaders stressed the value of analytics translators who understand both technology and business needs.

  “We’re investing in this layer we call product leads,”

  Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

  4. Consider production costs early

  Moving from proof-of-concept to production requires careful planning. As Johri noted :

  “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

  This includes :

  • Ongoing monitoring requirements
  • Maintenance needs
  • Regulatory compliance checks
  • Regular model updates

  Real-world applications

  ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

  Customer experience enhancement

  The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

  “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

  Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

  The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

  The bank also uses AI to personalise customer communications.

  “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

  noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

  Operational efficiency transformation

  ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

  “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

  Li Mandri explained. The bank developed an AI-powered system that :

  • Automates document verification
  • Flags potential compliance issues for human review
  • Maintains consistent standards across jurisdictions
  • Reduces processing time while improving accuracy

  This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

  In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

  Sustainable finance initiatives

  ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

  “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

  The bank developed AI models that :

  • Analyse company sustainability metrics
  • Compare environmental performance against industry benchmarks
  • Assess transition plans for high-emission industries
  • Monitor ongoing compliance with sustainability commitments

  This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

  “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

  Li Mandri noted.

  HSBC’s innovative approach

  Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

  Environmental monitoring through advanced technology

  HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

  “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

  Johri explained.

  The system provides several key capabilities :

  • Analysis of forest coverage and deforestation rates
  • Assessment of biodiversity impact in specific regions
  • Monitoring of environmental changes over time
  • Measurement of environmental risk in lending portfolios

  “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

  Johri noted. This technology enables HSBC to :

  • Make informed lending decisions
  • Monitor environmental commitments of borrowers
  • Support sustainability-linked lending programmes
  • Provide accurate environmental impact reporting

  Transforming document analysis

  HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

  “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

  Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

  The bank’s approach includes :

  • Natural language processing to understand complex documents
  • Machine learning models to extract relevant information
  • Validation systems to ensure accuracy
  • Integration with existing compliance frameworks

  “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

  This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

  Building future-ready analytics capabilities

  Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

  Developing clear governance frameworks

  “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

  Johri emphasised. Banks need to establish governance structures that :

  • Define acceptable uses for AI
  • Establish monitoring and control mechanisms
  • Ensure compliance with evolving regulations
  • Maintain transparency in AI decision-making

  Creating solutions that scale

  Li Mandri stressed the importance of building systems that grow with the organisation :

  “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

  Successful scaling requires :

  • Standard approaches to model development
  • Clear evaluation frameworks
  • Simple processes for model updates
  • Strong monitoring systems
  • Regular performance reviews

  Investing in people and skills

  Both leaders highlighted how important skilled people are to analytics success.

  “Having a good hiring strategy as well as creating that data literacy is really important,”

  Johri noted. Banks need to :

  • Develop comprehensive training programmes
  • Create clear career paths for analytics professionals
  • Foster collaboration between technical and business teams
  • Build internal expertise in emerging technologies

  Planning for the future

  Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

  • Adapting to new privacy regulations
  • Making AI decisions more explainable
  • Improving data quality and governance
  • Strengthening cybersecurity measures

  Practical steps for financial institutions

  The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

  Key steps for success

  Financial institutions looking to enhance their analytics capabilities should :

  1. Start with strong foundations
     • Invest in clear data governance frameworks
     • Set data quality standards
     • Build thorough documentation processes
     • Create transparent data tracking
  2. Think strategically about AI implementation
     • Focus on transformative rather than small changes
     • Consider the full costs of AI projects
     • Build solutions that can grow
     • Balance innovation with risk management
  3. Invest in people and processes
     • Develop internal analytics expertise
     • Create clear paths for career growth
     • Foster collaboration between technical and business teams
     • Build a culture of data literacy
  4. Plan for scale
     • Establish monitoring systems
     • Create governance frameworks
     • Develop standard approaches to model development
     • Stay flexible for future regulatory changes

  Learn more

  Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

  • Detailed technical insights from both banks
  • Extended Q&A with the speakers
  • Additional case studies and examples
  • Practical implementation advice

   

   

  Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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• Is Google Analytics Accurate ? 6 Important Caveats

  8 novembre 2022, par Erin

  It’s no secret that accurate website analytics is crucial for growing your online business — and Google Analytics is often the go-to source for insights. 

  But is Google Analytics data accurate ? Can you fully trust the provided numbers ? Here’s a detailed explainer.

  How Accurate is Google Analytics ? A Data-Backed Answer 

  When properly configured, Google Analytics (Universal Analytics and Google Analytics 4) is moderately accurate for global traffic collection. That said : Google Analytics doesn’t accurately report European traffic. 

  According to GDPR provisions, sites using GA products must display a cookie consent banner. This consent is required to collect third-party cookies — a tracking mechanism for identifying users across web properties.

  Google Analytics (GA) cannot process data about the user’s visit if they rejected cookies. In such cases, your analytics reports will be incomplete.

  Cookie rejection refers to visitors declining or blocking cookies from ever being collected by a specific website (or within their browser). It immediately affects the accuracy of all metrics in Google Analytics.

  Google Analytics is not accurate in locations where cookie consent to tracking is legally required. Most consumers don’t like disruptive cookie banners or harbour concerns about their privacy — and chose to reject tracking. 

  This leaves businesses with incomplete data, which, in turn, results in : 

  • Lower traffic counts as you’re not collecting 100% of the visitor data. 
  • Loss of website optimisation capabilities. You can’t make data-backed decisions due to inconsistent reporting

  For the above reasons, many companies now consider cookieless website tracking apps that don’t require consent screen displays. 

  Why is Google Analytics Not Accurate ? 6 Causes and Solutions 

  A high rejection rate of cookie banners is the main reason for inaccurate Google Analytics reporting. In addition, your account settings can also hinder Google Analytics’ accuracy.

  If your analytics data looks wonky, check for these six Google Analytics accuracy problems. 

  You Need to Secure Consent to Cookies Collection 

  To be GDPR-compliant, you must display a cookie consent screen to all European users. Likewise, other jurisdictions and industries require similar measures for user data collection. 

  This is a nuisance for many businesses since cookie rejection undermines their remarketing capabilities. Hence, some try to maximise cookie acceptance rates with dark patterns. For example : hide the option to decline tracking or make the texts too small. 

  

  Sadly, not everyone’s treating users with respect. A joint study by German and American researchers found that only 11% of US websites (from a sample of 5,000+) use GDPR-compliant cookie banners.

  As a result, many users aren’t aware of the background data collection to which they have (or have not) given consent. Another analysis of 200,000 cookies discovered that 70% of third-party marketing cookies transfer user data outside of the EU — a practice in breach of GDPR.

  Naturally, data regulators and activities are after this issue. In April 2022, Google was pressured to introduce a ‘reject all’ cookies button to all of its products (a €150 million compliance fine likely helped with that). Whereas, noyb has lodged over 220 complaints against individual websites with deceptive cookie consent banners.

  The takeaway ? Messing up with the cookie consent mechanism can get you in legal trouble. Don’t use sneaky banners as there are better ways to collect website traffic statistics. 

  Solution : Try Matomo GDPR-Friendly Analytics 

  Fill in the gaps in your traffic analytics with Matomo – a fully GDPR-compliant product that doesn’t rely on third-party cookies for tracking web visitors. Because of how it is designed, the French data protection authority (CNIL) confirmed that Matomo can be used to collect data without tracking consent.

  With Matomo, you can track website users without asking for cookie consent. And when you do, we supply you with a compact, compliant, non-disruptive cookie banner design. 

  Your Google Tag Isn’t Embedded Correctly 

  Google Tag (gtag.js) is a web tracking script that sends data to your Google Analytics, Google Ads and Google Marketing Platform.

  A corrupted gtag.js installation can create two accuracy issues : 

  • Duplicate page tracking 
  • Missing script installation 

  Is there a way to tell if you’re affected ?

  Yes. You may have duplicate scripts installed if you have a very low bounce rate on most website pages (below 15% – 20%). The above can happen if you’re using a WordPress GA plugin and additionally embed gtag.js straight in your website code. 

  A tell-tale sign of a missing script on some pages is low/no traffic stats. Google alerts you about this with a banner : 

  

  Solution : Use Available Troubleshooting Tools 

  Use Google Analytics Debugger extension to analyse pages with low bounce rates. Use the search bar to locate duplicate code-tracking elements. 

  Alternatively, you can use Google Tag Assistant for diagnosing snippet install and troubleshooting issues on individual pages. 

  If the above didn’t work, re-install your analytics script. 

  Machine Learning and Blended Data Are Applied

  Google Analytics 4 (GA4) relies a lot on machine learning and algorithmic predictions.

  By applying Google’s advanced machine learning models, the new Analytics can automatically alert you to significant trends in your data. [...] For example, it calculates churn probability so you can more efficiently invest in retaining customers.

  Vidhya Srinivasan, VP of Measurement, Analytics and Buying Platforms at Google

  On the surface, the above sounds exciting. In practice, Google’s application of predictive algorithms means you’re not seeing actual data. 

  To offer a variation of cookieless tracking, Google algorithms close the gaps in reporting by creating models (i.e., data-backed predictions) instead of reporting on actual user behaviours. Therefore, your GA4 numbers may not be accurate.

  For bigger web properties (think websites with 1+ million users), Google also relies on data sampling — a practice of extrapolating data analytics, based on a data subset, rather than the entire dataset. Once again, this can lead to inconsistencies in reporting with some numbers (e.g., average conversion rates) being inflated or downplayed. 

  Solution : Try an Alternative Website Analytics App 

  Unlike GA4, Matomo reports consist of 100% unsampled data. All the aggregated reporting you see is based on real user data (not guesstimation). 

  Moreover, you can migrate from Universal Analytics (UA) to Matomo without losing access to your historical records. GA4 doesn’t yet have any backward compatibility.

  Spam and Bot Traffic Isn’t Filtered Out 

  Surprise ! 42% of all Internet traffic is generated by bots, of which 27.7% are bad ones.

  Good bots (aka crawlers) do essential web “housekeeping” tasks like indexing web pages. Bad bots distribute malware, spam contact forms, hack user accounts and do other nasty stuff. 

  A lot of such spam bots are designed specifically for web analytics apps. The goal ? Flood your dashboard with bogus data in hopes of getting some return action from your side. 

  Types of Google Analytics Spam :

  • Referral spam. Spambots hijack the referrer, displayed in your GA referral traffic report to indicate a page visit from some random website (which didn’t actually occur). 
  • Event spam. Bots generate fake events with free language entries enticing you to visit their website. 
  • Ghost traffic spam. Malicious parties can also inject fake pageviews, containing URLs that they want you to click. 

  Obviously, such spammy entities distort the real website analytics numbers. 

  Solution : Set Up Bot/Spam Filters 

  Google Analytics 4 has automatic filtering of bot traffic enabled for all tracked Web and App properties. 

  But if you’re using Universal Analytics, you’ll have to manually configure spam filtering. First, create a new view and then set up a custom filter. Program it to exclude :

  • Filter Field : Request URI
  • Filter Pattern : Bot traffic URL

  Once you’ve configured everything, validate the results using Verify this filter feature. Then repeat the process for other fishy URLs, hostnames and IP addresses. 

  You Don’t Filter Internal Traffic 

  Your team(s) spend a lot of time on your website — and their sporadic behaviours can impair your traffic counts and other website metrics.

  To keep your data “employee-free”, exclude traffic from : 

  • Your corporate IPs addresses 
  • Known personal IPs of employees (for remote workers) 

  If you also have a separate stage version of your website, you should also filter out all traffic coming from it. Your developers, contractors and marketing people spend a lot of time fiddling with your website. This can cause a big discrepancy in average time on page and engagement rates. 

  Solution : Set Internal Traffic Filters 

  Google provides instructions for excluding internal traffic from your reports using IPv4/IPv6 address filters. 

  

  Session Timeouts After 30 Minutes 

  After 30 minutes of inactivity, Google Analytics tracking sessions start over. Inactivity means no recorded interaction hits during this time. 

  Session timeouts can be a problem for some websites as users often pin a tab to check it back later. Because of this, you can count the same user twice or more — and this leads to skewed reporting. 

  Solution : Programme Custom Timeout Sessions

  You can codify custom cookie timeout sessions with the following code snippets : 

  • _setSessionCookieTimeout. Set a custom new session cookie timeout in milliseconds.
  • _setVisitorCookieTimeout. Sets a custom Google Analytics visitor cookie expiration time frame in milliseconds.

  Final Thoughts 

  Thanks to its scale and longevity, Google Analytics has some strong sides, but its data accuracy isn’t 100% perfect.

  The inability to capture analytics data from users who don’t consent to cookie tracking and data sampling applied to bigger web properties may be a deal-breaker for your business. 

  If that’s the case, try Matomo — a GDPR-compliant, accurate web analytics solution. Start your 21-day free trial now. No credit card required.

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