Sur d’autres sites (11477)

• CCPA vs GDPR : Understanding Their Impact on Data Analytics

  19 mars, par Alex Carmona

  With over 400 million internet users in Europe and 331 million in the US (11% of which reside in California alone), understanding the nuances of privacy laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is crucial for compliant and ethical consumer data collection.

  Navigating this compliance landscape can be challenging for businesses serving European and Californian markets.

  This guide explores the key differences between CCPA and GDPR, their impact on data analytics, and how to ensure your business meets these essential privacy requirements.

  What is the California Consumer Privacy Act (CCPA) ?

  The California Consumer Privacy Act (CCPA) is a data privacy law that gives California consumers control over their personal information. It applies to for-profit businesses operating in California that meet specific criteria related to revenue, data collection and sales.

  Origins and purpose

  The CCPA addresses growing concerns about data privacy and how businesses use personal information in California. The act passed in 2018 and went into effect on 1 January 2020.

  Key features

  • Grants consumers the right to know what personal information is collected
  • Provides the right to delete personal information
  • Allows consumers to opt out of the sale of their personal information
  • Prohibits discrimination against consumers who exercise their CCPA rights

  Key definitions under the CCPA framework

  • Business : A for-profit entity doing business in California and meeting one or more of these conditions :
    • Has annual gross revenues over $25 million ;
    • Buys, receives, sells or shares 50,000 or more consumers’ personal information ; or
    • Derives 50% or more of its annual revenues from selling consumers’ personal information
  • Consumer : A natural person who is a California resident
  • Personal Information : Information that could be linked to, related to or used to identify a consumer or household, such as online identifiers, IP addresses, email addresses, social security numbers, cookie identifiers and more

  What is the General Data Protection Regulation (GDPR) ?

  The General Data Protection Regulation (GDPR) is a data privacy and protection law passed by the European Union (EU). It’s one of the strongest and most influential data privacy laws worldwide and applies to all organisations that process the personal data of individuals in the EU.

  Origins and purpose

  The GDPR was passed in 2016 and went into effect on 25 May 2018. It aims to harmonise data privacy laws in Europe and give people in the European Economic Area (EEA) privacy rights and control over their data.

  Key features

  • Applies to all organisations that process the personal data of individuals in the EEA
  • Grants individuals a wide range of privacy rights over their data
  • Requires organisations to obtain explicit and informed consent for most data processing
  • Mandates appropriate security measures to protect personal data
  • Imposes significant fines and penalties for non-compliance

  Key definitions under the GDPR framework

  • Data Subject : An identified or identifiable person
  • Personal Data : Any information relating to a data subject
  • Data Controller : The entity or organisation that determines how personal data is processed and what for
  • Data Processor : The entity or organisation that processes the data on behalf of the controller

  CCPA vs. GDPR : Key similarities

  The CCPA and GDPR enhance consumer privacy rights and give individuals greater control over their data.

  Dimension	CCPA	GDPR
  Purpose	Protect consumer privacy	Protect individual data rights
  Key Rights	Right to access, delete and opt out of sale	Right to access, rectify, erase and restrict processing
  Transparency	Requires transparency around data collection and use	Requires transparency about data collection, processing and use

  CCPA vs. GDPR : Key differences

  While they have similar purposes, the CCPA and GDPR differ significantly in their scope, approach and specific requirements.

  Dimension	CCPA	GDPR
  Scope	For-profit businesses only	All organisations processing EU consumer data
  Territorial Reach	California-based natural persons	All data subjects within the EEA
  Consent	Opt-out system	Opt-in system
  Penalties	Per violation based on its intentional or negligent nature	Case-by-case based on comprehensive assessment
  Individual Rights	Narrower (relative to GDPR)	Broader (relative to CCPA)

  CCPA vs. GDPR : A multi-dimensional comparison

  The previous sections gave a broad overview of the similarities and differences between CCPA and GDPR. Let’s now examine nine key dimensions where these regulations converge or diverge and discuss their impact on data analytics.

  

  #1. Scope and territorial reach

  The GDPR has a much broader scope than the CCPA. It applies to all organisations that process the personal data of individuals in the EEA, regardless of their business model, purpose or physical location.

  The CCPA applies to medium and large for-profit businesses that derive a substantial portion of their earnings from selling Californian consumers’ personal information. It doesn’t apply to non-profits, government agencies or smaller for-profit companies.

  Impact on data analytics

  The difference in scope significantly impacts data analytics practices. Smaller businesses may not need to comply with either regulation, some may only need to follow the CCPA, while most global businesses must comply with both. This often requires different methods for collecting and processing data in California, Europe, and elsewhere.

  #2. Penalties and fines for non-compliance

  Both the CCPA and GDPR impose penalties for non-compliance, but the severity of fines differs significantly :

  CCPA	Maximum penalty
  	$2,500 per unintentional violation $7,500 per intentional violation

  “Per violation” means per violation per impacted consumer. For example, three intentional CCPA violations affecting 1,000 consumers would result in 3,000 total violations and a $22.5 million maximum penalty (3,000 × $7,500).

  

  The largest CCPA fine to date was Zoom’s $85 million settlement in 2021.

  

  In contrast, the GDPR has resulted in 2,248 fines totalling almost €6.6 billion since 2018 — €2.4 billion of which were for non-compliance.

  GDPR	Maximum penalty
  	€20 million or 4% of all revenue earned the previous year

  So far, the biggest fine imposed under the GDPR was Meta’s €1.2 billion fine in May 2023 — 15 times more than Zoom had to pay California.

  Impact on data analytics

  The significant difference in potential fines demonstrates the importance of regulatory compliance for data analytics professionals. Non-compliance can have severe financial consequences, directly affecting budget allocation and business operations.

  Businesses must ensure their data collection, storage and processing practices comply with regulations in both Europe and California.

  Choosing privacy-first, compliance-ready analytics platforms like Matomo is instrumental for mitigating non-compliance risks.

  #3. Data subject rights and consumer rights

  The CCPA and GDPR give people similar rights over their data, but their limitations and details differ.

  Rights common to the CCPA and GDPR

  • Right to Access/Know : People can access their personal information and learn what data is collected, its source, its purpose and how it’s shared
  • Right to Delete/Erasure : People can request the deletion of their personal information, with some exceptions
  • Right to Non-Discrimination : Businesses can’t discriminate against people who exercise their privacy rights

  Consumer rights unique to the CCPA

  • Right to Opt Out of Sale : Consumers can prohibit the sale of their personal information
  • Right to Notice : Businesses must inform consumers about data collection practices
  • Right to Disclosure : Consumers can request specific information collected about them

  Data subject rights unique to the GDPR

  • Right to be Informed : Broader transparency requirements encompass data retention, automated decision-making and international transfers
  • Right to Rectification : Data subjects may request the correction of inaccurate data
  • Right to Restrict Processing : Consumers may limit data use in certain situations
  • Right to Data Portability : Businesses must provide individual consumer data in a secure, portable format when requested
  • Right to Withdraw Consent : Consumers may withdraw previously granted consent to data processing

  	CCPA	GDPR
  Right to Access or Know	✓	✓
  Right to Delete or Erase	✓	✓
  Right to Non-Discrimination	✓	✓
  Right to Opt-Out	✓
  Right to Notice	✓
  Right to Disclosure	✓
  Right to be Informed		✓
  Right to Rectification		✓
  Right to Restrict Processing		✓
  Right to Data Portability		✓
  Right to Withdraw Consent		✓

  Impact on data analytics

  Data analysts must understand these rights and ensure compliance with both regulations, which could potentially require separate data handling processes for EU and California consumers.

  #4. Opt-out vs. opt-in

  The CCPA generally follows an opt-out model, while the GDPR requires explicit consent from individuals before processing their data.

  Impact on data analytics

  For CCPA compliance, businesses can collect data by default if they provide opt-out mechanisms. Failing to process opt-out requests can result in severe penalties, like Sephora’s $1.2 million fine.

  Under GDPR, organisations must obtain explicit consent before collecting any data, which can limit the amount of data available for analysis.

  #5. Parental consent

  The CCPA and GDPR have provisions regarding parental consent for processing children’s data. The CCPA requires parental consent for children under 13, while the GDPR sets the age at 16, though member states can lower it to 13.

  Impact on data analytics

  This requirement significantly impacts businesses targeting younger audiences. In Europe and the US, companies must implement different methods to verify users’ ages and obtain parental consent when necessary.

  The California Attorney General’s Office recently fined Tilting Point Media LLC $500,000 for sharing children’s data without parental consent.

  #6. Data security requirements

  Both regulations require businesses to implement adequate security measures to protect personal data. However, the GDPR has more prescriptive requirements, outlining specific security measures and emphasising a risk-based approach.

  Impact on data analytics

  Data analytics professionals must ensure that data is processed and stored securely to avoid breaches and potential fines.

  #7. International data transfers

  Both the CCPA and GDPR address international data transfers. Under the CCPA, businesses must only inform consumers about international transfers. The GDPR has stricter requirements, including ensuring adequate data protection safeguards for transfers outside the EEA.

  

  Other rules, like the Payment Services Directive 2 (PSD2), also affect international data transfers, especially in the financial industry.

  PSD2 requires strong customer authentication and secure communication channels for payment services. This adds complexity to cross-border data flows.

  Impact on data analytics

  The primary impact is on businesses serving European residents from outside Europe. Processing data within the European Union is typically advisable. Meta’s record-breaking €1.2 billion fine was specifically for transferring data from the EEA to the US without sufficient safeguards.

  Choosing the right analytics platform helps avoid these issues.

  For example, Matomo offers a free, open-source, self-hosted analytics platform you can deploy anywhere. You can also choose a managed, GDPR-compliant cloud analytics solution with all data storage and processing servers within the EU (in Germany), ensuring your data never leaves the EEA.

  #8. Enforcement mechanisms

  The California Attorney General is responsible for enforcing CCPA requirements, while in Europe, the Data Protection Authority (DPA) in each EU member state enforces GDPR requirements.

  Impact on data analytics

  Data analytics professionals should be familiar with their respective enforcement bodies and their powers to support compliance efforts and minimise the risk of fines and penalties.

  #9. Legal basis for personal data processing

  The GDPR outlines six legal grounds for processing personal data :

  • Consent
  • Contract
  • Legal obligation
  • Vital interests
  • Public task
  • Legitimate interests

  The CCPA doesn’t explicitly define lawful bases but focuses on consumer rights and transparency in general.

  Impact on data analytics

  Businesses subject to the GDPR must identify and document a valid lawful basis for each processing activity.

  Compliance rules under CCPA and GDPR

  Complying with the CCPA and GDPR requires a comprehensive approach to data privacy. Here’s a summary of the essential compliance rules for each framework :

  

  CCPA compliance rules

  • Create clear and concise privacy policies outlining data collection and use practices
  • Give consumers the right to opt-out
  • Respond to consumer requests to access, delete and correct their personal information
  • Implement reasonable security measures for consumers’ personal data protection
  • Never discriminate against consumers who exercise their CCPA rights

  GDPR compliance rules

  • Obtain explicit and informed consent for data processing activities
  • Implement technical and organisational controls to safeguard personal data
  • Designate a Data Protection Officer (DPO) if necessary
  • Perform data protection impact assessments (DPIAs) for high-risk processing activities
  • Maintain records of processing activities
  • Promptly report data breaches to supervisory authorities

  Navigating the CCPA and GDPR with confidence

  Understanding the nuances of the CCPA and GDPR is crucial for businesses operating in the US and Europe. These regulations significantly impact data collection and analytics practices.

  Implementing robust data security practices and prioritising privacy and compliance are essential to avoid severe penalties and build trust with today’s privacy-conscious consumers.

  Privacy-centric analytics platforms like Matomo enable businesses to collect, analyse and use data responsibly and transparently, extracting valuable insights while maintaining compliance with both CCPA and GDPR requirements.

  Start your 21-day free trial today !

  no credit card required

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

  3 juillet, par Joe

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

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

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

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

  What is server-side tracking ? 

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

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

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

  Client-side tracking vs. server-side tracking 

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

  Let’s understand their differences. 

  Client-side tracking : Convenience with caveats

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

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

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

  However, there are some challenges. 

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

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

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

  

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

  Server-side tracking : Control and reliability

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

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

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

  Benefits of server-side tracking 

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

  Improved data accuracy

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

  Data minimisation

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

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

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

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

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

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

  Cross-device tracking capabilities

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

  In-depth event tracking

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

  Enhanced privacy compliance

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

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

  

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

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

  Extended cookie lifetime

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

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

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

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

  Server-side tracking : Responsibilities and considerations

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

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

  How does server-side tracking work ? 

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

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

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

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

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

  How to implement server-side tracking ?

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

  1. Server-side tag management

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

  

  (Image Source) 

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

  2. Direct server-to-server tracking via APIs

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

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

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

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

  3. Using analytics platforms with built-in server SDKs

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

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

  4. Hybrid approaches

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

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

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

  Using server logs

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

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

  Using the Matomo tracking API (Server-side SDKs)

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

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

  Data privacy, regulations and Matomo

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

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

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

  

  (Image Source) 

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

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

  Balancing data collection and user privacy

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

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

• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required.