Recherche avancée

Sur d’autres sites (4699)

• What is Funnel Analysis ? A Complete Guide for Quick Results

  25 janvier 2024, par Erin

  Your funnel is leaking.

  You’re losing visitors.

  You’re losing conversions and sales.

  But you don’t know how it’s happening, where it’s happening, or what to do about it.

  The reason ? You aren’t properly analysing your funnels.

  If you want to improve conversions and grow your business, you need to understand how to properly assess your sales funnels to set yourself up for success.

  In this guide, we’ll show you what funnel analysis is, why it’s important, and what steps you need to take to leverage it to improve conversions.

  What is funnel analysis ?

  Every business uses sales funnels, whether they know it or not.

  But most people aren’t analysing them, costing them conversions.

  

  Funnel analysis is a marketing method to analyse the events leading to specific conversion points. 

  It aims to look at the entire journey of potential customers from the moment they first touch base with your website or business to the moment they click “buy.”

  It’s assessing what your audience is doing at every step of the journey.

  By assessing what actions are taking place at scale, you can see where you’re falling short in your sales funnel.

  You’ll see :

  1. Where prospects are falling off.
  2. Where people are converting well.

  By gaining this understanding, you’ll better understand the health of your website’s sales funnels and overall marketing strategy.

  With that knowledge, you can optimise your marketing strategy to patch those leaks, improve conversions and grow your business.

  Why funnel analysis is important

  Funnel analysis is critical because your funnel is your business.

  When you analyse your funnel, you’re analysing your business.

  You’re looking at what’s working and what’s not so you can grow revenue and profit margins.

  Funnel analysis lets you monitor user behaviour to show you the motivation and intention behind their decisions.

  Here are five reasons you need to incorporate funnel analysis into your workflow.

  

  1. Gives insights into your funnel problems

  The core purpose of funnel analysis is to look at what’s going on on your website.

  What are the most effective steps to conversion ?

  Where do users drop off in the conversion process ?

  And which pages contribute the most to conversion or drop-offs ?

  Funnel analysis helps you understand what’s going on with your site visitors. Plus, it helps you see what’s wrong with your funnel.

  If you aren’t sure what’s happening with your funnel, you won’t know what to improve to grow your revenue.

  2. Improves conversions

  When you know what’s going on with your funnel, you’ll know how to improve it.

  To improve your conversion funnel, you need to close the leaks. These are areas where website visitors are falling off.

  It’s the moment the conversion is lost.

  You need to use funnel analysis to give insight into these problem areas. Once you can see where the issue is, you can patch that leak and improve the percentage of visitors who convert.

  For example, if your conversion rate on your flagship product page has plateaued and you can’t figure out how to increase conversions, implementing a funnel analysis tactic like heatmaps will show you that visitors are spending time reading your product description. Still, they’re not spending much time near your call to action.

  

  This might tell you that you need to update your description copy or adjust your button (i.e. colour, size, copy). You can increase conversions by making those changes in your funnel analysis insights.

  3. Improves the customer experience

  Funnel analysis helps you see where visitors spend their time, what elements they interact with and where they fall off.

  One of the key benefits of analysing your funnel is you’ll be able to help improve the experience your visitors have on your website.

  For example, if you have informational videos on a specific web page to educate your visitors, you might use the Media Analytics feature in your web analytics solution to find out that they’re not spending much time watching them.

  This could lead you to believe that the content itself isn’t good or relevant to them.

  But, after implementing session recordings within your funnel analysis, you see people clicking a ton near the play button. This might tell you that they’re having trouble clicking the actual button on the video player due to poor UX.

  In this scenario, you could update the UX on your web page so the videos are easy to click and watch, no matter what device someone uses.

  With more video viewers, you can provide value to your visitors instead of leaving them frustrated trying to watch your videos.

  4. Grows revenue

  This is what you’re likely after : more revenue.

  More often than not, this means you need to focus on improving your conversion rate.

  Funnel analysis helps you find those areas where visitors are exiting so you can patch those leaks up and turn more visitors into customers.

  Let’s say you have a conversion rate of 1.7%.

  You get 50,000 visitors per month.

  Your average order is $82.

  Even if you increase your conversion rate by 10% (to 1.87%) through funnel analysis, here’s the monthly difference in revenue :

  Before : $69,700
  After : $76,670

  In one year, you’ll make nearly $80,000 in additional revenue from funnel analysis alone.

  Different types of funnel analysis

  There are a few different types of funnel analysis.

  How you define success in your funnel all comes down to one of these four pillars.

  Depending on your goals, business and industry, you may want to assess the different funnel analyses at different times.

  1. Pageview funnel analysis

  Pageview funnel analysis is about understanding how well your website content is performing. 

  It helps you enhance user experience, making visitors stay longer on your site. By identifying poor performing pages (pages with high exit rates), you can pinpoint areas that need optimisation for better engagement.

  2. Conversion funnel analysis

  Next up, we’re looking at conversion funnel analysis.

  This type of funnel analysis is crucial for marketers aiming to turn website visitors into action-takers. This involves tracking and optimising conversion goals, such as signing up for newsletters, downloading ebooks, submitting forms or signing up for free trials. 

  The primary goal of conversion funnel analysis is to boost your website’s overall conversion rates.

  3. E-commerce funnel analysis

  For businesses selling products online, e-commerce funnel analysis is essential. 

  It involves measuring whether your products are being purchased and finding drop-off points in the purchasing process. 

  By optimising the e-commerce funnel, you can enhance revenue and improve the overall efficiency of your sales process.

  How to conduct funnel analysis

  Now that you understand what funnel analysis is, why it’s important, and the different types of analysis, it’s time to show you how to do it yourself.

  To get started with funnel analysis, you need to have the right web analytics solution.

  Here are the most common funnel analysis tools and methods you can use :

  1. Funnel analytics

  If you want to choose a single tool to conduct funnel analysis, it’s an all-in-one web analytics tool, like Matomo.

  

  With Matomo’s Funnel Analytics, you can dive into your whole funnel and analyse each step (and each step’s conversion rate).

  

  For instance, if you look at the example above, you can see the proceed rate at each funnel step before the conversion page.

  This means you can improve each proceed rate, to drive more traffic to your conversion page in order to increase conversion rates.

  In the above snapshot from Matomo, it shows visitors starting on the job board overview page, moving on to view specific job listings. The goal is to convert these visitors into job applicants.

  However, a significant issue arises at the job view stage, where 95% of visitors don’t proceed to job application. To increase conversions, we need to first concentrate on improving the job view page.

  Try Matomo for Free

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

  Start for free

  No credit card required

  2. Heatmaps

  Heatmaps is a behaviour analytics tool that lets you see different visitor activities, including :

  • Mouse movement
  • How far down visitors scroll
  • Clicks

  You can see which elements were clicked on and which weren’t and how far people scroll down your page.

  

  A heatmap lets you see which parts of a page are getting the most attention and which parts go unnoticed by your users.

  For example, if, during your funnel analysis, you see that a lot of visitors are falling off after they land on the checkout page, then you might want to add a heatmap on your checkout page to see where and why people are exiting.

  3. Session recordings

  Want to see what individual users are doing and how they’re interacting with your site ?

  Then, you’ll want to check out session recordings.

  A session recording is a video playback of a visitor’s time on your website.

  

  It’s the most effective method to observe your visitors’ interactions with your site, eliminating uncertainty when identifying areas for funnel improvement.

  Session recordings instill confidence in your optimisation efforts by providing insights into why and where visitors may be dropping off in the funnel.

  4. A/B testing

  If you want to take the guesswork out of optimising your funnel and increasing your conversions, you need to start A/B testing.

  An A/B test is where you create two versions of a web page to determine which one converts better.

  

  For example, if your heatmaps and session recordings show that your users are dropping off near your call to action, it may be time to test a new version.

  You may find that by simply testing a different colour button, you may increase conversions by 20% or more.

  5. Form analytics

  Are you trying to get more leads to fill out forms on your site ?

  Well, Form Analytics can help you understand how your website visitors interact with your signup forms.

  You can view metrics such as starter rate, conversion rate, average hesitation time and average time spent.

  This information allows you to optimise your forms effectively, ultimately maximising your success.

  Let’s look at the performance of a form using Matomo’s Form Analytics feature below.

  

  In the Matomo example, our starter rate stands at a solid 60.1%, but there’s a significant drop to a submitter rate of 29.3%, resulting in a conversion rate of 16.3%.

  Looking closer, people are hesitating for about 16.2 seconds and taking nearly 1 minute 39 seconds on average to complete our form.

  This could indicate our form is confusing and requesting too much. Simplifying it could help increase sign-ups.

  See first-hand how Concrete CMS tripled their leads using Form Analytics in Matomo.

  Try Matomo for Free

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

  Start for free

  No credit card required

  Start optimising your funnels with Matomo today

  If you want to optimise your business, you must optimise your funnels.

  Without information on what’s working and what’s not, you’ll never know if your website changes are making a difference.

  Worse yet, you could have underperforming stages in your funnel, but you won’t know unless you start looking.

  Funnel analysis changes that.

  By analysing your funnels regularly, you’ll be able to see where visitors are leaking out of your funnel. That way, you can get more visitors to convert without generating more traffic.

  If you want to improve conversions and grow revenue today, try Matomo’s Funnel Analytics feature.

  You’ll be able to see conversion rates, drop-offs, and fine-tuned details on each step of your funnel so you can turn more potential customers into paying customers.

  Additionally, Matomo comes equipped with features like heatmaps, session recordings, A/B testing, and form analytics to optimise your funnels with confidence.

  Try Matomo free for 21-days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

• Banking Data Strategies – A Primer to Zero-party, First-party, Second-party and Third-party data

  25 octobre 2024, par Daniel Crough — Banking and Financial Services, Privacy

  Banks hold some of our most sensitive information. Every transaction, loan application, and account balance tells a story about their customers’ lives. Under GDPR and banking regulations, protecting this information isn’t optional – it’s essential.

  Yet banks also need to understand how customers use their services to serve them better. The solution lies in understanding different types of banking data and how to handle each responsibly. From direct customer interactions to market research, each data source serves a specific purpose and requires its own privacy controls.

  Before diving into how banks can use each type of data effectively, let’s look into the key differences between them :

  Data Type	What It Is	Banking Example	Legal Considerations
  First-party	Data from direct customer interactions with your services	Transaction records, service usage patterns	Different legal bases apply (contract, legal obligation, legitimate interests)
  Zero-party	Information customers actively provide	Stated preferences, financial goals	Requires specific legal basis despite being voluntary ; may involve profiling
  Second-party	Data shared through formal partnerships	Insurance history from partners	Must comply with PSD2 and specific data sharing regulations
  Third-party	Data from external providers	Market analysis, demographic data	Requires due diligence on sources and specific transparency measures

  What is first-party data ?

  

  First-party data reveals how customers actually use your banking services. When someone logs into online banking, withdraws money from an ATM, or speaks with customer service, they create valuable information about real banking habits.

  This direct interaction data proves more reliable than assumptions or market research because it shows genuine customer behaviour. Banks need specific legal grounds to process this information. Basic banking services fall under contractual necessity, while fraud detection is required by law. Marketing activities need explicit customer consent. The key is being transparent with customers about what information you process and why.

  Start by collecting only what you need for each specific purpose. Store information securely and give customers clear control through privacy settings. This approach builds trust while helping meet privacy requirements under the GDPR’s data minimisation principle.

  What is zero-party data ?

  

  Zero-party data emerges when customers actively share information about their financial goals and preferences. Unlike first-party data, which comes from observing customer behaviour, zero-party data comes through direct communication. Customers might share their retirement plans, communication preferences, or feedback about services.

  Interactive tools create natural opportunities for this exchange. A retirement calculator helps customers plan their future while revealing their financial goals. Budget planners offer immediate value through personalised advice. When customers see clear benefits, they’re more likely to share their preferences.

  However, voluntary sharing doesn’t mean unrestricted use. The ICO’s guidance on purpose limitation applies even to freely shared information. Tell customers exactly how you’ll use their data, document specific reasons for collecting each piece of information, and make it simple to update or remove personal data.

  Regular reviews help ensure you still need the information customers have shared. This aligns with both GDPR requirements and customer expectations about data management. By treating voluntary information with the same care as other customer data, banks build lasting trust.

  What is second-party data ?

  

  Second-party data comes from formal partnerships between banks and trusted companies. For example, a bank might work with an insurance provider to better understand shared customers’ financial needs.

  These partnerships need careful planning to protect customer privacy. The ICO’s Data Sharing Code provides clear guidelines : both organisations must agree on what data they’ll share, how they’ll protect it, and how long they’ll keep it before any sharing begins.

  Transparency builds trust in these arrangements. Tell customers about planned data sharing before it happens. Explain what information you’ll share and how it helps provide better services.

  Regular audits help ensure both partners maintain high privacy standards. Review shared data regularly to confirm it’s still necessary and properly protected. Be ready to adjust or end partnerships if privacy standards slip. Remember that your responsibility to protect customer data extends to information shared with partners.

  Successful partnerships balance improved service with diligent privacy protection. When done right, they help banks understand customer needs better while maintaining the trust that makes banking relationships work.

  What is third-party data ?

  

  Third-party data comes from external sources outside your bank and its partners. Market research firms, data analytics companies, and economic research organizations gather and sell this information to help banks understand broader market trends.

  This data helps fill knowledge gaps about the wider financial landscape. For example, third-party data might reveal shifts in consumer spending patterns across different age groups or regions. It can show how customers interact with different financial services or highlight emerging banking preferences in specific demographics.

  But third-party data needs careful evaluation before use. Since your bank didn’t collect this information directly, you must verify both its quality and compliance with privacy laws. Start by checking how providers collected their data and whether they had proper consent. Look for providers who clearly document their data sources and collection methods.

  Quality varies significantly among third-party data providers. Some key questions to consider before purchasing :

  • How recent is the data ?
  • How was it collected ?
  • What privacy protections are in place ?
  • How often is it updated ?
  • Which specific market segments does it cover ?

  Consider whether third-party data will truly add value beyond your existing information. Many banks find they can gain similar insights by analysing their first-party data more effectively. If you do use third-party data, document your reasons for using it and be transparent about your data sources.

  Creating your banking data strategy

  

  A clear data strategy helps your bank collect and use information effectively while protecting customer privacy. This matters most with first-party data – the information that comes directly from your customers’ banking activities.

  Start by understanding what data you already have. Many banks collect valuable information through everyday transactions, website visits, and customer service interactions. Review these existing data sources before adding new ones. Often, you already have the insights you need – they just need better organization.

  Map each type of data to a specific purpose. For example, transaction data might help detect fraud and improve service recommendations. Website analytics could reveal which banking features customers use most. Each data point should serve a clear business purpose while respecting customer privacy.

  Strong data quality standards support better decisions. Create processes to update customer information regularly and remove outdated records. Check data accuracy often and maintain consistent formats across your systems. These practices help ensure your insights reflect reality.

  Remember that strategy means choosing what not to do. You don’t need to collect every piece of data possible. Focus on information that helps you serve customers better while maintaining their privacy.

  Managing multiple data sources

  

  Banks work with many types of data – from direct customer interactions to market research. Each source serves a specific purpose, but combining them effectively requires careful planning and precise attention to regulations like GDPR and ePrivacy.

  First-party data forms your foundation. It shows how your customers actually use your services and what they need from their bank. This direct interaction data proves most valuable because it reflects real behaviour rather than assumptions. When customers check their balances, transfer money, or apply for loans, they show you exactly how they use banking services.

  Zero-party data adds context to these interactions. When customers share their financial goals or preferences directly, they help you understand the “why” behind their actions. This insight helps shape better services. For example, knowing a customer plans to buy a house helps you offer relevant savings tools or mortgage information at the right time.

  Second-party partnerships can fill specific knowledge gaps. Working with trusted partners might reveal how customers manage their broader financial lives. But only pursue partnerships when they offer clear value to customers. Always explain these relationships clearly and protect shared information carefully.

  Third-party data helps provide market context, but use it selectively. External market research can highlight broader trends or opportunities. However, this data often proves less reliable than information from direct customer interactions. Consider it a supplement to, not a replacement for, your own customer insights.

  Keep these principles in mind when combining data sources :

  • Prioritize direct customer interactions
  • Focus on information that improves services
  • Maintain consistent privacy standards across sources
  • Document where each insight comes from
  • Review regularly whether each source adds value
  • Work with privacy and data experts to ensure customer information is handled properly

  Enhance your web analytics strategy with Matomo

  

  The financial sector finds powerful and compliant web analytics increasingly valuable as it navigates data management and privacy regulations. Matomo provides a configurable privacy-centric solution that meets the requirements of banks and financial institutions.

  Matomo empowers your organisation to :

  • Collect accurate, GDPR-compliant web data
  • Integrate web analytics with your existing tools and platforms
  • Maintain full control over your analytics data
  • Gain insights without compromising user privacy

  Matomo is trusted by some of the world’s biggest banks and financial institutions. Try Matomo for free for 30 days to see how privacy-focused analytics can get you the insights you need while maintaining compliance and user trust.

• What Is Data Misuse & How to Prevent It ? (With Examples)

  13 mai 2024, par Erin

  Your data is everywhere. Every time you sign up for an email list, log in to Facebook or download a free app onto your smartphone, your data is being taken.

  This can scare customers and users who fear their data will be misused.

  While data can be a powerful asset for your business, it’s important you manage it well, or you could be in over your head.

  In this guide, we break down what data misuse is, what the different types are, some examples of major data misuse and how you can prevent it so you can grow your brand sustainably.

  What is data misuse ?

  Data is a good thing.

  It helps analysts and marketers understand their customers better so they can serve them relevant information, products and services to improve their lives.

  But it can quickly become a bad thing for both the customers and business owners when it’s mishandled and misused.

  

  Data misuse is when a business uses data outside of the agreed-upon terms. When companies collect data, they need to legally communicate how that data is being used. 

  Who or what determines when data is being misused ?

  Several bodies :

  • User agreements
  • Data privacy laws
  • Corporate policies
  • Industry regulations

  There are certain laws and regulations around how you can collect and use data. Failure to comply with these guidelines and rules can result in several consequences, including legal action.

  Keep reading to discover the different types of data misuse and how to prevent it.

  3 types of data misuse

  There are a few different types of data misuse.

  If you fail to understand them, you could face penalties, legal trouble and a poor brand reputation.

  

  1. Commingling

  When you collect data, you need to ensure you’re using it for the right purpose. Commingling is when an organisation collects data from a specific audience for a specific reason but then uses the data for another purpose.

  One example of commingling is if a company shares sensitive customer data with another company. In many cases, sister companies will share data even if the terms of the data collection didn’t include that clause.

  Another example is if someone collects data for academic purposes like research but then uses the data later on for marketing purposes to drive business growth in a for-profit company.

  In either case, the company went wrong by not being clear on what the data would be used for. You must communicate with your audience exactly how the data will be used.

  2. Personal benefit

  The second common way data is misused in the workplace is through “personal benefit.” This is when someone with access to data abuses it for their own gain.

  The most common example of personal benefit data muse is when an employee misuses internal data.

  While this may sound like each instance of data misuse is caused by malicious intent, that’s not always the case. Data misuse can still exist even if an employee didn’t have any harmful intent behind their actions. 

  One of the most common examples is when an employee mistakenly moves data from a company device to personal devices for easier access.

  3. Ambiguity

  As mentioned above, when discussing commingling, a company must only use data how they say they will use it when they collect it.

  A company can misuse data when they’re unclear on how the data is used. Ambiguity is when a company fails to disclose how user data is being collected and used.

  This means communicating poorly on how the data will be used can be wrong and lead to misuse.

  One of the most common ways this happens is when a company doesn’t know how to use the data, so they can’t give a specific reason. However, this is still considered misuse, as companies need to disclose exactly how they will use the data they collect from their customers.

  Laws on data misuse you need to follow

  Data misuse can lead to poor reputations and penalties from big tech companies. For example, if you step outside social media platforms’ guidelines, you could be suspended, banned or shadowbanned.

  But what’s even more important is certain types of data misuse could mean you’re breaking laws worldwide. Here are some laws on data misuse you need to follow to avoid legal trouble :

  General Data Protection Regulation (GDPR)

  The GDPR, or General Data Protection Regulation, is a law within the European Union (EU) that went into effect in 2018.

  The GDPR was implemented to set a standard and improve data protection in Europe. It was also established to increase accountability and transparency for data breaches within businesses and organisations.

  The purpose of the GDPR is to protect residents within the European Union.

  The penalties for breaking GDPR laws are fines up to 20 million Euros or 4% of global revenues (whatever the higher amount is).

  The GDPR doesn’t just affect companies in Europe. You can break the GDPR’s laws regardless of where your organisation is located worldwide. As long as your company collects, processes or uses the personal data of any EU resident, you’re subject to the GDPR’s rules.

  If you want to track user data to grow your business, you need to ensure you’re following international data laws. Tools like Matomo—the world’s leading privacy-friendly web analytics solution—can help you achieve GDPR compliance and maintain it.

  With Matomo, you can confidently enhance your website’s performance, knowing that you’re adhering to data protection laws. 

  Try Matomo for Free

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

  Start for free

  No credit card required

  California Consumer Privacy Act (CCPA)

  The California Consumer Privacy Act (CCPA) is another important data law companies worldwide must follow.

  Like GDPR, the CCPA is a data privacy law established to protect residents of a certain region — in this case, residents of California in the United States.

  The CCPA was implemented in 2020, and businesses worldwide can be penalised for breaking the regulations. For example, if you’re found violating the CCPA, you could be fined $7,500 for each intentional violation.

  If you have unintentional violations, you could still be fined, but at a lesser fee of $2,500.

  The Gramm-Leach-Bliley Act (GLBA)

  If your business is located within the United States, then you’re subject to a federal law implemented in 1999 called The Gramm-Leach-Bliley Act (GLB Act or GLBA).

  The GLBA is also known as the Financial Modernization Act of 1999. Its purpose is to control the way American financial institutions handle consumer data. 

  In the GLBA, there are three sections :

  1. The Financial Privacy Rule : regulates the collection and disclosure of private financial data.
  2. Safeguards Rule : Financial institutions must establish security programs to protect financial data.
  3. Pretexting Provisions : Prohibits accessing private data using false pretences.

  The GLBA also requires financial institutions in the U.S. to give their customers written privacy policy communications that explain their data-sharing practices.

  4 examples of data misuse in real life

  If you want to see what data misuse looks like in real life, look no further.

  Big tech is central to some of the biggest data misuses and scandals.

  

  Here are a few examples of data misuse in real life you should take note of to avoid a similar scenario :

  1. Facebook election interference

  One of history’s most famous examples of data misuse is the Facebook and Cambridge Analytica scandal in 2018.

  During the 2018 U.S. midterm elections, Cambridge Analytica, a political consulting firm, acquired personal data from Facebook users that was said to have been collected for academic research.

  Instead, Cambridge Analytica used data from roughly 87 million Facebook users. 

  This is a prime example of commingling.

  The result ? Cambridge Analytica was left bankrupt and dissolved, and Facebook was fined $5 billion by the Federal Trade Commission (FTC).

  2. Uber “God View” tracking

  Another big tech company, Uber, was caught misusing data a decade ago. 

  Why ?

  Uber implemented a new feature for its employees in 2014 called “God View.”

  The tool enabled Uber employees to track riders using their app. The problem was that they were watching them without the users’ permission. “God View” lets Uber spy on their riders to see their movements and locations.

  The FTC ended up slapping them with a major lawsuit, and as part of their settlement agreement, Uber agreed to have an outside firm audit their privacy practices between 2014 and 2034.

  

  3. Twitter targeted ads overstep

  In 2019, Twitter was found guilty of allowing advertisers to access its users’ personal data to improve advertisement targeting.

  Advertisers were given access to user email addresses and phone numbers without explicit permission from the users. The result was that Twitter ad buyers could use this contact information to cross-reference with Twitter’s data to serve ads to them.

  Twitter stated that the data leak was an internal error. 

  4. Google location tracking

  In 2020, Google was found guilty of not explicitly disclosing how it’s using its users’ personal data, which is an example of ambiguity.

  The result ?

  The French data protection authority fined Google $57 million.

  8 ways to prevent data misuse in your company

  Now that you know the dangers of data misuse and its associated penalties, it’s time to understand how you can prevent it in your company.

  

  Here are eight ways you can prevent data misuse :

  1. Track data with an ethical web analytics solution

  You can’t get by in today’s business world without tracking data. The question is whether you’re tracking it safely or not.

  If you want to ensure you aren’t getting into legal trouble with data misuse, then you need to use an ethical web analytics solution like Matomo.

  With it, you can track and improve your website performance while remaining GDPR-compliant and respecting user privacy. Unlike other web analytics solutions that monetise your data and auction it off to advertisers, with Matomo, you own your data.

  Try Matomo for Free

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

  Start for free

  No credit card required

  2. Don’t share data with big tech

  As the data misuse examples above show, big tech companies often violate data privacy laws.

  And while most of these companies, like Google, appear to be convenient, they’re often inconvenient (and much worse), especially regarding data leaks, privacy breaches and the sale of your data to advertisers.

  Have you ever heard the phrase : “You are the product ?” When it comes to big tech, chances are if you’re getting it for free, you (and your data) are the products they’re selling.

  The best way to stop sharing data with big tech is to stop using platforms like Google. For more ideas on different Google product alternatives, check out this list of Google alternatives.

  3. Identity verification 

  Data misuse typically isn’t a company-wide ploy. Often, it’s the lack of security structure and systems within your company. 

  An important place to start is to ensure proper identity verification for anyone with access to your data.

  4. Access management

  After establishing identity verification, you should ensure you have proper access management set up. For example, you should only give specific access to specific roles in your company to prevent data misuse.

  5. Activity logs and monitoring

  One way to track data misuse or breaches is by setting up activity logs to ensure you can see who is accessing certain types of data and when they’re accessing it.

  You should ensure you have a team dedicated to continuously monitoring these logs to catch anything quickly.

  6. Behaviour alerts 

  While manually monitoring data is important, it’s also good to set up automatic alerts if there is unusual activity around your data centres. You should set up behaviour alerts and notifications in case threats or compromising events occur.

  7. Onboarding, training, education

  One way to ensure quality data management is to keep your employees up to speed on data security. You should ensure data security is a part of your employee onboarding. Also, you should have regular training and education to keep people informed on protecting company and customer data.

  8. Create data protocols and processes 

  To ensure long-term data security, you should establish data protocols and processes. 

  To protect your user data, set up rules and systems within your organisation that people can reference and follow continuously to prevent data misuse.

  Leverage data ethically with Matomo

  Data is everything in business.

  But it’s not something to be taken lightly. Mishandling user data can break customer trust, lead to penalties from organisations and even create legal trouble and massive fines.

  You should only use privacy-first tools to ensure you’re handling data responsibly.

  Matomo is a privacy-friendly web analytics tool that collects, stores and tracks data across your website without breaking privacy laws.

  With over 1 million websites using Matomo, you can track and improve website performance with :

  • Accurate data (no data sampling)
  • Privacy-friendly and compliant with privacy regulations like GDPR, CCPA and more
  • Advanced features like heatmaps, session recordings, A/B testing and more

  Try Matomo free for 21-days. 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