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• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now. 

• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• Understanding Data Processing Agreements and How They Affect GDPR Compliance

  9 octobre 2023, par Erin — GDPR

  The General Data Protection Regulation (GDPR) impacts international organisations that conduct business or handle personal data in the European Union (EU), and they must know how to stay compliant.

  One way of ensuring GDPR compliance is through implementing a data processing agreement (DPA). Most businesses overlook DPAs when considering ways of maintaining user data security. So, what exactly is a DPA’s role in ensuring GDPR compliance ?

  In this article, we’ll discuss DPAs, their advantages, which data protection laws require them and the clauses that make up a DPA. We’ll also discuss the consequences of non-compliance and how you can maintain GDPR compliance using Matomo.

  What is a data processing agreement ?

  A data processing agreement, data protection agreement or data processing addendum is a contractual agreement between a data controller (a company) and a data processor (a third-party service provider.) It defines each party’s rights and obligations regarding data protection.

  A DPA also defines the responsibilities of the controller and the processor and sets out the terms they’ll use for data processing. For instance, when MHP/Team SI sought the services of Matomo (a data processor) to get reliable and compliant web analytics, a DPA helped to outline their responsibilities and liabilities.

  A DPA is one of the basic requirements for GDPR compliance. The GDPR is an EU regulation concerning personal data protection and security. The GDPR is binding on any company that actively collects data from EU residents or citizens, regardless of their location.

  As a business, you need to know what goes into a DPA to identify possible liabilities that may arise if you don’t comply with European data protection laws. For example, having a recurrent security incident can lead to data breaches as you process customer personal data.

  The average data breach cost for 2023 is $4.45 million. This amount includes regulatory fines, containment costs and business losses. As such, a DPA can help you assess the organisational security measures of your data processing methods and define the protocol for reporting a data breach.

  Why is a DPA essential for your business ?

  If your company processes personal data from your customers, such as contact details, you need a DPA to ensure compliance with data security laws like GDPR. You’ll also need a DPA to hire a third party to process your data, e.g., through web analytics or cloud storage.

  But what are the benefits of having a DPA in place ?

  

  A key benefit of signing a DPA is it outlines business terms with a third-party data processor and guarantees compliance with the relevant data privacy laws. A DPA also helps to create an accountability framework between you and your data processor by establishing contractual obligations.

  Additionally, a DPA helps to minimise the risk of unauthorised access to sensitive data. A DPA defines organisational measures that help protect the rights of individuals and safeguard personal data against unauthorised disclosure. Overall, before choosing a data processor, having a DPA ensures that they are capable, compliant and qualified.

  More than 120 countries have already adopted some form of international data protection laws to protect their citizens and their data better. Hence, knowing which laws require a DPA and how you can better ensure compliance is important.

  Which data protection laws require a DPA ?

  Regulatory bodies enact data protection laws to grant consumers greater control over their data and how businesses use it. These laws ensure transparency in data processing and compliance for businesses.

  

  The following are some of the relevant data privacy laws that require you to have a DPA :

  • UK GDPR
  • Brazil LGPD
  • EU GDPR
  • Dubai PDPA
  • Colorado CPA
  • California CCPA/CPRA
  • Virginia VCDPA
  • Connecticut DPA
  • South African POPIA
  • Thailand PDPA

  Companies that don’t adhere to these data protection obligations usually face liabilities such as fines and penalties. With a DPA, you can set clear expectations regarding data processing between you and your customers.

  Review and update any DPAs with third-party processors to ensure compliance with GDPR and the laws we mentioned above. Additionally, confirm that all the relevant clauses are present for compliance with relevant data privacy laws. 

  So, what key data processing clauses should you have in your DPA ? Let’s take a closer look in the next section.

  Key clauses in a data processing agreement

  GDPR provides some general recommendations for what you should state in a DPA.

  

  Here are the elements you should include :

  Data processing specifications

  Your DPA should address the specific business purposes for data processing, the duration of processing and the categories of data under processing. It should also clearly state the party responsible for maintaining GDPR compliance and who the data subjects are, including their location and nationality.

  Your DPA should also address the data processor and controller’s responsibilities concerning data deletion and contract termination.

  Role of processor

  Your DPA should clearly state what your data processor is responsible for and liable for. Some key responsibilities include record keeping, reporting breaches and maintaining data security.

  Other roles of your data processor include providing you with audit opportunities and cooperating with data protection authorities during inquiries. If you decide to end your contract, the data processor is responsible for deleting or returning data, depending on your agreement.

  Role of controller

  Your DPA should inform the responsibilities of the data controller, which typically include issuing processing instructions to the data processor and directing them on how to handle data processing.

  Your DPA should let you define the lawful data processes the data processor should follow and how you’ll uphold the data protection rights of individuals’ sensitive data.

  Organisational and technical specifications

  Your DPA should define specifications such as how third-party processors encrypt, access and test personal data. It should also include specifications on how the data processor and controller will maintain ongoing data security through various factors such as :

  • State of the technology : Do ‌third-party processors have reliable technology, and can they ensure data security within their systems ?
  • Costs of implementation : Does the data controller’s budget allow them to seek third-party services from industry-leading providers who can guarantee a certain level of security ?
  • Variances in users’ personal freedom : Are there privacy policies and opt-out forms for users to express how they want companies to use their sensitive data ?

  Moreover, your DPA should define how you and your data processor will ensure the confidentiality, availability and integrity of data processing services and systems.

  What are the penalties for DPA GDPR non-compliance ?

  Regulators use GDPR’s stiff fines to encourage data controllers and third-party processors to follow‌ best data security practices. One way of maintaining compliance is through drafting up a DPA with your data processor.

  The DPA should clearly outline the necessary legal requirements and include all the relevant clauses mentioned above. Understand what goes into this agreement since data protection authorities can hold your business accountable for a breach — even if a processor’s error caused it.

  Data protection authorities can issue penalties now that the GDPR is in place. For example, according to Article 83 of the GDPR, penalties for data or privacy breaches or non-compliance can amount to up to €20 million or 4% of your annual revenue.

  There are two tiers of fines : tier one and tier two. Violations related to data processors typically attract fines on the tier-one level. Tier one fines can cost your business €10 million or 2% of your company’s global revenue.

  Tier-two fines result from infringement of the right to forget and the right to privacy of your consumer. Tier-two fines can cost your business up to €20 million or 4% of your company’s global revenue.

  GDPR fines make non-compliance an expensive mistake for businesses of all sizes. As such, signing a DPA with any party that acts as a data processor for your business can help you remain GDPR-compliant.

  How a DPA can help your business remain GDPR compliant

  A DPA can help your business define and adhere to lawful data processes.

  

  So, in what other ways can a DPA help you to remain compliant with GDPR ? Let’s take a look !

  1. Assess data processor’s compliance

  Having a DPA helps ensure that the data processor you are working with is GDPR-compliant. You should check if they have a DPA and confirm the processor’s terms of service and legal basis.

  For example, if you want an alternative to Google Analytics that’s GDPR compliant, then you can opt for Matomo. Matomo features a DPA, which you can agree to when you sign up for web analytics services or later.

  2. Establish lawful data processes

  A DPA can also help you review your data processes to ensure they’re GDPR compliant. For example, by defining lawful data processes, you better understand personally identifiable information (PII) and how it relates to data privacy.

  Further, you can allow users to opt out of sharing their data. As such, Matomo can help you to enable Do Not Track preferences on your website.

  

  With this feature, users are given the option to opt in or out of tracking via a toggle in their respective browsers.

  Indeed, establishing lawful data processes helps you define the specific business purposes for collecting and processing personal data. By doing so, you get to notify your users why you need their data and get their consent to process it by including a GDPR-compliant privacy policy on your website.

  3. Anonymise your data

  Global privacy laws like GDPR and ePrivacy mandate companies to display cookie banners or seek consent before tracking visitors’ data. You can either include a cookie consent banner on your site or stop tracking cookies to follow the applicable regulations.

  Further, you can enable cookie-less tracking or easily let users opt out. For example, you can use Matomo without a cookie consent banner, exempting it from many countries’ privacy rules.

  Additionally, through a DPA, you can define organisational measures that define how you’ll anonymise all your users’ data. Matomo can help you anonymise IP addresses, and we recommend that you at least anonymise the last two bytes.

  

  As one of the few web analytics tools you can use to collect data without tracking consent, Matomo also has the French Data Protection Authority (CNIL) approval.

  4. Assess the processor’s bandwidth

  Having a DPA can help you implement data retention policies that show clear retention periods. Such policies are useful when ending a contract with a third-party service provider and determining how they should handle your data.

  A DPA also helps you ensure the processor has the necessary technology to store personal data securely. You can conduct an audit to understand possible vulnerabilities and your data processor’s technological capacity.

  5. Obtain legal counsel

  When drafting a DPA, it’s important to get a consultation on what is needed to ensure complete compliance. Obtaining legal counsel points you in the right direction so you don’t make any mistakes that may lead to non-compliance.

  Conclusion

  Businesses that process users’ data are subject to several DPA contract requirements under GDPR. One of the most important is having DPAs with every third-party provider that helps them perform data processing.

  It’s important to stay updated on GDPR requirements for compliance. As such, Matomo can help you maintain lawful data processes. Matomo gives you complete control over your data and complies with GDPR requirements.

  To get started with Matomo, you can sign up for a 21-day free trial. No credit card required.

  Disclaimer

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