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• What is last click attribution ? A beginner’s guide

  10 mars 2024, par Erin

  Imagine you just finished a successful marketing campaign. You reached new highs in campaign revenue. Your conversion was higher than ever. And you did it without dramatically increasing your marketing budget.

  So, you start planning your next campaign with a bigger budget.

  But what do you do ? Where do you invest the extra money ?

  You used several marketing tactics and channels in the last campaign. To solve this problem, you need to track marketing attribution — where you give conversion credit to a channel (or channels) that acted as a touchpoint along the buyer’s journey.

  One of the most popular attribution models is last click attribution.

  In this article, we’ll break down what last click attribution is, its advantages and disadvantages, and examples of how you can use it to gain insights into the marketing strategies driving your growth.

  What is last click attribution ?

  Last click, or last interaction, is a marketing attribution model that seeks to give all credit for a conversion to the final touchpoint in the buyer’s journey. It assumes the customer’s last interaction with your brand (before the sale) was the most influential marketing channel for the conversion decision.

  

  Example of last click attribution

  Let’s say a woman named Jill stumbles across a fitness equipment website through an Instagram ad. She explores the website, looking at a few fitness bands and equipment, but she doesn’t buy anything.

  A few days later, Jill was doing a workout but wished she had equipment to use.

  So, she Googles the name of the company she checked out earlier to take a look at the fitness bands it offers. She’s not sure which one to get, but she signs up for a 10% discount by entering her email.

  A few days later, she sees an ad on Facebook and visits the site but exits before purchasing. 

  The next day, Jill gets an email from the store stating that her discount code is expiring. She clicks on the link, plugs in the discount code, and buys a fitness band for $49.99.

  Under the last click attribution model, the fitness company would attribute full credit for the sale to their email campaign while ignoring all other touchpoints (the Instagram ad, Jill’s organic Google search, and the Facebook ad).

  3 advantages of last click attribution

  Last click attribution is one of the most popular methods to credit a conversion. Here are the primary advantages of using it to measure your marketing efforts :

  

  1. Easiest attribution method for beginners

  If something’s too complicated, many people simply won’t touch it.

  So, when you start diving into attribution, you might want to keep it simple. Fortunately, last click attribution is a wonderful method for beginner marketers to try out. And when you first begin tracking your marketing efforts, it’s one of the easiest methods to grasp. 

  2. It can have more impact on revenue

  Attribution and conversions go hand in hand. But conversions aren’t just about making a sale or generating more revenue. We often need to track the conversions that take place before a sale.

  This could include gaining a new follower on Instagram or capturing an email subscriber with a new lead magnet.

  If you’re trying to attribute why someone converted into a follower or lead, you may want to ditch last click for something else.

  But when you’re looking strictly at revenue-generating conversions, last click can be one of the most impactful methods for giving credit to a conversion.

  3. It helps you understand bottom-of-funnel conversions

  If SEO is your focus, chances are pretty good that you aren’t looking for a direct sale right out of the gate. You likely want to build your authority, inform and educate your audience, and then maybe turn them into a lead.

  However, when your primary focus isn’t generating traffic or leads but turning your leads into customers, then you’re focused on the bottom of your sales funnel.

  Last click can be helpful to use in bottom-of-funnel (BoFu) conversions since it often means following a paid ad or sales email that allows you to convert your warm audience member.

  If you’re strictly after revenue, you may not need to pay as much attention to the person who reads your latest blog post. After they read the article, they may have seen a social media post. And then, maybe they saw your email with a discount to buy now — which converted them into a paying customer.

  3 challenges of last click attribution

  Last click attribution is a simple way to start analysing the channels that impact your conversions. But it’s not perfect.

  Here are a few challenges of last click attribution you should keep in mind :

  

  1. It ignores all other touchpoints

  Last click attribution is a single-touch attribution model. This type of model declares that a single channel gets 100% of the credit for a sale.

  But this can overlook impactful contributions from other channels.

  Multi-touch attribution seeks to give credit to multiple channels for each conversion. This is a more holistic approach.

  2. It fragments the customer journey

  Most customers need a few touchpoints before they’ll make a purchase.

  Maybe it’s reading a blog post via Google, checking out a social media post on Instagram, and receiving a nurture email.

  If you look only at the last touchpoint before a sale, then you ignore the impact of the other channels. This leads to a fragmented customer journey. 

  Imagine this : You tell your marketing leaders that Facebook ads are responsible for your success because they were the last touch for 65% of conversions. So, you pour your entire budget into Facebook ads.

  What happens ?

  Your sales drop by 60% in one month. This happens because you ignored the traffic you were generating from SEO blog posts that led to that conversion — the nurturing that took place in email marketing.

  3. Say goodbye to brand awareness marketing

  Without a brand, you can’t have a sustainable business.

  Some marketing activities, like brand awareness campaigns, are meant to fuel brand awareness to build a business that lasts for years.

  But if you’re going to use last click attribution to measure the effectiveness of your marketing efforts, then you’re going to diminish the impact of brand awareness.

  Your brand, as a whole, has the ability to generate multiples of your current revenue by simply reaching more people and creating unique brand experiences with new audiences.

  Last click attribution can’t easily measure brand awareness activities, which means their importance is often ignored.

  Last click attribution vs. other attribution models

  Last click attribution is just one type of attribution model. Here are five other common marketing attribution models you might want to consider :

  

  First interaction

  We’ve already touched on last click interaction as a marketing attribution model. But one of the most common models does the opposite.

  First interaction, or first touch, gives full credit to the first channel that brought a lead in. 

  First interaction is best used for top-of-funnel (ToFU) conversions, like user acquisition.

  Last non-direct interaction

  A similar model to last click attribution is one called last non-direct interaction. But one major difference is that it excludes all direct traffic from the calculation. Instead, it assigns full conversion credit to the channel that precedes it.

  For instance, let’s say you see someone comes to your website via a Facebook ad but doesn’t purchase. Then one week later, they go directly to your website through a bookmark they saved and they complete a purchase. Instead of giving attribution to the direct traffic touchpoint (entering your site through a saved bookmark), you attribute the conversion to the previous channel.

  In this case, the Facebook ad gets the credit.

  Last non-direct attribution is best used for BoFu conversions.

  Linear

  Another common attribution model is called linear attribution. Here, you split the credit for a conversion equally across every single touchpoint.

  This means if someone clicks on your blog post in Google, TikTok post, email, and a Facebook ad, then the credit for the conversion is equally split between each of these channels.

  This model is helpful for looking at both BoFu and ToFu activities.

  Time decay

  Time decay is an attribution model that more accurately credits conversions across different touchpoints. This means the closer a channel is to a conversion, the more weight is given to it.

  The time decay model assumes that the closer a channel is to a conversion, the greater that channel’s impact is on a sale.

  Position based

  Position-based, also called U-shaped attribution, is an interesting model that gives multiple channels credit for a conversion.

  But it doesn’t give equal credit to channels or weighted credit to the channels closest to the conversion.

  Instead, it gives the most credit to the first and last interactions.

  In other words, it emphasises the conversion of someone to a lead and, eventually, a customer.

  It gives the first and last interaction 40% of the credit for a conversion and then splits the remaining 20% across the other touchpoints in the customer journey.

  If you’re ever unsure about which attribution model to use, with Matomo, you can compare them to determine the one that best aligns with your goals and accurately reflects conversion paths. 

  

  In the above screenshot from Matomo, you can see how last-click compares to first-click and linear models to understand their respective impacts on conversions.

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  Use Matomo to track last click attribution

  If you want to improve your marketing, you need to start tracking your efforts. Without marketing attribution, you will never be certain which marketing activities are pushing your business forward.

  Last click attribution is one of the most popular ways to get started with attribution since it, very simply, gives full credit to the last interaction for a conversion.

  If you want to start tracking last click attribution (or any other previously mentioned attribution model), sign up for Matomo’s 21-day free trial today. No credit card required.

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• 7 Reasons to Migrate from Google Analytics to Matomo Now

  15 mai 2022, par Erin

  The release of Google Analytics 4 (GA4), and the subsequent depreciation of Universal Analytics, has caused a stir amongst webmasters, SEO experts, marketers and the likes.

  Google’s Universal Analytics is the most widely used web analytics platform in the world, but from 1 July 2023, it will no longer process any new data. Google is now pushing users to set up GA4 tracking imminently.

  If you’re like many and wondering if you should upgrade to Google Analytics 4, there are two key reasons why this might be a risk :

  1. GDPR violations : recent rulings have deemed Google Analytics illegal in France and Austria, and it’s likely that this trend will continue across the EU.
  2. Data loss : users switching to Google Analytics 4 can’t migrate their data from Universal Analytics.

  To mitigate these risks, many organisations are looking to switch to a Google Analytics alternative like Matomo. This is an ideal option for organisations that want to take ownership of their data, get compliant with privacy regulations and save themselves the stress of Google deprecating the software they rely on.

  Whilst there are two major reasons to steer clear of Google Analytics 4, there are 7 reasons why migrating to Matomo instead could save your business time, money and peace of mind.

  If you want to avoid the pitfalls of GA4 and are thinking about migrating from Universal Analytics to Matomo, here’s why you should make the switch now.

  1. Keep your historical Universal Analytics data

  Users switching to Google Analytics 4 will be disappointed to find out that GA4 does not accept data imports from Universal Analytics. On top of that, Google also announced that after Universal Analytics stops processing new data (1 July 2023), users will only be able to access this data for “at least six months”. 

  Years of valuable insights will be completely wiped and organisations will not be able to report on year over year results.

  Fortunately, any organisation using Universal Analytics can import this data into Matomo using our Google Analytics Importer plugin. So you can reduce business disruptions and retain years of valuable web analytics data when you switch to Matomo.

  Our comprehensive migration documentation features a handy video, written guides and FAQs to ensure a smooth migration process.

  2. Ease of use

  Web analytics is complicated enough without having to navigate confusing platform user interfaces (UIs). One of GA4’s biggest drawbacks is the “awful and unusable” interface which has received an overwhelming amount of negative backlash online. 

  Matomo’s intuitive UI contains many of the familiar features that made Universal Analytics so well-liked. You’ll find the same popular features like Visitors, Behaviour, and Acquisition to name a few.

  

  User Flow in Matomo

  When you switch to Matomo you can get up to speed quickly and spend more time focusing on high-value tasks, rather than learning about everything new in GA4.

  3. 100% accurate unsampled data

  GA4 implements data sampling and machine learning to fill gaps. Often what you are basing critical business decisions on is actually an estimate of activity. 

  Matomo does not use data sampling, so this guarantees you will always see the full picture.

  “My primary reason to use Matomo is to get the unsampled data, [...] if your website gets lots of traffic and you can’t afford an enterprise level tool like GA premium [GA360] then Matomo is your best choice.”

  With Matomo you can be confident your data-driven decisions are being made with real data.

  4. Privacy by design

  Built-in privacy has always been at the core of Matomo. One key method we use to achieve this, is by giving you 100% data ownership of your web analytics data. You don’t ever have to worry about the data landing in the wrong hands or being used in unethical ways – like unsolicited advertising. 

  On the contrary, Google Analytics is regularly under fire for controversial uses of data. While Google has made changes to make GA4 more privacy-focused, it’s all just smoke and mirrors. The data collected from Google Analytics accounts is used by Google to create digital profiles on internet users, which is then used for advertising. 

  Consumers are becoming increasingly concerned about how businesses are using their data. Businesses that develop privacy strategies, utilise privacy-focused tools will gain a competitive advantage and a loyal customer-base. 

  Prioritise the protection of your user data by switching to a privacy-by-design analytics solution.

  5. Compliance with global privacy laws

  To date, Google Analytics has been deemed illegal to use in France and Austria due to data transfers to the US. Upgrading to GA4 doesn’t make this problem go away either since data is still transferred to the US. 

  Matomo is easily configured to follow even the strictest of privacy laws like GDPR, HIPAA, CCPA, LGPD and PECR. Here’s how :

  • Matomo’s opt-out mechanism lets users opt-out of web analytics tracking
  • You can configure analytics for data retention of raw data and aggregated reports
  • Users can anonymise IP addresses as well as implement other data anonymisation techniques
  • Matomo can respect DoNotTrack setting
  • Users can set up Matomo so it doesn’t process any personal data or PII (personally identifiable information)
  • It’s easy to set shorter expiration dates for tracking cookies
  • Matomo allows you to disable Visits Log and Visitor Profile
  • Users aren’t tracked across websites unless specifically enabled

  Matomo can also be used without cookie consent banners (unlike with Google Analytics, which will always need user consent to track). Matomo has been approved by the French Data Protection Authority (CNIL) as one of the select few web analytics tools that can be used to collect data without tracking consent.

  Every year more countries are drafting legislation that mirrors the European Union’s GDPR (like the Brazilian LGPD). Matomo is designed to stay data-privacy law compliant, and always will be.

  Stay on top of global privacy laws and reduce the time you spend on compliance by switching to a privacy-compliant solution. 

  6. All-in-one web analytics

  Matomo gives you easy access to Heatmaps, Session Recordings, A/B testing, Funnels analytics, and more right out of the box. This means that digital marketing, UX and procurement teams won’t need to set up and manage multiple tools for behavioural analytics – it’s all in one place.

  Learn more about your audience, save money and reduce complexity by switching to an all-in-one analytics solution.

  Check out Matomo’s extensive product features.

  

  Page Scroll Depth in Matomo

  7. Tag Manager built-in

  Unlike GA4, the Matomo Tag Manager comes built-in for an efficient and consistent user experience. Matomo Tag Manager offers a pain-free solution for embedding tracking codes on your website without needing help from a web developer or someone with technical knowledge.

  Help your Marketing team track more website actions and give time back to your web developer by switching to Matomo Tag Manager.

  Final Thoughts

  Google Analytics is free to use, but the surrounding legal issues with the platform and implications of switching to GA4 will make migrating a tough choice for many businesses. 

  Now is the chance for organisations to step away from the advertising tech giant, take ownership of web analytics data and get compliant. Switch to the leading Google Analytics alternative and see why over 1 million websites choose Matomo for their web analytics.

  Ready to get started with your own Google Analytics to Matomo migration ? Try Matomo free for 21 days now – no credit card required. 

  21 day free trial. No credit card required.

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• A Guide to GDPR Sensitive Personal Data

  13 mai 2024, par Erin

  The General Data Protection Regulation (GDPR) is one of the world’s most stringent data protection laws. It provides a legal framework for collection and processing of the personal data of EU individuals.

  The GDPR distinguishes between “special categories of personal data” (also referred to as “sensitive”) and other personal data and imposes stricter requirements on collection and processing of sensitive data. Understanding these differences will help your company comply with the requirements and avoid heavy penalties.

  In this article, we’ll explain what personal data is considered “sensitive” according to the GDPR. We’ll also examine how a web analytics solution like Matomo can help you maintain compliance.

  What is sensitive personal data ?

  The following categories of data are treated as sensitive :

  1. 1. Personal data revealing :
        • Racial or ethnic origin ;
        • Political opinions ;
        • Religious or philosophical beliefs ;
        • Trade union membership ;
     2. Genetic and biometric data ;
     3. Data concerning a person’s :
        • Health ; or
        • Sex life or sexual orientation.

  

  Sensitive vs. non-sensitive personal data : What’s the difference ?

  While both categories include information about an individual, sensitive data is seen as more private, or requiring a greater protection. 

  Sensitive data often carries a higher degree of risk and harm to the data subject, if the data is exposed. For example, a data breach exposing health records could lead to discrimination for the individuals involved. An insurance company could use the information to increase premiums or deny coverage. 

  In contrast, personal data like name or gender is considered less sensitive because it doesn’t carry the same degree of harm as sensitive data. 

  Unauthorised access to someone’s name alone is less likely to harm them or infringe on their fundamental rights and freedoms than an unauthorised access to their health records or biometric data. Note that financial information (e.g. credit card details) does not fall into the special categories of data.

  

  Legality of processing

  Under the GDPR, both sensitive and nonsensitive personal data are protected. However, the rules and conditions for processing sensitive data are more stringent.

  Article 6 deals with processing of non-sensitive data and it states that processing is lawful if one of the six lawful bases for processing applies. 

  In contrast, Art. 9 of the GDPR states that processing of sensitive data is prohibited as a rule, but provides ten exceptions. 

  It is important to note that the lawful bases in Art. 6 are not the same as exceptions in Art. 9. For example, while performance of a contract or legitimate interest of the controller are a lawful basis for processing non-sensitive personal data, they are not included as an exception in Art. 9. What follows is that controllers are not permitted to process sensitive data on the basis of contract or legitimate interest. 

  The exceptions where processing of sensitive personal data is permitted (subject to additional requirements) are : 

  • Explicit consent : The individual has given explicit consent to processing their sensitive personal data for specified purpose(s), except where an EU member state prohibits such consent. See below for more information about explicit consent. 
  • Employment, social security or social protection : Processing sensitive data is necessary to perform tasks under employment, social security or social protection law.
  • Vital interests : Processing sensitive data is necessary to protect the interests of a data subject or if the individual is physically or legally incapable of consenting. 
  • Non-for-profit bodies : Foundations, associations or nonprofits with a political, philosophical, religious or trade union aim may process the sensitive data of their members or those they are in regular contact with, in connection with their purposes (and no disclosure of the data is permitted outside the organisation, without the data subject’s consent).
  • Made public : In some cases, it may be permissible to process the sensitive data of a data subject if the individual has already made it public and accessible. 
  • Legal claims : Processing sensitive data is necessary to establish, exercise or defend legal claims, including legal or in court proceedings.
  • Public interest : Processing is necessary for reasons of substantial public interest, like preventing unlawful acts or protecting the public.
  • Health or social care : Processing special category data is necessary for : preventative or occupational medicine, providing health and social care, medical diagnosis or managing healthcare systems.
  • Public health : It is permissible to process sensitive data for public health reasons, like protecting against cross-border threats to health or ensuring the safety of medicinal products or medical devices. 
  • Archiving, research and statistics : You may process sensitive data if it’s done for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.

  In addition, you must adhere to all data handling requirements set by the GDPR.

  Important : Note that for any data sent that you are processing, you always need to identify a lawful basis under Art. 6. In addition, if the data sent contains sensitive data, you must comply with Art. 9.

  Explicit consent

  While consent is a valid lawful basis for processing non-sensitive personal data, controllers are permitted to process sensitive data only with an “explicit consent” of the data subject.

  The GDPR does not define “explicit” consent, but it is accepted that it must meet all Art. 7 conditions for consent, at a higher threshold. To be “explicit” a consent requires a clear statement (oral or written) of the data subject. Consent inferred from the data subject’s actions does not meet the threshold. 

  The controller must retain records of the explicit consent and provide appropriate consent withdrawal method to allow the data subject to exercise their rights.

  Examples of compliant and non-compliant sensitive data processing

  Here are examples of when you can and can’t process sensitive data :

  • When you can process sensitive data : A doctor logs sensitive data about a patient, including their name, symptoms and medicine prescribed. The hospital can process this data to provide appropriate medical care to their patients. An IoT device and software manufacturer processes their customers’ health data based on explicit consent of each customer. 
  • When you can’t process sensitive data : One example is when you don’t have explicit consent from a data subject. Another is when there’s no lawful basis for processing it or you are collecting personal data you simply do not need. For example, you don’t need your customer’s ethnic origin to fulfil an online order.

  Other implications of processing sensitive data

  If you process sensitive data, especially on a large scale, GDPR imposes additional requirements, such as having Data Privacy Impact Assessments, appointing Data Protection Officers and EU Representatives, if you are a controller based outside the EU.

  Penalties for GDPR non-compliance

  Mishandling sensitive data (or processing it when you’re not allowed to) can result in huge penalties. There are two tiers of GDPR fines :

  • €10 million or 2% of a company’s annual revenue for less severe infringements
  • €20 million or 4% of a company’s annual revenue for more severe infringements

  In the first half of 2023 alone, fines imposed in the EU due to GDPR violations exceeded €1.6 billion, up from €73 million in 2019.

  Examples of high-profile violations in the last few years include :

  • Amazon : The Luxembourg National Commission fined the retail giant with a massive $887 million fine in 2021 for not processing personal data per the GDPR. 
  • Google : The National Data Protection Commission (CNIL) fined Google €50 million for not getting proper consent to display personalised ads.
  • H&M : The Hamburg Commissioner for Data Protection and Freedom of Information hit the multinational clothing company with a €35.3 million fine in 2020 for unlawfully gathering and storing employees’ data in its service centre.

  One of the criteria that affects the severity of a fine is “data category” — the type of personal data being processed. Companies need to take extra precautions with sensitive data, or they risk receiving more severe penalties.

  What’s more, GDPR violations can negatively affect your brand’s reputation and cause you to lose business opportunities from consumers concerned about your data practices. 76% of consumers indicated they wouldn’t buy from companies they don’t trust with their personal data.

  Organisations should lay out their data practices in simple terms and make this information easily accessible so customers know how their data is being handled.

  Get started with GDPR-compliant web analytics

  The GDPR offers a framework for securing and protecting personal data. But it also distinguishes between sensitive and non-sensitive data. Understanding these differences and applying the lawful basis for processing this data type will help ensure compliance.

  Looking for a GDPR-compliant web analytics solution ?

  At Matomo, we take data privacy seriously. 

  Our platform ensures 100% data ownership, putting you in complete control of your data. Unlike other web analytics solutions, your data remains solely yours and isn’t sold or auctioned off to advertisers. 

  Additionally, with Matomo, you can be confident in the accuracy of the insights you receive, as we provide reliable, unsampled data.

  Matomo also fully complies with GDPR and other data privacy laws like CCPA, LGPD and more.

  Start your 21-day free trial today ; 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.

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