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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.

  Try Matomo for Free

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• 6 Crucial Benefits of Conversion Rate Optimisation

  26 février 2024, par Erin

  Whether investing time or money in marketing, you want the best return on your investment. You want to get as many customers as possible with your budget and resources.

  That’s what conversion rate optimisation (CRO) aims to do. But how does it help you achieve this major goal ? 

  This guide explores the concrete benefits of conversion rate optimisation and how they lead to more effective marketing and ROI. We’ll also introduce specific CRO best practices to help unlock these benefits.

  What is conversion rate optimisation ?

  Conversion rate optimisation (CRO) is the process of examining your website for improvements and creating tests to increase the number of visitors who take a desired action, like purchasing a product or submitting a form.

  The conversion rate is the percentage of visitors who complete a specific goal.

  

  In order to improve your conversion rate, you need to figure out :

  • Where your customers come from
  • How potential customers navigate or interact with your website
  • Where potential customers are likely to exit your site (or abandon carts)
  • What patterns drive valuable actions like sign-ups and sales

  From there, you can gradually implement changes that will drive more visitors to convert. That’s the essence of conversion rate optimisation.

  6 top benefits of conversion rate optimisation (and best practices to unlock them)

  Conversion rate optimisation can help you get more out of your campaigns without investing more. CRO helps you in these six ways :

  1. Understand your visitors (and customers) better

  The main goal of CRO is to boost conversions, but it’s more than that. In the process of improving conversion rates, you’ll also benefit by gaining deep insights into user behaviour, preferences, and needs. 

  Using web analytics, tests and behavioural analytics, CRO helps marketers shape their website to match what users need.

  Best practices for understanding your customer :

  First, analyse how visitors act with full context (the pages they view, how long they stay and more). 

  In Matomo, you can use the Users Flow report to understand how visitors navigate through your site. This will help you visualise and identify trends in the buyer’s journey.

  

  Then, you can dive deeper by defining and analysing journeys with Funnels. This shows you how many potential customers follow through each step in your defined journey and identify where you might have a leaky funnel. 

  

  In the above Funnel Report, nearly half of our visitors, just 44%, are moving forward in the buyer’s journey after landing on our scuba diving mask promotion page. With 56% of potential customers dropping off at this page, it’s a prime opportunity for optimising conversions.

  Think of Funnels as your map, and pages with high drop-off rates as valuable opportunities for improvement.

  Once you notice patterns, you can try to identify the why. Analyse the pages, do user testing and do your best to improve them.

  2. Deliver a better user experience

  A better understanding of your customers’ needs means you can deliver a better user experience.

  

  For example, if you notice many people spend more time than expected on a particular step in the sign-up process, you can work to streamline it.

  Best practices for improving your user experience : 

  To do this, you need to come up with testable hypotheses. Start by using Heatmaps and Session Recordings to visualise the user experience and understand where visitors are hesitating, experiencing points of frustration, and exiting. 

  You need to outline what drives certain patterns in behaviour — like cart abandonment for specific products, and what you think can fix them.

  

  Let’s look at an example. In the screenshot above, we used Matomo’s Heatmap feature to analyse user behaviour on our website. 

  Only 65% of visitors scroll down far enough to encounter our main call to action to “Write a Review.” This insight suggests a potential opportunity for optimisation, where we can focus efforts on encouraging more users to engage with this key element on our site.

  Once you’ve identified an area of improvement, you need to test the results of your proposed solution to the problem. The most common way to do this is with an A/B test. 

  This is a test where you create a new version of the problematic page, trying different titles, comparing long, and short copy, adding or removing images, testing variations of call-to-action buttons and more. Then, you compare the results — the conversion rate — against the original. With Matomo’s A/B Testing feature, you can easily split traffic between the original and one or more variations.

  

  In the example above from Matomo, we can see that testing different header sizes on a page revealed that the wider header led to a higher conversion rate of 47%, compared to the original rate of 35% and the smaller header’s 36%.

  Matomo’s report also analyses the “statistical significance” of the difference in results. Essentially, this is the likelihood that the difference comes from the changes you made in the variation. With a small sample size, random patterns (like one page receiving more organic search visits) can cause the differences.

  If you see a significant change over a larger sample size, you can be fairly certain that the difference is meaningful. And that’s exactly what a high statistical significance rating indicates in Matomo. 

  Once a winner is identified, you can apply the change and start a new experiment. 

  3. Create a culture of data-driven decision-making

  Marketers can no longer afford to rely on guesswork or gamble away budgets and resources. In our digital age, you must use data to get ahead of the competition. In 2021, 65% of business leaders agreed that decisions were getting more complex.

  CRO is a great way to start a company-wide focus on data-driven decision-making. 

  Best practices to start a data-driven culture :
  

  Don’t only test “hunches” or “best practices” — look at the data. Figure out the patterns that highlight how different types of visitors interact with your site.

  Try to answer these questions :

  • How do our most valuable customers interact with our site before purchasing ?
  • How do potential customers who abandon their carts act ?
  • Where do our most valuable customers come from ?

  Moreover, it’s key to democratise insights by providing multiple team members access to information, fostering informed decision-making company-wide.

  4. Lower your acquisition costs and get higher ROI from all marketing efforts

  Once you make meaningful optimisations, CRO can help you lower customer acquisition costs (CAC). Getting new customers through advertising will be cheaper.

  As a result, you’ll get a better return on investment (ROI) on all your campaigns. Every ad and dollar invested will get you closer to a new customer than before. That’s the bottom line of CRO.

  Best practices to lower your CAC (customer acquisition costs) through CRO adjustments :

  The easiest way to lower acquisition costs is to understand where your customers come from. Use marketing attribution to track the results of your campaigns, revealing how each touchpoint contributes to conversions and revenue over time, beyond just last-click attribution.

  You can then compare the number of conversions to the marketing costs of each channel, to get a channel-specific breakdown of CAC.

  This performance overview can help you quickly prioritise the best value channels and ads, lowering your CAC. But these are only surface-level insights. 

  You can also further lower CAC by optimising the pages these campaigns send visitors to. Start with a deep dive into your landing pages using features like Matomo’s Session Recordings or Heatmaps.

  They can help you identify issues with an unengaging user experience or content. Using these insights, you can create A/B tests, where you implement a new page that replaces problematic headlines, buttons, copy, or visuals.

  

  When a test shows a statistically significant improvement in conversion rates, implement the new version. Repeat this over time, and you can increase your conversion rates significantly, getting more customers with the same spend. This will reduce your customer acquisition costs, and help your company grow faster without increasing your ad budget.

  5. Improve your average order value (AOV) and customer lifetime value (CLV)

  CRO isn’t only about increasing the number of customers you convert. If you adapt your approach, you can also use it to increase the revenue from each customer you bring in. 

  But you can’t do that by only tracking conversion rates, you also need to track exactly what your customers buy.

  If you only blindly optimise for CAC, you even risk lowering your CLV and the overall profitability of your campaigns. (For example, if you focus on Facebook Ads with a $6 CAC, but an average CLV of $50, over Google Ads with a $12 CAC, but a $100 CLV.)

  Best practices to track and improve CLV :

  First, integrate your analytics platform with your e-commerce (B2C) or your CRM (B2B). This will help you get a more holistic view of your customers. You don’t want the data to stop at “converted.” You want to be able to dive deep into the patterns of high-value customers.

  The sales report in Matomo’s ecommerce analytics makes it easy to break down average order value by channels, campaigns, and specific ads.

  

  In the report above, we can see that search engines drive customers who spend significantly more, on average, than social networks — $241 vs. $184. But social networks drive a higher volume of customers and more revenue.

  To figure out which channel to focus on, you need to see how the CAC compares to the AOV (or CLV for B2B customers). Let’s say the CAC of social networks is $50, while the search engine CAC is $65. Search engine customers are more profitable — $176 vs. $134. So you may want to adjust some more budget to that channel.

  To put it simply :

  Profit per customer = AOV (or CLV) – CAC

  Example :

  • Profit per customer for social networks = $184 – $50 = $134
  • Profit per customer for search engines = $241 – $65 = $176

  You can also try to A/B test changes that may increase the AOV, like creating a product bundle and recommending it on specific sales pages.

  An improvement in CLV will make your campaigns more profitable, and help stretch your advertising budget even further.

  6. Improve your content and SEO rankings

  A valuable side-effect of focusing on CRO metrics and analyses is that it can boost your SEO rankings. 

  How ? 

  CRO helps you improve the user experience of your website. That’s a key signal Google (and other search engines) care about when ranking webpages. 

  

  For example, Google’s algorithm considers “dwell time,” AKA how long a user stays on your page. If many users quickly return to the results page and click another result, that’s a bad sign. But if most people stay on your site for a while (or don’t return to Google at all), Google thinks your page gives the user their answer.

  As a result, Google will improve your website’s ranking in the search results.

  Best practices to make the most of CRO when it comes to SEO :

  Use A/B Testing, Heatmaps, and Session Recordings to run experiments and understand user behaviour. Test changes to headlines, page layout, imagery and more to see how it impacts the user experience. You can even experiment with completely changing the content on a page, like substituting an introduction.

  Bring your CRO-testing mindset to important pages that aren’t ranking well to improve metrics like dwell time.

  Start optimising your conversion rate today

  As you’ve seen, enjoying the benefits of CRO heavily relies on the data from a reliable web analytics solution. 

  But in an increasingly privacy-conscious world (just look at the timeline of GDPR updates and fines), you must tread carefully. One of the dilemmas that marketing managers face today is whether to prioritise data quality or privacy (and regulations).

  With Matomo, you don’t have to choose. Matomo values both data quality and privacy, adhering to stringent privacy laws like GDPR and CCPA.

  Unlike other web analytics, Matomo doesn’t sample data or use AI and machine learning to fill data gaps. Plus, you can track without annoying visitors with a cookie consent banner – so you capture 100% of traffic while respecting user privacy (excluding in Germany and UK).

  And as you’ve already seen above, you’ll still get plenty of reports and insights to drive your CRO efforts. With User Flows, Funnels, Session Recordings, Form Analytics, and Heatmaps, you can immediately find insights to improve your bottom line.

  And our built-in A/B testing feature will help you test your hypotheses and drive reliable progress. If you’re ready to reliably optimise conversion rates (with accuracy and without privacy concerns), try Matomo for free for 21 days. No credit card required.

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

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• Multivariate Testing vs A/B Testing (Quick-Start Guide)

  7 mars 2024, par Erin

  Traditional advertising (think Mad Men) was all about slogans, taglines and coming up with a one-liner that was meant to change the world.

  But that type of advertising was extremely challenging to test, so it was hard to know if it worked. Most of the time, nobody knew if they were being effective with their advertising.

  Enter modern marketing : the world of data-driven advertising.

  Thanks to the internet and web analytics tools like Matomo, you can quickly test almost anything and improve your site.

  The question is, should you do multivariate testing or A/B testing ?

  While both have their advantages, each has a specific use case.

  In this guide, we’ll break down the differences between multivariate and A/B testing, offer some pros and cons of each and show you some examples so you can decide which one is best for you.

  What is A/B testing ?

  A/B testing, or split testing, is testing an individual element in a medium against another version of the same element to see which produces better results.

  

  A/B tests are conducted by creating two different versions of a digital landmark : a website, landing page, email, or advertisement.

  The goal ? Figure out which version performs better.

  Let’s say, for example, you want to drive more sales on your core product page.

  You test two call-to-action buttons : “Buy Now” and “Add to Cart.”

  After running the test for two weeks, you see that “Buy Now” produced 1.2% conversions while “Add to Cart” produced 7.6%.

  In this scenario, you’ve found your winner : version B, “Add to Cart.”

  By conducting A/B tests regularly, you can optimise your site, increase engagement and convert more visitors into customers.

  Keep in mind that A/B testing isn’t perfect ; it doesn’t always produce a win.

  According to Noah Kagan, founder of AppSumo, only 1 out of 8 A/B tests his company conducts produces significant change.

  Advantages of A/B testing

  A/B testing is great when you need to get an accurate result fast on a specific element of your marketing efforts.

  Whether it’s a landing page or product page, you can get quick results without needing a lot of traffic.

  A/B testing is one of the most widely accepted and used testing methods for marketers and business owners.

  When you limit the number of tracked variables used in a test, you can quickly deliver reliable data, allowing you to iterate and pivot quickly if necessary.

  This is a great way to test your marketing methods, especially if you’re a newer business or you don’t have substantial traffic yet.

  Splitting up your traffic into a few segments (like with multivariate testing) will be very challenging to gain accurate results if you have lower daily traffic.

  One final advantage of A/B testing is that it’s a relatively easy way to introduce testing and optimising to a team, decision-maker, or stakeholder since it’s easy to implement. You can quickly demonstrate the value with a simple change and tangible evidence.

  Disadvantages of A/B testing

  So, what are the downsides to A/B testing ?

  Although A/B testing can get you quick results on small changes, it has limitations.

  A/B testing is all about measuring one element against another.

  This means you’re immediately limited in how many elements you can test. If you have to test out different variables, then A/B testing isn’t your best option since you’ll have to run test after test to get your result.

  If you need specific information on how different combinations of elements interact with one another on a web page, then multivariate is your best option.

  What is multivariate testing ?

  If you want to take your testing to the next level, you’ll want to try multivariate testing.

  Multivariate testing relies on the same foundational mechanism of A/B testing, but instead of matching up two elements against one another, it compares a higher number of variables at once.

  Multiple + variations = multivariate.

  Multivariate testing looks at how combinations of elements and variables interact.

  Like A/B testing, traffic to a page is split between different web page versions. Multivariate testing aims to measure each version’s effectiveness against the other versions.

  Ultimately, it’s about finding the winning combination.

  

  When to use multivariate testing

  The quick answer on when to use multivariate testing is if you have enough traffic.

  Just how much traffic, though ?

  While there’s no set number, you should aim to have 10,000 visitors per month or more, to ensure that each variant receives enough traffic to produce meaningful results within a reasonable time frame.

  Once you meet the traffic requirement, let’s talk about use cases.

  Let’s say you want to introduce a new email signup.

  But you want to create it from scratch and aren’t sure what will make your audience take action.

  So, you create a page with a signup form, a header, and an image.

  To run a multivariate test, you create two lengths of signup forms, four headlines, and two images.

  Next, you would create a test to split traffic between these sixteen combinations.

  Advantages of multivariate testing

  If you have enough traffic, multivariate testing can be an incredible way to speed up your A/B testing by testing dozens of combinations of your web page.

  This is handy when creating a new landing page and you want to determine if specific parts of your design are winners — which you can then use in future campaigns.

  Disadvantages of multivariate testing

  The main disadvantage of multivariate testing is that you need a lot of traffic to get started.

  If you try to do a multivariate analysis but you’re not getting much traffic, your results won’t be accurate (and it will take a long time to see accurate data).

  Additionally, multivariate tests are more complicated. They’re best suited for advanced marketers since more moving parts are at play.

  Key differences between multivariate and A/B testing

  Now that we’ve covered what A/B and multivariate tests are, let’s look at some key differences to help clarify which is best for you.

  

  1. Variation of combinations

  The major difference between A/B and multivariate testing is the number of combinations involved.

  With A/B testing, you only look at one element (no combinations). You simply take one part of your page (i.e., your headline copy) and make two versions.

  With multivariate testing, you’re looking at combinations of different elements (i.e., headline copy, form length, images).

  2. Number of pages to test

  The next difference lies in how many pages you will test.

  With an A/B test, you are splitting traffic on your website to two different pages : A and B.

  However, with multivariate testing, you will likely have 4-16 different test pages.

  This is because dozens of combinations can be created when you start testing a handful of elements at once.

  For example, if you want to test two headlines, two form buttons and two images on a signup form, then you have several combinations :

  • Headline A, Button A, Image A
  • Headline A, Button A, Image B
  • Headline A, Button B, Image A
  • Headline A, Button B, Image B
  • Headline B, Button A, Image A
  • Headline B, Button A, Image B
  • Headline B, Button B, Image A
  • Headline B, Button B, Image B

  In this scenario, you must create eight pages to send traffic to.

  3. Traffic requirements

  The next major difference between the two testing types is the traffic requirements.

  With A/B testing, you don’t need much traffic at all.

  Since you’re only testing two pages, you can split your traffic in half between the two types.

  However, if you plan on implementing a multivariate test, you will likely be splitting your traffic at least four or more ways.

  This means you need to have significantly more traffic coming in to get accurate data from your test. If you try to do this when your traffic is too low, you won’t have a large enough sample size.

  4. Time requirements

  Next up, just like traffic, there’s also a time requirement.

  A/B testing only tests two versions of a page against each other (while testing a single element). This means you’ll get accurate results faster than a multivariate test — usually within days.

  However, for a multivariate test, you might need to wait weeks. This is because you’re splitting your traffic by 4, 8, 12, or more web page variations. This could take months since you need a large enough sample size for accuracy.

  5. Big vs. small changes

  Another difference between A/B testing and multivariate testing is the magnitude of changes.

  With an A/B test, you’re looking at one element of a page, which means changing that element to the winning version isn’t a major overhaul of your design.

  But, with multivariate testing, you may find that the winning combination is drastically different than your control page, which could lead to a significant design change.

  6. Accuracy of results

  A/B tests are easier to decipher than multivariate testing since you only look at two versions of a single element on a page.

  You have a clear winner if one headline yields a 5% conversion rate and another yields a 1.2% conversion rate.

  But multivariate testing looks at so many combinations of a page that it can be a bit trickier to decipher what’s moving the needle.

  Pros and cons : Multivariate vs. A/B testing

  Before picking your testing method of choice, let’s look at some quick pros and cons.

  

  A/B testing pros and cons

  Here are the pros and cons of A/B testing :

  Pros

  • Get results quickly
  • Results are easier to interpret
  • Lower traffic requirement
  • Easy to get started

  Cons

  • You need to be hyper-focused on the right testing element
  • Requires performing test after test to optimise a web page

  Multivariate testing pros and cons

  Here are the pros and cons of multivariate testing :

  Pros

  • Handy when redesigning an entire web page
  • You can test multiple variables at once
  • Significant results (since traffic is higher)
  • Gather multiple data insights at once

  Cons

  • Requires substantial traffic
  • Harder to accurately decipher results
  • Not as easy to get started (more advanced)

  Use Matomo to start testing and improving your site

  

  You need to optimise your website if you want to get more leads, land more conversions and grow your business.

  A/B testing and multivariate testing are proven testing methods you can lean on to improve your website and create a better user experience.

  You may prefer one testing method now over the other, and that’s okay.

  The main thing is you’re starting to test. The best marketers and analysts in the world find what works through testing and double down on their winning tactics.

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