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Hello everyone, thank you for joining another session of MatomoCamp.

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Today we will learn more on how to get started with intranet analytics in Matomo.

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The host for this session is Marcus, who has been working with web since 1998 and with

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Matomo for the last 12 years. His experience from a public sector includes roles such as

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lead analyst, senior advisor in GDPR projects and product owner for Matomo and other analytics

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products. This session is recorded but Marcus will be available in the chat room and you

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can drop any questions there.

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Hi, let's begin the session on how to get started with intranet analytics in Matomo.

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This is the context, the things I will be talking about in this session. I think some

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of you would agree with me that web analytics is a common bad conscience. In my experience

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intranet analytics is even worse. I will be dealing with a couple of questions that is

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good to know to do intranet analytics right. For instance, how to regard consent as the

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legal basis to process employees' personal data. Why is Matomo a good choice for intranet

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analytics? And then I will guide you through the process of analytics, giving suggestions

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on how to make good use of Matomo. I will begin with introducing myself. I have been

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working in the industry since 1998 in different roles. I have been a consultant a couple of

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times and jumped the fence to work on the customer end as well in the Swedish public

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sector mostly. I contributed to get rid of Google analytics in favor of Matomo or back

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then peak before it was cool. In 2015, at Sweden's largest organization in the public

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sector, I have been responsible for search analytics, product owner for Matomo and performing

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a couple of advisor assignments such as implementing GDPR compliance and also curiously enough

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artificial intelligence in life science. In my spare time, I have been writing a couple

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of books on web strategy and web analytics. The latter is used as literature at some of

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Sweden's universities. Enough about me. Let's talk intranets. So what is the definition

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of an intranet in contrast to an extranet or a public website on the internet? Yeah,

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this might be both quite tricky and simple, right? Some people include everything connected

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in a form or another in the term internet. Some don't. I am one of those inclusive people.

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In my world, the internet includes internet, extranets and etc. But when using internet

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as a term, I think its main attribute is that it's an isolated network. Its users are the

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organization's own employees. And ideally, the internet facilitates the work to be done

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and making administrative tasks such as leave applications less of a burden compared to

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its paper equivalent. Regarding an internet's attribute as being inaccessible to non-employees,

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in Sweden, we also have a movement towards open internet when it comes to public sector

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organizations. Making the definitions less logical sometimes and legal compliance for

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analytics is not that straightforward. So what makes Matomo especially good at internet analytics?

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I possibly could ramble over the feats of using Matomo for way too long. But that shows two main

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points. First, to keep internal matters internal. With Matomo, you could actually gather personal

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data that would be breach of contract with some of the other popular products on the market.

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My second point is the more legal aspects of compliance, as in not sharing data with

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unnecessary outsiders, especially those with ties to third country legislation, in essence,

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United States. If you do, then you have exposed the data to actors you and your employees cannot

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hold accountable on what happens with that data. It's irresponsible. Also, to gather

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data on internet use has its own unique legal problems, as I will address shortly.

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Thirdly, for a smaller organization, it's not that hard to get going. Even without an IT department,

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you could make use of a web host, since many of them have one-click installers for Matomo.

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Now I am going to dive into the details. There might be differences in terms of configuration

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between Matomo internet tracking and Matomo for a public website. Technically, if you have

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another Matomo setup for your internet, you don't really have to worry about the same type of

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things as with a Matomo reachable from the public internet. The internet is not exposed in the same

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way. On an internet property in Matomo, you get a more even load, for instance, since the things

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being tracked is behind a firewall, it is not exposed to refer, spam, or denial of service attacks.

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Perhaps the most important point is to own your data, being able to use and reuse the data when

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you see fit. A Swedish term that might be lost in translation is rådighet. It translates to

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resourcefulness, which is not bad, but it lacks, in my opinion, the comforting and trustworthiness

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in its Swedish counterpart. Matomo as a product gives you control and sovereignty if you want to.

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By the way, I am not advocating solely for self-hosting Matomo, but when you don't have

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to make an effort to look at the processor and the eventual sub-processor of the data in question.

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What are the rules in terms of privacy when it comes to an internet? The e-privacy regulation in

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the European Union is not automatically applicable for internets, but if the user is not an employee,

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then yes, because sometimes the user of an internet is a journalist, a member of the public,

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or a non-employee. If you have never heard of European e-privacy regulation, you might have

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heard of something on the lines of the cookie law. It was introduced in the early 2000s and recently

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updated. In an EU perspective, all of us got the GDPR decade and the ifs, hows and whens of processing

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personal data, and all form of handling of personal data is considered processing, such as the

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collection, processing, sharing, etc. The only way we can process personal data is if we have at

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least one of six of the conditions that apply. That is A to F in this slide. All other cases are not

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lawful. I worked as a web analytics specialist in Sweden's largest region when we had the GDPR

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project, and it was really obvious during talks with all our lawyers that consent is not great on

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a public website, and consent is even less applicable on an internet, since the relation

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between the employee and employer is not living up to the voluntary part of the consent requirements

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stated in the GDPR. Consent is not lawful if it's not freely given, as in there is an option to turn

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it down. So that dictates if we can ask for consent on an internet, and the other law, the e-privacy

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regulation, makes asking for cookies look like a consent that might itself be illegal on the GDPR,

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if on an internet, or employees at least. The way to process employees' data, such as IP addresses,

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for an internet analytics, could possibly be lawful through B, the contract between the employees

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and the employer. This is rather difficult, but worth having in mind when an organization makes

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up their mind on the Matoma implementation for their internet. The easy way out is to use log

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analytics, to at least not having to consider e-privacy. But data in logs still is regulated

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by GDPR, and the default is that any personal data only belongs to the data subject, the person,

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in question. So how to set up an internet analytics with Matomo by yourself, in order to have a

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sandbox to test Matomo for your internet. I am myself playing with Matomo at a local webhost

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when stakes are low. Think of it as playing in a sandbox. For anyone interested in a one-click

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installer on Matomo, when having a webhost, the package is present in Softaculous, for instance.

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This is of course not suitable for everyone. Well, the time when major design decisions were made

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based on subjective measures, like someone's personal taste, ought to be behind us. Now you

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can and should evaluate almost everything based on data. Previously performed tests and the

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insights gained from working with analytics could be readily available to internet teams. Instead of

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attending pointless meetings about what shade of blue you like, you can evaluate alternatives based

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on real user of your internet. Users, through their clicks, provide signals on what works,

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how to find things, and which routes they take. This talk is about how to get started with the

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analytics in an initial review of an internet. Often no one knows the usefulness of most of the

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internet content, and there is little effort toward measuring the impact of the internet

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and the performance of the business, at least beyond noting the number of users. For many teams,

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the reason is they do not have a training in basic analytics, and which all the statistics

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to make use of. I hope you want to start evaluating your internet in a structured way and set tangible

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business goals for measuring what success looks like on the internet. The internet is not just

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a collection of pages, images, and documents. Rather, it's there to support employees in their

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daily work, efficiently carrying out work tasks like reserving a place on the training course,

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filing an application for leave, or finding the phone number of the person in the human resource

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department. Working with web analytics for an internet is about improving or simplifying

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activities like these, activities that the internet should support. Internet analytics is not

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about accumulating vast amounts of data. It is about using the data to gain insights in the

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user's experience of the internet, with the intent of improving that experience.

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It is easy to focus on analyzing the collected traffic data. However, all kinds of tools can

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contribute to getting an overview of your internet performance, and should be included in the concept

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of internet analytics. Some of these tools are soulless premium plugins for Matomo, such as

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session recording, but Matomo also evolves. I think it will be in version 4.1, which will be

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evolved. I think it will be in version 4.0. The performance report will be introduced out of the

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box. Problems such as not fulfilling accessibility requirements, using unnecessarily high resolution

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images, and many more may be easy to identify without using statistics. But with Matomo,

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you could develop a plugin to enhance the dashboard with that kind of data as well.

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A key performance indicator, KPI, sometimes, sometimes for internet schemes, we call it the

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metric. It's a measurable value that demonstrates how effectively business objectives are being

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attained. Organizations use KPIs to evaluate their success as reaching their targets. There are two

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fundamental categories of KPIs and metrics. We need them both to understand the big picture.

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Quantitative measurements are figures on how the internet is performing, such as all the data

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about the users as a group we find in Matomo, for instance. These measurements provide answers on

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how, when, and what about users, what devices they use when they visit the internet, if they

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have actively interacted with internet content in any way, where did they come from, and so on.

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Simply put, behavioral data. Qualitative measures is about how users experience the internet. This

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data is in the form of text or film rather than numbers. The methods may include an employee

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survey, guerrilla testing, interviews, usability evaluations, user observation, or features on the

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site where the user can report what they thought of their experience. In some cases, we hope to

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serve the user so we can take the opportunity to ask follow-up questions if necessary. Your internet

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should have a regular ongoing review process and major review should take place if you are considering

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an upgrade or change of platform. Reviews are often called content audits, although their

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conclusions are not restricted to the quality of content. When reviewing the current internet, both

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quantitative and qualitative measures can be used to find out what is working and what needs to be

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improved. The basic level of review should enable you to group content into useful pages that have

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an obvious value to actual users and all the rest at the best dubious value. When looking through the

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content, there are some things to consider when deciding which groups the content belongs to,

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which parts of your internet support the important activities. This part needs to be analyzed to

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identify improvement opportunities, optimal placement of buttons, understandable texts,

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how easy it is to get there, etc. How easy it is to find your way between the different activities.

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Then consider your navigation structure. Is it easy to find your way back to a starting point if

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you got a direct link by email? Is it easy to switch between tasks? Is it easy to switch from

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applying for vacation to the task to correct an improperly filed holiday application?

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How easy is it for users to complete an activity? Just like a store survival is conditional on

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customers' ability to pay for the goods, you need to make sure there are as few obstacles as possible

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on your internet in order for users to complete an activity. What defines a successful session on

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the internet? What activities must have occurred for you to be able to objectively refer to a

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user's session on the internet as a successful visit? Is it enough that the user fills out their

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timesheet or maybe that they land on home page, read some internal news and then disappear?

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Think carefully about which behavior indicates a successful visit. Content that does not relate to

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this point is often distracting and it should probably be removed or at least be hidden out

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of sight and not indexed by your search engine. Please do define requirements for new pages to

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include a clearly displayed call to action and having an explicit connection to the goals for

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the internet. Talking about call to actions, I just mentioned it. This is the definition of the term.

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Often a CTA is a button or any other interactive design element that is enabling the user to take

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the next logical step towards a conversion on a goal. This process is what the rest of my talk

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will discuss. For analytics to be meaningful, you need to first define what is good. Good content,

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successful task accomplishment, good quality and what is good for business. These are the things

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that are worth analyzing. Otherwise, the risk is that you are working to improve things that lack

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the prospect of worthwhile improvement. The working process for internet analytics is as follows.

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You start at the upper left of this never-ending loop. First, develop business goals or evaluate

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existing business goals. Then you produce reports and develop methods for analyzing

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KPIs and selecting metrics. Thirdly, you analyze and fourthly improve the internet based on any

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findings in the analysis and then you iterate. This is not the project. Start by developing the

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business goal and you're never done. Begin the second iteration with reflection of the measurability

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of the business goals that can be improved on another iteration of the process. Now it's time

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to go through each step in this working process and examine what is worth thinking about.

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Last year, it's pretty mind-boggling that only 34% of internet managers, according to

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web service awards, still had no clear objective for their internet. A clear majority also say

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that they have no defined objectives for any of the internet's sub-pages.

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Obviously, it's difficult to know how useful the internet is if you do not know what it's supposed

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to achieve or how to measure it. The very first step of internet analytics is to set down a list

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of goals. You should avoid vaguely worded wishes such as enable findability or keep employees

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informed with news. Instead, try to stick with concrete activities and tasks employees need to

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be able to do to get their job done and bring value to the business. Good examples of these include

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reporting time at work, updating skills profile, or submitting requests for absence.

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Management documents such as the organization's vision, plans, and the budget often provide

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already defined objectives, KPIs, that can be reused or adapted as measures for your internet.

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Break down each goal to something that can be isolated and measured as part of the user

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experience of the internet. A challenge may be to align the measurable business objectives with

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the long-term objectives. It is important to try to anticipate the long-term consequences of

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the goals we define in order to avoid negative side effects. In addition to the business objectives,

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it can be beneficial to review other metrics such as the number of page views per visit.

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They may tell the backstory of why the objectives performed the way they did. However,

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most generic metrics struggle to prove what value they indicate is the result unequivocally good

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or bad. These are not where you should put most of your effort. Instead, they help you investigate

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things afterwards and gather data helping you to tell the story of the users when you are reporting.

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The amount of page views, unique users, sessions, and similar does not provide any evidence of

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increased business performance. These may be nice anecdotes between internet geeks, but they provide

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very little insight in which way the internet is successful. Instead, they make for personal

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glorifications and a good gut feeling of not great business. Another term is segmentation. It is a

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feature of Matuomo and you find it at the toolbar in the top of the user interface.

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A segmentation is a group or a subset of users that have some attribute in common. It may be

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that they all use a specific type of device. It can be the geographical location or whatever Matuomo

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has gathered as common data points for them. Segments are often overlooked, so I will take

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some time to pitch it to you. But before I go into even more segmentation, we have a fundamental

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term to discuss, click-through rate or CTR for short. CTR is the share of users that do a specific

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thing, such as that, for instance, 73% of users click on one of the featured articles on the front

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page. If you have a low CTR, that indicates the need to improve the usability of the place

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where many users disappear. Or perhaps they get cold feet because of some poor design decision

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you need to improve. Think about the goal that at least 90% should succeed with submitting their

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application of lead. If 25% of them drop out at the very first step in the process, then we have

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to try to figure out what caused it or possibly set a more reasonable goal we are actually able

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to achieve. It is particularly interesting to create a segment of users to find out when we

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are failing the users. The segment, those who do not complete all the steps in the conversion funnel,

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might reveal some interesting pattern. Sometimes you are able to alleviate. Are there any common

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denominators where the users fail or flee? Where do they go? Is there possibly an indistinct call

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to action that users do not understand, see or discover? The conversion funnel does not require

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many steps to be suitable as a visualization. It might be obvious in itself presenting the results

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to internal stakeholders. A simple use case for a conversion funnel can be to show the amount of

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users of the search engine that actually click on any of the results. How many times they rephrase

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the search terms and how big the drop-off is. It could be one of several means to evaluate whether

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an enhanced relevance model for the search engine has been an improvement according to its users.

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A conversion funnel is not limited to the internet. It could very well be the measure

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of impact of a newsletter by email or even something in the real world. One thing is certain

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though. Do not report data or specific numbers, so-called data puking. It ought to be the last

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option to try out on stakeholders. Very few are interested in data alone. Even fewer actually

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understand the data. You are supposed to tell them the story of the data and visualize the impact.

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Conversion funnels are in a way the opposite of data puking since it's in a visual way telling

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a story reminding you to explain where the users end up and your hypothesis on why. Back to segments.

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Here I'll make some suggestions on segments in Matomo for internets for you to think about.

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A feature of Matomo that, at least in my experience, many seem to overlook is to compare two

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segments against each other. My first suggestion is such a comparison. Depending on the content of

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your internet, there might be a use case for a segment singling out users with a management

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position. This is something you can inform Matomo about using the internet as an example.

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You can inform Matomo about using the custom dimension feature directly from the code of your

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internet. So, what is the point? Well, if you have content on the internet aiming towards users

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within management, the obvious question is if that content is used by its intended audience, right?

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This is how you get to figure that out. Other suggestions on segments is the user's organizational

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position, since it can be relevant as a context when looking at conversions on the internet.

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For instance, if a part of the organization is unexpectedly underperforming on some of the

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internet goals, you have a deviation to investigate further. Another suggestion is to segment on

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different geographic locations or perhaps different office buildings in the same city or floors in the

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same building. So, how exactly is custom segments done in Matomo? One way is to let the internet

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feed the info to Matomo through the front-end code to end up in a custom dimension. An internet

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usually has integration to the local directory of employees, who is logged on, the actual computer,

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and where do they work in the organization. That might make the cut for a custom dimension,

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since they ought not to be of temporary nature or triviality for a few involved. But there might

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also be possibilities to track signals for segments using Matomo's event tracking. The upside with

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the Matomo's way of having segments as inclusive filters is that if a user session consists of an

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event signal of your choice, they are segment compatible. And you can see this slide as a

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critique on the sometimes oversimplistic views on segments, personas, and other ways of describing

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our users. Another difficulty is which potentially crucial details are hidden behind the persona or

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target group we have. Here we have an illustrative example some of you may have seen previously.

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It is the criticism of personas as a concept. That is, that you create a fictional person who

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is named and assigned a number of characteristics. But criticism is useful in many contexts.

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I have put together a persona I call Welfare William. He is rich, has children, is British,

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homeowner, etc. The description fits both the, until recently, Prince Charles and Ossie Osborn,

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two quite different people. One of them lived 70 years of his life as a literal prince. The other

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one is a prince of sorts, the prince of darkness, perhaps. One had fried sparrows flying into his

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mouth since birth. The other has literally bitten the necks of bats on concept stages, so the blood

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spurted. One is the very definition of upper class, the other began his life in total social

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vulnerability. What unites Ossie and the now King Charles may actually be irrelevant, but they have

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very different life experiences. And if you were to design a communication campaign and are aware

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that these two groups exist within your target group, yes, I suppose you will see the challenge

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in how the appeal is, how flexible these two people are to act on your call to action.

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There are two things you can take away from this discussion, though. One is that personas should

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not be about demographic data, such as income, marital status, etc. Rather, it is the person's

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problem to solve and their challenges, because that's where you become relevant

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with whatever matching offer or service you have. The second one is to dig deeper, otherwise you

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may miss details that are possibly relevant. So I leave it up to you and the internet managers to

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figure out if management position is a relevant segment where you work, or if it's just another

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wealthy William case. Segments help you not to get lost in all the averages and aggregates you

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encounter in Matomo. A made-up illustration is now in this image, showing that the fact that the

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average user, the grey circle in the middle, has 10 page views per visit might not be that insightful

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if actual users typically split into two groups with either one page view or 20. Segmentation is

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important because of the risks of looking at average values. Average values are sometimes not

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representative for the majority of users, and average value is meaningful only if the data is

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in a normal distribution, meaning that the average value has relatively few surprising deviations.

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This is an illustration of a normal distribution. Most data is predictably similar to the average

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value. Not all things we are interested in is distributed this way, just as the made-up example

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of average value of 10 page views per visit is not that meaningful a value. Sometimes the average

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does not describe the reality. More interesting is the observation that there are at least two

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very different groups in this data. To get clarity around what groups you need to inspect,

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you use segmentation to divide the data into different groups that can be explored separately.

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The idea is to filter out a subset of the users, see how they perform, and if they stand out in

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some way. Segmentation is done to be aware of differences, differences in how each segment

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behaves, depending on where they are located, their profession, which sub-pages they visit,

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and so forth. When reviewing your internet using analytics, it is a good idea to focus on

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to focus on few segments per repetition of the analysis. One segment could be to filter out

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users who only have a single page view and inspect where those user sessions are taking place.

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Another segment could be to watch the behavior among those who use the internet on a daily basis,

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what differentiates them from monthly users, for instance. Yet another segment may be to look at

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those who repeatedly only make a single page view. Looking at these different usage patterns

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could lead to some significant improvement actions. If, for example, you conclude that

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from your analysis that the single page viewer segment have simply given up on the internet,

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the targeted campaign could be implemented, aiming at figuring out a way of convincing them

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to try out some new features. As mentioned previously, average values do not always provide

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a particularly accurate picture of reality or actionable insights. The majority of users may have

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few usability issues. The aim of segmentation is to reveal the portion of users within the whole

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who have problems. With internet analytics, you can simultaneously manage several user groups

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in a structured way. Examples of business and operational goals measurable on an internet.

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Content published on the internet needs to align with business goals and the intentions we have

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for the internet. It is not nearly enough to assert a public interest or to continue treating

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the internet as an information dump just because some of us like to produce content.

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I have set out some examples of business goals for the internet, such as that at least 90% of

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those who begin the process to apply for BLEEM through the internet have to succeed. Otherwise,

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they are wasting their time. Perhaps you could have efficiency metrics as well.

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Metrics such as employees should easily find their contacts within the human resources

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or finance department whenever needed. That is very much a task to be done

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thing for the internet. As you notice, these examples of business goals are not limited by

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what statistics are captured automatically in Matuomo. To measure real business goals,

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you have to work both with quantitative metrics and qualitative metrics such as

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users' subjective opinions. Depending on the business goal, you might also need to look at

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other systems than Matuomo. Regarding accessibility and a measurable way to evaluate it,

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the design should probably adhere to accessibility guidelines. One such guideline is

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VCAG 2.2 at level AA, which in the European Union is a mandatory regulation since

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two years ago, I think, at least for the public sector.

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Step two out of four of the process, then. If you mention internet, you probably have access to some

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standard reporting tools. I would also guess that tool might be Matuomo since you attend this

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conference or that you are considering Matuomo in the future. In order to monitor how well the

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internet delivers on goals and metrics, we need to gather appropriate data using these data.

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It's almost certainly necessary to tune your reporting tool so that your

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reports actually measure your business goals. When you need adjustments or variations of your

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standard reports in Matuomo, you can consider premium plugin custom reports. In my experience,

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that plugin is a lifesaver. It can give you customized reports, filter data, and answer

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metric questions without the need to over and over again look at multiple other reports.

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It is good to collect data that tells the story about the goal so that we learn why something

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performed better, not only that it actually did. It may prove useful to work with a web developer

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until you have gained some knowledge of the systems involved. A large part of the benefit

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of working with the reports and methods is the learning process. Working with the system itself

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to get to know the user's behaviors and needs is something that makes each iteration of this work

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begin on a new and higher level. There are a number of widely used techniques, methods,

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and templates we can use to improve how your reports are designed and I will go through some

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of these now. First off is the conversion funnel, also premium plugin. This is funnel visualization.

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It shows that no one followed the desired path from the landing page to one at the top.

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This funnel has that first step as optional and we can see in the middle that users coming in

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the funnel on that level converts at 25 percent rate towards the goal called signed up. A funnel

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like this indicates room for improvement. Probably we are distracting the users with

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more tempting content or in this case the landing page is not optimized as pushing users down the

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funnel. The conversion is when a user action results in a measurable contribution towards

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a business goal, for instance signing up for the corporate newsletter or being successful in some

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self-service process at the internet. Probably the most common method to visualize a multi-step

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process is to use the conversion funnel. It is particularly well suited to visualize where

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users fail in a process with several stages. You define the starting point and then measure the

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click-through rate on each part, that is how many users you lose on the road towards the goal.

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All objectives cannot be measured by using the collected data, the already collected data in

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Matomo, but some do depending on your implementation. It is not solely about users click and their

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behavior. We need to evaluate other forms of quality indicators. This is a list of quality

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indicators not drawn from statistics tools that a communications department can use to measure

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quality. Things like to list heavy images that are slow to load especially from mobile users,

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find duplicate content,

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documents called something inappropriate such as new word document, has no title at all or other

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common mistakes that make it difficult to find the content on the internet, or that content lacks

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crucial metadata like page description or keywords to help them turn on search engine.

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Talking about search, an easy test you can perform on your own internet is to search for what's not

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supposed to give any results, as in the illustration here when I search for Microsoft Word which ends

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up being the document title if a user is foundling. Where I used to work until recently we have about

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one million pages and documents in our search index with the help of the search engine. You can

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behind the scenes compile a lot of interesting statistics, at least if you consider the search

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feature just as any other user interface to analyze. For instance we could see from search

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robot statistics that 89 percent of the internet lacks keywords, but we could also see that

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statistics that 89 percent of the internet lacks keywords, in other words nine out of ten

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pages and documents are not really competing in the battle to reach the top of the internal search

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engine. Then it's no wonder that the search engine has a hard time figuring out what is

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the most relevant content to some search queries. Most internets do have some sort of search feature,

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at the very least you ought to try to learn what keywords or phrases the users use. An extremely

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actionable thing is if you also track searches that return no result, zero hits. If a search phrase is

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reasonable then it should have a result. You might have issues with synonyms or that perhaps vital

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content is lacking. You could also consider to track the click behavior in the search results

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there is an expected distribution on how much clicks the first entry would get, the second

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would get about half of that etc. And perhaps you would track clicks that are not the top three in

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the results or that an entry got twice as much clicks than is expected for its ranking in the

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results etc etc. And I highly recommend Louise Rosenfeld's book called Search Analytics for

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Your Site if you are curious about a method to evaluate search features and ranking.

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The next step is of course to look at, compare and analyze all the data you have gathered.

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The objective of the analysis phase is to understand why users behave in a certain way or

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think the way they do. This is where you identify the obstacles that prevent users from carrying out

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activities and what you can do to remove the obstacles, improve efficiency, conversion and

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usability. The first thing you need to consider in your analytics is whether you have enough data

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to draw conclusions. Even if you find that you have too little data to draw large and far-reaching

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conclusions your data can suggest how you can transition the test to a larger scale and please

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don't get stuck in this phase. It's easy to find that you have spent more time analyzing than

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actually improving the internet. And you should also be aware of any seasonal variations that

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can affect the results of your review. There are peak times during the year for specific content

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and support for used on internet. For example it's common for people to forget their passwords over

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the summer holidays so demand for replacement passwords peaks when people return to work.

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This is an example of how to use your internet analytics to support the priorities of the

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editorial work on the internet. If people have extraordinary difficulties remembering their

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passwords at the end of holiday season then it's probably efficient to introduce some well-placed

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support information on how to act when one has forgotten one's password. And you should

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I think document the analytic efforts and findings based on the data you collected, processed and

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analyzed. You document the findings and lessons learned and you may choose to introduce your

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conclusions by stating the level of confidence you have in the analytics. This is wise especially

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if major decisions are likely to be taken on the basis of your analysis. Nothing wrong with

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being humble. I think this is not an exact science after all. So keep a log for future reference

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writing down which methods were used for each test etc. And also note what needs improvement

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and require another iteration through the entire analysis process. It's not unusual that you cannot

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find conclusive proof to make the case for one option or the other. And the last step in this

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process is to make improvements based on the evaluated hypothesis and the conclusions drawn

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from the analysis phase. You make a list of prioritized improvements. The improvements you

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think will matter most. It is not always easy to make those judgments. Some efforts may have

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minor impact while being an easy task and other tasks can be complex because they have external

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dependencies. No matter what, now at least you have a list of things to do and stuff you know

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is going to improve the user experience. An example of task in the improvement phase is to

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rearrange the positions of buttons to enhance usability. Something perhaps only beneficial for

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the user segment with smaller screens. And as I said before, you need to be able to

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do that. And after making your improvements, it's time for another iteration of the process.

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Internet analytics is not a project and it's never finished. The usefulness of each iteration of the

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analysis is that you get the chance to look critically at objectives, goals, and metrics

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to see if you think they still prove useful. Most likely over time you will revise, supplement,

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and verify them. And that's it. Something's up. I hope you got some great takeaways from this talk

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and to repeat the most important points I would like to pick these for. Try to set some measurable

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goals with your internet. It makes using Matomo more self-explanatory since you have conversions

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to indicate something of value inside of Matomo. Don't do internet statistics, do analytics.

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And that entails looking for actionable insights. You should use both qualitative

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and quantitative methods. They complement each other. Don't measure activity, focus on results.

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Good luck. And the consoling thought is that most other organizations also seem to suck at

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internet analytics. Let's do better. Any questions? Then you find me in the conference chatroom.

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If you'd like, you could also email me. Thank you for your attention.

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Thank you everyone for joining this session. As Markus mentioned, you can continue the conversation

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in the chat if you have further questions. I also wanted to let you know that the next round of

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sessions will start at 1 p.m. after the lunch break. So we hope to see you then.