Hello everyone, thank you for joining another session of MatomoCamp.
Today we will learn more on how to get started with intranet analytics in Matomo.
The host for this session is Marcus, who has been working with web since 1998 and with
Matomo for the last 12 years. His experience from a public sector includes roles such as
lead analyst, senior advisor in GDPR projects and product owner for Matomo and other analytics
products. This session is recorded but Marcus will be available in the chat room and you
can drop any questions there.
Hi, let's begin the session on how to get started with intranet analytics in Matomo.
This is the context, the things I will be talking about in this session. I think some
of you would agree with me that web analytics is a common bad conscience. In my experience
intranet analytics is even worse. I will be dealing with a couple of questions that is
good to know to do intranet analytics right. For instance, how to regard consent as the
legal basis to process employees' personal data. Why is Matomo a good choice for intranet
analytics? And then I will guide you through the process of analytics, giving suggestions
on how to make good use of Matomo. I will begin with introducing myself. I have been
working in the industry since 1998 in different roles. I have been a consultant a couple of
times and jumped the fence to work on the customer end as well in the Swedish public
sector mostly. I contributed to get rid of Google analytics in favor of Matomo or back
then peak before it was cool. In 2015, at Sweden's largest organization in the public
sector, I have been responsible for search analytics, product owner for Matomo and performing
a couple of advisor assignments such as implementing GDPR compliance and also curiously enough
artificial intelligence in life science. In my spare time, I have been writing a couple
of books on web strategy and web analytics. The latter is used as literature at some of
Sweden's universities. Enough about me. Let's talk intranets. So what is the definition
of an intranet in contrast to an extranet or a public website on the internet? Yeah,
this might be both quite tricky and simple, right? Some people include everything connected
in a form or another in the term internet. Some don't. I am one of those inclusive people.
In my world, the internet includes internet, extranets and etc. But when using internet
as a term, I think its main attribute is that it's an isolated network. Its users are the
organization's own employees. And ideally, the internet facilitates the work to be done
and making administrative tasks such as leave applications less of a burden compared to
its paper equivalent. Regarding an internet's attribute as being inaccessible to non-employees,
in Sweden, we also have a movement towards open internet when it comes to public sector
organizations. Making the definitions less logical sometimes and legal compliance for
analytics is not that straightforward. So what makes Matomo especially good at internet analytics?
I possibly could ramble over the feats of using Matomo for way too long. But that shows two main
points. First, to keep internal matters internal. With Matomo, you could actually gather personal
data that would be breach of contract with some of the other popular products on the market.
My second point is the more legal aspects of compliance, as in not sharing data with
unnecessary outsiders, especially those with ties to third country legislation, in essence,
United States. If you do, then you have exposed the data to actors you and your employees cannot
hold accountable on what happens with that data. It's irresponsible. Also, to gather
data on internet use has its own unique legal problems, as I will address shortly.
Thirdly, for a smaller organization, it's not that hard to get going. Even without an IT department,
you could make use of a web host, since many of them have one-click installers for Matomo.
Now I am going to dive into the details. There might be differences in terms of configuration
between Matomo internet tracking and Matomo for a public website. Technically, if you have
another Matomo setup for your internet, you don't really have to worry about the same type of
things as with a Matomo reachable from the public internet. The internet is not exposed in the same
way. On an internet property in Matomo, you get a more even load, for instance, since the things
being tracked is behind a firewall, it is not exposed to refer, spam, or denial of service attacks.
Perhaps the most important point is to own your data, being able to use and reuse the data when
you see fit. A Swedish term that might be lost in translation is rådighet. It translates to
resourcefulness, which is not bad, but it lacks, in my opinion, the comforting and trustworthiness
in its Swedish counterpart. Matomo as a product gives you control and sovereignty if you want to.
By the way, I am not advocating solely for self-hosting Matomo, but when you don't have
to make an effort to look at the processor and the eventual sub-processor of the data in question.
What are the rules in terms of privacy when it comes to an internet? The e-privacy regulation in
the European Union is not automatically applicable for internets, but if the user is not an employee,
then yes, because sometimes the user of an internet is a journalist, a member of the public,
or a non-employee. If you have never heard of European e-privacy regulation, you might have
heard of something on the lines of the cookie law. It was introduced in the early 2000s and recently
updated. In an EU perspective, all of us got the GDPR decade and the ifs, hows and whens of processing
personal data, and all form of handling of personal data is considered processing, such as the
collection, processing, sharing, etc. The only way we can process personal data is if we have at
least one of six of the conditions that apply. That is A to F in this slide. All other cases are not
lawful. I worked as a web analytics specialist in Sweden's largest region when we had the GDPR
project, and it was really obvious during talks with all our lawyers that consent is not great on
a public website, and consent is even less applicable on an internet, since the relation
between the employee and employer is not living up to the voluntary part of the consent requirements
stated in the GDPR. Consent is not lawful if it's not freely given, as in there is an option to turn
it down. So that dictates if we can ask for consent on an internet, and the other law, the e-privacy
regulation, makes asking for cookies look like a consent that might itself be illegal on the GDPR,
if on an internet, or employees at least. The way to process employees' data, such as IP addresses,
for an internet analytics, could possibly be lawful through B, the contract between the employees
and the employer. This is rather difficult, but worth having in mind when an organization makes
up their mind on the Matoma implementation for their internet. The easy way out is to use log
analytics, to at least not having to consider e-privacy. But data in logs still is regulated
by GDPR, and the default is that any personal data only belongs to the data subject, the person,
in question. So how to set up an internet analytics with Matomo by yourself, in order to have a
sandbox to test Matomo for your internet. I am myself playing with Matomo at a local webhost
when stakes are low. Think of it as playing in a sandbox. For anyone interested in a one-click
installer on Matomo, when having a webhost, the package is present in Softaculous, for instance.
This is of course not suitable for everyone. Well, the time when major design decisions were made
based on subjective measures, like someone's personal taste, ought to be behind us. Now you
can and should evaluate almost everything based on data. Previously performed tests and the
insights gained from working with analytics could be readily available to internet teams. Instead of
attending pointless meetings about what shade of blue you like, you can evaluate alternatives based
on real user of your internet. Users, through their clicks, provide signals on what works,
how to find things, and which routes they take. This talk is about how to get started with the
analytics in an initial review of an internet. Often no one knows the usefulness of most of the
internet content, and there is little effort toward measuring the impact of the internet
and the performance of the business, at least beyond noting the number of users. For many teams,
the reason is they do not have a training in basic analytics, and which all the statistics
to make use of. I hope you want to start evaluating your internet in a structured way and set tangible
business goals for measuring what success looks like on the internet. The internet is not just
a collection of pages, images, and documents. Rather, it's there to support employees in their
daily work, efficiently carrying out work tasks like reserving a place on the training course,
filing an application for leave, or finding the phone number of the person in the human resource
department. Working with web analytics for an internet is about improving or simplifying
activities like these, activities that the internet should support. Internet analytics is not
about accumulating vast amounts of data. It is about using the data to gain insights in the
user's experience of the internet, with the intent of improving that experience.
It is easy to focus on analyzing the collected traffic data. However, all kinds of tools can
contribute to getting an overview of your internet performance, and should be included in the concept
of internet analytics. Some of these tools are soulless premium plugins for Matomo, such as
session recording, but Matomo also evolves. I think it will be in version 4.1, which will be
evolved. I think it will be in version 4.0. The performance report will be introduced out of the
box. Problems such as not fulfilling accessibility requirements, using unnecessarily high resolution
images, and many more may be easy to identify without using statistics. But with Matomo,
you could develop a plugin to enhance the dashboard with that kind of data as well.
A key performance indicator, KPI, sometimes, sometimes for internet schemes, we call it the
metric. It's a measurable value that demonstrates how effectively business objectives are being
attained. Organizations use KPIs to evaluate their success as reaching their targets. There are two
fundamental categories of KPIs and metrics. We need them both to understand the big picture.
Quantitative measurements are figures on how the internet is performing, such as all the data
about the users as a group we find in Matomo, for instance. These measurements provide answers on
how, when, and what about users, what devices they use when they visit the internet, if they
have actively interacted with internet content in any way, where did they come from, and so on.
Simply put, behavioral data. Qualitative measures is about how users experience the internet. This
data is in the form of text or film rather than numbers. The methods may include an employee
survey, guerrilla testing, interviews, usability evaluations, user observation, or features on the
site where the user can report what they thought of their experience. In some cases, we hope to
serve the user so we can take the opportunity to ask follow-up questions if necessary. Your internet
should have a regular ongoing review process and major review should take place if you are considering
an upgrade or change of platform. Reviews are often called content audits, although their
conclusions are not restricted to the quality of content. When reviewing the current internet, both
quantitative and qualitative measures can be used to find out what is working and what needs to be
improved. The basic level of review should enable you to group content into useful pages that have
an obvious value to actual users and all the rest at the best dubious value. When looking through the
content, there are some things to consider when deciding which groups the content belongs to,
which parts of your internet support the important activities. This part needs to be analyzed to
identify improvement opportunities, optimal placement of buttons, understandable texts,
how easy it is to get there, etc. How easy it is to find your way between the different activities.
Then consider your navigation structure. Is it easy to find your way back to a starting point if
you got a direct link by email? Is it easy to switch between tasks? Is it easy to switch from
applying for vacation to the task to correct an improperly filed holiday application?
How easy is it for users to complete an activity? Just like a store survival is conditional on
customers' ability to pay for the goods, you need to make sure there are as few obstacles as possible
on your internet in order for users to complete an activity. What defines a successful session on
the internet? What activities must have occurred for you to be able to objectively refer to a
user's session on the internet as a successful visit? Is it enough that the user fills out their
timesheet or maybe that they land on home page, read some internal news and then disappear?
Think carefully about which behavior indicates a successful visit. Content that does not relate to
this point is often distracting and it should probably be removed or at least be hidden out
of sight and not indexed by your search engine. Please do define requirements for new pages to
include a clearly displayed call to action and having an explicit connection to the goals for
the internet. Talking about call to actions, I just mentioned it. This is the definition of the term.
Often a CTA is a button or any other interactive design element that is enabling the user to take
the next logical step towards a conversion on a goal. This process is what the rest of my talk
will discuss. For analytics to be meaningful, you need to first define what is good. Good content,
successful task accomplishment, good quality and what is good for business. These are the things
that are worth analyzing. Otherwise, the risk is that you are working to improve things that lack
the prospect of worthwhile improvement. The working process for internet analytics is as follows.
You start at the upper left of this never-ending loop. First, develop business goals or evaluate
existing business goals. Then you produce reports and develop methods for analyzing
KPIs and selecting metrics. Thirdly, you analyze and fourthly improve the internet based on any
findings in the analysis and then you iterate. This is not the project. Start by developing the
business goal and you're never done. Begin the second iteration with reflection of the measurability
of the business goals that can be improved on another iteration of the process. Now it's time
to go through each step in this working process and examine what is worth thinking about.
Last year, it's pretty mind-boggling that only 34% of internet managers, according to
web service awards, still had no clear objective for their internet. A clear majority also say
that they have no defined objectives for any of the internet's sub-pages.
Obviously, it's difficult to know how useful the internet is if you do not know what it's supposed
to achieve or how to measure it. The very first step of internet analytics is to set down a list
of goals. You should avoid vaguely worded wishes such as enable findability or keep employees
informed with news. Instead, try to stick with concrete activities and tasks employees need to
be able to do to get their job done and bring value to the business. Good examples of these include
reporting time at work, updating skills profile, or submitting requests for absence.
Management documents such as the organization's vision, plans, and the budget often provide
already defined objectives, KPIs, that can be reused or adapted as measures for your internet.
Break down each goal to something that can be isolated and measured as part of the user
experience of the internet. A challenge may be to align the measurable business objectives with
the long-term objectives. It is important to try to anticipate the long-term consequences of
the goals we define in order to avoid negative side effects. In addition to the business objectives,
it can be beneficial to review other metrics such as the number of page views per visit.
They may tell the backstory of why the objectives performed the way they did. However,
most generic metrics struggle to prove what value they indicate is the result unequivocally good
or bad. These are not where you should put most of your effort. Instead, they help you investigate
things afterwards and gather data helping you to tell the story of the users when you are reporting.
The amount of page views, unique users, sessions, and similar does not provide any evidence of
increased business performance. These may be nice anecdotes between internet geeks, but they provide
very little insight in which way the internet is successful. Instead, they make for personal
glorifications and a good gut feeling of not great business. Another term is segmentation. It is a
feature of Matuomo and you find it at the toolbar in the top of the user interface.
A segmentation is a group or a subset of users that have some attribute in common. It may be
that they all use a specific type of device. It can be the geographical location or whatever Matuomo
has gathered as common data points for them. Segments are often overlooked, so I will take
some time to pitch it to you. But before I go into even more segmentation, we have a fundamental
term to discuss, click-through rate or CTR for short. CTR is the share of users that do a specific
thing, such as that, for instance, 73% of users click on one of the featured articles on the front
page. If you have a low CTR, that indicates the need to improve the usability of the place
where many users disappear. Or perhaps they get cold feet because of some poor design decision
you need to improve. Think about the goal that at least 90% should succeed with submitting their
application of lead. If 25% of them drop out at the very first step in the process, then we have
to try to figure out what caused it or possibly set a more reasonable goal we are actually able
to achieve. It is particularly interesting to create a segment of users to find out when we
are failing the users. The segment, those who do not complete all the steps in the conversion funnel,
might reveal some interesting pattern. Sometimes you are able to alleviate. Are there any common
denominators where the users fail or flee? Where do they go? Is there possibly an indistinct call
to action that users do not understand, see or discover? The conversion funnel does not require
many steps to be suitable as a visualization. It might be obvious in itself presenting the results
to internal stakeholders. A simple use case for a conversion funnel can be to show the amount of
users of the search engine that actually click on any of the results. How many times they rephrase
the search terms and how big the drop-off is. It could be one of several means to evaluate whether
an enhanced relevance model for the search engine has been an improvement according to its users.
A conversion funnel is not limited to the internet. It could very well be the measure
of impact of a newsletter by email or even something in the real world. One thing is certain
though. Do not report data or specific numbers, so-called data puking. It ought to be the last
option to try out on stakeholders. Very few are interested in data alone. Even fewer actually
understand the data. You are supposed to tell them the story of the data and visualize the impact.
Conversion funnels are in a way the opposite of data puking since it's in a visual way telling
a story reminding you to explain where the users end up and your hypothesis on why. Back to segments.
Here I'll make some suggestions on segments in Matomo for internets for you to think about.
A feature of Matomo that, at least in my experience, many seem to overlook is to compare two
segments against each other. My first suggestion is such a comparison. Depending on the content of
your internet, there might be a use case for a segment singling out users with a management
position. This is something you can inform Matomo about using the internet as an example.
You can inform Matomo about using the custom dimension feature directly from the code of your
internet. So, what is the point? Well, if you have content on the internet aiming towards users
within management, the obvious question is if that content is used by its intended audience, right?
This is how you get to figure that out. Other suggestions on segments is the user's organizational
position, since it can be relevant as a context when looking at conversions on the internet.
For instance, if a part of the organization is unexpectedly underperforming on some of the
internet goals, you have a deviation to investigate further. Another suggestion is to segment on
different geographic locations or perhaps different office buildings in the same city or floors in the
same building. So, how exactly is custom segments done in Matomo? One way is to let the internet
feed the info to Matomo through the front-end code to end up in a custom dimension. An internet
usually has integration to the local directory of employees, who is logged on, the actual computer,
and where do they work in the organization. That might make the cut for a custom dimension,
since they ought not to be of temporary nature or triviality for a few involved. But there might
also be possibilities to track signals for segments using Matomo's event tracking. The upside with
the Matomo's way of having segments as inclusive filters is that if a user session consists of an
event signal of your choice, they are segment compatible. And you can see this slide as a
critique on the sometimes oversimplistic views on segments, personas, and other ways of describing
our users. Another difficulty is which potentially crucial details are hidden behind the persona or
target group we have. Here we have an illustrative example some of you may have seen previously.
It is the criticism of personas as a concept. That is, that you create a fictional person who
is named and assigned a number of characteristics. But criticism is useful in many contexts.
I have put together a persona I call Welfare William. He is rich, has children, is British,
homeowner, etc. The description fits both the, until recently, Prince Charles and Ossie Osborn,
two quite different people. One of them lived 70 years of his life as a literal prince. The other
one is a prince of sorts, the prince of darkness, perhaps. One had fried sparrows flying into his
mouth since birth. The other has literally bitten the necks of bats on concept stages, so the blood
spurted. One is the very definition of upper class, the other began his life in total social
vulnerability. What unites Ossie and the now King Charles may actually be irrelevant, but they have
very different life experiences. And if you were to design a communication campaign and are aware
that these two groups exist within your target group, yes, I suppose you will see the challenge
in how the appeal is, how flexible these two people are to act on your call to action.
There are two things you can take away from this discussion, though. One is that personas should
not be about demographic data, such as income, marital status, etc. Rather, it is the person's
problem to solve and their challenges, because that's where you become relevant
with whatever matching offer or service you have. The second one is to dig deeper, otherwise you
may miss details that are possibly relevant. So I leave it up to you and the internet managers to
figure out if management position is a relevant segment where you work, or if it's just another
wealthy William case. Segments help you not to get lost in all the averages and aggregates you
encounter in Matomo. A made-up illustration is now in this image, showing that the fact that the
average user, the grey circle in the middle, has 10 page views per visit might not be that insightful
if actual users typically split into two groups with either one page view or 20. Segmentation is
important because of the risks of looking at average values. Average values are sometimes not
representative for the majority of users, and average value is meaningful only if the data is
in a normal distribution, meaning that the average value has relatively few surprising deviations.
This is an illustration of a normal distribution. Most data is predictably similar to the average
value. Not all things we are interested in is distributed this way, just as the made-up example
of average value of 10 page views per visit is not that meaningful a value. Sometimes the average
does not describe the reality. More interesting is the observation that there are at least two
very different groups in this data. To get clarity around what groups you need to inspect,
you use segmentation to divide the data into different groups that can be explored separately.
The idea is to filter out a subset of the users, see how they perform, and if they stand out in
some way. Segmentation is done to be aware of differences, differences in how each segment
behaves, depending on where they are located, their profession, which sub-pages they visit,
and so forth. When reviewing your internet using analytics, it is a good idea to focus on
to focus on few segments per repetition of the analysis. One segment could be to filter out
users who only have a single page view and inspect where those user sessions are taking place.
Another segment could be to watch the behavior among those who use the internet on a daily basis,
what differentiates them from monthly users, for instance. Yet another segment may be to look at
those who repeatedly only make a single page view. Looking at these different usage patterns
could lead to some significant improvement actions. If, for example, you conclude that
from your analysis that the single page viewer segment have simply given up on the internet,
the targeted campaign could be implemented, aiming at figuring out a way of convincing them
to try out some new features. As mentioned previously, average values do not always provide
a particularly accurate picture of reality or actionable insights. The majority of users may have
few usability issues. The aim of segmentation is to reveal the portion of users within the whole
who have problems. With internet analytics, you can simultaneously manage several user groups
in a structured way. Examples of business and operational goals measurable on an internet.
Content published on the internet needs to align with business goals and the intentions we have
for the internet. It is not nearly enough to assert a public interest or to continue treating
the internet as an information dump just because some of us like to produce content.
I have set out some examples of business goals for the internet, such as that at least 90% of
those who begin the process to apply for BLEEM through the internet have to succeed. Otherwise,
they are wasting their time. Perhaps you could have efficiency metrics as well.
Metrics such as employees should easily find their contacts within the human resources
or finance department whenever needed. That is very much a task to be done
thing for the internet. As you notice, these examples of business goals are not limited by
what statistics are captured automatically in Matuomo. To measure real business goals,
you have to work both with quantitative metrics and qualitative metrics such as
users' subjective opinions. Depending on the business goal, you might also need to look at
other systems than Matuomo. Regarding accessibility and a measurable way to evaluate it,
the design should probably adhere to accessibility guidelines. One such guideline is
VCAG 2.2 at level AA, which in the European Union is a mandatory regulation since
two years ago, I think, at least for the public sector.
Step two out of four of the process, then. If you mention internet, you probably have access to some
standard reporting tools. I would also guess that tool might be Matuomo since you attend this
conference or that you are considering Matuomo in the future. In order to monitor how well the
internet delivers on goals and metrics, we need to gather appropriate data using these data.
It's almost certainly necessary to tune your reporting tool so that your
reports actually measure your business goals. When you need adjustments or variations of your
standard reports in Matuomo, you can consider premium plugin custom reports. In my experience,
that plugin is a lifesaver. It can give you customized reports, filter data, and answer
metric questions without the need to over and over again look at multiple other reports.
It is good to collect data that tells the story about the goal so that we learn why something
performed better, not only that it actually did. It may prove useful to work with a web developer
until you have gained some knowledge of the systems involved. A large part of the benefit
of working with the reports and methods is the learning process. Working with the system itself
to get to know the user's behaviors and needs is something that makes each iteration of this work
begin on a new and higher level. There are a number of widely used techniques, methods,
and templates we can use to improve how your reports are designed and I will go through some
of these now. First off is the conversion funnel, also premium plugin. This is funnel visualization.
It shows that no one followed the desired path from the landing page to one at the top.
This funnel has that first step as optional and we can see in the middle that users coming in
the funnel on that level converts at 25 percent rate towards the goal called signed up. A funnel
like this indicates room for improvement. Probably we are distracting the users with
more tempting content or in this case the landing page is not optimized as pushing users down the
funnel. The conversion is when a user action results in a measurable contribution towards
a business goal, for instance signing up for the corporate newsletter or being successful in some
self-service process at the internet. Probably the most common method to visualize a multi-step
process is to use the conversion funnel. It is particularly well suited to visualize where
users fail in a process with several stages. You define the starting point and then measure the
click-through rate on each part, that is how many users you lose on the road towards the goal.
All objectives cannot be measured by using the collected data, the already collected data in
Matomo, but some do depending on your implementation. It is not solely about users click and their
behavior. We need to evaluate other forms of quality indicators. This is a list of quality
indicators not drawn from statistics tools that a communications department can use to measure
quality. Things like to list heavy images that are slow to load especially from mobile users,
find duplicate content,
documents called something inappropriate such as new word document, has no title at all or other
common mistakes that make it difficult to find the content on the internet, or that content lacks
crucial metadata like page description or keywords to help them turn on search engine.
Talking about search, an easy test you can perform on your own internet is to search for what's not
supposed to give any results, as in the illustration here when I search for Microsoft Word which ends
up being the document title if a user is foundling. Where I used to work until recently we have about
one million pages and documents in our search index with the help of the search engine. You can
behind the scenes compile a lot of interesting statistics, at least if you consider the search
feature just as any other user interface to analyze. For instance we could see from search
robot statistics that 89 percent of the internet lacks keywords, but we could also see that
statistics that 89 percent of the internet lacks keywords, in other words nine out of ten
pages and documents are not really competing in the battle to reach the top of the internal search
engine. Then it's no wonder that the search engine has a hard time figuring out what is
the most relevant content to some search queries. Most internets do have some sort of search feature,
at the very least you ought to try to learn what keywords or phrases the users use. An extremely
actionable thing is if you also track searches that return no result, zero hits. If a search phrase is
reasonable then it should have a result. You might have issues with synonyms or that perhaps vital
content is lacking. You could also consider to track the click behavior in the search results
there is an expected distribution on how much clicks the first entry would get, the second
would get about half of that etc. And perhaps you would track clicks that are not the top three in
the results or that an entry got twice as much clicks than is expected for its ranking in the
results etc etc. And I highly recommend Louise Rosenfeld's book called Search Analytics for
Your Site if you are curious about a method to evaluate search features and ranking.
The next step is of course to look at, compare and analyze all the data you have gathered.
The objective of the analysis phase is to understand why users behave in a certain way or
think the way they do. This is where you identify the obstacles that prevent users from carrying out
activities and what you can do to remove the obstacles, improve efficiency, conversion and
usability. The first thing you need to consider in your analytics is whether you have enough data
to draw conclusions. Even if you find that you have too little data to draw large and far-reaching
conclusions your data can suggest how you can transition the test to a larger scale and please
don't get stuck in this phase. It's easy to find that you have spent more time analyzing than
actually improving the internet. And you should also be aware of any seasonal variations that
can affect the results of your review. There are peak times during the year for specific content
and support for used on internet. For example it's common for people to forget their passwords over
the summer holidays so demand for replacement passwords peaks when people return to work.
This is an example of how to use your internet analytics to support the priorities of the
editorial work on the internet. If people have extraordinary difficulties remembering their
passwords at the end of holiday season then it's probably efficient to introduce some well-placed
support information on how to act when one has forgotten one's password. And you should
I think document the analytic efforts and findings based on the data you collected, processed and
analyzed. You document the findings and lessons learned and you may choose to introduce your
conclusions by stating the level of confidence you have in the analytics. This is wise especially
if major decisions are likely to be taken on the basis of your analysis. Nothing wrong with
being humble. I think this is not an exact science after all. So keep a log for future reference
writing down which methods were used for each test etc. And also note what needs improvement
and require another iteration through the entire analysis process. It's not unusual that you cannot
find conclusive proof to make the case for one option or the other. And the last step in this
process is to make improvements based on the evaluated hypothesis and the conclusions drawn
from the analysis phase. You make a list of prioritized improvements. The improvements you
think will matter most. It is not always easy to make those judgments. Some efforts may have
minor impact while being an easy task and other tasks can be complex because they have external
dependencies. No matter what, now at least you have a list of things to do and stuff you know
is going to improve the user experience. An example of task in the improvement phase is to
rearrange the positions of buttons to enhance usability. Something perhaps only beneficial for
the user segment with smaller screens. And as I said before, you need to be able to
do that. And after making your improvements, it's time for another iteration of the process.
Internet analytics is not a project and it's never finished. The usefulness of each iteration of the
analysis is that you get the chance to look critically at objectives, goals, and metrics
to see if you think they still prove useful. Most likely over time you will revise, supplement,
and verify them. And that's it. Something's up. I hope you got some great takeaways from this talk
and to repeat the most important points I would like to pick these for. Try to set some measurable
goals with your internet. It makes using Matomo more self-explanatory since you have conversions
to indicate something of value inside of Matomo. Don't do internet statistics, do analytics.
And that entails looking for actionable insights. You should use both qualitative
and quantitative methods. They complement each other. Don't measure activity, focus on results.
Good luck. And the consoling thought is that most other organizations also seem to suck at
internet analytics. Let's do better. Any questions? Then you find me in the conference chatroom.
If you'd like, you could also email me. Thank you for your attention.
Thank you everyone for joining this session. As Markus mentioned, you can continue the conversation
in the chat if you have further questions. I also wanted to let you know that the next round of
sessions will start at 1 p.m. after the lunch break. So we hope to see you then.