Okay.
So you've built yourself a dashboard, but nobody's actually using it.
What did you do wrong?
Where could you do better?
Now the reality of it building an effective dashboard is not just
about putting data onto a canvas.
How can you get people to actually then use it.
In this video we have a clip from our Measure Masters live training
we've done with Andreea Popa from KPI's Studios.
She taught our Measure Masters, how they can enhance their Data Studio
Dashboards, with a few simple steps.
And this training was so well recieved that we wanted to share it with the
Measure School audience as well.
But before we go, it started, as I mentioned, this has been taken from our
Measure Masters live training, which we do every month inside of our membership,
where you will be also able to access courses, a community and a support
system, helping you to succeed with data.
If that sounds interesting to you, then check us out at measure
school.com/measuremasters With that out of the way, Andrea take it away.
So why should you help your clients understand their data?
Why wouldn't you just give them a crowded dashboard and let
them figure the data themselves?
I am not a huge fan of quotes, but, uh, I really resonated with this one here.
The idea is that in the end, if you help your clients do this,
you will give them meaning.
So they will learn to go to start going beyond just collecting the data
and they will see the value of it.
They will start applying it to do their business and growing their business
based on it, making better business decisions, knowing where to invest,
where to not invest, where to allocate the efforts and efforts and where not.
You will also save them a considerable amount of time and they will
definitely be grateful for that.
Also, don't forget about the fact that if you don't overwhelm them
with a dashboard, that from where they don't understand anything.
Uh, but from one that is, uh, clean and, um, gives them more
insights than just pure data.
They will see your services value and they will want to return for further help.
That is the bottom line.
Enhancement number three Add Time Series Annotations for your clients that
are used to add annotations in Google Analytics or the ones that have questions
about the spikes or dips in traffic.
You can create an annotation system in Google Data Studio
too using data blending.
And you can see here that, uh, I have page views and notes and, um, these are,
this time series contains three notes.
So your clients can see all the notes at once, or can just click on one
of the annotations and see exactly what event was recorded on that day.
So in this case on June the 1st we started the international children's days campaign
and we ran, paid advertising on Facebook.
So this is what caused the spike in the next days and probably the dip um,
that comes after pausing a campaign.
Like I said, you are going to use data blending.
In our case, it's, uh, the Google Analytics data source and for annotations,
we are going to use a Google Spreadsheet and add all the notes that we want.
In this case, we will use as a dimension, the dashboard annotations, because it
is text and it is recognized as Um, a dimension and we will add a column
with a date so that we, um, use the data blending with the join keys of date.
But what's important is that at the chart level, we will use both the page views
so the metric, the KPI what, uh, the, the KPI that you chose and
the, dashboard annotations, we will use them as metrics, as you
can see, the notes are counted here.
You will add the, uh, notes as a metric and they will be automatically
counted as an aggregation.
Also make sure that the page views are you set them up as a line
or whatever metric you choose.
You set them as a line and assign them to the left axis and the notes
you, um, mark them as bars, and then you assign them the right axis.
And that's it.
You have the time series annotations that is lacking in Google Data Studio,
but there's a workaround for it.
Enhancement number four group and simplify with Case When Formulas.
Case When Formulas are probably my favorite function in Data Studio for this
tip, I'm going to give you two examples.
One is a for simplifying the organic position.
Um, customizing and then putting in it into ranges and the other
one is for, uh, traffic channels.
So in this example here, uh, I will re select everything and we have
the average position, which you can see it's all over the place.
And we have this position when used.
The position range.
Is this a case one formula here that by the way is clickable and you can go and,
um, copy it and adapt it to your needs.
And I will show you how this functions.
So, um, let's say your client wants to focus on the keywords that are
between 11 and 20 in organic that have these positions and they can
select these and, uh, analyze them.
I covered them yes, analyze them and, um, I don't know, do something
about it and you will see that we have the position range here.
So yeah.
Instead of scrolling through hundreds, of, or I don't know if they want to reach, for
example, keywords that are on position 30 instead of scrolling all the way to there.
They can just select this more than 30 and get there directly.
There is an intermediary step here is, um, our formula for rounding the position
and also a data blending involved, but we can discuss this, uh, afterwards, if you
have, uh, the question that if you want to see more in depth, how this is done.
Okay.
Uh, then the traffic channels grouping, let's start with, uh, the traffic channel
grouping in Google Analytics so if you worked with the traffic channel
grouping there, you probably know that, um, it's really strict and if
you, uh, it's easy to mess up with it.
And if you make any changes, it will affect, um, how
you grouped all the traffic
and, um, it, it's also not looking at historical data.
And how it has, um, a lot of disadvantages, in my opinion, Data
Studio is much better for this channel grouping because it's instant it
rearranges reshuffles the traffic as you want and puts it in, um,
in the, um, the correct channel.
If you made any mistakes and then you will have the
accurate, accurate data instantly, unlike a Google Analytics, where if you make any
mistake, um, you, you cannot know for sure if the data you're looking at is correct.
Okay.
So in this case for this client, the, it's not a super complex
formula, but it's just an example.
So instead of paid search they can look at paid search for branded and
non-branded instead of email, they can look at email newsletter and email with
offers instead of social, they can look at a social paid and social organic, and
the possibilities are endless and yes, you will reach a super complex formula.
It can get much longer than this um, but like I said, you will be sure that
the client has the correct data, uh, the data that they can rely on and
the data they can use to grow their business, especially since you want to
know maybe where to invest more money and where to invest less effort and money.
My final enhancement recommendation is to Use Monthly Forecasts.
You can build simple predictions to see how your budget will look like
at the end of the month and, um, what is the remaining budget per day.
Also, this is a good chance for you to think about, is it a good thing
that I'll be 23% under the budget?
Is it a green zone or is it maybe the yellow zone or even red one?
There are a lot of clients that want all their budget spent so that they
can have a high return on investment.
How will you do this?
So in this case, we have, uh, the total monthly budget in a separate
data source uh, Google Spreadsheet, data source, and we're blending it
with, let's say a Facebook Ads data source the budget remaining this month
is a simple calculation between the monthly budget and the amount spent,
but don't forget, it's a data blend.
For the budget remaining per day, the calculations are a
bit more complex, so to say.
Don't forget, uh, again, that you can click on all of these and you can copy
the formulas and adapt them to your needs.
So feel free to do that.
Okay.
So step one is extracting the month from your date and then
defining how many, calculating how many days there are in a month.
With the Case When Formula and then we want to calculate how
many days passed this month and how many days are remaining this month.
So after we have all these, it's a simple, we have all the fields
that we need to calculate, um, the budget remaining per day.
And then for the end of month forecast, we will need these steps.
So we have the amount spent and we will multiply them by the day it's
passed this month, which means that we will get an average per day and
then we will multiply it by the days in the month, which means that we will
have a prediction for the total month based on what happened until now.
It doesn't mean that there won't be fluctuations, but remember that, um,
we will get an update everyday and then we divide it by the monthly budget.
Because it's a percentage, we will also need a minus one there
and this is a, what we get.
We get the, at the end of the month, we will be minus 23 under the budget
for the under, over the budget here,
this is a table like, uh, we used, uh, in the first enhancement and for this
one, I recommend that you're using it at the chart level only because you
will get this error message, but it will work so why not take advantage of it.
Also, don't forget to, to, uh, conditional format these values.
Not necessarily, but it's better.
It's easier for your clients to quickly understand what's going on and
define them with the colors that you think are right for your situation.
And this is it for the monthly forecasts.
Thank you.
Thank you, Andreea for these amazing tips, there was not all shared other interesting
visualization tips to enhance your data studio dashboards in the live training.
So if you want to check that out, head over to measure school.com/measure
masters, and maybe I'll see you in the next live training right there.
Now my name is Julian, till next time.