Likert Scales in Tableau can be an extremely powerful tool for measuring and analyzing customer satisfaction, user sentiment, and many other phenomena.
Here’s the transcript of the video:
Hello, it’s Peter Keobel here from Data Sciencing Consultants.
In this video I’m going
to create Likert scales in Tableau using
restaurants satisfaction survey data the
categories were food cleanse less
waiting time reservation process
reception ad server and the respondents
ranked those categories one to four
highly des satisfied dissatisfied
satisfied and highly satisfied and here
is the average score as you can see
server reception was Gordon really low
and food and Clinton scored really high
sort of start a new worksheet here and
of course we’re going to bring the
categories and we’ll bring in the
answers into each Oh
all right we’ll change these marks here
from going back to the Gantt bar and
this entire view next I’ll bring in the
percent this is going to help us
fill up those bars you can see here
these are the different responses
compute using answer and you can see
here this is where it’s starting to form
our bars
numbering in deep percentage a laser
calculated fields and I very very
established percentage seen
oops she met your size there we go
dr. Bartis and to make sure this is
should be set yet such a competing with
answer you can see these are already
separated okay sting ourselves a little
judge answered the color right here
somebody shows up over you again and
change these colors just something else
for mister screening so we’ll try colors
this time go green satisfied this color
green and purple
just picking random colors are not
really so seeing them with anything
specifically this just helps me more so
hiya satisfied as dark green and then
dissatisfied the light green light
purple is satisfied and I read that dark
purple plus we can tell these two
results are related and these two
results and we don’t need this header
should warm up first
is one yes one
because we can kind of already tell
relation really ratio of satisfy to this
highly satisfied to satisfied satisfied
by they satisfied by the curse since we
got right up the header of the values
which can get rid of nice lines
[Music]
there we go
all right don’t think lines now we will
fix this tool tip here so we hover we
don’t need all that information so you
know we can’t percent is so change this
to do percent category above answer yes
let’s give one other category first and
then answer yeah there we go
snowy hover you easily see categories
reception answers highly satisfied
percent let’s change that percent
percent too much force instead of
decimals over here go to over here size
format number percentage good alright
all right now we can see the percent
today – Destin places and it’s cleanest
let’s sort this by category and we can
see which ones are
so this actual actually probably to do
the average so aggregate by average so
we incorporate the average into this
church so let’s go from that
alright so bringing the average school
so here Savage so right-click some
thoughts up here game pic degradation
the house very carefully the average
they’re real alright this picked out the
separate than Buddy ISS fight satisfied
it’s basic coffee yeah Kent percent I
have the colors but we actually don’t to
do that because as we saw over here we
actually just wanted circles so you can
just see what specific average so
takeout answer and take a percent then
each 50 sometimes these that should they
have a score and those gamblers you want
circles and yeah label there you go
let’s change that to two decimal places
let’s see what’s here alignment of spins
middle let’s make them bigger
there we go that’s better
no I changed the signs of it’s easy to
see all right no no you’d see these are
there was these are the average scores
we need to change the tooltip let’s see
if we reset what that does
no there we go figured out what yeah I
want it uh let’s change the wording Ava
game Fiji score to average score alright
now we want these two accesses to mix so
we’ll right-click and Alexis and so you
don’t need to fix this so this is
actually the right spot
the average score of this for
cleanliness is three point one seven
this is where it is but this is access
we need to fix ID access
so these surveys went from one to four
there we go
and so now this lights up with Kent
percent we have down here so these are
in the right place now combining with
the gap percent bars don’t need these
headers anymore because this is kind of
self-explanatory this is this is where I
will sort these same score and so there
we go we have tapas food and cleansers
there wait time reservation process
reception server and let’s change these
sizings bars a little smaller change the
size of these circles make me a little
bit bigger now let’s go back and change
actual these smaller
actually change neal cassady of faded
there we go that’s pronounced because
the main focus of the chart is the
averages and we’re just wanted in
relation to the bars don’t need to know
specifically right away and yeah
depending on how thick you wanna make
the secret circles a lot bigger already
can make the bars a lot thinner but well
you look here for now and next look at
the demographics so we can check any
different how it was by age sex than
type of customers so I’m trying type of
customer
these were casual frequent and one offs
so just someone first time going there
and can see here the circles again just
those were first of all just the typical
customer to bit more frequency according
to the restaurant I love do this one
manually because tableau or most
computer don’t understand difference
less we rank these which is this is just
a lot easier steps up frequent and then
casually less frequent in than one offs
and we can circles in support so we can
read them better there we go and this is
a dependent on you want to analyze dad
if you want to just look at food you
compare the
responses from your frequent casual and
one of customers or if we just like
customer on this side they’ll break it
down by by the potential customer first
so you can see the frequent customers
and see how they responded then casually
one-off Sophie wanted to focus prudence
in here restaurant on satisfy the
frequent customers so you could focus on
here in this situation they all seem to
ranking receptions server as the lowest
so if you focus on helping out here or
proving quality for your frequent
customers by improving the reception
server that will also then incidentally
help the responses from your casual and
one-off customers but again if you
wanted to focus here on which ones are
worse overall it’s the same situation
casual is a bit more ok with the server
quality but frequent and one-off
customers still below a little away
response or low satisfaction for the
servant and and also then you can reward
see huge reward food is talk to you work
the kitchen staff and cleansiness also
get the highest second highest marks
there so let’s do it over here break
things up and then again
if you want you can actually with these
bars little thicker for presentation
purposes this be easier to see and then
you can also check out here age I
already created bins it’s 220 years open
oh here’s my boy here’s my thing so
these are in groups of 20 age groups 20
40 and 60 so you can get focus on near
demographics up to 20 year 2000 was
twenty to forty forty to sixty seven
sixty plus and see who would be want to
mark it to and then also checked by is
sex of the customer so again look at
just food for male-female about the same
for food just a bit more disparity with
the pleasantness and very close to
satisfaction here with the reception
server thank you
sit around and look just female
customers and just milk customers and
again you can you know use that that you
probably want for marketing recruitments
and rewarding of your staff yes it’s
like art skill it’s quick easy you can
see the average and kinda specially here
you can get a get a good education of a
percent so eight percent of customers it
scans what customers he responded with
their highly satisfied with food and you
can tell easily averages if you need to
look quick visualization to figure out
where you need to make improvements as
such thank you for watching.
If you need help with your Tableau, I’m Peter Koebel the owner of Data Sciencing Consultants. You can reach me at 204-770-6437, or email me at peter.koebel@datasciencing.com, or fill out the form on our website https://datasciencing.com. You can also check out our YouTube channel for more awesome Excel tips!
For more awesome Tableau and data viz tips, like this one about likert scales in Tableau, check out the Datasciencing Consultants blog.