Data
analysis
is
important
for
making
informed
decisions.
Analyzing
data
from
a
management
perspective
can
help
one
understand
where
to
put
money,
how
to
find
opportunities
for
growth,
how
to
forecast
income,
or
how
to
handle
unusual
circumstances
before
they
become
issues
(Calzon,
2023).
Three
possible
data
analysis
techniques
are:
cluster
analyis,
cohort
analysis,
and
regression
analysis.
Clustering
is
a
method
used
to
find
meaning
in
data.
In
e-commerce,
this
allows
marketers
to
separate
target
audiences
into
their
own
groups
and
optimize
their
marketing
campaigns.
Clustering
is
sensitive
to
the
choice
of
initial
centroid.
In
K-means
clustering,
different
initial
centroids
may produce
different
local
minima.
There's
no
right
way
to
choose
the
initial
centroid,
one
has
to
try
several
times
with
different
configurations
to
get
the
desired
result
(Mousse,
2016).
Cohort
analysis
compares
and
examines
a
specific
user
behavior
segment
using
historical
data
so
that
it
can
be
categorized
with
other
users
that
share
the
same
traits.
In
marketing,
cohort
analysis
can
be
used
to
understand
the
impact
of
the
campaign
on
specific
groups
of
customers.
Based
on
this
analysis,
marketers
can
adjust
their
campaigns
in
order
to
increase
retention
or
conversion
rates.
It's
worth
knowing
that
cohort
analysis
is
prone
to
bias.
Bias
can
come
from
sample
selection,
data
collection,
or
analysis
during
the
study's
design
phase
(Ramirez-Santana,
2018).
Finally,
regression
analyzes
past
data
to
determine
how
changes
in
one
or
more
independent
variables
(linear
regression)
or
multiple
variables
(multiple
regression)
affect
the
value
of
a
dependent
variable
(source).
This
type
of
analysis allows
us
to
build
a
model
that
correctly
describe
past
trends,
and
identify
future
trends.
Regression
is
prone
to
overfitting.
Overfitting
can
occur
when
there
are
too
many
independent
variables
in
the
model
compared
to
the
total
number
of
observations.
Word
count:
294.
References.
Calzon,
B.
(2023,
August
10).
What
is
data
analysis?
methods,
techniques,
types
&
how-to.
datapine.
https://www.datapine.com/blog/data-analysis-methods-and-
techniques/
Mousse,
A.
(2016).
Why
choosing
proper
initial
centroids
is
very
important
for
K-means?.
StackExchange.
https://stats.stackexchange.com/questions/214323/why-
choosing-proper-initial-centroids-is-very-important-for-k-means
Ramirez-Santana,
M.
(2018).
Limitations
and
biases
in
cohort
studies.
Cohort
Studies
in
Health
Sciences.
https://doi.org/10.5772/intechopen.74324
