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1
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.
B
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T
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1467
[
4
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T
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i
n
g
,
f
u
n
ctio
n
al
en
g
i
n
ee
r
in
g
an
d
v
is
u
aliza
tio
n
o
f
d
ata
to
s
u
p
p
o
r
t
s
tr
ateg
i
c
m
ar
k
et
in
g
d
ec
is
io
n
s
.
T
h
e
m
eth
o
d
o
lo
g
y
in
c
lu
d
es
clea
n
i
n
g
an
d
tr
an
s
f
o
r
m
atio
n
[
8
]
o
f
r
a
w
d
ata,
d
er
iv
in
g
m
ea
n
in
g
f
u
l
attr
ib
u
te
s
an
d
u
s
i
n
g
i
n
ter
ac
tiv
e
v
i
s
u
al
d
a
s
h
b
o
ar
d
s
f
o
r
co
m
m
u
n
ica
tio
n
o
f
k
e
y
f
i
n
d
in
g
s
.
T
h
e
ai
m
is
to
d
e
m
o
n
s
tr
ate
h
o
w
v
i
s
u
aliz
atio
n
[
9
]
tech
n
iq
u
e
s
ca
n
e
x
tr
ac
t
s
p
ec
ial
k
n
o
w
led
g
e
f
r
o
m
m
ar
k
e
tin
g
d
ata,
esp
ec
iall
y
w
h
en
id
en
ti
f
y
i
n
g
c
u
s
to
m
er
s
s
e
g
m
e
n
t
s
w
i
th
h
ig
h
v
alu
e
an
d
o
p
ti
m
izi
n
g
ca
m
p
a
ig
n
s
tr
ateg
ie
s
[
1
0
]
.
T
h
r
o
u
g
h
a
ca
s
e
s
t
u
d
y
u
s
i
n
g
a
d
ata
f
ile
f
o
r
a
r
ea
l
-
w
o
r
ld
m
ar
k
eti
n
g
ca
m
p
ai
g
n
,
w
e
will
in
v
esti
g
ate
h
o
w
v
is
u
aliza
t
io
n
to
o
ls
s
u
c
h
as
c
o
lu
m
n
c
h
ar
ts
,
t
h
er
m
al
m
ap
s
an
d
KP
I
ca
n
d
etec
t
p
atter
n
s
in
b
u
y
i
n
g
b
e
h
av
io
r
,
cu
s
to
m
er
d
e
m
o
g
r
ap
h
y
[
1
1
]
an
d
ca
m
p
aig
n
s
e
n
s
iti
v
it
y
.
T
h
e
f
i
n
d
i
n
g
s
n
o
t
o
n
l
y
v
er
if
y
th
e
p
o
ten
tial
o
f
v
is
u
aliza
t
io
n
in
m
ar
k
etin
g
a
n
al
y
s
i
s
,
b
u
t
also
p
r
o
v
id
e
s
p
ec
if
ic
r
ec
o
m
m
e
n
d
atio
n
s
f
o
r
s
tr
en
g
th
e
n
i
n
g
th
e
f
u
tu
r
e
d
esig
n
o
f
t
h
e
ca
m
p
ai
g
n
.
T
h
e
r
em
ai
n
i
n
g
p
ar
t
is
ar
r
a
n
g
ed
as
f
o
llo
w
s
:
Sectio
n
2
d
is
cu
s
s
es
t
h
e
liter
at
u
r
e
r
ev
ie
w
,
s
ec
tio
n
3
o
u
tlin
e
s
t
h
e
m
eth
o
d
o
lo
g
y
,
s
ec
tio
n
4
r
ep
r
esen
ts
e
x
p
er
i
m
e
n
ta
l
r
esu
lt
s
an
d
a
n
al
y
s
i
s
an
d
s
ec
t
io
n
5
clo
s
es
f
u
t
u
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
EW
Sev
er
al
s
t
u
d
ies
h
a
v
e
ex
p
lo
r
ed
ef
f
ec
ti
v
e
s
tr
ateg
ie
s
f
o
r
an
a
l
y
zi
n
g
m
ar
k
eti
n
g
ca
m
p
aig
n
d
ata
u
s
in
g
ad
v
an
ce
d
d
ata
tec
h
n
iq
u
es,
with
a
g
r
o
w
in
g
e
m
p
h
asi
s
o
n
d
ata
v
is
u
aliza
t
io
n
a
s
a
cr
itic
al
co
m
p
o
n
e
n
t
f
o
r
ex
tr
ac
ti
n
g
a
n
d
co
m
m
u
n
icati
n
g
in
s
ig
h
t
s
.
A
co
m
p
r
e
h
en
s
i
v
e
s
t
u
d
y
al
s
o
ev
a
lu
ated
t
h
e
e
f
f
ec
ti
v
en
e
s
s
o
f
m
ar
k
et
in
g
ca
m
p
aig
n
s
u
s
i
n
g
b
o
th
tr
ad
itio
n
al
a
n
d
o
n
li
n
e
d
ata
a
n
al
y
s
i
s
.
T
h
e
r
esear
ch
h
i
g
h
li
g
h
ted
ch
al
len
g
e
s
b
u
s
i
n
es
s
es
f
ac
e
w
h
e
n
ev
alu
a
tin
g
tr
ad
itio
n
al
m
ar
k
e
tin
g
d
u
e
to
lim
ited
m
etr
ics,
esp
ec
iall
y
co
m
p
ar
ed
to
d
ig
ital
m
ar
k
eti
n
g
.
Vis
u
a
lizatio
n
tec
h
n
iq
u
es
s
u
c
h
as
co
m
p
ar
ati
v
e
b
ar
ch
ar
ts
an
d
p
er
f
o
r
m
an
ce
d
as
h
b
o
ar
d
s
w
e
r
e
u
s
ed
to
r
ep
r
esen
t
ca
m
p
aig
n
p
er
f
o
r
m
a
n
ce
ac
r
o
s
s
ch
a
n
n
e
ls
,
h
elp
in
g
s
tak
e
h
o
l
d
er
s
u
n
d
er
s
ta
n
d
th
e
i
m
p
ac
t
o
f
k
e
y
f
ac
to
r
s
.
T
h
e
in
te
g
r
atio
n
o
f
tr
ad
itio
n
al
a
n
al
y
s
i
s
w
it
h
m
o
d
er
n
to
o
ls
w
as
s
h
o
w
n
to
e
n
h
an
ce
d
ec
is
io
n
-
m
ak
in
g
a
n
d
i
m
p
r
o
v
e
ca
m
p
aig
n
ev
a
lu
at
io
n
b
y
v
is
u
al
izin
g
tr
en
d
s
a
n
d
o
u
tco
m
e
s
ac
r
o
s
s
m
u
ltip
le
d
ata
s
o
u
r
ce
s
[
1
2
]
.
An
o
th
er
s
t
u
d
y
e
m
p
lo
y
ed
t
h
e
th
eo
r
etica
l
co
n
tex
t
-
co
n
ten
t
-
m
eth
o
d
o
lo
g
y
(
T
C
C
M)
f
r
a
m
e
w
o
r
k
to
an
al
y
ze
6
4
r
esear
ch
p
ap
er
s
,
i
d
en
tify
i
n
g
d
o
m
i
n
an
t
t
h
eo
r
ies,
k
e
y
v
ar
iab
les,
an
d
m
et
h
o
d
o
lo
g
ies
i
n
m
ar
k
eti
n
g
an
al
y
tics
.
V
is
u
aliza
tio
n
p
la
y
e
d
a
p
iv
o
tal
r
o
le
in
m
ap
p
i
n
g
t
h
e
d
is
tr
ib
u
tio
n
o
f
m
et
h
o
d
o
lo
g
ies
a
n
d
t
h
eo
r
etica
l
f
r
a
m
e
w
o
r
k
s
o
v
er
ti
m
e
u
s
i
n
g
t
i
m
eli
n
e
g
r
ap
h
s
an
d
b
u
b
b
le
p
lo
ts
.
T
h
ese
v
is
u
al
to
o
ls
e
f
f
ec
ti
v
el
y
ill
u
s
tr
ated
th
e
d
iv
er
g
e
n
ce
b
et
w
ee
n
tr
ad
itio
n
a
l
an
d
d
ata
-
d
r
iv
en
m
ar
k
et
in
g
a
p
p
r
o
ac
h
es
an
d
h
elp
ed
in
id
en
ti
f
y
in
g
r
esear
ch
g
ap
s
an
d
e
m
er
g
in
g
tr
en
d
s
[
1
3
]
.
T
h
e
d
ev
elo
p
m
en
t
o
f
d
ata
-
d
r
iv
en
m
ar
k
et
in
g
w
a
s
ex
p
lo
r
ed
b
y
R
o
s
ar
io
an
d
Dias
[
1
4
]
,
w
h
o
p
r
esen
ted
f
r
a
m
e
w
o
r
k
s
to
g
u
id
e
m
a
n
a
g
er
s
an
d
m
ar
k
e
ter
s
in
ap
p
l
y
i
n
g
d
ata
-
b
ased
s
tr
ateg
ies.
T
h
eir
s
tu
d
y
u
s
ed
f
lo
w
d
iag
r
a
m
s
a
n
d
co
n
ce
p
t
m
ap
s
to
v
is
u
alize
d
ec
is
io
n
-
m
a
k
i
n
g
p
ath
w
a
y
s
a
n
d
th
e
i
n
ter
ac
t
io
n
b
et
w
ee
n
d
ata
co
llectio
n
,
an
al
y
s
i
s
,
an
d
s
tr
ateg
y
i
m
p
l
e
m
e
n
tatio
n
.
T
h
es
e
v
is
u
al
aid
s
clar
if
ied
co
m
p
lex
p
r
o
ce
s
s
es
an
d
en
h
a
n
ce
d
co
m
p
r
eh
e
n
s
io
n
o
f
t
h
e
d
ata
-
d
r
iv
en
m
ar
k
e
tin
g
c
y
c
le
[
1
4
]
.
An
o
th
er
s
tu
d
y
ex
a
m
i
n
ed
h
o
w
b
i
g
d
ata
an
al
y
tic
s
h
as
tr
a
n
s
f
o
r
m
ed
m
ar
k
eti
n
g
s
tr
ate
g
ie
s
th
r
o
u
g
h
i
m
p
r
o
v
ed
d
ec
is
io
n
-
m
a
k
i
n
g
,
cu
s
to
m
er
s
eg
m
e
n
tatio
n
,
an
d
ef
f
icie
n
c
y
.
T
h
e
r
e
s
e
a
r
ch
e
r
s
em
p
l
o
y
e
d
v
a
r
i
o
u
s
v
is
u
a
l
t
e
c
h
n
i
q
u
es
s
u
ch
as
h
e
at
m
a
p
s
,
c
l
u
s
t
e
r
d
i
ag
r
am
s
,
a
n
d
d
e
c
is
io
n
t
r
e
es
t
o
i
l
lu
s
t
r
a
t
e
c
o
n
s
u
m
er
b
e
h
a
v
i
o
r
p
a
t
t
e
r
n
s
d
e
r
i
v
e
d
f
r
o
m
t
r
an
s
a
c
t
i
o
n
l
o
g
s
,
s
o
c
ia
l
m
e
d
i
a
i
n
t
e
r
a
c
ti
o
n
s
,
a
n
d
b
r
o
w
s
in
g
b
eh
av
i
o
r
.
T
h
e
s
e
v
is
u
a
li
z
a
ti
o
n
s
m
a
d
e
it
e
a
s
i
e
r
t
o
in
t
e
r
p
r
e
t
la
r
g
e
a
n
d
c
o
m
p
l
ex
d
a
t
as
e
ts
,
u
l
t
im
at
e
ly
s
u
p
p
o
r
t
i
n
g
d
a
t
a
-
d
r
iv
en
m
a
r
k
et
in
g
im
p
r
o
v
em
en
ts
[
1
5
]
.
M
a
r
k
et
in
g
m
ix
an
a
ly
s
i
s
th
r
o
u
g
h
th
e
l
en
s
o
f
b
u
s
in
e
s
s
in
t
e
ll
ig
en
c
e
w
as
d
is
cu
s
s
e
d
in
a
s
tu
d
y
b
y
F
an
et
a
l
.
[
1
6
]
,
w
h
ic
h
f
o
cu
s
ed
o
n
h
o
w
b
i
g
d
ata
ca
n
o
p
tim
ize
th
e
4
P
s
:
p
r
o
d
u
ct,
p
r
ice,
p
lace
,
an
d
p
r
o
m
o
tio
n
.
T
h
ey
u
tili
ze
d
m
u
lti
-
d
i
m
e
n
s
io
n
al
c
h
ar
ts
an
d
d
ash
b
o
ar
d
s
to
v
i
s
u
a
lize
t
h
e
i
n
ter
p
la
y
a
m
o
n
g
m
ar
k
eti
n
g
m
i
x
ele
m
e
n
ts
.
T
h
ese
to
o
ls
en
ab
led
b
etter
s
tr
ateg
ic
a
lig
n
m
e
n
t b
y
p
r
esen
t
in
g
p
er
f
o
r
m
an
ce
m
etr
ic
s
a
n
d
o
p
ti
m
izati
o
n
r
esu
l
ts
i
n
a
clea
r
,
in
ter
ac
ti
v
e
f
o
r
m
a
t [
1
6
]
.
A
s
tu
d
y
e
x
p
lo
r
ed
co
n
s
u
m
er
r
ep
u
r
ch
ase
b
eh
a
v
io
r
p
r
ed
ictio
n
u
s
i
n
g
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
ST
M)
n
eu
r
al
n
et
w
o
r
k
s
.
Vis
u
aliza
tio
n
tec
h
n
iq
u
es
s
u
c
h
as
lin
e
g
r
ap
h
s
o
f
p
r
ed
ictio
n
ac
cu
r
a
c
y
o
v
er
ti
m
e
a
n
d
h
ea
t
m
ap
s
o
f
co
n
s
u
m
er
en
g
ag
e
m
e
n
t
m
et
r
ics
w
er
e
e
m
p
lo
y
ed
to
d
em
o
n
s
tr
ate
th
e
m
o
d
el
’
s
e
f
f
ec
t
iv
e
n
es
s
.
T
h
ese
v
is
u
al
s
h
elp
ed
in
co
m
m
u
n
icati
n
g
th
e
p
o
w
er
o
f
d
ee
p
lear
n
in
g
i
n
ca
p
tu
r
in
g
s
eq
u
e
n
tial
p
u
r
c
h
ase
p
atter
n
s
,
u
lti
m
atel
y
s
u
p
p
o
r
tin
g
s
tr
ateg
ies
f
o
r
cu
s
to
m
er
r
eten
tio
n
[
1
7
]
.
T
h
ese
s
t
u
d
ies
h
i
g
h
l
ig
h
t
t
h
e
e
v
o
lv
in
g
la
n
d
s
ca
p
e
o
f
m
ar
k
eti
n
g
a
n
al
y
tics
,
e
m
p
h
a
s
izi
n
g
t
h
e
i
n
teg
r
at
io
n
o
f
b
ig
d
ata
tech
n
o
lo
g
ie
s
,
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
s
,
s
o
cial
m
ed
ia
i
n
s
i
g
h
ts
,
a
n
d
b
alan
ce
d
m
ar
k
et
in
g
s
tr
ateg
ie
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
4
6
6
-
1
475
1468
to
im
p
r
o
v
e
ca
m
p
a
ig
n
s
u
cc
es
s
r
ates.
T
h
is
ca
s
e
s
tu
d
y
d
ea
l
s
w
i
th
a
g
ap
in
a
v
ailab
le
f
r
a
m
es
o
f
m
ar
k
et
in
g
an
al
y
s
is
b
ased
o
n
v
i
s
u
a
lizati
o
n
th
at
s
u
p
p
o
r
ts
d
ec
is
io
n
-
m
ak
in
g
w
it
h
o
u
t
co
m
p
l
icati
n
g
m
o
d
eli
n
g
,
th
er
eb
y
in
cr
ea
s
i
n
g
t
h
e
to
o
ls
f
o
r
b
u
s
in
e
s
s
n
e
w
s
f
o
r
ex
p
er
ts
.
3.
M
E
T
H
O
D
A
s
tr
u
ct
u
r
ed
ap
p
r
o
ac
h
w
a
s
ad
o
p
ted
to
an
al
y
ze
t
h
e
m
ar
k
eti
n
g
ca
m
p
ai
g
n
d
ata
[
1
8
]
.
T
h
is
m
eth
o
d
o
lo
g
y
lev
er
ag
e
s
m
o
d
er
n
d
ata
s
cie
n
ce
to
o
ls
an
d
tech
n
iq
u
es
to
p
r
o
ce
s
s
,
ex
p
lo
r
e,
an
d
v
is
u
alize
t
h
e
d
ataset.
P
y
t
h
o
n
w
a
s
u
s
ed
as
th
e
p
r
i
m
ar
y
p
r
o
g
r
a
m
m
in
g
la
n
g
u
a
g
e
d
u
e
to
its
r
o
b
u
s
t
ec
o
s
y
s
te
m
o
f
d
ata
an
al
y
tics
lib
r
ar
ies.
Sp
ec
if
icall
y
,
th
e
P
an
d
as
lib
r
ar
y
f
ac
ilit
ated
d
ata
m
an
ip
u
latio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
,
wh
ile
Seab
o
r
n
an
d
Ma
tp
lo
tlib
w
er
e
e
m
p
lo
y
ed
f
o
r
cr
ea
tin
g
s
tati
s
tical
v
i
s
u
aliza
ti
o
n
s
.
T
h
ese
to
o
ls
o
f
f
er
f
lex
ib
il
i
t
y
,
ea
s
e
o
f
u
s
e,
an
d
s
ca
lab
ilit
y
,
m
a
k
in
g
t
h
e
m
id
ea
l f
o
r
ex
p
lo
r
ato
r
y
d
ata
an
al
y
s
is
(
E
DA
)
a
n
d
b
u
s
i
n
ess
i
n
tel
lig
e
n
c
e
task
s
.
T
h
e
an
al
y
s
i
s
p
r
o
ce
s
s
co
n
s
is
t
s
o
f
s
ev
er
al
es
s
en
tial
p
h
ase
s
:
d
ata
clea
n
in
g
,
f
ea
tu
r
e
en
g
in
ee
r
i
n
g
,
ex
p
lo
r
ato
r
y
d
ata
a
n
al
y
s
i
s
,
an
d
d
ash
b
o
ar
d
-
b
ased
v
is
u
aliza
t
io
n
f
o
r
k
e
y
p
er
f
o
r
m
an
ce
in
d
icat
o
r
(
KP
I
)
ex
tr
ac
tio
n
.
E
ac
h
p
h
ase
w
as
d
esi
g
n
ed
to
en
s
u
r
e
th
at
i
n
s
i
g
h
ts
d
r
a
w
n
f
r
o
m
t
h
e
d
ata
ar
e
ac
cu
r
ate,
ac
tio
n
ab
le,
an
d
ea
s
y
to
in
ter
p
r
et.
Fig
u
r
e
1
illu
s
tr
ates t
h
e
co
m
p
lete
d
ata
an
al
y
s
i
s
p
r
o
ce
s
s
t
h
r
o
u
g
h
f
lo
w
ch
ar
t.
Fig
u
r
e
1
.
Flo
w
c
h
ar
t o
f
d
ata
p
r
o
ce
s
s
in
g
a
n
d
v
is
u
aliza
t
io
n
p
ip
elin
e
3
.
1
.
Da
t
a
s
et
des
cr
iptio
n
T
h
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
w
a
s
o
b
tain
ed
f
r
o
m
a
p
u
b
licl
y
av
ailab
le
m
ar
k
e
tin
g
ca
m
p
ai
g
n
d
ataset
o
n
Kag
g
le
[
1
9
]
.
I
t
co
n
tain
s
a
n
o
n
y
m
i
ze
d
cu
s
to
m
er
d
ata
f
r
o
m
m
u
ltip
le
ca
m
p
a
ig
n
s
,
i
n
cl
u
d
in
g
d
e
m
o
g
r
ap
h
ic
d
etai
ls
,
p
u
r
ch
asi
n
g
b
eh
av
io
r
,
an
d
ca
m
p
aig
n
r
esp
o
n
s
es.
T
h
e
m
ai
n
attr
ib
u
tes ar
e
d
escr
ib
ed
:
−
I
D:
u
n
iq
u
e
id
en
ti
f
ier
f
o
r
ea
ch
cu
s
to
m
er
.
−
Yea
r
b
ir
th
:
y
ea
r
in
w
h
ic
h
th
e
c
u
s
to
m
er
w
as b
o
r
n
(
u
s
ed
to
ca
l
cu
late
ag
e)
.
−
E
d
u
ca
tio
n
:
ca
te
g
o
r
ical
v
ar
iab
l
e
d
escr
ib
in
g
t
h
e
ed
u
ca
tio
n
le
v
el
(
e.
g
.
,
b
asic,
g
r
ad
u
atio
n
,
m
as
ter
’
s
,
P
h
D)
.
−
Ma
r
ital stat
u
s
: r
elatio
n
s
h
ip
s
ta
tu
s
(
e.
g
.
,
s
i
n
g
le,
m
ar
r
ied
,
to
g
et
h
er
,
d
iv
o
r
ce
d
)
.
−
I
n
co
m
e:
cu
s
to
m
er
’
s
an
n
u
al
i
n
co
m
e
in
d
o
llar
s
(
clea
n
ed
b
y
r
em
o
v
in
g
cu
r
r
en
c
y
s
y
m
b
o
ls
f
o
r
n
u
m
er
ical
an
al
y
s
is
)
.
−
Kid
h
o
m
e:
n
u
m
b
er
o
f
ch
ild
r
en
in
th
e
h
o
u
s
eh
o
ld
.
−
T
ee
n
h
o
m
e:
n
u
m
b
er
o
f
teen
a
g
e
r
s
in
t
h
e
h
o
u
s
eh
o
ld
.
−
R
ec
en
c
y
:
n
u
m
b
er
o
f
d
a
y
s
s
in
c
e
th
e
cu
s
to
m
er
’
s
la
s
t p
u
r
ch
a
s
e.
−
Mn
tW
in
e
s
,
Mn
t
Fru
i
ts
,
Mn
t
Me
atP
r
o
d
u
cts,
Mn
tFi
s
h
P
r
o
d
u
cts,
Mn
t
S
w
ee
tP
r
o
d
u
cts,
Mn
t
Go
ld
P
r
o
d
u
cts:
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
B
u
s
in
ess
in
tellig
en
ce
th
r
o
u
g
h
d
a
ta
visu
a
liz
a
tio
n
:
a
ca
s
e
s
tu
d
y
u
s
in
g
ma
r
ke
tin
g
ca
mp
a
ig
n
…
(
A
d
iti B
a
n
s
a
l
)
1469
ex
p
en
d
it
u
r
e
o
n
v
ar
io
u
s
p
r
o
d
u
ct
ca
teg
o
r
ies.
−
Nu
m
Dea
ls
P
u
r
c
h
ase
s
: n
u
m
b
er
o
f
p
u
r
ch
ase
s
m
ad
e
w
it
h
d
is
co
u
n
t
s
.
−
Nu
m
W
eb
P
u
r
ch
a
s
es,
N
u
m
C
ata
lo
g
P
u
r
ch
ase
s
,
Nu
m
S
to
r
eP
u
r
ch
ases
: p
u
r
c
h
ases
b
y
r
e
s
p
ec
tiv
e
ch
an
n
el
s
.
−
n
u
m
w
eb
v
is
i
ts
m
o
n
t
h
:
n
u
m
b
er
o
f
w
eb
s
ite
v
i
s
it
s
in
t
h
e
p
ast
m
o
n
th
.
−
A
cc
ep
ted
C
m
p
1
–
5
: b
in
ar
y
in
d
i
ca
to
r
s
o
f
ca
m
p
aig
n
r
esp
o
n
s
e
s
.
−
R
esp
o
n
s
e
: o
v
er
all
ca
m
p
ai
g
n
r
esp
o
n
s
e
(
1
=
P
o
s
itiv
e,
0
=
Oth
er
w
i
s
e)
.
−
C
o
m
p
lai
n
: b
in
ar
y
f
la
g
i
n
d
icati
n
g
c
u
s
to
m
er
co
m
p
lai
n
ts
.
−
C
o
u
n
tr
y
: c
o
u
n
tr
y
o
f
r
esid
en
ce
(
e.
g
.
,
SP
,
C
A
,
US,
A
US,
GE
R
,
I
ND)
.
3
.
2
.
Da
t
a
clea
nin
g
Data
clea
n
in
g
w
as
p
er
f
o
r
m
ed
to
en
h
an
ce
ac
cu
r
ac
y
,
co
n
s
i
s
t
en
c
y
,
an
d
u
s
ab
il
it
y
[
2
0
]
.
T
h
e
f
o
llo
w
i
n
g
s
tep
s
w
er
e
i
m
p
le
m
e
n
ted
:
−
L
o
ad
in
g
d
ata:
th
e
d
ataset
w
as
lo
ad
ed
u
s
in
g
P
an
d
as
f
o
r
in
s
p
ec
tio
n
an
d
p
r
eli
m
i
n
ar
y
a
n
al
y
s
i
s
o
f
m
i
s
s
i
n
g
o
r
in
co
n
s
is
te
n
t
v
alu
e
s
.
−
I
n
co
m
e
co
lu
m
n
c
lean
i
n
g
:
th
e
in
co
m
e
f
ield
,
w
h
ic
h
in
c
lu
d
e
d
cu
r
r
en
c
y
s
y
m
b
o
l
s
an
d
tex
t
u
al
an
o
m
alies
,
w
a
s
clea
n
ed
an
d
co
n
v
er
ted
to
a
n
u
m
er
ic
f
o
r
m
at
f
o
r
p
r
ec
is
e
c
o
m
p
u
tatio
n
.
−
Ou
tlier
h
a
n
d
lin
g
:
b
o
x
p
lo
ts
w
e
r
e
u
s
ed
to
d
etec
t
o
u
tlier
s
in
in
co
m
e
a
n
d
ag
e.
Valu
e
s
ab
o
v
e
$
2
0
0
,
0
0
0
f
o
r
in
co
m
e
a
n
d
o
v
er
1
0
0
y
ea
r
s
f
o
r
ag
e
w
er
e
r
e
m
o
v
ed
.
−
Miss
i
n
g
v
a
lu
e
s
:
n
u
ll
e
n
tr
ies
i
n
I
n
co
m
e
w
er
e
r
ep
lace
d
w
it
h
t
h
e
m
ea
n
i
n
co
m
e
to
p
r
eser
v
e
d
at
aset
i
n
teg
r
it
y
an
d
p
r
ev
en
t
m
o
d
el
b
ias.
3
.
3
.
F
e
a
t
ure
eng
ineering
Ne
w
f
ea
t
u
r
es
w
er
e
e
n
g
i
n
ee
r
ed
to
im
p
r
o
v
e
a
n
al
y
s
i
s
an
d
m
o
d
e
l p
er
f
o
r
m
a
n
ce
:
−
Ag
e
ca
lc
u
latio
n
:
th
e
a
g
e
f
iel
d
w
as
ca
lc
u
lated
as
2
0
2
4
−
y
ea
r
b
ir
th
,
y
ie
ld
in
g
a
m
o
r
e
in
t
u
iti
v
e
ag
e
attr
ib
u
te.
−
C
ateg
o
r
ical
en
co
d
in
g
:
ca
te
g
o
r
ical
v
ar
iab
les
s
u
c
h
as
ed
u
ca
t
io
n
w
er
e
en
co
d
ed
o
r
d
in
all
y
(
e.
g
.
,
b
a
s
ic=
1
,
g
r
ad
u
atio
n
=2
,
m
aster
’
s
=3
,
P
h
D=
4
)
to
f
ac
ilit
ate
m
ea
n
i
n
g
f
u
l
n
u
m
er
ical
a
n
al
y
s
i
s
.
3
.
4
.
Da
t
a
a
na
ly
s
is
E
DA
w
a
s
co
n
d
u
cted
u
s
i
n
g
s
tat
is
tical
v
is
u
aliza
tio
n
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to
u
n
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v
er
h
id
d
en
p
atter
n
s
a
n
d
r
elatio
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s
h
ip
s
:
−
B
o
x
p
lo
ts
:
u
s
ed
to
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d
is
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ce
n
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alo
n
g
w
it
h
d
etec
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f
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tlier
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[
2
1
]
.
−
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t
m
ap
s
:
i
llu
s
tr
ated
co
r
r
elatio
n
s
b
et
w
ee
n
k
e
y
n
u
m
er
ical
v
a
r
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les
(
e.
g
.
,
p
r
o
d
u
ct
s
p
en
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d
in
co
m
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to
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n
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er
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ta
n
d
p
u
r
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asi
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g
b
eh
a
v
io
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s
[
2
2
]
.
−
His
to
g
r
a
m
s
:
p
r
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v
id
ed
f
r
eq
u
e
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c
y
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tr
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er
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ased
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co
m
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g
r
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p
s
an
d
ag
e
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g
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s
[
2
3
]
.
E
ac
h
v
i
s
u
aliza
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h
a
s
b
ee
n
s
elec
ted
to
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ess
a
s
p
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if
ic
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al
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q
u
e
s
tio
n
:
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ic
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cu
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to
m
er
s
’
s
eg
m
e
n
ts
s
p
en
d
t
h
e
m
o
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t?
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h
at
d
e
m
o
g
r
ap
h
y
d
o
m
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t
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p
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to
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m
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Ho
w
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p
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n
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w
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h
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elate
?
3
.
5
.
Da
s
hb
o
a
rd
v
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liza
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T
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p
p
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u
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ess
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ec
is
io
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-
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ak
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in
ter
ac
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d
as
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b
o
ar
d
w
a
s
b
u
i
lt
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s
i
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g
d
ata
v
i
s
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aliza
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to
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ls
to
p
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e
y
i
n
s
ig
h
t
s
in
an
ac
ce
s
s
ib
le
f
o
r
m
at:
−
B
ar
g
r
ap
h
s
: d
is
p
la
y
ed
cu
s
to
m
er
p
u
r
ch
ases
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e
g
m
e
n
ted
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ca
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le
v
el,
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n
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m
e,
an
d
a
g
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g
r
o
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p
s
.
−
P
ie
ch
ar
ts
:
r
ep
r
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d
em
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g
r
ap
h
ic
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m
ar
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ag
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−
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I
m
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en
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icato
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s
to
s
u
p
p
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r
t stra
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ic
p
lan
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n
g
[
2
4
]
.
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p
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ac
tic
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[
2
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d
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4.
RE
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I
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6
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esp
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ig
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Evaluation Warning : The document was created with Spire.PDF for Python.
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,
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6
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20
25
:
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T
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t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
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6930
T
E
L
KOM
NI
K
A
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elec
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m
m
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n
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o
m
p
u
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C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
6
,
Dec
em
b
er
20
25
:
1
4
6
6
-
1
475
1474
F
UNDIN
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NF
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DATA AV
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d
ataset
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s
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in
th
is
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y
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ttp
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Ka
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ter
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e.
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tifia
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w
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clu
d
ed
in
t
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d
ataset.
RE
F
E
R
E
NC
E
S
[
1
]
P
.
B
a
i
n
e
s,
C
.
F
i
l
l
,
a
n
d
S
.
R
o
se
n
g
r
e
n
,
Ma
r
k
e
t
i
n
g
.
O
x
f
o
r
d
U
n
i
v
e
r
si
t
y
P
r
e
ss,
2
0
1
7
.
[
2
]
P
.
F
i
f
i
e
l
d
,
M
a
rke
t
i
n
g
S
t
r
a
t
e
g
y
,
2
n
d
e
d
.
R
o
u
t
l
e
d
g
e
,
2
0
1
2
.
[
3
]
C.
-
K
.
H
u
a
n
g
,
T
.
-
Y
.
C
h
a
n
g
,
a
n
d
B
.
G
.
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a
r
a
y
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n
a
n
,
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i
n
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n
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h
a
n
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s
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o
rm
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t
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M
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t
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l
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1
6
,
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o
.
2
,
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p
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1
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o
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9
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-
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9
7
-
x.
[
4
]
D
.
A
.
R
e
i
d
a
n
d
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.
E
.
P
l
a
n
k
,
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u
si
n
e
ss
mark
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t
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n
g
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me
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g
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c
o
mp
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si
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f
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.
[
5
]
T
.
D
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su
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n
d
T
.
Jo
h
n
so
n
,
Ex
p
l
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t
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:
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0
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1
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0
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4
4
8
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5
4
.
[
6
]
N
.
H
e
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l
e
y
,
S
.
R
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f
f
i
n
,
a
n
d
B
.
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mm
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r
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h
e
a
p
p
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c
a
t
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f
mark
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g
p
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l
mark
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t
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g
c
a
mp
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n
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a
rke
t
i
n
g
I
n
t
e
l
l
i
g
e
n
c
e
&
Pl
a
n
n
i
n
g
,
v
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l
.
2
9
,
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o
.
7
,
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p
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6
9
7
–
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0
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2
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8
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2
6
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4
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8
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.
[
7
]
L
.
W
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st
b
r
o
o
k
,
“
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u
a
l
i
t
a
t
i
v
e
r
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se
a
r
c
h
me
t
h
o
d
s:
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r
e
v
i
e
w
o
f
maj
o
r
st
a
g
e
s,
d
a
t
a
a
n
a
l
y
si
s
t
e
c
h
n
i
q
u
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s,
a
n
d
q
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l
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t
y
c
o
n
t
r
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l
s,”
L
i
b
r
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ry
&
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n
f
o
rm
a
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o
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c
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o
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o
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:
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-
8
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8
8
(
9
4
)
9
0
0
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6
-
4.
[
8
]
S
.
B
e
c
k
e
r
,
L
.
G
r
u
n
sk
e
,
R
.
M
i
r
a
n
d
o
l
a
,
a
n
d
S
.
O
v
e
r
h
a
g
e
,
“
P
e
r
f
o
r
man
c
e
p
r
e
d
i
c
t
i
o
n
o
f
c
o
m
p
o
n
e
n
t
-
b
a
se
d
sy
st
e
ms:
A
su
r
v
e
y
f
r
o
m
a
n
e
n
g
i
n
e
e
r
i
n
g
p
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r
sp
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c
t
i
v
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,
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i
n
Arc
h
i
t
e
c
t
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n
g
S
y
st
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m
s
w
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t
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T
r
u
st
w
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o
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p
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n
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s
,
R
.
H
.
R
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u
ssn
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r
,
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A
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t
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f
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d
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a
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d
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.
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.
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z
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,
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r
g
:
S
p
r
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[
9
]
A
.
Z
u
b
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r
,
“
D
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t
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[
10]
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