Rationales
for
the
Program
1.
Revenue
and
Yield
Management
Hotel
profitability
was
acutely
sensitive
to
revenue.
A
trend
in
the
industry
was
to
appoint
a
"revenue
manager"
to
each
p
rop
1'ty
to
el;see
the
day-to-day
decisions
that
affected
hotel
revenue.
Yield
manag
em
.e.nt
m
d
Is
wer
·
probabilistic
a l
gorit
hm
s
that
helped
this
manager
set
reservation
s policy.
He
or
s
h.
e
used
past
hi
tory
and
other
statistical
data
to
make
continuou
sly
updated
recomm
'
ndation
s
regarding
hotel
b
king
palterns
an
.d
what
price
to
off
er
a
particular
guest.
Simulation
studie.s
had
sll
wn
that
when
book.ing
was
guided
by
a
good
yield
management
model,
a
comp
any's
rev
'nue
in crease
d
by
20
percent
over
a
simple
"first
come,
first
served,
fixed
price"
p
licy.
In
the
hotel
industry,
ffectiv
Iy
managing
yield
m
ean
t
utilizing
a
model
to
predict
that
a
room
was
highly
l
il<
Iy
tome
avaiJ
.
ab
l
due
to
cancella
tion
or
no-
show,
as
well
a
driving
bu
s iness
to
high
r-paying
or
longer
-s
taying
gu
sts.
Variable
pricing
meant
that
th ·'
rc
te
harged
for
c.
I'
om
depended
not
only
on
its
size
and
fit-
tings
but
also
on
the
day
of
booking,
the
day
of
occupation,
the
length
of
stay,
and
customer
characteristics.
Of
these
factors,
customer
characteristics
were
the
most
problematic.
Customer
characteristics
were
needed
by
the
model
to
estimate
"walking
cost,"
the
cost
of
turning
a
customer
away.
That
ost
in
turn
dep
nd
d
on
the
customer's
future
lifetime
value
to
the
chain,
a function
of
th
if
willingness
to
pay,
and
past
loyalty
to
the
chain.
These
were
considered
"soft"
vari
a
b.le
s,
notoriously
difficult
to
estimate.
The
better
the
historical
information
on
a customer,
however,
the
better
the
estimate.
As
Adam
Burke,
HHonors'
senior
directo
r of
marketing
for
North
Am
rica,
put
it,
"Who
gets
the
room-the
person
payin
g
$20
mar
·
that
you
may
nev
r
see
again,
or
the
guy
spending
thousands
of
dollars
in
the
sy
tem?
If
we
]
ave
th
right
da
ta, the
model
can
be
smart
enough
to
know
the
cliff
·enc
e ."
Sam
in
the
hotel
industry