Revenue Management under Customer Choice
Transcript of Revenue Management under Customer Choice
Revenue Management under Customer ChoiceBehaviour with Cancellations and OverbookingI
Dirk D. Sierag 1
1Center for Mathematics and Computer Science (CWI), Amsterdam, [email protected]
June 2014
IThis work is in collaboration with prof.dr. G.M Koole, prof.dr. R.D. van der Mei, dr. J.I. van der Rest, and
prof.dr. B. Zwart.
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
http://informsrmp2014.org/en/Accommodation-and-Transfer.html
One night: ($147 ≈ e105)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Two nights: (e103.50 ≈ $147)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Two nights: (e160 ≈ $220)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Our Research
Collaboration with 5 small independent hotels in theNetherlands
Research motivated by real hotel data
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Group Bookings
1 2+
010
2030
4050
6070
Group Bookings
Group size
% o
f res
erva
tions
Observation
Large part (41%) of all bookings are group bookings
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Networks/Multiple Night Stays
1 2 3+
020
4060
80
Network/Multi Night Stay
Length of stay (LOS)
% o
f res
erva
tions
Observation
Big part (29%) stays more than one night
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Cancellations
85+ 30−84 10−29 3−9 0−2
010
2030
4050
Cancellations per booking period
Days to arrival
% o
f can
cella
tions
per
seg
men
t
Observations
22% of all bookings are cancelled
Early booking =⇒ high cancellation probability
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Observations from the Data
Group bookings (41%)
Networks (multiple night stays) (29%)
Cancellations (22%)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Customer Choice Cancellation Model
Properties:
Customer choice behaviour
Cancellations
Overbooking
Related work:
Subramanian et alii (1999): Cancellations
Talluri and Van Ryzin (2004): Customer choice behaviour
Newman et alii (2010): Parameter estimation
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Other Application Areas
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Applying the Cancellation Model in Practice
Modelling cancellations and customer choice behaviour
Tractable and well-performing solution methods
Efficient parameter estimation method
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Example (Talluri & van Ryzin, 2004)
Hotel with
C = 20 rooms
n = 3 products with prices
r1 = 160 r2 = 100 r3 = 90
T days before arrival
λ = 1/4 probability that a customer arrives
xj number of reservations for product j (x = (x1, x2, x3))
γ(xj) = xj/100 probability that product j is cancelled
cj = rj costs if product j is cancelled
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Example (continued)
P(S , j) probability that customer buys product j ifS ⊂ {1, 2, 3} is offered
P(S , 0) probability that customer buys nothing
E.g. S = {1, 2} and
P(S , 1) = 0.1
P(S , 2) = 0.6
P(S , 3) = 0
P(S , 0) = 0.3
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
λS
Control
...
Product jReward rj
P(S , j)
...P(S, 0)
γj (x)Costs cj
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Objective
Which rooms in combination with price and conditions to offer?
Solution
Model as Markov decision process and solve with dynamicprogramming:
V (x , t) = maxS⊂N
{λ∑j∈S
P(S , j)(rj + V (x + ej , t − 1)
)+
n∑j=1
γj(x)(− cj(t) + V (x − ej , t − 1)
)+
(1− λ
∑j∈S
P(S , j)−n∑
j=1
γj(x)
)V (x , t − 1)
}.
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Properties
Reduced state space under equal and linear cancellationsassumption γj(x) = γxj
Heuristic performs well under this assumption
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
05
1015
20
0 1 2 3 4 5 6 7 8 9 10 11 12
Performance of Solution Methods under Different Cancellation Probabilities
γb
Rev
enue
loss
w.r.
t. ex
act s
olut
ion
(%)
Talluri and van Ryzin (TvR)Fast & Efficient (F&E)F&E with Inefficient Sets (F&E−I)F&E with Random Sets (F&E−R)Iterative & General (I&G)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Estimating Parameters
Maximum Likelihood Function:
L(λ, γ, β|x ,Z ,S , j) =∏t∈D
[λPtj(t)(β,Zt , St)
]aλ(t)×
n∏j=1
γj(xj)aj (t) ·
1− λ−n∑
j=1
γj(xj)
a(t)
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
New Parameter Estimation Algorithm
Based on Newman et alii (2010).
1 Estimate γ̂ (cancellations)
2 Estimate β̂ (customer choice behaviour)
3 Estimate α̂ and λ̂ using β̂ (market demand)
Upside: Fast; accurate; consistentDownside: Data collection difficult for independent hotels
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Example: customer choice behaviour estimate.
Family Double Twin Single Price Competition
β 9.43 0.36 -0.38 -10.43 -0.57 1.32
Observations:
Price elasticity: higher price =⇒ lower demand
Competition price higher =⇒ higher demand
Family room attractive, compensated by price.
Single room less attractive, compensated by lower price.
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Current Research: Applying the Cancallation Model
Pilot starting soon in several Dutch hotels
Hotels currently do not use RM system
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Collaborating hotels
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking
Conclusion
Cancellations have big impact on revenue
The heuristic approximates the optimal solution well
The new parameter estimation method performs well
Cancellation model is suitable for practitioners
Further Research
Application to Dutch hotels
Expand with group bookings and networks/multiple nightstays
Dirk D. Sierag Revenue Management under Customer Choice Behaviour with Cancellations and Overbooking