1. Add-on pricing: theory and evidence from the cruise industry
Marco Savioli
University of Salento
Lorenzo Zirulia
University of Bologna
SIE, 58ma Riunione Scientifica Annuale
Università della Calabria, Rende
19-21 ottobre 2017

2. Add-on pricing
In many industries, firms give consumers the opportunity to add (at a price) optional goods and services to a baseline product after the consumer has bought the baseline product
The market for add-on is typically less competitive than the market for baseline products
Often, prices for such add-ons are difficult (or costly) to observe before the baseline product is bought (add-ons as shrouded attributes)
What is observed are “low” prices for baseline products and “high” prices for add-ons (“add-on pricing strategy”)
Examples:
Hotels (minibar items)
Airlines (onboard meals)
Retailers (additional warranties, loss-leader strategy)
Printers producers (replacement cartridges)
Razors (blades)
Cruises (bars, casino, excursions, etc)

3. Literature and our contribution
Early contributions: Lal and Matutes (1994), Verboven (1999)
Chicago-school result of add-on pricing strategy irrelevance
More recent contributions: Ellison (2005), Gabaix and Laibson (2006), Schulman and Geng (2013), Zenger (2013), Wenzel (2014), Zegners and Kretchmer (2017), Lin (forthcoming)
Set-up with heterogeneous consumers and second-degree price discrimination (Ellison) or myopic consumers (Gabaix and Laibson) in which an add-on pricing strategy increases profits
Issues:
Business strategy
Competition policy (aftermarkets)
Boundedly rational consumers and consumer policy
Our contribution is theoretical and empirical
Theory: we consider that i) offering an add-on entails a fixed cost ii) firms may have a limited ability to capture the value from the add-on
Empirics: testing the theory on a sample of worldwide cruises

4. The model
Firms 0 and 1 compete. Firms offer a baseline product B and may offer an «add- on» A (e.g. casino, excursions)
Production costs (both for B and A) are 0
Offering A implies a fixed cost k
Baseline products B are horizontally differentiated à la Hotelling, and the two firms are located at the extremes of the segment
The add-ons offered by the two firms are identical, and consumers share the evaluation for the add-ons
Demand for A is 0/1, consumers can buy A only from the same firm they bought B
We assume that the maximum A price that firms can fix is α 𝑢 𝐴 , where 0≤𝛼≤1 is firm’s mkt pwr over A
«high» 𝛼 for casino and boutiques (onboard activities)
«low» 𝛼 for excursions (offboard activities)

5. The model (2) 𝑢 𝑖 0 = 𝑢 𝐵 − 𝑝 𝐵 0 −𝑡𝑥
Consumer i buys from 0, which offers B only
𝑢 𝑖 0 = 𝑢 𝐵 − 𝑝 𝐵 0 −𝑡𝑥
Consumer i buys from 0, which offers B+A
𝑢 𝑖 0 = 𝑢 𝐵 − 𝑝 𝐵 0 −𝑡𝑥+ 𝑢 𝐴 − 𝑝 𝐴 0
Consumer i buys from 1, which offers B only
𝑢 𝑖 1 = 𝑢 𝐵 − 𝑝 𝐵 1 −𝑡(1−𝑥)
Consumer i buys from 1, which offers A+B
𝑢 𝑖 1 = 𝑢 𝐵 − 𝑝 𝐵 1 −𝑡(1−𝑥)+ 𝑢 𝐴 − 𝑝 𝐴 1

6. The model (3) Timing t=0 t=1 t=2 Firms fix prices for B
Firms decide if offering A or not
Firms fix prices for B
Firms fix prices for A

7. The model (4)
Comments
The assumption that prices for A are chosen at t=2 is equivalent to assume that firms cannot commit to their A prices at t=1
In our set-up, commitment and bundling (selling A and B together) are equivalent. We consider bundling in an extension of the model
Consumers and firms are rational
In an extension we consider consumer biased beliefs
Limits to market power for A can be explained by the existence of a competitive fringe offering a low-quality alternative to the add-on
Our model assumes that consumers cannot take advantage of outside option utility if the specific firm from which they bought the baseline product does not offer the add- on
Tourists cannot value the services of a specialized tour operator in a destination, if this is not part of the cruise itinerary

8. Solution
t=2
In equilibrium, firms will fix the maximum price. Consumer will correctly predict prices at t=2, and use them to choose between firms at t=1
t=1
Firms fix prices for B in a standard Hotelling game, taking into account the total price paid by each consumer
t=0
Equilibrium profits for the four possible combinations firm 0/1- offering/not offering A are computed. The (Nash) equilibrium is determined

9. Results Proposition 1 If no firm offers A, 𝑝 0 𝐵 = 𝑝 1 𝐵 =𝑡
If only firm 0 offers A, 𝑝 0 𝐵 > 𝑝 1 𝐵 if α<2/3, higher otherwise
If both firms offer A, 𝑝 0 𝐵 = 𝑝 1 𝐵 =𝑡−α 𝑢 𝐴
Offering A has two effects:
i) consumers can get higher utility (differentiation effect) 𝑝 0 𝐵
ii) firms can appropriate surplus (revenue effect) 𝑝 0 𝐵
Proposition 2
For «low» k, both firms offer A
For «intermediate» k, a single firm offers A
For «high» k, no firm offers A

10. The cruise industry
The cruise industry is one of the fastest growing segment in the tourism market
Since the ‘80s, this industry has had an average annual passenger growth rate of 8.5% per annum
Despite the economic crisis, the number of passengers increased from 18.7 million to million between 2010 and 2014 (source: Cruise Lines International Association)
A cruise ship can be considered a “floating resort” as it includes all the facilities and activities of a tourist destination
This poses an interesting issue for pricing
Previous papers (e.g. Vogel, 2009 and 2011) have shown that cruise operators increasingly rely on extra-revenues (with respect to ticket price) in order to preserve profitability

11. Data
Observations: 2072 cruises worldwide, to be held during the period July-August 2013
Data were collected during April 2013 via the website cruise.com
Data refers to:
Price per night (=dependent variable, in log)
Cabin characteristics
Ship characteristics
Trip characteristics
We estimate a hedonic price regression model (Rosen, 1974) controlling for (15) markets and (26) cruise lines
Important since it corrects for possible omitted variables that are constant over markets and firms

12. Descriptive (1) Destination Frequency Caribbean/Bahamas 250
Caribbean/Bahamas
250
************************
Alaska
340
*********************************
Australia/New Zealand 12 *
Bermuda 68
*******
Europe-Northern
398
***************************************
Europe-Southern
391
**************************************
Europe-Rivers 23 **
Hawaii/Tahiti/S. Pacific 72
Mexico-Pacific Coast 30
***
Asia/Africa/M.E.
109
***********
Panama Canal & C. America 33
South America
US & Canada Eastern
102
**********
US & Canada Pacific 36
****
Other
196
*******************
Total
2072
Modelli empirici del mercato turistico

13. Descriptive (2) Modelli empirici del mercato turistico Cruise line
Average nights
Average price per night
Carnival Cruise
5.8
140
Costa Cruise
6.9
215
Disney Cruise Line
4.9
404
MSC Cruises
7.1
189
Norwegian Cruise Line
6.3
209
Princess Cruises
9.2
213
Royal Caribbean International
7.0
225
Azamara Club Cruises
8.6
468
Celebrity Cruises
8.5
247
Cunard Line
8.1
304
Holland America Line
11.6
187
Oceania Cruises
12.5
359
Crystal Cruises
7.3
635
Regent Seven Seas Cruises
518
Seabourn
184
SeaDream Yacht Club
720
Silversea
575
Star Clippers
8.0
462
Windstar Cruises
5.0
310
AmaWaterways
4.5
478
American Cruise Lines
6.1
437
American Safari Cruises
11.4
487
Avalon Waterways
12.3
268
Uniworld River Cruises
15.0
299
Viking River Cruises
11.0
399
Other
7.8
321
Modelli empirici del mercato turistico

14. Descriptive (3) On a ship WITHOUT casino: Cabin type Mean Median Max
Cabin type
Mean
Median
Max
Min
Inside cabin
234
235
496 93
Ocean view cabin
286
249
726
109
Balcony cabin
357
783
125
Suite
658
632
1249
172
Total
381
280
On a ship WITH casino:
160
135
749 33
197
171
899 50
256
221
1058 66
401
328
1491 83
254
205
Modelli empirici del mercato turistico

15. Hedonic price model estimations
Dependent Variable: Price per night (log of)
Complete model
Parsimonious model
Explanatory Variables:
Coefficient
Standard Error
Add-ons
Casino
-0.285***
(0.043)
-0.283***
(0.042)
Boutiques
-0.040**
(0.018)
-0.044**
Spa
0.136***
(0.041)
0.127***
Beauty salon
-0.037
(0.025)
-0.042*
(0.024)
Wi-Fi
-0.105***
(0.015)
-0.106***
Telephone
-0.000
(0.026)
Laundry
-0.009
Excursions per night (log of)
0.026***
(0.006)
0.028***
Modelli empirici del mercato turistico

16. Cabin characteristics
Dependent Variable: Price per night (log of)
Complete model
Parsimonious model
Explanatory Variables:
Coefficient
Standard Error
Controls
Cabin characteristics
Cabin type (Inside cabin reference category)
F(3,1993) = ***
F(3,2011) = ***
Ocean view cabin
0.196***
(0.018)
0.195***
Balcony cabin
0.444***
(0.019)
Suite
0.846***
(0.022)
0.848***
Cabin square footage (log of)
0.081***
(0.015)
0.079***
Private bath
0.111***
(0.030)
0.108***
(0.029)
Air conditioning
0.048**
0.049***
Refrigerator
0.028
(0.017)
0.033**
Individual safe
-0.006
(0.021) TV
-0.015
(0.038)
Music console
0.022
Hair dryer
-0.046*
(0.024)
-0.047**
Ship characteristics
Swimming pool
-0.059
(0.075)
Jogging track
-0.040**
-0.033**
Library
0.045**
0.051**
Fitness
0.035*
0.033*
Age (log of)
-0.085***
-0.082***
(0.014)
Capacity (log of)
-0.199***
(0.020)
-0.200***
Speed (log of)
-0.293***
(0.092)
(0.090)
Trip characteristics
Nights (log of)
-0.064***
-0.072***
Destination
F(14,1993) = 8.73***
F(14,2011) = 9.27***
Cruise line
F(25,1993) = 38.86***
F(25,2011) = 41.22***
Modelli empirici del mercato turistico

17. Conclusions
Conventional wisdom on add-on pricing is that offering add-ons should lower the price of baseline products
We show that this is not true in asymmetric configuration (not all firms offering the add-on) in which firms have a limited ability to capture its value for consumers
We find confirmation of this theoretical prediction in an empirical analysis of the cruise market
The relationship between ticket prices and the existence of add-ons depends on the characteristics of the additional activities which are offered
- effect on price: «high» 𝛼 (onboard) activities, i.e. casino and boutiques
+ effect on price: «low» 𝛼 (offboard) activities, i.e. excursions

18. Net onboard revenue/EBIT
Year
Carnival (%)
Royal Caribbean (%)
2001
101
118
2002 88
115
2003
105
144
2004 95
119
2005 87
113
2006
123
2007
146