strategie di prezzo per club calcistici
DESCRIPTION
Strategie Di Prezzo Per Club Calcisticilezione del prof. Carlo Amenta al corso di Marketing del prof. Gandolfo Dominici Facoltà di Economia università di PalermoTRANSCRIPT
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
When you walk through a storm
Hold your head up high
And don't be afraid of the dark
At the end of the storm
Is a golden sky
And the sweet silver song of a lark
Walk on through the wind
Walk on through the rain
Tho' your dreams be tossed and blown
Walk on, walk on
With hope in your heart
And you'll never walk alone
You'll never walk alone
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
I Prezzi nel marketing sportivo
● Prodotti sportivi;
● Eventi (Biglietti);
● Atleti;
● Merchandising e attrezzatura sportiva;
● Diritti TV;
● Sponsorizzazioni;
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Ricerca sul campo
● Analisi del calcio professionistico italiano con l’utilizzo di strumenti e teorie di strategia d’impresa ed econometria����articoli su:
● Le determinanti dei ricavi da media
● Accordi per la ripartizione di diritti televisivi
● competitive balance (Neale, 1964);
● Gestione degli stadi e strategie di coopetition
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Alcune questioni teoriche
● Inelastic sport pricing (Fort, 2004);
● Variable ticket pricing (Rascher et al., 2007);
● Profit maximization o utility maximizationowner(Sloane, 1971)?
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
The role of attendance
• I tifosi possono essere considerati un fattore di produzione per una squadra di calcio professionistica (strategie di cocreazione del valore)?
● Esiste il 12mo uomo in campo?
● Quali sono le conseguenze sulla vendita dei biglietti?
● Le strategie relative ai ricavi da gestione dello stadio possono avere un impatto sullo sviluppo di una strategia di CSR per le società di calcio professionistico?
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
The role of attendance
● L’esistenza del fattore campo è stata documentata per vari sport (Schwartz and Barsky, 1977; Pollard, 1986; Neville and Holder, 1999; Smith, 2005; Wolfson et al., 2005; Boyko R.H. et al. 2007)
● Il pubblico ha un impatto sulle performance degliatleti o sul comportamento dell’arbitro?
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Supply side
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Demand side
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Cross Section
Supply side: Sporting production function
iiii
iii
awaymanagerchgoalsdiffh
capattCwinperch
εβββ
ββ
++++
+++=
5 4 3
21 log0
Demand side
ιεβββββ
ββ
++++++
+++=
76 5 4 3
21
stardumserieapopaddedvalriot
avprwinperchCatt
iiii
iii 0506loglog 0
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Cross section – Season 2006/2007
Summary statistics
managerch 44442222 ....4444777766661111999900005555 ....5555000055554444888866667777 0000 1111 goalsdiffh 44442222 7777....777711114444222288886666 11111111....55554444999900002222 ----11119999 33338888 cap 44442222 33331111777788882222....11112222 22221111111111117777....99994444 8888444411112222 88881111111199993333 att 44442222 222266662222999900007777....1111 222211115555999955551111....8888 33336666555566663333 999911117777444499996666 riot 44442222 6666....444477772222666611119999 3333....999933330000444477775555 2222....88881111 22228888....66662222 addedval 44442222 22223333666644441111....11112222 5555888800003333....555511115555 11112222777766660000 33334444111188884444 avpr0506 44442222 44445555....77778888999966668888 22225555....55550000666622229999 11117777 111122228888 star 44442222 2222....777733338888000099995555 4444....333344445555222222222222 0000 11117777 dumseriea 44442222 ....4444777766661111999900005555 ....5555000055554444888866667777 0000 1111 pop 44442222 1111111100009999111155551111 1111000077770000777755552222 111177772222666677772222 3333888855554444111122227777 away 44442222 22220000....88885555777711114444 8888....555566667777000011114444 4444 44449999 winperch 44442222 ....4444777733336666888844442222 ....1111777799997777000088885555 ....111155557777888899995555 ....888899994444777733337777 Variable Obs Mean Std. Dev. Min Max
. sum
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Cross section – Supply
_cons ....0000222255555555333311119999 ....2222777788886666999977777777 0000....00009999 0000....999922228888 ----....5555333399996666999933333333 ....5555999900007777555577771111 away ....0000000022221111000088885555 ....0000000011119999888899996666 1111....00006666 0000....222299996666 ----....0000000011119999222266666666 ....0000000066661111444433335555 managerch ----....0000111155558888777799997777 ....0000222244443333222222222222 ----0000....66665555 0000....555511118888 ----....0000666655552222000077775555 ....0000333333334444444488881111 goalsdiffh ....0000111133332222888855558888 ....0000000011114444777744445555 9999....00001111 0000....000000000000 ....0000111100002222999955553333 ....0000111166662222777766662222 cap ----8888....44447777eeee----00007777 8888....33337777eeee----00007777 ----1111....00001111 0000....333311118888 ----2222....55554444eeee----00006666 8888....44449999eeee----00007777 logatt ....0000222277775555333399993333 ....0000222244442222222299999999 1111....11114444 0000....222266663333 ----....0000222211116666000011112222 ....0000777766666666777799998888 winperch Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 1111....33332222444411110000000088887777 44441111 ....000033332222222299995555111144443333 Root MSE = ....00007777111133335555 Adj R-squared = 0000....8888444422224444 Residual ....111188883333222244449999555511114444 33336666 ....000000005555000099990000222266664444 R-squared = 0000....8888666611116666 Model 1111....11114444000088885555111133336666 5555 ....222222228888111177770000222277771111 Prob > F = 0000....0000000000000000 F( 5, 36) = 44444444....88882222 Source SS df MS Number of obs = 44442222
. reg winperch logatt cap goalsdiffh managerch away
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Cross section – Demand
_cons 11112222....88885555888800001111 ....7777999944442222777766663333 11116666....11119999 0000....000000000000 11111111....22224444333388885555 11114444....44447777222211118888 star ....0000444488884444333333331111 ....0000222288888888111188889999 1111....66668888 0000....111100002222 ----....0000111100001111333333339999 ....1111000077770000000000001111 dumseriea ....7777000000004444888877771111 ....1111888877773333999977776666 3333....77774444 0000....000000001111 ....3333111199996666444499992222 1111....000088881111333322225555 pop 2222....22227777eeee----00007777 8888....55553333eeee----00008888 2222....66666666 0000....000011112222 5555....33334444eeee----00008888 4444....00000000eeee----00007777 addedval ----....0000000000000000222211116666 ....0000000000000000111133333333 ----1111....66663333 0000....111111112222 ----....0000000000000000444488886666 5555....33334444eeee----00006666 riot ....0000222222221111666655555555 ....0000111177777777444466667777 1111....22225555 0000....222222220000 ----....0000111133339999000000001111 ....0000555588882222333311111111 logavpr0506 ----....4444222244442222777799991111 ....1111888800003333000088888888 ----2222....33335555 0000....000022225555 ----....7777999900007777111100006666 ----....0000555577778888444477777777 winperch 1111....222200004444111144449999 ....5555000055552222000011114444 2222....33338888 0000....000022223333 ....1111777777774444555566665555 2222....222233330000888844442222 logatt Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 22222222....1111555599999999222244442222 44441111 ....555544440000444488885555999955555555 Root MSE = ....33338888888844444444 Adj R-squared = 0000....7777222200008888 Residual 5555....11113333000000003333222266663333 33334444 ....111155550000888888883333333311113333 R-squared = 0000....7777666688885555 Model 11117777....0000222299998888999911115555 7777 2222....44443333222288884444111166665555 Prob > F = 0000....0000000000000000 F( 7, 34) = 11116666....11112222 Source SS df MS Number of obs = 44442222
. reg logatt winperch logavpr0506 riot addedval pop dumseriea star
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
2SLS – 2 instruments
I n s t r u m e n t s : a w a y m a n a g e r c h g o a l s d i f f h c a p p o p a d d e d v a lI n s t r u m e n t e d : l o g a t t _ c o n s 1111 .... 5555 9999 7777 7777 5555 8888 1111 .... 6666 7777 8888 7777 0000 2222 0000 .... 9999 5555 0000 .... 3333 4444 8888 ---- 1111 .... 8888 0000 6666 8888 0000 8888 5555 .... 0000 0000 2222 3333 2222 4444 c a p 2222 .... 3333 1111 eeee ---- 0000 6666 3333 .... 4444 8888 eeee ---- 0000 6666 0000 .... 6666 6666 0000 .... 5555 1111 1111 ---- 4444 .... 7777 4444 eeee ---- 0000 6666 9999 .... 3333 6666 eeee ---- 0000 6666 g o a l s d i f f h .... 0000 1111 5555 5555 6666 3333 5555 .... 0000 0000 3333 1111 1111 9999 5555 4444 .... 9999 9999 0000 .... 0000 0000 0000 .... 0000 0000 9999 2222 3333 6666 9999 .... 0000 2222 1111 8888 9999 m a n a g e r c h ---- .... 0000 0000 8888 1111 4444 5555 9999 .... 0000 3333 4444 4444 5555 6666 5555 ---- 0000 .... 2222 4444 0000 .... 8888 1111 4444 ---- .... 0000 7777 8888 0000 2222 6666 9999 .... 0000 6666 1111 7777 3333 5555 1111 a w a y .... 0000 0000 1111 5555 1111 6666 3333 .... 0000 0000 2222 8888 0000 9999 1111 0000 .... 5555 4444 0000 .... 5555 9999 3333 ---- .... 0000 0000 4444 1111 8888 0000 7777 .... 0000 0000 7777 2222 1111 3333 4444 l o g a t t ---- .... 1111 1111 0000 2222 0000 4444 1111 .... 1111 4444 7777 0000 1111 3333 2222 ---- 0000 .... 7777 5555 0000 .... 4444 5555 8888 ---- .... 4444 0000 8888 3333 6666 0000 7777 .... 1111 8888 7777 9999 5555 2222 4444 w i n p e r c h C o e f . S t d . E r r . t P > | t | [ 9 5 % C o n f . I n t e r v a l ]
T o t a l 1111 .... 3333 2222 4444 1111 0000 0000 8888 7777 4444 1111 .... 0000 3333 2222 2222 9999 5555 1111 4444 3333 R o o t M S E = .... 0000 9999 8888 2222 8888 A d j R - s q u a r e d = 0000 .... 7777 0000 0000 9999 R e s i d u a l .... 3333 4444 7777 7777 5555 4444 3333 7777 7777 3333 6666 .... 0000 0000 9999 6666 5555 9999 8888 4444 4444 R - s q u a r e d = 0000 .... 7777 3333 7777 4444 M o d e l .... 9999 7777 6666 3333 4444 6666 4444 9999 4444 5555 .... 1111 9999 5555 2222 6666 9999 2222 9999 9999 P r o b > F = 0000 .... 0000 0000 0000 0000 F ( 5 , 3 6 ) = 2222 3333 .... 6666 0000 S o u r c e S S d f M S N u m b e r o f o b s = 4444 2222
I n s t r u m e n t a l v a r i a b l e s ( 2 S L S ) r e g r e s s i o n
_ c o n s 1111 1111 .... 8888 4444 3333 5555 2222 .... 4444 1111 8888 4444 4444 4444 5555 2222 8888 .... 3333 0000 0000 .... 0000 0000 0000 1111 0000 .... 9999 9999 4444 0000 3333 1111 2222 .... 6666 9999 3333 0000 1111 a d d e d v a l ---- .... 0000 0000 0000 0000 1111 2222 7777 .... 0000 0000 0000 0000 1111 4444 4444 ---- 0000 .... 8888 8888 0000 .... 3333 8888 5555 ---- .... 0000 0000 0000 0000 4444 1111 9999 .... 0000 0000 0000 0000 1111 6666 6666 p o p 1111 .... 8888 7777 eeee ---- 0000 7777 1111 .... 5555 7777 eeee ---- 0000 7777 1111 .... 1111 9999 0000 .... 2222 4444 2222 ---- 1111 .... 3333 2222 eeee ---- 0000 7777 5555 .... 0000 7777 eeee ---- 0000 7777 c a p .... 0000 0000 0000 0000 1111 6666 6666 6666 .... 7777 8888 eeee ---- 0000 6666 2222 .... 4444 5555 0000 .... 0000 1111 9999 2222 .... 8888 6666 eeee ---- 0000 6666 .... 0000 0000 0000 0000 3333 0000 4444 g o a l s d i f f h .... 0000 1111 9999 3333 2222 6666 4444 .... 0000 0000 9999 9999 0000 2222 2222 1111 .... 9999 5555 0000 .... 0000 5555 9999 ---- .... 0000 0000 0000 7777 7777 6666 2222 .... 0000 3333 9999 4444 2222 9999 m a n a g e r c h .... 0000 4444 4444 8888 5555 8888 1111 .... 1111 6666 5555 4444 9999 5555 2222 0000 .... 2222 7777 0000 .... 7777 8888 8888 ---- .... 2222 9999 1111 1111 1111 4444 9999 .... 3333 8888 0000 8888 3333 1111 2222 a w a y ---- .... 0000 1111 1111 6666 8888 6666 4444 .... 0000 1111 5555 2222 7777 7777 9999 ---- 0000 .... 7777 6666 0000 .... 4444 4444 9999 ---- .... 0000 4444 2222 7777 0000 2222 2222 .... 0000 1111 9999 3333 2222 9999 4444 l o g a t t C o e f . S t d . E r r . t P > | t | [ 9 5 % C o n f . I n t e r v a l ]
T o t a l 2222 2222 .... 1111 5555 9999 9999 2222 4444 2222 4444 1111 .... 5555 4444 0000 4444 8888 5555 9999 5555 5555 R o o t M S E = .... 4444 8888 4444 7777 2222 A d j R - s q u a r e d = 0000 .... 5555 6666 5555 3333 R e s i d u a l 8888 .... 2222 2222 3333 4444 0000 6666 1111 9999 3333 5555 .... 2222 3333 4444 9999 5555 4444 4444 6666 3333 R - s q u a r e d = 0000 .... 6666 2222 8888 9999 M o d e l 1111 3333 .... 9999 3333 6666 5555 1111 8888 6666 2222 .... 3333 2222 2222 7777 5555 2222 9999 9999 P r o b > F = 0000 .... 0000 0000 0000 0000 F ( 6 , 3 5 ) = 9999 .... 8888 9999 S o u r c e S S d f M S N u m b e r o f o b s = 4444 2222
F i r s t - s t a g e r e g r e s s i o n s
. i v r e g w i n p e r c h a w a y m a n a g e r c h g o a l s d i f f h c a p ( l o g a t t = p o p a d d e d v a l ) , f i r s t
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Cross section: more
L’interazione tra stadium capacity e attendance
non è significativa
Se utilizziamo l’attendance in livelli i
risultati non cambiano
Se utilizziamo il tasso di occupazione al posto
dell’attendance i risultati non cambiano
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Panel data model –
Supply side: Sporting production function
itititit
ititit
awaymanagerchgoalsdiffh
capattCwinperch
εβββ
ββγ ι
++++
++++=
5 4 3
21 log0
Demand side
titit
itititit
dumserieapop
addedvalriotwinperchCatt
ι
ι
εββ
βββγ
+++
+++++=
6 5
4 310log
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Panel data – 2004/05-2006/07
• Non abbiamo dati su Average price e
Star
• Abbiamo utilizzato un modello ad
effetti fissi per ragioni teoriche
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Panel data – 2004/05-2006/07 – Supply
F test that all u_i=0: F(44448888, 77772222) = 0000....88886666 Prob > F = 0000....7777111155555555 rho ....33339999222255550000999944447777 (fraction of variance due to u_i) sigma_e ....00006666666622228888777722223333 sigma_u ....00005555333322228888222255557777 _cons ....2222000055556666555566669999 ....4444333355557777444477776666 0000....44447777 0000....666633338888 ----....6666666622229999888899999999 1111....000077774444333300004444 away ....0000000022229999888844443333 ....000000001111555555551111 1111....99992222 0000....000055558888 ----....0000000000001111000077777777 ....0000000066660000777766662222 managerch ....0000000066663333777711115555 ....0000111188884444666644441111 0000....33335555 0000....777733331111 ----....0000333300004444333366661111 ....000044443333111177779999 goalsdiffh ....0000111122222222555566668888 ....0000000011110000222255559999 11111111....99995555 0000....000000000000 ....0000111100002222111111117777 ....0000111144443333000011119999 cap 1111....00003333eeee----00006666 1111....22224444eeee----00006666 0000....88883333 0000....444400009999 ----1111....44444444eeee----00006666 3333....55551111eeee----00006666 logatt ....0000000044443333444444447777 ....0000333344445555111155558888 0000....11113333 0000....999900000000 ----....0000666644444444666611112222 ....0000777733331111555500007777 winperch Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = ----0000....2222777777779999 Prob > F = 0000....0000000000000000 F(5555,77772222) = 44449999....11119999
overall = 0000....8888111188888888 max = 3333 between = 0000....8888444433330000 avg = 2222....6666R-sq: within = 0000....7777777733335555 Obs per group: min = 1111
Group variable: tttteeeeaaaammmm Number of groups = 44449999Fixed-effects (within) regression Number of obs = 111122226666
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Panel data – 2004/05-2006/07 – Demand
F test that all u_i=0: F(44448888, 77772222) = 11111111....00003333 Prob > F = 0000....0000000000000000 rho ....99992222333355559999777788884444 (fraction of variance due to u_i) sigma_e ....22221111888888887777555577772222 sigma_u ....77776666111100000000222277774444 _cons 11112222....77775555555577771111 1111....000044443333333300003333 11112222....22223333 0000....000000000000 11110000....66667777555599992222 11114444....8888333355555555 dumseriea ....3333999977776666999977778888 ....1111000055559999555522229999 3333....77775555 0000....000000000000 ....1111888866664444888844446666 ....6666000088889999111111111111 pop ----6666....33332222eeee----00008888 4444....99991111eeee----00008888 ----1111....22229999 0000....222200002222 ----1111....66661111eeee----00007777 3333....44446666eeee----00008888 addedval ----....0000000000000000333322222222 ....0000000000000000444455552222 ----0000....77771111 0000....444477779999 ----....0000000000001111222222222222 ....0000000000000000555577779999 riot ....0000000022226666666622224444 ....0000111133334444222288882222 0000....22220000 0000....888844443333 ----....0000222244441111000066662222 ....000022229999444433331111 winperch ....1111555500002222888877771111 ....2222222222220000555544444444 0000....66668888 0000....555500001111 ----....2222999922223333777700003333 ....5555999922229999444444444444 logatt Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = ----0000....0000888888885555 Prob > F = 0000....0000000022220000 F(5555,77772222) = 4444....22222222
overall = 0000....0000555577774444 max = 3333 between = 0000....0000666600002222 avg = 2222....6666R-sq: within = 0000....2222222266667777 Obs per group: min = 1111
Group variable: tttteeeeaaaammmm Number of groups = 44449999Fixed-effects (within) regression Number of obs = 111122226666
. xtreg logatt winperch riot addedval pop dumseriea, fe
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Panel data – 2004/05-2006/07 – 2SLS
Instruments: cap goalsdiffh managerch away pop addedvalInstrumented: logatt F test that all u_i=0: F(44448888,77772222) = 0000....33332222 Prob > F = 1111....0000000000000000 rho ....99991111111111113333888866661111 (fraction of variance due to u_i) sigma_e ....1111111155554444666688889999 sigma_u ....33336666999977774444333366662222 _cons 5555....444444448888000099998888 3333....999933331111111166669999 1111....33339999 0000....111166666666 ----2222....222255556666888855552222 11113333....11115555333300005555 away ----....0000000011111111222255553333 ....0000000044440000555544449999 ----0000....22228888 0000....777788881111 ----....0000000099990000777722228888 ....0000000066668888222222222222 managerch ....0000777788882222333333331111 ....0000666611118888888877775555 1111....22226666 0000....222200006666 ----....0000444433330000666644441111 ....1111999999995555333300003333 goalsdiffh ....0000000099994444666600005555 ....0000000022227777222255551111 3333....44447777 0000....000000001111 ....0000000044441111111199994444 ....0000111144448888000011117777 cap ----1111....00001111eeee----00006666 2222....66663333eeee----00006666 ----0000....33339999 0000....777700000000 ----6666....11118888eeee----00006666 4444....11115555eeee----00006666 logatt ----....4444111133333333888899996666 ....333311113333111177779999 ----1111....33332222 0000....111188887777 ----1111....000022227777222200009999 ....2222000000004444222299999999 winperch Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, Xb) = ----0000....8888888877774444 Prob > chi2 = 0000....0000000000000000 Wald chi2(5555) = 2222000033336666....55554444
overall = 0000....0000111199999999 max = 3333 between = 0000....2222111111114444 avg = 2222....6666R-sq: within = 0000....3333111122229999 Obs per group: min = 1111
Group variable: tttteeeeaaaammmm Number of groups = 44449999Fixed-effects (within) IV regression Number of obs = 111122226666
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
You can even walk alone…
• Nelle ultime tre stagioni del calcio italiano il
12mo uomo si è rilevato solo un mito � per
migliorare le performance sportive non serve avere
lo stadio stracolmo � allora c’è spazio per il
profitto nella vendita dei biglietti allo stadio
• Fine di lucro: il club può fissare prezzi elevati
per i posti di lusso e confortevoli (tipo sky box)
per coprire i costi di gestione dello stadio e per
i settori in cui oggi vanno i tifosi violenti
(curve)
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Conclusions: Need for a CSR policy
• Così il profitto dà spazio a strategie di
CSR nel senso di ruolo politico
nell’ambito della teoria del corporate
citizenship (Matten & Crane 2005)
• Le strategie di CSR possono essere
utilizzate per escludere i fans violenti
• Gli altri posti possono andare agli
stakeholder importanti: vivaio, studenti,
disabili
Pricing strategies - Amenta
Corso di Marketing
prof. Gandolfo DOMINICI Prof. Carlo Amenta
Conclusions: Proposal
• La legge italiana ha dato ai club il ruolo
di soggetti che garantiscono la sicurezza
negli stadi
• Le politiche di inclusione sociale possono
essere adottate creando fan club
controllati dalle squadre i cui membri
(opportunamente controllati) possono
ottenere sconti forti sui biglietti
Pricing strategies - Amenta