Exemples

On utilise les modèle d'efficacité des dépenses publicitaires 2.30 et 2.31 (publicité statique optimum est 10 et prix statique optimal = 6

  • Effets dynamiques des dépenses publicitaires, faible effet dynamique (0,2) dépense constante[2.59.1]
  • Effets dynamiques des dépenses publicitaires, faible effet dynamique (0,2) dépenses par impulsions [2.59.2]
  • Effets dynamiques des dépenses publicitaires, fort effet dynamique (0,8) dépense constante[2.59.3]
  • Effets dynamiques des dépenses publicitaires, faible effet dynamique (0,8) dépenses par impulsions [2.59.4]
  • Effets dynamiques des dépenses publicitaires, faible effet dynamique (0,8) dépense constante sous contrainte budgetaire[2.59.5]
  • Effets dynamiques des dépenses publicitaires, faible effet dynamique (0,8) dépenses par impulsions sous contrainte budgetaire[2.59.6]

Listing 21

1.                               x2=rep(6,10)

2.                               x1=rep(15,10)

3.                               alfa=0.8

4.                               lambda=0.2

5.                               ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

6.                               y<-rep(0,10)

7.                               y[1]<-ylong[1]

8.                               for(i in 2:10){

9.                               y[i]<-alfa*ylong[i]+lambda*y[i-1]

10.                             }

11.                             profit<-(x2-1.5)*y-x1

12.                             df<-data.frame(Ventes=y, Profit1=profit)

13.                             x1=rep(c(25,5),5)

14.                             alfa=0.8

15.                             lambda=0.2

16.                             ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

17.                             y<-rep(0,10)

18.                             y[1]<-ylong[1]

19.                             for(i in 2:10){

20.                             y[i]<-alfa*ylong[i]+lambda*y[i-1]

21.                             }

22.                             profit<-(x2-1.5)*y-x1

23.                             df$Profit2=profit

24.                             x1=rep(15,10)

25.                             alfa=0.2

26.                             lambda=0.8

27.                             ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

28.                             y<-rep(0,10)

29.                             y[1]<-ylong[1]

30.                             for(i in 2:10){

31.                             y[i]<-alfa*ylong[i]+lambda*y[i-1]

32.                             }

33.                             profit<-(x2-1.5)*y-x1

34.                             df$Profit3=profit

35.                             x1=rep(c(25,5),5)

36.                             alfa=0.2

37.                             lambda=0.8

38.                             ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

39.                             y<-rep(0,10)

40.                             y[1]<-ylong[1]

41.                             for(i in 2:10){

42.                             y[i]<-alfa*ylong[i]+lambda*y[i-1]

43.                             }

44.                             profit<-(x2-1.5)*y-x1

45.                             df$Profit4=profit

46.                             x1=rep(5,10)

47.                             alfa=0.2

48.                             lambda=0.8

49.                             ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

50.                             y<-rep(0,10)

51.                             y[1]<-ylong[1]

52.                             for(i in 2:10){

53.                             y[i]<-alfa*ylong[i]+lambda*y[i-1]

54.                             }

55.                             profit<-(x2-1.5)*y-x1

56.                             df$Profit5=profit

57.                             x1=rep(c(9,1),5)

58.                             alfa=0.2

59.                             lambda=0.8

60.                             ylong<-100*((+1*x1))^0.5*((+1*x2))^-2

61.                             y<-rep(0,10)

62.                             y[1]<-ylong[1]

63.                             for(i in 2:10){

64.                             y[i]<-alfa*ylong[i]+lambda*y[i-1]

65.                             }

66.                             profit<-(x2-1.5)*y-x1

67.                             df$Profit6=profit

68.                             df

69.                             matplot(1:10, df[,2:7], pch = 1:6, type = "o", col = 1:6,xlab="Valeurs de x", ylab="Ventes et/ou Profits"

70.                             legend(1, max(df[,2:7]),names(df)[2:7], lwd=3, col=1:6, pch=1:6)

Figure 28 - Depenses publicitaires constantes ou par impulsion et effets dynamiques

Michel Calciu calciu@iae.univ-lille1.fr; - Cours IAE de Lille 2004 - -