Data Gen

Q1) continues

Pantene Price(1-19) | Demand |

1 | 90 |

2 | 90 |

3 | 88 |

4 | 85 |

5 | 79 |

6 | 77 |

7 | 72 |

8 | 68 |

9 | 59 |

10 | 51 |

11 | 48 |

12 | 36 |

13 | 29 |

14 | 20 |

15 | 12 |

16 | 9 |

17 | 4 |

18 | 0 |

19 | 0 |

Q2)

Q3)

Q4)

| Ui | Pi |

Blendax | 4,01 | 4 |

Pantene | 12,76 | 1-19 |

Rejoice | 6,86 | 7 |

Head&Shoulders | 10,04 | 10 |

Clear | 8,08 | 8 |

Elidor | 6,32 | 6 |

Dove | 5,01 | 5 |

µ | 2,71697845949276 |

Variance | 12,14285714 |

Assumption: We took utilities as average of willingness to pay data for each brand

Q5)

Q6) Pricing is affected by some external and internal factors. External factors that is not relevant with company are market structure, consumer behaviors etc. On the other hand, internal factors controlling by the company are price, advertising, product quality etc. These factors assign the number of products at alternative prices. As a result of this, the relationship between alternative prices and number of sales is called the price response function.

In this case, we use different parameters and we provide different price-response functions and it is very useful to reach alternative results. As a result of this, the conducted survey does not provide another optional result. Therefore, P&G has very limited options for Pantene defined in the survey.

When P&G uses linear, exponential and MNL price-response functions, it has flexible functions. These means that, P&G can have larger set of data. The larger set of data provides better results.

Q7) The classic exceptions of demand properties are also valid here. First, an increase in the real product can create a perception of quality; unlike in the survey. If the price goes up; the consumer can think that there should be a reason for this. Second; conspicious consumption can take a role in...