Fueling Sales at Euro-pet Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

Fueling Sales at Euro-pet Case Solution

Question 04

Part A

The comparison between previous and current regression model shows that the R square of previous model is 24 percent; whereas, the R square of current regression model is 44 percent, thus depicting an increased goodness of the model. Additionally, there would be an increase in the sales, i.e.€189 for every increased degree. There is a slightly lower impact of TV advertisement on the sales as the coefficient has reduced from 12.19 to 11.42. The reason of the increased sales is the warm period rather than the advertisement, which keeps on having a similar impact.

Part B

The net impact of TV on sales is calculated by using the formula provided below:

Net impact = (coefficient*mf)*30% margin)*total stores – amount spent in ad

The net impact of TV over sales, is €727.94.

Part C

With the use of the TV advertisement for the c-stores sales; the coefficient should be positive, so that the TV advertisement would help in generating sales volume. Advertisement last positive impact over the company’s sales as it creates product or brand awareness among the customers.

Question 5

Part A

The regression equation is as follows:

Regression model = -3745.2+(5.59*TV)+(0.27*fuel volume)+(85.17*fuel price)+(68.93*temp)+(-138.5*prec)+(895.25*holiday)+(-157.25*visit)

Part B

There would be an increase of €5.59 in sales, for every increased add. Additionally, there would be an increase of €68.94 in sales, for every increased degree.There would be an increase of €0.28 in sales for every liter sold. Similarly, for every €1 in price of fuel; there would be an increase of €85.18 in sales.

The unintuitive signs includes holidays, which shows that there would be an increase of €895.2 in sales, if it is a holiday. Similarly, every add mm of precipitation would lead towards reduction of €138.50 in sales. Every added person comes ½ times per week, which tends to lead towards the reduction of €157.2 in sales.

Part C

The increase in TV GRP by 5 rating points, would bring a significant change in the sales volume of the c-store. The sales of the c-store would be affected by an increase in TV GRPs, in a way that the sales would increase from -3521.42 to -3493.45.

Part D

The net impact of new regression is calculated by using the formula provided below:

Net impact = (coefficient*mf)*30% margin)*total stores – amount spent in ad

The net impact of TV on sales is €727.94, showing that the ad is still profitable for the company.

Question 6

Part A

The regression equation (without radio),is as follows:

Sales = -4885.63+(6.03*TV)+(0.306*fuel volume)+(81.56*fuel price)+(57.78*temp)+(-131.71*prec)+(989.61*holiday)+(-180.42*dummy7)+(2045*dummy21)+(2102.04*dummy49)

Part B

The coefficient of the new dummies is significant as they are positive except dummy7. The relationship between sales and dummy variable is strong. There would be an impact of the dummy variable over the sales, only if the coefficient of dummy is 1.

Part C

The coefficient of dummy7 variable is -1821,which means that the dummy for week 7 would be a reduction of €1821 in sales; whereas, the dummy for week 21 and week 49 would havean increase of €2045 and €2102 in sales, respectively. Thus, the effect of the dummy variable for week 21 and week 49 is strong on the c-store’s sales.

Part D

Yes, the new regression would yield better prediction taking under consideration the fact that the R square has increased from 84 percent to 87 percent, thus accurately reflecting the percentage of dependent-variable's variation and making the regression more useful and meaningful.

Question 07

The impact of the holidays on the sales is estimated by performing regression analysis with or without holidays. With holidays, the sales of the c-store would be €17528 and without holiday, the sales of the c-store would be €16619. Thus, the impact of the TV advertisement in sales is not highly affected by the holidays.

Question 08

Part A

The regression equation (without radio) is as follow:

Sales = -3745+(5.59*TV)+(0.27*fuel volume)+(85.17*fuel price)+(-138.50*prec)+(-157.25*visits)+(895.25*holidays)+(68.93*temp)

Part B

The regression analysis is performed without radio as it does not meet the desired level of significance of Taylor.

Part C

The sales of c-store with holiday is €16435.73 and without holiday it is€16308.38 Thus, there would be a high impact of temperature if there will be a holiday.

Question 09

On the basis of the analysis, the variables that have significant impact over the c-store’s sales, include: temperature, fuel price, advertisement on TV and visits. Thus, the company is recommended to accelerate its efforts on TV advertisement only. The TV advertisement would lead towards an increase in brand or prediction of the awareness, thus increasing the sales on c-store. Taking under consideration the fact that the dummy variable could manipulate the results; the company should not use these variables..................

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Fueling Sales at Euro-pet Harvard Case Solution & Analysis