Pricing Simulation Report
Pricing Simulation
During twelve months, starting in October, we were responsible for setting the pricing strategy of Universal Rental Car Company, as the district manager for the Florida region of Orlando. It was a big role as Florida was the company’s worst performing region and had two major problems: “Stock outs”, which used to occur during demand peaks, and “unsold inventory”, which occurred in demand valleys.
Furthermore, we had to deal with the competitor in an intense price war, as the customers would only have two different options to rent a car and, of course, were intuitively very sensitive to prices.
We were able to run this situation three different times and therefore were able to apply different pricing strategies. In the end of each run, our aim was to improve the financial results of the company, either in terms of cumulative profit, financial market share, cumulative unit sales, capacity utilization and final month’s profit. For this, we could only change the prices applied by the company in weekdays and on weekends.
Another interesting input to this simulation was that there were two different kinds of customers: the ones who rented cars for business purposes and others who were spending vacations in Florida.
In every run, the price which was being practiced by Universal was 41$ and 34$, for the weekday and weekend respectively. On the other hand, the competitor wad charging a bit more in both periods (42$ during weekdays and 35$ during weekends) giving Universal a prior advantage.
Bearing all this in mind, we basically could follow two different main strategies (more extreme) which are characterized by the aggressiveness of the price increases or decreases, and another one that should be in the middle of these two:
Skim: setting a high price to capture the economic value of the product as customers may reveal insensitive to prices. Even though the sales volume normally decreases, the sale margins are high. Customers tend to differentiate a lot
During twelve months, starting in October, we were responsible for setting the pricing strategy of Universal Rental Car Company, as the district manager for the Florida region of Orlando. It was a big role as Florida was the company’s worst performing region and had two major problems: “Stock outs”, which used to occur during demand peaks, and “unsold inventory”, which occurred in demand valleys.
Furthermore, we had to deal with the competitor in an intense price war, as the customers would only have two different options to rent a car and, of course, were intuitively very sensitive to prices.
We were able to run this situation three different times and therefore were able to apply different pricing strategies. In the end of each run, our aim was to improve the financial results of the company, either in terms of cumulative profit, financial market share, cumulative unit sales, capacity utilization and final month’s profit. For this, we could only change the prices applied by the company in weekdays and on weekends.
Another interesting input to this simulation was that there were two different kinds of customers: the ones who rented cars for business purposes and others who were spending vacations in Florida.
In every run, the price which was being practiced by Universal was 41$ and 34$, for the weekday and weekend respectively. On the other hand, the competitor wad charging a bit more in both periods (42$ during weekdays and 35$ during weekends) giving Universal a prior advantage.
Bearing all this in mind, we basically could follow two different main strategies (more extreme) which are characterized by the aggressiveness of the price increases or decreases, and another one that should be in the middle of these two:
Skim: setting a high price to capture the economic value of the product as customers may reveal insensitive to prices. Even though the sales volume normally decreases, the sale margins are high. Customers tend to differentiate a lot