Universal Car Rental Pricing Simulation (Havard Busines School Pricing Simulations)

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Havard Business SCHOOL PAPER: Universal Car Rental Pricing Simulation
July 2012

Universal Car Rental Pricing Simulation

The objective of the simulation was to maximise profits of Universal Car Rental Company. The simulation was run across three cities in Florida; Tampa, Orlando and Miami.

Overall strategy:
We adopted a strategy of offering the highest price achievable whilst maintaining 100% capacity utilisation irrespective of market share. In the context of the scenario, where growth in demand outstripped supply and with only twelve ‘rounds’, we felt market share was not fundamentally important. In respect of setting the pricing level, we calculated the price elasticity of demand to give us an insight into the
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Each city had a different revenue mix between business and leisure users. Tampa had more business users, Orlando and Miami had more business and leisure users compared to Tampa. In Orlando and Miami, business users were more price insensitive compared to Tampa. Therefore, along with maximising our capacity utilisation we increased weekday prices at a higher rate in Orlando and Miami compared to Tampa. Table 2 confirms this, as it shows the percentage contribution of weekday car hires as a percentage of the overall contribution of each region. It indicates that weekday car hires account for a Florida grand total of 81% of contribution in October but this rises to 85% by December. In Orlando it is lower because the relatively higher contribution of weekend customers in that market.

Table 2: Percentage contribution of weekday car hire to overall contribution

All pricing decisions the team took are recorded in detail in appendix A, B and C. A summary of the same is presented in figure 1 below. Decisions were made with an informed understanding of the elasticity of demand, fleet utilisation, the gross profit, the contribution, the net profit, seasonal demand changes, the competitors pricing decisions and the context of the scenario.

After a few months of detailed scrutiny of the numbers, we were able to make pricing decisions more quickly by using the breakeven change in volume to set the new price. Based on our broad

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