Universal Car Rental Pricing Simulation

Havard Business SCHOOL PAPER: Universal Car Rental Pricing Simulation

July 2012

Universal Car Rental Pricing Simulation

Background:

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 increment we could increase the price. We concluded that price elasticity of supply was irrelevant in the context of this simulation.

Market Demand: customer price response

We were quickly able to observe that weekday and weekend demand outstripped supply, we deduced that weekday demand was a proxy for business users and weekend demand was a proxy for leisure users. After running the simulation for the first quarter we were able to analyse the Price Elasticity of Demand (PED). In general we found that demand from business users was price inelastic, whereas leisure users were price sensitive. The ranking of price inelasticity by market as calculated from our Quarter 1 numbers is shown in table 1. The graphical representation of the computations is recorded in Appendix D.

Table 1: PED from Quarter 1

In Tampa and Miami demand was more price elastic than in Orlando, where demand was price inelastic at the levels we had set prices in the first quarter. As a result of this observation we increased the weekday prices more rapidly than the weekend prices in Quarter 2 across all cities. However, in Orlando we also increased the weekend prices as a result of the analysis we had conducted.

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 understanding of the elasticity and the fact that demand outstripped supply, we would compare the fleet size to overall demand and increase the price by the percentage that demand exceeded our capacity. We would adjust this for the price elasticity using judgement.

Figure 1: Summary of pricing in Tampa, Miami and Orlando for the year

Cost Structure

The fixed costs of the business were