Simulation constitutes the principles of pricing in a market place or in a realistic business environment. Focusing on a Florida rental agency, ‘Universal Rental Car’ a study is carried out on how prices are set for rental cars across the two cities namely Miami and Orlando. Depending on the month of the year, the demand for cars varies based on whether leisure or business activities majorly take place in a given location. The demand of cars consequently has a direct impact on the prices, which calls for a periodic inventory adjustments to match the demands among the three locations. The main objective of this report is to analyze the price sensitivity between leisure and business travelers so as to initiate the strategies which will enable maximization of rentals in the three cities across weekdays and weekends and how price affects elasticity of demand as well as its contribution to competition.
Demand for cars is directly affected throughout the changing seasons in Florida. For the agency to ensure that there is gain of profit, it must ensure that it does not run out of cars and also the inventory is void of unrented cars. This is the main reason for developing strategies that will guarantee the agency’s maximum cumulated profits. Historically, Miami and Orlando run a mix of leisure travelers and business travelers, which are different, in each of the two cities. Demand variations are caused both by price sensitivity differences between the two cities and the demand between weekdays and weekends.
During simulation, the information obtained is sorted depending on the city that is under consideration. The difference in demand for cars is analyzed and accounted for in the two cities in relation to the set prices. Prices are entered in eleven rounds representing the same number of months across the year. In each month the demand for cars observed is compared to the simulated prices. For maximum determination of cumulative net profit, the cost structure in decision-making is compared with the number of orders made across the year. For final analysis of the simulation processes each of the information type obtained is then displayed on a dashboard, transferred to Excel and then analyzed accordingly.
In the study of price simulation in the Universal agency and its competitors, a forecast on market demand and historical data is important for easy comprehension of price elasticity in both market segments. The set prices have a direct impact on what the market share is and the size of the rental car market. It is observed during the simulation that higher prices affects two aspects which a customer has to weigh when going for a car rental decision, that is, the location to go for a car and also the decision whether to rent a car or not. The prices for Orlando and Miami remain the same in weekday and weekend although weekday prices are higher than weekend prices from the month of July to October. Despite the unchanging prices in that period, both demand and sales increase consistently from July to October.
The number of sales of cars is also affected by the prices, the demand and also the month of the year. An increase in price led to a decrease in demand for cars in Orlando and hence sales dropped. Thus, price against demand and sales made relate indirectly, for instance, an increase in demand in Florida from July to November records a respective increase in the number of sales. Demand is generally higher in Orlando and hence sales inferring that prices for rent of cars are lower than in Miami. Market demand for the two cities is the same for the first three months although higher in Florida due to the average demand, relatively constant in Orlando, decreases drastically in Miami. On average in Florida, there is a little drop beginning January but remains relatively constant in the remaining odd alternating months selected such as January and March and so on.
In the Universal rental car agency, it is observed that from the month of July to the month of October, the size of the fleet size in both Orlando and Florida increased consistently in both weekday and weekend. In Miami, there was also a consistent demand for cars from July to September on daily weekly orders but decreased in the month of October. However, daily weekend orders registered a constant increase until October. This data indicates that price and hence demand dynamics in all the months affected the size of daily orders that were made. Thus, it’s clear from the data that price simulation decisions caused a variation in the fleet size, depending on the month the order for rent of cars was placed. In Orlando, the price simulation favors greater weekend orders than weekday orders while a decrease of orders is registered for weekend orders compared to weekday in Miami and Florida on the whole, from the months of July to October.
From the simulation, it is obvious that a change in price causes a change in sales made and hence a change in profits made. To realize maximum profits, Universal rental agency needs to set prices, which will lead tithe number of car sales that meets the demand and fleet size and hence give rise to the desired profit. To achieve such desired results one example could be of a favorable decrease in price, for instance, 20% that will lead to an increase in demand for the cars in the city selected.
To ease of understanding, let us consider this example. If the company has a variable cost of $600, the new price per car may be calculated as (1-0.2) * $1000 =$800. Then the profit contribution per car is now $800-$600 =$200. If the company currently sells 100,000 cars at $1000, then the company makes a profit contribution of =100000 * ($1000-$600) = $40 MM. The relation could determine the extra units, given the value X, required maintaining the same profit:
100,000* ($1000-$600) =X * ($800 -$600)
$40MM= $200 *X
X= 2 00,000 cars
This means that the extra number of cars required to keep the same amount of profit is 100,000 cars which means doubling the number of cars (Sismeiro, n.d.).
A different approach used for price simulation is the Value Based Pricing which states that when there are different prices across a number of customers, changes in those prices for a length of period should reflect the difference or change in the value provided by the product or service to those customers. When this price simulation method is applied in one of the two cities, it is likely to earn 24% higher profits than the other cities engaging in the same business such as car rental.
After setting a price, the next and last important step is price promotion. This is done through time customization of the price where different prices are considered over a given range of time such as the 11 rounds of months and segmentation criteria, which refer to either search costs or willingness to hold inventory. In our case, search costs maybe the time available for shopping, for instance, choosing a suitable car and also opportunity cost time that depends on income and education. However, it is important to note that short- run “leakage’’ is one of the limitations of price promotions whereby lower prices are offered to all customers and yet some of these would have taken a car at a greater price.
The Main Points of Learning and Difficulty
I) The pricing strategy that was most suitable was increasing pricing in the increments by 10 % - 20 % but that depended on the demand in the particular city or market in question and also on a specific timeframe such as the month or either weekday or weekend. We had to change our pricing strategy a number of times and that too differently so that we didn’t lose out on maximizing the profits by meeting the demands.
II) Our main objective was to ensure maximization of profit by finding out the highest price the consumer was willing to pay. The theory of supply and demand is very applicable in this case.
III) It was learnt that during the week, demand was higher in both the cities and for the majority of other pricing experiments. It may be kept in mind that the two locations, Orlando and Miami are the major spots or destinations for conferences. For this reason, a business traveler could be charged higher than a vacationer and also for the reason that the money they spend for this service may not be theirs but of the company they work for.
IV) The behavior of a customer towards change in price may depend on the type of customer. The leisure traveler maybe more sensitive to price than the business traveler since the money to be spent is out of pocket or personal budget.
V) Since we assumed that business customers may be willing to pay more, our weekday pricing was more vigorous than the weekend pricing with price differentials of up to 25% or 30%.
VI) We were able to use the break-even calculator to predict the change in demand and sales volume to keep our profits high. However, we learnt that in many cases an increase in price did not lead to a fall in demand. This could be due to the characteristics of the car provided where by customers were willing to pay more than the price charged. In other instances, a fall in price lead to a greater increase in demand than predicted, also making firm our belief that consumers were not as price sensitive as predicted, rather having a great interest and demand for the cars provided by Universal Rental Car Company.
VII) Our understanding of general economics of business helped us to relate supply and demand. When the demand was higher than the supply higher prices were paid for cars especially by business travelers than by leisure travelers.
VIII) Capacity utilization tab provided us with a lot of information, which greatly influenced us in making our pricing decisions. We adjusted our prices, usually increasing them gradually to bring the demand curve down towards the supply curve, in turn maximizing our profits. As the demand fell below the supply, we decreased the prices vary slightly to generate interest in increase demand. Since it was noted that due to the peculiar nature or rather characteristics of the cars and services provided by the Universal Rental Car Company, only a very small dip in prices was needed to increase customer demand whenever it fell below our fleet size.
IX) It was more important and relatively easier to manage surplus than a shortfall. As mentioned earlier, we were able to increase our prices until the demand fell enough to match our full and additional fleet size.
X) The competition played a significant role in affecting our pricing decisions during the whole exercise, which was normally a few dollars above/below, our prices. The competitors’ aim was to undercut our market share, so that customers would go for their lower prices but since the demand was much higher than predicted, our average market share did not budge too much. In totality, we made more profits over the entire year. Unfortunately some of the competitive pricing decision for the competitor did not work as hoped for by him, instead aiding us in maximizing our profits by charging more since the demand was greater than hoped for and consumers not as price sensitive as expected.
XI) Across the eleven month period of the simulation exercise, it was observed that there was increase in demand during the warmer weather which may be attributed to the fact that there is an increased number of conferences and business travelers in addition to tourists and leisure travelers that prefer to go out more in the summer months than in the winter months.
XII) It should be noted from our pricing decisions and their impact on the demand that the demand in this overall market is relatively inelastic. This could be due to the reason that there weren’t alternative options from which the consumers could choose from or from the peculiar and much needed/demand characteristics of the cars provided by the Universal Rental Car Company.
XIII) The fact that the demand was relatively inelastic for most of the sales period was good for managing our business since we consistently made more profit than our competitor. It should also be noted that there was an overall constant demand for our product. In future if simulation allows and all variables remain same, we could look towards reinvesting in a larger inventory so as to increase our market share and maximize our profits even further.
Pricing is an important process in a business environment due to its contribution in creating a healthy and fair competition. Price simulation ensures that supply is well balanced with demand so that high quality products are availed at the right time and at the right price. Pricing strategies enable competing businesses to make a favorable profit without exploiting the customers or being exploited by the customer. The demand for a product is influenced by time and seasons and