Products Services and Prices in the Free Market Economy

PRICING: HAYES VS.SUPERIOR

Pricing decisions

When a firm decides to change the price of its good or services, the two driving factors are usually the cost and the competition. There is hardly another reason for change the price. Either the firm does it for itself as its costs are high and revenues are not significant or it does to beat the competition and gain bigger market share. Some theorists might add a third driving force behind pricing decision i.e. customer but this one is usually not as influential as the other two or simply doesn't play as major a role as do the two other factors. (Hyatt)

In the case of Hayes, the decision to change the price of aluminum wheel is driven by competition and not the cost. The firm already enjoys very large market share and is a global leader in this area but since it is Superior that is the market leader in North America, it is only natural for Hayes to come up with strategies to gain bigger market share and superceded Superior. Pricing is however not an easy decision. A slight change in price can produce unpredictable results and a large change may not make any noticeable change in demand. This is because it is not just the price that influences a person's decision to buy or reject a product; instead there are a host of other factors that determine price elasticity of demand for a certain product or service.

Demand Forecasting

Most firms understand that a change in price will affect demand and generally this is the main reason why price is changed in the first place. However what some firms might ignore are the factors that influence demand and may assume that lowering the price would automatically raise demand. This may not be so and the firm may be in for a rude shock. It is for this reason that before price is change, a firm must prepare demand forecast. A Demand Forecast "refers to an estimation of demand for a product for a future period. Forecasting is an attempt to foresee the future by examining the past. It is forward projection of data." (Gupta) Unlike sales forecasts that focus on sales in a particular period of time, demand forecasts study the relationship between the product and its potential quantity demanded while keeping in view all the possible factors that can affect demand. As we discussed earlier, demand is affected by factors other than price and when price is being changed, these factors cannot be ignored. Let us present a brief review of those factors and discuss a few others to explain how demand forecasts help.

Factors that influence demand or price elasticity of demand:

1. The rival products, how many they are and their quality:

The more rivals you have in a market, the greater the price elasticity of demand. This means that if people have access to many substitutes, they are more likely to switch over to another product or service in case of price increase by your firm. However quality must not be forgotten. You may have 10 competitors but if only 5 are close rivals and other 5 are producing low-quality copies of a product similar to yours, you need not worry about these five and must concentrate on the five close competitors only.

2. Transaction costs are also important:

When pricing decisions are being made, the firm must take into account the transaction costs involved with customer's switching between products. This may help in better understand demand behavior of the consumer. For example when Hayes decreases the price of its aluminum wheels by say 0.5%, it need to take into account the problems or hurdles associated with customer's decision to move to its product from Superior's wheels (if there be any hurdles at all). Similarly if Hayes decides to raise the price to attract more revenues, it must consider the transaction costs associated with rival's product which may help develop a more accurate demand forecast. These transaction costs may include additional costs that need to be incurred on fine-tuning of wheels, their maintenance, any contract signed with the manufacturer etc.

3. Rush season

At a time when aluminum wheels are in high demand for whichever reason, the firm can raise the price without worrying too much about change in demand. This is because during peak season, demand tends to remain price inelastic while it becomes suddenly more elastic during off-peak seasons.

4. definition of the product:

This may also play a significant role. When a firm defines its product broadly, it sets lower price elasticity automatically. However this may be a problem for Hayes because it specializes in one brand of wheels and its wheels are not defined as just 'wheels'. They are 'aluminum wheels' which means that demand for this good will be more price elastic than a firm that produces ordinary wheels.

Other factors include: the nature of service/good whether its necessary item or a mere luxury, the percentage of consumer's budget spent on your good, the time period, if the good is being consumed as a habitual item etc.

All these factors have an effect on demand and these are what demand forecasts closely study. Hayes need to have a demand forecast prepared before it decides to either increase or decrease the price.

The first important demand forecasting methodology is known as market response forecasting. Here the firm needs to check response of consumers to a range of prices. The firm estimates demand at various prices to determine at while price, demand will be the highest. This can help Hayes decide whether it should increase or decrease the price and by what percentage. D1 represents demand at different prices when other conditions are constant, D2 represents demand under different conditions.

The following graph illustrates how this forecasting helps:

D2

D1

PRICES

Demand forecasting thus helps a firm understand whether it should change the price or not and if yes, at what price will consumers be most satisfied. Price elasticity of demand can thus be checked with this method however errors are likely. However demand forecasts are still more reliable than sales forecasts for they study the relationship between an item and demand intent instead of simple sales forecasts based on nothing but previous sales data.

There are some other methods of forecasting demand. It is usually recommended that a firm uses more than one method to reach a close figure. A combination of methods used to estimate possible demand is likely to yield much better and accurate results than one method alone. Hayes can for example calculate demand using the market response method as well as the time-series analysis.

Conclusion: