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Analytical Approach
We will illustrate this through an example.
Example 1
A firm sells a product in a market with a few competitors. The average price charged by the competitors is Rs.10. The firm can follow any one of the pricing policies given below:
Match the competition price at Rs.10
Price two rupees above the competition at Rs.12
Price two rupees below the competition at Rs.8.
The firm knows the quantities it can sell at these prices:
Price (Rs.)
Quantity (Nos.)
8
10
12
15,000
10,000
7,500
The total cost of production is as below:
Quantity
Cost (Rs.)
95,000
80,000
75,000
To find out the price that the firm should charge, we must first determine the objective of the firm. Let us assume that the objective of the firm is to maximize profits. (The firm could easily have other objectives - to price the product always below the competitor's price in which case Rs.8 would be chosen or to price the product always above the competitors' price so that a higher price can be used to create the impression of a better quality in the minds of the consumers. In the latter case Rs.12 would be chosen).
To find out the price which would maximize the profits, we construct the following table:
Sales Quantity (Nos.)
Sales Value
Profit (Rs.)
1,20,000
25,000
1,00,000
20,000
90,000
We thus find that the profits are maximized at the price of Rs.8 per unit, and therefore this price should be chosen.
Though the analytical approach is quite simple and intuitive, it may not be possible to adopt this in all decision making situations. In reality, information regarding the average price charged by the competitors may not be available or may be dependent upon the price charged by the firm as the competitors may react to every change effected by the firm. The information regarding the exact quantities that can be sold at different prices may not be available or only a possible range of quantities may be known. Similarly, the cost of producing different quantities may not be exactly known.
The box plot displays the diversity of data for the totexp; the data ranges from 30 being the minimum value and 390 being the maximum value. The box plot is positively skewed at 1.
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