Logic-drive modeling is usually the first step to establish relationships through data driven models (using data collected from many sources to quantitatively establish model relationships.
Example
In the spring, a department store introduces a new line of bathing suits that sells for $70. The store purchases 1000 of these bathing suits. During the prime selling season, the store sells an average of 7 units per day at full price (40 days). On 10 sale days, the price is discounted 30% and sales increase to 32.2 units per day. Around July 4th, the price is marked down 70% to sell off remaining inventory. Determine total revenue from the bathing suits.
Assume a linear trend model between sales and price:
daily sales = a – b(price)
7 = a – b(70)
32.2 = a – b(49)
Daily sales = 91 – 1.2(price)
Revenue from full retail sales
= units sold * days * price
= (7)*(40)*(70)
= $19,600
Revenue from sale weekends
= (32.2)*(10)*(49)
= $15,778
Revenue from clearance sales
= leftovers * price
= (1000−7(40) − 32.2(10))*(21)
= (398)(21)
= $8,358
Total revenue = $43,736
