# Data-Driven Modeling

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