[11] This data can be combined with cost and inventory levels to develop a profitable price point for that product or service.
A seller then estimates how customers in different segments will respond to different prices offered through different channels.
[13] Given this information, determining the prices that best meet corporate goals can be formulated and solved as a constrained optimization process.
[1][14] If capacity is constrained and perishable and customer willingness-to-pay decreases over time, then the underlying problem is one of markdown management.
Here, the complexity of combinations and permutations is an example of a big data solution where the seller can create central pricing strategies that then can be applied and executed across the organization.