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Price optimization utilizes data analysis to predict the behavior of potential buyers to different prices of a product or service. Depending on the type of methodology being implemented, the analysis may leverage survey data (e.g. such as in a conjoint pricing analysis [7]) or raw data (e.g. such as in a behavioral analysis leveraging 'big data' [8] [9]).
A business can use a variety of pricing strategies when selling a product or service. To determine the most effective pricing strategy for a company, senior executives need to first identify the company's pricing position, pricing segment, pricing capability and their competitive pricing reaction strategy. [ 1 ]
Applications have included business services providers, industrial product manufacturers, and distributors of products ranging from technology to food to office supplies. Even airlines and other early practitioners began to revisit their original assumption that prices were a "given," a simple input to their optimization technology.
The strategy enables price changes to goods and services relative to increases or decreases in the product cost which are simple to communicate and justify to customers. [8] When there is little market intelligence, the use of a cost-plus pricing strategy compensates for the lack of information by setting prices based on actual costs. [9]
Dynamic pricing, also referred to as surge pricing, demand pricing, or time-based pricing, and variable pricing, is a revenue management pricing strategy in which businesses set flexible prices for products or services based on current market demands. It usually entails raising prices during periods of peak demand and lowering prices during ...
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Algorithmic pricing is the practice of automatically setting the requested price for items for sale, in order to maximize the seller's profits. Dynamic pricing algorithms usually rely on one or more of the following data. Probabilistic and statistical information on potential buyers; see Bayesian-optimal pricing. Prices of competitors.
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