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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]).
Typically, DaaS business is based on subscriptions and customers pay for a package of services or definite services. At the same time, investors must make sure that the revenue generated exceeds initial and operational costs of running the business. The pricing model is usually classified into 2 categories:
Repricing can be either competitor-based or value-based. While the former often increases revenue, it is also likely to cause price erosion. In contrast, value-based pricing tries to avoid a race to the bottom, by focusing more on the bottom-line margin. [11] There are several technology companies that specialize in repricing and pricing ...
In this setting, when periodic, or ad hoc decisions are made, analysis of historical transaction data sets is performed. This approach is often seen in large enterprises which have quantitative analysts familiar with the tools and, to various degrees, with Pricing Science methods, or which retain specialized consultants to perform the analysis.
The assumption underlying PSM is that respondents are capable of envisioning a pricing landscape and that price is an intrinsic measure of value or utility. Participants in a PSM exercise are asked to identify price points at which they can infer a particular value to the product or service under study.
In general business, price analysis is the process of evaluating a proposed price independent of cost and profit. [1] [2] Price analysis began in 1939 when economist Andrew Court decided to analyze prices to better understand the environmental factors that influence this practice. [3]
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.
Bayesian-optimal pricing (BO pricing) is a kind of algorithmic pricing in which a seller determines the sell-prices based on probabilistic assumptions on the valuations of the buyers. It is a simple kind of a Bayesian-optimal mechanism , in which the price is determined in advance without collecting actual buyers' bids.