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The estimation approaches based on functionality-based size measures, e.g., function points, is also based on research conducted in the 1970s and 1980s, but are re-calibrated with modified size measures and different counting approaches, such as the use case points [11] or object points and COSMIC Function Points in the 1990s.
Nonlinear Pricing Schedule - Nonlinear pricing is a pricing schedule in which quantity and total price are not mapped to each other in a strictly linear fashion [2] Affine Pricing - An affine pricing schedule consists of both a fixed cost and a cost per unit. Using the same notation as above, T(q) = k + pq, where k is a constant cost. [3]
The Gabor–Granger method is a method to determine the price for a new product or service. It was developed in the 1960s by Clive Granger and André Gabor. It is a variant of monadic price testing. To use the Gabor-Granger method in a survey, one must find the highest price that respondents are willing to pay.
The data used are prices and quantities in two time-periods, (t-1) and (t), for each of n goods which are indexed by i. If we denote the price of item i at time t-1 by p i , t − 1 {\displaystyle p_{i,t-1}} , and, analogously, we define q i , t {\displaystyle q_{i,t}} to be the quantity purchased of item i at time t, then, the Törnqvist price ...
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Developed in 1764 by Gian Rinaldo Carli, an Italian economist, this formula is the arithmetic mean of the price relative between a period t and a base period 0. [The formula does not make clear over what the summation is done.] =
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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]).