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Bass model

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  1. Introduction.
  2. Model development.
    1. Assumptions Of the bass.
    2. Model collaboration.
  3. Application & analysis.
    1. Output.
  4. Conclusion.
  5. References.

Most Companies recognise that a continuing stream of new product developments is essential to ensure long term organisational health, but they also recognise that innovation is accompanied by high cost and risks. These risks can be controlled through a well-conceived and professionally managed program of new product development. Presumably a new product is introduced by a company when a favourably estimate has been made of its future sales, profits and other impacts on the firm's objectives. New products sales are shaped by many factors, including the size of the potential market, the nature of competition, and the company's marketing plan and resources. The appropriate sales-forecasting model varies with the type of new product situation. These situations are distinguished by the degree of newness of the product re-purchasing.

[...] Pure Innovative Model: Fourt & Woodlock T-1 Qt = rQ(1 Qt = increment in cumulative sales as fraction of potential sales R = rate of penetration of untapped potential Q = total potential sales as fraction of all buyers t = time period Pure Imitative Diffusion model: Fisher and Pry (1971) df/ dt = b(1-f)f _ f = fraction of market having adopted new Q b = constant characterising growth to potential associated with particular technology Combined Innovative & Imitative: The Bass Model The Bass model has been used for many years in marketing, it is most appropriate for forecasting sales of an innovation (or new product) for which there are no closely competing alternatives existing in the marketplace. [...]

[...] the solution of the differential equation specification of the Bass model (New product diffusion models Journal of Marketing) NLS is thought to be a more superior estimation procedure than OLS, although maximum likelihood procedure is said to perform equally well when survey type diffusion data are used to estimate the parameters because of the dominance of sampling errors ( Mahajan, Mason and Srinivasan 1986, Srinivasan and Mason 1986) NLS (NonLinear Least Squares) estimation in the Bass Model coefficients are biased and that they change systematically as one extends the number of observations used in the estimation. [...]

[...] Top of the Document As a consequence of this assumption, the Bass model does not take into account stages in the adoption process (e.g. awareness, knowledge). Some of the attempts to extend the two-stage models to incorporate the multistage nature of the diffusion process include Midgley (1976) etc Diffusion of an innovation is not influenced by marketing strategies Since the pioneering work of Robinson and Lakhani (1975) that incorporated the impact of price in the Bass model, several efforts have been made to study systematically the impact of marketing mix variables such as price, advertising, promotion and personal selling, and distribution on product growth. [...]

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