A look at the Bass model: Report structure
- New product diffusion using the bass model.
- Meaning of new product diffusion model.
- An overview of the bass model.
- Using analogous products in diffusion modeling.
- The assumptions and limitations of the bass model.
- Technical description of the components of the bass model.
- Estimation procedures.
- Ordinary least squares estimation.
- Nonlinear least squares estimation.
- The Variants of the bass model: Other diffusion models explored in brief.
- The Mansfield model.
- The Gompertz model.
- Uses of Diffusion models.
- Descriptive and normative uses.
- A look at the model using the VCR data set.
- Report of the findings & implications.
Diffusion is a popular theory of communication used in marketing to model the first-purchase sales growth of a new product over time. As Mahajan et al (1990) put it diffusion theory suggests that a new product is first adapted by a few innovators who, in turn, influence others to adopt it. Taking this perspective on diffusion we can clearly see the pivotal role which interpersonal communication (word of mouth) between adopters and nonadopters plays in accounting for the rapid growth stage found in the diffusion process. It is important to remember that the value of diffusion modeling is not just restricted to historical data; rather leading academics in this field like Bass have made predictions based on early sales data which have resulted in successful predictions of diffusions before those products reached their peak. From a commercial perspective there are endless examples of diffusion processes, which will be elaborated on later, including: the diffusion of blockbuster movies; mobile phones and other analogous products. For the purposes of this assessment we will discuss the principal theories in this field playing particular attention to Bass and its variants, we will then apply these theories to the practical example of VCR diffusion.
[...] For the two other diffusion models considered in the study, (the Mansfield model and the Gompertz curve) the NLS again performs best and generally displays better predictions among the estimation procedures 6.1 Ordinary Least Squares Estimation (OLS) From Mahajan et al (1990), Bass uses a discrete analogue of his formula (as outlined above) and considers the case when the time intervals are equal to yearly data. Then, the probability that an initial purchase will be made in the ith time interval given that no purchase has yet been made, is expressed as a linear function of the number of previous adapters: = p+q/m N(ti-1) Advantages of OLS: Easy to implement Applicable to many different diffusion models Disadvantages of OLS: one may obtain parameter estimates that are unstable or possess wrong signs Standard Errors for the estimates are not available since the parameters m are nonlinear functions of ?1, ?2, ?3 A time interval bias is present in OLS approach since discrete time- series data are used for estimating a continuous-time model Nonlinear least squares estimation (nls) This estimation procedure is designed to overcome the shortcomings of the maximum likelihood approach. [...]
[...] A number of estimation procedures have been suggested to estimate these parameters in the Bass model. From a practical standpoint it will be desirable to use subjective managerial judgment and/or experience with analogous products to estimate parameters prior to the launch of a new product. However for the purposes of this paper we will ignore the role of subjective judgment and concentrate on objective estimation procedures that require the availability of a reasonable amount of data (usually including the inflection point) to produce reliable estimates of model parameters. [...]
[...] The advantage of this approach is that instead of managers merely guessing the new product sales they guess the inputs to a well established model, and the model provides the structure for incorporating these inputs in generating the sales forecasts Care must be taken in choosing the analogous products Report of the findings & implications (Summary of findings) As it can be seen from the comparison of output Bass NLS has the best fit for the data because it has the lowest RSE, MAD & MSE values when compared with the other models selected. [...]