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Thursday, May 10, 2012

Marketing Glossary


ADBUDG :-  A decision calculus model for the advertising budgeting decision. The model assumes that there is a fixed upper limit of response to saturation advertising, and it also assumes that there is a fixed lower limit to response under no advertising for an extended period. Within this range, increases in advertising spending increase response, and reductions in advertising spending lead to a decay in response over time. The model's parameters are calculated using subjective responses to a series of point-estimate questions concerning the likely impact of various advertising spending decisions (Little 1970). The effectiveness of the model's use has been discussed by Chakravarthi, Mitchell, and Staelin (1981) and Little and Lodish (1981).

ASSESSOR:- A model for predicting the market share of a new frequently purchased product using pretest market information. Perceptions and preferences of potential customers are measured via interview and a simulated shopping experience conducted at a central location. The prediction is based on the sample participants' reaction to advertising (exposure to advertisements for several brands), estimated level of product trial (based on the simulated shopping experience), estimated repeat purchase level (via follow-up interview), and brand preference judgments (Silk and Urban 1978). Evidence on the model's predictive validity has been reported by Urban and Katz (1983).


Beta Binomial Model:-  A probability mixture model commonly used to represent patterns of brand choice behavior or media exposure patterns. The model assumes that each individual's behavior follows a Bernoulli process. That is, an individual performs some behavior of interest (e.g., buying a brand of interest) with probability p on each possible opportunity (e.g., occasion on which a purchase is made from the product category of interest). The number of times that the behavior of interest is exhibited, out of a given number of opportunities, has the binomial distribution for any individual. The model further assumes that the probability values p vary across individuals according to a beta distribution (Greene 1982; Massy, Montgomery, and Morrison 1970). A generalization of this model to represent choice among more than two items is termed the Dirichlet multinomial model. These models are used to predict future brand choice or media exposure patterns based on individuals' past behavior.




BRANDAID:-                      A decision support system for determining the marketing mix for a particular brand. The model has submodels dealing with advertising spending level, price, and salesperson effort (i.e., dollars per customer per year). Its parameters can be calibrated by combining historical data (e.g., on sales, market share, advertising spending, etc.) with structured subjective judgments (Little 1975).