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Conjoint Analysis
Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to survey respondents and by analyzing how they make choices among these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs. Conjoint analysis originated in mathematical psychology and was developed by marketing professor Paul E. Green at the Wharton School of the University of Pennsylvania. Other prominent conjoint analysis pi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rule-developing Experimentation
Rule-developing experimentation or RDE is a systematized solution-oriented business process of experimentation that designs, tests, and modifies alternative ideas, packages, products, or services in a disciplined way using experimental design, so that the developer and marketer discover what appeals to the customer, even if the customer can't articulate the need, much less the solution. Explanation Rule-developing experimentation was developed by Moskowitz Jacobs Inc. in cooperation with Professor Jerry (Yoram) Wind (Wharton School of Business at University of Pennsylvania). The term was initially coined bHoward R. MoskowitzanAlex Gofmanin a series of articles and conference papers. The paradigm for systematic design and developing/using the rules in various applications was formalized in their book '' Selling Blue Elephants: How to Make Great Products That People Want Before They Even Know They Want Them'' (Wharton School Publishing, 2007).Moskowitz, H. and Gofman, A. (2007). Se ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Maximum Likelihood Estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. If the likelihood function is differentiable, the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when all observed outcomes are assumed to have Normal distributions with the same variance. From the perspective of Bayesian inference ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called '' simple linear regression''; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuse ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Test Market
A test market, in the field of business and marketing, is a geographic region or demographic group used to gauge the viability of a product or service in the mass market prior to a wide scale roll-out. The criteria used to judge the acceptability of a test market region or group include: #a population that is demographically similar to the proposed target market; and #relative isolation from densely populated media markets so that advertising to the test audience can be efficient and economical. Practical use The test market ideally aims to duplicate "everything" - promotion and distribution as well as "product" - on a smaller scale. The technique replicates, typically in one area, what is planned to occur in a national launch; and the results are very carefully monitored, so that they can be extrapolated to projected national results. The area may be any one of the following: *Television area *Internet online test *Test town *Residential neighborhood *Test site A number of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Configurator
Configurators, also known as choice boards, design systems, toolkits, or co-design platforms, are responsible for guiding the user through the configuration process. Different variations are represented, visualized, assessed and priced which starts a learning-by-doing process for the user. While the term “configurator” or “configuration system” is quoted rather often in literature, it is used for the most part in a technical sense, addressing a software tool. The success of such an interaction system is, however, not only defined by its technological capabilities, but also by its integration in the whole sale environment, its ability to allow for learning by doing, to provide experience and process satisfaction, and its integration into the brand concept. () Advantages Configurators can be found in various forms and different industries (). They are employed in B2B (business to business), as well as B2C (business to consumer) markets and are operated either by trained staf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Probability
Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses; that is, with propositions whose truth or falsity is unknown. In the Bayesian view, a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies a prior probability. This, in turn, is then updated to a posterior probability in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and form ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Revealed Preference
Revealed preference theory, pioneered by economist Paul Anthony Samuelson in 1938, is a method of analyzing choices made by individuals, mostly used for comparing the influence of policies on consumer behavior. Revealed preference models assume that the preferences of consumers can be revealed by their purchasing habits. Revealed preference theory arose because existing theories of consumer demand were based on a diminishing marginal rate of substitution (MRS). This diminishing MRS relied on the assumption that consumers make consumption decisions to maximise their utility. While utility maximisation was not a controversial assumption, the underlying utility functions could not be measured with great certainty. Revealed preference theory was a means to reconcile demand theory by defining utility functions by observing behaviour. Therefore, revealed preference is a way to infer the preferences of individuals given the observed choices. It contrasts with attempts to directly meas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Econometric Modeling
Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory. A simple example of an econometric model is one that assumes that monthly spending by consumers is linearly dependent on consumers' income in the previous month. Then the model will consist of the equation :C_t = a + bY_ + e_t, where ''C''''t'' is consumer spending in month ''t'', ''Y''''t''-1 is income during the previous month, and ''et'' is an error term measuring the extent to which the model cannot fully explain consumption. Then one objective of the econometrician is to obtain est ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Discrete Choice Analysis
In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in which the quantity of each good consumed is assumed to be a continuous variable. In the continuous case, calculus methods (e.g. first-order conditions) can be used to determine the optimum amount chosen, and demand can be modeled empirically using regression analysis. On the other hand, discrete choice analysis examines situations in which the potential outcomes are discrete, such that the optimum is not characterized by standard first-order conditions. Thus, instead of examining "how much" as in problems with continuous choice variables, discrete choice analysis examines "which one". However, discrete choice analysis can also be used to examine the chosen quantity when only a few distin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
