Sammenlign metoder

	Gabor-Granger Pricing	Choice-Based Conjoint
Fagområde	Marketing Research	Marketing Research
Familie	Process / pipeline	Regression model
Oprindelsesår	1966	1983
Ophavsperson	André Gabor & Clive W. J. Granger	Jordan J. Louviere & George Woodworth; Sawtooth Software (Bryan Orme)
Type	Direct purchase-intent pricing survey yielding a demand curve	Discrete-choice experiment for product preference and part-worth utilities
Oprindelig kilde	Gabor, A., & Granger, C. W. J. (1966). Price as an Indicator of Quality: Report on an Enquiry. Economica, 33(129), 43-70.	Louviere, J. J., & Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research, 20(4), 350-367.
Aliasser	Gabor-Granger, Gabor-Granger Technique, Direct Price-Response Method, Purchase-Intent Pricing	CBC, Discrete-Choice Conjoint, Choice Experiment Conjoint, Choice-Based Conjoint Analysis
Relaterede	4	4
Resumé	The Gabor-Granger method is a direct pricing-research technique that estimates a product's demand curve by asking respondents whether they would buy it at each of several price points. Developed by economists André Gabor and Clive Granger in the 1960s through surveys of how consumers perceive and react to prices, it asks a simple question, would you purchase at this price?, across a ladder of prices, usually presented in random order. Aggregating the share of people willing to buy at each price traces a stated demand curve, from which the analyst computes expected revenue at every price and identifies the price that maximizes it. Because it focuses on a single product rather than competitive trade-offs, Gabor-Granger is fast, intuitive, and well suited to setting or testing a price for an existing or clearly defined offering. It also yields a straightforward estimate of price elasticity. Its directness is both its appeal and its main weakness, since asking about price in isolation can prime respondents and overstate price sensitivity.	Choice-based conjoint analysis (CBC) measures how consumers value the features of a product by observing the choices they make among competing, attribute-defined profiles rather than by asking them to rate attributes directly. Each respondent completes a series of choice tasks, picking the single most preferred alternative (often with a 'none' option) from a small set, and the pattern of choices across many tasks reveals the implicit trade-offs people make. The method grew out of Louviere and Woodworth's 1983 integration of conjoint measurement with discrete-choice theory, which showed that controlled choice experiments could be analyzed with the multinomial logit model. Because the choice task mimics a real purchase decision, CBC has become the dominant form of conjoint in commercial marketing research, popularized by Sawtooth Software. Estimation recovers part-worth utilities for every attribute level, either at the aggregate level or, more commonly today, individually through hierarchical Bayes. Those utilities then feed market simulators that predict shares of preference for new or hypothetical product configurations.