Estimation of Willingness-to-Pay
BeschreibungWith the Price Estimation scene (PE scene) Christoph Breidert introduces a new method to estimate willingness-to-pay. It works as an additional interview scene appended to conjoint analysis and offers the respondents a dynamically generated sequence of product choices with assigned prices. The customers indicate whether they would actually purchase the presented product profiles.
InhaltsverzeichnisPricing in the marketing mix
Willingness-to-pay in marketing
Measurement of willingness-to-pay
Price Estimation scene (PE scene)
Empirical investigation at Nokia online shop Germany
PortraitDr. Christoph Breidert promovierte an der Wirtschaftsuniversität Wien bei Prof. Dr. Wolfgang Janko, Institut für Informationswirtschaft und Prof. Dr. Thomas Reutterer, Institut für Handel und Marketing. Er ist als Technology Analyst für PONTIS Venture Partners in Wien tätig.
Chapter 6 Conjoint Analysis in Pricing Studies (S. 68-69)
This chapter discusses how pricing studies are performed when conjoint analysis is apphed. The general approach is to include price in the conjoint study as yet another attribute. We will explain how this is done, and review a selection of pubhcations which exphcitly focus on the estimation of wilhngness-to-pay (WTP). Subsequent to this it will be developed what problems can arise from traditional approaches when price is included as an attribute. Three problems that can be identified will be the main focus of attention. These are (1) theoretical problems, (2) practical problems, and (3) estimation problems. Each of these problems will be discussed in detail. These problems will be emphasized and it will be illustrated how they can be overcome when the new estimation approach, the PE scene, is applied.
According to the literature pricing studies are one of the most important applications of conjoint analysis (e.g., Gustafsson et al. (2000, pp. 6-7)). In a study on conjoint applications in the US in the years 1981-1985 Wittink and Cattin (1989) surveyed 59 companies who carried out 1062 conjoint studies. 38% of the identified studies were pricing studies. In a similar study on the application of conjoint analysis in the European market in the years 1986-1991 Wittink and Burhenne (1994) surveyed 66 companies and reported a total of 956 conjoint studies. Out of these 46% were pricing studies. Baier (1999) carried out a smaller study in the German market. 8 companies were interviewed and 382 conjoint studies were identified, of which 62% were pricing studies. Hartmann and Sattler (2002a,b) surveyed 54 marketing research institutes in Germany, Austria, and Switzerland in the year 2001.
These institutes performed a total of 304 studies regarding preference measurement. 121 studies were
documented in greater detail by the marketing research institutes, showing that 48% were pricing studies. Not only surveys of the usage of conjoint analysis show the importance of pricing research. Publications of the application of conjoint analysis in scientific journals also illustrates their importance. In a broad review Voeth (1999) summarizes the pubhcations on conjoint analysis in German between the years 1976-1998. Most of the identified 150 studies were published in the 1990s. 31 studies exphcitly focused on pricing. Some of the best examples from the Hterature regarding pricing studies performed by conjoint analysis in important German and English scientific journals are Currim et al. (1981), Mahajan et al. (1982), Goldberg et al. (1984), Green and Krieger (1990), Balderjahn (1991), Green and Krieger (1992), Balderjahn (1994), Eggenberger and Christof (1996), and Green et al. (1997). As can be seen from practical applications and journal pubHcations, pricing studies are an important field of conjoint analysis.
Apparently, conjoint analysis is a method which is well suited for pricing studies (Diller, 2000, p. 202). In order to design a pricing strategy exceptionally insightful knowledge is needed regarding to the reaction of customers to different price schemes. Questions like the following must be answered: How many customers will buy a certain product at different price levels? What does the preference structure of the customers look like for different product configurations under different prices? Can variations of a specific feature for different products be assigned a monetary equivalent? Can customers be classified based upon their preference structure?