Products with Branded Components: An Approach for Premium Pricing and Partner Selection

An increasing number of products are being sold with components that themselves are brand names. Examples are personal computers with Intel microprocessors and diet soft drinks that use the NutraSweet formulation. Branded components may alter consumers' valuation of the bundle, necessitating changes in the ways firms identify and price such bundled products. Surprisingly, the existing marketing literature is silent on this issue. The purpose of our paper is to propose an analytical approach that helps marketers of products with branded components make optimal pricing and partner selection decisions. Our managerial objectives are twofold: a To the seller of the bundled product, we suggest the optimal bundled product offering, optimal selling prices of alternative products, revenues and profits; and b to the branded component manufacturers, we indicate the most favorable alliance partners, and payoff gains/losses of aligning with other branded components instead of unbranded alternatives. Our analytical approach is also likely to be helpful to academics researching bundling and ingredient branding issues. The specific problem modeled assumes the bundled product has two components that are consumed jointly. Furthermore, each component can be either a brand name or “unbranded.” In this model, the consumer has no control over the choice of the components in the bundle; the seller decides what form of bundle components i.e., branded or unbranded to offer. We assume that the product that is marketed eventually enjoys a monopoly. Drawing from research in signaling and brand alliance, we posit each branded component may either enrich or suppress the value of the partnering component in the bundle as perceived by the consumers. Our approach has three methodological stages. First, we build an individual level model to assess the valuation of alternative products and their principal components at the level of a randomly drawn consumer. To do so, we rely primarily on the theory of reservation prices and Weber's theory on price/value changes. Second, we aggregate such valuation across consumers to assess the market's overall valuation. For this purpose, we invoke parametric distributions based on integral transform theory. Third, we develop payoff functions based on market valuation of alternative products and supply-side costs. The managerial objectives are met based on results from this stage. Our approach was successfully applied to the context of a university bookstore that intends to sell “Windows”-based laptop computers. The two principal components of the laptop computer bundle are the microprocessor chip and the personal computer platform on which the chip is implanted. The seller may choose either Compaq with “Intel Inside” or a simpler bundle featuring one of these with an unbranded, complementary component, or an entirely unbranded bundle. We used data collected from 192 potential consumers via a survey. Included in the survey were measures of consumers' perceptions of functional aspects of the components, and their reservation prices for alternative bundled products. The approach identified the most profitable bundle for the seller and its optimal price. It also provided estimates of the revenue impact of the gain or loss for each branded component by partnering the other branded component instead of the unbranded alternative. As such, the model facilitates the determination of the optimal price, price premium, and profits for products with branded components, as well as the identification of the ideal partner for each branded component manufacturer. It is apt to caution that products with branded components need not always lead to price premiums or lead to win-win outcomes. Reasons such as incongruity between the branded components or domination of one of the components over the other may drive potential consumers away, thus hurting profits. Implications of the results to negotiate component prices are discussed in the paper. The study limitations and directions for future research are also presented.

[1]  K. B. Monroe Pricing: Making Profitable Decisions , 1990 .

[2]  R. Venkatesh,et al.  A Probabilistic Approach to Pricing a Bundle of Products or Services , 1993 .

[3]  Gary J. Russell,et al.  A Probabilistic Choice Model for Market Segmentation and Elasticity Structure , 1989 .

[4]  Carl D. Bodenschatz,et al.  Finding an H-Function Distribution for the Sum of Independent H-Function Variates , 1992 .

[5]  P. R. Nelson The algebra of random variables , 1979 .

[6]  C. Whan Park,et al.  Composite Branding Alliances: An Investigation of Extension and Feedback Effects , 1996 .

[7]  Gilbert A. Churchill,et al.  Marketing Research: Methodological Foundations , 1976 .

[8]  Donald G. Norris Ingredient Branding: A Strategy Option with Multiple Beneficiaries , 1992 .

[9]  M. Whinston,et al.  Multiproduct Monopoly, Commodity Bundling, and Correlation of Values , 1989 .

[10]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[11]  Janet L. Yellen,et al.  Commodity Bundling and the Burden of Monopoly , 1976 .

[12]  David C. Schmittlein,et al.  Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort? , 1988 .

[13]  S. Varadhan,et al.  A probabilistic approach to , 1974 .

[14]  Ward Hanson,et al.  Optimal bundle pricing , 1990 .

[15]  Peter S. Fader,et al.  Accounting for Heterogeneity and Nonstationarity in a Cross-Sectional Model of Consumer Purchase Behavior , 1993 .

[16]  V. Srinivasan,et al.  A Survey-Based Method for Measuring and Understanding Brand Equity and Its Extendibility , 1994 .

[17]  P. Kotler Marketing Management: Analysis, Planning, Implementation and Control , 1972 .

[18]  Yoram Wind,et al.  Conjoint Analysis of Price Premiums for Hotel Amenities , 1984 .

[19]  B. Wernerfelt,et al.  Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond , 1988 .

[20]  Wayne D. Hoyer,et al.  Promotion Signal: Proxy for a Price Cut? , 1990 .

[21]  Philip Rabinowitz,et al.  Numerical methods for nonlinear algebraic equations , 1970 .

[22]  Pradeep K. Chintagunta,et al.  Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data , 1991 .

[23]  P. R. Fisk,et al.  Distributions in Statistics: Continuous Multivariate Distributions , 1971 .

[24]  F. Bass,et al.  A Model of Stochastic Variety-Seeking , 1994 .

[25]  Gilbert A. Churchill,et al.  Research Design Effects on the Reliability of Rating Scales: A Meta-Analysis: , 1984 .

[26]  Jordan J. Louviere,et al.  Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data , 1983 .

[27]  I. D. Cook,et al.  Evaluation of the H-Function Integral and of Probability Distributions of Functions of Independent Random Variables , 1981 .

[28]  Charles B. Weinberg,et al.  Pricing a Bundle of Products or Services: The Case of Nonprofits , 1996 .

[29]  Richard Schmalensee,et al.  Gaussian Demand and Commodity Bundling , 1984 .