Optimal Bundling and Pricing under a Monopoly: Contrasting Complements and Substitutes from Independently Valued Products*

Venkatesh, R.; Kamakura, Wagner A.

doi:10.1086/367748

Cited by 255 publications

(164 citation statements)

References 19 publications

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“…Guiltinan (1987) refers to this assumption as the assumption of strict additivity. Venkatesh and Kamakura (2003) relax the strict additivity assumption and allow for substitutability and complementarity. In this study we analyze these cases as well.…”

Section: Model and Analysismentioning

confidence: 99%

“…When the two products are complements or substitutes, the assumption of strict additivity does not hold. Following the approach in Venkatesh and Kamakura (2003), we define θ to measure the degree of contingency (the degree of complementarity or substitutability). The degree of contingency θ is given by…”

Section: Superadditivity and Subadditivity Of Reservation Pricesmentioning

confidence: 99%

“…As noted in Venkatesh and Kamakura (2003), "correlation in reservation prices and the degree of contingency are two distinct notions. While the degree of contingency parameter θ captures perceived value enhancement or reduction within each consumer, the correlation in reservation prices for two products shows how stand-alone reservation prices relate to each other across consumers."…”

Section: Superadditivity and Subadditivity Of Reservation Pricesmentioning

confidence: 99%

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Bundle pricing of inventories with stochastic demand

Bulut

Gürler

Şen

2009

European Journal of Operational Research

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We consider a retailer selling a fixed inventory of two perishable products over a finite horizon. The products are sold individually or as part of a bundle. The customers arrive following a Poisson Process and each customer makes a purchasing decision based on her reservation prices of individual products: she either buys one of the individual products, buys the bundle or leaves without a purchase. Assuming that the customer reservation prices follow a bivariate distribution, we determine the optimal product and the bundle prices that maximize the expected revenue. The performances of three bundling strategies (mixed bundling, pure bundling and unbundling) under different reservation price distributions, demand arrival rates and starting inventory levels are compared. The impact of the shape of the reservation prices is also investigated by considering both the bivariate normal and bivariate gamma densities, the latter of which has not been considered before in the literature. Our numerical results based on the bivariate normal reservation prices indicate that the performances of the policies heavily depend on the parameters of the demand process and the initial inventory levels. Bundling is observed to be most effective with negatively correlated reservation prices and when the starting inventory levels are high. When the starting inventory levels of two products are equal, most of the benefits of bundling can be achieved through pure bundling in case of excess supply. However, the mixed bundling strategy clearly dominates the other two when the starting inventory levels are not equal. Our numerical results show that bundling becomes more effective and the expected revenues increase as the products become less substitutable and more complementary. We also observe that the correct modeling of the reservation price distribution is important, and the use of sub-optimal prices resulting from assuming bivariate normal density when in fact the appropriate distribution is bivariate gamma may result in significant losses in the expected revenue especially if the reservation prices are negatively correlated. Finally, the model is extended to allow for price changes during the selling horizon using a dynamic programming formulation. It is shown that offering price bundles mid-season may be a more effective mechanism than changing individual product prices.

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Section: Model and Analysismentioning

confidence: 99%

Section: Superadditivity and Subadditivity Of Reservation Pricesmentioning

confidence: 99%

Section: Superadditivity and Subadditivity Of Reservation Pricesmentioning

confidence: 99%

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Bundle pricing of inventories with stochastic demand

Bulut

Gürler

Şen

2009

European Journal of Operational Research

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“…Anderson and Leruth (1993) study whether complements produced by the same firm will be sold separately or bundled under monopoly and oligopoly. Venkatesh and Kamakura (2003) unveil the optimal bundling strategy of a monopolist depending on whether its products are complements, substitutes or independent. Nalebuff (2000) examines competition between integrated producers and independent producers, showing the interesting tradeoff between the gains from coordinated pricing and the effect of competition from the independent producers.…”

Section: Introductionmentioning

confidence: 99%

Substituting Complements

Dari‐Mattiacci¹,

Parisi²

2006

Journal of Competition Law &Amp; Economics

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The presence of multiple sellers in the provision of (non-substitutable) complementary goods leads to outcomes that are worse than those generated by a monopoly (with a vertically integrated production of complements), a problem known in the economic literature as complementary oligopoly and recently popularized in the legal literature as the tragedy of the anticommons. We ask the following question: how many substitutes for each complement are necessary to render the presence of multiple sellers preferable to a monopoly? Highlighting the asymmetries between Cournot (quantity) and Bertrand (price) competition and their dual models, we show that the results crucially depend on whether firms compete by controlling price or quantity. Two substitutes per component are sufficient when firms choose price. However, when firms choose quantity, the availability of substitutes, regardless of their number, is ineffective. Considering more complex cases of multi-complementarity, we ask the related question of how many complements need to be substitutable and offer comments on equilibrium prices and quantities under different scenarios.JEL classification: D43, D62, K11, L13.

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“…In a seminal work Adams, and Yellen (1976) examined the reason of using bundling as a prevalent strategy and express that bundling strategy has the ability to sort customers into groups with different reservation prices which can help to extracting customer surplus. Venkatesh and Kamakura (2003) focused on the practice of bundling substitutable or complementary products; and under a monopolistic environment studied the difference between joint pricing and bundling strategy of such products and interdependently valued products. Chew et al (2014) examined the strategy of mixed bundling of two products under a stochastic and monopolistic market.…”

Section: Literature Of Bundlingmentioning

confidence: 99%

Integrating assortment selection, pricing and mixed-bundling problems for multiple retail categories under cross-selling

Rabbani¹,

Salehi²,

Farshbaf‐Geranmayeh³

2017

10.5267/j.uscm

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This paper optimizes joint mixed bundling, assortment planning and pricing problems for complementary retail categories that comprise substitutable items. These categories are divided into one primary category and multiple secondary categories and based on cross-selling effect, customers of primary category can opt to buy from other categories. It is assumed that the bundle can comprise one product of each offered complementary categories. A mixed-integer nonlinear programming is proposed that maximizes total profit by optimizing strategy of bundling, assortment selection and prices of single products and the bundle under a maximumsurplus customer choice model. Then because of computational considerations, this model is linearized and converted to a mixed-integer linear programming; whereby exact solution for even large-scale problems can be obtained. The results show that if cross-selling effect between product categories is overlooked or bundling of products is not allowed, then significant profit losses, will be resulted. To the best of our knowledge, this is the first study on bundling strategies in assortment selection decisions.Growing Science Ltd. All rights reserved. 7

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Optimal Bundling and Pricing under a Monopoly: Contrasting Complements and Substitutes from Independently Valued Products*

Cited by 255 publications

References 19 publications

Bundle pricing of inventories with stochastic demand

Bundle pricing of inventories with stochastic demand

Substituting Complements

Integrating assortment selection, pricing and mixed-bundling problems for multiple retail categories under cross-selling

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