The practice of selling multiple products or services under nonlinear pricing has a long history in the business community. Consumers may face demand uncertainty when choosing a service plan, and preferences for multiple products or services may be inter-dependent. To examine a demand system with these features, we construct a two-stage discrete/continuous choice model for service bundle demand under three-part tariffs, allowing for interactive utility and preference correlations. Implementing a piecewise maximization approach to the consumer utility maximization problem, which is non-differentiable under three-part tariffs, we estimate the model via simulated method of moments. We then apply our model to data from a major wireless service provider in China. Finally, our counterfactual analysis simulates outcomes under three-part tariffs with interchangeable units. Compared to existing tariffs, the proposed ones contain fewer instruments without a significant loss of revenue. We also illustrate the implications of incorporating ex-post usage shocks in model estimates, elasticities and counterfactual outcomes.
{"title":"Estimating the Demand for Service Bundles under Three-Part Tariffs","authors":"Liangheng Chen, Yao Luo, Ping Xiao","doi":"10.2139/ssrn.3353982","DOIUrl":"https://doi.org/10.2139/ssrn.3353982","url":null,"abstract":"The practice of selling multiple products or services under nonlinear pricing has a long history in the business community. Consumers may face demand uncertainty when choosing a service plan, and preferences for multiple products or services may be inter-dependent. To examine a demand system with these features, we construct a two-stage discrete/continuous choice model for service bundle demand under three-part tariffs, allowing for interactive utility and preference correlations. Implementing a piecewise maximization approach to the consumer utility maximization problem, which is non-differentiable under three-part tariffs, we estimate the model via simulated method of moments. We then apply our model to data from a major wireless service provider in China. Finally, our counterfactual analysis simulates outcomes under three-part tariffs with interchangeable units. Compared to existing tariffs, the proposed ones contain fewer instruments without a significant loss of revenue. We also illustrate the implications of incorporating ex-post usage shocks in model estimates, elasticities and counterfactual outcomes.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122716165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the online marketplaces have flourished, the role of product and market information in how it affects an online marketplace and how it can be leveraged to improve the sales or sales volume of the platform has not been explored in the prior research. We study how the provision of product and/or market information affects buyers' and sellers' behavior in an online marketplace by establishing the (Pareto-dominant) equilibrium for the sellers' pricing decisions under various information structures. Our main findings are as followings. First, we show that in equilibrium while sales volume of the platform increases in both the size of the buyers' pool and the size of the sellers' pool, sales increase only in the size of the buyers' pool and are unimodal in the size of the sellers' pool. Second, by analytically characterizing the platform's optimal information strategy as a function of the underlying market parameters and whether the platform's goal is to maximize sales or sales volume, we find that providing product and/or market information may backfire on the platform by jeopardizing its financial performance. Third, we demonstrate using numerical studies that information is more valuable to the platform when the goal of the platform is to maximize sales rather than sales volume, and when it faces a seller's market (i.e., demand-to-supply ratio is greater than one) rather than a buyer's market (i.e., demand-to-supply ratio is less than one).
{"title":"The Role of Product and Market Information in an Online Marketplace","authors":"Shu Hu, Mike Mingcheng Wei, Shiliang Cui","doi":"10.2139/ssrn.3259465","DOIUrl":"https://doi.org/10.2139/ssrn.3259465","url":null,"abstract":"Although the online marketplaces have flourished, the role of product and market information in how it affects an online marketplace and how it can be leveraged to improve the sales or sales volume of the platform has not been explored in the prior research. We study how the provision of product and/or market information affects buyers' and sellers' behavior in an online marketplace by establishing the (Pareto-dominant) equilibrium for the sellers' pricing decisions under various information structures. Our main findings are as followings. First, we show that in equilibrium while sales volume of the platform increases in both the size of the buyers' pool and the size of the sellers' pool, sales increase only in the size of the buyers' pool and are unimodal in the size of the sellers' pool. Second, by analytically characterizing the platform's optimal information strategy as a function of the underlying market parameters and whether the platform's goal is to maximize sales or sales volume, we find that providing product and/or market information may backfire on the platform by jeopardizing its financial performance. Third, we demonstrate using numerical studies that information is more valuable to the platform when the goal of the platform is to maximize sales rather than sales volume, and when it faces a seller's market (i.e., demand-to-supply ratio is greater than one) rather than a buyer's market (i.e., demand-to-supply ratio is less than one).","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134323173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate how the adoption of price parity clauses (PPCs) by established platforms affects the listing decisions of suppliers. PPCs have been widely adopted by online travel agencies (OTAs) to force client hotels not to charge lower prices in alternative sales channels. We find that OTAs adopt PPCs when they are perceived as highly substitutable, and in order to prevent showrooming. PPCs allow OTAs to charge hotels higher commission fees. However, hotels can respond by delisting themselves from some OTAs. Hence, our analysis reveals that the removal of PPCs enables more hotels to resort to OTAs. This is beneficial for consumers, as prices decrease in absence of PPCs.
{"title":"Platform Price Parity Clauses and Segmentation","authors":"Joan Calzada, Ester Manna, A. Mantovani","doi":"10.2139/ssrn.3329157","DOIUrl":"https://doi.org/10.2139/ssrn.3329157","url":null,"abstract":"We investigate how the adoption of price parity clauses (PPCs) by established platforms affects the listing decisions of suppliers. PPCs have been widely adopted by online travel agencies (OTAs) to force client hotels not to charge lower prices in alternative sales channels. We find that OTAs adopt PPCs when they are perceived as highly substitutable, and in order to prevent showrooming. PPCs allow OTAs to charge hotels higher commission fees. However, hotels can respond by delisting themselves from some OTAs. Hence, our analysis reveals that the removal of PPCs enables more hotels to resort to OTAs. This is beneficial for consumers, as prices decrease in absence of PPCs.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Choice models are used frequently in marketing to help management make better or more informed product and pricing decisions. The most common statistical model for choice studies is the multinomial logit with heteregeneity via Hierarchcial Bayes (HB). The standard approach is to model main effects for each attribute, but interactions between attributes are also possible. Recently, a company that produces choice modeling software has introduced an automated interaction search feature creating additional buzz for interactions in choice studies. More needs to be known about interactions in the presence of heterogeneity before applying feature interactions willy-nilly. Here we propose a method for testing interaction terms with a special look at brand-price interactions (equivalent to brand specific price effects) that are often recommended in practice. We show that brand specific pricing is unnecessary when using HB estimation and the added terms can lead to devastating model overfit. We provide a general framework for testing interactions. The step-by-step process can be used without a set of dedicated holdout tasks or sample. When evaluating interactions and model specifications more generally, overfit as well as managerial inferences should both be evaluated.
{"title":"Brand - Price Interactions in Discrete Choice: But Should You?","authors":"Jake Lee","doi":"10.2139/ssrn.3282674","DOIUrl":"https://doi.org/10.2139/ssrn.3282674","url":null,"abstract":"Choice models are used frequently in marketing to help management make better or more informed product and pricing decisions. \u0000 \u0000The most common statistical model for choice studies is the multinomial logit with heteregeneity via Hierarchcial Bayes (HB). The standard approach is to model main effects for each attribute, but interactions between attributes are also possible. Recently, a company that produces choice modeling software has introduced an automated interaction search feature creating additional buzz for interactions in choice studies. \u0000 \u0000More needs to be known about interactions in the presence of heterogeneity before applying feature interactions willy-nilly. \u0000 \u0000Here we propose a method for testing interaction terms with a special look at brand-price interactions (equivalent to brand specific price effects) that are often recommended in practice. \u0000 \u0000We show that brand specific pricing is unnecessary when using HB estimation and the added terms can lead to devastating model overfit. \u0000 \u0000We provide a general framework for testing interactions. The step-by-step process can be used without a set of dedicated holdout tasks or sample. When evaluating interactions and model specifications more generally, overfit as well as managerial inferences should both be evaluated.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128915607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the response of markets to a firm-specific shock in a natural experiment setting. In 2006, a boycott of Danish products in several Arab countries was devastating for Danish cheese firms. In Saudi Arabia their market share collapsed from 16.5% in January to less than 1% in March and never fully recovered: it was 6.3% in 2009. By analyzing micro-level (scanner) price and expenditure data we find that (i) Danish firms lowered prices but kept the product mix the same; (ii) non-Danish firms kept prices constant but changed their product mix by introducing new products and new product bundles; and (iii) non-Danish firms chose to introduce products that were identical to the Danish in order to compete head-to-head. The finding that Danish firms adjusted to the negative demand shock through the intensive margin and non-Danish to the positive through the extensive is hard to reconcile with existing pricing theories or theories on multi-product firms. We offer two potential explanations that can help reconcile our findings with existing models.
{"title":"Micro-responses to Shocks: Pricing, Promotion, and Entry","authors":"Alexis Antoniades, Sofronis Clerides, Mingzhi Xu","doi":"10.2139/ssrn.3471070","DOIUrl":"https://doi.org/10.2139/ssrn.3471070","url":null,"abstract":"We study the response of markets to a firm-specific shock in a natural experiment setting. In 2006, a boycott of Danish products in several Arab countries was devastating for Danish cheese firms. In Saudi Arabia their market share collapsed from 16.5% in January to less than 1% in March and never fully recovered: it was 6.3% in 2009. By analyzing micro-level (scanner) price and expenditure data we find that (i) Danish firms lowered prices but kept the product mix the same; (ii) non-Danish firms kept prices constant but changed their product mix by introducing new products and new product bundles; and (iii) non-Danish firms chose to introduce products that were identical to the Danish in order to compete head-to-head. The finding that Danish firms adjusted to the negative demand shock through the intensive margin and non-Danish to the positive through the extensive is hard to reconcile with existing pricing theories or theories on multi-product firms. We offer two potential explanations that can help reconcile our findings with existing models.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115777614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Higher education pricing models have focused heavily on traditional student population analysis, net earnings, financial aid, and enrollment projections or unduplicated headcount. As the population of students shifts to a nontraditional majority, research of the effect of tuition price on nontraditional population segments is needed with a focus on persistence (the likelihood of re-enrollment in the next semester for a given student) rather than overall enrollment levels. It becomes prudent to re-evaluate pricing models and the associated coefficients from tuition pricing changes on persistence to more effectively serve the nontraditional population as nontraditional students rely less on financial aid and progress through their curriculum at an individualized pace consistent with their needs. The nontraditional population is, on average, older, with more professional experience, often with military affiliations (active duty, veteran, reservist, or family member), and education in progress. Using a quantitative longitudinal empirical case study, the researcher utilized student-level data from a private, nonprofit university in the Commission on Colleges of the Southern Association of Colleges and Schools regional accreditation territory to determine the effect of a tuition increase on nontraditional student (age 25 ) persistence. The data was analyzed using a linear regression interaction model in STATA. The researcher found statistical significance, with the counterintuitive finding that the effect of a 1 percent tuition increase for all students was an increase in persistence by 2.01 percent with a clear explanation for this finding of the overall tuition effect on persistence. Consistent with theory, this research finds that nontraditional students only increased their persistence by 0.62 percent, persisting less than traditional students. For every 1 percent increase in tuition, nontraditional online students are decreasing their persistence by 0.9 percent, persisting less than face-to-face students. These findings are important, as they provide contributions to Elasticity Theory, Tuition Elasticity Theory, and practice including application for higher education institutions, administrators and advisors in higher education, and customer relationship management software as service companies targeting students utilizing variations of predictive analytics to estimate persistence of different populations, estimate and understand tuition price increase effects on different populations, set recruiting and enrollment goals based upon expected attrition, and design customized communication plans to facilitate more in-depth relationships with those less likely to persist in an effort to overcome this statistic. These findings are also the first portion of exploring elasticities as they apply toward developing a pricing model for nontraditional student populations using the framework established by the TENEP model (Bryan & Whipple, 1995).
{"title":"Higher Education Pricing: Effects of Tuition Pricing on Nontraditional Student Persistence Moderated by Demographics","authors":"K. Spradley","doi":"10.2139/ssrn.3241757","DOIUrl":"https://doi.org/10.2139/ssrn.3241757","url":null,"abstract":"Higher education pricing models have focused heavily on traditional student population analysis, net earnings, financial aid, and enrollment projections or unduplicated headcount. As the population of students shifts to a nontraditional majority, research of the effect of tuition price on nontraditional population segments is needed with a focus on persistence (the likelihood of re-enrollment in the next semester for a given student) rather than overall enrollment levels. It becomes prudent to re-evaluate pricing models and the associated coefficients from tuition pricing changes on persistence to more effectively serve the nontraditional population as nontraditional students rely less on financial aid and progress through their curriculum at an individualized pace consistent with their needs. The nontraditional population is, on average, older, with more professional experience, often with military affiliations (active duty, veteran, reservist, or family member), and education in progress. Using a quantitative longitudinal empirical case study, the researcher utilized student-level data from a private, nonprofit university in the Commission on Colleges of the Southern Association of Colleges and Schools regional accreditation territory to determine the effect of a tuition increase on nontraditional student (age 25 ) persistence. The data was analyzed using a linear regression interaction model in STATA. The researcher found statistical significance, with the counterintuitive finding that the effect of a 1 percent tuition increase for all students was an increase in persistence by 2.01 percent with a clear explanation for this finding of the overall tuition effect on persistence. Consistent with theory, this research finds that nontraditional students only increased their persistence by 0.62 percent, persisting less than traditional students. For every 1 percent increase in tuition, nontraditional online students are decreasing their persistence by 0.9 percent, persisting less than face-to-face students. These findings are important, as they provide contributions to Elasticity Theory, Tuition Elasticity Theory, and practice including application for higher education institutions, administrators and advisors in higher education, and customer relationship management software as service companies targeting students utilizing variations of predictive analytics to estimate persistence of different populations, estimate and understand tuition price increase effects on different populations, set recruiting and enrollment goals based upon expected attrition, and design customized communication plans to facilitate more in-depth relationships with those less likely to persist in an effort to overcome this statistic. These findings are also the first portion of exploring elasticities as they apply toward developing a pricing model for nontraditional student populations using the framework established by the TENEP model (Bryan & Whipple, 1995).","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132084260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consumer showrooming – the behavior of examining a product in a brick-andmortar store and later buying it from an online retailer – is seen as a major threat to brick-and-mortar retailers. To combat showrooming, Best Buy announced a price-matching policy in 2012 to compete with major online retailers. In this paper, we examine the impact of Best Buy’s price-matching policy on the price competition between Best Buy and Amazon across a wide variety of product categories. We empirically explore Best Buy’s and Amazon’s pricing patterns using unique datasets collected from different sources, and find robust results that the competitive effect of the price-matching policy depends on the showrooming value of a product. For those products that offer consumers large value from physical store experiences – i.e., the “showrooming products” – the policy led to more intense price competition. Moreover, Amazon cut prices more aggressively than Best Buy. For those products that offer relatively small showrooming value – i.e., the “non-showrooming products” – it alleviated price competition. We also provide theoretical explanations for the findings and illustrate why the price matching policy did not reduce the price gaps between Amazon and Best Buy.
{"title":"Price Match Guarantees in the Age of Showrooming: An Empirical Analysis","authors":"Chunhua Wu, Kangkang Wang, Ting Zhu","doi":"10.2139/ssrn.3271305","DOIUrl":"https://doi.org/10.2139/ssrn.3271305","url":null,"abstract":"Consumer showrooming – the behavior of examining a product in a brick-andmortar store and later buying it from an online retailer – is seen as a major threat to brick-and-mortar retailers. To combat showrooming, Best Buy announced a price-matching policy in 2012 to compete with major online retailers. In this paper, we examine the impact of Best Buy’s price-matching policy on the price competition between Best Buy and Amazon across a wide variety of product categories. We empirically explore Best Buy’s and Amazon’s pricing patterns using unique datasets collected from different sources, and find robust results that the competitive effect of the price-matching policy depends on the showrooming value of a product. For those products that offer consumers large value from physical store experiences – i.e., the “showrooming products” – the policy led to more intense price competition. Moreover, Amazon cut prices more aggressively than Best Buy. For those products that offer relatively small showrooming value – i.e., the “non-showrooming products” – it alleviated price competition. We also provide theoretical explanations for the findings and illustrate why the price matching policy did not reduce the price gaps between Amazon and Best Buy.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126632787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.
{"title":"Pricing and Matching with Forward-Looking Buyers and Sellers","authors":"Yiwei Chen, Ming Hu","doi":"10.2139/ssrn.2859864","DOIUrl":"https://doi.org/10.2139/ssrn.2859864","url":null,"abstract":"Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129887643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
[Problem Definition] We incorporate heterogeneous customer valuation and the strategic customer behavior in the classical economic order quantity (EOQ) setting. The seller incurs setup costs when replenishing inventory, and can set the prices differently over time and implement capacity rationing. [Academic/ Practical Relevance] While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, there are new aspects when applying these concepts to retail outlets and supermarkets, because they usually face inventory replenishment problems. This makes the joint inventory replenishment and revenue management problem imperative. [Methodology] We adopt the mechanism design and dynamic programming approaches. [Results] We establish the optimality of cyclic intertemporal price discrimination, even if the customers are endowed with homogeneous valuations. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts the highest selling price when the inventory level is the lowest and plans discontinuous price discount at the replenishment point when the inventory is the highest. We also show that, under inventory-based pricing there is a direct mapping between deterministic and stochastic arrivals scenarios, and randomness on the arrivals per se does not alter the structure of the optimal policy. [Managerial Implications] This cyclic pricing scheme offers a stark contrast to the "low-price-every-day" scheme and achieves overall higher profit because customer strategic behavior offers additional flexibility to the seller in managing his inventory. Furthermore, because strategic customers are willingly backlogged, this creates opportunities for the seller to work with the customers to achieve a lower operational cost and a higher overall profit.
{"title":"Synchronizing Pricing and Replenishment to Serve Forward-Looking Customers with Lost Sales","authors":"Ying‐ju Chen, Leon Yang Chu","doi":"10.2139/ssrn.3216549","DOIUrl":"https://doi.org/10.2139/ssrn.3216549","url":null,"abstract":"[Problem Definition] We incorporate heterogeneous customer valuation and the strategic customer behavior in the classical economic order quantity (EOQ) setting. The seller incurs setup costs when replenishing inventory, and can set the prices differently over time and implement capacity rationing. [Academic/ Practical Relevance] While similar ideas of market segmentation and intertemporal price discrimination can be carried over from the travel industries to other industries, there are new aspects when applying these concepts to retail outlets and supermarkets, because they usually face inventory replenishment problems. This makes the joint inventory replenishment and revenue management problem imperative. [Methodology] We adopt the mechanism design and dynamic programming approaches. [Results] We establish the optimality of cyclic intertemporal price discrimination, even if the customers are endowed with homogeneous valuations. Under the optimal policy, the replenishments and price promotions are synchronized, and the seller adopts the highest selling price when the inventory level is the lowest and plans discontinuous price discount at the replenishment point when the inventory is the highest. We also show that, under inventory-based pricing there is a direct mapping between deterministic and stochastic arrivals scenarios, and randomness on the arrivals per se does not alter the structure of the optimal policy. [Managerial Implications] This cyclic pricing scheme offers a stark contrast to the \"low-price-every-day\" scheme and achieves overall higher profit because customer strategic behavior offers additional flexibility to the seller in managing his inventory. Furthermore, because strategic customers are willingly backlogged, this creates opportunities for the seller to work with the customers to achieve a lower operational cost and a higher overall profit.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116197607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this essay, I generalize the structure of network effect from a ""two-sided market"" to a pair-wised system. By adapting the adjacency matrix from the graph theory and the micro-foundation contributed from Ballerster et al. (2006), I discuss the monetization strategy for the platform that confronts the demand with pair-wised network effects, such as Facebook. I aim to compare the business and welfare implications by pricing users directly and by pricing the third party (e.g. advertisers). I also aim to provide the first tractable competition model when consumers choose how much (time) to consume in each platform, instead of which ""one"" to consume, as this ""multi-homing"" behavior is common in the digitized world.
{"title":"From Critical Mass to Key Players: A Network Approach to Platform Design","authors":"Yu-Hsin Liu","doi":"10.2139/ssrn.3492281","DOIUrl":"https://doi.org/10.2139/ssrn.3492281","url":null,"abstract":"In this essay, I generalize the structure of network effect from a \"\"two-sided market\"\" to a pair-wised system. By adapting the adjacency matrix from the graph theory and the micro-foundation contributed from Ballerster et al. (2006), I discuss the monetization strategy for the platform that confronts the demand with pair-wised network effects, such as Facebook. I aim to compare the business and welfare implications by pricing users directly and by pricing the third party (e.g. advertisers). I also aim to provide the first tractable competition model when consumers choose how much (time) to consume in each platform, instead of which \"\"one\"\" to consume, as this \"\"multi-homing\"\" behavior is common in the digitized world.","PeriodicalId":321987,"journal":{"name":"ERN: Pricing (Topic)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123243075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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