scholarly journals Managing Market Thickness in Online Business-to-Business Markets

2020 ◽  
Vol 66 (12) ◽  
pp. 5783-5822 ◽  
Author(s):  
Kostas Bimpikis ◽  
Wedad J. Elmaghraby ◽  
Ken Moon ◽  
Wenchang Zhang
Keyword(s):  
Operations Management ◽  
Natural Experiment ◽  
Recommendation System ◽  
Structural Model ◽  
Supply And Demand ◽  
Supply Side ◽  
Design Decisions ◽  
Business Markets ◽  
Online Business ◽  
Business To Business

We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders. This paper was accepted by Vishal Gaur, operations management.

scholarly journals Price Discrimination and Bargaining: Empirical Evidence from Medical Devices

2013 ◽  
Vol 103 (1) ◽  
pp. 145-177 ◽  
Author(s):  
Matthew Grennan
Keyword(s):  
Panel Data ◽  
Empirical Evidence ◽  
Price Discrimination ◽  
Medical Devices ◽  
Structural Model ◽  
Hospital Costs ◽  
Business Markets ◽  
Uniform Pricing ◽  
Theoretical Predictions ◽  
Business To Business

Many important issues in business-to-business markets involve price discrimination and negotiated prices, situations where theoretical predictions are ambiguous. This paper uses new panel data on buyer-supplier transfers and a structural model to empirically analyze bargaining and price discrimination in a medical device market. While many phenomena that restrict different prices to different buyers are suggested as ways to decrease hospital costs (e.g., mergers, group purchasing organizations, and transparency), I find that: (i) more uniform pricing works against hospitals by softening competition; and (ii) results depend ultimately on a previously unexplored bargaining effect. (JEL C78, L13, L14, L64)

Online Business-to-Business Markets for Industrial Product Reuse: Evidence from an Operational Policy Change

2020 ◽  
Author(s):  
Suvrat Dhanorkar ◽  
Karen Donohue ◽  
Kevin Linderman
Keyword(s):  
Policy Change ◽  
Detailed Examination ◽  
Problem Definition ◽  
Product Mix ◽  
Business Markets ◽  
Managerial Implications ◽  
Online Business ◽  
Technology Substitution ◽  
Product Reuse ◽  
Business To Business

Problem definition: We examine the importance of expert services in online materials and waste exchanges (OMWEs), which are online business-to-business markets for coordinating transactions of industrial surplus, by-products, and waste. Academic/practical relevance: OMWEs face unique challenges because of their product mix and market characteristics. Many OMWEs have traditionally relied on a combination of routine services (online aggregation, filtered search, etc.) and expert services (selective and spatial matching, contract facilitation, etc.). Although OMWEs employ varying levels of expert services, the ultimate value of expert services in promoting transactions is not fully understood. From a managerial perspective, our study provides insights into the importance of balancing routine and expert services, offering guidance on when expert services offer the most benefits. From an academic perspective, we expand on the type of product and market attributes that should be considered in tailoring OMWE designs. Methodology: We use transactional data from a unique OMWE setting (MNExchange.org), which consists of approximately 3,500 product listings from 700+ supplier firms, collected during 2001–2007. We use various econometric techniques (survival analysis, regression discontinuity, etc.) to examine the changes in performance, including transaction rates and time to market, attributable to an operational policy change that occurred in 2004. We further conduct a detailed examination of mechanisms, alternative explanations, and counterfactual analysis. Results: The results show that eliminating expert services in OMWEs can adversely affect transaction outcomes in OMWEs. In particular, the results show that OMWEs should consider their product mix and market characteristics when making decisions about the appropriate use of expert services. Managerial implications: The study provides insights for improving the potential of online reuse marketplaces in the circular economy. From a broader perspective, the paper contributes to the debate on the role of technology in sustainable development and technology substitution for human tasks.

Personalized Priority Policies in Call Centers Using Past Customer Interaction Information

2021 ◽  
Author(s):  
Brett Alan Hathaway ◽  
Seyed Morteza Emadi ◽  
Vinayak Deshpande
Keyword(s):  
Customer Service ◽  
Operations Management ◽  
Bayesian Learning ◽  
Structural Model ◽  
Call Centers ◽  
Waiting Times ◽  
Operational Performance ◽  
System Throughput ◽  
History Data ◽  
Individual Level

To increase revenue or improve customer service, companies are increasingly personalizing their product or service offerings based on their customers' history of interactions. In this paper, we show how call centers can improve customer service by implementing personalized priority policies. Under personalized priority policies, managers use customer contact history to predict individual-level caller abandonment and redialing behavior and prioritize them based on these predictions to improve operational performance. We provide a framework for how companies can use individual-level customer history data to capture the idiosyncratic preferences and beliefs that impact caller abandonment and redialing behavior and quantify the improvements to operational performance of these policies by applying our framework using caller history data from a real-world call center. We achieve this by formulating a structural model that uses a Bayesian learning framework to capture how callers’ past waiting times and abandonment/redialing decisions affect their current abandonment and redialing behavior and use our data to impute the callers’ underlying primitives such as their rewards for service, waiting costs, and redialing costs. These primitives allow us to simulate caller behavior under a variety of personalized priority policies and hence, collect relevant operational performance measures. We find that, relative to the first-come, first-served policy, our proposed personalized priority policies have the potential to decrease average waiting times by up to 29% or increase system throughput by reducing the percentage of service requests lost to abandonment by up to 6.3%. This paper was accepted by Vishaul Gaur, operations management.

An Introduction to Business Marketing

2011 ◽  
Author(s):  
Chris Fill ◽  
Scot McKee
Keyword(s):  
Supply Chains ◽  
Relationship Marketing ◽  
Perceived Value ◽  
Marketing Mix ◽  
Business Markets ◽  
Principal Characteristics ◽  
B2b Marketing ◽  
Goods And Services ◽  
Business To Consumer ◽  
Business To Business

This chapter explores some of the principal characteristics used to define business markets and marketing. It establishes the key elements of business-to-business (B2B) marketing and makes comparisons with the better-known business-to-consumer (B2C) sector. This leads to a consideration of appropriate definitions, parameters and direction for the book. After setting out the main types of organisations that operate in the B2B sector and categorising the goods and services that they buy or sell, the chapter introduces ideas about the business marketing mix, perceived value, supply chains, interorganisational relationships and relationship marketing. This opening chapter lays down the vital foundations and key principles which are subsequently developed in the book.

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