Coordination of Purchasing and Bidding Activities Across Markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. Most of the existing research related to trading agents assumes that there exists a combinatorial market mechanism in which buyers (or sellers) can bid (or sell) service or merchant bundles. Todayâ??s prevailing e-commerce practice, however, does not support this assumption in general and thus limits the practical applicability of these approaches. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-price markets and various online auctions that sell single items. It uses trading agents to coordinate a buyerâ??s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-price markets with fixed transaction costs. Flat shipping costs, a common e-tailing practice, are captured in these models. We observe that making optimal purchasing decisions in this context is NP-hard in the strong sense and suggest several efficient computational methods based on discrete location theory. The second set of models is concerned with the coordination of bidding activities across multiple online auctions. We study the underlying coordination problem for a collection of first or second-price sealed-bid auctions and derive the optimal coordination and bidding policies.

Model Selection in an Information Economy : Choosing what to Learn

As online markets for the exchange of goods and services become more common, the study of markets composed at least in part of autonomous agents has taken on increasing importance. In contrast to traditional completeinformation economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period. By explicitly considering the both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.Online learning; information economics; model selection; direct search

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

E-COMMERCE IN AGRICULTURE: DEVELOPMENT, STRATEGY, AND MARKET IMPLICATIONS

Marketing,

An Agent Based Market Design Methodology for Combinatorial Auctions

Auction mechanisms have attracted a great deal of interest and have been used in diverse e-marketplaces. In particular, combinatorial auctions have the potential to play an important role in electronic transactions. Therefore, diverse combinatorial auction market types have been proposed to satisfy market needs. These combinatorial auction types have diverse market characteristics, which require an effective market design approach. This study proposes a comprehensive and systematic market design methodology for combinatorial auctions based on three phases: market architecture design, auction rule design, and winner determination design. A market architecture design is for designing market architecture types by Backward Chain Reasoning. Auction rules design is to design transaction rules for auctions. The specific auction process type is identified by the Backward Chain Reasoning process. Winner determination design is about determining the decision model for selecting optimal bids and auctioneers. Optimization models are identified by Forward Chain Reasoning. Also, we propose an agent based combinatorial auction market design system using Backward and Forward Chain Reasoning. Then we illustrate a design process for the general n-bilateral combinatorial auction market. This study serves as a guideline for practical implementation of combinatorial auction markets design.Combinatorial Auction, Market Design Methodology, Market Architecture Design, Auction Rule Design, Winner Determination Design, Agent-Based System

Consumer Expertise or Credit Risk? An empirical analysis of mortgage pricing

Loan mortgage interest rates are usually the result of a bank-customer negotiation process. Credit risk, consumer cross-buying potential, bundling, financial market competition and other features affecting the bargaining power of the parties could affect price. We argue that, since mortgage loan is a complex product, consumer expertise could be a relevant factor for mortgage pricing. Using data on mortgage loan prices for a sample of 1055 households for the year 2005 (Bank of Spain Survey of Household Finances, EFF-2005), and including credit risk, costs, potential capacity of the consumer to generate future business and bank competition variables, the regression results indicate that consumer expertise-related metrics are highly significant as predictors of mortgage loan prices. Other factors such as credit risk and consumer cross-buying potential do not have such a significant impact on mortgage prices. Our empirical results are affected by the credit conditions prior to the financial crisis and could shed some light on this issue.Financial support from MICINN (ECO2009-09120 and ECO 2010-20792) and Gobierno Vasco (IT-313-07 and IT 473-10) is gratefully acknowledged

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

Bundling in advance sales: Theory and evidence from round-trip vs two one-way tickets

We develop a model to derive an optimal price for a bundle of two goods when buyers are risk averse and uncertain about the valuation of each good. In theory, the optimal bundle price depends not only on the probability of a positive valuation of each good, but also on the correlation between the two valuations. We analyze a unique airlines dataset in which we directly observe the prices of both bundled (round trip ticket) and unbundled items (two one-way tickets) for identical itineraries. We find that airlines offer bundle discounts, and that these discounts increase when the correlation between outbound and inbound demands is higher. Moreover, higher certainty about demand decreases bundling discounts. We also find that bundling discounts decrease with competition

A Major Setback for Retirement Savings: Changing how Financial Advisers are Compensated could Hurt Less-than-Wealthy Investors Most

If regulators were to simply outright prohibit Canadians with low and middle incomes from seeking financial advice, it would obviously constitute a massive setback for individual wealth accumulation and, ultimately, for the economy. In Canada, after all, the well-being of a large proportion of retirees relies heavily on their voluntary personal and private wealth accumulation, in part due to the shrinking proportion of Canadian employees covered by a defined-benefit pension plan. As it is, between a quarter and a third of households of all income levels not covered by a defined-benefit plan are not set up to retire comfortably. And yet, currently, regulators are entertaining a change to the financial services industry that will almost certainly have the net effect of keeping the vast majority of Canadians from accessing financial advice. It is not quite a ban, but given the effect it will have, it almost could be. The role of financial advice is pivotal in helping people prepare for retirement. Evidence shows that the average individual’s knowledge of basic financial products and concepts is quite limited. Research indicates that Canadian households using a financial adviser to assist in saving and investment matters and plan their retirement accumulated 1.58 times as much wealth as did non-advised households after four to six years; after 15 years, that had increased to 2.73 times. That has an effect on the rest society, too, since wealthier retirees enjoy a better quality of life, are less burdensome on government income supplements and contribute more to the economy. One thing that could prove immensely counterproductive to helping Canadians access financial advice to better prepare for retirement is the proposal, being considered by regulators, to unbundle adviser fees from financial products. The rationale for the move is compelling: If advisers receive different commissions depending on the financial products they convince their clients to purchase, the advisers are prima facie in a conflict of interest situation. There is an incentive for them to recommend products that offer them higher commissions — and to turn over their sales (or churn) more frequently — even if the recommendations are not in the best interests of their clients. That may seem entirely logical, although studies that investigate adviser behaviour have found surprisingly little evidence that advisers provide unsuitable advice as a matter of course and that other structures of remuneration lead advisers to adopt practices that are better aligned with their clients’ interests. Indeed, research has found that turnover is even higher in unbundled feefor-advice portfolios and that advisers tend to recommend to their clients investments that they, themselves, place in their own portfolio. Nevertheless, one thing arguably more problematic than clients receiving potentially conflicted advice is clients not having access to any advice at all. And based on the experience of other jurisdictions that have ordered fees to be unbundled and instead be structured as upfront fees, that is the result that ends up occurring for investors below a certain income level. In the U.K., after the decision was made to unbundle fees, the number of financial advisers fell from more than 40,000 in 2011 to just over 31,000, and has not recovered. Major banks, meanwhile, cancelled their financial advice services for clients that had only modest assets. The opening of investment accounts worth less than 100,000 pounds fell by half. After Australia required fees to be unbundled, there was a similar effect. There is little to suggest that Canadians would not be left with the same income-related “advice gap” were regulators to require fees unbundled here. Simply put, many clients are unwilling to pay upfront for unknown results. And any reform that causes investors to separate from their advisers, or to never hire one, would be counterproductive to the public policy goals of helping Canadians better prepare for retirement. If it is adviser conflicts that regulators are worried about, there are better ways to address them — for example, the regulatory regime governing fiduciary duty and the potential to enhance the competencies, proficiency and professionalism of financial advisers — than creating a system that results in fewer people providing financial advice, and fewer people willing to seek it