iPrice: A Collaborative Pricing Model for e-Service Bundle Delivery

Information goods pricing is an essential and emerging topic in the era of information economy. Myriad researchers have devoted considerable attention to developing and testing methods of information goods pricing. Nevertheless, in addition; there are still certain shortcomings as the challenges to be overcome. This study encompasses several unexplored concepts that have attracted research attention in other disciplines lately, such as collaborative prototyping, prospect theory, ERG theory, and maintenance from design, economic, psychological, and software engineering respectively. This study proposes a novel conceptual framework for information goods pricing and investigates the impact of three advantages: (1) provides collaborative process that could generate several prototypes via trial and error in pricing process, (2) deliberates the belief of consumer and producer by maximizing utility and profit, and (3) offers an appropriate service bundle by interacting with consumer and discovering the actual needs

Towards personalized data-driven bundle design with QoS constraint

Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ

iPrice: A Collaborative Pricing System for e-Service Bundle Delivery

[[abstract]]Information goods pricing is an essential and emerging topic in the era of information economy. Several researchers have devoted considerable attention to developing and testing methods of information goods pricing. Nevertheless, in addition, there are still certain shortcomings and challenges to be overcome. This study encompasses several unexplored concepts that have attracted research attention in other disciplines lately, such as collaborative prototyping, prospect theory, ERG theory, and maintenance from design, economic, psychological, and software engineering, respectively. Compared to other methods, our pricing model not only provides the feasibility but the applicability for information goods.[[journaltype]]國

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,

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

Tapping into the pulse of the market : essays on marketing implications of information flows

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2006.Includes bibliographical references (p. 113-115).As the Internet continues to penetrate consumer households, online marketing is getting increasingly important for firms. By adapting to online strategies, firms are blessed (or doomed) with a plethora of new business models. The information flows created in the process poses both opportunities and challenges for marketers. On one hand, information flows captured online are usually easier to be stored and processed, thus empowering firms to be better informed about the consumers or the market itself. On the other hand, how to use the information flows to make the correct managerial decisions is still a challenging task for managers and academics alike. My dissertation studies the marketing implications of these information flows. Broad as the research question is, my dissertation focuses on specific market settings. I adopt both analytical and empirical methodologies to study information flows in these markets. Overall, this dissertation concludes that information flows can engender new market mechanisms, can provide valuable information of unobservable market forces, and can be created to improve social welfare. Essay 1: Innovation Incentives for Information Goods. Digital goods can be reproduced costlessly.(cont.) Thus a price of zero would be economically-efficient for consumers. However, zero revenues would eliminate the economic incentives for creating such goods in the first place. We develop a novel mechanism which tries to solve this dilemma by decoupling the price of digital goods from the payments to innovators while maintaining budget balance and incentive compatibility. Specifically, by selling digital goods via large bundles the marginal price for consuming an additional good can be made zero for most consumers. Thus efficiency is enhanced. Meanwhile, we show how statistical sampling can be combined with tiered coupons to reveal the individual demands for each of the component goods in such a bundle. This makes it possible to provide accurate payments to creators which spurs further innovation. In our analysis of the proposed mechanism, we find that it can operate with an efficiency loss of less than 0.1. Essay 2: Edgeworth Cycles in Keyword Auctions. Search engines make a profit by auctioning off advertisement positions through keyword auctions. I examine the strategies taken by the advertisers.(cont.) A game theoretical model suggests that the equilibrium bids should follow a cyclical pattern- "escalating" phases interconnected by "collapsing" phases - similar to a pattern of "Edgeworth Cycles" that was suggested by Edgeworth (1925) in a different context. I empirically test the validity of the theory. With an empirical framework based on maximum likelihood estimation of latent Markov state switching, I show that Edgeworth price cycles exist in this market. I further examine, on the individual bidder level, how strategic these bidders are. My results suggest that some bidders in this market adjust their bids according to Edgeworth predictions, while others not. Finally, I discuss the important implications of finding such cycles. Essay 3: The Lord of the Ratings. Third-party reviews play an important role in many contexts in which tangible attributes are insufficient to enable consumers to evaluate products or services. In this paper, I examine the impact of professional and amateur reviews on the box office performance of movies. I first show evidence to suggest that the generally accepted result of "professional critics as predictors of movie performance" may no longer be true.(cont.) Then, with a simple diffusion model, I establish an econometrics framework to control for the interaction between the unobservable quality of movies and the word-of-mouth diffusion process, and thereby estimate the residual impact of online amateur reviews on demand. The results indicate the significant influence of the valence measure (ratings) of online reviews, but their volume measure (propensity to write reviews) is not significant once I control for quality. Furthermore, the analysis suggests that the variance measure (disagreement) of reviews does not play a significant role in the early weeks after a movie's opening. The estimated influence of the valence measure implies that a one-point increase in the valence can be associated with a 4-10% increase in box office revenues.by Xiaoquan (Michael) Zhang.Ph.D

Bundle recommendation in ecommerce

Recommender system has become an important component in modern eCommerce. Recent research on recommender systems has been mainly concentrating on improving the relevance or profitability of individual recommended items. But in reality, users are usually exposed to a set of items and they may buy multiple items in one single order. Thus, the relevance or profitability of one item may actually depend on the other items in the set. In other words, the set of rec-ommendations is a bundle with items interacting with each other. In this paper, we introduce a novel problem called the Bundle Recommendation Problem (BRP). By solving the BRP, we are able to find the optimal bundle of items to recommend with respect to preferred business objective. However, BRP is a large-scale NP-hard problem. We then show that it may be sufficient to solve a significantly smaller version of BRP depending on properties of input data. This allows us to solve BRP in real-world applications with mil-lions of users and items. Both offline and online experimen-tal results on a Walmart.com demonstrate the incremental value of solving BRP across multiple baseline models