On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities

Padmanabhan, Balaji; Tuzhilin, Alexander

doi:10.1287/mnsc.49.10.1327.17310

Cited by 98 publications

(53 citation statements)

References 58 publications

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“…Thus, the marginal profit from adding one unit of product i increases as the inventory level of product j increases. Note that part b) is in sharp contrast to the ER model in which the optimal value function was separable in inventories of all products and hence supermodularity held trivially as an equality in (26).The intuition behind Proposition 5c can be explained as follows: the larger the inventory of product i, the more opportunities there will be to sell this product in the future. Therefore, the package offered to a customer requesting product i can be priced high as there will be other opportunities in the future to sell the same package.…”

Section: Dynamic Pricing Under the Static Packagingmentioning

confidence: 94%

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Revenue Management through Dynamic Cross-Selling in E-commerce Retailing

2004

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We consider the problem of dynamically cross-selling products (e.g., books) or services (e.g., travel reservations) in the e-commerce setting. In particular, we look at a company that faces a stream of stochastic customer arrivals and may offer each customer a choice between the requested product and a package containing the requested product as well as another product, what we call a "packaging complement. " Given consumer preferences and product inventories, we analyze two issues: (1) how to select packaging complements, and (2) how to price product packages to maximize profits.We formulate the cross-selling problem as a stochastic dynamic program blended with combinatorial optimization. We demonstrate the state-dependent and dynamic nature of the optimal package selection problem and derive the structural properties of the dynamic pricing problem. In particular, we focus on two practical business settings: with (the Emergency Replenishment Model) and without (the Lost-Sales Model) the possibility of inventory replenishment in the case of a product stockout. For the Emergency Replenishment Model, we establish that the problem is separable in the initial inventory of all products, and hence the dimensionality of the dynamic program can be significantly reduced. For both models, we suggest several packaging/pricing heuristics and test their effectiveness numerically. AbstractWe consider the problem of dynamically cross-selling products (e.g., books) or services (e.g., travel reservations) in the e-commerce setting. In particular, we look at a company that faces a stream of stochastic customer arrivals and may offer each customer a choice between the requested product and a package containing the requested product as well as another product, "packaging complement". Given consumer preferences and product inventories, two issues are analyzed: (1) how to select packaging complements and (2) how to price product packages to maximize profits.We formulate the cross-selling problem as a stochastic dynamic program blended with combinatorial optimization. We demonstrate the state-dependent and dynamic nature of the optimal package selection problem and derive structural properties of the dynamic pricing problem. In particular, we focus on two practical business settings: with (the Emergency Replenishment model) and without (the Lost Sales model) the possibility of inventory replenishment in the case of a product stock-out. For the Emergency Replenishment model, we establish that the problem is separable in the initial inventory of all products and hence the dimensionality of the dynamic program can be significantly reduced. For both models several packaging/pricing heuristics are suggested and their effectiveness is tested numerically.

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Section: Dynamic Pricing Under the Static Packagingmentioning

confidence: 94%

“…To process the significant amounts of data necessary for making decisions, companies utilize data mining techniques (see Padmanabhan and Tuzhilin [26] for a survey of relevant methodologies). This approach is generally termed "personalization" (see Adomavicius and Tuzhilin [5]).…”

Section: Related Literaturementioning

confidence: 99%

Revenue Management through Dynamic Cross-Selling in E-commerce Retailing

2004

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“…Padmanabhan and Tuzhilin (2003) have provided a comprehensive overview of further opportunities for the use of optimization in DM for CRM. In addition, optimization methods from OR have been successfully employed to support DM methods, in particular for data and variable selection (Meiri and Zahavi, 2006;Yang and Olafsson, 2006) and variable pre-processing through linear programming (Bryson and Joseph, 2001) or simulated annealing (Debuse and Rayward-Smith, 1999).…”

Section: Computer-intensive Methodsmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

226

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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“…For instance, consideration for where data resides, the number of systems required for integration, the usability of and resistance to the system by users, and the expected outcomes and system functionality are commonly overlooked (Q. Chen & Chen, 2004;Fjermestad & Romano, 2003;Lin & Huang, 2007;Padmanabhan & Tuzhilin, 2003).…”

Section: Systems Integration and Datamentioning

confidence: 99%

“…Therefore, knowing what to collect, how to collect it, and subsequently understanding how to use data is critical. Having a clear focus on what data you need and how you will use it to optimize the lifetime value of a customer is where the power of eCRM resides (Padmanabhan & Tuzhilin, 2003). …”

Section: Systems Integration and Datamentioning

confidence: 99%

Stakeholder Perceptions Regarding eCRM: A Franchise Case Study

O’Reilly

Paper

2009

InformingSciJ

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Using a dual lens of Stakeholder Theory (ST) and Relationship Marketing (RM), this exploratory case study considers the perceptions and attitudes of each stakeholder regarding the initial design, development, and subsequent testing of an electronic customer relationship management (eCRM) tool. Through the use of in-depth interviews, member-checking interviews, and data from actual customer transactions, this study attempts to fill the gap in the literature by gaining a clear understanding of the objectives, goals, and perceptions of an eCRM vendor, franchisor senior company management, franchisees, and customers regarding eCRM. The results of this study reveal important implications for the informing sciences including the need to balance human and computer interaction, issues related to control, the impact that "electronic" systems may have on marketing relationships, and the importance of balancing the interests and expectations of all stakeholders.

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On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities

Cited by 98 publications

References 58 publications

Revenue Management through Dynamic Cross-Selling in E-commerce Retailing

Revenue Management through Dynamic Cross-Selling in E-commerce Retailing

Forecasting and operational research: a review

Stakeholder Perceptions Regarding eCRM: A Franchise Case Study

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