TAILIEUCHUNG - A Parsimonious Model of Stock-Keeping Unit Choice

A higher-risk CMO tranche could, for example, have an average life that changes from 2 years to 20 years with even a modest increase in interest rates. Higher risk refers here to the cash flow variability of the tranche, not its credit quality, although underwriters can create structured securities that combine higher average life sensitivity with lower credit quality. The highest yields go to those tranches that, by design, exhibit the most volatile average lives. Such tranches receive excess principal cash when prepayments rise, and pay off early. When prepayments are slower, these tranches may. | A Parsimonious Model of Stock-Keeping Unit Choice Teck-Hua Ho and Juin-Kuan Chong1 1 Teck-Hua Ho is the William Halford Jr. Family Professor of Marketing at the Haas School of Business University of California at Berkeley email hoteck@ . Juin-Kuan Chong is Assistant Professor of Marketing at the NUS Business School National University of Singapore email bizcjk@ . The authors thank Andrew Ainslie Greg Allenby David Bell Eric Bradlow Colin Camerer Tulin Erdem Peter Fader John Little Len Lodish Carl Mela Alan Montgomery Gary Russell David Schmittlein Richard Staelin Christophe Van den Bulte Russ Winer and seminar participants at Marketing Science Conference at Syracuse Duke MIT National University of Singapore Singapore Management University University of Texas Dallas Washington University St. Louis and Wharton for their helpful suggestions. The authors are especially grateful to John Lynch for his help in developing a behavioral underpinning for their model. David Bell Pete Fader and Bruce Hardie generously provided the data. The authors also thank three anonymous for their many suggestions. Wagner Kamakura provided numerous detailed and helpful suggestions. A Parsimonious Model of Stock-Keeping Unit Choice Teck H. Ho and Juin-Kuan Chong Managerial Summary This article develops a model to describe and predict consumer stock-keeping unit SKU choice in frequently bought product categories. The model posits that a product category consists of several salient attributes and that each attribute has different levels and represents a SKU as an attribute-level combination. Our latent-class 2-segment model has a fixed number of 59 parameters for a category with 3 salient attributes and in general has 11 12 K 1 parameters for a K-attribute product category. We achieve this model parsimony by neither discarding data nor aggregating the level of analysis beyond the SKU level. Since the number of parameters does not depend on either the number of SKUs .

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