""Counting Your Customers" One by One: An Individual Level RF Analysis Based on Consumer Behavior Theory"

In customer relationship management (CRM), ad hoc rules are often employed to judge whether customers are active in a "non-contractual" setting. For example, a customer is considered to have dropped out if he or she has not made purchase for over three months. However, for customers with a long interpurchase time, this three-month time frame would not apply. Hence, when assessing customer attrition, it is important to account for customer heterogeneity. Although this issue was recognized by Schmittlein et al. (1987), who proposed the Pareto/NBD "counting your customers" framework almost 20 years ago, today's marketing demands a more individual level analysis. This research presents a proposed model that captures customer heterogeneity through estimation of individual-specific parameters, while maintaining theoretically sound assumptions of individual behavior in a Pareto/NBD model (a Poisson purchase process and a memoryless dropout process). The model not only relaxes the assumption of independence of the two behavioral processes, it also provides useful outputs for CRM, such as a customer-specific lifetime and retention rate, which could not have been obtained otherwise. Its predictive performance is compared against the benchmark Pareto/NBD model. The model extension, as applied to scanner panel data, demonstrates that recency-frequency (RF) data, in conjunction with customer behavior and demographics, can provide important insights into direct marketing issues, such as whether long-life customers spend more and are more profitable.

""Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model"

This research extends a Pareto/NBD model of customer-base analysis using a hierarchical Bayesian (HB) framework to suit today's customized marketing. The proposed HB model presumes three tried and tested assumptions of Pareto/NBD models: (1) a Poisson purchase process, (2) a memoryless dropout process (i.e., constant hazard rate), and (3) heterogeneity across customers, while relaxing the independence assumption of the purchase and dropout rates and incorporating customer characteristics as covariates. The model also provides useful output for CRM, such as a customer-specific lifetime and survival rate, as by-products of the MCMC estimation. Using three different types of databases --- music CD for e-commerce, FSP data for a department store and a music CD chain, the HB model is compared against the benchmark Pareto/NBD model. The study demonstrates that recency-frequency data, in conjunction with customer behavior and characteristics, can provide important insights into direct marketing issues, such as the demographic profile of best customers and whether long-life customers spend more.

"A Two-Stage Prediction Model for Web Page Transition"

Utilizing data from a log file, a two-stage model for step-ahead web page prediction that permits adaptive page customization in real-time is proposed. The first stage predicts the next page of a viewer based on a variant of a Markov transition matrix computed from page sequences of other visitors who read the same pages as that viewer did thus far. The second stage re-analyzes the incorrect exit/continuation predictions of the first stage through data mining, incorporating the visitor's viewing behavior observed from the log file. The two-stage process takes advantage of a robust, theory-driven nature of statistical modeling for extracting the overall feature of the data, and a flexible, data-driven nature of data mining to capture any idiosyncrasies and complications unresolved in the first stage. The empirical result with a test site implies that the first stage alone is sufficiently accurate (50.3%) in predicting page transitions. Prediction of site exit was even better with 100% of the exit and 90.8% of the continuation predictions being correct. The result was compared against other models for predictive accuracy.

A Distributive Comparison of Enterprise Size in Korea and Taiwan

Research to date on the economic development of the Republic of Korea and Taiwan has frequently contrasted the two economies by depicting the former as centered on large-scale enterprises and the latter on small and medium-size enterprises (SMEs). The purpose of this study is to see if the appropriateness of this perception will also be verified by the statistical data. In Section I the authors utilized census data on the Korean and Taiwanese manufacturing sectors to compare the distribution pattern of the sizes of enterprises in the two economies. However, on examining the available data for making this comparison, the authors discovered that for Korea the statistics provided are those at the level of the establishment (a physical unit engaging in industrial activities such as a factory, workshop, office, or mine) while the statistics for Taiwan are those at the enterprise level. Mindful of this difference, the authors looked at the portion of the economy accounted for by large-scale establishments in Korea that employed 500 workers or more and by enterprises in Taiwan employing the same number of workers, and they discovered that the portion that these large-scale businesses account for, especially in the area of output, has steadily declined since the 1980s. When comparing the share of total production that these large-scale establishments/enterprises account for in the two economies, the authors concluded that those in Korea accounted for a larger share of that economy's production than did their counterparts in Taiwan. The authors then compared the portion of the economy accounted for by establishments in Korea and enterprises in Taiwan that employed less than ten workers, and they found that the portion of the two economies that these very small-scale production units accounted for has also been on the decline. Section II compares the portions of the two economies accounted for by large business groups. After comparing the percentage of GDP accounted for by the total sales of these business groups, the authors found that large business groups in Korea have played a far more important role in Korean economy than has been the case for such groups in Taiwan. This difference in the importance of such business groups in the two economies has also played an significant part in fostering the perceived dichotomy of large-scale enterprises playing the important role in Korea versus SMEs being the important players in Taiwan. Section III compares the percentage of total exports accounted for by SMEs, and shows that SMEs in Taiwan account for a larger share of exports than do their counterparts in Korea. This section also shows that in Taiwan the share of export sales for SMEs has consistently exceeded that for non-SMEs, while in Korea the relationship between enterprise size and the rate of export sales has been directly proportional. This difference in the size of the major export players is another factor fostering the perception of the Korean economy being centered on big business while Taiwan's is on SMEs. Although there were difficulties and limitations when comparing the data of the two economies, the statistical comparison undertaken in this study shows that in general big business has played the major role in the development of the Korean economy while in Taiwan's economic development this role has been played by SMEs. Thus the statistical data also verifies the perceived dichotomy of these two economies.Small and medium-scale enterprises, Large-scale enterprises, Economic development, South Korea, Taiwan

Holomorphic line bundles and Cartier divisors on domains in a Stein orbifold with disrete singularities

Let X be a Stein orbifold of pure dimension n such that Sing(X) is discrete. Let D be an open set of X such that Hᵏ(D,O) = 0 for 2 ≤ k ≤ n - 1 and every topologically trivial holo-morphic line bundle on D is associated to some Cartier divisor on D.Then D is Stein

Topic management in L2 task-based written interactions

How online interactants advance their talk in written interactions has been extensively investigated in different areas, including language learning. Applying a conversation analysis technique, this study examined how second language (L2) learners managed topics in L2 task-based text-chat interactions and the effects of L2 proficiency on their interactional practices. Participants were 53 dyads, comprising 106 Japanese learners of English across three proficiency levels. The findings demonstrated that only high-proficiency learners jointly constructed differences in topicality between on- and off-task interactions, by implementing resources available in the textual communication medium. Mid- and low-proficiency learners tended to recycle similar formats to create new sequences. In addition, low-proficiency learners never showed orientation to topic transition in an observable way; mid-proficiency learners did show an indication of such an orientation to topic transition, although they failed to achieve it interactionally. The findings were partially verified by quantifying the question format How about you?, which was predominantly used by low- and mid-proficiency learners. Based on the findings, this study proposes a hypothetical developmental pathway and pedagogical implications for teaching and assessment of interactional competencies in L2 written interactions

"Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling"

The Multinomial Logit (MNL) model is still the only viable option to study nonlinear responsiveness of utility to covariates nonparametrically. This research investigates whether MNL structure of inter-brand competition is a reasonable assumption, so that when the utility function is estimated nonparametrically, the IIA assumption does not bias the result. For this purpose, the authors compare the performance of two comparable nonpara-metric choice models that differ in one aspect: one assumes MNL com-petitive structure and the other infers the pattern of brands' competition nonparametrically from data.