Sangkil Moon

Dynamic Effects Among Movie Ratings, Movie Revenues, and Viewer Satisfaction

This research investigates how movie ratings from professional critics, amateur communities, and viewers themselves influence key movie performance measures (i.e., movie revenues and new movie ratings). Using movie-level data, the authors find that high early movie revenues enhance subsequent movie ratings. They also find that high advertising spending on movies supported by high ratings maximizes the movies revenues. Furthermore, they empirically show that sequel movies tend to reap more revenues but receive lower ratings than originals. Using individual viewer–level data, this research highlights how viewers’ own viewing and rating histories and movie communities’ collective opinions explain viewer satisfaction. The authors find that various aspects of these ratings explain viewers’ new movie ratings as a measure of viewer satisfaction, after controlling for movie characteristics. Furthermore, they find that viewers’ movie experiences can cause them to become more critical in ratings over time. Finally, they find a U-shaped relationship between viewers’ genre preferences and genre-specific movie ratings for heavy viewers.

Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach

Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based on the principle that customer preference similarity stemming from prior purchase behavior is a key element in predicting current product purchase. The proposed recommendation model is dependent on two complementary methodologies: joint space mapping (placing customers and products on the same psychological map) and spatial choice modeling (allowing observed choices to be correlated across customers). Using a joint space map based on past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends on the customers relative distance to other customers on the map. An empirical study demonstrates that the proposed approach provides excellent forecasts relative to benchmark models for a customer database provided by an insurance firm.

Estimating Promotion Response When Competitive Promotions Are Unobservable

This study addresses a problem commonly encountered by marketers who attempt to assess the impact of their sales promotions—namely, the lack of data on competitive marketing activity. In most industries, competing firms may have competitive sales data from syndicated services or trade organizations, but they seldom have access to data on competitive promotions at the customer level. Promotion response models in the literature either have ignored competitive promotions, focusing instead on the focal firms promotions and sales response, or have considered the ideal situation in which the analyst has access to full information about each firms sales and promotion activity. The authors propose a random coefficients hidden Markov promotion response model, which takes the competitors unobserved promotion level as a latent variable driven by a Markov process to be estimated simultaneously with the promotion response model. This enables the authors to estimate cross-promotion effects by imputing the level of competitive promotions. The authors test the proposed model on synthetic data through a Monte Carlo experiment. Then, they apply and test the model to actual prescription and sampling data from two main competing pharmaceutical firms in the same therapeutic category. The two tests show that compared with several benchmark models, the proposed random coefficients hidden Markov model successfully imputes unobserved competitive promotions and, accordingly, reduces biases in the own- and cross-promotion parameters. Furthermore, the proposed model provides better predictive validity than the benchmark models.

Measuring the success of retention management models built on churn probability, retention probability, and expected yearly revenues

We claim that often marketers have not all the information to develop various marketing campaign models. For example, marketers may have sufficient information to build a model for predicting possible churners, while they may have no clues of which customers are most likely to accept a retention campaign. In this paper, we first show that the information useful for a successful churner prediction model alone is not sufficient to develop a successful retention marketing program. In such a case, we claim that only theory-based simulation approach is feasible. In particular, it is claimed that optimal retention management models should consider not only churn probability but also retention probability and expected revenues from target customers. To validate our claim, we develop and compare five retention management models based on churn probability, retention probability, expected revenues, and combination of these models along with different evaluation metrics. Our experimental results show that the retention management model with the highest accuracy in predicting possible churners is not necessarily optimal because it does not consider the probability of accepting retention promotions. In contrast, the retention management model based on both churn and retention probability is the best in terms of predicting customers who are most likely to positively respond to retention promotions. Ultimately, the model based on expected yearly revenue of customers accrues the highest revenues across most target points, making it the best model out of five churn management models.

Conditional efficiency, operational risk and electronic ticket pricing strategies for the airline industry

As the USA moves towards an economic slowdown and possible recession, the airline industry is particularly vulnerable to consumer price sensitivity regarding the purchase of electronic tickets. In this research, we provide a methodological approach that utilises operational risk metrics to examine the impact of various electronic ticket pricing strategies on profitability. Specifically, the work presented herein is differentiated from previous airline revenue management studies in the following ways: 1) we develop a Monte Carlo simulation model which can be used effectively to construct conditionally efficient discount ticket pricing strategies, 2) we show that the operational value at risk and expected shortfall are effective measures for weighing the risk-return tradeoffs for efficient discount ticket pricing strategies along the constructed frontier and 3) we implement a demand-recapturing variable in the simulation model where prior studies have assumed independent demand among various consumer classes.