Rutger van Oest

Customer Complaints and Recovery Effectiveness: A Customer Base Approach

Although customer complaints are a well-studied aspect of business, no study has measured the impact of actual complaints and recoveries on subsequent customer purchasing. The authors develop a customer base model to investigate the effectiveness of recovery in preventing customer churn. They calibrate it on panel data that track actual purchases, complaints, and recoveries for 20,000 new customers of an Internet and catalog retailer over 2.5 years. Complaints are associated with a substantial increase in the probability that the customer stops buying, but the size of the increase depends on prior customer experiences: prior purchases mitigate the effect, and their impact is long-lasting, whereas prior complaints exacerbate the effect, but their impact is short-lived. Thus, unless the customer leaves the company after a complaint, or a second failure occurs shortly after the first, the relationship quickly returns to normal. Recovery counters the effect of the complaint but, in almost all cases, does not entirely offset it. The authors use simulation to translate the results to financial impact and discuss implications for researchers and managers.

Simulation Based Bayesian Econometric Inference: Principles and Some Recent Computational Advances

In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the MetropolisHastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator.

Return on Roller Coasters: A Model to Guide Investments in Theme Park Attractions

Despite the economic significance of the theme park industry and the huge investments needed to set up new attractions, no marketing models exist to guide these investment decisions. This study addresses this gap in the literature by estimating a response model for theme park attendance. The model not only determines the contribution of each attraction to attendance, but also how this contribution is distributed within and across years. The model accommodates saturation effects, which imply that the impact of a new attraction is smaller if similar attractions are already present. It also captures reinforcement effects, meaning that a new attraction may reinforce the drawing power of similar extant attractions, especially when these were introduced recently. The model is calibrated on 25 years of weekly attendance data from the Efteling, a leading European theme park. Our return on investment calculations show that it is more profitable to invest in multiple smaller attractions than in one big one. This finding is in remarkable contrast with the current “arms race” in the industry. Furthermore, even though thrill rides tend to be more effective than theme rides, there are conditions under which one should consider to switch to the latter.

A Joint Framework for Category Purchase and Consumption Behavior

We propose a consistent utility-based framework to jointly explain a households decisions on purchase incidence, brand choice and purchase quantity. The approach differs from other approaches, currently available in the literature, as it is able to take into account consumption dynamics. In the model, households derive utility from consumption, and they relate their purchase behavior to consumption planning. We illustrate our model for yogurt purchases, and show that our model yields important additional insights. One such insight is that the reservation price of households is not fixed, but depends on the available inventory stock. Furthermore, we find that promotional activities increase sales through more purchases in the product category and brand switching, but the effect through larger purchase quantities is limited.

Customer Inconvenience and Price Compensation: A Multiperiod Approach to Labor-Automation Trade-Offs in Services

Managers are faced with complex decisions when considering automating the front end of a service, where the firm interacts with its customers (e.g., check-in at airports). We develop an analytical model for the optimal decisions as to whether to automate the service and which price to charge. The model accounts for automation-induced customer inconvenience in the short run and differences in service quality and production costs in the long run. We show that it may be optimal not to automate, even if automated service reduces production costs for the firm and is ultimately desired by customers. In other situations, automated service is optimal, even though customer inconvenience may trigger financial losses in the short run. Automated service may also become optimal, as customers become more sensitive to service quality, but only if the quality of the automation technology is sufficiently high. We show that the firm should compensate customers for automation-induced inconvenience, but this price compensation can be reduced as customers become more comfortable with the service. Although automated service is cheaper to produce than labor-produced service, the firm should charge a price premium if the quality of the automated service is sufficiently superior.