581 research outputs found
Modeling CLV:A test of competing models in the insurance industry
Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions
Using Preferred Outcome Distributions to estimate Value and Probability Weighting Functions in Decisions under Risk
In this paper we propose the use of preferred outcome distributions as a new method to elicit individuals' value and probability weighting functions in decisions under risk. Extant approaches for the elicitation of these two key ingredients of individuals' risk attitude typically rely on a long, chained sequence of lottery choices. In contrast, preferred outcome distributions can be elicited through an intuitive graphical interface, and, as we show, the information contained in two preferred outcome distributions is sufficient to identify non-parametrically both the value function and the probability weighting function in rank-dependent utility models. To illustrate our method and its advantages, we run an incentive-compatible lab study in which participants use a simple graphical interface - the Distribution Builder (Goldstein et al. 2008) - to construct their preferred outcome distributions, subject to a budget constraint. Results show that estimates of the value function are in line with previous research but that probability weighting biases are diminished, thus favoring our proposed approach based on preferred outcome distributions
Using Preferred Outcome Distributions to Estimate Value and Probability Weighting Functions in Decisions under Risk
In this paper we propose the use of preferred outcome distributions as a new method to elicit individualsâ value and probability weighting functions in decisions under risk. Extant approaches for the elicitation of these two key ingredients of individualsâ risk attitude typically rely on a long, chained sequence of lottery choices. In contrast, preferred outcome distributions can be elicited through an intuitive graphical interface, and, as we show, the information contained in two preferred outcome distributions is sufficient to identify non-parametrically both the value function and the probability weighting function in rank-dependent utility models. To illustrate our method and its advantages, we run an incentive-compatible lab study in which participants use a simple graphical interface â the Distribution Builder (Goldstein et al. 2008) â to construct their preferred outcome distributions, subject to a budget constraint. Results show that estimates of the value function are in line with previous research but that probability weighting biases are diminished, thus favoring our proposed approach based on preferred outcome distributions
Transient behavior of photorefractive gratings in a polymer
The transient behavior of photorefractive gratings in the polymer composite poly(N-vinyl carbazole) (PVK), 2,4,7-trinitro-9-fluorenone (TNF), and N,N-diethyl-para-nitroaniline (EPNA) doped with various amounts of 4-(diethylamino)benzaldehyde diphenylhydrazone (DEH) is presented. The influence on the hole drift mobility due to the change in the trap density induced by DEH, was directly measured. (C) 1995 American Institute of Physics
A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries
In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the battery temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the battery temperature and several EIS-based temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the battery temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the temperature with an average absolute bias of View the MathML sourceC and an average standard deviation of View the MathML sourceC using a single impedance measurement for the battery under consideration
Preference Dynamics in Sequential Consumer Choice with Defaults
This research examines the impact of defaults on product choice in sequential-decision settings. Whereas prior research has
shown that a default can affect what consumers purchase by promoting choice of the preselected option, the influence of defaults
is more nuanced when consumers make a series of related choices. In such a setting, consumer preferences may evolve across
choices due to âspilloverâ effects from one choice to subsequent choices. The authors hypothesize that defaults systematically
attenuate choice spillover effects because accepting a default is a more passive process than either choosing a nondefault option in
the presence of a default or making a choice in the absence of a default. Three experiments and a field study provide compelling
evidence for such default-induced changes in choice spillover effects. The findings show that firmsâ setting of high-price defaults
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Cognitive control in belief-laden reasoning during conclusion processing: An ERP study
Belief bias is the tendency to accept conclusions that are compatible with existing beliefs more frequently than those that contradict beliefs. It is one of the most replicated behavioral findings in the reasoning literature. Recently, neuroimaging studies using functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) have provided a new perspective and have demonstrated neural correlates of belief bias that have been viewed as supportive of dual-process theories of belief bias. However, fMRI studies have tended to focus on conclusion processing, while ERPs studies have been concerned with the processing of premises. In the present research, the electrophysiological correlates of cognitive control were studied among 12 subjects using high-density ERPs. The analysis was focused on the conclusion presentation phase and was limited to normatively sanctioned responses to validâbelievable and validâunbelievable problems. Results showed that when participants gave normatively sanctioned responses to problems where belief and logic conflicted, a more positive ERP deflection was elicited than for normatively sanctioned responses to nonconflict problems. This was observed from â400 to â200âms prior to the correct response being given. The positive component is argued to be analogous to the late positive component (LPC) involved in cognitive control processes. This is consistent with the inhibition of empirically anomalous information when conclusions are unbelievable. These data are important in elucidating the neural correlates of belief bias by providing evidence for electrophysiological correlates of conflict resolution during conclusion processing. Moreover, they are supportive of dual-process theories of belief bias that propose conflict detection and resolution processes as central to the explanation of belief bias
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