A story to sell: The influence of storytelling on consumers' purchasing behavior

Storytelling can arouse consumers' emotions and affect purchasing behavior through desires and attitudes. While the marketing literature discusses storytelling, there is a lack of consensus because of the diverse conceptual and operational definitions used. To untangle the complexities and consolidate the fragmented knowledge about storytelling in marketing, this research examines how the marketing literature has addressed the influence of storytelling on consumers' purchasing behavior. The findings aid in understanding how the topic has been discussed from a marketing perspective in consumer behavior studies. Through a systematic literature review using a bibliometric analysis, we demonstrate that the marketing literature features four strands about the uses of storytelling to influence consumers' purchasing behavior. First, storytelling stimulates the consumer's identification with the brand. Second, storytelling allows consumers to experience emotional value. Third, storytelling supports engagement behaviors. Finally, storytelling has a downside in that it also propagates harmful speech. This study concludes with a roadmap for future research about how storytelling impacts consumers' purchasing behavior.     

Improving risk classification and ratemaking using mixture-of-experts models with random effects

In the underwriting and pricing of nonlife insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this paper, we apply a flexible regression model with random effects, called the Mixed Logit-weighted Reduced Mixture-of-Experts, which leverages both policyholder information and their claim history, to categorize policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness-of-fit to data, while offering intuitive and interpretable characterization of policyholders' risk profiles to adequately reflect their claim history.