The key issue that Yang (2019) describes in her article relates to the losses that mattress companies suffer due to widespread at-home trials provided to consumers. Clients using the services of different sellers use the purchased goods temporarily and then return them, which is allowed under the terms of guarantees. In this regard, there is an urgent need to find alternative strategies to sell products profitably and avoid fraud from buyers.
For instance, reducing the terms of free use can be a potentially effective measure, as well as monitoring the purchasing history of customers to prevent precedents. As Garnefeld et al. (2019) argue, there is a relationship between payment schemes and the frequency of fraud, which requires revising sales pricing policies. Utilizing the tool of marginal analysis can help mattress companies to assess the effectiveness of potential alternative solutions.
As a mechanism of work, the marginal analysis may involve assessing the most significant elements affecting sales. Chen et al. (2019) mention “the marginal mean structure” as a strategy that allows defining variables for measuring profit (p. 951). In this case, the term of at-home trials is an important criterion, and by lowering this indicator, sellers can evaluate the effectiveness of this approach.
Monitoring consumer buying history is a practice that may also reduce fraud by analyzing purchases of similar products. These alternative solutions may be accompanied by risks, for instance, the loss of potential clients due to too tight sales policies. However, this information is necessary to identify the threat of costs and ensure profit. The cases described by Yang (2019) are unique examples of gaps made by sellers and encouraging customer fraud. Consumers can change mattresses unintentionally, making sure of their insufficient quality. Nevertheless, fraud may become a common practice if effective alternative measures are not taken to revise sales policies.
References
Chen, C., Shen, B., Zhang, L., Xue, Y., & Wang, M. (2019). Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness. Biometrics, 75(3), 950-965.
Garnefeld, I., Eggert, A., Husemann-Kopetzky, M., & Böhm, E. (2019). Exploring the link between payment schemes and customer fraud: A mental accounting perspective. Journal of the Academy of Marketing Science, 47(4), 595-616.
Yang, S. (2019). Unintended perk of the online mattress boom: Never-ending free trials. The Wall Street Journal.