Problem definition: This paper empirically investigates how customer email engagement affects the profitability of subscription service providers and retailers. They have been using email engagement to increase customer retention. However, it is unclear whether email engagement improves their profitability. The existing literature focuses on email engagement’s benefit of customer retention but ignores its associated operating cost to serve retained customers. Methodology/results: We analyze the outcome of a field experiment conducted by a large U.S. car wash chain that offers tiered subscription services to consumers and employs an radiofrequency identification-based technology to track subscriber service events. We apply survival analysis and difference-in-differences methods to estimate the effects of email engagement on subscribers’ retention and service consumption. We find that a one-month engagement with two emails separated by a half-month interval increased the likelihood of subscriber retention by 7.4% five months after the experiment started and decreased the subscriber churn odds by 26.3% for the entire five-month duration. Meanwhile, we find that the same engagement increased a subscriber’s per-period service consumption by 7.0%. We provide suggestive evidence for two behavioral mechanisms that explain the effect of email engagement on subscribers’ service consumption. First, the engagement effect decays over time and exhibits fatigue after the second email, suggesting that emails act as reminders to subscribers. Second, the engagement effect persists after engagement ends but weakens over time, suggesting the habit formation of subscribers. By computing subscriber lifetime value and the operating cost of service, we find that email engagement increases profit when deployed on mid-level infrequent-use subscribers and top-level subscribers but decreases profit when deployed on mid-level frequent-use subscribers and basic-level subscribers. Therefore, we recommend that the company use a selective strategy by sending engagement emails to only profitable subscribers. Managerial implications: Our study highlights that email engagement is a double-edged sword; it increases both customer retention and service consumption, and it may decrease profitability when the increased operating cost to serve retained customers outweighs the benefit of customer retention. We recommend that subscription service providers and retailers adopt a data-driven approach to optimize their email engagement strategies.

As the fashion industry increasingly embraces artificial intelligence (AI), we investigate how a fast-fashion retailer should choose between using a manual design strategy or an AI-assisted design strategy to enhance existing products. A manual design is a traditional and basic approach that involves human designers only, whereas an AI-assisted design is a more innovative approach that involves both human designers and AI technologies. In this paper, the overall product enhancement is measured by two key attributes: product quality and product trendiness. Product quality can be measured by the product’s longevity as reflected by the quality of the materials and types of fabric and stitching used, where the product’s improvement level can be determined by the retailer in a continuous range. Consequently, the retailer may choose different levels of product quality under different design strategies. The two design approaches also lead to different natures of product trendiness, which is reflected by features such as styles, new materials, and colors, to name just a few. Specifically, we assume that the traditional manual design can predict well how trendy or popular the new product is. Hence, the trendiness attribute under the manual design is deterministic. However, given the uncertain nature of the AI-assisted design technology and the needed coordination between human designers and the adopted technologies, the trendiness of the new product designed under the AI-assisted approach is assumed uncertain. Two sets of designing costs are considered in product enhancement: the fixed design cost that is irrespective of the production volume and the variable marginal cost. Our analysis of the base model highlights the importance of decomposing different costs in determining the optimal design strategy. Specifically, the manual design is preferred when the fixed cost carries more weight, whereas the AI-assisted design is preferred when the marginal cost is a more important factor. Moreover, a higher level of innovation uncertainty under the AI-assisted design gives this strategy an advantage over the manual design. In our extended models, we demonstrate that (1) these results are robust even if the retailer does not have the flexibility to offer the existing product when the AI-assisted design is unpopular, and (2) the relative position of human designers in the two design approaches has an impact on the effects of these costs. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0315 .