Service that falls below customer expectations is framed as a service failure. While many researchers have investigated service failures, they have tended to focus on large service failures. This is likely because large failures are more noticeable by firms and more likely to prompt customer complaints than small failures. However, we argue that smaller service failures can cause as much damage as larger failures, and in some cases even more. We introduce the concept of service microfailures, which we define as instances when a customer's expectations go unmet in some small way. While minor in isolation, repeated service microfailures that go unnoticed and unrecovered can compound in effect and drive customer defection. For this reason, we propose that service microfailures are a potentially much larger managerial problem than they may appear on the surface. In this article, we conceptualize microfailures as a distinct form of service failure and outline the mechanism through which they cause damage. We then develop a multifaceted approach through which managers can detect, repair, and prevent service microfailures.

Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI's adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.