In their informative article, Speer, Dutta, Chen, and Trussell (2019) have created a resource for predicting turnover by using available data to help organizations know if employees are "here to stay or go." In an article with colleagues, we explored the other side of this coin-how employees decide, "Should I stay or should I go?" (Rothausen, Henderson, Arnold, & Malshe, 2017). We have two broad cautions for consultants in response to Speer et al. from our findings in this article and related work (Rothausen & Henderson, 2019). First, the "detrimental effects to company productivity, financial performance, : : : and morale : : : " referred to by Speer et al. (2019, p. 277) are not only, or perhaps even primarily, caused by losing employees but rather by the impact of the job, as delivered by the organization, on employees' lives. Our data show that the organizational treatment of employees not only leads some to leave but also impacts "stayers" directly rather than primarily through their colleagues' leaving. Second, though quantitative data are good at answering certain questions, there are broader questions about turnover quantitative data cannot answer, of which consultants and researchers should not lose sight. In our study, we took a different direction from the dominant turnover models reviewed by Speer et al. (2019), in order to broaden attrition modeling, because reviews indicate that researchers are not content with the predictive power of current models of turnover (e.g., Hom, Mitchell, Lee, & Griffeth, 2012). The bounds of the extant models Speer et al. review may be due in part to over-reliance on limited frameworks, types of data, and methodologies in turnover research (Russell, 2013). There have been calls in the literature to expand understanding of turnover by moving beyond reductionist measures from employees, for example by exploring leavers' self-reports and in-depth exit interviews (Bergman, Payne, & Boswell, 2012; Maertz, 2012), which is what we did. We began by interviewing leavers across four industries and later added leavers from other industries as well. Our findings demonstrate that the process of turnover is more complex, and goes deeper, than extant models suggest. Our findings also demonstrate that the process of turnover begins well before the timeframes common in extant research. Once we learned this, we also interviewed "stayers" to make sure the process we uncovered was actually happening pre-leaving and was not post-hoc rationalizing. Our findings suggest that how current employees are-and how former employees were-treated by the organization has the potential to cost the organization much more than the cost of replacing and training new employees. Another way of saying this is that we offer an alternative attrition model based on what employees told us about why they leave or stay in organizations, that is not limited to what happens inside the organizational walls but carries over into the impact of these experiences on people's whole lives. In line with others' suggestions (e...