Abandonment rate is one of the most broadly used online user satisfaction metrics. In this paper we discuss the notion of potential good abandonment, i.e. queries that may potentially result in user satisfaction without the need to click on search results (if search engine result page contains enough details to satisfy the user information need). We show that we can train a classifier which is able to distinguish between potential good and bad abandonments with rather good results compared to our baseline. As a case study we show how to apply these ideas to IR evaluation and introduce a new metric for A/B-testing — Bad Abandonment Rate.