Extended Conversion: Capturing Successful Interactions in Voice Shopping

Being able to measure the success of online shopping interactions is crucial in order to evaluate and optimize the performance of e-commerce systems. It is especially challenging in the domain of voice shopping, typically supported by voice-based AI assistants. Unlike Web shopping, which offers a rich amount of behavioral signals such as clicks, in voice shopping a non-negligible amount of shopping interactions frequently ends without any immediate explicit or implicit user behavioral signal. Moreover, users may start their journey using a voice-enabled device, but complete it elsewhere, for example on their smartphone mobile app or a Web browser. We explore the challenge of measuring successful interactions in voice product search based on users' behavior, and propose a medium-term reward metric named Extended ConVersion (ECVR). ECVR extends the notion of conversion beyond the usual purchase action, which serves as an undisputed measure of success in e-commerce. More specifically, it also captures purchase actions that occur at a later stage during a same shopping journey, and possibly on different channel than the one on which the interaction started. In this paper, we formally define the ECVR metric, describe multiple ways of evaluating the quality of a metric, and use these to explore different parameters for ECVR. After selecting the most appropriate parameters, we show that a ranking system optimized for ECVR, set up with these parameters, leads to improvements in long-term engagement and revenue, without compromising immediate conversion gains.