Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.