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Mobile Health (mHealth) apps, such as COVID-19 contact tracing and other health-promoting technologies, help support personal and public health efforts in response to the pandemic and other health concerns. However, due to the sensitive data handled by mHealth apps, and their potential effect on people's lives, their widespread adoption demands trust in a multitude of aspects of their design. In this work, we report on a series of conjoint analyses (N= 1,521) to investigate how COVID-19 contact tracing apps can be better designed and marketed to improve adoption. Specifically, with a novel design of randomization on top of a conjoint analysis, we investigate people's privacy considerations relative to other attributes when they are contemplating contact-tracing app adoption. We further explore how their adoption considerations are influenced by deployment factors such as offering extrinsic incentives (money, healthcare) and user factors such as receptiveness to contact-tracing apps and sociodemographics. Our results, which we contextualize and synthesize with prior work, offer insight into the most desired digital contact-tracing products (eg, app features) and how they should be deployed (eg, with incentives) and targeted to different user groups who have heterogeneous preferences.