  Chatbots have garnered interest as conversational interfaces for a variety of
tasks. While general design guidelines exist for chatbot interfaces, little
work explores analytical chatbots that support conversing with data. We explore
Gricean Maxims to help inform the basic design of effective conversational
interaction. We also draw inspiration from natural language interfaces for data
exploration to support ambiguity and intent handling. We ran Wizard of Oz
studies with 30 participants to evaluate user expectations for text and voice
chatbot design variants. Results identified preferences for intent
interpretation and revealed variations in user expectations based on the
interface affordances. We subsequently conducted an exploratory analysis of
three analytical chatbot systems (text + chart, voice + chart, voice-only) that
implement these preferred design variants. Empirical evidence from a second
30-participant study informs implications specific to data-driven conversation
such as interpreting intent, data orientation, and establishing trust through
appropriate system responses.
