Tracecast automatically summarizes prospects with stories: natural language highlights of the key actions and properties for a prospect. Stories are fine-tuned to be easily scannable by sales reps to quickly understand a prospect’s recent behavior.

There are two types of stories: (1) an overview, which consists of a prospect’s properties (i.e. a lead’s current pricing plan or industry), and (2) an activity summary, which consists of a prospect’s recent events – timestamped actions that are typically generated by analytics tools (i.e. last sign in date or date of visit to a pricing page).

Tracecast automatically generates personas by uncovering common patterns and themes across all lead data. Personas let sales reps easily categorize and understand trends across their assigned leads. Each prospect uploaded to Tracecast is assigned a persona.

While Tracecast generates personas out of the box, admins can also create custom personas (coming soon).

Tracecast has an AI-native data model, which means that all data in the platform can be retrieved and analyzed through a chat experience. The chat lets users query their prospects across across any dimension, such as “Show me all leads in the healthcare industry” or “Which leads signed up for the free tier last week?” Users can then add groups of leads to email sequences from directly within the chat flow.

Recommended actions surface key intent signals from within a sales rep’s assigned leads. They can include notable increases or decreases in lead behavior and engagement, and include a list of associated leads.