Repeat Client Radar

Problem The Property Coaches team at PropHero lacks an efficient way to prioritize past buyers who might reinvest. Although client data exists, key buying signals are scattered across various platforms (call transcripts, emails, HubSpot notes). Consequently, advisors waste time calling low-potential clients while missing higher-value opportunities.

Solution & Impact We implemented an AI agent to identify and prioritize past buyers with the highest repeat-buy potential. This will reduce unproductive calls, reactivate the existing customer base, and increase sales from trusted clients.

Key Capabilities of the AI Agent Scoring: Assigns a "commercial temperature score" to past buyers based on liquidity, satisfaction, intent, timing, and past behavior.

Categorization: Generates a prioritized list for each coach, dividing clients into hot, warm, cold, and risk/no-call categories.

Actionable Insights: Provides the reasoning behind each score, recommends the next step, and suggests a personalized message for the client.