How do robo-advisors analyze support insights?

Deploy a Chatref AI agent trained on your robo-advisor's help center, fee schedules, and investment methodology docs. The agent resolves routine questions - such as "What are my management fees?" or "How do I rebalance?" - directly in the website widget. Every interaction is logged, giving you a complete, searchable record of customer contacts without manual data entry.

Chatref's conversation-tags automatically classify each exchange by topic: "tax implications," "account linking," "performance concern," and more. You can also apply manual tags for nuanced issues like "regulatory query" or "advisor complaint." This structured customer data becomes the foundation for support metrics - no guesswork, just real patterns from real chats.

The insights engine synthesizes tagged conversation data and surfaces what matters most. Weekly digest emails highlight sudden spikes in certain question types, your AI agent's deflection rate, and topics that still require human escalations. This insight generation loop transforms raw logs into a prioritized to-do list: update that FAQ on capital gains, tweak the agent's response for inheritance questions, or add new training docs on crypto taxation.

Your conversation-tags and insights together reveal key support metrics: deflection rate (AI-resolved vs. human-handled), top-10 question categories, escalation frequency, and handoff response time. By tracking how these shift week-over-week, you can measure the impact of training updates and decide where to invest support resources. More deflections on fee inquiries? Your fee explainer document just paid for itself.

How can robo-advisors use insights to improve support?

Robo-advisors can use Chatref's insights digests to identify recurring customer confusion - like frequent questions about retirement withdrawal rules or ESG portfolio options - and then update the AI agent's training documents to pre-empt those inquiries. Insight reports also pinpoint topics that lead to human escalations, signaling where the knowledge base needs richer detail. This continuous feedback reduces repeat contacts and lifts first-touch resolution.

What are the key metrics for robo-advisor support?

Key metrics you can track directly inside Chatref include deflection rate (the share of conversations resolved entirely by the AI agent), topic distribution (fee queries vs. onboarding questions vs. performance concerns), and handoff frequency. These metrics help you optimize both the agent's knowledge base and the workload of your human support team, ensuring every question gets answered at the lowest cost.

How do robo-advisors track customer satisfaction?

By using conversation-tags to mark conversations as "resolved" or "unresolved" and then watching the ratio in insight reports, robo-advisors can infer satisfaction trends. A high deflection rate paired with a low re-contact rate on the same issue indicates strong satisfaction. If insight digests show a sudden rise in a specific "unresolved" tag - say, around advisory fee changes - teams can immediately intervene with clearer content or a human follow-up.