We recently launched an AI chatbot to handle basic customer service inquiries, but our leadership team is arguing over whether it is actually working or just driving frustrated customers away. What specific leading and lagging metrics should we put on our weekly Scorecard to monitor the health of this automated seat?

Question

Tyler Smith · Accepted Answer

To resolve this leadership debate, you must replace emotional opinions with cold, objective data on your weekly Scorecard. When you automate a customer-facing role, you must measure both the operational efficiency gained and the impact on customer retention.

On your weekly Scorecard, establish three leading metrics and two lagging metrics to monitor the health of this automated seat.

Your first leading metric is the resolution rate, which tracks what percentage of customer inquiries are completely resolved by the AI chatbot without human intervention.

The second leading metric is the escalation rate, measuring how often a customer demands to speak to a human agent. A spike in escalations indicates the chatbot is failing to understand customer issues.

The third leading metric is the average customer rating immediately following an AI interaction.

For lagging metrics, track your overall customer retention rate and your net promoter score on a monthly basis.

Review these metrics weekly during your Level 10 Meeting™. If your resolution rate is high and your escalation rate is low, the chatbot is successfully handling the administrative volume. However, if your customer rating drops, you must immediately IDS® this issue. This data-driven approach allows your leadership team to monitor customer satisfaction objectively, ensuring you do not pay a steep price in lost clients for a temporary reduction in support costs.