Measuring Your AI Support Performance Understanding the Support Analytics Dashboard – Rep

Measuring Your AI Support Performance: Understanding the Support Analytics

Dashboard Introduction The Support Analytics dashboard provides valuable insights into how your AI Concierge assists with customer service inquiries. This powerful tool helps you measure automation effectiveness, understand human escalation patterns, and quantify the efficiency gains from using Rep AI to

handle support tickets. Accessing the Support Analytics Dashboard Log in to your Rep AI Console

Navigate to "Analytics" in the left sidebar menu
Select the "Support" tab
Understanding Your Support Dashboard Metrics

Resolved by AI What it shows: The percentage of support interactions handled entirely by your AI Concierge without human intervention. Why it matters: A higher percentage indicates greater automation efficiency, reducing your team's workload. Handled by Humans What it shows: The percentage of conversations that were escalated to a human support agent.

Why it matters: This helps you understand how often customers need human assistance beyond what the AI can provide. Question-Answering Rate What it shows: The percentage of customer questions your AI was able to successfully answer. Why it matters: This metric reflects your AI's knowledge breadth and accuracy, helping you

identify areas for improvement. Unanswered Questions What it shows: The number of customer questions your AI couldn't answer effectively. Why it matters: This highlights knowledge gaps that you can address to improve your AI's performance. Deflected Support Tickets What it shows: The number of customer-initiated support conversations that were fully handled

by your AI. Why it matters: Each deflected ticket represents time saved for your support team. Potential Savings from Ticket Reduction What it shows: An estimate of cost savings from AI-handled support tickets (calculated as a set amount per deflected ticket). Why it matters: This helps quantify the ROI of your AI investment in concrete financial terms.

Customer Satisfaction Survey What it shows: Feedback from customers who interacted with your AI, including helpful/not helpful ratings. Why it matters: This provides direct insight into how customers perceive their AI support experience. Support Skill Performance The bottom section of your dashboard displays specific support skills and their performance

metrics

Skill: The type of support task your AI can handle (e.g., Cancel Order, Change Order Address) Created At: When the skill was added to your AI Conversations: Number of times this skill was used Successfully Handled: How many conversations were fully resolved by the AI Success Rate: Percentage of successful resolutions

Using Filters to Refine Your Analytics

To gain more targeted insights, use the filtering options available

Date Range Selection

Click the date picker to select a custom time period

Use preset options like Today, Yesterday, This Week, or Last Month Apply custom date ranges using the calendar interface

Customer Segments

Filter by New or Returning customers

See how different customer types interact with your AI

Traffic Source

Filter by Direct, Referral, Search, or Social traffic

Understand how acquisition channel affects support needs

Platform

Select Web (Desktop + Mobile), Desktop only, Mobile only, or Simulator

Compare AI performance across different platforms View messaging platform data when available (Instagram DM, Facebook DM, Whatsapp DM) Making the Most of Your Support Analytics Compare to Previous Periods: Toggle the comparison feature to see how your metrics have changed over time View Conversations: Click "See conversations" links to review actual chat transcripts related

to each metric Address Unanswered Questions: Review and update your AI's knowledge base to fill identified gaps Calculate ROI: Use the Potential Savings metric to demonstrate the value of your AI investment Troubleshooting Low Performance

If your metrics show room for improvement

Review unanswered questions to identify knowledge gaps

Check your Support Skills settings to ensure they're properly configured
Consider expanding your AI's training with additional product information or customer
service policies

Adjust escalation thresholds if too many or too few conversations are being handed to

human agents