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
Email Resolution Chart
If you use AI Email Answering, set the Platform filter to Helpdesk to see the Email Resolution Chart. It is a pie chart showing what happened to every email ticket your AI processed, so you can measure how much work it took off your team and fine-tune the rules that control which emails it answers.
The center of the chart shows Tickets Analyzed — every email ticket the AI processed in the selected date range — divided into four outcomes, each with a count and percentage:
Resolved
What it shows: The AI answered the customer and the conversation closed without a human stepping in.
Why it matters: This is your AI handling tickets end-to-end.
Pending Customer Response
What it shows: The AI replied and is waiting for the customer to respond.
Why it matters: The ticket is not stuck — the ball is in the customer's court.
Could Not Be Resolved (Escalated)
What it shows: The AI could not fully answer and left the ticket for a human.
Why it matters: A rising slice here points to knowledge gaps worth filling.
Blocked by Do-Not-Answer Rules
What it shows: The AI intentionally skipped the email because it matched a Do-Not-Answer rule or automatic spam filtering.
Why it matters: This is a good outcome — the right emails reach your team instead of getting an automated reply.
One ticket, one slice: Each ticket is counted only in its final outcome, so the four slices always add up to your Tickets Analyzed total. A ticket that was Pending and later Resolved appears under Resolved only.
Alongside the chart you will also see the resolution rate (resolved divided by analyzed), the block rate (blocked divided by analyzed), and period-over-period comparison on each slice — improvements shown in green, the opposite in red.
Drilling Down into Any Slice
Every part of the chart is clickable, so you can go from a number to the actual tickets behind it:
Tickets Analyzed total: the full list of email tickets processed, with subject, customer, date, and outcome.
Blocked by Do-Not-Answer Rules: a second pie chart breaking blocked tickets down by individual rule, each labeled Default (a Rep AI built-in rule) or Custom (one you created). Click a rule to see the exact tickets it caught.
Could Not Be Resolved: the escalated tickets, with the reason the AI could not answer.
Pending Customer Response: tickets awaiting a reply, including how long each has been open.
Resolved: the tickets the AI closed on its own.
To adjust the rules behind the Blocked slice, see the help article "Automate Customer Support with the AI-Powered Answer Emails Support Skill."
Seeing Why a Ticket Was Not Answered
When a drill-down opens an individual blocked or unanswered conversation, an indicator sits under the customer's message so you do not have to guess why the AI stayed quiet:
Blocked by a rule: the indicator shows the rule name and a Default or Custom label. Click it to review the rule — custom rules are editable, default rules open as read-only.
Could not be resolved: the indicator shows the reason with a neutral label, so you can tell the AI deliberately handed the ticket off rather than missing it.
Special Cases
No email answering yet, or not on your plan: the chart shows a prompt to set up AI Email Answering or to upgrade.
No activity in the period: the chart shows an empty state — try widening the date range.
Email answering toggled on or off mid-period: only tickets from the days when it was active are counted, keeping your numbers accurate.
If the Chart Looks Off
The chart is not showing: make sure the Platform filter is set to Helpdesk, AI Email Answering is enabled, and your plan includes it.
The slices do not add up as expected: remember each ticket is counted once, in its final outcome only.
The Blocked slice is larger than expected: this usually means spam filtering and your rules are working. Click the slice to see which rules are catching tickets and adjust any that are too broad.
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)
Select Helpdesk to switch the dashboard to your email ticket metrics and view the Email Resolution Chart
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