Introduction
The Analytics dashboard in Rep AI provides comprehensive insights into how your AI chatbot is performing across different aspects of your business. This powerful suite of tools helps you measure key metrics, understand customer interactions, and make data-driven decisions to optimize your AI assistant's effectiveness. With Rep AI Analytics, you can track engagement rates, monitor sales conversions, assess support efficiency, and quantify the overall return on your AI investment.
Accessing Analytics
Log in to your Rep AI Console
Click on Analytics in the left navigation menu
Select the tab for the specific analytics type you want to view:
Engagement: Monitor how visitors interact with your AI
Sales: Track revenue and conversions driven by your AI
Support: Measure customer service efficiency and automation
Analytics Dashboard Overview
The Analytics section is divided into three specialized dashboards, each focusing on a different aspect of your AI's performance:
1. Engagement Analytics
The Engagement Analytics dashboard helps you understand how effectively your AI initiates and participates in conversations with website visitors. Key metrics include:
Engagement Rate: The percentage of site visitors who interact with your AI
Conversations by Traffic Source: Breakdown of AI interactions by visitor origin
Proactive vs. Shopper-Initiated: Balance between AI-started and customer-started conversations
Leads Collected: Number of email addresses or phone numbers gathered during conversations
Use this dashboard to optimize how your AI approaches visitors and converts passive browsers into active shoppers or support seekers.
2. Sales Analytics
The Sales Analytics dashboard tracks how your AI chatbot drives revenue, conversion, and product engagement. Key metrics include:
Conversion Rate: Percentage of AI conversations resulting in purchases
AI-Generated Sales: Total revenue attributed to chatbot-assisted conversations
Average Order Value (AOV): Average purchase amount from AI-assisted shoppers
Rep Funnel Visualization: Visual representation of the AI-driven customer journey
Use this dashboard to measure the ROI of your AI investment and identify opportunities to optimize your conversion funnel.
3. Support Analytics
The Support Analytics dashboard provides insights into how your AI handles customer service inquiries. Key metrics include:
Resolved by AI: Percentage of support interactions handled without human intervention
Question-Answering Rate: Success rate for customer questions
Deflected Support Tickets: Number of customer-initiated support conversations fully handled by AI
Potential Savings: Estimated cost savings from AI-handled support tickets
Use this dashboard to quantify efficiency gains, understand escalation patterns, and measure customer satisfaction with AI-powered support.
Working with Analytics Tools
Date Range Selection
All analytics dashboards allow you to select specific time periods for analysis:
Click the date picker in the top right corner
Choose from preset options (Today, Yesterday, This Week, Last Month)
Use the calendar interface for custom date ranges
Comparing Performance
Toggle the "Compare to previous period" switch to see how your current metrics compare to the previous time period of equal length. This helps you:
Identify trends over time
Measure the impact of changes to your AI configuration
Set benchmarks for ongoing performance
Filtering Your Data
Click the Filters button to refine your data by:
Customer Type: All, New, or Returning
Traffic Source: All, Direct, Referral, Search, or Social
Platform: All, Web (Desktop + Mobile), Desktop only, Mobile only, or Simulator
Viewing Detailed Conversations
Each metric card includes a "See conversations" link that allows you to review the actual chat transcripts that contributed to that metric. This provides valuable context for understanding the numbers and identifying specific interaction patterns.
Tips for Getting the Most from Analytics
Regular Review Schedule: Set aside time weekly to review your analytics across all three dashboards
Focus on Trends, Not Just Numbers: Look for patterns and changes over time rather than fixating on absolute values
Connect Insights Across Dashboards: For example, if Engagement Analytics shows high interaction but Sales Analytics shows low conversion, investigate the gap
Use Insights to Refine AI Training: Identify knowledge gaps or missed opportunities and update your AI's training accordingly
Test and Iterate: Make one change at a time to your AI configuration, then use Analytics to measure its impact
Share Insights with Your Team: Use the data to align marketing, sales, and support teams around AI performance goals
Troubleshooting Common Issues
Missing or Incomplete Data: Ensure your tracking is properly implemented. If you've recently installed Rep AI, it may take time to accumulate meaningful data.
Metric Discrepancies: If metrics seem inconsistent across different views or filters, try refreshing the page or temporarily disabling filters.
Low Performance Indicators: Check your AI's configuration settings and review conversation transcripts to identify specific areas for improvement.
Date Range Confusion: Remember that comparing to previous periods uses time ranges of equal length (e.g., comparing this week to last week).
Related Features
Conversations: Review individual chat transcripts in detail
AI Training: Configure your AI's knowledge base and behavior based on analytics insights
Sales Skills: Adjust proactive approach settings based on Engagement Analytics data
Support Skills: Refine automation capabilities based on Support Analytics metrics
By regularly using Rep AI Analytics, you'll gain valuable insights that help you continuously improve your AI chatbot's performance, drive more revenue, provide better customer support, and maximize your return on investment.