Analytics
📊 Analytics Dashboard - Understanding Your Chatbot's Performance
Track your AI BotKit's performance like a boss! Think of analytics as your chatbot's report card - it shows you exactly how well your AI assistant is doing and where it can improve.
🏠 Accessing Your Analytics Dashboard
📍 Where to Find It
Go to your WordPress admin panel
Click on "AI BotKit" in the sidebar
Select the "Analytics" tab
You'll see a beautiful dashboard with colorful graphs and important numbers! 📊
📈 Interactive Charts & Graphs
1. Daily Usage Chart
What it shows: How many people chat with your bot each day
Why it matters:
📈 Rising trend = Your chatbot is getting more popular!
📉 Dipping trend = Time to promote your chatbot more
🎯 Peak days = When your customers are most active
Real-world example: "I noticed my usage spikes on Mondays - that's when customers have weekend questions!"
2. Response Times Chart
What it shows: How fast your chatbot answers questions (in milliseconds)
Good response times:
🟢 Under 2000ms (2 seconds): Excellent - like instant human response!
🟡 2000-5000ms (2-5 seconds): Good - still feels natural
🔴 Over 5000ms (5+ seconds): Slow - users might get impatient
What affects speed:
Complex questions take longer
Large knowledge base = more processing time
API provider performance
3. Error Rate Chart
What it shows: Percentage of conversations that had problems
Healthy error rates:
🟢 0-2%: Excellent - your bot is rock solid!
🟡 2-5%: Good - minor issues, but manageable
🔴 Over 5%: Needs attention - time to investigate!
Common causes of errors:
API key issues
Knowledge base problems
Internet connectivity issues
Rate limit exceeded
4. Token Usage Chart
What it shows: How many AI tokens you're consuming daily
Why track this:
Budget planning - predict your monthly costs
Usage optimization - find ways to reduce costs
Growth tracking - see how your usage scales
Cost-saving tips:
Use smaller AI models for simple questions
Optimize your knowledge base
Set up smart caching
5. Top Query Types
What it shows: This table displays a breakdown of the most common types of user queries, along with their frequency, average quality score, and average response time.
Why track this:
Identify most common query types — Understand what users are asking most frequently.
Evaluate response quality — Monitor how well each query type is being handled.
Analyze response times — Detect any performance issues or delays in specific query types.
Prioritize feature improvements — Focus development efforts based on real usage patterns.
6. Recent Errors
What it shows: This table lists the most recent system errors, including the error type, how many times it occurred, which component was affected, and when it last happened.
Why track this:
Identify system issues quickly — Spot recurring or critical errors early.
Pinpoint affected components — Understand where in the system failures are occurring.
Track error frequency and timing — Monitor how often errors happen and their last occurrence.
Support debugging and testing — Provide context for developers to resolve issues faster.
Improve system reliability — Reduce downtime and enhance overall user experience.
🔍 Troubleshooting Common Issues
"I Don't See Any Data!"
Possible causes:
Too early: Analytics need time to collect data
Wrong date range: Check your time filter
Analytics disabled: Check your settings
Technical issue: Contact support
📉 "My Metrics Look Wrong!"
Check these:
Time zone settings: Ensure correct timezone
Data caching: Clear cache and refresh
Browser cache: Try a hard refresh (Ctrl+F5)
Different device: Check on another computer
🚨 "Everything Shows as Errors!"
Emergency checklist:
API Key: Is it valid and has credits?
Internet: Is your connection stable?
Billing: Are your AI provider payments up to date?
Support: Time to get help!
Need help interpreting your analytics? Check our troubleshooting guide or contact our support team! 💪
Last updated