Chatbot error handling is crucial because chatbots can mess up, frustrating users and potentially hurting your business. Here’s how to handle errors effectively:
- Clear error messages
- Context-based responses
- Partial functionality
- Helping users recover
- Preventing errors
- Always improving
- Switching to human help
Why it matters:
- 75% of US shoppers ditch companies with poor user experiences
- 60% of users worry chatbots won’t get their questions
Don’t panic. Good chatbot error handling keeps users engaged, builds trust, and gets them back on track.
Let’s break it down:
| Strategy | Benefit | Example |
|---|---|---|
| Clear messages | Less frustration | "Oops! Can you rephrase that?" |
| Context-based | More natural | "Can’t book that flight, but I can check prices." |
| Partial function | Core features work | Offering cached data when live data fails |
| User recovery | Guides users back | Suggesting alternative commands |
| Error prevention | Stops issues early | Using input validation |
| Continuous improvement | Learns from mistakes | Updating based on feedback |
| Human handover | Solves complex issues | Smooth transfer to live agent |
The goal? Keep the conversation flowing, even when things go wrong.
Related video from YouTube
Types of chatbot errors
Chatbots stumble in various ways:
1. No-match: Bot doesn’t understand input.
2. No-input: Bot gets no response.
3. Dead ends: Chat can’t continue.
4. Info overload: Too much info at once.
5. Failure to escalate: Complex issues not passed to humans.
Impact on users
- 96% might leave after bad service
- Brand damage
- Lost sales
Why it’s tough to fix
- Users phrase things in countless ways
- Bots need constant updates
- Must be helpful without overwhelming
| Challenge | Impact | Solution |
|---|---|---|
| Unpredictable inputs | Dead ends | Comprehensive decision trees |
| Outdated info | Wrong answers | Regular knowledge updates |
| Complex queries | Frustration | Clear paths to human agents |
Plan for errors and keep improving. By 2027, chatbots will be the main customer service channel for 25% of organizations.
1. Clear error messages
Clear messages keep users happy when bots mess up. Bad ones frustrate and damage your brand.
To write good messages:
- Say something went wrong
- Use simple words
- Don’t blame the user
- Be specific
- Offer help
| Element | Description | Example |
|---|---|---|
| Tone | Friendly | "Sorry, we didn’t catch that." |
| Length | Short | 8-14 words max |
| Next steps | Clear actions | "Try rephrasing or pick an option:" |
| Look | Easy to spot | Use colors or icons |
Include calls to action. Spotify nails it: "Oops! Something went wrong. Try again or check our help area."
"Good chatbot error messages are readable and easily understood by the user." – ChatBot Academy
To improve:
- Create different versions
- Ask for feedback
- Update your message library often
2. Context-based responses
Make error messages fit the situation. Analyze the chat, spot key topics, and craft relevant replies.
Example: For a hotel booking error, say: "Can’t find rooms for those dates. Try different dates or a nearby spot?"
Use what the user said:
- Mention their words
- Acknowledge their goal
- Offer related options
Bank of America’s Erica does this well: "Can’t help with that, but I can check your balance or recent transactions. Want to do that?"
Keep the chat flowing:
- Give clear next steps
- Offer choices
- Be friendly
Example:
User: What's your shoe return policy?
Bot: Sorry, I don't have that info. Want me to:
1. Check our general return policy?
2. Connect you with a human for shoe questions?
3. Show you popular shoe styles?
This keeps the conversation going, even when the bot can’t directly answer.
3. Partial functionality
Graceful degradation keeps bots working when things go wrong. It’s about keeping core functions running.
How it works:
- Build a bot that works well normally
- Add backup plans
- Keep basic features working during errors
Backup strategies:
- Use old data when new data fails
- Give simple answers when complex ones won’t work
- Fall back on pre-written responses
Example: A weather bot without live data could say:
"Can’t get the latest forecast. Yesterday was sunny, 75°F. Want a notification when I get current info?"
Focus on:
- Always responding
- Letting users move between main sections
- Suggesting other ways to get help
| Feature | Normal | During Errors |
|---|---|---|
| Responses | Personal, context-aware | Pre-set, general |
| Data | Real-time, full | Limited, old |
| Options | All services | Core services only |
| Processing | Complex queries OK | Simple queries only |
sbb-itb-58cc2bf
4. Helping users recover
Guide users back on track:
- Say there’s an error
- Explain what went wrong
- Give steps to fix it
Example:
"Couldn’t book your flight. Please:
- Check your destination
- Make sure travel dates are future
- Try ‘Book a flight to New York on July 15’"
Offer options:
- Suggest other commands
- Give buttons for common actions
- Offer human help
After an error, try:
"Not sure what you mean. You can:
- Type ‘Help’ for commands I know
- Click a button for popular services
- Say ‘Talk to an agent’ for human help"
To keep users chatting:
- Remind them what the bot can do
- Offer to start over
- Suggest related topics
| Strategy | Description | Example |
|---|---|---|
| Remind capabilities | What the bot can do | "I help with accounts, products, and orders." |
| Reset chat | Start over | "Want to start from the beginning?" |
| Suggest topics | Offer related info | "I can’t help with that, but I can explain returns or shipping." |
5. Preventing errors
Use data to predict common chatbot error handling mistakes. Spot patterns in past chats and adjust responses.
Check user input early:
- Text validation: Ensure text-only inputs
- Number validation: Check for expected ranges
- Date validation: Verify correct format and timeframe
Use regular expressions for complex checks like email or phone formats.
Design clear conversations:
- Break long messages into chunks
- Use buttons for common options
- Give clear instructions
| Design Element | Purpose | Example |
|---|---|---|
| Short messages | Easy to read | "How do you want it shipped?" |
| Option buttons | Reduce typing errors | [Standard] [Express] [Next Day] |
| Clear prompts | Guide input | "Enter your 5-digit zip code" |
6. Always improving
Learn from past errors. ChatGPT showed this in a demo with Microsoft’s CEO, admitting and correcting a mistake about South Indian food.
Use AI to spot errors:
- Detect issues in real-time
- Flag problems quickly
- Learn new error types from data
Home Depot improved their system with:
- Better search
- Real-time error reporting
- Clearer project details
Result? Fewer errors, happier customers.
Keep updating:
- Check error rates often
- Update bot knowledge
- Fix common issues fast
69% of customers want to solve problems alone. Regular updates help more users find answers without errors.
| Step | Action | Benefit |
|---|---|---|
| Get feedback | Ask if responses helped | Find weak spots |
| Check unanswered questions | See what stumps the bot | Add missing info |
| Update base prompts | Change how the bot thinks | Improve accuracy |
7. Switching to human help
Know when humans should step in:
- Complex issues
- Frustrated users
- Requests for human help
- Low bot confidence
MongoDB‘s bot lets users ask for humans if they’re unhappy.
Make handovers smooth:
- Tell users they’re being transferred
- Give wait times
- Share the agent’s name
Temu lets users type "I want a human agent" for help. The agent then reviews previous messages.
Manage handovers well:
- Share context with agents
- Use the same chat window
- Give agents chat history access
| Step | Action | Purpose |
|---|---|---|
| Pre-handoff | Get user info | Prep agent |
| Wait | Update user on status | Set expectations |
| Post-handoff | Agent reviews chat | Avoid repetition |
Payoneer makes it simple: Users write a prompt to talk to a human, leading to a quick connection.
Putting strategies into action
To improve your bot’s error handling:
- Check current performance
- Pick relevant strategies
- Add them gradually
- Test thoroughly
- Get user feedback
Useful tools:
| Tool | Purpose | Key Feature |
|---|---|---|
| Dialogflow | Understand queries | Better intent recognition |
| Botpress | Smooth conversations | Create error recovery paths |
| Chatfuel | Track performance | Monitor error rates and satisfaction |
| MobileMonkey | Test approaches | Compare error handling methods |
Measure success:
- Watch key metrics (user retention, response success, chat duration)
- Check user feedback
- Review error logs
- Test with real users
"Good chatbots typically handle 40% to 80% of queries." – Healthspan (Talkative customer)
Conclusion
Recap: 7 key strategies for chatbot error handling:
- Clear messages
- Context-based responses
- Partial functionality
- User recovery
- Error prevention
- Continuous improvement
- Human handover
Future outlook:
- AI will boost error detection and fixing
- Bots will grasp context better
- Multi-channel integration for smoother experiences
Remember:
- 75% of US shoppers avoid companies with poor experiences
- 60% worry bots won’t understand them
- 40% don’t mind bots if they get help
Good chatbot error handling is key to chatbot success.
