20 Essential Chatbot Analytics Metrics to Track

Want to supercharge your chatbot’s performance? Here are the 20 key metrics you need to monitor:

Total users
Active users
Engaged users
New users
User retention rate
Total conversations
Average conversation length
Messages per conversation
Conversation completion rate
Human handover rate
Response time
Goal completion rate
Fallback rate
Self-service rate
Confusion triggers
Customer Satisfaction Score (CSAT)
Net Promoter Score (NPS)
User sentiment analysis
Feedback and ratings
Task success rate

Tracking these metrics helps you:

Spot issues in your chatbot’s performance
Improve user experience
Boost customer satisfaction
Make data-driven decisions

Here’s a quick comparison of key performance indicators:

Metric	What it measures	Good score
CSAT	User satisfaction	>80%
NPS	Likelihood of recommendation	>50
Response time	Speed of bot replies	<5 seconds
Goal completion rate	Task fulfillment	>90%
Self-service rate	Issues solved without human help	>80%

Remember: Regular analysis and continuous improvement are crucial for chatbot success.

How chatbot analytics works

Chatbot analytics is all about understanding how your bot performs. It’s like a report card for your chatbot, showing you what’s working and what’s not.

Collecting chatbot data

Chatbots are data-gathering machines. They collect:

Messages from users
Specific info tags (attributes)
Similar meanings (entities)
User categories (segments)
Files users send

What the numbers tell you

Analytics give you the scoop on:

How many people use your bot
How well conversations go
How fast and accurate your bot is
If users are happy

Here’s a cool example of chatbot analytics in action:

Company	What they measured	What happened
Telepass Group	Sales	13% more in 6 months
Würth Italia	Bot handling chats	96% of chats
Santander Consumer Bank	Messages sent	100,000+ in 5 months

With these numbers, you can:

Spot trends
Fix bot weak spots
Make smart choices
See if your bot’s helping your business

It’s like having a crystal ball for your chatbot. You see what’s happening and can make it better.

User engagement metrics

Let’s look at five key metrics that show if your chatbot is doing its job:

1. Total users

This is how many people have ever talked to your bot. It shows your bot’s reach.

Domino’s Pizza’s chatbot "Dom" hit 500,000 users in its first year on Facebook Messenger. People were clearly into ordering pizza via chat.

2. Active users

These are the regulars who keep coming back. They’re your bot’s fan club.

Timeframe	Who counts
Daily	Chat at least once a day
Weekly	Chat at least once a week
Monthly	Chat at least once a month

3. Engaged users

These folks chat with your bot often, usually daily or weekly. They’re getting real value from it.

Sephora‘s Kik chatbot saw 70% of its 13-24 year old users chatting more than twice a week in 2019. That’s a lot of makeup talk!

4. New users

This tracks your bot’s first-timers over time. It shows if your marketing is working.

H&M’s Kik chatbot grew new users by 20% each month for six months after launch in 2016. People were clearly spreading the word.

5. User retention rate

This shows how many people keep coming back. It’s all about loyalty.

Rate	What it means
High (>70%)	Your bot’s a keeper
Medium (40-70%)	Some like it, but it could be better
Low (<40%)	Most users don’t come back

Rare Beauty‘s Facebook Messenger chatbot kept 78% of users coming back after 30 days in 2018. That’s a lot of happy chatters!

Conversation metrics

Want to know if your chatbot’s pulling its weight? These five metrics spill the beans:

6. Total conversations

This one’s simple: how many chats your bot’s had. It shows if people are actually using it.

Sephora’s Facebook Messenger bot chatted over 1 million times in 2022. That’s a lot of lipstick talk!

7. Average conversation length

How long do chats usually last? Short chats might mean your bot’s quick. Long ones? It could be thorough… or lost.

Chat Length	What’s Going On?
< 10 seconds	Users bail fast
30-60 seconds	Quick fixes
2-5 minutes	Deep dives
> 5 minutes	Bot’s spinning its wheels

8. Messages per conversation

This counts the back-and-forths. It hints at how complex your bot’s convos are.

Domino’s "Dom" bot? 3-5 messages to order a pizza. That’s fast food for real.

9. Conversation completion rate

Does your bot finish what it starts? A high rate means it’s solving problems solo.

10. Human handover rate

How often does your bot wave the white flag? Lower is usually better, but some stuff will always need human brains.

Rare Beauty’s bot only calls for human backup 15% of the time. Not bad for AI!

Performance metrics

Let’s look at the numbers that show if your chatbot’s doing its job:

11. Response time

How fast does your bot reply? Users hate waiting. A Harvard Business Review study found that if a customer doesn’t get a response within 5 minutes, the chance of qualifying a lead drops by 400%. Ouch!

12. Goal completion rate

This shows how often your bot helps users finish tasks. For example:

E-commerce: Successful checkouts
Banking: Completed application forms

A bot that completes tasks 95% of the time is doing well. If your rate is low, it’s time to fix your bot’s scripts.

13. Fallback rate

This tracks when your bot gets confused. A high rate means users often hear "Sorry, I didn’t understand that." Not good.

14. Self-service rate

This measures how many users solve problems without human help. Higher is better – it means your bot’s doing its job.

15. Confusion triggers

These are the spots where users often get lost. Finding them helps you fix issues fast.

Here’s a quick look at what these metrics mean for your bot:

Metric	Good	Bad	Action
Response time	< 5 seconds	> 10 seconds	Speed up responses
Goal completion	> 90%	< 70%	Improve task flows
Fallback rate	< 10%	> 20%	Expand bot’s knowledge
Self-service	> 80%	< 60%	Boost bot’s problem-solving

User satisfaction metrics

People need to like your chatbot for it to be successful. Here’s how to check if they do:

16. Customer Satisfaction Score (CSAT)

CSAT measures how happy users are with your bot. It’s simple:

Ask users to rate their experience (1-5 stars)
Add up the scores
Divide by total responses
Multiply by 100

If 75 out of 100 users give 4 or 5 stars, your CSAT is 75%.

Pro tip: Use your bot to ask for ratings right after chats. It’s quick and easy.

17. Net Promoter Score (NPS)

NPS shows if users would recommend your bot. Here’s how:

Ask: "How likely are you to recommend our chatbot?" (0-10 scale)
Group responses:
- Promoters (9-10)
- Passives (7-8)
- Detractors (0-6)
Subtract % of Detractors from % of Promoters

If 50% are Promoters and 20% are Detractors, your NPS is 30.

18. User sentiment analysis

This looks at the emotional tone of chats. Tools like IBM Watson can help. They check if users sound:

Happy
Frustrated
Confused

More positive sentiment? Your bot’s doing well.

19. Feedback and ratings

Don’t just look at numbers. Ask users what they think:

"Was this helpful?" (Yes/No)
"What could we improve?"

Many chatbot platforms let you add surveys right in.

20. Task success rate

This shows how often your bot helps users finish tasks. Track things like:

Completed purchases
Answered questions
Solved problems

Aim for over 80% success.

Here’s a quick comparison:

Metric	Measures	How to collect	Good score
CSAT	Satisfaction	Post-chat surveys	>80%
NPS	Recommendations	Surveys	>50
Sentiment	Emotional tone	AI analysis	>70% positive
Feedback	Likes/dislikes	Open questions	N/A
Task success	Goal completion	Track actions	>80%

These numbers aren’t just for show. Use them to improve your bot. Low scores? Find out why and fix it.

How to analyze chatbot metrics

Want to supercharge your chatbot? You need to know how it’s doing. Here’s how to dig into the data:

Tools for tracking metrics

These tools can help you collect and analyze chatbot data:

Tool	Key Features	Best For
Dashbot	User behavior tracking, sentiment analysis	In-depth conversation analysis
Botanalytics	User lifecycle data, conversation transcripts	Individual user tracking
Chatbase	Active user metrics, retention rates	Google-powered insights

These platforms offer dashboards to view your metrics at a glance. They’ll help you spot trends and areas to improve.

Tips for understanding data

1. Set clear goals

What does success look like for your chatbot? Is it solving customer issues? Generating leads? Use these goals to guide your analysis.

2. Focus on key metrics

Don’t get lost in the numbers. Pay attention to metrics that align with your goals, like:

Goal completion rate
User satisfaction scores
Conversation length

3. Look for patterns

When does your bot perform better? What questions does it struggle with? These insights can help you make targeted improvements.

4. Use visuals

Charts and graphs make complex data easier to understand. Most analytics tools have built-in visualization options.

5. Compare over time

Track how your metrics change week-to-week or month-to-month. This shows if your improvements are working.

6. Gather qualitative data

Numbers don’t tell the whole story. Use exit surveys or review conversation transcripts for more context.

7. Act on insights

Don’t just collect data—use it. High fallback rate? Work on improving your bot’s responses in those areas.

Analyzing chatbot metrics isn’t a one-time thing. Keep testing, learning, and refining to create a better user experience.

"The best chatbots are always changing. They use what they learn from talking to people to get better and better."

Improving chatbot performance with metrics

Let’s put those chatbot insights to work. Here’s how to boost your bot’s performance:

Fixing problem areas

1. High fallback rates

If your bot often misunderstands users:

Add more varied customer queries to its knowledge base
Create clear menus for common issues
Route complex questions to human agents

Mobily, a UAE telecom company, cut their first response time from 20 minutes to 6 seconds with AI chatbots using these tactics.

2. Low self-service rates

To help users solve problems without human help:

Update bot content regularly
Improve the chat interface
Make the bot easy to find on your website

3. Negative feedback

Use sentiment analysis and CSAT scores to spot unhappy users. Then:

Review transcripts to find pain points
Rewrite confusing bot responses
Fix frustrating conversation flows

Ongoing improvements

Chatbot optimization never ends. Keep getting better by:

1. Regular reviews

Schedule time to:

Check key metrics (engagement rate, goal completion)
Analyze new queries the bot couldn’t handle
Test bot responses for accuracy

2. Learning from human agents

Your customer service team has valuable insights. Ask them about:

Common issues they handle
Opportunities to automate more interactions
Ideas for better bot responses

3. A/B testing

Let data guide your decisions:

Try different greetings or conversation flows
Compare user responses to different answer formats
Test bot personality and tone

4. Watching the competition

Keep an eye on other chatbots:

Try competitor bots for ideas
Look for service gaps you can fill
Stay ahead of changing user expectations

Even small tweaks matter. A healthcare chatbot cut misunderstood inputs by 25% and boosted user retention by 35% through ongoing improvements to conversation quality.

Conclusion

Chatbot analytics are crucial for boosting performance and customer satisfaction. By tracking the right metrics, you can:

Spot issues
Refine responses
Deliver better experiences

Why do these analytics matter? They show how your bot handles queries, highlight improvement areas, and reveal customer preferences.

Don’t aim for perfection. Even top bots don’t hit 100% automation. Healthspan‘s 88% deflection rate? That’s excellent.

To maximize your analytics:

1. Focus on metrics that match your goals

2. Review data regularly

3. Make data-driven improvements

These practices help create a bot that serves customers and supports your business.

Take Mobily’s success:

"Mobily moved their offline interactions to modern digital channels, specifically Twitter, Facebook, and WhatsApp, using Sprinklr’s conversational AI chatbots that could juggle multiple customers and serve quick, contextual answers to routine queries, eventually increasing the first response time by a whopping 99.6%." – Mubarak Alharbi, Mobily

That’s the power of well-implemented, analytics-backed chatbots.

FAQs

What is KPI in chatbot?

KPI in chatbot means Key Performance Indicator. It’s how we measure if a chatbot is doing its job well. Think of KPIs as a report card for your chatbot.

What do chatbot KPIs look at?

How much people use it
If conversations make sense
Whether it gets things done
If users are happy with it

Here’s a quick example: response time. If your bot is slow, users get frustrated. In fact, if you don’t respond within 5 minutes, you’re 4 times less likely to get a good lead. That’s according to Harvard Business Review and InsideSales.com.

How to measure the impact of a chatbot?

Want to know if your chatbot is making a difference? Keep an eye on these:

Metric	What it means
Activity volume	How many chats happen
Bounce rate	Users who leave after one message
Retention rate	Users who come back
Use rate	Active chats at any time
Target audience sessions	Chats from your ideal users
Response volume	Questions the bot answers
Conversation length	How long chats last

These numbers tell you if your chatbot is hitting the mark. For example, Cardiff insurance company’s bot handles 56% of incoming calls right off the bat. That’s a big win for customer service efficiency.

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