If you’ve ever launched a chatbot and then wondered if it’s actually doing anything useful, this guide is for you. We’re digging into how to track and analyze chatbot performance metrics in Landbot, with a clear focus: helping you actually improve go-to-market (GTM) results, not just stare at dashboards. No fluff, no buzzwords—just practical steps, honest advice, and a few heads-ups about what to ignore.
Why Chatbot Metrics Matter (And Where Most People Go Wrong)
A chatbot is only as good as its results. But “results” depends on your goals—lead gen, support, bookings, whatever. The mistake most teams make is either tracking everything (then drowning in data) or tracking nothing (flying blind). The sweet spot is tracking what moves the needle for your GTM strategy.
Here’s what you don’t need: vanity metrics. Impressions, total messages sent, or “engagement rate” without context often mean nothing. Instead, focus on metrics you can act on.
Step 1: Get Clear On Your GTM Goals
Before you even look at Landbot’s analytics, nail down what “success” looks like for your chatbot. Ask yourself:
- Is the bot supposed to capture leads? Book demos? Answer FAQs and reduce support tickets?
- Which part of the funnel does it impact—top, middle, or bottom?
Pro tip: Write down one sentence: “If the bot is working, I’ll see more [X].” That’s your north star.
Step 2: Set Up Tracking In Landbot
Landbot gives you a built-in analytics dashboard, but you can (and probably should) set up extra tracking if you want to get serious. Here’s how to get started:
Enable Landbot’s Built-In Metrics
When you create or edit a bot, Landbot starts tracking basics out of the box:
- Number of users: How many people started a chat.
- Completion rate: How many finished the flow you designed.
- Drop-off points: Where users bail out.
You’ll find these in the Analytics tab for each bot. Don’t overthink it—this is a solid place to start.
Add Custom Variables
If you want to track specifics (like which marketing source sent the user, or if they clicked a certain button), create custom variables in your bot flow. You can:
- Use “Set Variable” blocks to capture user data or actions.
- Pass UTM parameters from your landing pages into the bot.
- Store answers to key qualifying questions.
Connect To Google Analytics Or Other Tools
Landbot lets you send events to Google Analytics, Google Tag Manager, or even Zapier. This is worth doing if you want to:
- Track conversions alongside your website stats.
- Trigger CRM updates or email notifications.
How to do it:
In Landbot’s builder, add the Google Analytics or GTM integration block where your key event happens (like after a user submits contact info).
Honest take: Setting up integrations can be fiddly, but it’s worth it if you need to connect bot data with your wider marketing stack. If you’re just starting out, don’t stress—start with built-in reports, then upgrade later.
Step 3: Pick Metrics That Actually Matter
Here’s where a lot of teams get lost. Don’t just track what Landbot shows by default—pick metrics tied to your GTM goals. Here are the ones that usually matter:
For Lead Generation Bots
- Qualified leads captured: Not just any email, but leads who meet your criteria.
- Completion rate: Did people finish the flow and submit info?
- Drop-off rate at critical questions: Where are you losing good prospects?
- Time to completion: Are people bailing because it’s too long or confusing?
For Support Bots
- Ticket deflection rate: How many users solved their problem without reaching an agent?
- CSAT (if you ask for feedback): How happy are users post-chat?
- Repeat questions: Are you getting the same unanswered queries over and over?
For Booking Bots (Demos, Calls, etc.)
- Booked meetings: Simple—how many times did the bot actually book something?
- No-show rate (if you can track it): Are people showing up, or just clicking to get rid of the bot?
- User ratings/comments: Any feedback on the booking process?
Ignore: “Total chats” or “messages sent” unless you have a good reason. They rarely tie to real outcomes.
Step 4: Analyze Drop-Offs and Bottlenecks
Most chatbots lose users somewhere in the flow. That’s normal—but you want to fix bottlenecks, not just accept them.
How To Find Drop-Offs In Landbot
- Go to the Analytics tab for your bot.
- Check the “flow visualization” or “funnel” view (Landbot calls this the “flow analytics”).
- Look for steps with a big drop in users.
What to do:
- If lots of users drop off before giving you their email, your ask is too soon or too aggressive.
- If people bail at a question, maybe it’s confusing or feels invasive.
- If most users finish, but you’re getting junk leads, tighten your qualifying questions.
Pro tip: Run through the bot yourself, or watch a few sessions if Landbot allows. You’ll spot stuff that numbers alone won’t show.
Step 5: Use A/B Testing (But Don’t Overcomplicate It)
Landbot supports A/B testing, which lets you compare two bot flows or questions.
- Test different opening questions, button labels, or order of questions.
- Only test one change at a time, or you won’t know what made the difference.
Honest take: If you have under 100 users a month, don’t bother with A/B testing yet. You need more data for results to mean anything.
Step 6: Turn Insights Into Action
Metrics are only useful if you actually change something. Here’s how to put your findings to work:
- High drop-off? Simplify the flow, reword questions, or move key asks later.
- Low conversion but high engagement? Add a stronger call to action or a time-limited offer.
- Junk leads? Add qualifying questions or make the ask more specific.
Change one thing at a time, then check your metrics again next week. Rinse and repeat.
Step 7: Share Results With Your GTM Team
Don’t keep bot analytics siloed. Share what you’re seeing with sales, marketing, and support. For example:
- If the bot is booking too many unqualified demos, sales will want to know.
- If support tickets are down, marketing can use that win in their reports.
- If you’re getting the same complaints, feed them back to product.
What To Ignore (And Why)
There’s a lot of hype around “AI-powered insights” and “sentiment analysis.” Here’s the truth:
- Landbot is great for structured flows, not for deep AI magic. Don’t expect it to read between the lines.
- Sentiment scores are fun to look at, but rarely move the needle for GTM unless you have a massive volume and a dedicated analyst.
- “Chatbot NPS” is often too noisy to be useful unless you’re running huge numbers.
Stick to what you can control and improve.
Keeping It Simple (And Why That Works)
Most teams overcomplicate chatbot analytics. You don’t need a PhD in data science—just clear goals, basic tracking, and a willingness to tweak things each week. Start simple, track what matters, change what’s broken, and share what you learn.
You’ll get better GTM results—and spend a lot less time arguing over dashboards.