Improve your customer service chatbot with analysis

Chatbots have become a standard fixture on company websites, and the reason is cost. A well-used chatbot handles routine questions far more cheaply than a phone call or an email answered by an agent. But that saving only materialises if the bot actually helps the customer.

Too often it does not. A customer opens the chat, tries to get help, realises the bot cannot resolve their issue, and falls back to phone or email — the exact channels the chatbot was meant to relieve. When that happens repeatedly, the investment in the bot is wasted and costs go up, not down.

Installing a chatbot is not the finish line. Making one that genuinely deflects contacts takes continuous analysis of what customers ask it — and what it fails to answer.

A chatbot is never "install and forget"

From a company's point of view, chatbots and self-service are the cheapest way to help customers. That is precisely why a bot that fails quietly is so expensive: every customer it cannot help is pushed to a costlier channel, and you rarely notice until volumes tell you.

Improving a chatbot depends on two things:

• Understanding what customers actually ask it, so you can teach it to handle the recurring questions.
• Seeing where it fails, because those failures point to fixes for self-service and the website, not just the bot.

Without that analysis you are optimising blind — adding answers you think customers need rather than the ones they are actually asking for.

Improve the bot through analysis and targeted training

Feelingstream's conversation analytics covers chat alongside calls and email. It shows the topics customers raise with the chatbot, lets you search chats by topic, and reveals which topics the bot resolves well and which it cannot.

From there the work is targeted. You can train the bot on the recurring questions it currently fumbles, and use the failed chats to spot changes needed in self-service and on the website so customers can help themselves next time. Every issue a bot resolves — or correctly hands off to self-service — is an avoided call or email.

This is efficiency by design: rather than employing a large team to answer the same repetitive questions through expensive channels, you teach the bot and tidy self-service so those questions never reach an agent. For the broader approach, see efficiency with AI.

What analysis reveals in practice

Analysis tends to surface concrete, fixable gaps. Two examples of the kind of issue clients uncover:

1. Vague billing answers. When customers asked about their invoice, the bot pointed them to a general billing page. After analysis and training, it could direct them to the exact place in self-service to find their billing information.
2. Ignored context. When the bot asked what the customer had already tried, it did not use that answer later in the conversation. After updating, it carried the extra information forward and gave more personal, relevant support.

Neither fix required rebuilding the bot. Both came from reading real chat transcripts and acting on what they showed.

Optimise every channel, not just the bot

There will always be issues a chatbot cannot solve, and that is fine. The goal is to let the bot and self-service absorb the routine volume so that phone and email — your costly, human channels — are reserved for the customers who truly need them. With that volume deflected, agents have more room to focus on relationships and sales.

Frequently asked questions

Why do customers abandon chatbots and call instead?

Usually because the bot cannot resolve their issue. Analysing failed chats shows exactly where it breaks down, so you can train it or fix the underlying self-service gap.

How does analysing chats improve the chatbot?

It reveals the topics customers ask about and which ones the bot handles poorly. You can then train it on recurring questions and correct the specific answers that fail.

Can chat analysis help channels other than the bot?

Yes. Failed chats often point to missing or unclear self-service and website content. Fixing those helps customers across every channel, not just the chatbot.

Does a better chatbot really reduce cost?

Yes. Every question the bot resolves or routes to self-service is a contact that never reaches a more expensive phone or email queue.

Where to go next

• Deflect more contacts: Knowledge base agent
• The efficiency picture: Efficiency with AI in customer service
• Improve self-service too: Improve your self-service portal
• See it on your data: Request a demo

Want your chatbot to deflect more contacts instead of frustrating customers? We will analyse your chats and show you where to improve. Book a demo.