Amplifying User Intelligence with Chatbot Feedback Loops

Why Feedback Loops are Crucial to Chatbots

The absence of a feedback loop in your AI Chatbot is that you’re restricting the intelligence that you could be getting back from your users. Also, you are missing an easy chance to develop by adapting to user needs. Let’s begin by identifying the ways that feedback loops can help you offer a great experience for your users.

— First and foremost, Understand your users. They’re a way to know what your users really want. It is because chatbots are fundamentally open-ended, your users will say things that you never expected.

-Secondly, Keep it fresh. They help you keep your chatbot updated. If we see the failure cases, you’ll be able to grasp the big changes and ignore one of the most common chatbot problems. You’ll have to develop in new catchphrases, hot topics, and product names to keep your chatbot update.

- Thirdly, Adapt to evolving needs. They permit you to accomplish user expectations. However, great chatbots manage user expectations, people will generally have higher expectations for conversational than non-conversational interfaces.

How to Collect Chatbot Feedback

Let’s review some of the most common sources here.

In-conversation feedback sources

Apart from edge cases, you’ll naturally be registering the complete conversation between your bot and user.
Just take a sample of conversations or turns and hand-score them stating to your own success basis. Here are a few sample questions you could consider using (on a 0–5 or 0–3 scale). If you’re looking at turns, consider:

“Did my chatbot understand the user’s intent?”
“Was the chatbot’s response appropriate to the user’s query?”
If you’re looking at entire conversations at a time, you might ask:

“Am I proud of my chatbot’s performance in this conversation?”
“Did the user ultimately accomplish at least one goal of the conversation?”

If the answer to the above questions is not “yes!”, then keep track of the reasons that are holding you back.

While the above method is exceptionally helpful, so you might also want to try something that takes a little less effort on your part.

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External feedback sources

Depending on the purpose of your bot, at a later stage, some of your most valuable feedback might come from data outside the chatbot itself. For example, you might evaluate a customer service chatbot by observing whether users that talk to it heave at a higher or lower rate than others.
Just be careful when designing these metrics that they’re not picking up on other things. If you’re developing chatbots as part of a suite of products or channels, don’t avoid your standard product feedback sources. This incorporates support emails, usability testing, social channels and whatever else you’re using.

How Do I Turn My Feedback into Improvements

There are two main ways of doing this:

Manual, human-led improvements

A lot of what we’ve talked about above falls into this point. You take a look at your feedback data and understand that what’s going wrong and why. After that, you prioritize fixes or improvements accordingly.

Semi-automated improvements

If you have a more sophisticated chatbot, you have more options at your disposal. If you have generated more and more conversational data, you can use this data to retrain the prime machine learning models.

Is My Feedback Loop Working?

Here are a few ways of assessing your chatbot feedback loop:

— Plotting bot success metrics — before and after the feedback loop.

If your chatbot has clearly defined success metrics, benchmark them before setting up your feedback loop or creating any changes to how it runs. This will help in accidental consequences, especially if you’re using a semi-automated feedback loop.

— Measuring intent coverage over time.

The two most frustrating chatbot experiences are

a. misunderstanding your user, and

B. having to repeatedly tell them you don’t understand what they’re asking. Take a sample of conversational turns and evaluate whether the chatbot mapped the user’s message to the correct intent. It will tell you if your feedback loop is boosting intent coverage over time.

— Developing a measure of momentum

After a feedback loop is established, performance metrics are defined, and you’ve had the chance to collect data during a few rounds of improvement. Assess how the metrics are changing over time, and examine performance data to recognize additional chances for improvement.

— Embrace Feedback Loops for Competitive Advantage

As chatbot technology and conversational AI tool sets become highly mature, your users will obviously have higher expectations, and you’ll have to work to set yourself apart from your competition.