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First Published: July 2018 Last updated: July 2021
A while back, I worked with a client who had added a Dialogflow bot to their website.
To me, it seemed that they had unfortunately set up their bot to fail. Here is what I mean:
If you want to improve your chatbot deflection rate, you should first be clear how you are going to measure it.
First, let us look at some terminology.
The U-M-M method
A while back, I wrote a post which linked to Chatbase’s UMM method which provides a way to reason about your chatbot’s accuracy. While it is a good idea and I do derive some ideas from it, it is not particularly useful because there isn’t any way to measure the accuracy using the UMM method.
What I am proposing here is much simpler.
The confusion matrix
You may be familiar with the term error matrix or confusion matrix. If not, don’t worry!
It is a way to measure if classification techniques work well, and it is quite appropriate in this case because under the hood, Dialogflow takes the user’s input and classifies it to the nearest matching intent.
So let us define the following terms:
Regular intent = An intent which is not a fallback intent
Correct mapping = the user’s phrase was mapped to the expected, appropriate intent. By the way, if you find the idea of “correct mapping” subjective rather than objective, you probably need to improve the way you are defining your intents.
True Positive (TP) = A user phrase is mapped to a regular intent correctly
True Negative (TN) = A user phrase is mapped to a fallback intent correctly (this means, we haven’t yet declared an intent to handle the user’s phrase)
False Positive (FP) = A regular intent is triggered, but it should have either been mapped to a different regular intent, or it should have been mapped to the fallback intent because we don’t yet handle the phrase. Instead it is wrongly mapped to a regular intent.
False Negative (FN) = A fallback intent is triggered, but we actually have already defined a regular intent which should have been mapped to the user’s phrase. An excellent example of this is when the user types a message which is nearly identical to a training phrase except for a small typo.
Consider the last 100 user messages to your bot. If you don’t have that many, get some 10 or 15 beta testers to try out your bot for a few minutes.
Let TP be the number (out of the 100) messages which were true positive mappings.
Similarly, TN = number of true negative mappings
FP = number of false positive mappings
FN = number of false negative mappings
Let Correct Mapping (CM) = TP + TN
Let Incorrect Mapping (IM) = FP + FN
Accuracy = CM / CM + IM
Since CM + IM = 100 (if you got the correct sample size), the value is already a percentage.
Tips to improve your bot’s accuracy
In my Improving Dialogflow ES accuracy course, I provide a set of tips which allow you to improve your Dialogflow ES bot’s accuracy.
 However, it works best with my existing recommendations such as avoiding slot filling, using a context lifespan of 1 etc. That makes the bot much easier to analyze.
The recently released Zoho SalesIQ v2 allows non-programmers to build chatbots using an easy-to-use code less bot builder. What is really unique about Zoho SalesIQ is the fact that you can also integrate AI into their code less bot builder. In my Zoho SalesIQ chatbots course, I explain how to use Zoho SalesIQ to add a chatbot to your website.
"The magic key I needed as a non-programmer" The custom payload generator was the magic key I needed (as a non-programmer) to build a good demo with rich responses in DialogFlow Messenger. I've only used it for 30 minutes and am thrilled. I've spent hours trying to figure out some of the intricacies of DialogFlow on my own. Over and over, I kept coming back to Aravind's tutorials available on-line. I trust the other functionalities I learn to use in the app will save me additional time and heartburn. - Kathleen R Cofounder, gathrHealth