Measure Chatbot Customer Effort Using Disambiguation & Auto Learning
Gauge Expended Customer Effort For Problem Solving
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Introduction
- What is Customer Effort, and how can it be measured in chatbot conversations?
- And, how can Disambiguation improve Customer Effort?
- Can Automatic Learning be employed to improve Customer Effort over time?
Here we will look at three elements, which combined, will have a huge impact on the conversational experience you are building:
- Disambiguation
- Automatic Learning
- Customer Effort
What Is Disambiguation?
At the bottom of this page there are links to in-depth articles on disambiguation. However, in short…
When a user enters an ambiguous utterance, instead of defaulting to the intent with the highest score; the chatbot checks the differences between the top intent scores.
If there is no clear winner, the top 3 or 4 intents are presented to the user. This presentation takes the form of a menu.
Allowing the user to disambiguate their own utterance.
This is a far more elegant solution than sending the user off on a conversational path which was not intended.
In other words, disambiguation allows the chatbot to request clarification from the user.
A list of related options should be pretested to the user, allowing the user to disambiguate the dialog by selecting an option from the list.
The list presented should be relevant, even if only vaguely, to the context of the utterance. Hence only contextual options must be presented.
What Is Auto Learning?
It is really a win-win situation when the feedback from the user can be used to improve your NLU model via automatic learning on the fly; as this is invaluable training data vetted by the users.
