casevoice assistant application.
productGoogle Assistant .
Voice is the new chatbot making customer interactions even more human. By simply speaking, it makes it more easy to interact with their favorite brands or get the information they need. We’ve created a voice application for Expert. With Voice they hope to give their shoppers the ultimate customer experience.
This feature allows customers to find their nearest stores, with a direct link to Google Maps to easily access directions.
Easy overview of the latest offers from Expert, with convenient links to the relevant store pages.
The assistant will ask the user questions, catered by Experts expert sales team, to discern which TVs on offer best suit their needs and desires.
Customers can also use the assistant to directly filter specifics and find the TV they’re after, or perhaps something better which meets their criteria.
Creating the conversation flow.
Before we start writing a conversation flow, it’s important to take into account that what should always come first is that the conversation between the Google Assistant and the user should be as human as possible. The more human the conversation feels, the less users have to learn about how the application works. Before a flow is established a few goals must also be described along with which steps must be taken to achieve these goals. i.e. for televisions, this goal is to provide advice to the user and determine which steps are required for this. When the script is complete, we visualize the flow. We made use of Miro to connect all the different sub-streams into a holistic view of the flow.
Building the application.
Once we have our flow, the application can be built. DialogFlow tries to discern what the user is after through using machine learning to match key phrases to a range of things users might be saying. This is referred to as the "intent". Every "intent" has a so-called "fulfillment". This adds to the word, as it were, and collects the information from the internet. Through "webhooks" data is sent to a "custom endpoint". Once here we talk to Expert’s API in order to collect the matching information from their systems. In the case of Expert, the API is based off a Magento2 platform. With the help of this data we can find the right match such as a television based on your preferences. Consider the image quality, format and brand of a television. As soon as the correct match is found, it is sent directly to the user.