In today’s digitised age, customers want to be able to get immediate responses to their queries. This has led to businesses beginning to add chatbots to their websites to provide a lightening-speed reply to queries. How is this possible? For most online conversations in B2C industries, it is perfectly possible to programme chatbots to automatically have conversations that answer queries and help to boost conversions.
The technology is still relatively in its infancy; however, it is growing rapidly, and we should expect to see a far greater use of this exciting facility in the future months. If you are considering your own chatbot, what do you need to do next?
A chatbot investment is an investment like any other. This means you need to plan for it carefully. Start by defining the target audience that it will be engaging with, working with your developer to create defined feedback mechanisms that will support the mechanical learning capability. This will mean constantly evaluating support logs and regularly checking analytics for performance.
Set the purpose
An agency such as Redsnapper that specialises in web design and development services in London will recommend picking a focus for your chatbot. This will tend to fall in the category of support, e-commerce or news, with this early definition defining the end goal. This could be customer engagement, a clear and easy user experience, or additional sales.
Create the conversation plan
Your chatbot will learn how to provide the right actions that respond to user goals. This will depend on the queries you create and the scenarios for each chatbot interaction. Plan for as many possible dialogue branches as possible.
As far as the user is concerned, the chatbot experience will differ very little from a more familiar human agent chat window; however, you will need to ensure that certain elements are in place for the UX, such as a persistent menu; get started, reply and smart reply buttons (for pre-defined response options); and cards to store huge information chunks in manageable blocks, allowing the chatbot to provide tips and quick-fire information.
This will give you your structural start point from which your developer will begin to use coding programs to define the logic that will help the machine learning to take place.