You can provide generic answers or more or less personalised ones. Personalisation is based on available user data, as well as data provided by the user during the conversation. Here are the features available to personalise your dialogues :
This enables you to complete an answer or to configure an action, based on data provided during the dialogue. The bot identifies pre-defined words and triggers an action.
For example, the bot could trigger an escalation to a human operator after 3 misunderstandings or if the user utters the words “terminate my contract”.
The bot can answer the same question differently if it is asked several times. This improves the conversational experience, by making the bot sound more natural.
For example, if a user asks “how to contact customer services” twice in the same conversation, the chatbot can provide the same answer phrased differently, because it has alternative answers for this question.
Push rules (behavioural targeting)
This feature allows to observe the end-user’s browser behaviour and trigger an action at the right moment. For example, the chatbot could pop up and offer to help if the user is inactive on the site for more than X minutes.
This feature allows to condition a bot’s answer according to the value of a variable. To answer the question “I’m 21 years old, can I vote?”, the bot tests the value of the age variable (greater than or equal to 18 years) and provides the right answer according to this variable, instead of a generic answer, such as “any French citizen can vote from the age of 18”.
This enables the chatbot to adapt the answer to the user, depending on criteria such as which device they are using, or whether or not they are logged into their customer account (different answers to the same question according to these criteria).
Let’s take the example of a bot with two consultation spaces (mobile and PC) and a user who asks, “how to share my schedule”. If the user is asking this question from a mobile phone, the answer will be in text form. But if the user is using a PC, then the chatbot will provide a step-by-step tutorial in the sidebar.
User data can be retrieved during a conversation and used in an answer or API call. These variables can come from an explicit user declaration, a transfer to the chatbot (via its web page), or information from an API (e.g., name, age, type of contract, etc.).
By using web services from your chatbot’s knowledge base, you can access external information, such as tracking an order, or push information out to feed a CRM or ticket management tool, for example. If the bot is connected to a specific company app (CRM, IS, HRIS, etc.) via a web service, it can use the data to provide personalised answers.