Article updated: August 16, 2022
Use auto responders in Sparkcentral
Auto responders are messages sent automatically to contacts on direct messaging channels. They gather common information from contacts using pre–written scripts. This can help save time until a human agent is needed to take over the conversation.
To create a new auto responder, follow these steps.
- Go to Admin settings, expand Virtual agents, and then select Auto responders.
- Select Add auto responder.
- Enter a name. This name will appear in the conversation view as Name VA.
- Select whether you want the auto responder to be activated as an Inception agent or a Delegation agent. See Activation types for more information.
- Set timeout rules for the virtual agent and the contact. See Timeout rules for more information.
- Fill out the optional Scripts fields as you like, including any business hours and out-of-office messages, translations, escape words, or an inactive duration setting, and then select Save. See Scripts for more information.
- Scroll down to the Channel access section and use the toggles to select channels where this auto responder can be used.
- Inception - An inception virtual agent is like a typical chat bot. It automatically picks up any private conversations that start in the channels it has access to. If a human agent becomes required, it moves the conversation to the New queue. If no human assistance is needed, it resolves the conversation.
- Delegation - A delegation virtual agent does not automatically pick up conversations. A human agent can give a conversation to a delegation virtual agent at any point in a conversation, at which point the delegation virtual agent takes over. If a delegation virtual agent needs a human agent again, it uses the Handoff rule to decide whether to send the conversation to the New queue or the previous agent.
When a timeout occurs, the conversation is “handed off” to either the New or Resolved queue. You can configure two types of timeout rules for auto responders:
- Timeout virtual agent - Determines how long before a handoff occurs if the auto responder stops responding for any reason. The maximum you can set is 1 hour.
- Timeout contact - Determines how long before a handoff occurs if the contact stops responding. The maximum you can set is 23 hours.
You can add auto responder scripts in multiple languages and both during and outside of business hours. Scripts can be general questions, questions that collect contact attribute data, or statements.
|Script type||Message||Contact type|
|Question||Would you please provide a brief description of what we can help you with?||(none)|
|Question - Contact Attribute||Thanks for reaching out to us! To help serve you better, may I please have your email address?|
|Statement||Thank you. An agent will be with you shortly.||(none)|
The contact’s language is automatically detected. If the language can’t be determined, a default language is used. To learn more, see Language detection in Sparkcentral.
To add your own script translation to an auto responder, under Scripts, select the Add translation tab, and then choose a language from the list.
You can add scripts for both during and outside of regular business hours. Learn how to set business hours by reading Business hours in Sparkcentral.
Use quick replies
You can add quick replies to auto responders for Facebook Messenger, Twitter direct message, In-Web, and In-App channels. Quick replies are buttons that your customers can click to reply to questions.
For example, you could create a Question script with the message, "Where are you based?%[EMEA](reply:EMEA) %[APAC](reply:APAC) %[LATAM](reply:LATAM) %[NA](reply:NA)"
Prioritize important conversations
Auto responders, along with automation and prioritization, can help you prioritize important conversations based on keywords in user messages. User responses to auto responder questions can trigger automatic application of topics and priorities to conversations. Messages with the highest priority appear at the top of your queue.
To learn more, see Use automations in Sparkcentral and Sparkcentral conversation prioritization.
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