Built in System Actions on Tickets (Merging, Following, Linking)
6 guides
Followers and Following Tickets
The article explains the process of following tickets in a support system. It describes how both agents and end users can add themselves or others as followers to stay updated on ticket progress. Agents can follow a ticket by pressing the Eye icon, while end users can do so through the self-service portal. The article provides detailed instructions on configuring settings to control who can be added as followers and highlights the benefits, such as ensuring that less experienced staff learn from ticket resolutions. It also notes visibility options and search functionalities for larger organizations.
Internal Conversations
Merging Tickets
This article explains the process of merging and unmerging tickets in Halo. Merging involves combining two tickets into one, transferring all actions from one ticket to another, typically to consolidate duplicate issues. The article provides step-by-step instructions on how to merge tickets, including navigating the merge screen and confirming the selection. It also covers the unmerging process, which allows tickets to be separated again, detailing the effects on each ticket after unmerging.
Problem/Resolution Finder
The Problem/Resolution Finder is an optimization tool for incident and problem resolution by leveraging historical and current tickets from the database. It suggests related knowledge base articles and similar tickets based on matching criteria such as ticket summary, custom fields, and categories. Agents benefit by receiving suggestions without manually searching the knowledge base. The tool's configuration options allow customization of matching logic and criteria for article and ticket suggestions. It also provides settings for where and how the resolution finder is used within the agent interface and portal.
Ticket Suggestions- without the use of AI
This article explains how to configure and use ticket suggestions in Halo without relying on AI. Ticket suggestions help automate ticket processing by advising agents on decisions based on historical data. Instead of AI-based suggestions, the system uses SQL searches to find similarities in ticket fields for matching. Configuration involves enabling the AI module, setting a default connection, and determining match criteria. Various suggestion types can be set up, like setting the estimate or linking tickets. These suggestions are prioritized based on rules, and agents can apply or dismiss them to assist with ticket processing.
Using "Drag and Drop"