Automated Ticketing System Using Bot and Pega 7.3.1

Here is what an automated system can do as part of the ticketing system:

• Continuously monitors the log, and automatically detects an issue to create a ticket.
• Automatically assign tickets, triggers can be created so if a ticket is submitted via email or a portal it goes to a specific agent or group.
• Remind customers to follow up, create a trigger for customer response.
• Close tickets automatically help to keep the ticket queues clean and provides an accurate assessment of the ticket volume. This accuracy can be crucial when the ticket volume rises.
• Get alerts for urgent tickets to help considerably in managing support tickets.

The bot solution gives Predictive Analytics, NLP, and Unified Architecture.

• AI with predictive analytics, and decision senses user need and offer hyper-personalized experience with the right solution on time.
• Natural Language Processing (NLP) and text analytics that makes it natural for people to join in the same way that they normally speak or type.
• A unified architecture that allows people to seamlessly move from one channel to the next – including from a bot to a human agent if needed – and pick up exactly where they left off.
• A code-free development environment that makes it easy for any business user to configure, simulate, and manage the bot to accomplish their business goals.
• Case Management and Business Process Management (BPM) that drive work after the scenes to make conversational UX (user experiences) more practical, useful, and functional.

Key characteristics of an efficient ticket management system:

• Quick response, follow-up, and documentation after resolving the issue
• Integration across all platforms
• Effective support software tracks every ticket from reception to completion and then logs the data to improve future communications.
• Prevents wasting time searching for missing tickets and issues arising from unanswered complaints.
• Can efficiently sort, classify, and flag incoming items depending on the issue
• Free up existing employees to do more high-level problem solving
• Reduces support requests by creating knowledge base articles.
• Enables ticket triage
• Recognizes the subtle patterns in tickets’ context, and then directs both the ticket and the best resolution to the appropriate queue.
• Enables to prioritize by urgency and route by skill.
• Cuts down on time-intensive, manual routing practices by finding patterns in past interactions, channel preferences, market segmentation, and more.

It turns the necessary channels into intelligent assistants that make personalized service and real work possible. This makes bots more intelligent, useful, and gratifying for customers and employees to use in a variety of scenarios, such as:
Consider the following scenario as an example case:
An employee tries to open an application and notices that it is not working. He checks if there were any exceptions in the previous two days and finds that there were many exceptions. It takes him a few hours to determine the exceptions and categorize them. He approaches the IT Service desk with the different log exceptions that he had captured and asks them to analyze the issues and creates tickets in the system. The IT team assesses the issue and delegates appropriate resources with skill sets related to the exception. They work on the exceptions and resolve the tickets. The employee starts working again but is still not happy with the time taken to get the application back on track. He looks at a possible solution to this repetitive issue.
This is where the Aaseya Ticket Management Application can be a perfect solution.
In this management application, the system checks the log entries, and detects all the encountered exceptions, saves it in a database, removes the duplicates, and creates a ticket for each one of them. All Cases are assigned to the IT team’s Workbasket.
They initiate the case which is profiled with data from the exceptions, and the system displays the names of operators having the required skills. The IT team can then assign the cases to the operators with the required skill sets. This will enable them to look into the issue and resolve the tickets.
Compared to the scenario before the Ticket Management Application was implemented, both the effort and time will be significantly lower. (Process Open Span Robot fetching Help Links from PDN based on Case Tag)
With the help of Pega Open Span Robotics and Case Management (Case Tagging), Aaseya’s Automated Ticket Management System can:

• Improves help desk ticket management tasks
• Manage routine ticket assignments
• Routing and escalation
• Save time significantly
• Improve help desk efficiency and productivity

Ticket automation saves time and money and avoids having to employ extra people to monitor and sort tickets; support ticket software can perform these tasks without the element of human error. Moving forward, Aaseya’s Automated Ticket Management System can become a larger part of customer experience as it not only saves business time and money but also improves the experience of customers.

• AI
• Automated Ticketing System
• Bot
• Pega