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Re-Inventing Ticket Management System with AI

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For a business or service provider, nothing is more painful than being slammed for slow and inactive customer support by their customers. And this is even more true in the era of social media. Any negative experience and a customer is more than ready to tag the company or service provider on social media channels complaining about the issue. Slow and delayed response not only leads to negative customer experience, it can also impacts sale and revenue of a business. After all, social media reviews have a profound influence on buying decisions of tech savvy customers.

According to a Gartner report, nearly 84% of millennial customers admitted that their buying decision is influenced by user generated content on the internet. This could be social media reviews, product review on company’s website and YouTube/Instagram videos created by strangers. Given the influence of social media on purchasing decision, enterprises have to ensure that the users are organically encouraged to post positive content across social media platform. One way of ensuring that is by implementing a full-proof ticketing system.

What is a Ticketing System?

Whether it’s the B2B or B2C segment, the customer support teams of any product or service based company receives numerous queries in a day. The origin of the these queries can be from any of the following sources –

  • Email
  • Social Media
  • In-app Chat Window
  • Website

A ticketing system allows customer support teams to segregate, manage and create tickets from multiple sources for effective query-handling. Ticketing software not only improves customer experience(CX), but also improves the efficiency of the customer support teams. The system follows a workflow that ensures end-to-end ticket management. Typically, ticket management system de-clutters support ticket in the following ways –

  • Organizes Emails

The system offers a single view of the problem. Multiple teams working on the raised ticket can collaborate efficiently. All communications related to the ticket are centralized, reducing the turnaround time.

  • Analytics & Reporting

Customer support teams can receive a mix of critical and non-critical queries. Irrespective of the nature of ticket, it cannot be overlooked or ignored. Analytics and reporting will give business insight into the nature of the tickets. If the frequency of mundane tickets are high, companies can include a self-help section on their website, reducing the burden on customer support teams.

  • Complete Automation

Automation is another way to streamline a number of mundane tasks. Auto notification of tickets, schedule follow-up emails can improve the efficiency of the customer support teams.

Re-Inventing Ticketing with AI

We all know, that overtime customer expectation has profoundly changed. Customers not only demand fast query handling, but they also expect a certain degree of empathy and earnestness from agents when handling the tickets.

Sometimes, high-stake tickets can easily get lost under the pile of new tickets. Since, support agents are periodically reminded that “every ticket is valuable”, they may miss out on critical  tickets that needs urgent attention. Thankfully, AI powered ticket management systems facilitates ticket prioritization through sentiment analysis.

What is Sentiment Analysis?

Sentiment analysis is a form of text mining that uses a predictive model to pick the exact sentiment of the customer. It is a category of customer experience AI that identifies the emotional status of the customer on a series of words. This enables agents to prioritize critical tickets to leverage customer experience and agent performance. AI enables contextual understanding of all text based communication in the following ways –

  • Heat Maps

Customer support agents often face the dilemma regarding – Which support ticket should they handle first? With so many interaction channels, the volume of tickets can be quite high each day. To enable agents to take informed decision, heat map assesses factors like time, status and priority of the ticket before suggesting the next best ticket to the agent. For that it creates a color coded heat index and denotes the criticality or importance of the ticket in descending order. This way agents can identify critical tickets easily.

  • Aggravated Chat Notification

This feature is particularly relevant for businesses that offer Live Chat support. Under the purview of aggravated chat notification, the AI instantly picks up all types of negative sentiments during an ongoing chat.

For any such occurrences, the supervisor is immediately notified and h/she can view the chat. The supervisor also has the option to barge into the conversation and offer a relevant solution to the irate customer. This reduces customer wait time, as the agent does not have to hold the conversation with the customer to resolve the issue.

  • Scoring System

Scoring system is part of sentiment analysis. Driven by prediction model, the AI can calculate the state and behavior of the customer by assigning score from 0 to 100. O being negative, 50 being neutral and 100 being very positive. This score grade is assigned after analyzing previous and current chats. The scoring system is helpful for agents when prioritizing and addressing the tickets.

End Result – Unmatched Customer Experience

To meet the terms of SLA, a fast ticketing system is absolutely necessary. It is also an important metric to measure customer experience. A good ticket management system improve efficiency of the customer service team by eliminating silly and repetitive mistakes. The system set rules for ticket management which is crucial for prioritizing and handling large volumes of ticket. The ultimate goal is to achieve unmatched customer experience.

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