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Aug 17, 2021 | 4 Mins read

Role of Machine Learning in Identifying Root Cause of Support Issues

IrisAgent is a proactive customer support platform that resolves customer tickets and issues efficiently and effectively using Machine Learning (ML), Natural Language Processing (NLP), and Artificial Intelligence (AI). The goal is to help customer support teams save companies time and money. IrisAgent speeds up the time to resolution for customer support cases caused by outages, bugs, and performance issues.

Understanding Role of Machine Learning in Customer Service

Machine Learning is a part of Artificial Intelligence that uses statistics and algorithms to learn from experience, find patterns in data, and make predictions. IrisAgent detects early product issues and uses Machine Learning to find the root cause of product issues. Support agents can get an overview of recent and ongoing incidents caused by a particular incident. They can quickly identify the root cause and, with the help of IrisAgent’s workflow automation capabilities, provide customers with the next steps and routes to resolution. By integrating with monitoring tools like Jira, PagerDuty, and several others, IrisAgent goes into the ‘why’ behind tickets associated with bugs, performance issues, and outages to create support workflows and recommend operational improvements. 

Applying Machine Learning in customer support improves the support experience for support agents and customers alike. Since customer support can contain many unstructured and unlinked data, Machine Learning structures and links them to relevant data. By linking and structuring data, support agents can easily connect incoming tickets to similar tickets and trace them to the root cause.

By 2022, Gartner anticipates that 72% of customer interactions will involve Machine Learning, chatbot, or mobile messaging.

There are many ways Machine Learning can be applied in customer support operations. IrisAgent applies Machine Learning by integrating IrisAgent with Jira, which helps identify the root causes of support tickets. 

What are the different uses of Machine Learning in customer support?

Machine learning has found diverse customer support applications, revolutionizing how businesses interact with their customers and handle inquiries. Here are some key uses of machine learning in customer support:

  1. Automated Chatbots: Machine learning powers intelligent chatbots that offer instant responses to common customer queries, providing 24/7 support. These chatbots can handle routine tasks, answer frequently asked questions, and even converse naturally.

  2. Ticket Routing and Prioritization: Machine learning algorithms can analyze the content of support tickets and automatically route them to the most suitable agents or teams based on their expertise. It also aids in prioritizing tickets according to urgency and complexity.

  3. Sentiment Analysis: Machine learning models can analyze customer messages and interactions to determine sentiment. This helps support teams identify frustrated or dissatisfied customers, enabling them to intervene promptly and provide appropriate solutions.

  4. Predictive Analytics: Machine learning can predict customer behavior and issues. For instance, it can forecast which customers are more likely to churn, enabling proactive retention strategies.

  5. Language Translation: Machine learning facilitates real-time language translation, enabling businesses to support customers across different languages and regions.

  6. Automated Email Responses: Machine learning can assist in generating automated, contextually relevant email responses to common customer inquiries, reducing response times and increasing efficiency.

Overall, machine learning enhances customer support processes' speed, accuracy, and efficiency, contributing to higher customer satisfaction, improved resource utilization, and enhanced insights for businesses.

Machine Learning for Jira Integration 

Engineering teams using agile methodologies use Jira to map product workflow, launches and track bugs. Connecting the Jira software to IrisAgent enables support teams to easily identify how support tickets relate to product bugs. Via its Jira integration, IrisAgent can identify when a product release or bug from Jira is the root cause of a support ticket. This information can help support teams resolve tickets faster with clear and accurate solutions. Support agents can get a clearer picture of product releases, updates, and bugs. This saves the time of the agents and prevents avoidable mistakes. 

incident alerts Jira

Installing the root cause of support tickets can help foster smoother collaboration between customer support teams, product teams, and engineering teams. Since the engineering team moves very fast with product launches and updates, it is important for support teams to understand what these launches and changes mean for the business and customers alike. They can link support tickets to product updates or bugs in Jira. 

Why is finding the root cause of support issues important?

  • Support agents can resolve complex support tickets faster when given access to relevant product contexts. They can instantly identify the root cause of incoming tickets by connecting them to product bugs and releases in Jira. This results in a faster time to resolve customer tickets and ultimately increases customer satisfaction.

  • It enables seamless collaboration between the customer support, engineering, and product teams. All teams can get a 360-degree view of product bugs and support tickets. This can close gaps in product understanding among teams in cross-functional organizations. Efficient alignment between teams in an organization can lead to a more sustainable decision-making process. 

  • Customer support teams can resolve support tickets faster and decrease ticket resolution time. This can save time for both support agents and customers. Consequently, employee satisfaction is increased and customers can enjoy a positive experience.

  • Product and engineering teams can prioritize product bugs with business context. Identifying bugs that customers are reacting more to, can help the engineering teams resolve bugs that keep the customer satisfied. This can result in increased customer loyalty and customer retention for the organization.

Supercharge your customer support team and close the product gap between your organization's support and engineering teams by starting with IrisAgent. Book a demo now!

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