Manage Support Ticket Priority with User Sentiment & Business Impact
Clearly, ticket prioritization matters. Get it right, and customers emerge from the interaction feeling positive about you and your product. Prioritize poorly, and you risk customer churn. But deciding which customer support tickets to solve first isn't as easy as it may seem. The approaches companies take vary as much as the companies themselves.
What is ticket prioritization in customer support?
Ticket prioritization in customer support is assigning varying degrees of urgency and importance to incoming customer support tickets based on specific predefined criteria. The goal of ticket prioritization is to ensure that customer issues are addressed in a timely and efficient manner, aligning with the impact and urgency they have on the customer and the business. Key factors that influence ticket prioritization include:
Severity of the Issue: Tickets indicating critical issues severely affecting the customer's ability to use the product or service are given higher priority.
Impact on Customer: Tickets related to high-value customers or those with a history of frequent purchases may be prioritized to maintain customer loyalty.
Response Time Expectations: Customer expectations for response times may influence ticket prioritization. Some issues require immediate attention, while others can wait longer.
SLAs (Service Level Agreements): Businesses often define SLAs that outline response and resolution times for different issues. Adhering to these SLAs helps in prioritizing tickets.
Volume and Queue Load: The current workload of customer support agents and the number of tickets in the queue can affect prioritization.
Complexity of the Issue: Tickets requiring in-depth investigation or coordination across different teams may need higher priority.
Regulatory or Legal Considerations: Compliance, privacy, or legal issues might be prioritized due to potential implications.
What are the different levels of ticket priority?
Ticket priority levels can vary depending on the organization and the nature of the products or services they offer. However, here are some standard levels of ticket priority:
Critical/Urgent: Issues that severely impact the customer's ability to use the product or service, leading to significant financial loss, safety concerns, or regulatory violations. These require immediate attention and quick resolution.
High: Important issues that affect the customer's ability to use the product/service effectively but don't pose an immediate risk. These require prompt attention to prevent escalation.
Medium: Issues that moderately impact the customer's experience but can wait for a reasonable period before resolution. These often include functional or usability problems.
Low: Minor issues that have minimal impact on the customer's experience or can be easily worked around. These can be addressed at a lower priority compared to more critical concerns.
Routine/Normal: Standard inquiries, requests for information, or general assistance that are not time-sensitive. These can be addressed as resources permit.
Different approaches for ticket prioritization
First-in, First-out (FIFO) — The simplest approach is to respond to tickets in the order they were submitted.
Customer-defined — Companies that pursue this approach typically include a field on their submission form asking customers to rate the severity of their issue on a scale from "minor" to "urgent".
Issue-defined — An issue-defined approach allows support teams to classify tickets into categories: Pre-sales, General, Service Outage, Troubleshooting, and Billing, for example.
Service-level agreement-defined — Customer contracts often include language defining how—and how quickly—their support requests are managed.
Challenges with Ticket Prioritization
Let's look at a scenario with two SaaS company customers with an average annual contract value (ACV) of $310K per customer.
Using the FIFO method, should a ticket submitted by the $100K customer before a ticket submitted by the $420K customer receive top priority? Take those same two companies and shift the priority assessment to customer-defined. Maybe the $100K customer defines their issue as “urgent” while the $420K customer defines their issue as “minor”. Does urgent always trump minor? What if we switch to issue-defined prioritization? Should a service outage-related ticket from a smaller customer outweigh a troubleshooting ticket from a larger one? What if the smaller company writes hostile requests? Do they get priority over another company with whom your team’s interactions are calm and polite? And finally, what happens if both customers submit similar tickets on the same day, but the smaller company has a 12-hour response requirement via their SLA? In comparison, the bigger company mandates a 24-hour response.
Customer Support agents try to reconcile conflicts like these all the time. But what if customer support teams could augment prioritization across these dimensions with case sentiment and business impact?
IrisAgent's AI for Effective Ticket Prioritization
User sentiment differs from customer satisfaction. Customer satisfaction is typically a post-interaction, self-reported metric most commonly collected in a survey. User sentiment reflects a natural language processing (NLP) analysis of the initial language and tone used when filing the case. For example, a case submitted with language like “need this fixed now” or “still can’t log in” shows frustration and anger and should factor into ticket prioritization.
Likewise, IrisAgent’s integration with CRMs like Salesforce puts each customer’s ACV at an agent's fingertips. Adding business impact to a support ticket helps agents account for the effect on the company’s bottom line when prioritizing open cases in real-time. In the image below, IrisAgent adds ACV to other, more customary metrics like wait time, case activity, and how individual agents interpret each case’s priority. Teams can customize these dimensions to meet their requirements.
The prioritization decision becomes much less binary when you add sentiment and revenue; it’s not which customer is first, which customer complains the loudest, which issue is more difficult to resolve, or the timeframe in which they expect a response. It all boils down to which customer–were they to churn–would have the most negative impact on the company’s business?
Frequently Asked Questions
How can user sentiment analysis contribute to support ticket prioritization?
By analyzing the language and tone used in customer inquiries, sentiment analysis can identify whether a customer service request represents a delighted customer with a simple query or a frustrated one with a critical issue. This analysis enables support teams to prioritize incoming support requests accordingly, promptly addressing urgent or dissatisfied customers, thus enhancing overall customer satisfaction. Additionally, sentiment analysis can help identify broader trends and issues in customer feedback, allowing businesses to improve their products or services proactively. Overall, it streamlines the ticket management process by addressing the most pressing issues first, leading to more efficient customer support and improved customer experiences.
What are the key metrics and data sources used for sentiment analysis in support ticket management?
Metrics include sentiment polarity (positive, negative, or neutral), sentiment intensity (how strongly a sentiment is expressed), and overall sentiment scores. Data sources primarily consist of customer-generated content such as text-based customer queries, feedback forms, emails, chat transcripts, and social media interactions. These sources provide a rich dataset for sentiment analysis algorithms to analyze customer sentiments, emotions, and opinions. Additionally, historical data, customer surveys, and feedback aggregators are valuable sources for training and refining sentiment analysis models, helping businesses comprehensively understand customer sentiment and effectively prioritize support tickets based on user emotions and needs.
How does automation play a role in the efficient prioritization of support tickets?
Through artificial intelligence and machine learning algorithms, automation can quickly analyze vast amounts of data, including customer sentiment and business impact, in real time. It can identify urgent issues, categorize and assign tickets, and suggest appropriate responses or actions. This automation speeds up the ticket triage process and reduces the risk of human error in prioritization decisions. Furthermore, automation can adapt and learn from historical data, continuously improving its ability to make accurate and data-driven ticket prioritization choices. By offloading repetitive tasks and decision-making to automation, support teams can focus on more complex customer interactions, enhancing efficiency, improved customer satisfaction, and overall better ticket management.