1. AI-powered ticket analysis

When a customer logs a complaint, your AI agent instantly analyzes the ticket using ML and NLP to understand its context. It assesses priority, urgency, and complexity while identifying whether:

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• You’re dealing with a first-time complaint from a new user
• It’s a follow-up on an unresolved issue
• It’s a new concern from a previously satisfied customer

2. Smart categorization

What comes next is categorization based on a combination of the metrics above. By urgency, tickets can be grouped into:

• High urgency needing immediate response, such as in a product security crisis or VIP complaints
• Standard resolution time following the SLA, such as in general troubleshooting
• Low urgency in cases of feedback requests and suggestions

By priority, we have:

• High priority when it affects the customer’s business operations, like in service outages
• Medium priority when it affects users but not business critical. Example is a feature malfunction
• Low priority if it’s just general requests and non-urgent inquiries

Lastly, based on complexity:

• Basic L1 requests such as repetitive inquiries
• L2 complaints, such as product bugs, and usually require technical interventions
• L3 tickets featuring highly technical issues like API failures

Another salient categorization method is based on the ticket’s relevant department. Complaints about subscriptions fall under billing and payments, while logins and password resets fall under account management.

3. Intelligent routing

Following the categorization above, an L1 ticket with low urgency and low priority will be automatically routed to AI chatbots. If it’s L1 with low urgency and high priority, it will still likely be routed to an AI chatbot but will be addressed before other L1 tickets of lower priority.

On the other hand, all L2 and L3 tickets are automatically assigned to relevant service reps or senior support agents. The urgency and priority of the tickets determine which ones are assigned first.

Your AI agent also considers the status of your support agents before assigning them a ticket. This includes their availability, ticket load, previous tickets successfully closed, categories of ticket topics they find easier to resolve, and how fast they complete the task.

4. Post-support sentiment analysis

Traditionally, you can collect post-support feedback through surveys or thumbs up/down options. However, AI agents take a more efficient route which involves using NLP to analyze the tone, phrases, or keywords used by customers during support resolution.

If there’s a negative sentiment, it triggers follow-up actions or even escalations. For neutral sentiments, AI agents consider what could have been done better.

• For instance, if the negative sentiment is because of agent-customer mismatch, your AI agent uses available data to refine its routing approach.
• If a ticket was successfully resolved but slowly, resulting in neutral sentiment, the AI model decides whether to increase the urgency of such issues in the future.

Also, compiled reports from these analyses can help your service reps refine their strategies and customer resolution approach.

Faster categorization and routing

Manual ticket sorting is a repetitively monotonous task and there are two problems with it. 

First, it’s slow—imagine having to categorize over 100 tickets in a day and still expecting to deliver a response to each complaint in less than 10 minutes. That’s virtually impossible.

Second, it consumes your service reps’ time and prevents them from focusing on the majors.

AI agents eliminate these outcomes and handle all the categorization processes in seconds, freeing your team to handle core tasks. This optimizes time to first response and time to resolution, which, in turn, enables faster response times to your customers.

Minimized ticket collision

Ticket collision is when two service reps unknowingly work on the same ticket, whether in parallel time or one after the other. This is usually as a result of errors in manual routing, delayed update of ticket status, and or lack of a centralized agent dashboard to track backend activities.

AI agents minimize the incidence of these errors and ensure each ticket gets updated—pending, ongoing, active, or closed—in real-time so as to avoid collision. 

As an omnichannel platform designed to scale your support team, Plivo CX’s Unified Agent Desktop also helps your reps visualize ticket statuses and eliminate collision.

Reduced service operational cost

A surge in tickets either means your brand is in high demand or you’re turning off your customers really bad. Whichever one it is, you’re going to need more hands on the desk to manage your tickets. And that’s cost-ineffective, especially if you have a limited budget.

AI agents reduce costs by handling the bulk of ticket management, no matter the volume. This minimizes the need for additional hires, allowing you to allocate resources toward more advanced AI solutions that further optimize customer support.

Also, faster response time as a result of a more efficient categorization and routing helps you retain customers. In fact, according to Bain & Company, increasing retention by 5% can boost profits by as much as 95%.

Less employee burnout and more efficiency

Ticket overload can result in customer service burnout. AI agents in customer service help prevent this through efficient distribution, notifications for tickets exceeding SLAs, and integration with AI chatbots for automated ticket resolution. 

This ensures that ticket backlogs do not build up, optimizes your team’s workflow so they can function at maximum efficiency, focuses on complex tasks, and increases their productivity by almost 66%.

1. Use ticket categorization agents

Traditional ticketing systems mainly follow pre-defined workflows. That means they lack some of the optimized features we’ve discussed such as NLP sorting and AI-powered post-support analysis. 

So, you can either use a full-stack AI categorization agent capable of handling ticketing hands down or integrate your existing ticketing system with an AI-powered customer support platform. 

An example of such a platform is Plivo CX’s unified agent desktop which enables integration with ticketing programs like Salesforce to automate your tickets, route to appropriate reps, and initiate proactive services.

2. Utilize AI-powered insights to optimize knowledge base

Remember we said AI agents can analyze ongoing conversations—tone, phrase, and keywords—using NLP, in order to figure out customer sentiments? They can also help you identify common or routine questions that do not require routing to your service reps.

Use this insight to optimize your knowledge base and fill in missing gaps. This will improve the effectiveness of your service options, reduce inbound requests, and free up your backlogs.

3. Integrate AI self-service chatbots

The giveaway in CS is that most of the requests your customers log in a day are routine queries—which AI chatbots can effectively handle. 

So, integrate an AI-powered chatbot directly with your AI categorization agent to cut your ticket load and help your reps focus on complex complaints.

‍Plivo CX’sOpenAI-powered self-service agentic chatbot is designed to relieve your team's pressure, escalate difficult queries to service reps, and reduce the time to resolution.