Calling Customer Support Managers: Here’s How to Further Your Career


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Why taking on an AI initiative can get you to your goals faster

The job of a customer support manager is not easy — that’s no secret. Working long hours, balancing a loaded case backlog, and still handling customer complaints with grace is no small task. All of these moving parts, combined with limited budget and limited daylight, can make it hard to keep your eye on the true customer support prize: improving customer satisfaction (CSAT).

CSAT scores are often used as an end-all-be-all customer support metric, but anyone who works in the industry knows this can be misleading. CSAT scores are squishy — irritable customers, product failure, and even individual bias can all unfairly affect CSAT scores independent of agent engagement. How can customer support managers improve CSAT in spite of all of these challenges?

CSAT is a resultant metric, which means it can be broken up into several smaller, controllable factors. By focusing on the individual facets of CSAT, and working “smarter”, you can move the needle more readily toward true customer satisfaction. The recipe for improved CSAT scores comes down to improving these three elements:

– First contact resolution (FCR)
– Average handle time (AHT)
– Agent happiness

Let’s explore each of these factors individually to better understand how to improve CXM efficiency and, in effect, boost CSAT scores with the help of AI for customer support.

This is the Holy Grail of customer support because your team’s ability to deliver quick and efficient resolution is a key contributor to customer satisfaction. This is fairly intuitive — when a customer reaches out for support, the last thing they want is to brave a maze of redirects!

The best way to measure FCR is coverage: the percentage of tickets that are resolved with one touch. While this might seem like a far-away ideal, the best support organizations actually resolve over 60% of e-cases with one touch.

You might think to yourself, “that sounds great, but my team’s cases are far too complex for quick resolution.” Challenge your team to simplify things. In the end, the simple answer is what your customers are looking for. There are tools available to make even the most complicated products and services eligible for FCR.

The best support managers can extrapolate ticket intent, and use common inquiries to help build simple decision trees. But this is easier said than done as a company grows and tickets pile up.

Here’s the solution: A good AI solution can help you read through your historical tickets and will give you an analysis of how to design decision trees and content that increases first contact resolution.

AI Solution: Ticket Deflection and Macros

There are two ways AI improves FCR for e-cases:

Ticket Deflection — AI increases FCR by digging into an established knowledge base and using this historically-proven insight to deliver helpful customer resolution with no agent interaction.

AI-based Macros: Businesses design macros for single touch resolution. AI systems use natural language processing to read through historical exchanges and suggest ticket categories that can be automated with high accuracy and without any agent interaction. Repetitive use of single touch content enables automation.

Consider the math — if you have 100 agents who manage e-cases at a rate of 6 minutes per response, that means even the best agents on staff can only handle 10 responses per hour. As your company grows, this is simply not sustainable.

Let’s say a typical support organization of 100 agents manages 80K requests. That makes 32,000 tickets eligible for automation!

When it comes to AHT, every second counts. This core metric has a significant effect on your team’s responsiveness and, even more importantly, your company’s bottom line. Reducing handle time is a combination of how fast an organization can assign and route a ticket to the right person, plus the time it takes for an agent to research and respond to a case.

AI Solution: Triage and Agent Response Time

It’s one thing to say you need to reduce AHT, but how can this practically be accomplished? Put simply, AHT = Triage time + Agent response time. If you can reduce one, or both, of these elements, AHT will in effect also decrease. Here’s how AI can help reduce these two important AHT factors:

Triage — Measurement of the time it takes for a ticket to enter your system to then be routed to the correct agent. Often, teams use rules-based routing (or even worse, manual triage). These methods cause significant inefficiencies such as a hefty backlog, misclassification, and re-routing for multi-touches. Inefficient triage can lead to agents looking at all tickets equally – free and paid users, for instance.

With AI integration your system can automatically classify and route tickets. Through Natural Language Processing, your system can detect ticket intent, priority, severity, sentiment, language, customer taxonomy (gold/silver/platinum), and spam.

Agent Response Time — Measurement of how quickly an agent responds to a ticket after it appears in their console. Unfocused and unorganized response processes are often the greatest deterrents to effective Agent Response Times. That’s why it’s important to equip agents with consistent, historically-proven response templates.

AI systems use neural networks to understand a request as soon as it arrives, and assists agents with top recommended responses from the bank of historical successful exchanges. AI can reduce AHT by at least 20%.

AI systems are obsessed about answering the ticket and can bring the best of knowledge base content and macro templates (email templates) all within one console with the top 3 suggested responses. The goal for these systems is to present the most accurate, relevant content within a second of opening a ticket.


Of course, one of the most important elements of not only your intenal work culture, but also CXM efficacy, is agent happiness. Nothing hinders agent happiness — and CSAT scores — quite like monotony. When faced with an avalanche of mundane, repetitive tasks, even the most resolute agents on the team are sure to check out.

That’s why achieving agent happiness is not as easy as simply hiring more agents. People enter this field to be creative, solve problems, and serve customers. So, challenge your agents. No one wants to resolve repetitive tickets and while it might sound reductive, improving agent happiness is as simple as eliminating repetitive tickets.

AI Solution: Automated Response and Ticket Deflection

Here’s how AI can help:

Automated Response — Easily resolve 20 – 30 percent of tickets by automating common responses. Thanks to Automated Response, customers receive the answers they need without any agent interaction — significantly freeing up agents to focus on tickets that require greater, more specialized human interaction. Automated Responses are based on historically-proven templates, so you can rest assured customers get the answers they need quickly.

Ticket Deflection — Solve issues at the source by helping customers help themselves. Self Assist suggests an answer from existing content sources so customers can self-discover the right answer on their own.


For all the utility of the above CXM strategies, you will never move the needle on CSAT without first improving your understanding of your support landscape. Important questions to ask yourself to begin improving customer experience include:

– What is AHT per response/per ticket?
– Do you have a robust Decision Tree?
– Do you have established Reason Code to back it up?
– Where are the gaps and redundancies in your issue heatmap and content coverage?
– What is the cost to serve a certain customer channel?

Most support systems offer simple metrics like AHT or Issue Heat Maps, but that’s simply not good enough these days. Without acute insight into Decision Trees and Content Heat-Maps, your system cannot reap the benefits of automation and therefore is falling significantly behind. Implementing AI-based systems puts you in control of your CXM and will make you and your support team shine.

AI features help reduce AHT and improve FCR — effectively promoting agent efficiency and, in turn, boosting CSAT scores. Ready for that promotion? Buckle up! (And, reach out to me directly if you’d like to talk more about this!)

Note: all images used in this post were created by AnswerIQ.

Pradeep Rathinam
Pradeep Rathinam (Paddy) is the Chief Customer Officer (CCO) at Freshworks. He leads a global team of customer experience employees that span customer success, professional services, and customer service. He founded AnsweriQ, an AI-based customer service automation company, which was acquired by Freshworks in 2020. Previously, he was the CEO of Aditi Technologies and spent over a decade at Microsoft as general manager of the Independent Software Vendors business. Pradeep holds a business management degree from Delhi University.


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