5 Effective Ways to Improve Agent Productivity with AI
Artificial intelligence (AI) helps businesses meet the digitally-inclined customer on their channel of choice. From ticket deflection to smart automation, the impact of AI on customer service is announcing itself in surprising ways. A customer can now start a conversation over chat, continue it via phone, and pick it up later over email within the same conversation thread.
The new wave of advancements in AI technology has provided businesses with easy access to better customer support tools and resources, empowering customer support agents to increase productivity and to focus on high-value tasks.
In this article, we’ll discuss:
- What is AI?
- AI in customer service
- Impact of agent productivity on customer service
- How can AI improve customer agent productivity?
- How to be AI-ready?
- What kind of AI do you need?
- Freddy AI for Freshdesk
What is AI?
Today, AI is everything but a buzzword. To help you understand the capabilities of AI better, let’s take a quick look at the underlying technology through the lens of customer service. AI encompasses
- Machine learning (ML)
- Natural language processing (NLP)
- Natural language understanding (NLU)
- Natural language generation (NLG)
Machine learning (ML)
ML is a subset of AI. It refers to how the AI tool learns to understand human language and generate appropriate answers through automatic self-learning, or in other words – without being explicitly programmed in a simple workflow. These tools are created by feeding large datasets pertaining to the customer language such as common questions, dialogue, and phrases, to a neural network. ML enables the algorithm to study the datasets within the context of customer questions and to identify combinations of words and phrases that indicate customer intent.
Note: A neural network is a set of algorithms or rules that is designed to recognize patterns in data.
Natural language processing (NLP)
It requires different levels of proficiency to read in a language and to be able to converse in that language. The same applies to machines as well. NLP is how the AI tool understands the customer’s language, recognizes their intent, and executes the required task.
Natural language understanding (NLU)
NLU is an advanced subfield of NLP that is capable of capturing subtle inferences that NLP might miss. It uses grammar and linguistic rules to understand the meaning of sentences in complex customer communication.
Natural language generation (NLG)
NLG refers to the capability of machines to sound more like humans and less like robots. Advanced AI products use NLG to not just offer appropriate answers to customer queries but also engage with the customer.
What is the role of artificial intelligence in customer service?
AI’s biggest impact is the transformation of customer service through automation and the use of AI-powered technology that can engage with customers in human-like dialogue. Gartner1 predicts that by 2022, 70% of customer interactions will be done using AI and machine learning (ML) applications, chatbots, and messaging apps.
Here are a few examples of what a leading AI customer support tool can do:
- Answer simple or open-ended customer questions
- Ask questions to the customer to get more information
- Automate both inbound and outbound customer communications
- Complete processes using input from the customer such as booking a dentist appointment
- Route customers to the right department
- Communication through various channels such as SMS, email, voice, web or social
- Integrate seamlessly with CRM systems
- Automatically create tickets and update statuses
Though AI and ML were initially thought to be a threat to the customer service industry, we can’t deny that these machines are here to help customer support agents improve productivity and deliver great customer experiences.
What is the impact of agent productivity on customer service?
Delighting customers and providing memorable experiences is a defining strategy for all companies. However, if your support agents are overwhelmed with customer queries and spend a good part of their day in repetitive tasks, delighting customers might not be their central focus. The more productive your agents are, the happier your customers will be.
However, the benefits of agent productivity go beyond customer happiness.
- By offloading mundane tasks and providing them with the right tools, support agents are better motivated to focus on high-value tasks and use their experience to optimize support processes.
- Motivated support agents contribute to improved KPIs such as time to resolution, first call resolution, customer satisfaction score, net promoter score, etc.
- Productive agents can adapt better to seasonal spikes and still deliver superior customer service.
- Lower agent churn — when agents are under-equipped, they become inundated by customer queries that lead to burnout.
Productivity challenges vary from one support channel to another. With businesses offering multi-channel support, a one-size-fits-all strategy will not work to improve agent productivity. You need to empower agents with the right tools to help them deal with specific challenges associated with each support channel.
How can AI improve customer support agent productivity?
Interminable queues, outdated systems and processes, frustrated customers, and stressed support agents don’t play out well when you’re trying to deliver a superior experience for the hyperconnected customer. Well, there’s help!
Using AI-powered agent assist bots and intelligent automation, here are five ways you can free up your agent’s time so that they can focus on doing what they are best at — empathetic support.
#1 Triaging customer requests to manage ticket volume better
If your support agents are flooded with customer requests, sorting the more urgent ones and routing them to the right teams or flagging them under the right categories is the stuff of nightmares.
AI and intelligent automation can improve ticket routing time so that it reaches the right experts, obtains quicker responses, and increases customer satisfaction.
ML algorithms are trained to automatically categorize incoming customer issues. This leaves the support agents with ample time to focus on more complex issues than sorting through support tickets.
Additionally, not all customer support issues require the assistance of a support agent. Most of the questions customers ask are already addressed in your knowledge base in the form of FAQs. By embedding an AI-powered chatbot on your website or app, the bot answers these FAQs and deflects them from landing into your agent’s inbox. For instance, PhonePe, a Freshdesk customer, deflects 60% of its customer queries through automation.
#2 Providing smart assistance and response recommendations
Though your support agents are product experts, it can get overwhelming for them to provide the right answers to every customer query in record time. However, with AI-powered agent assist bots, you can enable your support team to quickly sift through the most relevant resources from your content repositories such as knowledge base, blogs, and other internal sources of information to suggest the best solution. The ML algorithms are also capable of automatically learning the right solutions from tickets resolved in the past.
#3 Smart automation of complex processes and repetitive tasks
By automating repetitive and mundane tasks, AI frees up your support agents’ time that they would have otherwise spent on backend tasks. They are not only time-consuming but also error-prone processes. With AI, you can take the manual work out of the process and elicit all the information you need in just a click.
Additionally, updating ticket properties after each customer interaction will eat into your support team’s time. With an AI feature such as Freddy’s Auto Triage that automatically suggests ticket fields for new tickets, you can significantly reduce ticket assignment and resolution times.
Pro tip: Build smart workflows for your bot to save time spent on performing complex mundane tasks.
#4 Eliminate noise and look for customer intent
One of the key metrics that support agents are measured on, is the number of tickets they have been able to close. However, every time a customer writes back to the support agent, even thanking them for their assistance, the ticket in your helpdesk gets reopened. The support agent has to then manually update the ticket properties and resolve the ticket again – increasing duplication of effort and impacting their KPIs. This can be avoided with the help of AI which can detect the intent in such customer responses and prevent the ticket from being reopened.
Freddy’s Thank You Detector understands the intent behind responses such as a thank you note from the customer, or a request for help from an agent, before deciding whether or not to reopen a ticket.
#5 Look for social signals
With businesses providing support on social media, there’s a good chance that the social accounts get a lot of mentions from people. But not all these people are looking for support. Manually filtering through the noise is difficult for your support agents. You may also miss out on important issues that require immediate action from your team.
With AI, you can quickly scan through all the noise, look for signals, and notify your team the instant a customer reaches out for support. Freddy’s Social Signals is an AI-enabled feature that automatically finds the most relevant tweets quickly and cancels out the noise from your Twitter handle.
How to be AI-ready?
There’s no AI if there’s no data. For your bot to function effectively, you need to feed it information. What you need is an AI strategy!
Know your customers
One of the important aspects of creating a good service knowledge base is to ensure that it is helpful and relevant to your customers. This not only helps your support agents and bots address customer issues faster, but it also builds a strong empathetic relationship with your customers. The first step towards achieving this is to create customer personas — who they are, what they are usually looking to solve, what kind of problems they are most likely to run into. Using this as a framework you can create relevant content that the bot is capable of understanding.
Pro tip: When designing your customer personas, thoroughly research and cross-reference your findings with any existing data that you might have. Ensure that you involve your team in this exercise and do not create more than 3 personas.
Identify customer problems
Once you’re done sketching these personas, begin mapping out your customer journey. This will help you identify all the potential touchpoints where your customers engage with your product. Using this information, you can start curating the list of problems they face at every touchpoint in the journey. Now that you have the problems deciphered and the questions identified, it’s time to start answering them.
So far, you’ve looked at the potential problems from the outside. Now, it’s time to look at it from the inside. Scan all your support channels and curate all the frequently asked questions. Once you have all these questions and their corresponding answers, map them under the respective customer touchpoints.
Structure your knowledge base content
By now, you’d have an extensive repository of questions and answers to every problem that a customer might have regarding your product. You need to add structure to it so that it’s easy for your customers and your bot to discover them quickly. Map articles with relevant metadata making it easier for the bot to scan and discover information. Here’s how we structure our knowledge base articles at Freshdesk.
Pro tip: The best way to structure your knowledge base content is to group the content based on the touch-points within your customer’s journey. Additionally, keep your content simple and consistent, adding visuals such as screenshots, GIFs, and videos where necessary.
Keep your knowledge base up-to-date
As your product evolves, so should your knowledge base. Make sure that you keep your knowledge base content constantly updated, reflecting everything that is being added to or improved on within the product. Since ML algorithms enable the bot to constantly learn from new data or content in the knowledge base, your bots will be up-to-date providing only the right responses to customer queries.
Pro tip: Constantly monitor the feedback you receive on your knowledge base articles. This way you can make necessary changes, provide relevant answers to your customers, and keep them happy.
Monitor, measure, improve
You can’t manage what you can’t measure! One of the important aspects to focus on in your AI strategy is to figure out whether or not the strategy is working for your customers and your support team. This means you need to constantly track the performance of your bot and make necessary changes to improve it.
Ask yourself the following question:
- Do your customer personas interact with your chatbots?
- How many questions does your chatbot help you deflect?
- Do you train your chatbot regularly?
Answers to such questions will help you understand the impact of your AI strategy.
What kind of AI do you need?
In a huge market filled with a variety of AI tools for customer support, how do you decide which kind of AI will offer the most value for your support team and your business? Well, generally, there are three key elements that you need to consider when weighing your AI customer service products.
Ease of use
Is this AI tool something your support agents have to spend six months to learn or is it something you can get started with right away? If the tool takes a longer time to implement or learn, you will experience a longer time to value. This will not prove to be a beneficial or wise investment.
Robustness
Can the AI tool perform complex tasks? Can it be integrated with existing tools that your support team uses? Can it be implemented without breaking your existing support processes? Can customized business rules be integrated into its automation? These are some of the questions you need to ask yourself when deciding on an AI platform.
Vendor accountability
Is the vendor good enough to be partnered with? Is the vendor capable of understanding your customer support and business requirements? Does the vendor have the resources to help your team with the product onboarding process? Does the vendor understand how to implement their product to help you achieve your business goals?
AI product selection matrix
Here’s a framework you can use to explore and determine the best AI-product for your customer service team.
Freddy AI for Agent Assistance
Great customer service will always be your biggest competitive differentiator. Hence, AI is one of the most powerful tools you should be investing in right now. If you are looking for an enterprise-grade AI software for customer support, Freshdesk is your wingman.
With Freddy AI for CX, Freshdesk’s powerful solution for AI that offers features such as chatbot, email bot, agent scripts, social signals, thank you detector, auto triage, and bot reporting, you can automate most of your support processes leaving only the complex issues for the support agents to handle. It can be launched on multiple channels and in multiple languages to deliver well-rounded customer service. And the best part? Freshdesk does not require any coding skills and is easy to set up.
If you are looking for a cost-effective customer support solution, let’s talk.
Source:
1 – https://www.gartner.com/smarterwithgartner/top-cx-trends-for-cios-to-watch/
