What are the Benefits of AI in Customer Service?
Do you know why ChatGPT became immensely popular within days of its inception?
“People are much more likely to accept what something says when it’s automated,” says Margaret Mitchell, the chief ethics scientist at the AI company Hugging Face, when asked about her opinion on ChatGPT’s unbelievable popularity.
If you think about it, the Internet already has all the information you want, but it's scattered. ChatGPT or any AI tool streamlines these datasets and provides faster, automated, and personalized responses to every query. And that is the comfort we all have been looking for.
Support queries from multiple channels like emails and service desks often overwhelm customer service agents. But, with the strategic application of AI in customer service, a significant percentage of these routine queries can be automated. That would reduce customers’ waiting time to seconds and allow agents to focus on complex queries.
The benefits of AI in customer service are exceptional; in this article, we will discuss that.
What is AI in Customer Service?
When an organization uses AI to streamline customer support operations, it is called using AI in customer service. There are multiple approaches to using AI in customer service, the most common being AI chatbots.
Let’s take the example of Hotjar, a SaaS website analytics solution. As soon as you enter this site, you will find an AI chatbot that greets you, answers your most common questions, offers you knowledge resources to answer product-related queries, helps you identify the most suitable pricing plan, and routes you to a human agent for addressing complex queries.
This is a great example of AI in customer service as it responds to each customer personally, reduces their waiting time, and provides the exact solution they are looking for faster.
AI chatbots are just one example of an AI application in customer support. It is still an evolving process in which business leaders and AI enthusiasts are learning and iterating their AI maturity models on the go.
An AI maturity model typically starts with manual, reactive services and goes on to build personalized automated models that understand customers’ emotions and respond proactively to their queries. These models automate more than 95% of service interactions and are expected to evolve further in the coming years.
What are the Benefits of AI in Customer Service?
As of 2023, 79% of customer service professionals consider it to be somewhat or essential in their strategy execution process.
This massive adaption towards AI didn’t happen in a single day. Multiple factors, like increased customer acceptance of conversational AI and the revolution of relevant technologies like instant messaging platforms, play a significant role in the large-scale use of AI in customer service.
But what are the biggest benefits of AI in customer service? Let’s find out!
Drive personal conversations beyond routine queries
A simple chatbot answers the common queries of a customer and routes them to a human agent for complex queries. These chatbot features were relevant a few years ago when users weren’t as familiar with AI as they are now.
A 2023 survey showed that 38% of respondents have already used generative AI at least once. This explains how far the users have come in terms of their comfort level with using AI models, and these findings also tell us the potential of using generative AI in customer service.
Chatbots no longer just automate routine queries. AI technology has evolved to meet higher customer expectations. Today, chatbots can mix and match multiple built-in capabilities:
- Customize conversational tones according to customers’ moods
- Directs customers across different stages of their journey, starting with pre-purchase, onboarding, offboarding
- Respond to customers in their preferred language and customize messages in the format and brevity of their choice
41% of customer service professionals use chatbots as a significant application of AI in customer service, which goes beyond automating routine questions and answers.
Provide support where your customers need it the most
Suppose you are targeting B2B decision-makers whose preferred communication channel is Slack (Since 80% of Fortune 100 companies are using this tool for team communication). But you continue to use only the website chatbot as the primary customer support channel.
This means customers and support executives must frequently switch between multiple applications. Integrating an AI chatbot into your Slack support channel can streamline B2B customer support communication.
Customers seek personalized support, meaning instantly responding to their queries in their preferred channel. This could be WhatsApp, Facebook Messenger, Discord, Zendesk, or any other platform where your customers actively engage with the support agents.
Conversational AI platforms provide channel connectors and custom API solutions to integrate the chatbot into your preferred channel. It is a great way to streamline customer requests and resolve their queries without toggling between different tools, saving the support executives’ time and increasing productivity.
Encourage users to self-serve themselves with AI-powered help centers
Users hardly reach out to customer support teams when they first encounter an issue. Their first approach is always to solve it on their own.
Six out of 10 US customers prefer digital self-serve solutions as their go-to channel for simple website and mobile application queries. However, they are often unable to solve even the simplest queries because of insufficient documentation and help centers.
With the power of generative AI in customer service, you can create self-help bots that go through your knowledge bases and answer customers’ queries instantly.
So, if a customer asks, “How do I reset my account password?” Engaging a human agent to address such generic queries is unnecessary. Any simple conversational AI tool will tell them the steps involved. Some even help customers with screenshots or share video tutorials so they can take matters into their own hands.
Coming back to the example of Hotjar, watch how the bot provided us with simple steps to install the tool and also shared a detailed guide for further clarifications:
Improves agent’s efficiency by predicting customer’s behavior
Suppose a customer is frustrated because they have not received a refund on an item they returned eight days ago. If an agent decides to provide the same old information like a return gets processed within eight to ten days and they should wait two more days, it would only frustrate the customer.
One of the greatest benefits of AI in customer service is that conversational platforms have a built-in sentiment analysis tool. This tool helps interpret customers’ responses, predicts their moods, and directs agents on how to approach them.
For example, in the above scenario, while you ask the customer to wait two more days, you can also offer them a discount coupon to show that the online store regrets the delay.
Tools like Grammarly offer suggestions to make a write-up friendlier and more empathetic. It is a great example of sentiment analysis.
When agents realize customers’ intents in advance, they are prepared to respond empathetically and offer incentives if necessary. This also means taking action faster and resolving more queries within less time.
Sort and route incoming requests with AI-driven workflows
Remember when calling customer support meant waiting for hours only to be routed to another department? If your chatbot keeps routing the users to different departments and agents to solve a complex query, you are replicating the same customer experience with never-ending waiting time.
But with conversational AI, you can build intelligent workflows that provide support faster and route customers to the most efficient agent at the first attempt who can address their query effortlessly.
A typical AI-powered customer support workflow looks like this:
Customer query ➡️Understanding customer’s intent through sentiment analysis ➡️ Assessing agent capacity ➡️ Evaluating agent's skillset to ensure they can address this query ➡️Route customer to the suitable agent
With AI, this workflow should take a few seconds, reducing waiting time and optimizing your support team’s efficiency.
How does Conversational AI Help Different Industries?
The applications of conversational AI differ from industry to industry. Here, we have narrowed down the benefits of AI in customer service for four different sectors:
- Banking
- Finance
- SaaS
- Insurance
Let’s get to the details:
AI in banking customer service
Suppose a customer is considering a credit card but has questions before selecting the most suitable one.
These questions include: "Which credit card is most suitable for me with a $10,000 monthly income, and I spend on dining and travel?" or “Are there any joining fees associated with card X?”
With conversational AI, banks can easily create a product discovery assistant trained with the bank’s product collateral pages and documentation. A leading Asian bank recently deployed a similar product discovery chatbot and reduced the time to access information from 15 minutes to less than a minute.
AI in SaaS customer service
Matchbook, a SaaS company in sports, introduced a help center with detailed video recordings and documentation. The goal was to encourage customers to develop teams to integrate API keys to access Matchbook’s features. But the biggest challenge in deploying the help center was accessibility. Information discovery was taking a long time, leading to frustrated customers.
Matchbook paired with Alltius, a conversational AI platform for customer support executives. Alltius swiftly integrated an on-brand widget into the Matchbook platform and built a powerful AI assistant by training Matchbook’s extensive help resources. To ensure that complex queries are routed to the human agents, an intent detection tool was also implemented that predicted customers’ emotions.
This AI assistant was accessible only to authenticated Matchbook users. It successfully answered 95% of customer queries and reduced time to information discovery by 80%.
AI in FinTech customer service
Most FinTech companies don’t have a streamlined process for registering customer queries and complaints. A lot of a FinTech customer support executive’s day is spent collecting customer queries from emails, in-app chats, and calls. Support executives take longer to assign tickets, leading to longer response times. No wonder customers are easily frustrated with waiting for as long as five hours.
A robust and secure AI assistant can solve this challenge. Once coached with a large dataset involving website documentation, resolved customer conversations, and support agent playbooks, rigorous training will help the assistant become well-versed in the typical solutions to customer queries, agents’ communication styles, and objection-handling strategies.
Such an assistant can auto-draft initial email responses and integrate with service desks to handle initial responses and routing. A renowned digital lender has deployed a similar assistant that drafts up to 5000 emails daily and reduced customer waiting time by 50%. This also resulted in a 2X increase in employee productivity for the FinTech organization.
AI in insurance customer service
Assurance IQ, a US-based insurance company, generates millions of traffic to its website, and a significant percentage of it gets converted into leads. These leads are directed to the sales contract center, where insurance agents assist them in selecting suitable insurance plans.
The challenges with this process are that agents struggle to stay updated about the plan details as there are over 6,000 insurance plans. The challenge is more critical for new agents as it takes them at least six months to get trained.
Assurance IQ used Alltius to address this challenge. The first solution was to create a sales agent who narrowed prospective customers’ searches to five to ten relevant plans addressing three to five specific needs. The agent could then compare these plans based on their benefits, inclusions, and exclusions specific to customers’ needs.
With more personalized answers to customers’ queries, Alltius’s AI assistant could lift the call-to-conversion rate by up to 15%.
Reduce your Support Costs and Close Deals Faster with Alltius
If you want to create generative AI assistants for your support team to automate customer interactions, gain insights about their behavioral patterns, and free up your service agents’ time, Alltius is your go-to solution.
Some of its significant features are:
Kno Plus: These purpose-driven, personalized AI assistants are trained using different resources, such as 200-page PDFs, support ticket history, product wikis, and video libraries. They are deployable across different channels and allow you to refresh sources at your desired frequency. They are 300X faster in identifying targeted answers to specific queries.
Kno Widget: Embed Kno Widget within your products to keep them undistracted, coach them on your sources, and deploy them on your applications. With a simple, low-code installation process, Kno Widget comes with an intent library to predict customers’ behavior. It has a proven track record of doubling contact center productivity.
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Read more:
- How to make your own AI?
- Best AI sales assistant software in 2024
- Top AI customer support software
- Top customer service chatbots
- Top conversational AI platforms
- Best conversational AI platforms
- Best enterprise AI platforms
- How to build conversational AI?
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