3 Ways To Leverage AI in Customer Service

Long wait times. Inconvenient hours. Questions going unanswered. These rank consistently among the most common customer service complaints, and unsurprisingly so.

And yet so much rides on customer service. Good reviews can bring in more customers, while even just one negative review can drive customers away. What if there was a way to ensure more consistently positive customer interactions and customer retention?

Making artificial intelligence (AI) part of your customer service strategy is one such way. While it might sound like the stuff of science fiction, the technology is here, and your competitors are making plans to use it. AI is one of the top two tech trends that small business leaders told GetApp in our 2021 Top Technology Trends Survey* that they’re planning to adopt in the next 12 to 18 months (the other is virtual reality). And almost one third (31%) of those surveyed already have a budget set aside for the technology.

Artificial intelligence can enable your business to provide better, faster, and more informed customer service at any hour of the day, resulting in higher customer retention and a more positive customer experience overall. These benefits are especially appealing to customer service professionals working for small to midsize businesses (SMBs) and can be helpful in presenting a business case for investing in AI to small-business leaders. 

With these benefits in mind, here are three ways your business can leverage AI in customer service.

The chatbot is defined by Gartner as a conversational interface that uses an app, messaging platform, social network, or chat solution for its conversations. Chatbots can be text- or voice-based, or a combination of both.

The AI chatbot is great for simple tasks, such as the following:

• Answering customers’ most frequently asked questions (FAQs).

• Queueing a human customer service agent for questions that fall outside the FAQs, or for requests that need to be escalated.

• Assisting a customer who is having trouble logging into their account.

• Scheduling appointments, if linked to a calendar.

• Collecting details such as business needs before passing a customer to sales.

In sum, chatbots are extremely useful for tasks that involve gathering and forwarding information, and they provide relief for customer service reps who might feel like bots themselves due to answering the same questions again and again. They also free up those same customer service agents to assist customers with more complicated questions, or to work on higher-value tasks. If your customer service team spends a lot of time assisting customers with any of the above, we recommend that you start with a chatbot.

As soon as 2022, Gartner predicts that VAs will be taking on more complex, knowledge-based functions such as providing basic healthcare diagnoses or sales-lead qualification (full content available to Gartner clients). What’s more, VAs are able to understand customer intent and even gauge their level of frustration. This capability is called sentiment analysis, which we’ll discuss further in the next section.

Sentiment analysis refers to the study of longer sentence fragments to identify the more nuanced aspects of language, such as tone and slang. This information is then turned into actionable insights that can help you better understand your customers and their needs.

According to expert.ai, sentiment analysis achieves the same goals as traditional focus groups and surveys, but without the time and expense. It can also scan online reviews and social media for customer feedback so that you know what’s being said about your products or services—good or bad—in real time. 

Sentiment analysis can also help your own customer support team improve customer service interactions by reviewing calls. One example of a platform that offers sentiment analysis by way of speech analytics is Oto.

Screenshot of Oto’s Deeptone Speech Detection feature, taken by the author

Oto’s Deeptone Speech Detection feature, shown above, can provide insights into different tones expressed in a customer service call such as boredom, enthusiasm, or irritation. Platforms such as Oto can help you fine-tune your customer engagement strategy by giving you insight into which calls are most effective, and where your team could use more training.

Platforms that use sentiment analysis are exciting in that they delve a bit deeper into more advanced machine learning capabilities. We recommend incorporating them into a longer-term business case if:

• The thought of saving time and money on focus groups and surveys appeals to you.

• You’re looking to gain insights from customer service calls.

• You’re seeking assistance in predicting customer behavior, such as purchasing cycles or stock market trends.

Next, we’ll discuss how machine learning can help you respond to these predictions in ways that will help you better serve your customers.

Gartner’s definition of machine learning (ML) can be boiled down to an umbrella term for advanced algorithms that are guided by existing information. Machine learning enables your business to gather customer analytics data and turn it into actionable insights. 

According to Gartner, customer analytics is the use of data and analytics to understand customer needs, values, and satisfaction. It is used to segment customers for acquisition, growth, and retention, and to develop targeted marketing, sales, and service interactions to improve the customer experience (full content available to Gartner clients).

By gathering customer analytics data, machine learning helps you understand your customers’ needs and provide those customers with real-time responses. Here are some examples of what this might look like:

• More personalized and timely interactions: Offer services or information to customers based on the date of their last purchase. Are they due for a refill on pet medication, or is it time for their next oil change? ML can automate correspondence that suggests that they buy a subscription, or sign up for recurring services.

• Knowledge of your customers’ communication preferences: Whether you reach out to customers by phone, text, email, or snail mail, ML can use behavioral data to observe how different communication channels perform and determine which to use and at what time for optimal results.

• Lowered customer churn: ML can collect churn data on customers who have been inactive or have canceled products or services. If your customer data shows when your business is most at risk of losing customers, you can take steps to retain those customers through added incentives or discounts.

If the leaders of your small business are concerned that investing in AI would lead to laying off employees, you can reassure them that this is not the case, and that AI can actually help your customer service team perform their jobs more efficiently. 

According to McKinsey Global Institute, less than 5% of occupations consist of activities that are 100% automatable. And from a practical standpoint, people will need to continue working alongside machines to meet global demands. It isn’t in any business’s best interest to replace humans with robots, at least not entirely.

The same goes for AI powered customer service. Customer service is one area in particular that benefits massively from “the human touch.” If you’ve ever repeatedly dialed zero until you were connected with a human agent, you know this.

This is why using artificial intelligence to enhance rather than replace human interaction is the way to go. If you think of the most common customer service grievances we mentioned earlier as squeaky wheels, AI technology is the grease that makes everything run more smoothly. 

To get your small business on board with AI in customer service, check out our catalog of customer service software, and look for systems that include chatbots or virtual assistants, sentiment analysis, and machine learning capabilities.