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More than buzzwords: How AI & ML transform customer care

by Russ Garcia | Jan 14, 2021

If it seems like people in every industry are talking about artificial intelligence (AI) and machine learning (ML), it’s because they are. Do a quick Google search and you’ll find countless articles and think pieces about its benefits and risks, predictions for its adoption, and how it’s been steadily seeping its way into our day-to-day lives for years. Once feared by many (the robots will take over!), AI and ML have instead provided practical applications that improve a wide variety of processes for brands around the world.

Within the vast umbrella created by this digital transformation, customer service has proven to be one of the best AI use cases. It’s been shown to positively impact customer care by increasing efficiency, empowering customer self-help, and improving job satisfaction for customer service agents.

There is, however, one hiccup. While over half of service organizations are actively looking for ways to integrate AI into their operations, many still aren’t sure how to do so. Furthermore, we found that 65% of consumers worry that brands are overusing AI which can actually make things more difficult if not implemented properly — so it’s important to have a thorough understanding of the ways AI can be used and when it’s appropriate to do so.

In this article, we’ll cut through the buzz surrounding AI and ML to share realistic ways your brand can start benefiting from these new and expanding technologies now.

AI vs. ML: Understanding the difference

Before diving into the specific ways your brand can take advantage of AI and ML, it’s helpful to understand exactly what these terms mean. While often used interchangeably, AI and ML are not exactly the same thing.

Artificial intelligence (AI) refers to the broader concept that machines can perform a process typically associated with human problem solving or cognitive tasks. Machine learning (ML), which falls under the umbrella of AI, improves algorithmic processes, predictions, or decisions over time using training data.

Two core differences are important to remember:

  • AI increases process efficiency, speed, quality, or success rates. AI models that are also equipped with ML do the same thing, but are set up to improve those outcomes (speed, quality, etc) by feeding training data back into the model

  • AI is decision making, whereas ML uses data to help AI make better decisions

ML is a key component of AI, and both can be used to improve processes. When broken down into their base descriptions, the idea of further adopting them might not be as daunting. Let’s take a look at how AI and ML improve customer service when applied.

The bot balancing act | Khoros

How to enhance your brand’s customer service with AI

In our 2021 Guide to Building Consumer Trust, we found that nearly nine in ten customers have a preference for human-led or human-assisted interactions to occur alongside AI while only 9% of people are comfortable with interactions that are mostly or entirely AI-led. While consumers are open to AI, brands need to carefully weave it into their care strategy.

AI can help customer service teams by providing agents with intuitive tools, smarter workflows, and automating certain processes. And it’s easiest to adopt AI when your tools already have those capabilities baked right in. Khoros Care empowers brands to become early adopters of AI in customer service, making it easier for them to improve the experience for customers and agents alike.

AI use cases: The different kinds of chatbots

When people think of AI and customer service, chatbots are one of the first things that come to mind. By far the flashiest AI use case, chatbots’ futuristic qualities have both intrigued and concerned people for a long time. According to an article by Inc., people will soon be more likely to have a conversation with a robot than their spouse.

But despite sometimes warranted concerns about bots, there’s an incredibly useful time and place for them: customer care messaging. Here are a few examples of three different types of bots that completely transform customer service management:

Customer-facing chatbots

The type of bot people most often hear about is customer facing, automatically responding to customer inquiries on any channel. For example, a chatbot can be used to respond with basic welcome messages and expectation management (e.g. how long until you hear back from a customer care representative). They can also be leveraged, however, to fully handle common questions or processes autonomously. Think, providing routing details, sending canned responses, directing a customer to an informative article, etc.

We found that when customers were asked what factors help them feel connected to a brand, the second most popular answer was being able to resolve a question or issue without having to make a phone call, with 80% of customers noting this is important to them. Customer-facing chatbots can quickly handle common issues so consumers don’t have to spend time waiting on the phone, ultimately helping them feel more connected to your brand without ever having a person-to-person interaction.

Suggested response chatbots

Our research found that 71% of customers say they’re more likely to buy from brands whose digital interactions are human-led with some AI, and suggested response chatbots are the perfect representation of this concept. These chatbots are agent-facing rather than consumer, and automatically suggest the best response for an agent to use throughout a conversation. These responses are fully customizable and built using a brand’s historic conversation transcript data.

Though this capability feels magical, it is not, in fact, magic. Chatbots created with natural language processing (NLP) recognize the intent of a customer’s message and pair it with a response that’s most likely to resolve the inquiry. The longer a brand utilizes suggested response chatbots, the better they’ll become.

Intent detection chatbots

These chatbots don’t interact with agents or consumers. Rather, they identify a customer’s intent and tag the conversation to categorize and prioritize. This is one of the most useful applications of chatbot capabilities, as it helps customer service teams better understand (and provide data about) the types of inquiries they most often receive, which funnels to other areas of the brand, be that marketing or product.

Intent detection also helps manage viral spikes. Say, you’re at the beginning on a pandemic and a large volume of customers has questions about order delays. These chatbots help to prioritize requests as they come in, as well as route conversations to the appropriate members of a customer service team.

The do's and don'ts of chatbot design

AI is the key to digital-first customer care

Shifting to an era of digital-first strategy, brands are being forced to modernize at a pace like never before. Artificial intelligence plays a major role in this, enabling brands to streamline processes in the face of rising customer expectations and an influx of inquiries.

Khoros helps brands implement AI and ML across all customer engagement channels, including contact centers, social media, and more. Request a demo today to learn more about how Khoros can help improve your customer service processes, building trust with customers while reducing operating costs along the way.


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