Leveraging Artificial Intelligence for Customer Service—Without Losing the Human Touch

Customers have high expectations for brands—and that includes customer service. According to Forrester Research, 67% of adults feel that the most important thing a company can do to provide good customer service is value their time. And when it comes to making a purchase, Gartner found that 64% think customer service is actually more important than price. Furthermore, the number one reason a customer switches products or services is because they feel unappreciated by the brand.

But the cost of human support is high—according to Forrester Research, it can cost a company as much as $12 per contact depending on the channel. So how do companies meet high customer expectations while still making them feel valued? Introducing Artificial Intelligence (AI) into its operations is one way companies can control costs while upping their customer service game, without losing the human touch that makes customers feel appreciated.

Relegate the mundane

Everyone has had the experience of needing a simple question answered by a brand, only to dread having to jump through customer service hoops just to get someone on the phone who may or may not have the answer. Conversational chatbots can make these conversations more seamless. Not only do conversational platforms help cut costs by 30%, they also can help your customer service scale and your agents have more meaningful and productive conversations with your customers when it matters the most.

Amazon, for instance, uses chatbots to leverage the data the company collects on all of its customers about their past orders. By programming chatbots with information about the customer’s past preferences, you can have conversational platforms interact with customers up to the point where an agent is needed. Then, once the conversation is transferred to an agent, the agent can pick up where the chatbot left off. This way, when it comes time for human interaction, the customer and the agent can have a more productive conversation without the customer having to repeat the information they provided to the chatbot.

Eventually you can program your chatbot to not only acquire customer information, but also recommend the actions customers and agents should take next. If a customer simply needs a common question answered about a product they already purchased, the chatbot can direct them to a FAQ without an agent getting involved. All of these interactions can be automatically tagged in the system, so they’re easy to track and reference, while also improving future recommendations. Besides streamlining processes, think of how much happier you’ll make your customer service agents—and happy customer service agents means happier customers.

Automate business processes

Consider this: Every minute you add back to an agent’s day by eliminating tedious tasks translates into more conversations per existing agent, while also giving them the time they need to handle high-value customers or go deeper on complex questions without feeling rushed. So how do you find more minutes in the day? Robotic process automation (RPA) can be used to handle the necessary, but routine tasks that keep support agents from interacting with customers in meaningful ways. RPA can track user actions within an application to complete a task and then perform the task, working across multiple digital systems. It can range from automatically replying to emails to routing conversations.

A global insurance provider has deployed RPA for a wide-variety of purposes from streamlining policy renewals to speeding customer claims. In one instance, RPA is taking information from customer communications with the company and matching it with the appropriate claims forms. Taking a process that once took 4 minutes down to 42 seconds. KPMG estimates that companies using RPA to automate business processes can reduce costs by up to 75%.

Turn agents into specialists

According to IBM, 80% of tier 1 support inquiries can be handled by a chatbot and elevated to a human agent if necessary. In the past, automated phone systems performed data dips, moving customers through a phone tree (“press 1 for a current reservation”) without handing the agent any information that the phone system captured. AI eliminates this unnecessary process. If a customer is calling about a product that’s discontinued, for example, there might not be a need for a human agent to talk to the customer only to relay that same information.

By using AI to capture information about the customers, and then passing customers and the information collected to agents only when absolutely necessary, agents can have more meaningful conversations and become more knowledgeable about the areas of the business that matters. If a customer still wants to talk to a human even after discovering their product is discontinued, the agent can immediately begin the conversation by offering recommendations for other products the customer may like. AI doesn’t eliminate the need for humans (as many people incorrectly assume when they hear talk of using AI in customer service). Instead it augments the human team and allows them to be better at their jobs.

Better management, better business

Gone are the days of randomly auditing customer service calls. By using AI to monitor your support operations, you can predict when conversations will start to go south allowing managers to intercede accordingly. AI can also help monitor which responses result in reopened tickets. If response A, for instance, tends to resolve inquiries quickly, but response B results in the ticket repeatedly being opened, the system can recommend you eliminate response B in order to set your agents up for success. Managers and executives can use the data generated by AI to oversee customer service operations in both clearer and more efficient ways—and this is a win for everyone.

Integrating AI into your customer service isn’t about replacing humans. Rather, it is about arming your customer service agents with the information they need to have more meaningful conversations with your customers, and using data to personalize your customers’ experience with your brand. Build an incremental strategy to roll out AI in your organization and use analytics to leverage the data collected. By using AI to build a more complete view of a customer’s relationship with the brand, companies can meet the high customer expectations for exemplary customer service and come across as anything but artificial.

Ready to learn how Kustomer can help you drive personalized, efficient, and effortless customer service? Discover AI trends in this customer service webinar.


Kustomer Tech Recap: New SLA and Custom Workflow Rules Capabilities

Our engineering and product teams have been busy launching new features, improving integrations, and making your top requests a reality. Here are the highlights of what we’ve added to Kustomer over the past few weeks:

  • Added ability to use custom message attributes when building workflow rules and searches.
  • Added ability to use queue assignments when building SLA rules.
  • Ensured previously-assigned conversations are allocated to an agent’s capacity when they make themselves available.
  • Added exporting of “Conversations Viewed” events data to better understand agents productivity and occupancy within the platform.
  • Added support for Twi language in Global Languages, Snippets, and web chat.
  • Added support for creating Snippets in Belarusian and Kazakh.
  • Eliminated a customer’s need to refresh the chat window to immediately view a message sent by a Proactive Messaging campaign.
  • Improved syncing of multi-level list options in Conversational Assistant flows, so that options deleted by the admin are removed from selection in the customer view.
  • Improved translation accuracy for the “End Chat” prompt in chat window.
  • Added a new notification for agents when sending a Twitter Direct Message (DM) that is not delivered due to customer privacy settings that block DMs from sources they are not following and have not DMed.
  • Ensured that the unread message count indicator will update properly when a customer leaves their chat window open and idle.


Doug Jarvis is the Director of Product Marketing at Kustomer.

Keep AI From Feeling Like Sci-Fi With Our Terminology Guide

When the conversation turns to AI, there’s often a Sci-Fi novel’s worth of terminology and jargon that the uninitiated reader has to decode. If you’re looking at using automation for service, then here’s a quick guide to the difference between AI, Machine Learning, and Deep Learning.

Watch Our Webinar with Solvvy here – The Truth About Bots and Intelligent Automation

Artificial Intelligence as a concept has been around since at least the ancient Greeks, who designed some mechanical devices that could be loosely-termed as intelligent. However the term itself is around 60 years old, and the first applicable AI technologies have only just started coming to market in the last few years.

Machine Learning is a more specific subset of AI. It describes machines’ ability to learn from their mistakes and improve over time. A good example of Machine Learning in practice example is the recent Google AI that beat a world champion at Go. The more the AI plays, the better it becomes at spotting patterns and predicting its opponents’ moves.

Deep Learning is a further iteration of machine learning. It describes machine learning algorithms that run on multiple layers, mirroring how our own neurons function. A now common example of deep learning is the way that smart assistants like Alexa or Siri process speech.

Also important is Natural Language Processing. NLP is the ability for a computer program to understand human speech, regardless of slang or dialect. By being able to make sense of written or spoken language in the messy and error-filled ways humans normally express it, AI capabilities become much more applicable to everyday life.

What does this mean for service? Artificial intelligence and intelligent automation can take over existing tasks and create new efficiencies that your organization couldn’t dream of previously. Machine Learning is just one example. By suggesting responses agents can use to common customer queries, a partially-automated system could learn the most effective replies and language for your customer base. Deep learning capabilities should extend to IVR trees, and put an end to the common “Sorry, I didn’t get” response from many systems that currently rely on processing speech. And NLP is crucial for chatbots, and for analytics that look at all of the conversations your agents have across chat, social, and any other text-driven medium.

It’s important to build a solid understanding of these exciting technologies as they become more prevalent and relevant to the service and customer experience sphere. To learn more, listen to our webinar with Solvvy: The Truth About Bots and Intelligent Automation.

Kustomer Expands Artificial Intelligence & Machine Learning Offerings

Customer Support technology is evolving fast and can be tough to keep up with. Lots of you have asked us about new technology like AI, bots and more. Today we’re announcing our two first entrants to Kustomer Labs that expands our artificial and machine learning offerings. We’ve been working with both for several months and love what they are doing.

What Is Kustomer Labs?

Kustomer Labs is an internal group here at Kustomer that evaluates the newest cutting edge Support technologies for you. We pick the best ones and offer them as integrations/plugins to the Kustomer platform.

Our goal is to offer our more visionary and adventurous users access to the coolest new things we see. We will collect feedback and determine how we will integrate the Kustomer Labs companies more deeply with Kustomer.

Want to learn more? labs@kustomer.com.

Init.ai: Conversation Intelligence

Training new and existing Support staff is time consuming and expensive. This NY-based company uses cutting edge AI to provide suggested responses, automate routine conversations and analyze interactions at scale for actionable insights.

We introduced them to the team at Sticker Mule who have already signed up and are preparing to go live with them! Team members at Sticker Mule rely on Init.ai to recommend a response or suggest an action that they can then personalize to deliver a consistent, friendly response to their customers.

“Thanks to the Init.ai integration, our team will be able to resolve issues, with fewer back-and-forth questions to the customer,” said Anthony Constantino, CEO, Sticker Mule. “The combination of Init.ai and Kustomer allows us to have a glue between those customer conversations and the data in our CRM.”

Want to learn more? labs@kustomer.com.

Abot Labs: Work Automation

We’ve spent hundreds of hours with Support teams. One of the most common complaints we get is that they want to spend more time interacting with customers and less time on menial tasks like basic response.

Abot Labs is newer to Kustomer Labs. Abot helps businesses and customers save time by making automated help more human. Their AI-powered agent enables companies to meet customer expectations and scale more quickly.

Their technology enables your team to spend less time on the painfully boring questions like Password resets and more time building high quality relationships with customers.

Want to learn more? labs@kustomer.com.

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