How to Improve the Customer Experience with AI

Written by Emily Cummins  on   Sep 25, 2018

Customer service today is broken, marred by inefficiency that leaves customers frustrated and support employees disenchanted.

Consumers experience long hold times or are an unwitting participant in the virtual game of pinball, getting knocked from one agent to the other until you may get your need resolved. Or there’s the hours-long wait many consumers experience to hear back on a customer support email or social message (the average wait time on social media is an astonishing nine hours). Customers expect businesses to respond to their emails within an hour, and most companies don’t come close to delivering on this today.

The stakes for good customer service are higher than ever: 33% of Americans say they’ll consider switching companies after just a single instance of poor service. On the bright side, taking a customer-centric approach to support pays off: 7 out of 10 U.S. consumers say they’ve spent more money to do business with a company that delivers great service.

Without using AI, it’s almost impossible, and very expensive, to meet consumer demands for fast, high-quality resolutions at scale. Let’s take a look at 6 different ways customer service can be elevated through intelligent support automation.

6 ways AI-powered customer service improves the customer experience

  • Always convenient for your customers

AI connects to all of your business systems in order to surface the right information to personalize the exchange and drive in-the-moment relevance. Companies can treat every customer differently on-demand. AI also enables an omnichannel experience deployed across all channels where customers are. A single AI can exist on social, mobile, chat and email. AI Agents are available 24/7, whenever your customers need them.

  • The instantaneous, accurate response to repeatable issues

Over 50% of the tickets your company receives span repeatable, simple questions and scenarios. AI can be used to increase your brand’s responsiveness and customer satisfaction via quick high-quality resolutions to these queries as soon as your customers reach out.

  • Better, faster service from human agents

AI-centric customer service is not about replacing human agents; it’s about uplifting humans to work smarter and faster, and allowing them to focus on high-value work and things that require uniquely human characteristics like complex problem solving and having empathy. AI can empower agents to respond quickly with recommended replies and actions, and surface the critical information that will help your agent serve better. The customer experience is enhanced as human agents are focusing solely on enabling better assistance to fewer tickets.


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  • Support becomes proactive and predictive

AI enables a fundamental shift away from solely reactive service to preemptive service. First, we will see a wave of proactive service capabilities where companies anticipate issues based on changes to things like delivery or order status and alert customers before they have a chance to reach out to the brand. Within the next few years, we will also see support become predictive, where companies will anticipate issues based on contextual signals. For instance, a person who is stuck in traffic on their way to the airport and has missed their flight will be preemptively rebooked on the next flight. They will get a message from an airline’s AI agent with new ticketing information.

  • Disseminate the right information at the right time

AI-enabled support enables companies to educate and intervene at the exact moment it can lead to overall higher customer satisfaction (and also drive a sale).

    • Pre-sale:  More than half of Americans have scrapped a planned purchase or transaction because of bad service, according to the American Express 2017 Customer Service Barometer. An AI agent can act on signals like time spent on a page, clicks or mouse movement to identify customers who are not yet ready to make a purchase or are on the fence. The AI can provide information relevant to their exact behavior, offer to answer questions or even recommend questions other customers have had at that point in the purchase journey. A more informed purchase leads to fewer buyers remorse and returns, and a happier customer.
    • Post-sale: Companies can automate recommendations and instructions for service or care. For instance, 1 year after a coffee maker was purchased, based on the usual wear and tear, we’d recommend you do this service or cleaning to keep the device working best. AI can also help with feature discovery, alerting customers to new and different ways to enjoy a product.
  • Personalized engagement

AI customer support can enable resolutions to be more personal to the customer. Based on machine learning and understanding the variations amongst audience segments and how contextual factors influence whether an action resolves –  or fails to effectively resolve – specific issues, companies can provide more relevant personalized customer service to the individual.

A moderate increase in Customer Experience generates an average revenue increase of $823 million over three years for a company with $1 billion in annual revenues, according to the Temkin Group. Even if your revenue is nowhere near this, providing friendly, convenient, high-quality support at scale can have an enormous impact on your consumer experience and your company’s bottom line.

To learn more about AI-enabled customer service and how Netomi can transform your customer support, schedule a demo today.

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The Turing Test Holds No Value In Assessing Conversational AI

Written by Puneet Mehta  on   Sep 25, 2018

This originally published in VentureBeat

AI is becoming the new user interface. From self-driving cars and Amazon’s Alexa to Robo-advisors and facial recognition locks, consumers are interacting with AI like never before. And this is just the beginning.

For years, AI enthusiasts have used the Turing test as a guide for developing conversational bots. Developed in 1950, the Turing test focuses on believability, analyzing a machine’s ability to behave indistinguishable from a human; researchers have long considered passing the test as the holy grail of AI. This benchmark, though, was created in an era when AI wasn’t common, and teams created machines with the goal of creating a human clone.

Over the past few decades, Hollywood’s portrayal of AI in movies like Her and I, Robot also sought to replicate human characteristics. Tinseltown’s version goes way beyond what today’s commercial tech can achieve, but we still seem to measure modern applications of AI against these fictitious interpretations.
 

Solve problems, don’t just ape humans

Today, we’re somewhere between the Turing test and Hollywood’s in-your-face robots. AI is surpassing human capacity in subtle but powerful ways like diagnosing diseases. It’s the technology powering some of the most advanced applications in the consumer tech market, and we’re just on the cusp of implementation.

In the application of modern AI, the number one goal is to solve problems. Reproducing human characteristics is only one ingredient in a complex concoction of an effective AI, and many human characteristics are even counterproductive. Yet we still see engineers building things like time delays in conversational AI responses to make it appear as though a bot is “thinking” and similar tactics to contort technology into passing the Turing test.

When aeronautical engineers designed the 747, they tested whether it could cross the Atlantic — they didn’t try to build a mechanical pigeon. Similarly, self-driving cars learn in a unique way and behave much differently than cars with a human behind the wheel. Why should AI have to hew to the human model?

With conversational AI’s growing prominence, it is critical to have a universal, realistic understanding of what we consider to be a success and what we deem fails to meet today’s standards. AI will make a different set of mistakes than humans do and will also learn from these mistakes differently. This means we need to measure success for machines differently than we do for humans.


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New success metrics for AI

So how do we update the Turing test for practical applications of conversational AI? We need to get away from how “advanced” it feels and focus on the primary goal: efficiency. We should regard AI as providing a significantly better alternative to how we solve problems today. As we move forward, we also need to widen the scope to encompass all intelligent behavior that could be useful to the end-user. Here are several KPIs researchers could use to more accurately measure the success of AI.

How AI applies context

AIs should not work in a vacuum, but be situationally aware. Conversational AIs have increasing access to various contextual triggers that should tailor the experience, and they are in a unique position to leverage this data in ways that would not necessarily work with human agents. For instance, as a consumer, if a human customer service rep knew exactly where I was, I might feel creeped out. With an AI, I might think it’s cool, especially if it’s giving me something with immediate relevance.

How AI learns over time

An AI should learn from every interaction. For example, a researcher might consider if a bot provided the right information based on a person’s response and tone. They also might want to look more closely at the user questions that the machine is unable to answer. The sign of a good AI is not top performance on day one, but an upward-trending curve.

How comprehensive and connected an AI is

Most “great” conversational AIs so far are really good at one single thing, which is not practical for the long term. AIs need to connect with various systems to span the entire customer journey, enabling the person to complete everything in a single place. A retailer AI, for instance, needs to personalize product recommendations, manage a CRM, conduct orders, provide status updates, and manage customer support.


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How well an AI holds memory

A person should never have to reintroduce themselves. Conversational AIs need to have short-term and long-term memory, keeping and acting on what an individual has liked in the past. When you call customer service, send an email, or walk into a store today, you’re a stranger; your preferences, past purchases, and social comments on a brand all are unknown. A compelling AI will act on the data seeds a person has sprinkled over the entire conversation.

How AI predicts needs

AIs need to tap into predictive algorithms that can anticipate what a consumer might need based on their historical context and current situation. The AI should analyze aggregate data to identify the best course of action from what has resulted in the most positive sentiment in similar circumstances.

How flexible AI is

AIs need to be where the consumer is. Good AIs can’t be available only on chat, or websites, or voice calls. What will distinguish successful AIs from the rest will be cross-platform performance and the ability to hold the same knowledge base at every touchpoint.

AI is not human. And humans are not AI. There are always going to be things that a human will do better — having empathy and solving complex first-time issues are a couple of good examples. Only when AIs exhibit the ability to solve problems more quickly and intelligently than humans can we start flying over oceans, sidestepping the blueprint of the mechanical pigeon.

Learn more about AI for customer service here.

For more information on conversational AI, discover how to provide brilliant AI-powered salesforce chatbot solutions to every customer, every time.