Are KPIs Keeping Your Customer Support Team Down?

Written by Emily Cummins  on   Mar 20, 2019

How AI-powered automation can deliver on a customer support organization’s most critical KPIs

It’s not easy being held accountable to customer support KPIs these days.

Agents are pulled in a million different directions; forced to jump between various systems to answer a single question while interacting with often frustrated customers. They’re having to do so on an increasing number of channels as well: email, chat, social, mobile and so on. It’s no wonder that the turnover rate in customer support is among the highest of any profession. It doesn’t have to be this way.

Meeting modern customer expectations is also getting harder to do; they expect quick, convenient high-quality resolutions on their terms. We live in an on-demand, personal world. Leveraging AI to automate support is a means to get there.

There has been a lot of talk on how AI customer support can be used within a support organization for years, with many organizations wondering how much of it is hype…. What impact can it really have? Will it really make a difference in what’s important to your organization and customers?

When deployed correctly, AI can have an immediate and immense impact on customer service by automating responses to expensive, repeatable tickets (usually about 50% of all tickets) and uplifting agents to do their work more efficiently. The customer support KPIs that AI can improve include:

  • First Response Time (FRT): Customers do not like to wait. It’s pretty astounding how long it takes companies to respond; the average response time is 36 hours over email, while 75% of customers expect it within 5 minutes. Leveraging AI to automate customer service responses, whether it’s providing an instantaneous resolution or acknowledging the customer’s needs and collecting necessary information before a human gets involved lets the customer know that they are being heard. The automation effect is noticeable from day 1: our customer WestJet gets back to customers in less than 1 second.
  • Average handle time (AHT): Getting back to your customers quickly is one thing, but how long it takes for you to actually resolve an issue is even more important. AI-powered automation helps in a few ways:
    • Automating resolutions to repeatable issues: Leverage AI to respond instantaneously to the high-volume, simple queries like order status and inventory checks.  Make sure your AI has the authority to resolve issues by connecting with core business systems. Set rules to minimize business risks like enabling the automation of refunds under a certain dollar amount or free upgrades based on loyalty status.
    • Empowering human agents to work faster: Use AI to gather data from any customer, such as account or order number, and pull information from other business systems like your CMS and OMS. The AI can package up all relevant data to pass along to an agent who can quickly review, make a decision and communicate with the customer.
  • Consistent Resolutions: Leveraging AI in customer support also promises to provide standardized resolutions. There is no human bias, subjectivity or bad days. A bot will review the facts and based on how it has been trained, act consistently every time. If there are any questions or uncertainties, a human agent will always be looped in.
  • Customer Satisfaction Score (CSAT): According to Forrester, 73% of Americans say that valuing their time is the most important thing a company can do to provide them with good customer service. In our own consumer research, we found that quality and consistency in a company’s service and experience is key. As discussed in the points above, automation delivers on all of these.
  • Employee Satisfaction Score (ESAT): Automation allows you to give your agents back their time:  the time spent doing mundane tasks is now spent on more fulfilling and high-impact work. Agents no longer have to dig deep to find specific information needed to make a decision from various systems. AI gathers it for them before they even get involved. Agents are empowered with the information to make a quality decision quickly.
  • Cost Savings: The cost benefits of automation fall into two primary buckets:
    • Deflection from human agents:  By automating repeatable tickets, you will see a significant deflection from your call centers of tickets that are never even created.
    • Less agent turnover: With happier and more fulfilled agents, turnover will decrease resulting in savings tied to hiring and training new employees.

For further reading on KPIs, please check out our post on 15 customer service KPI metrics you need to know.

We’ve worked directly with our partners to learn the pain points companies are facing to design an automated solution that is designed to create an immediate, measurable impact. Are you ready to have a customer support organization that delivers on what customers and agents need? Let’s chat

For more information on customer support, click here.

5 Winning AI Strategies for Customer Service

Written by Can Ozdoruk  on   Nov 17, 2018

Forrester’s Ian Jacobs recently joined Netomi’s Founder & CEO, Puneet Mehta, as a featured guest in a webinar where they unveiled how world-class companies can create real-world AI in customer service. No more buzz or hype. It’s all actionable strategies that can be used today to deploy AI that serves both your customers and employees.

Watch this video to learn Forrester’s perspective on how AI can be effective within customer service and recommendations on how you can start achieving efficiency today. Netomi’s Puneet Mehta revealed how AI allows you to create unique customer experiences in this video and Ian and Puneet together outline a Customer Service AI Roadmap here.

Puneet and Ian outlined 5 strategies for winning AI customer service:

AI Strategies, Part #1 – Automate the right support issues with AI [Watch Here]

Watch the video to learn how to:

  • Identify the right problems to delegate to AI
  • Automate issue resolution while delivering to your brand promise
  • Maintain human intelligence and use AI to empower agents

AI Strategies, Part #2 – Improve agent performance with AI [Watch Here]

Watch the video to learn:

  • How to use AI as a workforce multiplier, making work less stressful for agents
  • How to leverage AI to handle the prep work, so agents can focus on resolution
  • Different human+AI collaboration approaches and what works for your company
  • Ways to let AI triage and route issues to the right human agent for faster resolution

AI Strategies, Part #3 – Achieve high CSAT with AI [Watch Here]

Watch the video to learn how to:

  • Build an AI so you can avoid a “50 First Dates” moment with your valued customers
  • Design AI experiences that work for your customers—respectful, friendly, accurate
  • Leverage AI the right way: speedy resolution at times, and more empathetic, deeper connections for others

AI Strategy, Part #4 – Measure AI as if it were your employee [Watch Here]

Watch the video to learn how to:

  • The shift from technology KPIs to measuring the business impact of AI
  • Evaluate AI’s ability to resolve, learn, work with others, and be proactive
  • Go beyond surveys and take a holistic approach when analyzing CSAT

AI Strategy, Part #5 – The new approach for customer service [Watch Here]

Watch the video to learn about:

  • Real-life scenarios where the predictive and proactive resolution will soon be a reality
  • How AI can help you identify signals to anticipate customer needs
  • How you can reduce support tickets by resolving issues before customers reach out

This was a captivating discussion on the current state of AI in customer service. If you’re thinking about modernizing your customer service operation without disrupting current agent processes, don’t miss these highlights!

For more information on customer service, check out everything you need to know about the omnichannel experience for customers in 2022.

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.


Discover The 16 Best AI Chatbot Vendors With Reviews and Features.


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.

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.