5 Strategies for Launching Conversational AI

A picture of a robot sat at a desk talking to an agent

Filed under - Industry Insights,

Puneet Mehta of Freshworks shares five strategies you need to know when launching conversational artificial intelligence (AI) for  customer experience (CX) improvement.

Customer service is arguably more important than ever before. Support has emerged as a critical differentiator that now directly impacts loyalty, spending, and growing your business.

Agents Need Support to Serve the Modern Customer

The role of the human agent has become much more complex. Agents often have to access multiple systems to resolve a single ticket. As volume increases, agents need to resolve multiple tickets simultaneously.

This comes at a time when customers are already demanding more than ever before. According to our State of Customer Service 2020 report, we found that over 60% of consumers say speed is critical and nearly half expect convenience.

To support agents, companies have started bringing AI into their workforce. AI-powered virtual agents and chatbots offload work and some responsibility from human agents.

AI agents act as the first line of defence, automatically resolving repeatable tickets or drafting responses for agents to help agents work faster.

The relief AI can provide agents and benefit to customers is further multiplied in times of crisis, like the current coronavirus outbreak, where agents, now working from home, are on the front lines fielding a surge in customer service tickets.

With an influx of tickets seemingly overnight, AI is helping customer service teams scale up in seconds, resolving tickets across channels without human intervention.

This enables human agents to focus on critical and high-touch issues. AI also identifies new and trending issues, helping the company get out in front of issues and prepare a business response or update a policy.

Customer Service AI: 5 Strategies for Successful Automation

The best way to identify if your AI-driven approach is working and keeping customers happy is to look at how your metrics are improving.

To ensure higher Customer Satisfaction (CSAT) and Net Promoter Scores (NPS) as well as improved agent satisfaction, there are five key considerations that will ensure high customer satisfaction and a successful AI program.

1. Automate the Right Customer Needs

First, understand what your customers most require. Always prioritize the user experience over delegating more use cases to AI. It’s about the quality of the CX, over the quantity of use cases an AI is tasked to manage.

Not every customer query should be automated, especially those that are critical or high-risk. Instead, leverage AI to automatically respond to queries that are high-volume, have low-to-medium business risk and have low-to-medium exception management.

Examples include order status and refund policies for a retailer, order modifications and cancellation requests for a subscription company, and baggage policies and upgrade requests for an airline.

2. Integrate With Core Business Systems

Leverage virtual agents to offer a full resolution within the interaction and avoid simply linking out to help articles.

When you integrate with various back-end systems, like CRM platforms or order management systems, your virtual agent will be able to provide highly personalized and meaningful interactions.

For instance, this would allow a meal kit delivery company to allow a person to skip next week’s order, instead of telling a person to log in to their account and follow the next steps.

3. Carefully Delegate Work Between Humans and Machines

AI should augment work and automate specific tasks, not replace humans agents.

There are many things that will never – and should never – be automated: showing empathy, creativity and complex problem-solving.

Situations that require any of these should always remain with a human to ensure the customer experience is never compromised and is authentic.

4. Prioritize Day One AI Training and Incorporate Regular Optimization

Take advantage of your historical data to launch a more accurate AI on Day 1.

To minimize frustration, you’ll want to use past tickets to expand the speech training of each use case.

For example, a retailer might determine that customers ask about order status hundreds of ways:

  • Where’s my package?
  • Is my delivery coming tonight?
  • Where’s my sweater?

You’re not going to be able to anticipate every way a person might ask a question, but the more data that you use during training, the better probability AI will correctly classify a person’s intent.

Additionally, AI gets better with each interaction and training is a continuous process.

The longer AI is interacting with customers, or even working behind-the-scenes drafting replies for agents, the smarter it’s getting.

Monitor the success of your AI so tweaks to the experience can be made and further training conducted.

5. Measure AI Correctly

Many companies measure AI like other technical systems, but this isn’t the best approach.

Conversational AI within a support organization is performing human work, and so it should be measured like a human.

Look at how AI is impacting CSAT and resolution time, how quickly it’s learning and improving performance over time, as well as how it impacts agent productivity.

Customer service is the new marketing. It’s the driver behind long-term loyalty and relationships.

To turn customer service into a business driver, follow these five strategies to ensure high CSAT and agent productivity.

This blog post has been re-published by kind permission of Freshworks – View the original post

For more information about Freshworks, visit www.freshworks.com

Published On: 14th May 2020 - Last modified: 19th May 2020
Read more about - Industry Insights,

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