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How Machine Learning Can Help Build a Successful Response Library

When responding to a ticket, customer service agents with a typical response library will most likely have a few options for reply. For example, they may start typing a response from scratch. They might have a template of their own that they revisit and adjust. Or, they could search the company’s response library to see if an appropriate response already exists, and customize it as needed. How Machine Learning Can Help Build a Successful Response Library

All three of these actions are possible, and they all take a few minutes of the agent’s time. However, the third one—looking up and customizing an approved template in the company’s response library—is the most efficient. After all, when responding to common issues or recurring questions, there’s no reason to start from scratch every time.

The importance of a customer service response library

There are many reasons why it’s important to have a library of response templates (or macros) on hand:

  • Using an approved macro ensures consistency with brand voice and tone
  • Approved responses can be customized more quickly than starting from scratch
  • A response library offers an organized resource for training new agents

When agents know that there may already be a template for the response they want to send, they can look up that response template, open it, and customize it themselves—spending far less time than they would if they had to write it from a blank page. However, this all relies on the company having a resource library in the first place, which can prove to be a big challenge for any customer service department.

The challenges of building a response library

Creating a robust library of templated response is a daunting prospect. It requires customer support leaders and content curators to not only determine which issues are appropriate for the library, but also manually create broad templates for each situation.

For this reason, it may seem easier for agents to create responses as they go. In fact, some agents may already be creating their own templates for common questions or complaints when they notice a gap in the company’s library. And because your agents are smart, competent employees, they may have started to develop their own personal content repositories.

This is problematic for two key reasons:

  1. Customer support leaders cannot maintain control over the tone or language in these responses. If the responses need improvement or better brand alignment, managers may not always be able to see this and coach the agents appropriately.
  2. If the responses are excellent, the rest of the customer support team will be missing out on a company-wide resource.

Reasons to let machine learning help develop your library

When you are able to look at the previous dialogues between customers and agents, you can better understand the consistencies that exist, and thus, where a template might be helpful.

Machine learning (ML) applications can help automate and improve these interactions by intelligently selecting from past communications, discovering when an issue merits a templated reply, and then giving customer service managers the chance to customize it, polish it, and add it to the system as an official response. This drives consistency across the organization, with the option to fully automate certain responses, with time. It also empowers agents as they have the ability to contribute to a library of responses.

The risks of complete automation

Of course, there’s valid concern over letting ML take over all customer interactions. Just look at what happened to Microsoft when they let “Tay” the robot loose on Twitter.

That’s why there always must be another step involved. Smart applications search, find, and present templates that—according to historical data—might solve the customer’s problem. Then, agents have the chance to verify the content and adjust it with their own voice or brand-approved adjustments to customize the response.

This marks the essential shift from full automation to augmentation—using ML to expedite the process, but giving humans the final decisions. It also frees up agents’ time for more complex, high-level interactions, while preserving their authority over the final communications.

The benefits of content management

As a customer support leader, your goal is to create and curate content for your agents to discover, customize, and communicate to customers. Machine learning can help you do this more effectively by:

  1. Harvesting the best responses from all your agents and making them available company-wide
  2. Ensuring consistent communications for a better customer experience
  3. Allowing you to control the tone of the responses and capture the company’s voice
  4. Supporting your agents by providing an approved content resource
  5. Offering new agents a way to learn the types of tickets that come in, select from the approved responses available, and customize them as needed

This type of ML software captures the best content from your staff, allows for proper modifications, and then releases it as a resource for the entire team. While this ensures a level of quality control, it also leverages your agents’ good work to enrich your company’s content library over time.

How Wise can help

When you’re looking to optimize your customer service response library, we can help analyze and understand your data to improve efficiency and increase customer satisfaction—without investing in expensive technological tools or human resources. Learn more about how Wise can help you implement automation tools, including building a successful response library.

The Essential Guide to Automating Customer Services, Wise.io

Topics: Machine Learning, Customer Success, Customer Support