3 actions to prepare for machine customers in service organizations

There are more machines with the potential to act as customers than humans on the planet. Here’s how to handle them.

Many customer service organizations believe “machine customers” are but a distant trend, yet they’re already here and impacting our daily lives. Maybe you’ve recently asked Siri, Alexa or some other smart product to call customer service or wait on hold for you. The number of tasks assistants can perform is increasing all the time, as more industries see the potential to deliver low-effort CX in this way. 

Today, there are more machines with the potential to act as customers than humans on the planet. By 2025, nearly 40% of customers will try using a digital assistant to interact with customer service on their behalf, according to a Gartner prediction. What’s more, CEOs and CIOs believe one-fifth of their total revenue could come from machine customers by 2030.

Machine customers are proliferating. It’s time for customer service teams and leaders to prepare for the impact they will have on their function. After all, if machine customers haven’t already arrived in your industry, they will sooner rather than later.

Defining what a machine customer is (and is not) 

Products are increasingly connected and producing data that can be analyzed to understand performance, which allows for automated process fulfillment and intuitive self-healing.

Advances in conversational AI and Internet of Things (IoT) will enable these products and virtual personal assistants to become machine customers — requesting and performing service on behalf of their owners for lower customer effort.

A common misconception is that chatbots within the service function, which provide issue triaging or resolution, are machine customers. But actually, a machine customer doesn’t live inside the organization at all. 

Rather, a machine customer is something in your customers’ homes, cars, businesses or on their phones. They are products or bots that request and perform service on behalf of their owner for lower customer effort, such as:

  • Identifying issues.
  • Reporting them to customer service.
  • Obtaining resolution.

In the most effortless experiences, the entire service journey takes place without the customer having to say or do anything at all. 

Machine customers process large quantities of data much faster and more consistently than human equivalents, executing transactions when required, especially for repeatable tasks.

As one example, many U.S. and U.K. homes now have smart electricity, gas or water meters installed, which serve as machine customers. They make recurring transactions on behalf of users (e.g., automatically reporting meter readings to generate a bill, rather than the homeowner having to manually read and report the meter monthly). 

As another example, a major electric vehicle company continuously gathers product information in its vehicles in order to self-diagnose the need for repair. Once an issue is identified, it can submit a service request which automatically pre-orders spare parts to the nearest repair center. The only activity the customer undertakes is to pick which date they want to drop off their vehicle for the replacement part to be fitted. 

Implications to the customer service function

First and foremost, machine customers will reset customer expectations about what constitutes a low-effort experience, creating a greater competitive gap with other customer service organizations. Those that embrace machine customers will be able to differentiate their value and close the gap by meeting this new standard for effortless service. 

Machines acting on behalf of their customers will not respond to sentiment and empathy. Instead, they will deliver a different customer experience and service response that focuses solely on objective outcomes (e.g., availability of data, meeting SLAs).

But while automation and reduced human involvement can lower operating costs and offer lower-effort experiences for customers, it also risks a less personal customer relationship, especially if automation is designed more for operational benefit than for better customer experience.

This is likely to challenge service functions that are increasingly being tasked with growing the business and ensuring customer loyalty — activities that benefit from human interaction.

Recommendations for customer service and support leaders

Many customers who have already used and benefited from a machine customer likely can not imagine switching to a different product that does not offer this low-effort customer service.

From a customer service organizational perspective, all it takes is a competitor to innovate and offer machine customer functionality to be left behind as the customers you serve opt for the competitor’s lower-effort service. 

Certain industries have more machine customers in action already, namely e-commerce, retail and utilities, necessitating an urgent response if customer service leaders in these industries have yet to explore the potential of machine customers. 

Customer service leaders in verticals such as banking, insurance and telecommunications are up next in thinking about enabling machine customers. Those in arts, entertainment and education have time but should look to next steps.

Regardless of your industry, here are three things customer service leaders must do now in anticipation of machine customers.

1. Prepare for an influx of machine customers

Identify service requests that could be initiated by bots or internet-connected products and design workflows to accommodate these requests.

Answer the question: Which steps in the customer journey are lower value and rote?

2. Identify who is responsible for developing machine customers

Collaborate with relevant customer experience teams in your organization to develop machine-centric personas and journey maps. Understand how machine customers’ experience expectations differ from those of human customers.

3. Plan for machine customers while maintaining relationships with their human counterparts

Determine at which point in the interaction it is appropriate to loop in the human customer by:

  • Cataloging machine inquiry use cases.
  • Creating automatic responses that would satisfy the machine while building a feedback loop to the human.

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About The Author

Christopher Sladdin

Christopher Sladdin is a Senior Principal within Gartner’s Customer Service & Support practice. In his role, Mr. Sladdin works with Heads of Customer Service to develop strategies and execute on key initiatives consistent with the practices of leading Customer Service organizations. His areas of expertise include recruitment and retention strategies for frontline talent and measuring and understanding the customer service experience. He also helps leaders to identify and prioritize CX improvement opportunities.