Prepare for Machine Customers’ Impact to Customer Service and Support

The growth of machine customers is projected to be more significant to enterprises than the arrival of digital commerce. In the next few years, customer service and support leaders must prepare for and adapt to this shift in their landscape.

Gartner predicts that by 2030, one billion requests for customer service will be raised automatically by company-owned bots. Even sooner than that, by 2025, nearly 40 percent of customers will try using a virtual assistant to interact with customer service.

So what are machine customers, and why are they so important to customer service and support leaders?

A machine customer is a nonhuman economic actor that obtains goods or services in exchange for payment. Authorized machines will function like a form of digital agent that acts on behalf of the customer—this could be a virtual assistant, like Siri or Alexa, or a physical object connected to the internet, such as a car or even a washing machine.

Customers can now have their own virtual concierge that will take care of many of their more repeatable and low-complexity tasks, reducing customer effort to virtually zero.

Given the rapid emergence of the Internet of Things (IoT) and artificial intelligence (AI), smart machines are able to perform a growing number of tasks on behalf of human customers, from requesting service, receiving messages, making recurring transactions, to reporting issues and searching for information.

Gartner’s "Machines as Customers Survey" found that 61 percent of senior business executives believe about one-fifth of their revenue will come from machine customers within the decade.

Customer service and support leaders must understand the impact of machine customers to their business, and establish a strategy to enable them in order to stay competitive. After all, human customers will have more and more opportunities to leverage machines to interact with customer service and support on their behalf.

Three Steps to Stay Ahead of the Machine Customers Curve

Forward-thinking service organizations have already begun to enable machine customers to significantly reduce human customer effort and improve CX.

As adoption of machine customers grows, organizations that continue to focus on only the human customer will find that they are being left behind. Here are some vital steps for customer service & support leaders to take in order to stay ahead of the curve:

1. Identify best-fit use cases for bot-to-bot interactions.

Bot-to-bot (machine customer-to-enterprise chatbot) task execution can be extremely useful when dealing with customer intents that are low in complexity, but high in frequency. Leaders should review their customer journeys to identify use cases that can be executed by machine customers.

Then, leverage the following criteria to vet the use cases for enabling bot-to-bot interaction:

• Appropriately sequenced: Ensure the task being considered does not fall immediately after a task that must be performed by a human customer on a live channel.
• Easy to program: Look for transactions that are not too complex requiring extensive programming.
• Static: Tasks where the processes, products or policies related to the task are unlikely to change frequently.
• Driving live volume: Tasks that are driving live volume up and where automation will provide significant ROI.
• Approved by legal: Tasks should be reviewed and approved by the legal, risk and compliance groups.

2. Invest in scalable conversational AI platform (CAIP).

Highly functional machine customers already exist and will continue to evolve in the customer service and support function. For example, virtual assistants can make calls on behalf of customers in a natural-sounding human voice.

Chatbots leverage conversational AI technology and can have natural language (text and speech) understanding and generation capabilities to serve as the front line customer engagement channel. Chatbots can be deployed for simple to highly complex use cases. Organizations are implementing and using chatbots to expand their internal and external conversational AI capabilities.

To create a strong foundation for organizations to meet the demands of current and future customer service, invest in a scalable chatbot platform to enable bot-to-bot transactions (see Figure 1).

Figure 1. Humans and Machines Interact With Enterprise Chatbots to Transact

(Source: Gartner, Inc.)

3. Measure for continuous improvement.

To get the most from their chatbots, customer service and support leaders should evaluate their organization’s chatbot applications for fit with bot-to-bot interactions, and continually evolve chatbot applications by monitoring bot-to-bot interactions. Compare the historical performance of bot-to-bot transactions over time to gauge success by:

• Revising baseline metrics based on the first 30 days of bot-to-bot performance.
• Establishing a cadence to analyze the trends in metrics. For example, review at what step the bot-to-bot conversation is being abandoned or getting stuck and whether it actually needed a live agent or was a failure of comprehension on the part of the chatbot.
• Working with all stakeholders to review the findings and get consensus on prioritization of items for improvements.
• Implementing the changes and continuing to monitor.

Machine customers reduce the effort and friction experienced by human customers. Starting now, customer service & support leaders must prepare for a growing number of interactions with machine customers. Organizations that proactively invest in identifying the business opportunities with machine customers to reduce customer effort will lead the market in growth, efficiency and productivity.

Uma Challa is a senior director analyst within Gartner’s Customer Service and Support Practice, covering digital customer service, CX and customer service and support strategy/leadership. Learn more about machine customers in the Gartner book, When Machines Become Customers.