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Machine customers explained: Everything you need to know

In this fast-paced digital age, the concept of a machine customer has emerged, changing how businesses interact with their online platforms and services.

A machine customer uses AI and machine learning to interact independently with online services or platforms as if it were a human customer. A machine customer freely engages with businesses or services, and responds to human and machine queries and commands through a chatbot, virtual assistant or any other automated process.

A significant area where machine customers can come into play is as a purchase customer, automating a task that would otherwise require human intervention. For example, today, if a printer is running low on toner, a warning light comes on or a message pops up on the user's computer so that they can place an order with Staples or Amazon. In a machine customer role, the printer places the order and then alerts the person of its action.

This has several benefits such as immediacy, lack of distraction and traceability. The entire process is logged and accounted for. But there is still room for error, which is why Gartner predicts it will take years for machine customers to catch on.

The IT research and advisory firm sees a three-step process of machine customer adoption. In the first stage, machine customers are programs driven and manipulated by humans; preset rules determine each purchase. By 2026, we'll see the rise of the adaptable customer, where humans and machines work together, operating autonomously based on rules. Finally, by 2036, machines will be fully autonomous shoppers. They will still rely on rules, preferences and context for their decision-making, but the AI will be much more involved than it is now.

How do machine customers work?

Machine customers employ several AI-related technologies, including machine learning, natural language processing (NLP) and sometimes even deep learning. Though the process varies between industries and operations, here's an overview of how machine customers work:

  • Input processing. In this first step, a user interacts with a machine customer, either typing a message or speaking a command.
  • Language understanding and recognition. The machine customer uses NLP techniques to understand the user's input and intent.
  • Response generation. After processing the input and retrieving necessary information, the machine customer produces a contextual response based on the input and its estimated intent.
  • Natural language generation. In a generative AI scenario, the machine customer can use natural language generation techniques to create humanlike responses.
  • Output delivery. Finally, the machine customer delivers its response to the user or target machine via text-based chat, voice or another medium.

Throughout this process, machine learning algorithms continuously monitor the interaction to improve the performance and accuracy of machine customers. They learn from interactions with users and adjust the models accordingly.

Evolution of machine customers

Machine customers are a relatively new phenomenon, and their evolution parallels advancements in AI, NLP and user experience design.

Early machine customers were rule-based systems that followed rigid, predefined decision trees or scripts. Input from users, such as keywords, required exact matches, limiting their utility.

Basic chatbots were among the first to use rule-based systems and suffered from an inability to recognize queries unless they specifically matched the keywords in the rules. The emergence of NLP and intent recognition in recent years coincided with chatbots' improved understanding of complex and even imprecise inquiries.

Machine customers then began to incorporate user data including past interactions, preferences and user history to provide more relevant and tailored responses. Next, they expanded beyond text-based interactions to support voice, touch and gesture. This allowed machine customers to interact with smartphones, smart speakers and chat interfaces.

AI advancements continue to expand the capabilities of machine customers, which currently generate more humanlike responses, understand contextual nuances and engage in more meaningful, natural conversations with users.

Who uses machine customers and why?

Many organizations and individuals use machine customers to aid their human customers and improve customer experiences. Here's a breakdown of who uses them and why:

  • Businesses. The most obvious consumers of machine customers, businesses use them to automate repetitive tasks such as inventory management, operating continuously and reducing errors by using predefined rules. Businesses also turn to machine customers for data-driven decisions, providing a more objective perspective, and for suggesting products to customers based on previous purchases.
  • Customer service centers. They use machine customers to manage human customer inquiries and support requests, as well as handle account management, troubleshooting, and providing information about products or services.
  • Financial institutions. Banks and other financial institutions use machine customers to assist human customers in managing their finances, as well as to provide services such as account inquiries, bill payments, funds transfers and financial advice.
  • Healthcare providers. Machine customers improve patient engagement, assisting patients with appointment scheduling, medication reminders and accessing medical information to reduce the administrative load.
  • Educational institutions. Machine customers answer student inquiries, aid in course enrollment and complete other administrative tasks.
  • E-commerce. Online retailers employ machine customers to help humans find products, provide personalized recommendations, answer product and order questions, promote events and ease the checkout process.
  • Hospitality and travel. In these sectors, machine customers handle hotel bookings, flight reservations, itinerary planning and customer inquiries, as well as provide travel recommendations, local information and assistance during the booking process.
  • Government and public services. While they might not have a product to sell, government agencies and public service organizations deploy machine customers to improve citizen engagement, provide information about government services, answer common questions, and assist with applications and form submissions.

The effects of machine customers on businesses

Machine customers -- chatbots and virtual assistants among them -- significantly improve human engagement and satisfaction across a variety of industries, leading to long-term organizational benefits too. Here are some of the key effects:

  • Improved customer service. Machine customers, always at work, provide faster response times and continuous support availability, which improves customer satisfaction and fosters customer retention.
  • Cost savings. By automating repetitive tasks and reducing human intervention, machine customers lower operational costs, particularly in customer service and support functions.
  • Improved efficiency. Machine customers never tire and never stop. They process multiple inquiries simultaneously and provide relevant information or assistance immediately, leading to more efficient workflows.
  • Scalability. Born in the cloud and exercising its elastic nature, machine customers use their scalability to ramp up and manage increasing volumes of customer interactions as needed, then subside as demand decreases.
  • Data collection and analysis. As part of their basic operations, machine customers collect valuable data on human customers' interests, preferences and behavior. Businesses then analyze this data to gain insights into customer needs, on both an individual and collective basis, and to make better-informed strategic decisions.
  • Personalization. Building on the data collection above, machine customers then deliver a more unique experience for each human customer.
  • Sales and marketing support. Machine customers provide lead generation, product recommendations and sales support, including promotions, discounts and upcoming events.
  • Improved productivity. By offloading routine and mundane tasks to machine customers, employees can focus on the more complex, human-related aspects of their roles, such as strategic planning.

Andy Patrizio is a technology journalist with almost 30 years' experience covering Silicon Valley who has worked for a variety of publications -- on staff or as a freelancer -- including Network World, InfoWorld, Business Insider, Ars Technica and InformationWeek. He is currently based in Southern California.

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