Rise of Machine Customers: What CSOs Need to Prepare
Updated: May 8
Machine customers are ready to transform B2B sales as the number of smart devices rises. Chief Sales Officers (CSOs) must comprehend machine customers and include them in their sales strategy in order to be ready.
Sales managers need to develop a strategy to maintain their competitiveness and comprehend how machine customers affect their business. According to Gartner simulations, machine customers will be directly involved in or have influence over $30 trillion worth of purchases by the year 2030. And CEOs anticipate that machine clients will account for 25% of their business's sales.
What is Machine Customers?
Machine customers are nonhuman economic agents that engage in the exchange of goods or services for cash. Machine customers come in two different forms virtual and physical.
Physical machine customers are intelligent physical objects that can communicate with one another across a local or public network or the internet. Both internal information (machine performance, demands, etc.) and external information (motion detection, temperature, etc.) are collected, interpreted, and communicated by these devices. We see examples of these technologies all around us, including Google Nest, HP Instant Ink printers, Tesla vehicles, and more. According to Statista prediction, there will be 19 billion smart devices on the planet by 2025, up from 13 billion in 2022.
Virtual machines customers provide robust interfaces for doing transactions with human consumers. These are purchasing algorithms and virtual customer assistants (VCAs). Examples include the virtual assistants found in virtual worlds such as Alibaba's Tmall Genie, Amazon's Alexa, Apple's Siri, and digital nonplayable characters (NPCs). The strongest basis for machine customers is provided by the increasing deployment of these VCAs and their capacity to continuously learn from millions of interactions.
Three Phases of Machine Customer Evolution
There are three phases of the machine customer evolution. The first stage in a three-phase progression is the bound customer. Today, limited tasks can be automatically carried out as a "co-customer" on the owner's behalf by services like HP Instant Ink, Amazon Dash Replenishment, and Tesla vehicles. Rules are made by people, and machines carry them out in a predetermined ecosystem.
In the second, developing phase, humans continue to set the rules for machines as "adaptable customers," despite the fact that AI technology may choose and act on behalf of a person with little to no human participation for some activities. The Staples Easy System, robotrading, and financial "roboadvisors" like Betterment, Free2Spend, and Wealthfront are other examples. Toyota, Tesla, and Google's autonomous vehicle systems all fit in here.
These new economic participants are "autonomous customers" in the final stage. They control the majority of the transaction's process phases and are intelligent enough to operate independently on behalf of humans with great judgment. Although it is not a sentient machine, this one will still have needs of its own, including those for upkeep and software updates, which it will take care of on its own. An example of a client using an autonomous machine is Aidyia, an AI-enabled automated hedge fund that can run completely independently from human interaction, according to company developers. Aidyia reads the news, examines a lot of economic data, finds cryptic patterns, forecasts market movements, and makes investments based on her findings.
How Machine Customers Differ from Human Customer
The commonality among the machine clients from each step is that they will differ from humans in three different ways while making decisions. These variations have important implications for business and operations:
To a certain extent, they are transparent. Machines are founded on logic and rules. To resolve a problem is what drives them. Their actions, regulations, and inquiries will all reflect the assumptions they have. People frequently conceal their objectives when making purchases. There is no such thing as a "poker face" for machines. They will concentrate on finding a solution, but it may not be obvious how they achieve it, particularly when intricate algorithms are involved. The opaqueness of the machine's decision-making process in these situations has drawn the attention of regulators, who are implementing accountability rules.
They have the capacity to process a lot of data before making a choice. As a result, they will carefully gather and weigh the information to make an informed decision free from the effect of emotion.
They don't have to be happy. Machines concentrate on finishing things quickly. A machine can't be courted for loyalty, and you don't have to. If the sales and fulfillment process runs smoothly and simply complies with the service-level agreement standards, it is more likely that the customer will choose that supplier.
Why Do Sales Leaders Need to Pay Attention?
There are currently 7.98 billion individuals on the earth and 13 billion machines that have the ability to serve as customers. Effectively marketing to machine customers will not be a "tech thing" or a "marketing thing." Everyone will be affected by it. All C-suite executives must participate in adaptation; otherwise, it won't be complete, effective, or conclusive. More so than the introduction of digital commerce, the emergence of machine clients will be a competitive advantage for expansion. In the future, machines will be a company's best clients, not specific people or businesses.
How Can Sales Executives Get Ready?
When their consumers are machines, sales leaders will need to think about how their organization's methods for selling must change. Understanding the underlying principles and reasoning that behind a machine's purchase behavior will be crucial. Machine customers will probably require a lot more information to make decisions than their human counterparts do. Sales managers must get ready to have the data accessible and usable. In terms of decision-making, robotic customers will differ from human customers. Selling to a machine requires logic, knowledge, and rapid processing, whereas selling to a human requires persuasion, empathy, and emotion. Selling will be data science oriented and automated in a future with machine customers.
It will be an incredible thing to have a computer as a customer in situations where decision-making is primarily logical or transactional. Machine customers may be more dependable and effective than human ones in the future. Selling to a machine customer will be challenging, though, if emotion is a big factor in the choice to buy or the project is complicated (such as specialized IT projects). It might not occur at all.
When nonhuman actors drive sales, providing high-quality intelligence to drive interactions with machine customers will be essential to success. Modern advanced analytics and AI that tailor marketing offers, goods, services, and content for individuals will need to be machine-friendly. Improved B2B sales forecasting, which is a tool for predicting and qualifying leads, and process automation, for instance, would depend on strict data management that produces high-quality data. To support machine-driven sales, data and analytics leaders will also need to strengthen their capacities.
In order to get ready for the emergence of machine consumers, CSOs should:
1. Investigate market opportunities by formulating and pondering one to three queries, such as:
What kinds of smart items might emerge in the circumstances or pursuits in which clients already employ your goods and services?
Who may possibly designate and/or oversee such machine customers?
How might serving machine customers alter your sales strategy?
2. Strengthen the relationship between the sales team and IT, product management, analytics, and finance in order to coordinate and take advantage of this new opportunity.
3. Make investments in your e-commerce platform to make sure that every data offered to a machine customer is simple to access, up to date, and pertinent to its position in the purchasing process.
As the rise of machine customers transforms the B2B sales landscape, it's essential for sales executives to prepare by developing a strategy and investing in technology and collaboration across departments. Don't wait until it's too late - take action now to understand the opportunities and challenges presented by machine customers and stay ahead of the competition. Start by investigating market opportunities, strengthening relationships with other departments, and investing in e-commerce platforms that provide relevant and up-to-date information to machine customers. The future is now - prepare for the machine customer era today.
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