Although organizations have struggled for years to get a deep understanding of their customers, with AI, organizations can tap into their data to generate digital twins of their customers. These can then be used to research customer preferences and what is needed to optimize and personalize customer journeys.
"Digital twins represent an interactive training ground for organizations and AI to fine-tune the customer experience," explained Carmit DiAndrea, director, engineering, AI data management CX for NICE.
Digital twins enable organizations to understand their customers on a much deeper level—think having a carbon copy of a customer to interact with just as you would the actual customer.
When AI is trained using a digital twin, it can test different types of interactions and responses to figure out the most optimal way to interact with an individual customer.
Whereas learning about customers through methods like focus groups or surveys takes much longer and are reliant on human schedules, digital twins let AI train on customer data independent of people’s schedules.
"This allows organizations to learn about their customers faster and more in-depth," DiAndrea said.
Digital Twins Can Be Powerful Tools
Cresta CEO Ping Wu explained in contact center environments, digital twins can be a powerful tool for business leaders to derive insights from their customers and better anticipate their needs.
For example, digital twin technology could reveal customer frustrations with the identity and verification process – thus highlighting the need for the business to streamline the process, before it becomes a pain point for agents.
"In addition to creating virtual replicas of customers, digital twin technology can be used to develop replicas of agents and supervisors," he said.
For example, organizations can leverage aggregate data to build digital twins of human agents – amplifying that info into their virtual agent offerings.
Additionally, business leaders can use existing knowledge bases, training materials, and top agent behaviors to build replicas of contact center supervisors.
"These digital twins can be used to scale role-play training with new and existing agents – helping agents onboard more quickly," Wu said.
DiAndrea said as AI gets smarter, it will be even easier for organizations create and manage digital twin technology.
"AI’s role with digital twins is two-fold," she said. "It can be used to create the digital twin, tapping into an organization’s data to generate an accurate customer replica."
AI can also train itself to be smarter using the digital twin, but the key to this is using the right, purpose-built AI for CX, as AI only trained on CX data can generate the most accurate digital twin.
Key Stakeholders, Best Practices
Wu said when identifying and implementing new CX technologies such as digital twins, CTOs, CIOs, and CXOs are often the primary decision makers within an organization.
"These leaders will review whether the technology meets the overall business objectives of the company, if it can enhance customer engagement, and if the organization is ready for broad implementation," he said.
He added business leaders also need to consider the cost benefit.
"In the case of digital twins the business will need to focus on developing integrations and analyzing large amounts of data, which can be quite costly," Wu said.
Organizations that have implemented AI purpose-built for CX have already started their journey to digital twins. DiAndrea said using purpose-built AI for CX to generate actionable insights into CX data is the first step.
"Additionally, organizations need to use a single, interaction-centric cloud platform," she said, noting the creation of digital twins requires massive amounts of data.
That’s why it’s important that organizations already have robust CX data management in place through a cloud platform.
From Wu's perspective, the primary challenge in developing and maintaining digital twin technology lies within an organization’s data.
"Businesses cannot create effective and accurate digital twins without having access to large amounts of data," he said.
He added business leaders will also need to determine whether digital twin technology will easily integrate into existing systems.
"If the selected technology doesn’t seamlessly integrate with existing systems like Salesforce, the business will have to develop custom integrations, which can be time-intensive and costly," he cautioned.
Avoiding Pitfalls and Minimizing the Risks of Digital Twins
With the large amount of data required to create and maintain digital twins, it is paramount that organizations have robust security and compliance measures in place to ensure that data is secure.
Ensuring data security and compliance is why implementing digital twin technology takes a cross-departmental approach--including IT and information security--to oversee and monitor the development of the technology.
Wu cautioned digital twins are only as good as the data used to train them: While they can be a useful tool in predicting customer behavior, digital twins are still limited by the data available and can be inaccurate in how they react.
"Digital twins also don’t account for unexpected human behaviors and emotions," he said. "These virtual replicas can’t always accurately simulate how an escalated customer would behave."
DiAndrea said it’s important to build employee trust of digital twin technology, as this is a new concept that could take some getting used to by those within an organization.
"Organizations should have a plan in place to educate employees throughout the rollout and make them feel comfortable with using them," she said.
She added digital twin development can cost organizations a lot of time and money, which can be alleviated if an organization already has a solid foundation of an interaction-centric cloud platform and AI purpose-built for CX.
"This can greatly reduce costs and the time to value," DiAndrea said.