To successfully monetize their infrastructure investments and make 5G-based capabilities a reality, telecom operators need to explore both enterprise and consumer markets. Digital twins have become a common practice for this and for monetizing 5G in the form of keeping customers and subscribers as happy as possible—the discipline of customer management (also known as customer value management and customer experience management).
But even the digital twin technology itself is starting to evolve, and now, out of necessity, a new generation of digital twins is emerging in the form of what we call active digital twins.
First—let’s look at how the digital twin technology became useful for customer management.
5G and the new age of customer management
According to Deloitte’s 2021 benchmark telecommunications industry outlook, telecommunications, media, and entertainment companies need to focus on what their customers want and when they want it. This focus on ensuring telco operator customers have the best experience possible and thus become high-value customers is broadly called customer management.
Now, collecting information about customers to understand their behavioral patterns is not something new; it’s been done for quite a few years now, either in the form of data warehouses or, more recently, big data platforms.
However, these approaches are starting to fail in the face of 5G-enabled, single-digit millisecond latency, where being able to process data rapidly and act on it even faster are key to managing customer experience in real time.
Enter the need for digital twins.
What are digital twins?
Digital twins are digital representations of processes, products, and services. An organization can use digital twins to collect and store data about the physical counterparts in an intelligible form so that it (the organization) can apply machine learning algorithms to determine what that data is telling them.
Why do telco-space enterprises need digital twins?
Because their customers are no longer just human subscribers—they are also now connected things, such as devices, that are using the network the same way a human would, but at a much more rapid pace heading towards automation.
Every subscriber—or thing—has a digital twin that represents their activities and preferences in the context of the service(s) they subscribe to. This helps the operators and enterprises provide the most valuable services and offers when they are most needed at a quality and service level required for success, however that success is measured.
However, the traditional method of storing all data and querying it when necessary is quickly becoming insufficient. Be it in industrial process automation or the individual subscriber universe, every action demands a corresponding reaction. This requires what we call active digital twins.
What are active digital twins?
Acting intelligently on events in real time requires applying machine learning insights to event streams to make the most impactful decision possible and consequently drive the most appropriate actions.
Unlike regular digital twins, active digital twins don’t just collect data for some possible future analytical use but instead treat streams of data as streams of events. By analyzing these events in the context of past events, an enterprise or operator can determine the most opportune moment to engage a customer or ensure the quality of service.
The advantages of active digital twins
Digital twins as they are today have many advantages:
- They create a staging area for data thinning.
- They provide a repository for physical assets’ state information.
- They provide a data set for machine learning algorithms to convert into information and then into insights.
By allowing digital twins to actively participate in the business processes, the insights and learnings that the traditional aspects of digital twins provide can then be put to use for decision and process automation.
This brings several additional benefits, including:
- The elimination of human errors in decisions.
- Faster actions and quicker decisions.
- Dynamic incorporation of evolving decision criteria from new feature extractions and feature thresholds.
This “activation” of the digital twins ultimately allows for better monetization while optimizing resource utilization, which results in better top-line revenues and better bottom-line income.