If your company is not yet using a digital twin of customer profiles, your sales could be affected and your team could be missing out on substantial opportunities.
Over the past few years, digital twins of a product (DToP) have popped up across industries to simulate business processes and create virtual models that enable companies, like manufacturers, to predict and implement more efficient production. These digital twins can lead to millions of dollars saved in operational costs by allowing organizations to better predict system and machine maintenance or evolve production workflows.
But what if we applied this concept to improving and streamlining the customer experience (CX) in any industry?
Think of a digital twin of a customer (DToC) as the evolutionary cousin of its DToP predecessor. While DToP focuses on predicting the behaviors of machines and systems, DToC instead attempts to predict consumers’ complex patterns and behaviors.
According to a recent Gartner report, “The digital twin of a customer builds on the familiar concept of a marketing persona to provide context and predictions of future consumer behavior. It relies on both online and physical interactions to accurately simulate the customer experience and adjust those simulations in real time.”
But DToC isn’t just a predictive model. And it also isn’t a simulated alternate reality like the metaverse. Rather, it represents an entire set of interconnected predictions and simulations, based on real customer data, that work together to emulate a customer’s range of possible experiences with a brand.
To create a true DToC (and not just a marketing persona), organizations need to pull together data from a host of different sources to encompass the “total experience.” This data, which spans online touchpoints, physical store visits, and transactions, can then be augmented with modern data and AI best practices, where they are transformed into a real-time “map” that charts a customer’s entire relationship with a brand.
Let’s set the scene. A retail store has a large shipment of new inventory on the way for the holidays. Before it decides how to promote the product, set up inventory in the store, and support sales, imagine if it could create digital twins of its customers to better understand how they would respond to factors such as inventory levels, store layout, in-store associate support, and timely offers.
What does this mean for your sales team? Well, by predicting the future, your team can target how, when, and what to supply the end users – in this case a store, but it could be any company that sells products your company can (and does) supply.
By rolling these simulations into the aggregate, a retail store can begin to understand the potential effect of each factor across an entire segment of the customer base. Now, there are insights to use to maximize every touchpoint in the customer experience associated with this product – pinpointing offers only to the groups that need it, designing the in-person experience to maximize spend and customer satisfaction, allocating inventory across stores and fulfillment centers, and supporting a variety of other core business objectives.
Let’s take a brief look at a few ways a DToC can pay off:
Manufacturing and retail aren’t the only industries that can benefit from creating digital twins, and customers aren’t the only possible variable. Updating varied and detailed data sources in real time to be able to predict multiple potential future outcomes are use cases in other industries.
Factories, hospitals, shipping and logistics, and commercial real estate all rely on the reliable performance of physical assets – whether that’s a cherry picker or an MRI machine. Modeling physical assets and keeping track of maintenance, transportation, and average performance can help these industries learn where they can improve.
For example, in the energy industry, “fingerprinting” a house or business (even anonymized) allows energy companies to know how they are performing based on their energy consumption, habits, and peaks.
Turning the DToC concept around to reflect the employee allows businesses to examine and consider processes and performances. What range of behaviors do employees engage in? How can workforce management software data be used to detect problems and predict future performance, or design more useful training experiences for employees based on products or geographies served? Ultimately, answering these questions with a digital twin of an employee helps achieve the same goals as a DToC: budget savings and operational efficiencies.
A digital twin of a community can help organizations in industries like healthcare and the public sector create better data models on a block, neighborhood, or zip-code level. Finding ways to achieve better population health outcomes or understanding the way city infrastructure investments might impact different populations differently – whether that’s building a new hospital or closing a school – can help make more informed and equitable decisions. These models can also measure progress against goals.
DToC is still an emerging capability, but the use cases, data, and AI best practices are there to make it happen. By measuring, synthesizing, and utilizing the data that already exists, companies can unlock the potential of digital twins in their business.
Aaron Schroeder is director of analytics and insights at TTEC Digital, one of the largest global CX technology and services innovators.