How digital twins can prepare retailers for the upcoming holiday season

Most brands take pride in knowing who their customers are: knowing not just their demographics, but understanding, to some degree, their challenges, wants, needs, and priorities. But that understanding is quickly becoming a baseline expectation, not an advantage. According to a recent article from Forbes, "Brands that succeed in 2023 are likely to be those that take their experience to the next level by analyzing and predicting customers' behaviors, interests, and preferences in a consistent and accurate way."

Imagine this scenario: A retailer has a large shipment of new inventory coming in, arriving just in time for the holiday shopping season. Before this retailer decides how to set up inventory in-store and promote these new products, imagine they could better understand how customers would respond to various factors, such as inventory levels, store layout, in-store support, and specific offers, and then optimize every variable for the best possible customer experience.

How can a retailer accomplish this? With digital twins.

What is a digital twin?

A digital twin of a customer (sometimes called a DToC) is a simple concept that evolved from the idea of both a marketing persona and a digital twin of a product (DToP). But while a DToP focuses on predicting the behaviors of machines and systems that go into the making and supplying of a product, a DToC instead attempts to predict and understand the complex patterns that go into consumer behavior.

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."

While a DToC can provide predictions of future consumer behavior, a DToC isn't just a predictive model: rather, it represents an entire set of interconnected predictions and simulations based on real customer data and changing external variables. These simulations and predictions work together to represent the wide range of a customer's possible experiences with a brand.

A DToC is obviously more complicated than its predecessor, the DToP, or even its cousin, a marketing persona. Predicting the many variables of human behavior is a little more complex than predicting machinery upkeep. Because of these extra layers of complication, digital twins of customers require more data, pulled together from more diverse sources to accurately encompass the total experience a customer has with a brand. This data — which spans online touchpoints, physical store visits, and transactions — can then be augmented with different data analysis, AI, or machine learning models, where they are transformed into a real-time "map" that charts a customer's entire relationship with that brand.

Once you have mapped out a customer's relationship with your brand, you can use it as a guide to solving several different business challenges.

How can a digital twin of a customer solve business challenges?

When you understand the variables behind your customer's behavior, you can solve different complex business challenges.

A digital twin of a customer can help you:

• Optimize processes: Simulating and predicting a customer's range of behaviors can help streamline the processes brands create for their customers. In some cases, it could also inform where automation can play a supporting role.
• Uncover opportunities: One of the most powerful benefits of a DToC strategy is the ability to adjust engagement tactics in real-time. While one engagement tactic might work for a while, it likely won't be that way forever. By staying connected to the pulse of customer, brands can track and adapt to subtle changes in customer preference and behavior.
• Predict performance: The ability to simulate customer behavior enables brands to vet proposed activities or events before they occur, reinforcing strong strategies and flagging activities that could miss the mark.
• Detect problems early: Each time a negative customer experience is identified and proactively remedied, brands can avoid significant costs or lost loyalty.

Digital twins in the real world

Let's revisit our holiday retailer from earlier. The retailer expecting the new shipment has all the data they need on-hand to build a digital twin model. By rolling these simulations into the aggregate, they are now beginning to understand the potential effects of each factor (store layout, inventory levels, store associate availability, etc.) across entire segments of its customer base.

Now, they have the insights they need to maximize experiences associated with this specific product, including pinpointing offers only to the groups who want or need it, designing the in-person shopping experience to maximize spend and satisfaction, or correctly allocating inventory across stores and fulfillment centers. They also have the insights to support a variety of other core business objectives across every other touchpoint in the customer experience.

DToC is still an emerging capability, but the use cases and data and AI best practices are there to make it happen. By measuring, synthesizing, and utilizing the data you already have, you too can unlock the potential of digital twins in your business in time for the holiday season.