Why digital twins for retail?
What is a digital twin?
Step 1: Define scope
Digital twins have varying levels of sophistication. The sophistication level is determined by the complexity of the digital twin, the degree of integration with data sources, and what use case it is being deployed for.
A digital twin built and deployed for enhancing customer experiences may only require that the twin be a physically accurate digital representation of an asset or product that emulates its real-world counterpart. However, a digital twin built for store planning with integrations for process optimization, what-if simulation, and machine learning requires more capabilities.
Defining the digital twin’s use case and the required level of complexity is an essential first step.
Step 2: Collect relevant data
Digital twins designed for retail are built by importing the appropriate data for all the components that will be incorporated in the final model.
For example, to build a digital twin for a product configurator, it is essential to include product data like color, size, and materials. On the other hand, a digital twin for store planning should include data on store layout, aisle configuration, shelving and display stands, as well as location and spatial data.
You can collect this data through various methods. Deckers Brands used photogrammetry, a technique of taking multiple overlapping photographs and deriving measurements from them, to create 3D models of objects. You can also import computer-aided design (CAD) or building information modeling (BIM) data.
Step 3: Build the digital twin
Next, you’ll need to consolidate all relevant data into a single, centralized platform. This process requires data transformation tools to optimize existing data in preparation for interactive visualization applications, for example, by converting CAD models into mesh data.
Once the data has been prepared, you can use real-time 3D visualization tools to build and refine the digital twin. This enables physical and virtual data, as well as real-time information like customer interactions and product customizations, to be combined.
Step 4: Deploy the digital twin
Regardless of the use case or maturity level, it’s important to consider how people will access your digital twin application. For example, customers may prefer accessible devices like mobile phones, whereas store planners may find an extended reality (XR) application more effective for visualization.
You can deploy digital twins built with Unity’s real-time 3D technology to more than 17 platforms, enabling customers and stakeholders to engage on multiple devices and platforms, including, web, iOS, Android, PC, Mac, virtual reality (VR), and augmented reality (AR).
Step 5: Gather impactful data
Digital twins help unlock the full power of data for better decision-making. For example, customer preference data collected from product configurators can help go-to-market teams make more informed decisions about their strategies.
Additionally, a more mature digital twin for store planning can use machine learning or artificial intelligence to analyze vast amounts of data and generate predictions about future store plans, including layout and display performance.