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April 28, 2025
Are Digital Twins a Game Changer for Stores?
At the University of Arizona’s Lundgren Consumer Science Lab, students wearing Meta Quest 3 headsets explore how store layouts impact shopper behavior via a virtual reality “digital twin” of a physical store.
The lab’s physical location, which is mirrored in the digital twin, features a staged retail space with advanced in-store analytics technology that tracks and records shoppers’ movement via four sensors. Students are able to rearrange shelves at the click of a button, shift product displays instantly, and track and study shopper movements in real time.
“I saw an opportunity for students to learn in a three-dimensional, interactive environment,” said Soo Hyun Kim, a retailing and consumer science assistant professor of practice, in an article on the University of Arizona’s news site. “In order to complete certain projects in class, they visit virtual stores and investigate how shoppers move, what they buy and where they spend their time. Not only do we use real data in a virtual environment, but students also gain experience with advanced technology before competing for jobs.”
Guess’ use of a digital twin of a mock store at its corporate headquarters in Los Angeles since 2017 has helped merchandisers setting in-store layouts increase their productivity by 200%. With iPads, changes are made without in-person visits. Digital twins have also sped up Guess’ employee onboarding, reduced team travel by 30% with remote collaboration, and slashed departmental paper and printer ink costs by 95% by eliminating the typically manual process of printing and shipping materials to stores.
Guess has similarly created a digital twin of its showroom to support virtual buyer visits.
Digital twins were a hot topic at the recent NRF 2025 conference.
At one session, Azita Martin, VP and general manager of retail & CPG at NVIDIA, said Lowe’s has created 3D digital twins of all of its stores, updating operational and inventory data several times a day to simulate layout changes in real time. She said, according to Retail TouchPoints, “A digital twin that uses ‘physics AI’ means it understands the weight, depth and size of these products, which really matters.”
Walmart, Home Depot, and Target have similarly implemented digital twins of their stores to optimize store layouts, product placement, and inventory replenishment, while Amazon has implemented digital twins of fulfillment centers to optimize the flow of goods.
At another NRF session, Walmart U.S. CEO John Furner likened digital twins to a video game. He said, “It’s an idea in your head, and there’s a reality you’ve created, you simulate, and play in it, and results happen. You can do the same thing in business.”
Discussion Questions
What are the benefits and limitations of using digital twins to virtually optimize store layouts and inventory replenishment?
In what ways does in-person strategic decision-making remain important for merchandisers?
Poll
Loading …BrainTrust
Carol Spieckerman
President, Spieckerman Retail
Brad Halverson
Principal, Clearbrand CX
Doug Garnett
President, Protonik
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When it comes down to the precise nuances, there is very little uniformity across a chain of stores. This creates complexity for teams in central functions such as buying and merchandising, and it can also create implementation challenges for those working in individual shops. A digital twin of a store makes planning far easier because you know what layout and space you’re working with. When coupled with AI, digital twins of stores can also help predict what things like traffic and sales might look like with various layout changes. But even with all of this, stores still need good people on the ground to maintain standards and disciplines.
Exactly, Neil! We will always need solid associates on the frontline.
I agree with Neil Saunders and Georganne Bender that digital twins combined with physical, in-store analysis IRL (in real life) is the ideal formula.
Digital twins act as a planning blueprint, only better because it’s 3D and you can experiment in a cost-effective, virtual environment before spending to build anything physical (and expensive). Once you’re confident in your digital twin (blueprint) and have tested it on screen, you build IRL. Minimize errors in your planning phase before spending to build and rebuild.
When it comes to store planning, digital twinning is winning.
A real upside in having digital twins for store planning is seeing what a store experience or department space might look like from the customers view. Sometimes a floor plan and the supporting math doesn’t tell the whole story when it comes to the impact of merchandising and fixture heights, signage, busy corners, and narrow corridors. Having a sense of what the customer actually sees can help for better planning and in making adjustments. Making adjustments prior to being filled or set will save money and time prior to making heavy moves on site.
I think there’s tremendous value in digitally mocking up a store before physically making the changes. (I’m not really sure what a digital “twin” is because they don’t exist.) That said, any digital mockup of anything — from store to furniture to photographs of art — miss a great deal of nuance which is important to human reality. Thus, weights, feelings of presence, and much more cannot be sense within a digital mockup. Just as is true with pre-mockups of advertising, those who use them must remember they are NOT THE REAL THING. It takes considerable skill to project from an animatic — for example — to what a finished spot might be. Just so, there is tremendous skill needed to learn through a digital store mockup. And, we should almost never ever ask consumers for their opinions about a digital mockup as they lack the training and experience to fully comprehend what it includes.
Digital twins are an exciting frontier for retail merchandising (and distribution center management). We’re in the test-and-learn phase now and it will be fascinating to see where things go as seemingly endless scenarios are processed and patterned over time.
Digital twins help retailers optimize store layouts by simulating different configurations virtually, enabling them to test layouts and analyze foot traffic in real time. This data can guide the strategic placement of popular products.
However, digital twins lack the human insight merchandisers provide, such as noticing customer expressions and body language. Merchandisers can also adapt quickly to unexpected situations, like customer feedback or seasonal trends.
We had great success with VR virtual twins of retail environments at Kantar. You could test things that would be too difficult or too costly to do in a live store. You are also able to create new data from shopper research on retail changes without ever revealing your work in the physical world.
Digital twins have existed for over 40 years – supply chain modelling pioneered by Fritolay in the 1980’s has arguably paved the way for possibilities we see today. Over time things have expanded and grown to allow close to real-time understanding and replanning of supply chains.
To model individual stores in real-time is however a hugely exciting opportunity – albeit not without challenges. It offers the ability to understand how promotions are performing, how consumers are reacting to in-store advertising and product placement – allowing rapid changes, rollouts to other stores, and ability to modify in close to real-time.
The technology challenge involved is however huge. Arguably IoT technology – a key component – has been 2-3 years away from bringing real value in retail for the past 10 years. The big barrier is however the underlying technology infrastructure. Like a bridge across a river – people – or different business stakeholders – benefit from it but no individual wants to pay for it.
Despite that, there are layers of digital twin type approaches that can be applied without this significant effort. For example real-time understanding of in-store inventory – by aligning data from multiple systems. What is needed for this is a consistent data layer to consolidate.
The efficiency of digital twins in store planning is clear, and it was recently underscored by Walmart US CEO when discussing their use for front-end planning during an investor presentation (In April). Crucially, the accuracy of any digital twin, or any model for that matter, depends entirely on the data it’s fed. We at Amoobi are proud to provide the in-store customer tracking technology highlighted in the article and video to the center. By measuring exactly what shoppers do in real-time within the store, we deliver the essential “truth” about their behavior, which is vital for refining and adjusting digital models. This combination of precise in-store measurement and effective modeling is the key to unlocking the future of store planning and merchandising optimization.