Extended reality, or XR, is the umbrella term for immersive technologies that blend physical and digital environments, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). In retail, XR is used for everything from virtual store testing and planogram evaluation to AR-assisted field execution and associate training.
But the technology itself is not what makes XR valuable.
For retail teams, the real question is whether an XR environment can produce data accurate enough to predict shopper behavior before decisions are made in store. That has been InContext Solutions’ focus since 2009, when founder Rich Scamehorn built the world’s first online 3D virtual marketing research platform. In the years since, InContext has captured the purchase behaviors of more than 2 million shoppers across 65 CPG categories, working with more than 100 major retailers and brands to plan, test, and execute in-store strategy.
What separates organizations that get meaningful results from those running expensive pilots is not the headset, the 3D environment, or the visualization layer. It is the behavioral data and research methodology behind the experience.
What Is Extended Reality?
Extended reality is a category of immersive technologies that includes virtual reality, augmented reality, and mixed reality. In retail, XR can be used to simulate store environments, test shelf arrangements, visualize display concepts, validate planogram execution, and support associate training, all before or during physical implementation.
The three key technologies under the XR umbrella are distinct, and each addresses a different point in the retail planning and execution cycle.
How Extended Reality Works in Retail: VR, AR, and MR
Virtual reality creates a fully immersive 3D environment. The merchandising team walks the store layout, evaluates a planogram at eye level, and explores a display concept before it’s built. The immersion isn’t aesthetic; it’s methodological. In a properly constructed VR environment, products above or below eye level require the viewer to physically look up or down. Scale is accurate. That physical realism is what makes virtual testing predictive of real-world behavior in ways that flat or 2D tools simply can’t replicate.
Augmented reality overlays digital information onto the physical world through a mobile device. In retail execution, a field rep points a device at a shelf and sees the approved planogram mapped onto what’s actually there. “It’s intuitive, impactful, and only requires a mobile device to use,” said Erin Feeney, chief product officer at InContext Solutions, in the 2023 Store Operations Benchmark Survey conducted by Retail TouchPoints in partnership with InContext Solutions. The same survey found that 28% of retailers already use mobile devices specifically for auditing visual merchandising and displays.
Mixed reality uses a depth-sensing headset (like Microsoft HoloLens) to spatially map the physical environment. Unlike AR, which overlays digital content on a camera feed without understanding the space around it, MR anchors digital objects to real surfaces. A planogram can be evaluated against actual shelf dimensions. A display can be repositioned within a real aisle. MR is more hardware-intensive and less widely deployed than VR or AR today, but for retail planning applications where spatial accuracy matters, that environmental awareness is exactly the point.
Why XR Technology Alone Is Not Predictive
The value of extended reality in retail does not come from immersion. It comes from whether the environment is realistic enough, methodologically sound enough, and data-rich enough to predict what shoppers will actually do.
Most XR platforms can render a shelf. Far fewer can produce behavioral data that holds up against real-world outcomes.
The difference comes down to fidelity. In a 2D shelf study, shoppers see the entire set on a single screen without needing to move. Products at non-eye-level positions get inflated purchase rates because the friction of finding them has been removed. That’s not how shoppers behave in a store, and planogram decisions made on that data reflect the distortion.
In a properly constructed 3D environment, shoppers have to look for products the same way they would in a physical store. Scale is accurate. Product adjacency affects navigation. Visibility constraints shape the purchase decision. The behavioral data produced reflects actual shelf dynamics, not an artifact of the research method.
That distinction is the foundation of InContext’s methodology, and it is why the company’s virtual shopping research correlates with in-store shopping behavior at .90–.95 across thousands of completed studies.
InContext’s 15-Year Foundation in Virtual Retail Research
When Rich Scamehorn founded InContext Solutions in 2009, the premise was straightforward: By building a virtual retail environment accurate enough to produce reliable shopper behavior data, you give brands and retailers a way to test decisions before making them in store. What wasn’t straightforward was building the methodology, fielding the studies, and doing it consistently enough across enough categories to know when the results could be trusted.
That is what InContext has been doing ever since. More than 2 million observed shopping trips. Sixty-five CPG categories. More than 100 major retailers and brands. The result is a data lake that took 15 years to build and an AI model trained on it that no organization could replicate from scratch today.
Technology is replicable. Data accumulated over 15 years of fielded studies is not.
What 2 Million Shopping Trips Make Possible
Building a machine learning model for planogram optimization requires more than a virtual environment. It requires enough observed shopping behavior across categories, store formats, assortment scenarios, and shopper demographics for the model to generalize accurately. InContext’s own research established that a valid prediction model requires a minimum of 1 million observed shopping trips as a foundation.
InContext passed that threshold. The result is Arrangement AI: a model trained on more than 2 million observed shopping trips across 65 CPG categories, built to predict planogram arrangement outcomes before a study is fielded. It correlates at .8 or higher with full virtual study results, making it a practical tool for rapid iteration, exploratory research, and planning cycles where there isn’t time for a full study. It is not a replacement for deep behavioral research but rather a meaningful accelerant when speed matters.
No organization building a virtual merchandising capability today is starting with that dataset. They are starting from zero. That gap doesn’t close quickly.
Retail Outcomes from Predictive Extended Reality
The methodology is one measure of InContext’s effectiveness. The client outcomes are another. InContext’s work across retailers and brands shows where predictive XR creates the most consistent operational value.
Better category decisions
A shelf arrangement study surfaced $40 million in growth potential, with an 800:1 ROI on the research investment. InContext’s work with the National Pork Board, on behalf of Midan Marketing, used virtual category research to determine whether expanding pork SKU count would grow the category or simply redistribute sales across existing brands.
Faster planning cycles
A snack foods manufacturer and a national retailer cut their category reset research timeline by six months by running the collaboration through a shared virtual environment rather than sequential physical iterations. A mass merchandiser increased cross-category purchases 2% in 12 weeks through virtual category testing.
Stronger field execution
InContext’s work with Albertsons shows how XR can close the gap between planning and execution. Instead of relying on printed PDFs, field teams access the current plan in store and compare it against the actual shelf using AR-powered compliance tools.
Better retailer sell-in
A manufacturer replaced a 50-page quarterly PowerPoint with a virtual display execution video, improving retailer sell-in 19%. When a concept is tangible rather than described, the sell-in conversation changes.
Enterprise adoption ROI
A CPG brand that began working with InContext in 2012 has documented 9x ROI from its enterprise adoption of virtual planning tools across more than a decade of category planning, testing, and execution work.
The Adoption Reality
By 2023, 44% of retailers had already deployed AR/VR solutions and another 25% planned to add them within 12 months, according to the 2023 Store Operations Benchmark Survey, a trajectory that signaled XR was moving from experimental investment toward operational adoption. When the survey was published, VR- and AR-assisted training adoption had already grown 73% year over year. Among the more than 120 retail executives surveyed, 62% had increased their in-store technology budget between 2022 and 2023, and 82% were arming associates with mobile devices.
“From a store planning and operations point of view, forward thinkers understand that technology such as virtual reality and AR are not just consumer-facing,” Feeney said in the survey. “Digital twin technology has become a fast, cost-effective way to gain insight into shopper behavior and preferences and visualize in-store concepts.”
Retail XR adoption is no longer just about whether to use immersive tools. It is about whether those tools are connected to a reliable planning, testing, and execution workflow, and whether the behavioral data behind them is rigorous enough to be trusted.
Frequently Asked Questions
What is extended reality in retail? Extended reality in retail refers to the use of virtual reality, augmented reality, and mixed reality technologies to simulate, visualize, test, or support physical store decisions. Applications include virtual store testing, planogram evaluation, AR-assisted field execution, and associate training.
How is virtual reality used in retail planning? Virtual reality allows retailers and brands to evaluate store layouts, planograms, displays, and category changes in a fully immersive digital environment before making physical changes in store. When the VR environment is built to accurate scale with real-world shelf constraints, it produces shopper behavior data that predicts in-store outcomes.
How is augmented reality used in retail execution? Augmented reality can overlay a digital planogram or display guide onto a physical shelf, helping field teams compare the approved plan to what is actually in store. This supports real-time compliance verification and reduces the gap between what headquarters planned and what ends up on the shelf.
What makes extended reality data reliable? XR data becomes more reliable when virtual environments reflect real-world shopping constraints, including shelf height, product scale, product visibility, adjacency, and shopper movement. Environments that remove those constraints produce distorted behavioral data. InContext’s methodology correlates with in-store shopping behavior at .90–.95 because it maintains the physical friction of real shopping conditions.
Why does behavioral data matter in extended reality? Immersive technology alone does not prove what shoppers will do. Retail XR becomes predictive when virtual environments are validated against real shopping behavior at scale and the models built on that data have been calibrated across enough categories and scenarios to generalize accurately. The data and methodology behind the experience are what determine whether XR produces useful retail intelligence or just better-looking visualizations.
Where to Go from Here
Extended reality is becoming easier to access. Predictive extended reality is not. In retail, the advantage belongs to organizations with the behavioral data, validation methodology, and operational workflow to make immersive tools useful before decisions hit the shelf.
InContext Solutions works with retailers and brands to identify where predictive virtual testing, AR-assisted execution, or AI-powered planogram optimization can create the most leverage in their planning cycle. Talk to InContext about where to start.
