There’s no shortage of buzz around retail tech right now. On one hand, AI promises faster answers. On the other, technologies such as VR promise lifelike testing. But when it comes to making real merchandising decisions, the question is: Which tool do you actually use, and when?
At InContext, we cut through the noise with two proven platforms. Arrangement AI gives you a way to forecast planogram (POG) performance in days using predictive models built on more than 2 million shopping trips. ShopperMX lets you place those ideas inside a virtual store, test them with real respondents, and see how they perform before you commit in the real world.
Both are designed to work in complementary ways, but they solve different problems. In this post, we’ll walk you through each tool, where it shines, and how to match it to the business questions you face. By the end, you’ll know exactly when to turn to Arrangement AI for speed and when a VR simulation in ShopperMX will give you the deeper read you need.
Start with Your Goals
Before diving into the tools themselves, it helps to pause and do a quick mental inventory. New technology works only if it matches your objectives. Ask yourself:
-
- “What decision am I trying to make?” Are you comparing two planograms, validating a full reset, or fine-tuning an assortment?
- “How fast do I need answers?” Do you need directional feedback in days or shopper-tested results before a seasonal launch?
- “What level of detail do I need?” Are you looking for a predictive read on sales lift or a full picture of how shoppers interact with your layout?
- “What’s my budget and risk tolerance?” Do you need a cost-effective first pass, or is the cost of a wrong in-store move high enough that full simulation is worth it?
- “Who will use the results?” Will your findings guide a quick merchant meeting, or serve as proof points for an enterprise-wide rollout?
Once you have those answers in mind, you’ll be better equipped to choose the tool that fits.
Why Retailers Need More Than One Tool
Retail today moves faster than most teams are staffed to handle. Merchandising resets that used to take months now need answers in weeks. Shoppers expect fresh assortments, while supply chain pressure demands leaner planning cycles.
This creates two broad kinds of challenges. Some questions require quick forecasts and data-backed predictions, such as determining which POG will lift sales more. Others demand visualization and behavior testing, such as finding out how shoppers will react when they see a certain endcap in context.
What Arrangement AI Delivers
Arrangement AI is InContext’s predictive analytics engine, built to give retail teams speed without sacrificing accuracy. At its core, it draws on a data lake of more than 2 million virtual shopping trips. That sheer volume grounds its predictions in how shoppers actually behave in store aisles, not in abstract models or assumptions.
The platform runs advanced algorithms across that massive dataset and blends it with your category inputs. In less than 10 days, you get a forecast of which POGs are most likely to deliver a sales lift. That speed means decisions don’t sit in meetings or get bogged down in spreadsheets. They land while your teams are still planning, not after displays are already built.
What Arrangement AI can do:
-
- Forecast planogram performance. Identify which POGs are most likely to drive higher sales and category growth.
- Compare multiple layouts. Test different shelf, endcap, or pack arrangements side by side before they hit the floor.
- Fine-tune strategy. Verify that your current approach holds up, or adjust quickly when data points another way.
- Scale insights. Apply findings across multiple planograms without needing dozens of separate in-store tests.
- Save costs. Run predictive studies for a fraction of the price of traditional in-store testing.
Where it works best:
-
- Seasonal shifts. A beverage brand prepping for summer promotions wants to know which 12-pack layout will maximize lift. Arrangement AI runs scenarios and flags the top performer before the first cooler reset.
- Category resets. A snack brand testing a new flavor can compare placement options across planograms to see which scenario drives trial.
- Merchant meetings. Category managers walk in with fast, data-backed recommendations instead of opinions.
Arrangement AI shines when teams need speed, iteration, and predictive confidence. It’s the right fit when you want to compare ideas quickly, get directional answers, and stretch budgets without losing the grounding of real shopper behavior.
What ShopperMX Delivers
ShopperMX is InContext’s virtual store platform, designed to help retailers and brands see their ideas in context before they reach the sales floor. Instead of relying on sketches, mockups, or trial-and-error in physical stores, teams can build, test, and refine concepts inside digital twins of real retail environments.
At its core, ShopperMX gives you the ability to create a 3D replica of the store, aisle or shelf, and drop in products, fixtures, signage, and displays. You can walk through the virtual store, stream video, and share screenshots with colleagues and partners, all before spending a dollar on physical build-outs.
What ShopperMX can do:
-
- Visualize at scale. Access more than 80 store environments, complete with SKUs, endcaps, and full categories.
- Test merchandising ideas. Explore assortments, signage, or seasonal displays in a digital twin without disrupting live stores.
- Collaborate in real time. Share walkthroughs and iterations across teams, partners, and merchants in a risk-free space.
- Build custom assets. Tap into InContext’s global 3D modeling pipeline to add products or displays not yet on shelf.
- Reduce risk. Validate how concepts look and function before committing to time-consuming in-store tests.
Where it works best:
-
- Retail execution. A retailer planning a nationwide endcap rollout can model the display across different store formats, making sure it works everywhere before launch.
- Shopper research. A CPG brand can run virtual studies within the environment to see which layout captures attention.
- Collaboration. Merchandising, insights, and sales teams can meet “inside” the store to align on strategy and show merchants exactly what the plan will look like.
ShopperMX excels when the goal is to visualize and align on in-store execution while minimizing risk. It’s the right fit when you need to validate shopper experience at scale, present to retail partners with confidence, and avoid costly mistakes that come from guesswork.
Why the Distinction Matters
Retailers and brands don’t have months to experiment anymore. Launch windows are shorter, shopper expectations are higher, and competition is constant. Picking the wrong tool slows decisions, drains budgets, and risks missing sales opportunities.
Arrangement AI is built to quickly answer “what sells best?” ShopperMX is built to show how shoppers will respond in-store. Teams that know when to apply each one move from idea to execution with speed and confidence. And when both tools are used together, the result is fewer missteps and stronger outcomes.
Arrangement AI vs. ShopperMX: How They Compare
Both tools are designed for merchandising success, but they solve different problems.
Arrangement AI: Predictive analytics that simulates shopper behavior across millions of virtual shopping trips. Best for rapid, data-driven answers about which planogram or arrangement will drive the most sales.
ShopperMX: A full virtual store environment for testing, collaboration, and shopper research. Best for seeing how an arrangement plays out in real space, capturing shopper reactions, and aligning teams on execution.
Think of it this way. Arrangement AI helps you pick the winning concept on paper. ShopperMX shows you how that concept will live, breathe, and perform in the store.
Bring Your Merchandising Strategy Into Focus
Whether you need predictive data, full-store visualization, or both, InContext gives you the tools to move faster with less risk. Arrangement AI helps you optimize planograms with speed and accuracy. ShopperMX lets you visualize and test those concepts in realistic virtual stores. Together, they form an end-to-end workflow designed for today’s retail pace.
Ready to see which tool fits your goals? Contact InContext to start your next project with confidence.
