Pretend you’re a category manager armed with 18 months of point-of-sale data, plus rich shopper demographics and competitive intelligence on how rival brands are performing at the shelf. You know exactly how your category is performing, yet when it comes time to roll out a new planogram to 400 stores, you’re still nervous. Why do you think that is? Most likely it’s because deep down you know that data tells you what happened but not what will happen. It shows you the past but not always the future. And that gap between historical analysis and future reality is where most category resets see their biggest flaws.
Whether you’re a brand category manager pitching planograms to retail partners or a retailer optimizing your store layouts, you face the same challenge. Historical reports have their place, but they can’t keep up with the pace retailers are dealing with now. Virtual testing, however, lets teams confirm their category management decisions while the opportunity is still in front of them, giving teams a better chance of making the right move the first time.
The Data Trap
Category managers today have more data than ever before.
You have access to data sources such as:
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- Syndicated panels
- Loyalty card insights
- E-commerce search and browse behavior
- Social media sentiment and trend signals
- Real-time POS data
- Shopper segmentation studies
The volume is staggering. However, simply having more data doesn’t always lead to better decisions. In fact, it can do the opposite and cause your category management process to become bogged down by information overload rather than clarity.
When you have multiple data sources pointing in different directions, or when the data supports two equally plausible strategies, you can end up in decision paralysis. Should you go with Option A or Option B? What if the data supports both? You could spend weeks building additional analyses, pulling even more reports, and still end up making a judgment call based on intuition rather than evidence. This is especially true when stakeholders across different business units disagree on interpretation, further complicating your category management strategy.
The core issue is that almost all this data looks backward. It explains what shoppers did last quarter, not necessarily how they’ll respond to a shelf you haven’t built yet. You might know that SKU X drives 15% of category sales, which is useful for understanding its role in the mix, but it still doesn’t answer the practical questions that matter when you’re planning a reset.
Things like:
Where should it sit?
Does it belong at eye level?
Should it anchor an endcap?
Should it live next to private label to encourage trade-up or away from it to reduce cannibalization?
Historical data can’t tell you any of that, because it reflects yesterday’s layout, not the one you’re about to execute.
This gap in quality data is where most resets face their biggest hurdle, and with shopper behavior shifting faster than many retailers can react, those decisions only get harder. Testing gives teams a way to validate the layout before committing to labor and weeks of in-market learning.
What If You Had a Test Kitchen?
Chefs don’t design an innovative new dish and then serve it to customers without tasting it first. Instead, they create the dish in a controlled kitchen environment, taste it, refine it, get feedback from sous chefs, make adjustments, and only then serve it. The test kitchen is where raw ideas become proven recipes. This mirrors successful category management where strategic sourcing and continuous improvement drive better outcomes.
A new planogram is a significant operational initiative affecting your entire category. You’re doing everything from reorganizing shelf space and changing adjacencies to adding private-label facings and shifting where premium items sit. A lot happens throughout the category management process, and for each decision you make, there’s likely a logical reason on paper. But when it comes down to it, will shoppers actually navigate the shelf the way you predicted? Will they trade down to private label, or will they skip the category entirely because they can’t find their preferred brand? These are questions that only testing can answer, making your category management process more robust.
If you’re looking to win at shelf in retail, build yourself a test kitchen of your own. Companies using this approach can test multiple planogram variations before committing to any single approach. They’re getting real shopper feedback before rollout, and they’re measuring which version drives the best conversion, the largest basket size, the strongest category growth. That means walking into retailer meetings with confidence backed by evidence, not hope backed by data analysis.
All of this represents a fundamental shift in how category management process and procurement strategies are executed, and it all comes down to testing.
How Virtual Store Testing Works
Imagine you’re planning a seasonal reset for a snacking category. You have three planogram ideas competing for approval and two endcap concepts you want to test. You’re uncertain whether to expand private-label offerings in the main planogram or keep them limited to the endcap.
Without testing, you pick the strongest option based on your data and market intelligence, roll it out across locations, and hope for the best. Unfortunately, this approach leaves risk unmanaged. But at InContext, we remove that risk.
Using our virtual store platform, ShopperMX, you can build 3D digital twins of your concept. In this situation, you would load your planogram variations and endcap concepts, and then InContext would conduct shopper research in these virtual environments. This enables teams to understand how shoppers will actually react to your in-store ideas. Such tests can reveal which layouts work, where customers hesitate, how different audience segments respond to planogram changes, and more.
Because you’re working in a virtual environment, you can test multiple concepts and iterate quickly without the time and cost of physical test stores. You can quickly compare which planogram variations resonate most with your target customer segments and identify which endcap concepts drive the strongest response. With this shopper research data, you’ll move forward with confidence. Your stakeholders will see clear metrics showing which concepts will succeed, and your procurement teams will gain actionable insights to guide vendor negotiations and supplier relationships.
How Arrangement AI Lets You Move Even Faster
There are also times when you just need a quick temperature check or when want to simply fine-tune your recommendations. This is when AI can come in handy. Arrangement AI is a predictive model built on an exclusive data lake of more than 2 million virtual shopping studies, and can forecast planogram sales lift. It learns patterns so that in less than 10 days you’re testing, learning, and refining based on predictive intelligence about how various customer segments will respond. Procurement leaders get the data they need to make confident decisions, and teams break down silos because everyone’s working from the same insights. And most important, you see measurable cost savings and operational efficiency gains that justify the investment.
The Retailer Conversation Changes When You Have Evidence
When you walk into a retailer meeting to propose a category reset, what’s your evidence?
The Old Approach
It might sound something like this: “Our data shows that private label is underperforming relative to our target state. I recommend we shift three facings to eye level to increase trial and grow this segment.”
The retailer responds that it’s interesting, but they’ve always done it another way, and asks about the risk if it doesn’t perform the way you’re projecting. You’re now left defending a recommendation without proof.
With Testing and AI Prediction
You can confidently walk in and say something along the lines of “We tested this planogram with shoppers and saw strong performance in key segments. We also ran this through our predictive AI model trained on 2 million shopping trips, and it forecasts similar performance patterns across your store base in your value-focused segments. We’ve optimized for your specific market conditions and store mix.”
Now you have real evidence. Retailers are dramatically more willing to support bigger, bolder category resets when they see real data backing them up. You reduce the back-and-forth negotiation about risk because you’ve already tested it, and you can defend your recommendation with numbers. Your credibility as a category expert goes up, and you have category resets that have a higher probability of success and retailers who become willing partners.
Your Test Kitchen Awaits
Category managers today are drowning in data but starving for insight. They have more information than ever before, yet they’re still making category decisions with uncertainty because data analysis can’t always predict how shoppers will respond to shelf changes. And although you might think collecting more data is the answer, the true solution is to use the right data to test faster and learn smarter through continuous improvement of your category management process.
A test kitchen for the shelf, where you can predict how various segments will respond across market conditions and refine concepts before they go live, is the way category management evolves from informed guesswork to scalable strategy. It’s how procurement teams and business units align around shared business objectives grounded in data and how you streamline processes and eliminate the guesswork that costs your organization time, money, and market opportunity.
The best category managers are the ones who test and adapt fastest and who treat category management as a continuous improvement discipline. Ready to build your category management test kitchen and transform how you approach category management strategy? Start with InContext’s virtual store testing to see how your next planogram, display, or seasonal reset will actually perform across your customer segments and store clusters before you commit.
Category Management FAQ
Why does data alone fail to improve category performance?
Data tells you what happened in the past, but it can’t predict how shoppers will respond to a shelf configuration you haven’t built yet. Category managers have access to more data than ever, including POS reports, shopper analytics, and market intelligence, yet they still struggle with category performance because historical data reflects yesterday’s strategies, not tomorrow’s opportunities. This gap between what data shows and what actually happens in-store is where most category resets stumble. Virtual testing bridges this gap by providing real-world insights into shopper behavior before you commit budget and retail space.
How does virtual testing support better decision-making in category management?
Virtual testing enables informed decisions by allowing teams to test multiple planogram variations in a risk-free digital environment before committing to physical rollout. Instead of relying on historical data alone, category managers can see how real shoppers respond to different layouts, pricing strategies, and merchandising concepts. This automation of the testing process dramatically reduces the time and cost traditionally required for in-market testing while providing actionable data that supports strategic decision-making across business units.
What are the benefits of category management when combined with AI-powered testing?
You’re getting data-backed insights faster, allowing category teams to create agile strategies. Teams gain the ability to benchmark multiple concepts simultaneously, identify which strategies deliver the strongest category performance, and make adjustments based on real shopper behavior rather than assumptions. This approach supports risk mitigation by validating ideas before rollout, ensures better alignment with business strategy, and delivers measurable improvements in operational efficiency and spend management.
How does virtual testing address changing market conditions and business needs?
Market changes happen faster than traditional testing cycles can accommodate. Virtual testing allows category managers to respond to shifting shopper preferences, competitive pressures, and pricing dynamics quickly. By testing concepts in days rather than weeks, teams can adapt their category management strategy to meet evolving business needs without the lag time of physical pilots. This agility is critical for staying competitive in fast-moving procurement categories such as CPG, where delays in decision-making can result in lost market share.
How do testing and predictive AI impact spend data and cost management?
Testing provides granular spend data on what actually drives performance, helping procurement teams allocate resources more effectively across indirect spend categories. When combined with predictive AI, this data translates into segment-specific forecasts that guide sourcing strategies, supply chain management decisions, and cost-reduction opportunities. The result is improved spend management, better supplier negotiations, and more efficient use of trade spend and marketing budgets—all contributing to stronger profitability and category performance
