How Machine Learning Helps Retailers Shift From Batch Planning to Adaptive Assortments

A shelf plan can make perfect sense the week you build it. But by the time it reaches stores, shopper behavior shifts, competitors move, or your key SKU starts outrunning its space. Suddenly the mix you were confident in is already fighting the shelf.

This is the trap of batch planning: big decisions made far from the aisle, using data that’s aging by the day. And the tough part is, the plan hasn’t even had a chance to breathe before the shelf moves on.

While machine learning promises to help, most tools still make teams work in long cycles.

Adaptive assortments change that rhythm with faster adjustments, backed by evidence from real stores.

Batch Assortment Planning Freezes Decisions for Months

Planning your assortments in bulk means assuming that stores behave like each other and stay stable long enough for one season’s plan to hold.

The reality is they don’t. Instead:

• Local demand shifts faster than management can react. Shopper preferences change by neighborhood, and sometimes overnight.
• In-store conditions drift. Facings get stretched, displays move, and resets take more time than expected.
• Space constraints vary by store. The “ideal” planogram on paper often doesn’t match the actual shelf because capacity is misread.
• New products and flavors throw off the plan. Especially in categories where innovation happens every month.

The result is a mismatch between what was planned and what the shopper sees—costing sales, labor, and credibility with retailers and field teams.

Machine Learning Makes Assortment Respond to What’s Happening in the Aisle

An AI model works best when  become part of the ongoing process, not a bolt-on feature. By learning from store behavior data—what sells where, at what rate, and what happens when space shifts—it can make store-specific predictions. This makes shelves easier to adjust without starting from scratch.

But machine learning in retail only works when it connects to the indicators that are actually relevant for planners:

Real Demand From Buyers

Machine learning can spot demand patterns early, including:

• High-velocity SKUs that deserve extra space
• Items losing steam before the drop becomes obvious
• When one SKU absorbs demand from another (demand transfer)
• The hidden upside in core items that get crowded out

Instead of relying on old movement reports, you get a living view of what shoppers are reaching for today.

Space Limitations That Drive Real Outcomes

A model only works if it understands the shelf.

Space-aware assortment decisions take into account:

• Actual shelf dimensions
• Cooler capacity
• Required facings
• How often stock rotates
• Seasonal or weekly constraints

Without this, assortment planning becomes guesswork—no matter how good the algorithm is.

Neighborhood-Level Patterns and Store Demographics

A chain can be national, but demand is always local.

Machine learning can separate the patterns from hundreds of stores and highlight:

• Rural vs. suburban vs. urban preferences
• Seasonal variations by climate
• Income-driven shifts
• Cultural and demographic patterns
• Commuter-heavy store behavior vs. residential

Today, shoppers aren’t comparing stores to each other anymore—they’re comparing them to everything else they interact with. Personalization it’s no longer limited to online shopping. 

Those expectations show up directly in assortment: shoppers want the shelf to feel curated for their store, not built for the chain average. With image recognition and AI providing faster inputs, retailers finally have the ability to respond with assortments that feel tailored, not templated.

Early Indicators of Drift and Compliance Gaps

Even the best plan falls apart if it doesn’t get executed the way it was intended.

Using image-based data to feed the AI model, you can see where planograms have drifted, catch items that lost facings, and understand why an item is underperforming. This means you can correct issues before the next reset window.

Machine learning also gives planners a quicker read on where space is helping or hurting. Maybe a fast mover in the South is draining capacity, while the same item lags in the Northeast. Or a new SKU draws demand away from a long-time top seller. 

Small shifts like these are easy to test before stores feel the impact—something teams like Clorox and Mars have put into practice by using Curate to model different scenarios before choosing the one that makes the most sense.

How Curate Helps Teams Move From Static Plans to Adaptive, Space-Aware Assortments

Curate gives teams an easy way to test, refine, and approve smarter assortment choices before they ever hit the store.

It supports the shift toward adaptive assortments by:

• Simulating scenarios using the retailer’s real movement and space data. Its AI model adjusts to your business, learning from actual shopper behavior.
• Accounting for physical constraints on every shelf. You see the changes that actually fit in each store.
• Packaging strategies so planners and merchants can review them. No guessing what the model did. Everything is transparent and adjustable.

And because Vision Group’s team is full of people who’ve lived this for decades, clients get guidance that blends the science with real retail sense.

Ready to See How Machine Learning Would Change Your Next Assortment Cycle?

Curate handles the “what should the product mix look like?” part. But assortments only stay sharp when the inputs stay clean and the feedback loop stays alive. Vision Group’s product library, OmniPIX, keeps product data consistent, and Store360 brings back shelf-level truth as conditions shift. The combination helps the strategy stay grounded in real stores, not just the plan you started with.

If you want to see how that can look for your team, book a quick walkthrough and we’ll show you the difference between a plan that stays stuck on paper and one that evolves as stores change.

Frequently Asked Questions