Predicting Consumer Behavior: AI's Role in Shaping Retail Assortments

Oct 09, 2025 | Couture AI Team

Consumer behavior prediction is used to indicate assumptions.

You'd pull last year's numbers. Check what the competition was doing. Run a few focus groups. Then place orders six months out and hope for the best.

That doesn't work anymore.

Here's what transformed: your customers leave data trails everywhere. Every purchase. Every click. Every product they browse but don't buy. The information exists - sitting in your POS systems, loyalty programs, and website analytics.

The concern not only exists in getting the data, perhaps knowing what to do with it.

And that's where AI merchandising comes into play.

The global AI in retail market size is already valued at USD 11.61 billion in 2024 and is projected to reach USD 40.74 billion by 2030 a CAGR of 23%.

These numbers make one thing clear: AI stands experimental and is becoming core infrastructure for the future of retail.

According to a NVIDIA State of AI in Retail 2024 survey, 42% of retailers already use AI, and an additional ~34% are piloting or evaluating it.

Why Your Current Forecasting Method Is Costing You Sales

Most retail teams still forecast the same way they did 20 years ago.

They use:

Historical sales averages that ultimately miss new trends
Last year's seasonal patterns by assuming nothing changes
Manual adjustments based on experience are inconsistent across teams
Category-level forecasts are neglecting what's happening at the SKU level

All of these share one massive flaw.

They presume tomorrow will look like yesterday.

But consumer behavior doesn't work that way now. Supply chain issues push customers toward alternatives. Weather patterns shift and throw your entire forecast off.

You can't rely on quarterly reviews that tell you what you should've done three months ago.

In fact, in pilots using AI/ML forecasting, some retailers achieved a 33% reduction in forecast error compared to legacy methods

You need AI systems that adapt as customer behavior changes.

How AI Sees What Your Team Can't

Retail data analytics powered by AI works differently.

Instead of just tracking what sold, it figures out why things sold. It connects patterns across data sources that a human team is unable to manually track.

Here's what modern systems can process simultaneously:

Transaction patterns across thousands of products
External factors like weather, local events, and economic shifts
Real-time inventory levels and competitor pricing
Social media trends and search behavior
Customer journeys from first click to final purchase

The algorithms find connections your team might overlook.

In a knitwear pilot, a dynamic algorithm rack approach improved forecast accuracy from ~60% to ~80%.

Like how running shoe sales spike exactly three days after marathon registration opens in your area. Or how certain food items see demand jumps when specific recipes trend online.

This isn't future tech. Retailers are using this right now.

McKinsey predicts AI-based demand forecasting is now reducing forecasting errors by 20-50% and cutting lost sales up to 65% in some implementations.

Demand Sensing: Your Early Warning System

Demand sensing takes consumer behavior prediction to another level.

Traditional forecasting tells you what happened last month. Demand sensing tells you what's about to happen next week.

That difference matters when you're deciding:

How much inventory to order
Which products to promote
Where to allocate shelf space
When to adjust pricing

Demand sensing monitors:

Point-of-sale velocity changes across all locations
Inventory depletion rates in real-time
Online search spikes and cart activity
Competitor stock-outs and price changes
External triggers (events, weather, news cycles)

When these signals shift, you receive alerts before they appear in your sales reports.

One footwear retailer (FLO) used AI demand forecasting + allocation to reduce lost sales by 12%.

Glimpse how real-time demand sensing can improve your inventory decisions. Explore Couture.ai's solutions and discover how leading retailers are using AI to remain ahead of demand curves.

Building Smarter Assortments Without The Manual Work

Your assortment shouldn't be identical across every store.

Customer preferences change based on location, demographics, and local context. But managing hundreds of store-specific assortments manually? Nearly impossible.

AI merchandising automates this at scale.

The system analyzes every SKU's performance at every location. It identifies which products work for specific customer segments. Then it recommends assortment changes tailored to each store's demand profile.

This means you can:

Allocate shelf space to items with the highest local demand
Remove underperformers that waste valuable real estate
Test new products in stores where success is most likely
Adjust seasonally based on local climate patterns

One grocery chain used AI merchandising for its beverage category. They discovered stores near gyms needed different sports drink varieties than stores near schools.

Just from better product-location matching, the category saw measurable revenue growth.

The algorithms do in minutes what takes merchandising teams weeks. And they do it continuously - not just during quarterly reviews.

What This Actually Looks Like In Practice

Let's break down a real scenario.

Let's suppose for summer coming a category manager for athletic apparel needs to decide

Which swimwear styles to stock
How much of each size to order
Which stores get which products

The old way:

Look at last summer's sales. Assume similar patterns. Order based on gut feel about what's "trending." Wait and see what happens.

The AI-powered way:

The system analyzes:

Current search trends for swimwear styles
Social media conversations about summer fashion
Weather forecasts for your regions
Past purchase patterns for similar customers
Inventory performance from similar products
Competitor pricing and availability

It predicts:

High-waisted bottoms will outperform last year by 40% in coastal stores
One-piece suits are trending up in suburban family areas
Plus sizes will sell faster than historical averages suggest
Certain color palettes match local preference patterns

You order smarter. Stock arrives where it'll sell. Less markdown waste. Higher margins.

That's the difference between guessing and knowing.

The Outcomes That Truly Count

Here's what changes when you get consumer behavior prediction right:

Less inventory waste. You stock what will sell, not what you think might sell. Markdown rates drop because you're not stuck with products nobody wants.
Better margins. When you have what customers want, you don't need aggressive discounting to move inventory. Products sell at full price.
Higher customer satisfaction. Shoppers find what they're looking for. They don't walk out empty-handed because you're out of stock or carrying the wrong products.
Faster decision-making. Your team spends less time in spreadsheets and more time on strategic initiatives. The system flags what needs attention.
Competitive advantage. While others are reacting to last month's trends, you're positioned for next month's demand.

Conclusion: Key Takeaways

Customer expectations keep rising. Margin pressure keeps intensifying. Inventory costs keep climbing. You need every advantage you can get.

Consumer behavior prediction gives you that advantage. It helps you see what's coming instead of reacting to what already happened.

Keen to see what AI can do for your assortments? Book a free demo call with Couture.ai to explore how our solutions help retailers predict demand and optimize merchandising decisions. See the difference real-time analytics can make in your category performance.