Retail & E-Commerce
5,000 SKUs. 3 channels. The optimal price for each.
Demand elasticity, competitor pricing, seasonal patterns, channel constraints. kint calculates the prices that maximize margin across every product and every channel.
1-3%
revenue increase from pricing optimization
Boston Consulting Group
€400B
annual dead stock cost in European retail
EHI Retail Institute
10-25%
of pricing decisions are suboptimal
McKinsey Pricing & Promotions
When Thousands of Prices Change Everything
A competitor just dropped prices on 200 products. Do you match? Which ones? How does that affect the margin on complementary products? What about the seasonal inventory that needs to clear? Your merchandising team knows the market. But calculating the optimal response across 5,000 interdependent prices isn't a human-scale problem.
Price interactions are invisible
Dropping the price on product A increases demand for product B. These cross-elasticity effects compound across thousands of SKUs. No spreadsheet can model them.
Channel conflicts erode margin
Online, in-store, and wholesale prices need to be consistent yet optimized per channel. Manual coordination breaks down at scale.
Seasonal clearance timing is guesswork
Mark down too early, you lose margin. Too late, you're stuck with dead stock. The optimal timing depends on demand curves.
Competitor response creates urgency
When a competitor changes prices, your window to respond is hours, not days. Manual repricing at scale is too slow.
Comparison
Status Quo vs. With kint
Pricing decisions
Weekly team review
Daily automated optimization
Gross margin
Baseline
+7% average improvement
Dead stock
12-18% of inventory
24% reduction
Price change response
2-3 days
Same day
Assortment planning
Quarterly, gut-feel
Continuous, data-driven
Process
How It Works for Retail & E-Commerce
Connect your sales and pricing data
POS data, competitor prices, inventory levels, margin targets. Any format your systems already export.
kint learns your market
Demand elasticity, cross-product effects, seasonal patterns. Your team validates the model against their experience.
Optimize and review
kint calculates optimal prices per SKU per channel. Your merchandisers review and approve.
Monitor and re-optimize
Continuous feedback loop. New competitor data or inventory changes trigger re-optimization automatically.
Use Cases
What kint solves in Retail & E-Commerce.
Pricing Optimization
“Optimize prices for 5,000 SKUs across 3 channels”
kint computes profit-maximizing prices factoring in demand elasticity, competitor pricing, inventory levels, and channel constraints.
Each SKU gets a channel-specific price that maximizes total margin. Cross-product effects are accounted for. Your merchandisers see exactly why each price was set.
+7%
gross margin
+3%
revenue
daily updates
Assortment Planning
“Select optimal product mix for 120 store locations”
kint determines the best assortment per location based on local demand, shelf space, supplier terms, and margin targets.
Each store gets a tailored product mix. High-demand items get more shelf space. Low performers are flagged for removal. The assortment maximizes sales per square meter.
+11%
sales/sqm
-24%
dead stock
+5%
margin
Workforce Scheduling
“Schedule 300 staff across 45 locations for peak season”
kint generates optimal shift plans matching predicted foot traffic, skill requirements, labor laws, and budget constraints.
Staffing matches demand curves per location and per hour. Peak hours get more staff. Quiet periods get fewer. Labor law compliance is guaranteed by the model.
-20%
labor cost
+15%
coverage
98%
fill rate
Your Problem
These are examples. kint is not limited to predefined use cases.
FAQ
Common Questions About Retail & E-Commerce Optimization
Yes. Any constraint you define is respected. Minimum margins, maximum discounts, competitor matching rules, bundle pricing logic. kint optimizes within your guardrails.
Promotions are modeled as temporary constraint changes. kint optimizes the promotional price, predicts the demand lift, and calculates the net impact on margin.
Start small. Run kint on one product category, measure the impact, then expand. Most customers start with their highest-volume or most competitive categories.
kint trains models from your historical sales data. Price changes, promotions, and seasonal patterns reveal how demand responds. The more data, the more accurate the model.
For OEM Partners
Building POS, merchandising, or e-commerce platforms?
Your customers want pricing optimization. Build it into your product with kint.
Show Us Your Retail & E-Commerce Problem
30 minutes. Your data. We'll show you what optimal looks like.