Markdown Optimization
“When should we markdown each product?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
When should we markdown each product?
Retailers markdown $300B+ in merchandise annually (Coresight Research), typically using calendar-based rules: 20% off at week 8, 40% off at week 12, 70% off at week 16. This one-size-fits-all approach ignores that some products still have strong demand and could be sold at a smaller discount, while others should be marked down earlier to avoid total write-offs. A fashion retailer generating $2B in annual revenue loses $200-300M in margin to poorly timed markdowns, either marking down too early (leaving money on the table) or too late (forcing deep clearance that barely covers cost).
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects products, sales velocity, inventory positions, customer browsing patterns, competitive pricing, and seasonal trends into a relational graph. The model predicts that Product P-7012 (Fall Jacket, Navy) still has 18 days of full-price demand because browsing sessions are increasing, the weather forecast shows a cold snap, and similar products at competitors are sold out. Meanwhile, P-7015 (Summer Dress, Floral) should be marked down 25% immediately because browsing has dropped 80%, remaining inventory is 3x what will sell in the next 30 days at current price, and similar items on competitor sites are already at 40% off.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
PRODUCTS
| product_id | name | category | full_price | cost | season |
|---|---|---|---|---|---|
| P-7012 | Fall Jacket, Navy | Outerwear | $189.00 | $68.00 | Fall 2025 |
| P-7015 | Summer Dress, Floral | Dresses | $79.00 | $22.00 | Summer 2025 |
| P-7020 | Classic Wool Sweater | Knitwear | $129.00 | $45.00 | Fall 2025 |
INVENTORY_POSITION
| product_id | units_remaining | weeks_on_floor | sell_through_pct |
|---|---|---|---|
| P-7012 | 420 | 6 | 58% |
| P-7015 | 680 | 14 | 32% |
| P-7020 | 310 | 4 | 69% |
SALES_VELOCITY
| product_id | units_sold_7d | units_sold_30d | velocity_trend |
|---|---|---|---|
| P-7012 | 45 | 180 | Accelerating |
| P-7015 | 8 | 52 | Decelerating |
| P-7020 | 38 | 160 | Stable |
MARKET_SIGNALS
| product_id | browsing_trend_7d | competitor_status | weather_outlook |
|---|---|---|---|
| P-7012 | +35% | Sold out at 2 competitors | Cold snap forecast |
| P-7015 | -80% | 40% off at 3 competitors | Warm fall |
| P-7020 | +10% | In stock, full price | Seasonally normal |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SALES_VELOCITY.UNITS_SOLD_7D, 0, 28, days) FOR EACH PRODUCTS.PRODUCT_ID ASSUMING PRODUCTS.MARKDOWN_PCT = 0.25
Prediction output
Every entity gets a score, updated continuously
| PRODUCT_ID | NAME | MARKDOWN_REC | OPTIMAL_TIMING | MARGIN_SAVED_VS_CALENDAR |
|---|---|---|---|---|
| P-7012 | Fall Jacket, Navy | 0% (hold) | Week 10+ | +$18,900 |
| P-7015 | Summer Dress, Floral | 30% now | Immediate | +$8,400 |
| P-7020 | Classic Wool Sweater | 0% (hold) | Week 8+ | +$6,200 |
Understand why
Every prediction includes feature attributions — no black boxes
Product P-7015 (Summer Dress, Floral)
Predicted: Mark down 30% immediately
Top contributing features
Browsing interest collapse
-80% in 7d
30% attribution
Inventory vs remaining demand
3x oversupply
25% attribution
Competitor markdown pressure
40% off at 3 sites
20% attribution
Seasonal relevance declining
Warm fall forecast
14% attribution
Sell-through rate below target
32% vs 60% goal
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Recover 15-30% of margin typically lost in calendar-based clearance events, saving $30-90M annually for a $2B fashion retailer.
Related use cases
Explore more retail & e-commerce use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
