Demand Forecasting for Retail
How Increff Forecasts True Demand
- Designed for Fashion & Retail Demand
- Self-Learning Algorithms
- Seasonal, Event-Driven & Scenario Planning
- From Forecast to Profitable Action
Designed for Fashion & Retail Demand
Self-Learning Algorithms
Seasonal, Event-Driven & Scenario Planning
From Forecast to Profitable Action
Frequently Asked Questions
What is demand forecasting in retail?
Demand forecasting in retail is the practice of predicting how much of each product customers will actually buy, at a specific location and time, so that buying, allocation, replenishment, and markdown decisions are based on real demand rather than guesswork. It uses historical sales, seasonality, events, and product attributes to project future demand at the store and SKU level.
What is true demand, and how is it different from historical sales?
True demand is the quantity customers would have bought if a product had been fully available, whereas historical sales only capture what was actually sold. Historical sales are distorted by stockouts, broken size sets, and discount-driven liquidation, so forecasting on raw sales repeats past mistakes. True demand cleans out that noise to reveal genuine, full-price demand.
What is Rate of Sale (ROS), and what makes true ROS™ different?
Rate of Sale (ROS) is the pace at which a product sells over a given period. True ROS™ measures a daily rate of sale for each store–style using only the days a style was genuinely live and available with a minimum representation of pivotal sizes, so the rate is not diluted by stockout or broken-size days. This produces a far more accurate base for forecasting.
How does demand forecasting reduce markdowns and improve full-price sellthrough?
By predicting true demand at the store and SKU level, demand forecasting places the right depth of the right products where they will sell at full price, reducing overstock that later needs discounting. Better initial buys and allocation mean fewer markdowns and higher full-price sell-through — brands typically see a lift in full-price sell-through and improved margins.
How does Increff forecast demand at the store–SKU level?
Increff uses patent-pending, self-learning algorithms that consume raw sales data and automatically clean out stockout, brokenness, and liquidation noise. It forecasts every store individually — no forced clustering — across up to 17 levels of fashion attributes, while accounting for seasonality, events, and operating constraints such as planogram, MOQ, lead time, and safety stock.


.webp)
.webp)




.webp)
















