Forecast Demand for NewProducts WithoutHistorical Data

What problem this solves

New product launches involve significant uncertainty. Organizations need to estimate expected demand, production or inventory requirements, and revenue potential. However, new products do not have historical data. This makes forecasting difficult, highly dependent on assumptions, and prone to error.

Why existing approaches fall short

Common approaches include analog-based estimation (similar products), expert judgment, and small pilot studies. While useful, these methods lack consistency, do not scale well, and are difficult to validate. They also struggle to incorporate market conditions, pricing strategies, and external factors.

How Fount approaches new product forecasting

Fount uses a combination of historical data from similar products, category-level demand patterns, causal relationships (price, promotions, seasonality), and early signals (pre-orders, pilot data if available) to estimate demand for new products.

The system focuses on identifying relevant analogs, adjusting for differences, and incorporating current market conditions.

What the system produces

Fount provides: demand forecasts for new products, confidence ranges reflecting uncertainty, key assumptions and drivers, and scenario simulations (e.g., pricing, launch timing). This allows teams to plan inventory and production, evaluate launch strategies, and manage risk.

Where this creates value

New product forecasting enables better launch planning, reduced risk of overproduction or stockouts, improved alignment between teams, and more informed decision-making. The key benefit is structured estimation under uncertainty.