What is seasonality?
In demand forecasting, “seasons” are refined datasets used to build a forecast. Analysts working from historical data can choose between extensive datasets or more targeted selections, and the act of selecting and refining that data is seasonality. It’s refined further by day of week, since Tuesday demand can differ sharply from Saturday — and exploiting those differences improves accuracy.
Why it’s critical
Selecting the right historical data is essential to forecast accuracy, and the forecast drives pricing, promotions, budgets, and sales. Accurate forecasting improves revenue precisely because it informs every downstream decision.
Finding the best seasons
It’s a risk-reward balance. Should a property with 15 years of history use all of it, or only recent months? Using everything can miss recent trends and underforecast; using only six months can overforecast after a disruption like a pandemic. Quibble’s revenue managers use data and market intelligence to decide how many seasons a property needs, identify day-of-week patterns, and update seasonality to capture emerging trends while watching local and global shifts.