Proline Houdini

How we calculate the 90-day forecast: We look at each product's sales history and calculate how many we expect to sell in the next 90 days. The forecast considers: (1) recent sales trends (last 90 days), (2) seasonal patterns from previous years, and (3) whether sales are growing or slowing. We show three scenarios: Low (conservative estimate), Mid (most likely), and High (optimistic estimate).

How we calculate the 90-day forecast: We look at each accessory's sales history and calculate how many we expect to sell in the next 90 days. The forecast considers: (1) recent sales trends (last 90 days), (2) seasonal patterns from previous years, and (3) whether sales are growing or slowing. We show three scenarios: Low (conservative estimate), Mid (most likely), and High (optimistic estimate).

Method: Average weekly sales for each 52-week period compared to the previous period. Shows whether the business is growing or shrinking year-over-year.

Method: Shows CAGR (Compound Annual Growth Rate) from Sep 15, 2024 baseline to each month through latest data (updates weekly). Each bar represents the monthly growth rate needed to reach that month's sales from the baseline. Uses IQR outlier detection for weekly sales within each month.

Method: For each year, outlier weeks (sales jump >2x or drop >50%) are replaced with the yearly average. Each week's % deviation from that year's average is calculated, then averaged across all years. Shows which weeks are typically above/below average. Excludes accessories.

Method: Shows the average percentage of each product type sold per month across all years. Outlier months are removed before averaging.