Data Driven: Responsive Forecasting
Breaking Down the Curve
Across the top of the chart is a standard B-to-C Fall/Holiday order curve for the U.S. market. This "standard" is compiled from numerous business categories, and includes historical data from the past two years. The realities of the new economy during the past two years are reflected in a higher percentage of forecasted orders within the first six weeks of activity than previously seen. Again, because this incorporates data across a wide array of industries, it should only be used as a benchmark rather than an accurate curve for any one particular business.
Two mailed campaigns are planned for October, the first one in-home the week of Oct. 9 (allowing for some early delivery in limited areas during the latter part of the week of Oct. 2), and a second drop planned to hit the week of Oct. 23. Both drops are planned for $100,000 in revenue/gross sales.
It is important to note the impact of the two major U.S. holidays during the campaign: Thanksgiving and Christmas. Sales one week prior and one week following Thanksgiving week generally see an accelerated sales boost, while Thanksgiving week is generally much slower for mailed campaigns. The week before Christmas and Christmas week itself are affected by the last day to order for guaranteed Christmas delivery dates. Please note that, in the chart, both of these holidays are accounted for in the order curve. Similar nuances may be necessary for Easter during a spring campaign, depending upon your industry.
Let's assume your organization hires significant seasonal call center and warehouse staff to help fulfill holiday orders. If you find that, coming into the week of Nov. 13, the cumulative percentage of planned orders-to-date is 70 percent for the first drop and 40 percent for the second drop, you can use the order response curve to re-forecast your total season upward. Then you will be able to hire additional call center and warehouse staff to meet the revised season forecast increase.