Responsive Forecasting


How do you create a response order curve to accurately reflect how close you will be to plan mid-way through a marketing campaign? How can that curve help efficiently manage staffing and/or inventory concerns? How do major holidays affect those 
forecasted curves?

Forecasting, by its very nature, leads to criticism. Unless you are 100 percent accurate every time out, you have “failed.” However, reasonably accurate order response curve forecasts can allow an organization to efficiently manage staffing requirements during peak periods, solve potentially catastrophic inventory issues mid-season, and reasonably plan cash flow operations.

Finding Order
An order response curve is the historical calculation of orders—and percentage of orders—received weekly from a direct response campaign. Construct, from the past two years, a weekly response pattern of each promotional campaign. This is the percentage of orders received by week for the drop. If you have four mailings a year, you need to develop four weekly response curves, one for each season. If you have remailings of each major campaign, those need to be tracked and a separate response pattern built for each drop. Ideally, a direct marketer will have two or three years of history to compare for each seasonal drop.

From this information, you can identify the “half life” of each mailpiece or catalog—the point when 50 percent of the orders are in. The control buying team will use this “doubling point” when placing 
merchandise reorders.


A sample of an actual order response curve is shown in the chart. (Note: While the response order curves presented below deal with print campaigns, the same concepts hold true for either pure-play Web marketing campaigns or multichannel initiatives.)

Be forewarned, however. Even though a standard order curve is presented, you cannot assume that this response curve is appropriate for your company. You must build a response curve for your marketing campaign, one that incorporates your industry’s nuances and seasonality. In addition, while order response curves are built upon historical data, major changes in product line, competitive landscape or economic outlook need to be factored in to maintain a reasonable level 
of accuracy.

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.