How Promotions Should Affect Forecasts
By Jon and Matt Schreibfeder

Promotions are designed to increase sales of particular products. They can take the form of a price reduction, special advertising, demonstrations, or something else to increase customer interest. Keep in mind there are two types of promotions:

  • Those designed to reduce surplus, excess, or otherwise unwanted stock.
  • Those designed to increase future sales of specific products

To ensure you have adequate inventory to fulfill the anticipated increase in sales, you must add a collaborative element to your forecasts based on past usage history. To obtain optimal results, we suggest you follow these steps:

1. Define the promotions that should be monitored. Estimate the anticipated increase in sales. At first these will probably be rough approximations, but these predictions should improve as you gain experience in planning promotions. Be sure that your buyers have adequate time to bring in the needed additional inventory to fulfill the increased anticipated demand.

2. Specify the beginning and ending month and day that sales will be affected by each occurrence of the promotion. Sometimes there may be a lag between when a promotion begins and when sales will be affected. For example, a promotion campaign may begin on the date a flyer goes in the mail. But we don’t expect sales to start to increase until the day after the advertisement is delivered to potential customers.

3. After the promotion ends, calculate the sales per day (in units) of each item in the week before the start of the event, the sales per day during the event, and the sales per day in the week following the event.

4. With the information from step #3 above calculate the percentage difference in sales between the period before the event and the period during the event as well as the difference in sales between the period before the event and the period after the event.

       Event           Start Day        End Day      Prior Wk     Event        Post Wk    Prior-        Prior-
                                                                         Sls/Day       Sls/Day      Sls/Day    Event %    Post %
Promotion-01   January 1      January 14      82              104              74           26.8%      -9.8%
Promotion-01   June 1            June 14           86                98              82           14.0%       -4.7%
Promotion-02   February 7   February 10    74                86              70           16.2%       -5.4%
Promotion-02   October 14  October 21      93              102              89            9.7%        -4.3%

Why are we interested in what will happen to usage after the promotion has ended? Many promotions are followed by a “boomerang effect” or reduction in sales. After all, people considering buying the product probably purchased it during the promotion. In order for a promotion to be successful, the usage increase during the promotion must be greater than the boomerang effect after the promotion has ended. If it isn’t, you have only given away profit dollars or increased your costs without realizing higher sales.

Usage should be adjusted to take away the effects of the promotion because we cannot be sure that the same promotion will be offered at the same time each year. For example, we must adjust January’s usage to reduce usage per day by 26.8% for fourteen days (January 1 through January 14) and increase usage by 9.8% for the seven days after the promotion 01 has ended. This is referred to as “normalized usage.” We can calculate what usage would have been without the effects of the promotion with the equation:

Actual Usage ÷ (1+ % Difference from Normal Usage)

In the first occurrence of Promotion-01 above actual usage was 104 pieces during the event, a 26.8% increase over the prior week’s usage. Using the equation, we predict that usage would have been 82 pieces per day had the promotion not occurred:

104 ÷ (1 + 26.8%) = 82 pieces per day

After the event sales fell by 9.8% using the same formula, we would predict that usage would have been 81 pieces per day had the promotion not occurred:

74 ÷ (1- 9.8%) = 81 pieces per day

Note that normalized usage will not always be equal to usage recorded immediately prior to the event. Other factors (such as seasonality) will influence the results.

5. Accumulated promotion history can help guide buyers and inventory planners as they anticipate what will happen to sales when the promotion is offered again in the future. For example, promotion #1 has resulted in an average increase in usage of 20.4% [(26.8% + 14.0%) ÷ 2]. When promotion 01 is offered again, a buyer should consider increasing the results from the forecast demand formula by 20.4% to compensate for the
anticipated additional sales.

Implementing “best practice” promotion planning will help your organization increase sales and achieve the goal of effective inventory management. Please let us know if you have any questions.