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Estimating Rebate Liability
Accurately predicting response rates is probably the most hotly debated and least understood dynamic in sales promotion today. The reason is there is no longer a valid standard model with which to predict consumer behavior. In years past, one could with reasonable certainty, say that on-pack offers would get “x” percent response, FSIs would generate “y” percent, and direct mail would result in “z” percent.
Then what has changed that makes this a challenge?
The first thing is that manufacturers no longer run the same types of offers quarter after quarter and year after year. They also no longer have a database of 200 similar offers from which to draw reasonable response rate estimates. Additionally, corporate turnover has virtually eliminated the one internal resource that used to know everything. Then came the variables. Today’s marketing environment is more robust than at any time in the history of sales promotion. Retail influence is still phenomenal- and unpredictable – as is the influence of the internet – on direct selling, marketing, and sales promotion. And those are in addition to the already mind- boggling array of more traditional variables that influence a consumer’s propensity to respond to rebates and other sales promotion initiatives.
Some of those variables appear below:
· Media (where and how the rebate is advertised – FSI, take-ones, on-pack)
· Original purchase price of item
· Sale or featured price of item
· Any in ad support with “net after rebate” price featured
· Rebate amount as a percent of selling price
· Seasonality (if applicable)
· Degree of difficulty
· Competitive offers in the marketplace at the same time
· Perceived value
· Radio mentions
· TV support
· Internet support
· Value of proof of purchase
· Quantity of proofs required
· Requirement for multiple and or unrelated purchases
· Market share of product sold
· Sales channel
It is the unique combination of these factors that make it virtually impossible for most marketing and financial managers to accurately predict the outcome of every offer.
Estimating accurately is not only common sense for fiscal responsibility, but is also a critical component of proper budgeting. For instance, the Task Force from the Financial Accounting Standards Board recently recommended that “if the amount of future rebates or refunds cannot be reasonably and reliably estimated, a liability (or “deferred revenue”) should be recognized for the maximum potential amount of the refund or rebate (that is, no reduction for breakage should be made”).
The two most important considerations in predicting response rates are: the quantity of available mail-in certificates, and the quantity of actual products in the market during the entire duration of the offer. It will be very helpful to compare the estimated response rate of the media to the estimated percent of products anticipated to rebated against for a more accurate prediction. For instance, if there are ten million mail-in certificates available, and a 3% response rate is predicted (300,000) – does that number make sense if 15,000,000 products are in the market during the rebate (i.e. is it logical that 20% of all product sold during the rebate period will have been submitted for a rebate).
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