We consider the problem of utilizing data at the retail/market level on sales and marketing mix variables to help manufacturers optimize the allocation of trade promotional budgets across areas. Major consumer packaged goods manufacturers budget at least one-half of their total marketing expenses to trade promotions. Trade promotional deals are designed to encourage retailers to promote products by temporarily reducing the price, putting them in in-store displays, or advertising in local media. A profit-based trade promotional allocation system will require estimates of the responsiveness of sales at each retailer to a given promotion. A major barrier to the use of retailer data is the proliferation of incorrectly signed coefficients in standard least squares analyses. Even more sophisticated adaptive shrinkage methods will not remove the problem of improper signs. We propose a hierarchical model to modeling retailer response that uses a first-stage prior with inequality constraints on the regression coefficients. We demonstrate the usefulness of our modeling approach with data on more than 75 retailers. We find substantial profit opportunities from our response-based promotional allocation scheme over and above what might be achieved by a standard volume-oriented allocation scheme.