When it comes to generating Requests For Proposals – the bane and the breadwinner for many companies – some organizations have made a science of the process by using proposal optimizers that automatically generate proposals based on the request.
Yet when it comes to maximizing ad sales, few companies have implemented systems to gain the optimal revenue. Perhaps they don’t realize they are only a formula away from improving proposal optimization results, reserving inventory, and driving Cost Per Thousand (CPM).
A common way companies have implemented an ad sales optimization formulation to gain their optimal revenue is described by Bollapragada (2002). In this model, ad spots are valued based on a sales management ranking of selling titles. This approach at best offers improved Request For Proposal response times, but the top-line results might be lacking.
Instead, a preferred approach, which ensures optimal revenue results, relies on a market valuation of each spot that considers total advertiser demand and elasticity. Expressed mathematically, the optimization model formulation needs to be augmented with the following:
In this model, $latex r_stp$ is the price for a spot in selling title s, week t for proposal p. $latex x_stp=1 $ if a spot in s, t is assigned to proposal p, and the demand for s, t $latex (D_st) $ is a function of the spot prices.
In addition, it is also preferred to use a gradual relaxation of advertiser requirements when they are infeasible.
Companies that utilize this approach will optimize their total network revenue under realistic market valuations of spots while still generating Requests For Proposals quicker. That’s the best of both worlds and a significant competitive edge over those who can’t – or don’t – employ a Revenue optimization model.
Bollapragada, S., et al, NBC’s Optimization Systems Increase Revenues and Productivity, Interface, 2002.