Information Retrieval for Media Planning

A critical problem, continually faced by managers in developing "effective" resource allocation plans, is that only limited partial information is available about the system under consideration on which to base these plans. This is particularly true in planning involving the limited information typically available on the relevant behavior and characteristics of large human populations. This paper develops the insight that partial information--which at first scrutiny appears to contain only severely limited information about the system--in many important cases actually contains (or stores) a great deal of the relevant total system information needed for planning (or control). This insight leads to powerful methods-for retrieving the needed information about the behavior and characteristics of the total system from the information contained in the partial information data. In the present paper, we illustrate these information retrieval methods by developing and applying them to partially specified media audience exposure systems to retrieve certain critical total system information needed for media planning. More specifically, partial information on the audience of a combination of advertising media vehicles consisting of only two information quantities--the sum of the individual audiences, and the sum of the audiences of all pairs of media vehicles in the combination--is shown, in fact, to contain a significant amount of the information about the audience exposure pattern of the combination that is needed for media planning evaluation. Theory and methods are developed which retrieve, from these two quantities, operationally useful information on the unduplicated audience, the average exposure frequency, and the frequency distribution of audience exposure of the combination. The retrieval methods developed in this paper have been successfully applied to evaluating and developing "effective" media selection programs. These retrieval methods apply as well to management information systems involving marketing, demographic, socio-economic and related partially specified information systems in which individuals are classified in terms of the presence or absence of each of a large number of relevant attributes. In addition, these retrieval methods can be extended to retrieve additional information about the total system, where required, including the development of usefully precise single value estimates of the critical information quantities of the total system.