eTURF: A competitive TURF algorithm for large datasets

Although Total Unduplicated Reach and Frequency (TURF) analysis has repeatedly demonstrated value in a variety of market research applications, TURF analyses on large problem sizes have historically either been unacceptably slow or approximate in their solutions. To resolve this dilemma, we begin by identifying and explaining the principle of non-synergy that is present in all situations for which TURF analyses apply. We use this principle to provide a competitive algorithm, called eTURF, for determining exact solutions to large TURF problems in reasonable time and we provide an illustrative example to demonstrate how eTURF can obtain considerable speed improvements. We then discuss how eTURF is useful not only in terms of providing optimal solutions but also in providing a metric by which to gauge the quality of heuristic TURF solutions.

► A novel algorithm (eTURF) to find best TURF solutions on large datasets is given.
► The principle of non-synergy is identified to improve computational efficiency.
► eTURF can be used to evaluate the quality of heuristic TURF solutions.
► eTURF can be used to evaluate the quality of pre-specified portfolios.