When performing a TURF (Total Unduplicated Reach and Frequency) analysis, clients often ask: How much does each item contribute to the success of the portfolio of items? For example, in a portfolio of three ice cream flavors {Cherry, Chocolate, Coconut} which reaches 97% of customers, how much does each individual flavor contribute? This article shows how order of entry matters to this question, so a Shapley Value Attribution approach averages across all potential orders of entry to compute the contribution of each item in the portfolio to the total reach of the portfolio. It’s a computationally intensive but correct way to address the question of item contribution to the reach of a given portfolio.

TURF stands for "Total Unduplicated Reach and Frequency" and is an optimization search routine for finding optimal sets of items (brands, claims, attributes) that motivate (reach) respondents. This introductory paper describes how TURF works within our software offering TURF optimization called MaxDiff Analyzer. Many different types of data may be used with our TURF routine including Likert scales, consideration (pick any), and MaxDiff.

Traditional TURF optimization uses an exhaustive search approach, examining all possible portfolios (sets of items) of a specific size en route to discovering the optimal portfolio(s). However, problems can quickly become too large for exhaustive search to compute within reasonable time frames. A shortcut, heuristic approach is described called Stepwise TURF + Swaps that finds all 300 top portfolios in three very large commercial data sets. The heuristic searches take at most a few minutes, whereas the exhaustive search would take many days or years to compute.