방법 비교

	TURF Analysis	Importance-Performance Analysis
분야	Marketing Science	Marketing Science
계열	Process / pipeline	Process / pipeline
기원 연도	2013	1977
창시자	Gene Miaoulis & Valentine Free (media-planning origins); formalized as optimization by Daniel Serra	John A. Martilla & John C. James
유형	Combinatorial optimization pipeline for product-line / assortment reach maximization	Two-dimensional diagnostic grid for prioritizing attribute improvements
원전	Serra, D. (2013). Implementing TURF analysis through binary linear programming. Food Quality and Preference, 28(1), 382-388.	Martilla, J. A., & James, J. C. (1977). Importance-Performance Analysis. Journal of Marketing, 41(1), 77-79.
별칭	Total Unduplicated Reach and Frequency, Reach Maximization Analysis, Product Line Optimization (TURF), Assortment Reach Analysis	IPA, Importance-Performance Mapping, Action Grid Analysis, Quadrant Analysis
관련	3	3
요약	TURF analysis — Total Unduplicated Reach and Frequency — answers a portfolio question: which limited set of products, flavors, features, or messages reaches the largest number of distinct customers with at least one option they like? The reach-and-frequency idea originated in media planning, where reach is the share of an audience exposed at least once and frequency is the average number of exposures, and was carried into product-line research by Gene Miaoulis and colleagues. The defining word is 'unduplicated': a customer who likes three items in the set is still only one person reached, so TURF rewards complementary, non-overlapping appeal rather than piling up popular-but-redundant items. Daniel Serra formalized the selection problem as binary linear programming, showing it can be solved exactly and efficiently even for large candidate sets instead of relying on exhaustive enumeration. Wedel and Kamakura situate TURF within assortment and segmentation strategy as a tool for choosing a product line that covers a heterogeneous market. The output is a recommended assortment of a chosen size together with its reach curve, guiding line extensions, menu design, and message portfolios.	Importance-Performance Analysis (IPA) is a simple, durable diagnostic for deciding where to focus improvement effort by combining how much customers care about each attribute with how well the offering performs on it. John Martilla and John James introduced it in a 1977 Journal of Marketing note, using automobile-dealer service data to show that satisfaction depends jointly on the salience of attributes and judgments of actual performance. The technique plots each attribute as a point on a two-dimensional grid — importance on one axis, performance on the other — divided into four quadrants by crosshairs, and reads off a managerial action for each quadrant. The headline insight is that high-importance, low-performance attributes are where to 'concentrate here,' while resources poured into low-importance, high-performance attributes represent 'possible overkill.' Because it rests on a clear conceptual link between salient-attribute importance and performance, IPA pairs naturally with structured customer-needs work such as the Voice of the Customer. Its visual action grid makes priorities legible to managers without statistical training, which is why it has spread far beyond its original marketing context.