방법 비교

	TURF Analysis	Voice of Customer Analysis
분야	Marketing Science	Marketing Science
계열	Process / pipeline	Process / pipeline
기원 연도	2013	1993
창시자	Gene Miaoulis & Valentine Free (media-planning origins); formalized as optimization by Daniel Serra	Abbie Griffin & John R. Hauser
유형	Combinatorial optimization pipeline for product-line / assortment reach maximization	Structured qualitative-to-structured pipeline for eliciting, organizing, and prioritizing customer needs
원전	Serra, D. (2013). Implementing TURF analysis through binary linear programming. Food Quality and Preference, 28(1), 382-388.	Griffin, A., & Hauser, J. R. (1993). The Voice of the Customer. Marketing Science, 12(1), 1-27.
별칭	Total Unduplicated Reach and Frequency, Reach Maximization Analysis, Product Line Optimization (TURF), Assortment Reach Analysis	VoC Analysis, Voice of the Customer, Customer-Needs Elicitation, VoC for Quality Function Deployment
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.	Voice of Customer (VoC) analysis is a structured method for hearing what customers actually need, in their own words, and turning that into a prioritized, organized set of requirements for product development. Abbie Griffin and John Hauser established its modern foundations in their 1993 Marketing Science article, which examined the customer-needs component of Quality Function Deployment and answered practical questions: how many customers to interview, how to extract needs from verbatims, how to structure them, and whether one-on-one interviews or focus groups are more efficient. Their key empirical findings — that needs accumulate toward saturation, that a modest number of interviews uncovers most needs, and that one-on-one interviews are at least as productive per dollar as focus groups — turned VoC from an art into a repeatable research process. The method distills raw customer statements into solution-free need statements, organizes them into a primary-secondary-tertiary hierarchy through customer sorting, and assigns importance weights using survey priorities, an idea closely tied to importance-performance thinking. Those weighted, structured needs then feed Quality Function Deployment, where they are mapped onto engineering attributes to drive design decisions.