Sammenlign metoder

	Latent-Class Choice Segmentation	TURF Analysis
Fagfelt	Marketing Science	Marketing Science
Familie	Latent structure	Process / pipeline
Opprinnelsesår	1989	2013
Opphavsperson	Wagner A. Kamakura & Gary J. Russell	Gene Miaoulis & Valentine Free (media-planning origins); formalized as optimization by Daniel Serra
Type	Finite-mixture choice model for simultaneous segmentation and response estimation	Combinatorial optimization pipeline for product-line / assortment reach maximization
Opprinnelig kilde	Kamakura, W. A., & Russell, G. J. (1989). A Probabilistic Choice Model for Market Segmentation and Elasticity Structure. Journal of Marketing Research, 26(4), 379-390.	Serra, D. (2013). Implementing TURF analysis through binary linear programming. Food Quality and Preference, 28(1), 382-388.
Alias	Finite-Mixture Logit Segmentation, Latent-Class MNL, Mixture Choice Model, Concomitant-Variable Latent-Class Choice Model	Total Unduplicated Reach and Frequency, Reach Maximization Analysis, Product Line Optimization (TURF), Assortment Reach Analysis
Relaterte	3	3
Sammendrag	Latent-class choice segmentation estimates consumer market segments and their preferences at the same time, by fitting a finite mixture of discrete-choice models to individual purchase or choice data. Wagner Kamakura and Gary Russell introduced the approach in their 1989 Journal of Marketing Research paper, which fit a probabilistic choice model whose latent segments differ in both brand preference and price sensitivity, yielding a unified picture of market structure and elasticities. Rather than clustering consumers first and modeling choice afterward, the method treats segment membership as an unobserved (latent) variable and recovers it jointly with the segment-level choice parameters by maximum likelihood. Each segment is a multinomial logit model with its own coefficient vector, and the mixing proportions describe how large each segment is. Michel Wedel and Wagner Kamakura's authoritative monograph later codified the finite-mixture framework as the methodological backbone of model-based market segmentation. The result links the pattern of brand switching to the magnitudes of own- and cross-price elasticities, giving managers a behaviorally grounded segmentation tied directly to demand response.	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.