Editing Conjoint analysis (section)

{{Short description|Survey-based statistical technique}}{{Mcn|date=January 2025}}[[File:Ice-cream-experiment-example.png|thumb|Example choice-based conjoint analysis survey with application to marketing (investigating preferences in ice-cream)]]

'''Conjoint analysis''' is a survey-based [[Statistics|statistical]] technique used in [[market research]] that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service.

The objective of conjoint analysis is to determine the influence of a set of attributes on respondent choice or decision making. In a conjoint experiment, a controlled set of potential products or services, broken down by attribute, is shown to survey respondents. By analyzing how respondents choose among the products, the respondents' valuation of the attributes making up the products or services can be determined. These implicit valuations ([[Utility|utilities]] or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.

Conjoint analysis originated in [[mathematical psychology]] and was developed by marketing professor [[Paul E. Green]] at the [[Wharton School of the University of Pennsylvania]]. Other prominent conjoint analysis pioneers include professor [[V Srinivasan|V. "Seenu" Srinivasan]] of Stanford University who developed a [[linear programming]] (LINMAP) procedure for rank ordered data as well as a self-explicated approach, and Jordan Louviere (University of Iowa) who invented and developed choice-based approaches to conjoint analysis and related techniques such as [[best–worst scaling]].

Today it is used in many of the social sciences and applied sciences including [[marketing]], [[product management]], and [[operations research]]. It is used frequently in testing customer acceptance of [[new product development|new product designs]], in assessing the appeal of [[advertising|advertisements]] and in [[service design]]. It has been used in [[positioning (marketing)|product positioning]], but there are some who raise problems with this application of conjoint analysis.

Conjoint analysis techniques may also be referred to as '''multiattribute compositional modelling''', '''discrete choice modelling''', or '''stated preference research''', and are part of a broader set of trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade-Off, [[Simalto]], and mathematical approaches such as [[Analytic hierarchy process|AHP]],<ref>{{cite journal|title=A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation|vauthors=Ijzerman MJ, van Til JA, Bridges JF|year=212|pmid=22185216|doi=10.2165/11587140-000000000-00000|volume=5|issue=1|journal=The Patient |pages=45–56|s2cid=207299893|url=https://research.utwente.nl/en/publications/comparison-of-analytic-hierarchy-process-and-conjoint-analysis-methods-in-assessing-treatment-alternatives-in-stroke-rehabilitation(fddc10ca-bb9d-4c47-b2eb-1b5075a5c27a).html}}</ref> [[Potentially All Pairwise RanKings of all possible Alternatives|PAPRIKA]],<ref>{{cite journal|title=Clinical decision-making for thrombolysis of acute minor stroke using adaptive conjoint analysis |vauthors=Liberman AL, Pinto D, Rostanski SK, Labovitz DL, Naidech AM, Prabhakaran S |year=2019 |doi=10.1177/1941874418799563 |volume=9|issue=1|journal= The Neurohospitalist |pages=9–14 |pmid=30671158 |pmc=6327243 }}</ref><ref>{{cite journal|title=Cloud computing adoption decision modelling for SMEs: a conjoint analysis |vauthors=Al-Isma'ili A, Li M, Shen J, He Q | year=2016 | doi= 10.1504/IJWGS.2016.079157 |volume=12|issue=3|journal= International Journal of Web and Grid Services |pages=296–327 |url=https://www.inderscience.com/info/inarticle.php?artid=79157 |url-access=subscription }}</ref> [[evolutionary algorithms]] or rule-developing experimentation.