Conjoint analysis is a survey-based 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 what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to survey respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.
Conjoint originated in mathematical psychology and was developed by marketing professor Paul E. Green at the Wharton School of the University of Pennsylvania and Data Chan. Other prominent conjoint analysis pioneers include professor V. "Seenu" Srinivasan of Stanford University who developed a linear programming (LINMAP) procedure for rank ordered data as well as a self-explicated approach, Richard Johnson who developed the Adaptive Conjoint Analysis technique in the 1980s 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 designs, in assessing the appeal of advertisements and in service design. It has been used in 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 is 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 AHP, evolutionary algorithms or rule-developing experimentation.
- 1 Conjoint design
- 2 Types
- 3 Information collection
- 4 Analysis
- 5 Advantages and disadvantages
- 6 Practical applications of conjoint analysis
- 7 Available software tools for conjoint analysis
- 8 See also
- 9 References
- 10 External links
A product or service area is described in terms of a number of attributes. For example, a television may have attributes of screen size, screen format, brand, price and so on. Each attribute can then be broken down into a number of levels. For instance, levels for screen format may be LED, LCD, or Plasma.
Respondents would be shown a set of products, prototypes, mock-ups, or pictures created from a combination of levels from all or some of the constituent attributes and asked to choose from, rank or rate the products they are shown. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank orders, or preferences among alternative combinations.
As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data are available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider.
The earliest forms of conjoint analysis were what are known as Full Profile studies, in which a small set of attributes (typically 4 to 5) are used to create profiles that are shown to respondents, often on individual cards. Respondents then rank or rate these profiles. Using relatively simple dummy variable regression analysis the implicit utilities for the levels can be calculated. Two drawbacks were seen in these early designs.
Firstly, the number of attributes in use was heavily restricted. With large numbers of attributes, the consideration task for respondents becomes too large and even with fractional factorial designs the number of profiles for evaluation can increase rapidly. In order to use more attributes (up to 30), hybrid conjoint techniques were developed. The main alternative was to do some form of self-explication (rating of separate components) before the conjoint tasks and some form of adaptive computer-aided choice over the profiles to be shown.
The second drawback was that the task itself was unrealistic and did not link directly to behavioural theory. In real-life situations, the task would be some form of actual choice between alternatives rather than the more artificial ranking and rating originally used. Jordan Louviere pioneered an approach that used only a choice task which became the basis of choice-based conjoint analysis and discrete choice analysis. This stated preference research is linked to econometric modeling and can be linked to revealed preference where choice models are calibrated on the basis of real rather than survey data. Originally choice-based conjoint analysis was unable to provide individual level utilities as it aggregated choices across a market. This made it unsuitable for market segmentation studies. With newer hierarchical Bayesian analysis techniques, individual level utilities can be imputed back to provide individual level data.
Data for conjoint analysis are most commonly gathered through a market research survey, although conjoint analysis can also be applied to a carefully designed configurator or data from an appropriately design test market experiment. Market research rules of thumb apply with regard to statistical sample size and accuracy when designing conjoint analysis interviews.
The length of the research questionnaire depends on the number of attributes to be assessed and the method of conjoint analysis in use. A typical adaptive conjoint questionnaire with 20-25 attributes may take more than 30 minutes to complete. Choice based conjoint, by using a smaller profile set distributed across the sample as a whole may be completed in less than 15 minutes. Choice exercises may be displayed as a store front type layout or in some other simulated shopping environment.
Depending on the type of model, different econometric and statistical methods can be used to estimate utility functions. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features. The actual estimation procedure will depend on the design of the task and profiles for respondents, in the type of specification, and the scale of measure for preferences (it can be ratio, ranking, choice) which can have a limited range or not. For rated full profile tasks, linear regression may be appropriate, for choice based tasks, maximum likelihood estimation, usually with logistic regression are typically used. The original methods were monotonic analysis of variance or linear programming techniques, but contemporary marketing research practice has shifted towards choice-based models using multinomial logit, mixed versions of this model, and other refinements. Bayesian estimators are also very popular. Hierarchical Bayesian procedures are nowadays relatively popular as well.
Advantages and disadvantages
- estimates psychological tradeoffs that consumers make when evaluating several attributes together
- measures preferences at the individual level
- uncovers real or hidden drivers which may not be apparent to the respondent themselves
- realistic choice or shopping task
- able to use physical objects
- if appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation
- designing conjoint studies can be complex
- with too many options, respondents resort to simplification strategies
- difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
- respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to
- poorly designed studies may over-value emotional/preference variables and undervalue concrete variables
- does not take into account the number items per purchase so it can give a poor reading of market share
Practical applications of conjoint analysis
One practical application of conjoint analysis in business analysis is given by the following example: A real estate developer is interested in building a high rise apartment complex near an urban Ivy League university. To ensure the success of the project, a market research firm is hired to conduct focus groups with current students. Students are segmented by academic year (freshman, upper classmen, graduate studies) and amount of financial aid received. Study participants are given a series of index cards. Each card has 6 attributes to describe the potential building project (proximity to campus, cost, telecommunication packages, laundry options, floor plans, and security features offered). The estimated cost to construct the building described on each card is equivalent. Participants are asked to order the cards from least to most appealing. This forced ranking exercise will indirectly reveal the participants' priorities and preferences. Multi-variate regression analysis may be used to determine the strength of preferences across target market segments.
Federal courts in the United States have allowed expert witnesses to use conjoint analysis to support their opinions on the damages that an infringer of a patent should pay to compensate the patent holder for violating its rights. Nonetheless, legal scholars have noted that the Federal Circuit's jurisprudence on the use of conjoint analysis in patent-damages calculations remains in a formative stage.
Pharmaceutical manufacturers need deeper and deeper market information they can rely on to make the right decisions and to identify the most promising market opportunities. They can obtain great benefits from understanding physicians’ prescription preferences and opinions, as these are the key people in prescribing medical treatments. Consequently, conjoint analysis research projects might represent a remarkable help for pharmaceutical companies during the development of new drugs, and if properly conducted they might even allow the estimation of product sale and market share. Conjoint analysis is a multivariate statistical technique based on the study of the joint effects on consumers of the elements that compose a product or service; it allows eliciting the relative importance of such elements. Therefore, by an additive model, it allows the estimation of the total utility of different profiles or combinations of attributes, and consequently, it allows the identification of the optimal configuration for a new or existing product or service. This technique was first developed in the early 1970s by Green and Rao. Since then, it has received increasing academic and private sector attention.
Prescription-based conjoint model (healthcare)
These models are adequate when respondents’ evaluations apply to products or services to be used by themselves (fast-moving consumer goods, durable goods, financial products, etc.). However, for some projects, respondents have to provide evaluations for products or services to be used by a group of people, that is the case of physicians with respect to their patients, when they are asked to assess the preference for a new or existing drug or medical treatment. The healthcare researcher wants to assess what physicians will do for a number of patients. More precisely, he/she intends to elicit the preference for a new treatment and to estimate in the most precise way the related preference share that eventually will lead to the estimation of the market share for such a treatment. Consequently, a prescription-based model seems to be an appropriate tool for this research situation. Basically, it consists in exposing physicians to several treatments profiles at-a-time, where all current treatments appear next to the new treatment (conjoint task). Physicians are then asked to assign some points to each treatment in the task. More precisely, in this exercise they are asked to think of the next 100 patients with the disease of concern and to record the number of prescriptions that they would prescribe for each of the treatments outlined in the task presented. Consequently, all treatment assignments in the task add up to 100 points or more. More than 100 points are allowed for projects involving treatments that could be co-prescribed. This framework is especially appropriate for a deep understanding of the relationships between the current medical treatments for a particular disease and different definitions for the product to be launched.
Available software tools for conjoint analysis
|Product||Developer||Initial public release||Headquarters||Delivery method||Cost||License|
|1000Minds||1000Minds||2002||New Zealand||SaaS||Free - POA||Proprietary|||
|Survey Analytics||QuestionPro||2002||San Francisco, United States||SaaS||US$1,000 per month||Proprietary|
|Sawtooth Studio||Sawtooth||2005||Orem, United States||Desktop software||From US$$11,500||Proprietary|
|support.CEs package (R)||Hideo Aizaki||2015||N/A||Package for the R programming language||Free||Open-source software|
- Marketing research
- Quantitative marketing research
- New product development
- Value-based pricing
- Discrete choice (choice modelling)
- TURF Analysis
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- J. Gregory Sidak & Jeremy O. Skog, Using Conjoint Analysis to Apportion Patent Damages, (Criterion Economics Working Paper, Jan. 29, 2016), https://www.criterioneconomics.com/using-conjoint-analysis-to-apportion-patent-damages.html.
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- Furlan, R.; Corradetti, R. "Aspects of Experimental Design in the Prescription-Based Conjoint Analysis Model". Proceedings of ENBIS 6. Wroclaw, Poland.
- Sculpher, M.; Bryan, S.; Fry, P.; DeWinter, P.; Payne, H.; Emberton, M. (2004). "Patients' preferences for the management of non-metastatic prostate cancer: discrete choice experiment". British Medical Journal. 328: 382–385.
- Green, P.E.; Rao, V.R. (1971). "Conjoint measurement for quantifying judgemental data". Journal of Marketing Research. 8: 355–63.
- Smith, K. F.; Fennessy, P. F. (2011). "The use of conjoint analysis to determine the relative importance of specific traits as selection criteria for the improvement of perennial pasture species in Australia". Crop and Pasture Science. 62 (4): 355–65. doi:10.1071/CP10320.
- Hideo Aizaki (2015). "support.CEs: Basic Functions for Supporting an Implementation of Choice Experiments".
- Green, P. and Srinivasan, V. (1978) Conjoint analysis in consumer research: Issues and outlook, Journal of Consumer Research, vol 5, September 1978, pp 103–123.
- Green, P. Carroll, J. and Goldberg, S. (1981) A general approach to product design optimization via conjoint analysis, Journal of Marketing, vol 43, summer 1981, pp 17–35.
- Srinivasan, V. (1988) A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences, Decision Sciences, Vol. 19, Spring 1998, 295-305.
- Green, P. E. and Srinivasan V. (1990) Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, Vol. 54, October 1990, 3-19.
- Marder, E. (1999) The Assumptions of Choice Modeling
- Output of a standard choice-based conjoint analysis study
- Conjoint Analysis, Related Modeling and Applications
- Conjoint Analysis Applications in Health