Conjoint analysis

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Example choice-based conjoint analysis survey with application to marketing (investigating preferences in ice-cream) Ice-cream-experiment-example.png
Example choice-based conjoint analysis survey with application to marketing (investigating preferences in ice-cream)

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.

Contents

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 choices among 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 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. "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 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 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 AHP, [1] PAPRIKA, [2] [3] evolutionary algorithms or rule-developing experimentation.

Conjoint design

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.[ citation needed ]

Respondents are 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 choices among alternative combinations.[ citation needed ]

Conjoint design involves four different steps:

  1. Determine the type of study
  2. Identify the relevant attributes
  3. Specify the attributes' levels
  4. Design questionnaire

1. Determine the type of study

There are different types of studies that may be designed:

2. Identify the relevant attributes

Attributes in conjoint analysis should:

3. Specify the attributes' levels

Levels of attributes should be:

4. Design questionnaire

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 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.[ citation needed ]

Earliest form and drawbacks

The earliest forms of conjoint analysis starting in the 1970s were what are known as Full Profile studies, in which a small set of attributes (typically 4 to 5) were used to create profiles that were shown to respondents, often on individual cards. Respondents then ranked or rated these profiles. Using relatively simple dummy variable regression analysis the implicit utilities for the levels could be calculated that best reproduced the ranks or ratings as specified by respondents. Two drawbacks were seen in these early designs.[ citation needed ]

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 that combined self-explication (rating or ranking of levels and attributes) followed by conjoint tasks. Both paper-based and adaptive computer-aided questionnaires became options starting in the 1980s.[ citation needed ]

The second drawback was that ratings or rankings of profiles were unrealistic and did not link directly to behavioural theory. In real-life situations, buyers choose among alternatives rather than ranking or rating them. 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 and researchers developed aggregated models to represent the market's preferences. This made it unsuitable for market segmentation studies. With newer hierarchical Bayesian analysis techniques, individual-level utilities may be estimated that provide greater insights into the heterogeneous preferences across individuals and market segments.[ citation needed ]

Information collection

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 designed test market experiment. Market research rules of thumb apply with regard to statistical sample size and accuracy when designing conjoint analysis interviews.[ citation needed ]

The length of the conjoint questionnaire depends on the number of attributes to be assessed and the selected conjoint analysis method. A typical adaptive conjoint questionnaire with 20–25 attributes may take more than 30 minutes to complete[ citation needed ]. 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.[ citation needed ]

Analysis

Sample output of conjoint analysis with application to marketing Sample-output-of-conjoint-analysis.png
Sample output of conjoint analysis with application to marketing

Because conjoint designs are complicated, they usually generate substantial measurement error (as indicated by low intra-respondent reliability), which can induce substantial bias in any direction by any amount; this bias must be corrected in statistical analyses of conjoint data. [4] 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 and the measurement scale used to indicate preferences (interval-scaled, ranking, or discrete choice). For estimating the utilities for each attribute level using ratings-based full profile tasks, linear regression may be appropriate, for choice based tasks, maximum likelihood estimation usually with logistic regression is typically used. The original utility estimation 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.[ citation needed ]

Advantages and disadvantages

Advantages

Disadvantages

Practical applications

Market research

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 shown a series of choice scenarios, involving different apartment living options specified on six attributes (proximity to campus, cost, telecommunication packages, laundry options, floor plans, and security features offered). The estimated cost to construct the building associated with each apartment option is equivalent. Participants are asked to choose their preferred apartment option within each choice scenario. This forced choice exercise reveals the participants' priorities and preferences. Multinomial logistic regression may be used to estimate the utility scores for each attribute level of the six attributes involved in the conjoint experiment. Using these utility scores, market preference for any combination of the attribute levels describing potential apartment living options may be predicted.[ citation needed ]

The market research approach, Mind Genomics (MG), is an application of Conjoint Analysis (CA). CA is carried out to evaluate consumer acceptance, presenting them with a set of product attributes and assessing their preferences for different attribute combinations by estimating the utility scores for different attribute levels. MG applying CA delves deeper into the psychological and emotional aspects that influence decision-making, assisting in the initial identification of the attributes that are most salient to consumers and helping researchers refine the attributes to be used in CA. [5]

Litigation

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. [6] 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. [7]

One example of this is how Apple used a conjoint analysis to prove the damages suffered by Samsung's copyright infringement, and increase their compensation in the case.[ citation needed ]

See also

Related Research Articles

Marketing research is the systematic gathering, recording, and analysis of qualitative and quantitative data about issues relating to marketing products and services. The goal is to identify and assess how changing elements of the marketing mix impacts customer behavior.

Concept testing is the process of using surveys to evaluate consumer acceptance of a new product idea prior to the introduction of a product to the market. It is important not to confuse concept testing with advertising testing, brand testing and packaging testing, as is sometimes done. Concept testing focuses on the basic product idea, without the embellishments and puffery inherent in advertising.

The theory of consumer choice is the branch of microeconomics that relates preferences to consumption expenditures and to consumer demand curves. It analyzes how consumers maximize the desirability of their consumption, by maximizing utility subject to a consumer budget constraint. Factors influencing consumers' evaluation of the utility of goods include: income level, cultural factors, product information and physio-psychological factors.

In marketing, market segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers based on shared characteristics.

Brand equity, in marketing, is the worth of a brand in and of itself – i.e., the social value of a well-known brand name. The owner of a well-known brand name can generate more revenue simply from brand recognition, as consumers perceive the products of well-known brands as better than those of lesser-known brands.

In the social sciences, scaling is the process of measuring or ordering entities with respect to quantitative attributes or traits. For example, a scaling technique might involve estimating individuals' levels of extraversion, or the perceived quality of products. Certain methods of scaling permit estimation of magnitudes on a continuum, while other methods provide only for relative ordering of the entities.

As part of consumer behavior, the buying decision process is the decision-making process used by consumers regarding the market transactions before, during, and after the purchase of a good or service. It can be seen as a particular form of a cost–benefit analysis in the presence of multiple alternatives.

Kansei engineering aims at the development or improvement of products and services by translating the customer's psychological feelings and needs into the domain of product design. It was founded by Mitsuo Nagamachi, Professor Emeritus of Hiroshima University. Kansei engineering parametrically links the customer's emotional responses to the properties and characteristics of a product or service. In consequence, products can be designed to bring forward the intended feeling.

Customer satisfaction is a term frequently used in marketing to evaluate customer experience. It is a measure of how products and services supplied by a company meet or surpass customer expectation. Customer satisfaction is defined as "the number of customers, or percentage of total customers, whose reported experience with a firm, its products, or its services (ratings) exceeds specified satisfaction goals." Enhancing customer satisfaction and fostering customer loyalty are pivotal for businesses, given the significant importance of improving the balance between customer attitudes before and after the consumption process.

The following outline is provided as an overview of and topical guide to marketing:

Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services in order to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.

Brand awareness is the extent to which customers are able to recall or recognize a brand under different conditions. Brand awareness is one of two dimensions from brand knowledge, an associative network memory model. Brand awareness is a key consideration in consumer behavior, advertising management, and brand management. The consumer's ability to recognize or recall a brand is central to purchasing decision-making. Purchasing cannot proceed unless a consumer is first aware of a product category and a brand within that category. Awareness does not necessarily mean that the consumer must be able to recall a specific brand name, but they must be able to recall enough distinguishing features for purchasing to proceed. Creating brand awareness is the main step in advertising a new product or bringing back the older brand in light.

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices in order to infer positions of the items on some relevant latent scale. Indeed many alternative models exist in econometrics, marketing, sociometrics and other fields, including utility maximization, optimization applied to consumer theory, and a plethora of other identification strategies which may be more or less accurate depending on the data, sample, hypothesis and the particular decision being modelled. In addition, choice modelling is regarded as the most suitable method for estimating consumers' willingness to pay for quality improvements in multiple dimensions.

Sawtooth Software, Inc. is a computer software company based in Provo, Utah, United States. The company provides survey software tools, and specializes in conjoint analysis.

Multivariate landing page optimization (MVLPO) is a specific form of landing page optimization where multiple variations of visual elements on a webpage are evaluated. For example, a given page may have k choices for the title, m choices for the featured image or graphic, and n choices for the company logo. This example yields k×m×n landing page configurations.

Best–worst scaling (BWS) techniques involve choice modelling and were invented by Jordan Louviere in 1987 while on the faculty at the University of Alberta. In general with BWS, survey respondents are shown a subset of items from a master list and are asked to indicate the best and worst items. The task is repeated a number of times, varying the particular subset of items in a systematic way, typically according to a statistical design. Analysis is typically conducted, as with DCEs more generally, assuming that respondents makes choices according to a random utility model (RUM). RUMs assume that an estimate of how much a respondent prefers item A over item B is provided by how often item A is chosen over item B in repeated choices. Thus, choice frequencies estimate the utilities on the relevant latent scale. BWS essentially aims to provide more choice information at the lower end of this scale without having to ask additional questions that are specific to lower ranked items.

Marketing engineering is currently defined as "a systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process".

Psychographic segmentation has been used in marketing research as a form of market segmentation which divides consumers into sub-groups based on shared psychological characteristics, including subconscious or conscious beliefs, motivations, and priorities to explain and predict consumer behavior. Developed in the 1970s, it applies behavioral and social sciences to explore to understand consumers’ decision-making processes, consumer attitudes, values, personalities, lifestyles, and communication preferences. It complements demographic and socioeconomic segmentation, and enables marketers to target audiences with messaging to market brands, products or services. Some consider lifestyle segmentation to be interchangeable with psychographic segmentation, marketing experts argue that lifestyle relates specifically to overt behaviors while psychographics relate to consumers' cognitive style, which is based on their "patterns of thinking, feeling and perceiving".

SIMALTO – SImultaneous Multi-Attribute Trade Off – is a survey based statistical technique used in market research that helps determine how people prioritise and value alternative product and/or service options of the attributes that make up individual products or services.

References

  1. Ijzerman MJ, van Til JA, Bridges JF (212). "A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation". The Patient. 5 (1): 45–56. doi:10.2165/11587140-000000000-00000. PMID   22185216. S2CID   207299893.
  2. Liberman AL, Pinto D, Rostanski SK, Labovitz DL, Naidech AM, Prabhakaran S (2019). "Clinical decision-making for thrombolysis of acute minor stroke using adaptive conjoint analysis". The Neurohospitalist. 9 (1): 9–14. doi:10.1177/1941874418799563. PMC   6327243 . PMID   30671158.
  3. Al-Isma'ili A, Li M, Shen J, He Q (2016). "Cloud computing adoption decision modelling for SMEs: a conjoint analysis". International Journal of Web and Grid Services. 12 (3): 296–327. doi:10.1504/IJWGS.2016.079157.
  4. Clayton, Katherine; Horiuchi, Yusaku; Kaufman, Aaron R.; King, Gary; Komisarchik, Mayya (2023). "Correcting Measurement Error Bias in Conjoint Survey Experiments". gking.harvard.edu. Retrieved 2023-01-31.
  5. Porretta, Sebastiano; Gere, Attila; Radványi, Dalma; Moskowitz, Howard (February 2019). "Mind Genomics (Conjoint Analysis): The new concept research in the analysis of consumer behaviour and choice". Trends in Food Science & Technology. 84: 29–33. doi:10.1016/j.tifs.2018.01.004.
  6. Cornell University v. Hewlett-Packard Co., 609 F. Supp. 2d 279 (N.D.N.Y. 2009); Sentius Int'l, LLC v. Microsoft Corp., No. 5:13-cv-00825, 2015 WL 331939 (N.D. Cal. January 23, 2015).
  7. J. Gregory Sidak & Jeremy O. Skog, Using Conjoint Analysis to Apportion Patent Damages, (Criterion Economics Working Paper, January 29, 2016), https://www.criterioneconomics.com/using-conjoint-analysis-to-apportion-patent-damages.html.