Conjoint Analysis

Conjoint Analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced which is a multivariate analysis technique introduced to the marketers in 1970's. Conjoint Analysis is basically a data de- compositional technique which tries to plot the output data on the joint space of the importance of each attribute. The important point to note is that the consumer is not asked to assign scores to different attribute separately. The main steps involved in using conjoint analysis include determination of the salient attributes for the given product from the points of view of the consumers, assigning a set of discrete levels or a range of continuous values to each of the attributes, utilizing fraction factorial design of experiment for designing the stimuli for experiment, physically designing the stimuli, data collection, conjoint analysis and determination of part worth utilities. The possible application, of conjoint analysis includes product design, market segmentation, swot analysis etc. In its original form, conjoint analysis is a main effects analysis-of-variance problem with an ordinal scale of-measurement dependent variable. Conjoint analysis decomposes rankings or rating-scale evaluation judgments of products into components based on qualitative attributes of the products. Attributes can include price, color, guarantee, environmental impact, and so on. A numerical utility or part-worth utility value is computed for each level of each attribute. The goal is to compute utilities such that the rank ordering of the sums of each product’s set of utilities is the same as the original rank ordering or violates that ordering as little as possible. When a monotonic transformation of the judgments is requested, a nonmetric conjoint analysis is performed. Nonmetric conjoint analysis models are fit iteratively. When the judgments are not transformed, a metric conjoint analysis is performed. Metric conjoint analysis models are fit directly with ordinary least squares. When all of the attributes are nominal, the metric conjoint analysis problem is a simple main-effects ANOVA model. In both metric and nonmetric conjoint analysis, the respondents are typically not asked to rate all possible combinations of the attributes. For example, with five attributes, three with three levels and two with two levels, there are 3×3×3×2×2 = 108 possible combinations. Rating that many combinations would be difficult for consumers, so typically only a small fraction of the combinations are rated. Typically, combinations are chosen from an orthogonal array which is a fractional-factorial design. The statistical technique of Fractional Factorial Design of Experiment finds out the minimum number of product designs which are necessary to use in the study and yet provide us all the information that we originally sought. These designs are also mutually independent (orthogonal) to avoid any redundancy in the data and allow the representation of each of the attributes and their respective levels in an unbiased manner.
Conjoint Analysis Steps
1. The respondent is given a set of stimulus profiles (constructed along factorial design principles in the full profile case). In the two-factor approach, pairs of factors are presented, each appearing approximately an equal number of times.
2. The respondents rank or rate the stimuli according to some overall criterion, such as preference, acceptability, or likelihood of purchase.
3. In the analysis of the data, part-worths are identified for the factor levels such that each specific combination of part-worths equals the total utility of any given profile. A set of part-worths is derived for each respondent.
4. The goodness-of-fit criterion relates the derived ranking or rating of stimulus profiles to the original ranking or rating data.
5. A set of objects are defined for the choice simulator. Based on previously determined part-worths for each respondent, each simulator computes an utility value for each of the objects defined as part of the simulation. 6. Choice simulator models are invoked which rely on decision rules (first choice model, average probability model or logit model) to estimate the respondent's object of choice. Overall choice shares are computed for the sample.
How to conduct Conjoint
While specific research objectives will dictate the direction of conjoint research, there are several components common to all conjoint engagements. These steps include: definition of attributes; establishment of attribute levels; choice of conjoint methodology; design of experiment; data collection; data analysis; and development of the market simulator.
Step 1: Definition of Attributes
To replicate the decision-making process, it is necessary to understand each of the attributes consumers consider when making an actual purchasing decision. Experience, previous research, and/or the specific research objectives will determine which attributes are of particular importance, and whether all product features should be displayed or only those most relevant to differentiating a product from competitive offerings.
Step 2: Establishment of Attribute
Levels Once attributes for the conjoint research have been defined, it must be determined how attributes will vary from one product concept to the next. This step involves the establishment of attribute levels. Attribute levels must be comprehensive enough to capture all of the products that exist, or soon exist, within the marketplace. However, as with the definition of attributes, care must be taken to avoid respondent fatigue, so only the most prevalent attribute levels will be chosen for testing (typically 3-5 attribute levels per attribute). Further, the number of attribute levels chosen has a direct impact on the number of concepts respondents will be asked to evaluate. The optimal number of attribute levels tested will be that which ensures research objectives are satisfied while minimizing the burden faced by respondents.
Step 3: Choice of Conjoint Methodology
Because no two product and/or service categories are exactly the same, there are a number of conjoint methodologies at a marketing researcher's disposal. The three primary methods used today include: conjoint value analysis (CVA), adaptive conjoint analysis (ACA), and choice-based conjoint analysis (CBC), with adaptive choice-based conjoint (ACBC) emerging as a new generation of conjoint analysis. For the purposes of this whitepaper, we will focus on CBC analysis, by far the most popular conjoint methodology currently used by researchers. Some types of Conjoint Methodologies include: 1. Choice-Based Conjoint (CBC) 2. Conjoint Value Analysis (CVA) 3. Adaptive Conjoint Analysis (ACA)
Step 4: Design of Experiment
Having established the methodology, attributes, and attributes levels to be tested; we can then create concept profiles (i.e., descriptions of product concepts using the attributes and attribute levels to be used in the research). Respondents are asked to evaluate a number of these concepts, and in the case of CBC determine which, if any, they would choose to purchase given the opportunity. Fortunately, it is not necessary that every potential product offering be evaluated. In fact, this would be quite impossible, as there are typically thousands of potential product configurations in any given study. For example, there are 1800 hypothetical products in the energy bar study (3 brands x 5 protein levels x 6 carbohydrate levels x 4 flavors x 5 price levels). However, with a carefully constructed conjoint design, we are able to calculate respondent preference for each attribute and attribute level. Therefore, assuming a simple additive model (i.e., product preference is the sum of preference for its attributes), we can estimate how respondents would react to any product offering.
Step 5: Data Collection
An online survey is recommended for almost all conjoint research engagements, as it provides the most effective, cost efficient, time sensitive, and highest quality solution. Respondents are required to consider a great deal of information, allowing them to visually assess the stimuli results in more reliable findings. An online presentation of product concepts and conjoint tasks allows respondents to complete the survey at their own pace, allowing time for thoughtful and accurate responses. With over 70% of U.S. adults accessing the Internet via computers at home, work, or school (Source: Pew Internet and American Life Project), an online methodology allows for data collection from a large sample set.
Step 6: Data Analysis
With a carefully constructed conjoint survey, we can statistically deduce the consumer values for each feature respondents may be subconsciously using to evaluate concepts. Analysis of conjoint data yields a series of scores for each respondent for each attribute level. These scores, known as part-worth, may be likened to the unit which is an arbitrary measurement of utility consumers associate with a product and its attributes. Each score reflects the value the respondent associates with each attribute level, and is the building block from which all analysis is conducted. By assuming a simple additive model, we are able to build products and pricing structures, and then calculate the value consumers find in that product. By comparing this to other potential products in the marketplace, we can begin to understand how consumers will choose products in the real world.
Step 7: Development of Market Simulator 
While preliminary analysis of conjoint data results in valuable insight regarding consumers and their preferences, the real value of conjoint analysis comes from the market simulators developed at the conclusion of the research engagement. The market simulator is a software program, similar to a spreadsheet, which allows users to conduct "what-if" analyses with data collected during conjoint fielding. As mentioned above, respondents can be asked to evaluate only a small fraction of concept profiles, yet still reveal how they would respond to any product offering. Therefore, it is possible to aggregate the preferences of all consumers to reveal how the market as a whole will respond to any product offering. Furthermore, we can assess how the marketplace will respond to two or more competing products by calculating the market’s share of preference for every product of interest.

Conjoint Analysis Survey Examples