While specific research objectives will dictate the direction of conjoint research, there are several components common to all conjoint engagements (see figure below). 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 attribute levels to be tested, W5 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, W5 is 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), W5 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, W5 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-worths, may be likened to the util 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, W5 is 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 (see Figure 6 below) 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, W5 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.