Technically Speaking
What is Conjoint Analysis?

Conjoint analysis is one of the terms used to describe a broad range of techniques for estimating the value people place on the attributes or features that define a product and service.

Conjoint Analysis is perfect for answering these types of questions:

• What is the optimal feature set for my product?
• What price level maximizes its profitability?
• What services are consumers willing to pay more for in the product?
• Which of these changes will increase our market share?
• What impact will this have on my current suite of products/cannibalization?

Conjoint analysis was first used in the early 1970's, and to this day represents an important market insight tool. The benefits of conjoint analysis include being able to quantify and compare the perceived importance of product or service attributes. Conjoint analysis from a practical sense allows customers to rank various product configurations, each made up of specific attributes and benefits. This analytical technique can also be used to understand how customers make “tradeoffs” in product features and benefits, and what dollar value they are wiling to pay for each of those features and benefits. Conjoint analysis permits us to address the question of which product features or benefits have the greatest effect on behavior or purchase intent.

What is the Goal of Conjoint Analysis?
The goal of any conjoint survey is to assign specific values to the range of options buyers consider when making a purchase decision. Armed with this knowledge, marketers can focus on the most important features of a product or service, and design products, services and messages that are most likely to resonate with target buyers.

How does Conjoint Analysis Work?
Conjoint analysis involves the measurement of consumer preferences, or acceptability between choice alternatives. The name "Conjoint Analysis" implies the study of the joint effects. In marketing applications, we study the joint effects of multiple product attributes on product choice. When asked to do so outright, many consumers are unable to determine the relative importance that they place on product attributes. For example, when asked which attributes are the more important ones, the response may be that “they all are important”. Furthermore, individual attributes in isolation are perceived differently than in the combinations found in a product. It is difficult for a survey respondent to take a list of attributes and mentally construct the preferred combinations of them. The task is easier if the respondent is presented with combinations of attributes that can be visualized as different product offerings. Fortunately, conjoint analysis can facilitate the process. Conjoint analysis is a tool that allows a subset of the possible combinations of product features to be used to determine the relative importance of each feature in the purchasing decision; the relative values of attributes considered jointly can better be measured than when considered in isolation.

How is the Data Analyzed?
There are several varieties of standard statistical methods used in conjoint analysis to translate respondents' answers into important values or utilities. The actual values obtained by these statistical methods are useful; however, the relative values or relationships between each of the attributes are of more importance.

• Evaluate respondents' answers in a manner that reveals the underlying value they consciously or subconsciously place on each attribute
• Conjoint analysis allows us to compute the relative value of all options considered in the research design

Conjoint analysis can be used to measure individuals' sensitivity to brand names, prices, and all other attributes in the research design.

An Example - An Airline Ticket*:

• $400 or $700 for a ticket
• Seat size regular or extra-wide
• Direct flight (3hrs) or indirect flight (5hrs)

*Based on three factors - eight possible ticket configurations could be tested. (2 x 2 x 2)

In a conjoint analysis, the respondent may be asked to arrange a list of combinations of product attribute in decreasing order of preference. Once this ranking is obtained, a statistical tool is used to find the utilities of different values of each attribute that would result in the respondent's order of preference. This method is efficient to the extent that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results, one can predict the desirability of the combinations that were not even tested. You can have over 10,000 different configurations and picking the optimal one without conjoint analysis is virtually impossible. This type of method allows you to show only ten to twenty combinations that can answer all of the 10,000+ different configurations.

References
The Marketing Revolution, A Radical manifesto for Dominating the Marketplace, Kevin J. Clancy, Robert S. Shulman Journal of Marketing Education

In the next Blueprints Newsletter… The Benefits of Market Segmentation