Full Conjoint Analysis

Powerful and Realistic Tools for Marketing Decision Making.


Many marketing decisions require complex decision making processes which incorporate many data points from various sources. Depending on the type of decision you need to make, some statistical techniques are more useful than others. In this three-part series of white papers we explore three different trade-off techniques frequently used for different marketing decisions. In our first white paper we explored Discrete Choice Modeling; in this second paper we discuss Full Profile Conjoint Analysis; and the third and final paper of this series will present Paired Trade-Off Analysis.

Like Discrete Choice Modeling, Full Profile Conjoint Analysis is a trade-off and simulation technique useful for studying these types of questions:

  1. Product/service design and pricing issues such as what features maximize preference or revenue
  2. To what extent customers value features and what impact that value has on preference for the feature
  3. Whether and how to bundle product or service features
  4. Anticipated increases or decreases in revenues based on the presence, absence, or combination of features
  5. Combinations of all these issues, as needed

Some of these issues can also be addressed by Discrete Choice Modeling. However, not all scenarios require the complexity of Discrete Choice Modeling; they can be quite effectively addressed using Conjoint Analysis. Furthermore, lower levels of complexity often involve lower levels of investment in the research.

Consider the following example. An original equipment manufacturer of automotive and trucking parts and services wants to develop a loyalty program to reward customers for their continued business. They have conducted enough preliminary research (including discussions with key internal stakeholders) to identify two different rebate program models, each of which offers a range of percentages for a cash-back program and different options for delivering the rebate. Furthermore, they have developed a list of non-cash incentives they would like to incorporate into the program which may also serve to increase customer loyalty. However, they want to know which rebate program at what percentage level and what combinations of non-cash features will be most appealing to their customers. They also want to know which program and features will have the greatest positive impact on customers’ spending levels with their business. These questions are quite effectively answered in the context of a Conjoint Analysis design.