Qualitative Techniques for Enhancing Conjoint Analysis

Most of the articles we publish in this newsletter focus on the quantitative aspects of conjoint analysis. This article highlights some of the more qualitative aspects that are often inadequately treated or entirely ignored.

Determinant Attributes

To model the decision process, we develop a list of determinant attributes. (The term determinance was first coined by James Myers and Mark Alpert nearly three decades ago.) A determinant attribute is a product feature that is both important to the buyer and on which competing brands are perceived to differ.

It is common for the researcher and the client to co-develop a list of attributes based on their knowledge of the product and the marketplace. Sometimes relying on "expert opinion" is not adequate to faithfully represent the key decision factors. Qualitative up-front work with respondents is often used to generate a list of determinant attributes. Jordan Louviere has suggested a simple series of questions to facilitate that process:

  1. Which products (services) in this product class do you buy (own), or would you consider buying (owning)?
  2. Which products (services) in this product class do you not buy (own), or would you consider not buying (owning)?
  3. You said that you would buy (own) or would consider buying (owning) brand(s) (products in Question 1 are now repeated). What is it about these products that makes them attractive to you?
  4. You said that you do not buy (own) brand(s) (products in Question 2 are repeated) or would not consider buying (owning) them. What is it about these products that is unattractive to you?
(Louviere, Jordan J. Analyzing Decision Making: Metric Conjoint Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, 1988, 07-067. Newbury Park, CA: Sage.)

Another technique for identifying determinant attributes is referred to as the repertory grid. Both Louviere's and Ray Poynter's (referenced below) papers mention this approach. Briefly, respondents are asked to compare products or brands three-at-a-time and then list in what ways the products are similar and different.

Assessing Perceptions

During the conjoint analysis interview, other questions can be used to better understand the decision process. One can probe how respondents perceive the brands/products to be positioned on the various attributes and why respondents have certain preferences.

Conjoint simulations assume that respondents both know and believe that the brands offer the specified features. In the actual marketplace, this assumption may not hold. Brands hindered by ineffective marketing communications campaigns may never achieve the degree of preference suggested by the simulator. In some markets, brands not perceived to offer essential features may never be considered. Therefore, it may be useful to include a series of questions outside of the conjoint exercise wherein respondents state how they perceive each brand is positioned on each attribute. Such information might be used to direct effective marketing communications strategy. Some researchers have advocated using the perceptions and the conjoint utilities together in conjoint simulations, by assigning each brand the utilities of the levels associated with it for each respondent. We have tried this ourselves, but generally have not been pleased with the results.

Asking "Why?"

In his 1999 Sawtooth Software Conference paper, Ray Poynter points out that researchers should not only learn what respondents prefer, but why they have those preferences: "Failure to answer this seemingly straightforward question," Ray argues, "will frequently result in the client doubting the wisdom of both the researcher and of the executive who commissioned the project" (Poynter, Ray. "But Why? Putting the Understanding into Conjoint," 1999 Sawtooth Software Conference Proceedings).

It is possible when using techniques such as Adaptive Conjoint Analysis (ACA) with the Ci3 system to reference the current respondent's conjoint utilities in real time and ask follow-up questions about specific preferences. For example, if your client is interested in why some customers strongly prefer packages of 12 rather than 24, you could program Ci3 to ask a qualitative follow-up question if the 12 package count was preferred to the 24 and if that attribute was one of the top three attributes in terms of conjoint importance.