Using Conjoint Analysis to Recommend Products and Close Sales over the Web

Over the last three decades, conjoint analysis has blossomed as a valuable technique to measure people's preferences for products and services and to predict how markets might react to different product offerings. In 1985, Sawtooth Software created one of the first commercially available software package for conjoint analysis, called ACA (Adaptive Conjoint Analysis). ACA soon became the most widely used conjoint research technique and software system (based on industry-wide studies in 1992 and 1997).

Early in our company's history, we recognized that the statistical techniques we were developing to understand people's complex preferences could have wider application than just strictly for marketing research. We felt that conjoint (trade-off) analysis could help buyers sort through many product offerings to make quicker and better decisions in complex decision-making environments. For example, in the 1980s, we developed a computer based system for a Real Estate client, to provide a way of matching home buyers with homes for sale in their area that would be expected to most appeal to them. It seems that the Real Estate world wasn't ready for that kind of system then, and resistance by Realtors doomed the project. (About that same time, we also made early attempts for automobile purchases, college selections, financial products and group executive decisions.)

Times have changed. The Internet has revolutionized the way we do business and communicate with customers. Our economy is increasingly both comfortable with and dependent on technology. Buyers have become empowered by the amount of information available at their fingertips over the Web. More than 50% of homes in the US are online. But with the proliferation of information, buyers are experiencing information overload that is actually impeding their ability to make decisions about some purchases. It is not surprising that a number of websites have appeared to help buyers make decisions about products.

Many sites like use a technology called Collaborative Filtering, which recommends new books to customers based on their previous purchases and what other customers with similar tastes have purchased. Collaborative filtering requires large amounts of data from many buyers. These systems cannot make recommendations for new products or when few customers have purchased/evaluated a product. But, they have the advantage of recommending products to buyers based on other like-minded individuals' choices.

Other sites let respondents specify the kinds of products they are interested in buying using cutoffs or acceptable ranges. A query to the database of available products returns potential candidates. Often times, buyers place too many restrictions, thus disqualifying all available products. We have seen this happen within the context of ACA's Unacceptables section. We have found that respondents are quick to say that certain levels of an attribute are unacceptable, when in reality they are willing to bend those rules if enough other aspects of the product are very desirable. Also, a cut-off based decision rule provides no way of ranking qualified products based on overall suitability.

Still other sites collect respondent preferences, through self-explicated models or with conjoint analysis, and then use those preferences to recommend products that might satisfy the buyer. Preference models have the advantage of being able to recommend new products that other buyers have not yet experienced. They also can prioritize products better for respondents by sorting them from most likely to be preferred to least likely.

A few academics have published research regarding the use of electronic or on-line recommendation agents. So far, the results are very encouraging. In a recent article by Gerald Haubl and Valerie Trifts of the University of Alberta, the authors conclude:

In sum, our findings suggest that interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality and the efficiency of consumers' purchase decisions in online shopping environments--shoppers are able to make much better decisions while expending substantially less effort. This suggests that interactive decision aids have the potential to drastically transform the way in which consumers search for product information and make purchase decisions.

"Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Haubl and Trifts, Marketing Science, Winter 1999.

The goals of Web-based recommendation agents are to:

  • personalize the process for the buyer,
  • help the buyer efficiently sift through large amounts of information to identify the products/services that are best for him/her,
  • instill the buyer with greater confidence in the decision and subsequent purchase,
  • increase sales,
  • provide feedback to the seller regarding the market's preferences for features.

As marketing scientists, we are excited about the opportunities in this area. Our work involves a fascinating blend of science and business strategy. We sincerely enjoy what we do and know the techniques we develop lead to better products for customers and more profitable industries.