Predicting Choice with Conjoint Analysis and Discrete Choice

Conjoint Analysis and Discrete Choice are popular techniques because they let marketers predict choice behavior. Follow this detailed example from Sawtooth Technologies.

Conjoint Analysis and Discrete Choice are popular techniques because they let marketers predict choice behavior. Follow this detailed example from Sawtooth Technologies.

Bringing a new or reconfigured product to market involves a number of complex, interrelated decisions. Marketers must decide what features a product should have, how to price it, and whom to target. And, all of this must be done against a background of anticipated competition. Conjoint analysis has become a popular technique for making these types of decisions because it lets marketers predict choice behavior.

I described the basics of conjoint analysis in, “Understanding Conjoint Analysis in 15 Minutes.” Because I touched only briefly on choice models in that article, I expand on that subject here using the same example. I will describe three of the most widely used models: First Choice, Share of Preference, and Likelihood of Purchase.

Suppose we want to market a new golf ball and have decided that the salient features and feature alternatives are:

Also suppose we have interviewed golfers to determine their preferences for these features. For one buyer these preferences are reflected in the following “utilities:”

A utility has the property that the higher its value, the more desirable its corresponding feature. Utilities can be added to yield a total value for a combination of features.

Suppose we were considering marketing one of two golf balls:

One way to predict which ball our buyer will choose is to add up the buyer’s utilities for each ball; the one with the higher total is expected to be the buyer’s first choice.

Given these two choices, we’d expect our buyer to choose the Long-Life Ball. Repeating this for the 100 buyers in our hypothetical sample, we might get:

We can use this approach to answer “what-if’ questions. For example, suppose we dropped the price of the Distance Ball from $ 1.50 to $ 1.25. Which ball would our buyer prefer? Let’s recompute the totals and see.

This price decrease is enough to make our buyer switch. For the total sample, the results for the lower-price Distance Ball might turn out to be:

We could also look separately at market segments. For example, our sample includes 50 males and 50 females. The split, keeping the Distance Ball at the lower price, might be:

We’d expect the Distance Ball to appeal to men and the Long-Life Ball to appeal to women.

This simple way of simulating behavior, which can be extended to any number of buyers and products, is referred to as the First Choice approach. Although conceptually and computationally simple, the First Choice model has one major drawback: it usually overstates choice for the more popular products. For this reason the Share of Preference model is often a better estimator of choice.

Unlike the First Choice model, which assigns a buyer’s entire purchase to the product with the highest utility, the Share of Preference model splits the probability of purchase among all competing products according to their utilities. Why should this be better? First, it recognizes that buyers do not always purchase the product for which they have the highest utility. And second, many products represent low-involvement purchases for buy­ers. For these products buyers often have no clear first choice.

To read the rest of this white paper on the Sawtooth Technologies website, click here.

This content was provided by Sawtooth Technologies. Visit their website at

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