A simple way to assess how much consumers value a product feature would be to directly ask the question. However, there are a few problems with this direct elicitation method. First, as there is no trade-off involved, consumers tend to say that every attribute is important. Second, consumers in the marketplace generally evaluate and choose a product as a whole, noting the features it includes rather than evaluating each attribute separately. Thus, directly asking customers to separately evaluate attributes is likely to yield unrealistic—and perhaps biased—results.
The idea behind conjoint analysis is to consider jointly the set of attributes that comprise a product as a template for understanding consumer preferences. By providing a complete product for evaluation (rather than individual features one at a time), conjoint analysis encourages consumers to make trade-offs between various product features. In a variation known as discrete conjoint analysis, multiple products are provided, and consumers are asked to choose among them making more complex (and realistic) trade-off between the products. By setting up the products appropriately, the researcher is able to understand the value placed by consumers on individual features.
For example, when two products that differ on a single feature (and price) are presented, the choice made by the survey respondents provides clear information on the trade-off made between the feature and price. Those who choose the more expensive product (which includes that feature) reveal that they are willing to pay more for it, while the opposite is true for those who choose the cheaper option. By presenting a sequence of such options, the researcher is able to identify the preferences of the market.
Quite often in practice, conjoint choice tasks also include a “none” option that allows consumers to indicate their lack of interest in any of the alternatives presented to them in that specific question. Based on the proportion of respondents who choose this “none” option, researchers can estimate primary demand for the product (i.e. how attractive the entire set of the attributes and levels is to the market as a whole). This is a significant and singular advantage of the conjoint approach.
Primary results from conjoint studies are “partworths”—essentially, attractiveness scores for each level of each feature. The more attractive a level is, the higher the partworth of that level. The total worth score (or attractiveness of the whole product) is simply the sum of the individual partworths of the features that comprise that particular product. In other words, partworths provide a common and convenient metric for evaluating the total value of a product. This, of course, makes it possible to compare two different products and evaluate their relative value in the eyes of the consumer. After that, it is only a short jump to figuring out how to evaluate the premium that consumers are willing to pay for specific features. To illustrate this methodology, consider two products that are very similar in every aspect, except price and the level of the touchscreen feature. Let’s say one product has a better touchscreen (say, “multi-touch”) and costs more, while the other one has an inferior touchscreen (“single-touch”) and costs less. If more people prefer the former, it implies that the higher quality touchscreen indeed has good value in the marketplace with consumers being willing to pay more. But how much more, exactly?
In conjoint analysis, a very useful tool called a market simulator is often used. Its input is the partworth scores of individual respondents. Assuming that respondents chose the product with the highest value, preference shares can be calculated for any product that can be constructed with the features and levels included in the study. The output is a simulation of the expected market share for each product.
A market with two products identical on every feature except one, and price, should be constructed, yielding different shares for each one based on attractiveness of the products. Then by changing (dropping) the price on the product with the lower share, the shares can be equalized. In practice, this means the value of the two products has equalized and the consumers are now indifferent between them. But the difference in price between the two products provides an estimate of the premium that can be charged for the superior touchscreen capability. Such an analysis allows calculation of consumers’ willingness to pay a price premium for certain features.
Conjoint analysis is a robust and widely used approach, commonly used to measure consumers’ willingness to pay for product features. This approach is particularly useful in new product design, in order to select which new features to develop. The approach has been applied across a variety of product categories, including: cars, hotels, credit cards, pharmaceutical drugs, and cameras. Conjoint analysis has also been frequently used in litigation cases involving billions of dollars in claimed damages.