24. Choice-Based Conjoint Analysis

OBJECTIVE

Identify customers and potential customers’ preferences for specific attributes of a product. It can also be used to define the willingness to pay and market share of different products.

DESCRIPTION

This method is preferred to conjoint analysis because it represents a more realistic purchase situation and, in the case of having a large number of possible combinations, because it is sufficient to show only a certain number of combinations to each respondent. Then the responses are analyzed together and the utility is defined at the aggregated level (not at the individual level as in conjoint analysis).

For this method it is also very important to choose carefully the attributes (as a rule of thumb, no more than seven including the price) and the product profiles to present, that is, the combinations of attributes. Once the attributes and product profiles have been defined, choice scenarios are designed. A scenario is a combination of several products that is presented to the respondents. When defining scenarios, these recommendations should be followed:

• A “none” choice should be included among the products presented in each purchase scenario;
• Each scenario should not have more than 5 products;
• Between 12 and 18 scenarios are usually presented to each respondent.

Usually, all the combinations cannot be presented in the same scenario, and a good practice is to show from two to five products in each scenario. When choosing the combination of products for each scenario, it is important that all the products are shown an equal number of times and that each product is compared equally with other alternatives.

Once the data have been collected, utilities are estimated at the aggregate level. The market share of each product can be calculated using the “share of preferences”:

• Products’ utilities are calculated by summing all the attributes’ utilities;
• Products’ utilities are exponentiated;
• The market share is calculated as the product’s exponentiated utility divided by the sum of all the exponentiated utilities.

To obtain utilities at the individual level, a method called “hierarchical Bayes” is used. This method enables us to calculate a more reliable market share based on the choice of each respondent using three main techniques:

• First choice: each respondent chooses the product that maximizes her utility (this technique is suggested for expensive products that imply a careful evaluation, such as houses and cars);
• Share of preference: each respondent purchases a share of each product based on the share of utilities (suggested when a product is purchased several times during a certain period);
• Randomized first choice: each respondent chooses one product with a probability proportional to its utility.

This method is also useful for predicting variations in the market share compared with competitors by creating simulations in which prices or other products’ attributes are changed. For example, we can analyze whether a discount can attract a big enough market share to compensate for the reduction in price. In this kind of simulation, we assume that competitors are not modifying both attributes and price, but in reality this could not be the case. This is why we should at least simulate several scenarios including possible competitors’ reactions. A more complex approach would be to include a game theory model (see 76. GAME THEORYMODELS).

TEMPLATE

Here you can find a template in Excel which can help you both in the design of the survey (definition of combinations and surveys including optimal reduction of options) and in the analysis using conditional multinomial logit.

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