Choice-based conjoint allows us to answer “what if” questions. What will happen if Coca-Cola increases the price of its drinks by 20%? What will happen if Wrigley introduces a new flavor of chewing gum? What will happen if United Airlines and American Airlines merge to become a single airline called United American?
Common applications of choice-based conjoint include:
- Testing the appeal of a new product
- Understanding product deletions
- Portfolio optimization
- Assessing the impact of changes in product design
- Pricing optimization
- Understanding psychology
- Product optimization
- Computing brand equity
- Calculating Willingness-To-Pay (WTP)
- Market segmentation
Testing the appeal of a new product
Choice-based conjoint analysis is widely used for testing the appeal of new products and services. For example, a new flavor of soft drink, a new cabin in an aircraft (premium economy), or a new transport option (hyperloop). It is preferred to simple techniques, like concept testing [link] when there is a desire to:
- Force people to trade-off different attributes.
- There is a need to get a deeper understanding of why people make decisions.
- We want to test lots of possible products, rather than having to lock in a concept for testing prior to collecting any data.
Understanding product deletions
Choice-based conjoint analysis can be used to work out what happens when a product is removed (deleted) from a market. The focus here is on understanding what customers buy if they cannot buy their existing alternative. This is particularly useful in situations where a company has a large collection of brands or SKUs. Choice-based conjoint done for this purpose tends to use a special type of experimental design known as an availability design, where the list of which brands or SKUs are shown varies from question to question. This is useful both in terms of portfolio planning and also in terms of understanding antitrust/monopoly cases.
Understanding product deletions naturally leads to designing optimal portfolios of products. For example, a company wishing to identify the optimal range of chewing gum flavors may use choice-based conjoint to understand how people will react to different portfolios of flavors.
Assessing the impact of changes in product design
Choice-based conjoint analysis is widely used to prioritize changes to product design, where the changes could be improvements (e.g., better packaging), or reductions in performance levels (e.g., less legroom on flights, replacing natural with nature-identical ingredients). Most commonly, this occurs as a part of a more general business case, where the choice model estimates the demand for the product and an economic analysis works out the impact on profit.
Choice-based conjoint analysis is widely used as an input to pricing, in terms of working out the impacts of price increases and decreases, as well as understanding how changes in price influence cannibalization.
Choice-based conjoint analysis is also used to identify which attributes people regard as being most important (i.e., purchase hierarchies), which is useful to know for a wide variety of marketing planning purposes (e.g., new product design, store layout, communications messaging).
Preferences for attribute levels
One of the key outputs of a choice-based conjoint analysis study is an estimate of the utility of different attribute levels (for an explanation of this jargon, see
The assumptions of choice-based conjoint). For example, in a study of chewing gum, the study will estimate the relative appeal of different flavors (spearmint, peppermint, apple, etc.). This is useful in planning future products and product modifications.
Where detailed information is known about the cost of different attribute levels, products can be optimized to maximize the net overall benefit. For example, working out the optimal structure of employment benefits (e.g., salary, versus lunch, versus health benefits).
Computing brand equity
Where a brand is used as an attribute in a choice-based conjoint analysis study, the appeal estimated for each brand becomes an estimate of brand equity, which is defined in this case as the relative strengths of brands when their features are at parity.
Calculating Willingness-To-Pay (WTP)
Willingness-to-pay (WTP) refers to the average price that people are prepared to pay for changes in attribute levels. For example, the average amount that people in a market would be prepared to pay for an extra six inches of legroom in an economy seat is known as the WTP for the six inches of leg room.
Willingness-to-pay has a number of applications. It is:
- A useful way of describing a preference for attribute levels. For example, as a way of calculating brand equity (see the previous section).
- A way of calculating consumer surplus in economics (e.g., when assessing damages in environmental court cases). For example, this approach has been used in working out damages in environmental cases (where, say, the damage of an oil leak can be viewed as a change in the product design of the environment), copyright cases in terms of the value of illegally used copyrighted materials, and patent violations, such as in the phone wars between Apple and Samsung.
A common mistake is to interpret WTP as the amount that can be charged for a product feature. While both concepts are expressed in dollars (or some other currency), they are different things, and the WTP is almost always higher than the optimal price for a change in a product design.
Choice models are routinely used for market segmentation, as it leads to segments that differ in terms of purchase hierarchies and preferences for attribute levels, which usually ensures that the segments are actionable in terms of marketing planning.
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