Research plays an important role in the marketing and development of products. Through research, a company can understand how their customer thinks, acts, and feels. What product features do they want, how much are they willing to pay, how will they react if services are bundled together or charged separately? If you introduce a value menu, will that cannibalize your existing premium offer?
Conjoint is a specialized research technique designed to investigate these questions. With this technique, the researcher can understand how customers value various features that make up a product (or service); how demand levels change for different product bundles, pricing or competitive situations. The power of conjoint comes from the capability to provide numerical answers to these questions. Imagine being able to say “the introduction of a value menu is estimated to eat a 5% share from the existing product line, but improve the overall bottom line by 20%…”
How conjoint works
There are several conjoint methods, but perhaps the most popular utilizes the discrete choice (or tradeoff) exercise. In a survey, participants are shown a set of product profiles and asked to choose their preferred one, or none at all. This simulates how consumers shop and make purchase decisions—through product and feature comparisons. Participants may go through a dozen such scenarios, receiving a new set of product profiles each time. Through careful design of the product profile set, participants make tradeoffs between certain features. Do they choose the higher-priced Apple brand, or the lower-priced Dell with slightly less processor speed? Enough data is collected for sophisticated mathematical models to numerically estimate how much each of the individual features influenced their decision when choosing a product (part-worth utilities).
Figure 1. A conjoint exercise asks a person to make a series of tradeoffs between different product options. Based on how they answer, a set of scores (part-worth utilities) for each feature can be statistically derived. Part-worth utilities tell us how much the feature impacts their choice.
With the part-worth utilities for each test feature, a market simulator can be built. In a market simulator, “what if” scenarios, are simulated – bundling different features to build a product and pit multiple product configurations against each other in a competitive set. In each case, preference shares are calculated for all the product profiles in the custom simulated marketplace. Want to know how an existing product will fair against a hypothetical budget tier product? How will preference share change when competitors are introduced? What happens when a lower-priced product with reduced processing speed is offered? These scenarios can be simulated and more.
Figure 2. Data gathered from a conjoint exercise is used to build a market simulator. Through a market simulator, “what if” scenarios are generated to see how preference levels changes as different products are introduced to the market.
Conjoint can be a valuable tool for product and marketing managers. By gathering feedback on how consumers value and tradeoff specific product features, conjoint provides insight on how a newly created or designed product might be expected to perform when taken to market. Then you can be sure your product offers a winning combination of features at the right price.