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Conjoint analysis is a market research technique that is intended to help predict people's decisions and what they will buy. A common method of gathering information about customers and their decision patterns, a conjoint analysis is used in new product development to figure out what choices people are likely to make. Companies use conjoint analysis in telephone and online surveys and at marketing panels to get information that helps them determine features, price, distribution methods, and advertising techniques for the product.
This type of analysis is usually used to decide minor features and traits of a product and is not usually used to develop an initial product offering. Rather, it is usually used in determining choices between fairly minor differences in products like flavor, packaging quantity, and product appearance or logo design differences. When used in market research, conjoint analysis measures the judgment differences and the impact of those differences on the choices people make. Knowing people's likely decisions help business make advertising decisions and determine product positioning that help market research professionals get a better idea of the target market for the product or service.
Generally, conjoint analysis is used by product management professionals to predict how they should market and sell the product. The data gathered in conjoint analysis can help a marketing professional decide who might buy their products and whether they are best marketed on the Internet, radio, on television, or in magazines or newspapers. This type of information can also tell marketing professionals which features of their products are most likely to receive a response from customers. Analyzing consumer response to product features helps a marketing professional decide which parts of the product should receive focus in advertising.
In customer service departments, conjoint analysis can help determine customers' preferred methods of receiving customer service. This information helps company's design executives put together a customer service interface best suited to the needs and desires of the customers. Customer service preference data is used in developing telephone and online customer service systems and talking scripts to help customer care personnel most effectively handle any issues that arise.
When a company decides to make some changes to an existing product, conjoint analysis can help tell the company how its customers might react to the changes. By gathering information about customer preferences before making changes, companies can avoid making expensive errors in judgment that customers do not like. Though it is not always completely accurate, if the data is properly collected, marketing professionals might be able to predict whether people will like the change, and if not, whether they will learn to live with it.
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