Conjoint analysis is a statistical technique used in market research to determine how people value different attributes or features that make up an individual product or service. The main idea is to understand the trade-offs people are willing to make between different features and to quantify the value they place on each of those features. This helps businesses to design products or services that closely match customer preferences.
Here’s a step-by-step explanation of how conjoint analysis works:
- Identification of Attributes and Levels: The first step is to identify the key attributes or features of a product or service. For instance, for a smartphone, attributes might include screen size, battery life, brand, camera quality, and price. Each of these attributes will have different levels. For example, screen size could have levels like 5 inches, 6 inches, and 6.5 inches.
- Creation of Profiles: Using the identified attributes and their levels, you can create a set of hypothetical products or profiles. Each profile represents a different combination of attribute levels.
- Survey Design: Respondents are then asked to evaluate or rank these profiles based on their preferences. The ranking can be direct (e.g., “rank these five profiles from 1 to 5”) or based on choice tasks (e.g., “choose the best option among these three profiles”).
- Data Collection: Respondents’ evaluations or rankings are collected through surveys.
- Analysis: The data is then analyzed to determine the utility or importance of each attribute level to the respondents. Utilities (also called part-worths) are numerical values that indicate the preference or value respondents place on each level of an attribute. The higher the utility, the more preferred that level is.
- Interpretation: By understanding the utilities, businesses can infer:
- Which attributes are the most important to customers?
- How much value customers place on each level of an attribute?
- What combinations of attributes are most preferred by customers?
- Applications: With this information, companies can make informed decisions about product design, pricing, positioning, or even promotional strategies. They can identify market segments, forecast market shares for new products, or optimize existing product features.
Example: Let’s say a company is planning to launch a new smartphone. Using conjoint analysis, they might discover that customers place the highest value on battery life and are willing to pay extra for a phone with a two-day battery life. At the same time, they might find out that most customers don’t place much value on having an ultra-high resolution camera.
In essence, conjoint analysis offers insights into customer preferences and the trade-offs they are willing to make, helping businesses tailor their offerings more effectively.