Conjoint analysis is a popular method for quantifying psychological judgments such as consumer preference. Consumers rarely make purchases that are exclusively based on a single product characteristic. Instead, the decision is based on a combination of several factors.
To deconstruct the factors impacting such decision-making, it is critical to analyze the differences between various alternatives. The analysis attempts to dissect the aggregate answers to determine the importance or value of each attribute. It estimates which products or services people will choose and quantifies how much weight they place on various factors that may have influenced their decision-making process. Through the analysis of the usefulness of each characteristic, it becomes possible to determine the optimal value for goods and services that a user will find most fulfilling.
Conjoint analysis can help you determine price, product features, product combinations, and package bundling among other things. It is beneficial because it resembles real-world purchasing scenarios. It uses statistical analysis to generate mathematical representations of the respondents’ preference for product attributes. In addition, it also simulates the effect of attribute changes on demand and even forecasts market acceptance of new items before their introduction.
Task 1: The product or service that needs investigation is broken down into its basic parts known as attributes and levels.
Task 2: Based on the attribute and level, respondents are asked to select their favorite attribute and level.
Task 3: Survey results are analyzed to derive preference scores or part-worth utilities for the attribute levels of the survey. In this case, a logit model combined with computational procedures such as Hierarchical Bayes (HB) can be used.
Conjoint analysis is further classified as:
- metric conjoint analysis, in which the dependent variable has metric value, and
- non-metric conjoint analysis.
Based on the option types, it can be further divided into several categories listed below:
Choice-Based Conjoint: This is the most prevalent conjoint in which respondents select the most attractive attributes and levels for each set. Each set comprises an equally distributed collection of ideas at random. This conjoint best represents customer behavior because each set consists of hypothetical items.
Another sort of conjoint analysis is Best/Worst Conjoint or MaxDiff conjoint. This method, like choice-based conjoint, offers a group of ideas to the respondents and asks them to select the best/worst level in each set.
Adaptive Conjoint: In this case, respondents choose the most attractive notion for each set. However, using this technique, the following batch of concepts are adjusted based on the previous replies instead of arbitrary selection:
Full-profile Conjoint: This approach presents several ideas to respondents and asks them to rate each one on the purchase probability.
Rating or Ranking Conjoint: In this case, respondents are given a collection of ideas and asked to rank or evaluate them.
Menu-Based Conjoint: Menu-based conjoint is a new method wherein respondents may choose from a menu of various levels. When customization is allowed and multiple options are accessible, we can use this strategy to investigate client preferences.
Conjoint analysis is a popular method among product and brand managers, and it has proven to be beneficial in a wide range of industries including air travel, computers, financial services, healthcare, real estate, electronics, and many more.
Kultar Singh – Chief Executive Officer, Sambodhi