conjoint analysis

Conjoint analysis is a popular research method for predicting how people make complex choices. Conjoint asks people to make trade-offs just like they do in their daily lives. Our clients can then figure out what elements are driving peoples’ decisions by observing their choices.

Every day as customers make choices between products and services, they are faced with trade-offs. Is convenience more important than low price and speed of service? Or is high quality more important than atmosphere and high price? Often these choices are not conscious rational decisions – nevertheless, these decisions are made because people do make choices between alternatives.

Conjoint analysis is based on the fact that the relative values of attributes can be accurately measured when considered jointly than when considered in isolation.

global vox populi conjoint analysis solutions

choice-based conjoint (cbc)

The target respondent is asked to indicate the option or package they are most likely to purchase. Choice-based conjoint represents choice sets that are similar to those faced in the marketplace. The importance and preference for attribute features and levels can be mathematically deduced from the trade-offs made while selecting one of the available choices

adaptive conjoint analysis (aca)

Adaptive conjoint analysis is used to handle larger problems that required more descriptive attributes and levels. A unique contribution of adaptive conjoint analysis was to adapt each respondent’s interview to the evaluations provided by each respondent. Early in the interview, respondent are asked to eliminate attributes and levels that would not be considered in an acceptable product under any conditions. The attributes are then presented for evaluation, followed by sets of full profiles, two at a time for evaluation. The choice pairs are presented in an order that increasingly focuses on determining the utility associated with each attribute

max-diff analysis

• Max-diff conjoint analysis presents product configurations as an assortment of packages and features. these packages are evaluated and selected under best-most preferred and worst-least preferred scenarios
• respondents can quickly indicate the best and worst items present in a list, but often struggle to decipher their feelings for the middle ground
• max-diff conjoint analysis provides an easier task because consumers are well programmed in the art of making comparative judgments
• Max-diff conjoint analysis is considered as an ideal methodology when the decision task is to evaluate product choice. an experimental design is employed to balance and properly represent the sets of items

full profile conjoint analysis

Full-profile conjoint analysis is seen as a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference testing/evaluations. Each product profile is designed as part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, our researcher estimate the respondent’s utility for each level of each attribute tested

hierarchical bayes conjoint analysis (hb)

Hierarchical bayes conjoint analysis is similarly used to estimate attribute level utilities from choice data. HB is useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. The HB approach uses averages (information about the distribution of utilities from all respondents) as part of the procedure in order to estimate attribute level utilities for each individual. This approach allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent

self-explicated analysis

Self-explicated conjoint analysis is a hybrid approach that focuses the evaluation on the various attributes of a product. The respondent rates their preference for each feature level rather than the preference for a bundle of features. Although the attribute level approach differs from full profile techniques, the outcome is still the high quality estimates of preference utilities

how conjoint analysis can affect your business?

In business, it’s important to understand how markets value different elements of your products and services. Identifying these elements of higher value will enable you to optimize product development and adjust your pricing structure around the customers’ willingness to pay for specific elements.