Conjoint Analysis – Meaning, Usage and its Limitations

Introduction

During the sixties, when researchers tried to understand consumers’ decision making process, they used a simple questionnaire or a form.

Respondents would generally answer what was on the top of their minds or what they assumed the researcher wanted to hear. However, this did not always correspond to their actual purchase decisions.

For example, consider a questionnaire designed to understand consumers’ perceptions of the most desirable smart phone features.

On a scale of 1 – 10 where 1 is least important and 10 is most important, rate:

• The hardware configuration of the device
• Operation System and its version
• Price of the phone
• Brand

The survey data would usually reveal that all features are extremely important and the user would want all the features at the lowest cost. Such a discovery is not actionable and hence not usable.

Users cannot have more of all features that are attractive and less of all features that are not desirable. Instead, they must compromise of few characteristics to get more of the others. This method wherein various characteristics are considered jointly to make a purchase is known as conjoint analysis. It enables market researches to anticipate purchases with more certainty.

Breakdown of the product or service

Conjoint analysis is also popularly called trade off analysis as buyers have to let go of certain product features that they consider lucrative to make a more practical purchase. For example a large number of people planning to buy a new smart phone might think that however much they want an iPhone 6, they will have to be content with a less expensive phone.

Thus we see that consumers are put in a situation where they are forced to evaluate the merit of the phone attributes such as configuration, OS, price, brand, etc. Thus, broadly conjoint analysis checks the compromises users make while selecting products or services.

Operational Development

The process of conjoint analysis is described in a simplified manner in the following steps:

1. Recognizing the product attributes: configuration, brand, price, etc in the above case.
2. Selecting the importance degree of these attributes.
3. Creating virtual products by fusing several degrees of these attributes.
4. Collecting responses through a survey.
5. Analyzing the data statistically.
6. Market simulation of the product.

Conjoint Analysis Usage

• Conjoint studies aid in advertising. By research the company can selected the most desirable attributes to be marketed. For example, after conjoint analysis, the company determines that brand and hardware configuration of the phone is most important to its users. It would then design advertisements that well promote these attributes and that do not focus on price which is a secondary concern in this case.

• The most common usage is new product development. Conjoint analysis identifies opportunities by fusing attributes to generate new products and services that are not yet in the market.

• The method is also good for test marketing as it provides information of the degrees of importance of each attribute. Prior to releasing the product full-fledged, it is feasible to foresee the success or failure of a product.

• Conjoint analysis is also applicable in situations where segmentation needs to be done. Certain clusters of users give preference to one set of attributes, whereas a different set would be more important to few others. Conjoint uncovers this pattern so that the company can target users accordingly.

Limitation and Conclusion

For certain kind of products, consumers do their evaluation built on intangible attributes or image. These products mostly comprise of luxury items where the emotional factor rather than the rational side dominates. In cases like these, the logic of conjoint analysis does not apply.

With an exception to this situation, conjoint is quite inexpensive as compared to other similar methods such as concept testing and hence is hugely popular.

In a nutshell, it is a versatile and powerful tool to predict consumer choices, foresee their purchase decisions and hence design and launch products accordingly.